EPA/600/R-14/051
RESEARCH AND DEVELOPMENT HIGHLIGHTS:
MOBILE SENSORS AND APPLICATIONS
FOR AIR POLLUTANTS
Prepared by
Margaret MacDonell, Michelle Raymond, David Wyker, Molly Finster,
Young-Soo Chang, Thomas Raymond, Bianca Temple, and Marcienne Scofield
Argonne National Laboratory
Environmental Science Division (EVS)
Argonne, IL
In collaboration with
Dena Vallano (AAAS Fellow),
Emily Snyder and Ron Williams
U.S. Environmental Protection Agency (EPA)
Research Triangle Park, NC
31 October 2013
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DISCLAIMER
This work was conducted in collaboration with the Argonne National Laboratory through
Interagency Agreement DW-89-92376701-0. The information in this document has been
subjected to review by the U.S. Environmental Protection Agency and approved for publication.
Approval does not signify that the contents reflect the views of the Agency, nor does mention of
trade names or commercial products constitute endorsement or recommendation for use. The
authors furthermore declare they have no conflict of interest.
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31 October 2013
RESEARCH AND DEVELOPMENT HIGHLIGHTS:
MOBILE SENSORS AND APPLICATIONS FOR AIR POLLUTANTS
TABLE OF CONTENTS
NOTATION v
CONVERSION TABLES xiii
EXECUTIVE SUMMARY ES-1
1 INTRODUCTION 1-1
1.1 Traditional Air Pollution Monitoring and Current Opportunity 1-1
1.2 Purpose and Scope of this Report 1-1
1.3 Report Organization 1-2
2 APPROACH 2-1
2.1 Literature Search 2-1
2.2 Candidate Pollutants 2-1
2.3 Data Compilation 2-3
3 RESULTS AND DISCUSSION 3-1
3.1 Initial Literature Search 3-1
3.2 Study Pollutants 3-1
3.2 Exposure Benchmarks 3-4
3.3 Example Concentrations in Air 3-7
3.4 Sensor Technologies and Techniques 3-15
3.5 Detection Capabilities 3-21
3.6 Architecture and Infrastructure Approaches and Apps 3-53
3.7 Highlights of Recent Federal Research Activities 3-64
4 GAPS AND OPPORTUNITIES 4-1
4.1 Sensor Technologies and Techniques 4-1
4.2 Architecture and Infrastructure Approaches and Air Quality Apps 4-3
4.3 Partnerships 4-14
5 SUMMARY 5-1
6 ACKNOWLEDGEMENTS 6-1
7 SELECTED INFORMATION RESOURCES 7-1
APPENDIX A: Supporting Details for the Literature Search Approach A-1
APPENDIX B: Overview of Exposure Benchmarks B-1
APPENDIX C: Context for Chemical Fate in Air C-1
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31 October 2013
TABLE OF CONTENTS (Cont'd.)
APPENDIX D: Example Concentration Summaries to Guide Regional and Local Inputs D-1
APPENDIX E: Summary of Sensors Represented on the Graphical Arrays E-1
APPENDIX F: Overview of Sensing Technologies and Techniques F-1
APPENDIX G: Evaluation of Selected Air Quality Apps G-1
TABLES
2-1 Key Pollutants from the 2005 National-Scale Air Toxics Assessment 2-4
2-2 Risk Characterization Categories Used to Identify Main Pollutants 2-4
2-3 Estimated Risks and Number of People Exposed per Pollutant Category 2-4
2-4 Candidate Pollutants for the Review of Recent Research Sensors 2-5
3-1 Air Pollutants and Other Measurands 3-2
3-2 Pollutant Study Set 3-3
3-3 Overview of Selected Inhalation Benchmarks 3-6
3-4 Pollutant Study Set and Associated Emission Sources 3-8
3-5 Example Airborne Concentrations and Emission Sources for the Study Pollutants 3-9
3-6 Technologies/Techniques Reflected in Research Sensors and Systems 3-16
3-7 Detection Capabilities for Selected Sensing Technologies/Techniques 3-22
3-8 Guide to Distinguishing Types of Benchmarks in the Graphical Arrays 3-23
3-9 Categories and Counts for Architecture/Infrastructure 3-54
3-10 Highlights of Recent Sensor Research Led or Funded by Federal Agencies 3-65
3-11 Highlights of Selected Research Activities at DOE National Laboratories 3-67
4-1 Reported Ability to Detect Exposure Benchmark Concentrations 4-2
4-2 Highlights of Sensor Gaps/Limitations and Opportunities 4-7
4-3 Highlights of Advantages and Limitations for Selected Technologies-Techniques 4-10
4-4 Example Comparison of Limitations and Opportunities for Three CO Sensors 4-11
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31 October 2013
TABLE OF CONTENTS (Cont'd.)
FIGURES
2-1 Literature Search Approach 2-2
3-1 Number of Sensors Reported to Detect the Study Pollutants 3-5
3-2 Detection Techniques Highlighted in Recent Research Literature 3-18
3-3 Detection Techniques Reflected in Sensors and Systems for the
Study Pollutants 3-19
3-4 Technology/Technique Counts by Year 3-20
3-5 Acetaldehyde: Comparison of Detection Levels to Exposure Benchmarks 3-25
3-6 Acrolein: Comparison of Detection Levels to Exposure Benchmarks 3-26
3-7 Ammonia: Comparison of Detection Levels to Exposure Benchmarks
and an Example Concentration 3-27
3-8 Benzene: Comparison of Detection Levels to Exposure Benchmarks
and Example Concentrations 3-28
3-9 1,3-Butadiene: Comparison of Detection Levels to Exposure Benchmarks
and Example Concentrations 3-29
3-10 Carbon Monoxide: Comparison of Detection Levels to Exposure Benchmarks 3-30
3-11 Formaldehyde: Comparison of Detection Levels to Exposure Benchmarks 3-31
3-12 Hydrogen Sulfide: Comparison of Detection Levels to Exposure Benchmarks 3-32
3-13 Lead: Comparison of Detection Levels to Exposure Benchmarks 3-33
3-14 Methane: Comparison of Detection Levels to Exposure Benchmarks 3-34
3-15 Nitrogen Dioxide: Comparison of Detection Levels to Exposure Benchmarks 3-35
3-16 Ozone: Comparison of Detection Levels to Exposure Benchmarks 3-36
3-17 Particulate Matter: Comparison of Detection Levels to Exposure Benchmarks 3-37
3-18 Sulfur Dioxide: Comparison of Detection Levels to Exposure Benchmarks 3-38
3-19 Acetaldehyde: Comparison of Detection Levels to Example Concentrations 3-41
3-20 Acrolein: Comparison of Detection Levels to Example Concentrations 3-42
3-21 Carbon Monoxide: Comparison of Detection Levels to Example Concentrations 3-43
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31 October 2013
TABLE OF CONTENTS (Cont'd.)
FIGURES (Cont'd.)
3-22 Formaldehyde: Comparison of Detection Levels to Example Concentrations 3-44
3-23 Hydrogen Sulfide: Comparison of Detection Levels to Example Concentrations 3-45
3-24 Lead: Comparison of Detection Levels to Example Concentrations 3-46
3-25 Methane: Comparison of Detection Levels to Example Concentrations 3-47
3-26 Nitrogen Dioxide: Comparison of Detection Levels to Example Concentrations 3-48
3-27 Ozone: Comparison of Detection Levels to Example Concentrations 3-49
3-28 PlVb.s: Comparison of Detection Levels to Example Concentrations 3-50
3-29 PMio: Comparison of Detection Levels to Example Concentrations 3-51
3-30 Sulfur Dioxide: Comparison of Detection Levels to Example Concentrations 3-52
3-31 Number of Sensors Using Selected Architecture/Infrastructure Approaches 3-55
IV
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31 October 2013
NOTATION
(This list includes acronyms and abbreviations used in this report and the companion master
table that summarizes sensor information from the literature reviewed; these notations include
abbreviations used to streamline those summaries. Selected acronyms and abbreviations that
are only used in limited tables are defined within those tables.)
ac
ACGIH
AEGL
AFC
Ag
Al
a-IGZO
AIHA
AL
Al
Alg/Mod
ANL
API
app
AQI
AR
As
A/SR
ATSDR
Au
avg
AVS
AZO
BaP
BC
BTEX
BTX
C
°C
C8-MPN
C8-PBP
CA
CAA
CAAQS
CAFO
CalEPA
CAS RN
CEAS
CEGL
CEL
CEMS
acute
American Conference of Governmental Industrial Hygienists
acute exposure guideline level (National Research Council)
automated fare collection
silver
ambient intelligence
amorphous indium gallium zinc oxide
American Industrial Hygiene Association
action level
aluminum
aluminum oxide
algorithm/modeling
Argonne National Laboratory
application programming interface
application
Air Quality Index
augmented reality
arsenic
activity/speech recognition
Agency for Toxic Substances and Disease Registry
gold
average
automated voltammetric system
aluminum-doped zinc oxide
benzo[a]pyrene
black carbon
benzene, toluene, ethylbenzene, xylene(s)
benzene, toluene, xylene(s)
ceiling (OSHA PEL value)
degree(s) centigrade (Celsius)
n-octanethiolate-monolayer-protected gold nanoparticle(s)
Pt2CU(1 ,3-butadiene)(pyridine)2
context awareness
Clean Air Act
California Ambient Air Quality Standards (California EPA)
concentrated animal feeding operation
California Environmental Protection Agency
Chemical Abstracts Service Registry Number
cavity enhanced absorption spectroscopy
continuous exposure guideline level(s)
continuous exposure limit
continuous emissions monitoring system(s)
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ChU
C2H2
C2H4
CH2O
C2H4O
chr
C\2
cm
cm3
CNT
CO
CO2
COCI2
concn
COSPEC
CPB
Cr
Cr(VI)
CSA
CTL
Cu
CVD
1-D
3-D
d
DAQ
DARPA
DB
dBm
DC
DCE
D/DM
DDP
DFB
DHHS
DHS
DIY(er[s])
DMA
DNT
DoD
DOE
DOT
DRIFT
DTRA
NOTATION (Cont'd.)
methane
acetylene
ethylene
1,3-butadiene
benzene
formaldehyde
acetaldehyde
acrolein
chronic
chlorine
centimeter(s)
cubic centimeter(s)
carbon nanotube(s)
carbon monoxide
carbon dioxide
cobalt(ll) chloride
concentration
correlation spectroscopy
cell phone-based
chromium
hexavalent chromium
camphor sulfonic acid
cataluminescence
copper
chemical vapor deposition
one-dimensional
three-dimensional
day(s)
data acquisition
Defense Advanced Research Projects Agency
data broadcasting
decibel-milliwatt
direct current
dichloroethylene
database/data mining
distributed data processing
distributed feedback (laser)
U.S. Department of Health and Human Services
U.S. Department of Homeland Security
do-it-yourself(er[s])
deoxyribonucleic acid
dinitrotoluene
U.S. Department of Defense
U.S. Department of Energy
U.S. Department of Transportation
diffuse reflectance infrared Fourier transform (spectroscopy)
Defense Threat Reduction Agency
VI
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NOTATION (Cont'd.)
E. co// Escherichia coli
EA exposure assessment
EEGL emergency exposure guidance level
EEL emergency exposure limit
EIS embedded/integrated sensor
e-nose electronic nose
EPA U.S. Environmental Protection Agency
ERPG emergency response planning guideline
ET-O economic trade-offs
eV electron volt(s)
EVS Environmental Science Division (DOE/Argonne)
FAA Federal Aviation Administration
FBAR (thin-)film bulk acoustic resonator
FDMS filter dynamics measurement system
FEHWCVD field-enhanced hot wire chemical vapor deposition
FID flame ionization detector
F/S-PSU fixed/semi-portable sensor unit
ft foot (feet)
FTIR Fourier transform infrared spectroscopy
Ga gallium
GaN gallium nitride
GC gas chromatograph(y)
GF cyclosarin
GHG greenhouse gas
GPRS general packet radio service
GPS global positioning system
GSM global system for mobile communications
hb hydrogen
HAP hazardous air pollutant(s)
HBCD(s) hexabromocyclododecane(s)
HC hydrocarbon(s)
HCI hydrogen chloride, or hydrochloric acid
HCN hydrogen cyanide
HDI hexamethylene diisocyanate
HF hydrogen fluoride
Hg mercury
HI hazard index
hr hour(s)
H2S hydrogen sulfide
H2SO4 hydrogen sulfate, sulfuric acid
HWCVD hot wire chemical vapor deposition
HWG hollow waveguide
Hz hertz
ICT
i.d.
information and communication technology(ies)
internal diameter
VII
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ID
IDE
IDLH
IDT
IGZO
in.
IOM
IP
IR
IRIS
ITO
IUR
JS
K
keV
kg
kHz
km
kV
LA
La-Sr-FeOs
LB
Ib
LCD
LDA
LDL
LED
LEL
LIBS
LIDAR
LPAS
LPG
m
m3
mA
max
MEG
MEK
MEMS
mg
mg/m3
MgO
ug
ug/m3
NOTATION (Cont'd.)
identifier
interdigitated electrode
immediately dangerous to life or health (NIOSH)
interdigitated transducer
indium gallium zinc oxide (also InGaZnCU)
inch(es)
indium oxide
Institute of Medicine
ionization potential
infrared
integrated risk information system (EPA)
indium-tin oxide
inhalation unit risk (EPA)
Java script
Kelvin
kiloelectronvolt(s)
kilogram(s)
kilohertz
kilometer(s)
kilovolt(s)
location awareness
lanthanum-strontium-iron oxide
Langmuir-Blodgett
pound(s)
liquid crystal display
linear discriminant analysis
lower detection limit
light-emitting diode
lower explosive limit
laser-induced breakdown spectroscopy
light detection and ranging
laser photoacoustic spectroscopy
liquefied petroleum gas
meter(s)
cubic meter(s)
milliampere(s)
maximum
military exposure guideline (DoD)
methyl ethyl ketone
microeletromechanical system(s)
milligram(s)
milligram(s) per cubic meter (air)
magnesium oxide
microgram(s)
microgram(s) per cubic meter (air)
VIII
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31 October 2013
NOTATION (Cont'd.)
urn micron(s)
us microsecond(s)
MIM metal-insulator-metal
min minute(s)
MIR mid-infrared (spectroscopy)
ml_ milliliter(s)
MLIAS multiple-line integrated absorption spectroscopy
mm millimeter(s)
Mn manganese
mo month(s)
Mods molybdenum trioxide
MOS metal oxide semiconductor(s)
MoS mobile sensing
MPa megapascal
MRL minimal risk level (ATSDR)
MS:MMP mountable sensor: micro- and miniature-scale platform
M-SS multi-sensor system
mW milliwatt(s)
MW molecular weight
MWCNT multi-walled carbon nanotube(s)
N nitrogen atom (nitride)
ISb nitrogen gas
NAAQS National Ambient Air Quality Standard(s) (EPA, CAA)
NASA National Aeronautics and Space Administration
NATA National-Scale Air Toxics Assessment (EPA)
NEMA National Electrical Manufacturers Association
Nhb amine
NHs ammonia
NhU ammonium
Ni nickel
Ni-Cr nickel-chromium
NIH National Institutes of Health
NiMH nickel-metal hydride (battery)
NIOSH National Institute for Occupational Safety and Health
NIST National Institute of Standards and Technology
nm nanometer(s)
NO nitric oxide
N2O nitrous oxide
NO2 nitrogen dioxide
NOX nitrogen oxide(s)
NRC National Research Council
NSF National Science Foundation
NST nanoscale technology
O2 oxygen
Os ozone
OEHHA Office of Environmental Health Hazard Assessment (CalEPA)
OEL occupational exposure level
IX
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31 October 2013
NOTATION (Cont'd.)
OP organophosphate(s)
OPC optical particle counters
ORD Office of Research and Development (EPA)
OSHA U.S. Occupational Safety and Health Administration
PAAEMA poly(2-(acetoacetoxy)ethyl methacrylate)
PAC protective action criteria (DOE)
PAH polycyclic aromatic hydrocarbon(s)
PANi polyaniline
Pb lead
PBDE polybrominated diphenyl ether(s)
PC personal computer
PCA principal component analysis
PCB polychlorinated biphenyl(s)
PCP pentachlorophenol(s)
P/CS participatory/citizen sensing
Pd palladium
PDA personal digital assistant
PDMS polydimethylsiloxane
PEGL permissible exposure guidance level (NRC/DoD)
PEL permissible exposure limit (OSHA)
PFC perfluorocarbon(s)
pg picogram(s)
PHs phosphine
PID photoionization detector
p-i-n diode with semiconductor stack of p-type, intrinsic, and n-type materials
p-ILJR provisional inhalation unit risk (EPA)
PM particulate matter
PM with an aerodynamic diameter of a nominal 1 micron or less
PM with an aerodynamic diameter of a nominal 2.5 microns or less
PM with an aerodynamic diameter of a nominal 10 microns or less
PMTFPS poly[methyl(3,3,3-trifluoropropyl)siloxane]
PO project officer
PoANIS poly(o-anisidine)
PPB photonic pass band
ppb part(s) per billion
PPEGL permissible public exposure guidance level (NRC/DoD)
ppm part(s) per million
PPRTV provisional peer reviewed toxicity value (EPA)
ppt part(s) per trillion
PPy polypyrrole
p-RfC provisional reference concentration (EPA)
PS prediction service
Pt platinum
PtCI2 platinum(ll) chloride
PTR-LIT photon-transfer reaction linear ion trap
QCL quantum cascade laser(s)
QCM quartz crystal microbalance
PM2.5
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31 October 2013
R&D
RBC
REL
REL
resp
RfC
RFID
RS
RSC
RSL
RS/M
RSS
RTR
sec
SAW
SC
SD
SEM
SF
Si
SMAC
SN/C
SnO2
SnOx
SO2
SO4
SOX
SOA
SNS
SPME
STEL
subc
SVOC
SWCNT
SWV
TCE
TD
TD-FTIR-HWG
TDI
TOLAS
temp
TEOM
Ti
TiO2
TLV
TNT
TWA
NOTATION (Cont'd.)
research and development
risk-based concentration (EPA)
recommended exposure limit (NIOSH)
reference exposure level (CalEPA)
response (time)
reference concentration (EPA)
radio frequency identification
radar system
risk-specific concentration (EPA)
Regional screening level(s) (EPA Regions)
remote sensor/monitoring
received signal strength
reel-to-reel
second(s)
surface acoustic wave
sensor calibration
secure digital
scanning electron microscope
slope factor
silicon
spacecraft maximum allowable concentration (NRC/NASA)
social networking/computing
tin dioxide
tin oxide
sulfur dioxide
sulfate
sulfur oxide(s)
service-oriented architecture
social networking services
solid phase micro-extraction (fiber)
short-term exposure limit (OSHA)
subchronic
semivolatile organic compound(s)
single-walled carbon nanotube(s)
square wave voltammetry
trichloroethylene
thermal desorption
thermal desorption Fourier transform infrared hollow waveguide
2,4-toluene diisocyanate
tunable diode laser absorption spectroscopy
temperature
tapered element oscillating microbalance
titanium
titanium dioxide
threshold limit value (ACGIH)
trinitrotoluene
time-weighted average
XI
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31 October 2013
NOTATION (Cont'd.)
ubicomp ubiquitous computing
UEL upper explosive limit
UF uncertainty factor
UFFI urea-formaldehyde foam insulation
UFP ultrafine particle(s)
UPSIDE unconventional processing of signals for intelligent data exploitation
UR unit risk (EPA)
USB Universal Serial Bus
USDA U.S. Department of Agriculture
UTL upper tolerance limit
UV ultraviolet
UV abs ultraviolet absorption
UV vis ultraviolet visible
UVNS ultraviolet non-solarizing (e.g., optical fiber)
V volt(s)
VA volt-ampere(s)
VAC volt(s) alternating current
VDC volt(s) direct current
VMU vehicle-mounted unit
VOC volatile organic compound(s)
VP vapor pressure
VR virtual reality
VR/S virtual reality/sensing
VS visual sensing
W watt(s)
W-B Web-based
wk week(s)
WOs tungsten trioxide
WSN wireless sensor network
wt % weight percent
XMLS Extensible Markup Language 5
XRF X-ray fluorescence
yr year(s)
YSZ yttria-stabilized zirconia
ZnO zinc oxide
ZrC>2 zirconium dioxide
XII
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31 October 2013
CONVERSION TABLE 1 Units of Area, Length, Mass, and Volume
Multiply
By
To Obtain
English/Metric Equivalents
acre (ac)
cubic foot (ft3)
cubic yard (yd3)
foot (ft)
inch (in.)
inch (in.)
mile (mi)
ounce (oz.)
pound (Ib)
short ton (tons)
short ton (tons)
square foot (ft2)
square yard (yd2)
square mile (mi2)
yard (yd)
4,047
0.02832
0.7646
0.3048
2.540
25,400
1.609
28.35
0.4536
907.2
0.9072
0.09290
0.8361
2.590
0.9144
square meter (m2)
cubic meter (m3)
cubic meter (m3) (= 106 cm3)
meter (m)
centimeter (cm)
micron (urn, or micrometer)
kilometer (km)
gram (g)
kilogram (kg)
kilogram (kg)
metric ton (t)
square meter (m2)
square meter (m2)
square kilometer (km2)
meter (m)
Metric/English Equivalents
centimeter (cm)
cubic meter (m3)
cubic meter (m3)
gram (g)
kilogram (kg)
kilogram (kg)
kilometer (km)
meter (m)
meter (m)
micron (urn, or micrometer)
milliliter (ml)
square kilometer (km2)
square meter (m2)
square meter (m2)
square meter (m2)
0.3937
35.31
1.308
0.03527
2.205
0.001102
0.6214
3.281
1.094
0.00003937
0.0002642
0.3861
0.0002471
10.76
1.196
inch (in.)
cubic foot (ft3)
cubic yard (yd3)
ounce (oz.)
pound (Ib)
short ton (tons)
mile (mi)
foot (ft)
yard (yd)
inches (in.)
gallon (gal)
square mile (mi2)
acre (ac) (1 ac = 43,560 ft2)
square foot (ft2)
square yard (yd2)
XIII
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31 October 2013
CONVERSION TABLE 2 Gas Concentration Equivalents in Aira
Pollutant
Acetaldehyde
Aero le in
Ammonia
Benzene
1,3-Butadiene
Carbon monoxide
Formaldehyde
Hydrogen sulfide
Methane
Nitrogen dioxide
Ozone
Sulfur dioxide
ppm
1
1
1
1
1
1
1
1
1
1
1
1
Concentration
mg/m3
1.801
2.291
0.696
3.193
2.211
1.145
1.227
1.393
0.656
1.880
1.962
2.619
,9/m3
1,801
2,291
696
3,193
2,211
1,145
1,227
1,393
656
1,880
1,962
2,619
a Values are at 25°C (or 298. 15 K), and 1 atmospheric pressure (or 1013.25 millibars).
This relationship can be expressed as: mg/m3 = (molecular weight/24. 5) * ppm.
For other temperatures and pressures, the following equation can be used:
concentration in mg/m3= P™»""»
concentration in ppm
(Note that standard temperature and pressure (STP) conditions are 0° Celsius (273 K) rather than 25°C. However,
using standard pressure (1 atm) and a reference temperature of 25°C is commonly accepted practice in the air
monitoring community.)
XIV
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31 October 2013
EXECUTIVE SUM MARY
ES.1 STUDY CONTEXT
The public has long been interested in understanding what pollutants are in the air they breathe
so they can best protect their environmental health and welfare. The current air quality
monitoring network consists of discrete stations with expensive equipment operated by state
and local agencies. Because both the number of stations and the pollutants they measure are
limited, location-specific data are relatively sparse. Thus, actual concentrations to which
individuals are exposed each day are generally unknown. Significant advances in mobile
sensors and software applications offer unique opportunities for citizen-based sensing that
could ultimately help fill these gaps. The Innovation Team of the U.S. Environmental Protection
Agency (EPA) Office of Research and Development (ORD) is leading an initiative to understand
the state of progress for mobile sensors and applications for air pollutants. Key objectives are
to identify opportunities for strengthening current monitoring programs and to catalyze and
facilitate community-based monitoring.
ES.2 APPROACH
Literature Review
A review of selected literature was conducted to support the EPA initiative for next-generation
air monitoring. This review began in late 2011 and primarily focused on the period from 2010 to
early 2012. More than 1,000 information sources were evaluated in pursuing relevant data,
including patent database entries, journal articles, abstracts and papers in conference and
workshop proceedings, meeting presentations available online, and organizational web pages.
The latter were particularly useful as gateways to recent sensor and app developments, as were
compilations from recent meetings. Nearly half the resources reviewed contained relevant
information, notably for technologies, sensing techniques, and architectures and infrastructures.
Study Pollutants
More than 100 measurands were identified from the literature review. To further focus the
assessment, 14 pollutants were selected for targeted study. This set was determined based on
an evaluation of the most recent National-Scale Air Toxics Assessment (NATA) and inputs from
EPA Program and Regional Offices that reflected community interests. Consisting of a dozen
gases plus particulate matter (PM) and lead (Pb), this set reflects several key emission sources
and pollutants of interest for fenceline communities, children, and personal health protection.
• Six criteria pollutants: carbon monoxide (CO), lead, ozone (Os), nitrogen dioxide (NO2),
PM, and sulfur dioxide (SO2).
• Five hazardous air pollutants (HAPs): acetaldehyde, acrolein, benzene, 1,3-butadiene,
and formaldehyde.
• Three indicator pollutants: ammonia (NHs), hydrogen sulfide (H2S), and methane (ChU).
Target Concentrations
Two types of concentrations were compiled for the study pollutants, to serve as practical targets
for comparing reported sensor detection levels. The first category consists of health-based
ES-1
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31 October 2013
benchmarks that have been established by EPA and other organizations to protect human
health under various conditions, ranging from one-time (acute) to daily (chronic) exposures.
These benchmarks can be organized into three exposure situations or conditions:
• Emergency response guides established for the general public (including sensitive
subgroups such as children) for exposures lasting up to one day, commonly 8 hours or
less.
• Ambient air quality standards and guidelines established for the general public, as
above, for continuous exposures that extend over a lifetime.
• Occupational standards and guidelines established for the workforce: Noncontinuous
exposures (e.g., discrete work shifts with evening and weekend breaks) that extend over
an adult working life (decades).
The second set of comparison concentrations consists of example pollutant concentrations that
have been reported for various settings and time intervals. The settings cover both indoor to
outdoor air, including for specific conditions such as freeway tunnel exits and forest fires. The
time intervals reflected include discrete measurements and annual averages. Together these
established exposure benchmarks and example reported concentrations provide anchors for
assessing sensor detection capabilities, toward identifying related gaps and opportunities to
inform targeted research investments.
ES.3 FINDINGS
Selected insights from the review of recent literature for mobile sensors and apps are
highlighted below.
Sensing Techniques and Technology
• The sensing techniques reflected in the literature reviewed can be grouped into three
categories: chemistry, spectroscopy, and ionization. These categories are listed in
order of their prominence, with chemistry and spectroscopy dominating the research and
development highlighted for mobile sensors.
• Nanotechnology, which is grouped within the chemistry technique, is a major research
theme. From a limited follow-on check of subsequent literature extending into 2013, this
trend appears to be sustained. Advances include development of nanomaterials of
different compositions, shapes, and sizes that can function as stand-alone sensing
materials or can be added to other sensor components (e.g., films or electrodes) to
improve sensitivity, selectivity, and sensor response time.
• Relatively few studies were found to reflect ionization techniques such as mass
spectrometry and gas chromatography systems with photoionization and flame
ionization detectors in mobile sensors or systems.
Pollutant Coverage
• Mobile sensors identified from both the targeted (study set) and broader literature review
appear to emphasize gases, notably the criteria pollutants.
ES-2
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31 October 2013
• Chemical-specific particle sensors appear to constitute a current gap. No research
sensors were found for Pb in air; laser-induced breakdown spectroscopy (LIBS) may
represent a potential opportunity area for this pollutant.
• The relatively few PM sensors identified are expensive, and they are generally
mass-based rather than chemical species-specific. Microelectromechanical systems
(MEMS) may represent a potential opportunity area for a broadly affordable, mobile PM
sensor. Nanoelectromechanical systems are also being pursued.
• Novel systems that use commercial sensors (or commercial systems alone) are
prevalent for a number of the study pollutants, including several criteria pollutants. This
finding is not unexpected given the long-standing regulatory status of those pollutants.
• Although research sensors or novel systems with commercial sensors were identified for
several of the study HAPs, sensors for acrolein and 1,3-butadiene appear limited.
Opportunities exist for developing lower-cost mobile sensors for these (and other)
pollutants, with general interest in such pollutants potentially increasing in the near term
due to emerging emission sources such as natural gas (notably shale gas) development
and biomass conversion facilities.
• Lower-cost sensors for benzene also represent an important area for research and
development, to fill this sensor gap and address practical information needs.
• Sensor systems that address multiple pollutants have commonly been modular, with
individual plug-ins to measure one pollutant at a time. Sensor arrays integrated into
mobile systems represent an expanding opportunity area for all-in-one multipollutant
sensing.
Detection Levels
In addition to the limitations for particle sensors indicated above, pollutant-specific insights from
the comparison of detection capabilities to target concentrations for the study gases follow.
• Current reported detection capabilities may not be sufficient to address the suite of
health-based benchmarks and example concentrations identified for the range of
settings and conditions considered in this evaluation.
• The detectability of illustrative values identified in this review is unknown for several
study pollutants, including acrolein. In part, this situation reflects a reporting issue,
because the detection range and/or maximum concentration tested is often missing from
the research literature, notably for contaminants beyond the gaseous criteria pollutants.
(The reported capability of a sensor to detect a relatively low concentration does not
necessarily translate to the same ability at a higher concentration, and vice versa.) In
addition, research results commonly reflect controlled conditions that may not directly
translate to field applications. More complete reporting of research sensor detection
ranges and field validation of these capabilities would improve detectability
determinations.
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• In terms of standards and guidelines, the reported sensor information indicates that most
health-based concentrations are potentially detectable by various research sensors or
novel systems that use commercial sensors, particularly for the criteria pollutants.
However, the relatively low (protective) guideline concentrations for continuous lifetime
exposures for certain HAPs may not be readily detected (including for acrolein; note
similar limitations apply to commercial sensors for this compound).
• Regarding detection limits, it is important for sensors to be able to measure
environmentally relevant pollutant concentrations, recognizing the range of
concentrations to which people are exposed under various circumstances. For example,
sensors would need to measure background levels in evaluating the protectiveness of
routine or chronic exposures or in assessing baseline conditions for natural settings.
Approaches to increase sensitivity such as via nanomaterials in films, coatings, or other
reactive surfaces of chemical sensors represent an ongoing opportunity.
• Relatively simple spectroscopic approaches may be well suited for situations in which
the detection level needed is relatively high, such as during emergency or episodic high-
pollution events, including wildfires, or in urban areas with chronic high emissions from
traffic or industrial facilities.
Architecture/Infrastructure
• General architecture: Portable, handheld and vehicle-mounted architectures are
relatively common; less common are wearable sensors. Sensor systems that leverage
existing infrastructure components (such as fixed and mobile elements of transportation
systems from the local to the national level) represent an opportunity for continued
advancements. (To illustrate, the City of San Francisco outlined an initiative that
involves attaching sensors to fixed infrastructure to assess exposures to pollutants from
vehicle exhaust, while cross-country trucks outfitted with simple sensors are collecting
data along their routes.) With regard to whether such initiatives would be broadly
sustainable, issues include who would pay and what advantages would be conferred.
• Devices: Sensor components are commonly integrated with mobile phones, tapping
Bluetooth/wireless networks. The trend toward increased use of other devices such as
tablets represents a further opportunity for mobile sensors and systems.
• Supporting infrastructure: Traditional field monitors are commonly supported by
substantial infrastructure to assure environmental controls. Reducing and eliminating
such housing and other support infrastructure while assuring reliable, automated
operation under a range of environmental conditions (with humidity and other
interferents) represents a continuing opportunity area for mobile sensors.
• Size and mobility: Mindful of the trade-off between size and sensitivity (which is affected
by the detection area), the trend is toward increasingly miniature sensors from
centimeter- to millimeter-scale and below. Spectroscopic and ionization sensors are
currently limited to a few cm, even with mirrors in cell pathways. Where larger sensors
are warranted, although they may not be suited for cell phones or wearable accessories
(e.g., watch or clip), they can be mounted to vehicles or other features/infrastructure to
support monitoring at the neighborhood level to community and metropolitan scales.
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• Sensitivity: Research to improve sensitivity includes the use of nanomaterial coatings to
increase reactive surface areas for chemical sensors. For spectroscopic and ionization
sensors, measures to increase sensitivity (i.e., lower the detection limits) include use of
pre-concentrators and tailored light sources as well as optimized light path designs.
• Selectivity and specificity: Chemical-specific measurement is a common issue, notably
in variable field conditions with multiple pollutants and interferents (e.g., high relative
humidity). Improvements being pursued in recent research include filtration mechanisms
and highly selective sorption media, with multiple-sensor arrays or electronic noses also
representing opportunities in this area.
• Response time: A number of studies are focusing on reducing the sensor response
time, both to reduce power consumption and to facilitate real-time measurements, which
are particularly important in dynamic environments. While nanomaterials are being used
to reduce response times for chemical sensors (by increasing reactive surface areas),
this increased reactivity can translate to a longer recovery time or can limit the sensor to
a single use. Similarly, although pre-concentrators can reduce response times for
spectroscopic and ionization sensors, the lag time required to accumulate a
concentration of pollutant sufficient to initiate the rapid response phase remains an
issue. Thus, improving response time represents an ongoing opportunity area for mobile
sensor research.
• Power consumption: Power requirements can be reduced by implementing a periodic
sampling approach, e.g., rather than sampling continuously, taking measurements only
when the sensor is in motion or at targeted times of the day when pollutants or
concentrations are expected to be changing or to be relatively high. Energy can also be
conserved by conducting passive versus active sampling campaigns. An evaluation of
novel detection techniques related to energy conservation indicates that reducing
operational temperatures, warm-up periods, and sampling times can reduce power
consumption as well as associated maintenance needs (and costs).
• Cost: Lower-cost mobile sensors are commonly qualitative or semiquantitative, such as
those relying on colorimetric techniques to indicate the presence of a pollutant or class
but not a specific concentration. More expensive sensor systems (such as those with
combined fixed and mobile architectures) may be indicated when data quality needs are
high (e.g., for enforcement purposes). Reducing system costs while maintaining high-
quality data represents an ongoing opportunity area.
• Energy sources: Novel energy sources (including human) and optimized sampling
regimens (e.g., targeted spatiotemporal campaigns guided by pollutant behaviors and
fate) offer opportunities for lower power use and more broadly affordable systems.
• Accuracy, precision, reliability and durability: Accurate and precise measurements
under dynamic field conditions across a wide variety of climatic and meteorological
conditions represent a continuing opportunity area for lower-cost mobile sensors.
Extensive sensor networks and saturation-sampling approaches (via concurrent
deployment of a large number of sensors) can facilitate comparisons across multiple
readings to assess sensor drift and link appropriate calibration and other maintenance
needs. Combined fixed and mobile sensor systems and advances such as
Web 4.0/lnternet of things represents opportunities for autocalibration. Opportunity areas
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for improved durability include reducing replacement or tuning needs for sensing media
and developing less expensive, reliable environmental controls (e.g., to address such
factors as humidity and other interferents).
Data Quality, Sharing, Management, and Analysis
• Algorithms and approaches for consistent data processing, quality assurance and
control, and data scrubbing (an error correction technique), transformation, integration,
visualization, and analysis represent active research areas.
• Fit for purpose is a key consideration for the type and quality of sensor data. With
anticipated needs ranging from compliance assessment and enforcement to establishing
environmental baselines and informing personal health decisions, the nature of the data
quality requirements differ across applications. The fit-for-purpose design of mobile
sensors and associated data systems represents an evolving opportunity area, to align
the technologies and software infrastructure to user needs across a variety of existing
programs and initiatives.
• Advances such as cloud computing and visualization tools are facilitating the
development and implementation of extensive networks for data upload, storage,
integration, display, and evaluation over time, including the ability to conduct
comparisons across readings. Tools highlighted in the research literature reviewed
include CaliBree (a self-calibration system for mobile sensor networks), Quintet (share
sensing resources among sensor devices), and Halo (facilitate the rendezvous of mobile
sensors with static infrastructure).
• Core protocols and platforms that underlie data collection, integration, sharing, storage,
and other data management systems represent ongoing opportunity areas. Frameworks
adaptable to multiple data sources, types, and uses (from facility compliance reporting
and enforcement to establishing environmental baselines and personal health
management) are being pursued to help strengthen the sharing, integration, and quality
assurance efforts for massive data sets.
• Privacy is a key concern related to citizen sensing. Tools for stripping data of identifiers
and other anonymizing techniques represent active research areas.
• Increasing automation and interconnectivity offer potential opportunities for increased
data coverage and integrated analyses, including collective autocalibration and self
repair (e.g., via the Internet of Things/Web 4.0, which has been described as an entire
web as a single operating system with information flowing from any point to any other).
Mobile Applications
• Only a limited number of mobile apps have been established for air quality, so this area
represents a key opportunity for research and development.
• Pollutants addressed by the existing apps are limited, reflecting the general lack of data
for pollutants other than the standard criteria set. Spatial coverage is also relatively
sparse per existing data limitations. Data availability often lags behind the collection
period. Recognizing the underlying limitations, development needs for mobile apps
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include addressing a much larger set of pollutants and providing real-time,
high-resolution spatial coverage across community, neighborhood, and individual scales.
• Computing advances indicated above provide opportunities for enhancing mobile apps
to accommodate real-time data upload, integration, distribution, display, and
interpretation. Providing context for that interpretation would increase the overall
usefulness of these apps. Considerations include links to existing standards and
guidelines as well as to previous data for that setting and similar settings (ranges and
averages), in addition to local, regional, and national trends. Beyond links to comparison
values, explanations would also be important (such as the exposure duration addressed
by a given standard or guideline, or the time frame represented by an historical
average).
• Indicators such as smart tags, alarms, and other notifications or warning features
triggered by measured pollutant levels also represent a research opportunity area for
mobile apps. A number of questions underlie this development area, including who
would define the warnings, on what basis, and for which pollutants.
• User interface design and programming components are active research areas toward
developing reliable interfaces that are stable across devices, that can accommodate the
rapid evolution of mobile phones and tablets, and that can be adapted to individual user
preferences.
Leveraging
• Do-it-yourselfers (DIYers) illustrate the evolving landscape of opportunity for citizen
participation in environmental monitoring.
• Leveraging the many organizations supporting related research and citizen capital
represents an opportunity for significantly increasing the characterization of air quality
nationwide. Collateral programmatic benefits including baselining for climate change
and adaptation planning, as well as exposure monitoring for environmental health
initiatives.
• Extending beyond integrated arrays for multipollutant sensing, links to biosensors (e.g.,
per personalized medicine applications) combined with insights from sensors for other
measurands offer opportunities for multipurpose sensing systems.
• Effective and efficient data and knowledge management approaches are being pursued,
with an eye toward common standards, infrastructure, and software for reporting,
accessing, sharing, storing/archiving, and maintaining data, considering both raw and
transformed data and topical syntheses and reports.
• Opportunity areas for mobile apps include providing high-resolution spatial coverage
(including at community and local scales) with displays for a full suite of pollutants.
These and other insights from recent research highlighted in this report and the companion
summary table are offered to help frame the EPA ORD roadmap for next-generation air
monitoring.
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1 INTRODUCTION
This report was prepared as part of the U.S. Environmental Protection Agency (EPA) Office of
Research and Development (ORD) initiative on mobile sensors and apps for air pollutants. A
key goal is to enhance community awareness and citizen involvement in environmental
monitoring and health protection programs. Additional goals are to facilitate compliance and
enforcement activities to improve and maintain air quality. The need underlying this initiative is
summarized in Section 1.1, the purpose and scope of this report are described in Section 1.2,
and the report organization is outlined in Section 1.3.
1.1 TRADITIONAL AIR POLLUTION MONITORING AND CURRENT OPPORTUNITY
A substantial number of air pollutants have been linked with adverse health effects, including
those associated with releases ranging from vehicle exhaust to industrial processes.
Understanding pollutant exposures is key to developing and implementing effective health
protection programs. The current standard system for monitoring air pollutants consists of
discrete stations with large, fixed sensors that are too expensive to be feasible for citizen-based
monitoring. These standard systems focus on a limited set of pollutants, in particular the six
criteria pollutants for which National Ambient Air Quality Standards (NAAQS) have been
established. Because both the number of these stations and the pollutants they measure are
limited, spatial and chemical coverage is relatively sparse.
For this reason, the pollutant levels to which individuals and communities are exposed is largely
unknown. Human exposures are dynamic and local variations are large because they are
affected by personal behaviors and activity levels as well as proximity to sources, the nature of
airborne releases, local meteorology, land use/land cover, and other factors. Thus,
measurements from regional metropolitan stations are unable to represent exposure levels at
the local scale. Pollutant concentrations at the neighborhood and individual levels constitute
key information needed to advance the development of practical health protection measures.
Recent advances in mobile sensing and related software applications (apps) provide a valuable
opportunity for addressing this need. Emerging detection technologies and techniques hold
significant promise for future cheap, mobile sensors that could fill in a network of air quality data
across the country. Concurrent developments in sensor architectures and information/
communication technologies (ICT), including apps, translate to opportunities for integrated
systems such as distributed sensor networks that can complement the existing monitoring
programs and facilitate community involvement.
1.2 PURPOSE AND SCOPE OF THIS REPORT
The purpose of this report is to provide an overview of recent research relevant to mobile
sensing of air pollution. The objective is to identify gaps as well as opportunities, to guide
investments toward making low-cost mobile sensors available to a wide variety of prospective
users, including individual citizens.
The science and technology literature on sensing technologies and techniques for air pollutants
is vast and growing. Because the purpose of this review is to indicate recent trends for mobile
sensors, rather than provide a comprehensive evaluation of current literature, the scope was
focused by several key considerations, as follows.
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• Size: Small, portable devices are the primary target. In some cases, larger sensors are
included to consider technologies that might present an opportunity for development of
more mobile devices in the future.
• Development phase: Sensors in an early stage of research and development to those
nearing deployment are the main focus. Commercial sensors incorporated into novel
sensor systems are also included.
• Time frame: The main emphasis is on literature information from 2010 to early 2012.
Some earlier publications are also included, for trends and other relevant context.
• Pollutant: An illustrative set was selected to guide the more detailed literature search.
The scope also includes practical context for assessing sensor detection capabilities. This
context is provided by two types of concentration values:
• Exposure benchmarks: Many agencies have established standards and guidelines for
chemicals in air as part of implementing specific health and safety programs. These
values range from emergency response guidelines to workplace standards and
reference concentrations considered safe for the general public over a lifetime of
exposures. Referred to as exposure benchmarks, these values serve as points of
comparison for assessing detection capabilities and opportunities for situation-specific
applications.
• Measured concentrations: A number of pollutants have been measured in a variety of
settings over different time periods. These include indoor and outdoor air, in urban and
rural areas that include settings where pollutants are markedly elevated, such as fires
and busy freeways. Some data reflect snapshot measurements while others reflect
systematic, long-term sampling programs. These illustrative concentrations provide
practical context for setting-specific applications.
Finally, sensing technologies and techniques are a key emphasis of this report. Information on
architectures and infrastructures for mobile sensors, including associated software, are also
included. Additional details are presented in supporting tables compiled separately from this
report (Raymond et al. 2013).
1.3 REPORT ORGANIZATION
This report is organized as follows:
• Chapter 2 summarizes the approach for conducting the literature search, developing the
candidate set of pollutants, and organizing the data.
• Chapter 3 provides results of the literature search, including the pollutant sets, main
categories of sensor technologies/techniques, and highlights of associated
architectures/infrastructures and apps. Also included are figures (graphical arrays) that
compare reported sensor detection levels to exposure benchmarks and example
measured concentrations.
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Chapter 4 describes gaps and opportunities for mobile sensors and apps for air
pollutants.
Chapter 5 gives a brief summary that highlights key findings of this report.
Chapter 6 acknowledges contributors to this report.
Chapter 7 lists selected information resources reviewed for this report.
Appendix A offers additional information about the literature search.
Appendix B presents an overview of exposure benchmarks and information sources.
Appendix C illustrates the role of environmental fate for air pollutants in air, to illustrate
the type of setting-specific information (such as relative humidity) and associated fate
products that can be used guide the development of integrated sensor systems.
Appendix D presents example concentrations of selected pollutants, organized in tables
designed to support data compilations for specific locations or regions of interest.
Appendix E provides supporting details about the sensors in Chapter 3 graphical arrays.
Appendix F briefly describes the sensing technologies and techniques reflected in this
report.
Appendix G presents an evaluation of selected air quality apps.
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2 APPROACH
The approach used to conduct the literature search is outlined in Section 2.1, development of
the candidate pollutant set is described in Section 2.2, and the general process for organizing
the information retrieved is summarized in Section 2.3. The general approach for searching and
reviewing the literature is illustrated in Figure 2-1, and supporting details are presented in
Appendix A.
2.1 LITERATURE SEARCH
Recognizing the extent of science and technology research relevant to mobile sensors and
apps, the purpose of this literature search is to identify general trends rather than provide a
comprehensive review. For this reason, the search focused primarily on information available
online. Information resources include:
• Journal articles, research reports, and other technical publications;
• Conference proceedings, presentations, and other meeting highlights;
• Patent databases, application and granting summaries;
• Organizational websites, including Federal, state, and international, agencies; academic,
national laboratory, and other research organizations; industry; and communities.
This search was conducted in two phases. The first phase was initiated in fall 2011 and
involved a broad search targeting abstracts and summaries, not limited to specific chemicals. Its
objectives were three-fold. First, an open approach would limit inadvertent omissions of key
technologies or techniques and avoid skewing suggested trends. Second, capturing a variety of
sensed parameters could offer additional insights into promising detection techniques for related
pollutants. Third, the measurands identified in a broader search would help guide selection of
the representative set of pollutants to be assessed in more detail.
The second phase of the literature search focused on a smaller set of pollutants, determined
from inputs by EPA ORD, Program and Regional scientists, combined with insights from
Phase I. This phase involved pursuing further details for sensing technologies/techniques,
architectures/infrastructures, exposure benchmark, and illustrative concentrations in air by
accessing full papers, reports, presentations, and other documentation. The information
compiled included details about the size, stage of development, and cost, as well as automation
and network capabilities, power requirements, response time, interferences, and other operating
conditions. In some cases, additional information was obtained from the individual researchers.
Most of this phase was conducted in winter to early spring 2012. Targeted follow-on searches
continued through 2013.
2.2 CANDIDATE POLLUTANTS
More than 40 pollutants of interest were identified during the early framing stage of this project.
These candidate pollutants reflected the following considerations:
• Criteria pollutants, i.e., those for which National Ambient Air Quality Standards (NAAQS)
have been established.
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Q>
I
1
1
i
CO
o
M
I
Review project aims and
selected research literature
to compile preliminary search terms
Initial search terms
Broad search
Science and
technology
literature (Web of
Science, PubMed,
sensor journals)
Conference, workshop,
other meeting materials
(agendas, proceedings,
presentations,
summaries, online and
via expert contacts)
Research
websites
(academia,
laboratory,
industry)
Calls for
proposals
(foundations,
agencies,
including
international)
Patent
databases
Technology
innovation
programs,
awards
(including
local groups)
Citizen
project
websites
/- Compilation of candidate
abstracts, meeting materials, project and program
^- descriptions, other data
Review
and
screen
i
for
relevance
,
Refined set of materials
for further evaluation
Check citations for additional
papers, and pursue as indicated
T
Refined search terms
(including study pollutants,
institutions, authors, journals)
Retrieve full papers,
pursue other details
T
Extract, organize,
and synthesize data
Additional materials
for integrati on
Phase SI Search, Retrieval, Review
FIGURE 2-1 Literature Search Approach
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31 October 2013
• Key pollutants from the National-Scale Air Toxics Assessment (NATA) study (EPA
2011), notably those identified as risk drivers and contributors. This set includes
chemicals listed as Hazardous Air Pollutants (HAPs) pursuant to the Clean Air Act
Amendments of 1990 and the subset subsequently evaluated as urban air toxics.
• Pollutants of potential interest to fenceline communities.
• Selected pollutants of interest to the EPA Children's Health Program.
These candidate pollutants are summarized in Tables 2-1 through 2-4. Tables 2-2 and 2-3 offer
two ways of summarizing the categories EPA used to identify key pollutants for the 2005 NATA
assessment (EPA 2011), "Drivers" and 'contributors" are largely based on the magnitude of the
risk estimate and the number of people affected. The two types of health effects considered
are: incremental risk of getting cancer over a lifetime, and the hazard index, which represents
the potential for noncancer effects (as the ratio of the exposure level to a "safe" daily level).
2.3 DATA COMPILATION
Results of the initial literature search were first compiled by information resource (e.g., patent
database or Ubicomp conference proceedings) then organized by common topic. The three
main topics are:
• Pollutants and other measured parameters (collectively referred to as measurands),
such as temperature and relative humidity.
• Sensor technologies and techniques.
• System architecture and infrastructure approaches, and associated apps.
Two additional themes are:
• Exposure benchmarks
• Example concentrations in air.
A number of data compilations were developed and refined during the literature review process,
as illustrated in the appendices. For example, Appendix A presents early tables that illustrate
data extraction and summary compilations. Similarly, details underlying the compilation of
exposure benchmarks are presented in Appendix B. Practical context that integrates
environmental fate considerations is presented in Appendix C, to illustrate how such information
can help frame the development of multi-pollutant sensors. Example outlines for compiling
measurement data for specific locations or regions are presented in Appendix D. In compiling a
summary of the original search results, entries were coded with identifiers (IDs) to facilitate
topical sorts, e.g., by pollutant or sensing technique. The master summary table is organized
alphabetically by measurand, and within that by sensor technology/technique (Raymond et al.
2013). A smaller subset table provides information for sensors considered for the graphical
arrays that compare detection levels to exposure benchmarks and example concentrations in
air; that table is presented in Appendix E. Results of limited additional check searches are also
presented in Appendix E. These "working" tables reflect the technology/ technique groupings
described in Appendix F. An overview of selected air quality apps is included in Appendix G.
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TABLE 2-1 Key Pollutants from the 2005 National-Scale Air Toxics Assessment3
Health Risk Driver
National
Acrolein
Formaldehyde
Regional
Benzene
Chlorine
(PM)
(HDI)
Naphthalene
Polycyclic aromatic hydrocarbons (PAHs)
(TDI)
Health Risk Contributor
National
Acetaldehyde
Acrylonitrile
Arsenic compounds
1,3-Butadiene
Carbon tetrachloride
Chromium compounds
Coke oven emissions
1,4-Dichlorobenzene
Ethylbenzene
Ethylene oxide
Tetrachloroethylene
Regional
1,3-Dichloropropene
Methylene chloride
Nickel compounds
Source: EPA (2011). Lighter font is used to indicate that the health effect characterization for the given chemical or
category of chemicals is based on a noncancer effect; regular font is used to indicate that the basis is cancer risk.
TABLE 2-2 Risk Characterization Categories Used to Identify Main Pollutants3
Number of People
Exposed (or more)
25 million
1 million
10,000
Cancer Risk
>10-4
Regional driver
>10-5
National driver
Regional driver
>10-6
National contributor
Noncancer Hazard
Index (HI) >1.0
National driver
Regional driver
TABLE 2-3 Estimated Risks and Number of People Exposed per Pollutant Category3
Risk Characterization
Category
National Scale
Driver
Contributor
Regional Scale
Driver
Contributor
Cancer
Risk exceeds:
Number exposed (or higher):
10-5
25 million
-I 0-6
25 million
-I 0-5 -I Q-4
1 million 10,000
-I Q-6
1 million
Noncancer
Hazard index (HI) exceeds:
Number exposed (or higher):
1.0
25 million
1.0
10,000
3 Tables 2-2 and 2-3 offer two ways of summarizing the characterization categories EPA used to identify key
pollutants from the 2005 NATA assessment (EPA 2011). As defined in the overview of that assessment:
- Cancer risk represents the upper-bound lifetime cancer risk (i.e., a plausible upper limit to the true probability that
an individual will contract cancer over a 70-year lifetime as a result of a given hazard, such as exposure to a
toxic chemical). This risk can be measured or estimated in numerical terms (e.g., one chance in a million).
- HI = sum of hazard quotients for substances that affect the same target organ or system. Because different
pollutants may cause similar adverse health effects, it is often appropriate to combine hazard quotients
associated with different substances to understand the potential health risks associated with aggregate
exposures to multiple pollutants.
More information regarding the bases for determining drivers and contributors can be found in EPA (2011).
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TABLE 2-4 Candidate Pollutants for the Review of Recent Research Sensors3
Air Pollutant
Acetaldehyde
Acrolein
Acrylonitrile
Arsenic compounds
Benzene
1,3-Butadiene
Carbon monoxide (CO)
Carbon tetrachloride
Chlorine
Chromium compounds
1,4-Dichlorobenzene
1,1-Dichloroethylene (1,1-DCE)
1,3-Dichloropropene
Ethyl benzene
Ethylene oxide
Formaldehyde
Hexabromocyclododecanes (HBCDs)
Hexamethyl diisocyanate
Hydrochloric acid
Hydrogen sulfide
Lead
Manganese compounds
Mercury
Methane
Methylene chloride
Naphthalene
Nickel compounds
Nitrogen oxides/dioxide (NOx/NCb)
Ozone
Particulate matter (PM)
Coke oven emissions
Diesel PM
Perchlorate
Perfluorocarbons (PFCs)
Phthalates
Polybrominated diphenyl ethers (PBDEs)
Polychlorinated biphenyls (PCBs)
Polycyclic aromatic hydrocarbons (PAHs)
Sulfur oxides/dioxide (SOx/SO2)
Tetrachloroethylene
Trichloroethylene
Toluene
2,4-Toluene diisocyanate
Xylene
Children's
Health
X
X
X
X
X
X
X
X
Region 6
Fenceline
Communities
X
X
X
X
X
X
X
X
X
X
NATA
c
N
N
N
R
N
N
N
N
R
N
N
N
R
R
R
N
R
N
nc
N
R
R
R
R
R
R
Criteria
Pollutant
(NAAQS)
X
X
X
X
X
X
EPA (2008)
Detection
Report
X
X
X
X
X
X
X
X
X
(HgCb-Hg)
X
X
X
X
X
X
aThis list reflects inputs from EPA staff in the Children's Health Program and Region 6, the criteria pollutants, and key
pollutants from NATA (EPA 2011) — including risk or hazard drivers (bold font), and risk or hazard contributors (in
italics)', c = cancer risk (not assessed for diesel PM), N = national risk (driver or contributor), NAAQS = National
Ambient Air Quality Standards, NATA = National-Scale Air Toxics Assessment, nc = noncancer hazard (driver or
contributor), R = regional risk contributor. EPA (2008) served as a resource for benchmarks and fate context.
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3 RESULTS AND DISCUSSION
3.1 INITIAL LITERATURE SEARCH
More than 400 sensors and systems were identified in the literature reviewed. Some of these
are novel sensors, while others are novel devices that incorporate commercial sensors.
Together, these sensors and systems address more than 100 pollutants and other measurands
(objects of measure), as highlighted in Table 3-1.
Non-pollutant measures include location, acceleration, temperature, and relative humidity. Both
temperature and humidity provide important context for assessing pollutant transformations and
subsequent fate products in air, as well as operating constraints that can affect sensor
suitability. (Additional information regarding the role of chemical fate in guiding potential
development of multi-pollutant sensors is presented in Appendix C.) Further details on the
sensors and measurands are available in the supporting master table compiled from the overall
literature review (Raymond et al. 2013).
3.2 STUDY POLLUTANTS
The initial candidate list of more than 40 pollutants that were identified from the NATA report
(EPA 2011) and inputs from EPA Regional and Program staff was winnowed to 14 pollutants,
based on further context gained from the initial literature search. In selecting a representative
study set, it was important to include both gases and particles because respective sensing
techniques differ. Additional factors considered included the types of activities, events, and
processes that led to their presence; their prevalence; and implications for human health and
welfare. Example emission sources for the study pollutants are included in Table 3-2.
The fourteen pollutants that comprise the study set comprise:
• Six criteria pollutants: carbon monoxide (CO), lead (Pb), ozone (Os), nitrogen dioxide
(NO2), particulate matter (PM), and sulfur dioxide (SO2).
• Five hazardous air pollutants (HAPs): acetaldehyde, acrolein, benzene, 1,3-butadiene,
and formaldehyde.
• Three indicator pollutants: ammonia (NHs), hydrogen sulfide (HbS), and methane (ChU).
The latter three are indicators of various airborne releases, including:
• Emissions from nuisance sources: For example, ammonia, hydrogen sulfide, and
methane are nuisance indicators of landfill and livestock operations.
• Greenhouse gas (GHG) emissions: Methane is a key GHG indicator.
• Emissions from specific activities, events, or processes: For example, benzene,
methane, and hydrogen sulfide may be indicators of natural gas exploration.
Regarding their physical state in air, lead and PM are particles and the other twelve are gases.
3-1
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31 October 2013
TABLE 3-1 Air Pollutants and Other Measurands3
I. Gas (in air)
II. Particle
A. Criteria Pollutants
Compliance Priority
Carbon monoxide (CO)
Nitrogen dioxide (NCb)
Nitric oxide (NO)
Nitrogen oxides (NOx)
Ozone (Os)
Sulfur dioxide (SCk)
Sulfur oxides (SOx)
Also of Interest
Carbon dioxide (CO2)
Compliance Priority
Lead
Particulate matter, PM
PMio
PM2.5
PMi
PM-black carbon, elemental carbon
PM-ultrafine particle
PM-nanoparticle
Also of Interest
Further PM:
Aerosol, nanoaerosol
Dust
Exhaust-auto, vehicle
Exhaust-diesel
Exhaust-gasoline
Exhaust-tailpipe
Mass-vehicle exhaust
B. Other Air Pollutants
Risk Priority
Acetaldehyde
Acrolein
Acrylonitrile
Ammonia (NH3, NH3-N)
Benzene
1,3-Butadiene
Carbon dioxide
Carbon tetrachloride
Chlorine
1.4-Dichlorobenzene (p-)
1,1-Dichloroethylene (-DCE)
1,3-Dichloropropene
Ethylbenzene
Ethylene oxide (oxirane)
Formaldehyde
Hexamethyl diisocyanate
Hydrochloric acid (HCI)
Hydrogen sulfide (H2S)
Methane (CH4)
Methylene chloride
Naphthalene
Perfluorocarbons (PFCs)
Tetrachloroethylene
Toluene
Trichloroethylene
Xylene
Toluene
Also of Interest
Acetone
Amine
Benzaldehyde
n-Butanol
Chloroform
Decane
Diethyl ether
Ethanol
Ethyl acetate
Exhaust gas (S)
Hexane
Isobutene
Isopropanol
Mercury vapor
Methanol
Methyl ethyl ketone
Oxygen
Phenol
Propane
Sulfur, sulfide
1,1,1-Trichloroethane
Trimethylamine
1 ,2,4-Trimethylbenzene
Risk Priority
Arsenic compounds
Chromium compounds
Coke oven emissions (also see PM)
Hexabromocyclododecanes (HBCDs)
Lead
Manganese compounds
Mercury
Nickel compounds
Polycyclic aromatic hydrocarbons
Perchlorate
Phthalates
Polybrominated diphenyl ethers
Polychlorinated biphenyls (PCBs)
Also of Interest
Ammonium (NhU)
Benzo(a)pyrene (BaP)
Cyclosarin (GF)
Dinitrotoluene (DNT)
Trinitrotoluene (TNT)
Sarin
Soman
C. Other Chemicals
Beer, wine, vodka
Chemical agents
Gas-combustible
Gas-liquid petroleum
Gas-natural
Mercaptans
Mixture: NO2-NH3
Odor
Organic vapors
VOCs
Vehicle exhaust (S)
Chemical explosives
Hydrocarbons
Organophosphates (OPs)
Pesticides (other)
Smoke
3-2
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31 October 2013
TABLE 3-1 (Cont'd.)
D. Physical and Environmental Parameters
Altitude, elevation
Illumination, light (visible, including light pollution)
Motion, acceleration (including earthquake)
Noise, sound, voice
Position, location
Pressure-air, barometric
Radiofrequency, RSS (received signal strength)
Rainfall
Relative humidity
Solar radiation
Temperature
Ultraviolet light
Wind speed, wind direction
E. Biological/Physiological
Parameters
Blood gas
Cotinine (nicotine metabolite)
Electrolytes
Glucose
Hormone residues
Nicotine
pH
F. Microbial Agents, Seasonal Events
Biological agent
£. co//
Viruses
Phenology (bud burst)
Bird migration
3 This table highlights selected measurands identified from the Phase I literature review. "Compliance priority"
indicates criteria pollutants. "Risk priority" indicates importance based on the 2005 National-Scale Air Toxics
Assessment (EPA 2011), Regional inputs for fenceline communities, and the EPA children's health program.
Polybrominated diphenyl ethers are commonly abbreviated as PBDEs, and polycyclic aromatic hydrocarbons are
commonly abbreviated as PAHs. The Phase I literature search results covered many of these priority chemicals.
Note that several biological/physiological parameters and microbial agents/seasonal events identified from the
literature search are listed in Sections E and F, respectively. Because the current effort focuses on sensors for air
pollutants, such measures are not discussed further in this report.
TABLE 3-2 Pollutant Study Set3
Pollutant
1. Acetaldehyde
2. Acrolein
3. Ammonia
4. Benzene
5. 1,3-Butadiene
6. Carbon monoxide
7. Formaldehyde
8. Hydrogen sulfide
9. Lead
10. Methane
11. Nitrogen dioxide
12. Ozone
13. Particulate matter
14. Sulfur dioxide
Formula or
Abbreviation
C2H4O
CshUO
Nhb
CeHe
C4H6
CO
CH20
H2S
Pb
CH4
N02
03
PM
SO2
Air Pollutant Category and Type
Criteria
Gas
Particle
Gas
Gas
Particle
Gas
HAP
Gas
Gas
Gas
Gas
Gas
(see footnote)
Indicator
Gas
Gas
Gas
Pollutants are listed with their primary category. The criteria pollutants are the six for which NAAQS have been
established. HAPs (hazardous air pollutants) were identified in the 1990 amendments to the Clean Air Act.
Hydrogen sulfide was inadvertently included in that original list and subsequently removed, but it is still subject to
accidental release provisions; it is also an indicator for various emission sources. Regarding indicators, ammonia,
methane, and hydrogen sulfide are nuisance indicators of sources such as landfills and livestock facilities; methane
and hydrogen sulfide are indicators for natural gas development; and methane is a key greenhouse gas (GHG)
indicator.
3-3
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31 October 2013
The number of sensors found for each study pollutant in the literature reviewed is shown in
Figure 3-1. Criteria pollutants are common sensor targets, as are several volatile organic
compounds (VOCs). Carbon monoxide was most frequently sensed (addressed by 63 sensors
overall), followed by nitrogen dioxide, PM, ammonia, sulfur dioxide, benzene, formaldehyde,
ozone, hydrogen sulfide, acetaldehyde, methane, acrolein, and 1,3-butadiene.
No sensors were found for atmospheric lead particulates. Although limited information was
found for sensing lead ions in water, this report focuses on air pollutants so that research is not
included in this report.
For the HAPs in the study set (all of which are VOCs), two of the top NATA cancer risk drivers
(see Table 2-1) are addressed by more than a dozen sensors each. These two pollutants are
benzene (a regional risk driver) and formaldehyde (a national risk driver). Eleven sensors were
found for one of the two national risk contributors, acetaldehyde. In contrast, only two sensors
each were identified for the other national risk driver, acrolein, and for the other national risk
contributor, 1,3-butadiene.
3.2 EXPOSURE BENCHMARKS
Exposure benchmarks are standards and guidelines established for chemicals in air by a
number of organizations under specific health and safety programs. Included in this set are the
air quality regulations established by EPA for six criteria pollutants in outdoor air, i.e., the
NAAQS. The overall suite of benchmarks covers different exposure durations for various
situations and in some cases different health effect levels, thus serving as practical points of
comparison for the detection levels reported for research sensors. That is, the range of
established benchmarks helps frame which technologies and techniques may be suited for a
given situation or not, for example, to assess whether a certain sensor is "fit for purpose" among
the range of purposes addressed by these benchmarks. Another aspect of this is proper
comparison by the device (e.g., to avoid false alarms when a 24-hr benchmark is exceeded by a
1-hour value). These health and safety benchmarks can be organized into three categories that
reflect the target group and situation or setting addressed:
• Emergency response (general public): Typically a single exposure lasting up to 8 hours.
• Outdoor air (general public): Continuous exposures, extending to a lifetime (70 years).
• Occupational exposure (adult workers): Workplace noncontinuous exposures (per
breaks between work shifts) over the work life (decades).
Federal and state agencies and several national organizations have established standards and
guidelines for chemicals in air to address specific program responsibilities and needs. These
agencies include U.S. EPA, U.S. Department of Health and Human Services, Centers for
Disease Control and Prevention, Agency for Toxic Substances and Disease Registry (ATSDR);
U.S. Department of Labor, Occupational Safety and Health Administration (OSHA);
U.S. Department of Defense, Department of the Army, Army Public Health Command; California
EPA (CalEPA); National Academies, National Research Council (NRC); National Institute for
Occupational Safety and Health (NIOSH); American Industrial Hygiene Association (AIHA); and
American Conference of Governmental Industrial Hygienists (ACGIH). A benchmark overview
is given in Table 3-3, and further information is provided in Section 3.5.1 and Appendix B.
3-4
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31 October 2013
70
•9
FIGURE 3-1 Number of Sensors Reported to Detect the Study Pollutants
(Counts include sensors that indicate the study pollutant, including systems involving novel architecture and infrastructure approaches.)
3-5
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TABLE 3-3 Overview of Selected Inhalation Benchmarks3
31 October 2013
Benchmark and
Organization
AEGL: Acute Exposure
Guideline Level
(NRC/EPA, DOE)
CEGL: Continuous
Exposure Guidance Level
(NRC/DoD)
EEGL: Emergency
Exposure Guidance Level
(NRC/DoD)
IDLH: (level) Immediately
Dangerous to Life or
Health
(NIOSH)
MEG: Military Exposure
Guideline
(DoD Army)
MRL: Minimal Risk Level
(ATSDR)
PEGL: Permissible
Exposure Guidance Level
(NRC/DoD)
PEL: Permissible
Exposure Limit
(OSHA)
PPEGL: Permissible
Public Exposure Guidance
Level
(NRC/DoD)
PPRTV: Provisional Peer-
Reviewed Toxicity Value
(EPA ORD)
RBC or RSC: Risk-Based
or Risk-Specific
Concentration
(EPA ORD)
REL: Recommended
Exposure Limit
(NIOSH)
Summary Description
Limits for acute airborne exposure to guide
emergency planning and response; targets
single release/exposure
Ceiling concentrations for continuous
exposures to avoid adverse health effects,
immediate or delayed
For rare emergency, ceiling to not cause
irreversible harm or prevent performance
of essential tasks
Maximum airborne concentration from
which a worker could escape after a short
exposure without harm or irreversible side
effects
Concentration for intakes in moderate and
arid climates to produce minimal to no
adverse effects (can adjust for the general
public by scaling intake)
Concentration based on no adverse
noncancer effects from continuous
exposures (note duration terms differ from
EPA definitions)
Repeated exposure of personnel during
military training exercises
Enforceable worker standard; can be a
time-weighted average (TWA, 8 hr), short-
term limit (STEL, 15 min), ceiling (C, to not
exceed; if direct monitoring is not sufficient,
ceiling assessed as 15-min TWA)
Repeated accidental exposure of the public
living or working near a military training
facility
Similar derivation process as for reference
concentration and unit risk, for provisional
RfC (p-RfC) and inhalation unit risk (p-ILJR)
Concentration corresponding to a given
target risk level (10~4, 10~5, or 1Q-6),
calculated from the inhalation unit risk
(IUR)
Guideline recommended for substances or
conditions that may be hazardous in the
workplace; TWA, STEL, and/or ceiling
Target
Group
General
public
Submarine
personnel
Submarine
personnel
Worker
Deployed
military
personnel
General
public
Military
personnel
Worker
General
public
General
public
General
public
Worker
Exposure
Duration
10 min, 30 min,
1 hr, 4 hr, 8 hr
90 d
15 min, 1 hr, 6 hr
30 min
1 hr
8hr
1 to 14 d
1 yr
Chronic (>1 yr)
Intermediate
(1 5-364 d)
Acute (1-1 4 d)
8 hr/d,
1,2,5 d/wk
8 hr/d,
40 hr/wk, yrs
8 hr/d,
1,2,5 d/wk
Chronic/lifetime
Chronic/lifetime
10 hr/d,
40 hr/wk, yrs
3-6
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31 October 2013
TABLE 3-3 Overview of Selected Inhalation Benchmarks3
Benchmark and
Organization
RfC: Reference
Concentration
(EPA ORD)
REL: Reference Exposure
Level
(Cal EPA OEHHA)
RSL: Regional Screening
Level
(EPA Regions)
SMAC: Spacecraft
Maximum Allowable
Concentration
(NRC/NASA)
TLV: Threshold Limit
Value
(ACGIH)
UR: Unit Risk
(EPA ORD; used to
calculate RBC, RSC)
Summary Description
Estimate of continuous daily inhalation
exposure concentration likely to be without
appreciable risk of adverse noncancer
effect over lifetime, including for sensitive
groups
Concentration associated with no adverse
effect, considering cancer and noncancer
Risk-based concentrations based on
generic exposure assumption, used as
initial screening concentration for
contaminated sites (e.g., Superfund)
Limits that monitor and control traces of
gas in a spacecraft cabin, to avoid
noncancer effects
Health-based concentration for the
workplace, designed for exposures without
experiencing adverse health effects; can
be a TWA, STEL and/or ceiling
Plausible upper-bound probability of
developing cancer from the exposure over
lifetime; assumes continuous exposure at a
unit concentration (e.g., 1 ug/m3)
Target
Group
General
public
General
public
General
public
Astronauts
Worker
General
public
Exposure
Duration
Chronic/lifetime
(limited set for
shorter durations,
e.g., acute, 24 hr)
Chronic/lifetime
Acute/1 hr, 8 hr
Chronic/lifetime
1 hr, 24 hr
7d, 30 d, 180 d
8 hr/d,
40 hr/wk,
throughout
working yrs
Chronic/lifetime
a These benchmarks are listed alphabetically; shading indicates the application category:
rose = emergency response; blue = occupational; green = ambient (continuous exposures that
extend to a lifetime). Note that benchmarks for the general public include sensitive subpopulations.
In deriving these values, the standard adult is taken to be 70 kg, and inhalation rates assumed for
occupational and public (residential) exposures are generally 10 m3 and 20 m3/d, respectively.
For benchmarks that address less-than-lifetime exposures, depending on the chemical and the
underlying study, some shorter-duration values may also be relevant for longer exposure durations,
e.g., where the underlying study(ies) addresses the longer period. Conversely, benchmarks for
shorter durations can also provide useful context for longer exposures (considering time scaling,
depending on the nature of the effect), and they can also serve as bounding indicators. Note many
of the guideline values undergo regular review to keep pace with evolving scientific knowledge.
In addition to benchmarks for the general public, occupational exposure levels (OELs) can also
provide useful context for assessing sensor detection capabilities and potential opportunities.
This context is not only with regard to considering "fit-for-purpose" opportunities for such
settings, these values may also be considered in assessing some public exposures (e.g., as
illustrated following the World Trade Center disaster, for pollutants without public benchmarks.)
3.3 EXAMPLE CONCENTRATIONS IN AIR
Pollutant concentrations in air result from a wide variety of releases, and Table 3-4 highlights
common emission sources for the study pollutants. Illustrative concentrations for the study
pollutants are shown in Table 3-5, together with example emission sources.
3-7
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31 October 2013
TABLE 3-4 Pollutant Study Set and Associated Emission Sources3
Pollutant
1. Acetaldehyde
2. Acrolein
3. Ammonia
4. Benzene
5. 1 ,3-Butadiene
6. Carbon monoxide
7. Formaldehyde
8. Hydrogen sulfide
g. Lead
10. Methane
11. Nitrogen dioxide
12 Ozone
13. Particulate matter
14. Sulfur dioxide
Example Sources
Vehicle
exhaust
S
^
^
^
^
S
S
S
^
^
Oil and gas
production,
petroleum
facilities
^
^
^
S
S
S
S
S
^
^
^
Fossil fuel
mining,
power plants
^
^
^
S
S
S
S
S
^
^
^
Metal
smelting,
refining
facilities
^
S
^
^
^
^
Pulp and
paper mills
^
^
^
^
^
^
Landfills
^
^
^
^
^
^
Agriculture,
livestock
^
^
^
^
^
Waste
incinerators
^
^
^
^
^
^
^
^
^
Biodiesel
production,
biomass
combustion
^
^
^
^
^
^
^
^
Tobacco
smoke
^
^
^
^
^
^
^
^
^
Criteria pollutants (in bold font) are those for which National Ambient Air Quality Standards have been established. (Note that vehicle exhaust was a source of
sulfur dioxide in the past, less so today). Of the remaining eight, six (all but ammonia and methane) were in the original list of HAPs (hazardous air pollutants)
from the 1990 amendments to the Clean Air Act. Hydrogen sulfide was inadvertently included in the original list and subsequently removed, but it is still subject
to accidental release provisions. All three indicators (ammonia, hydrogen sulfide, and methane) are nuisance pollutants associated with livestock operations.
Hydrogen sulfide and methane are also indicators for natural gas development (as is the HAP benzene), and methane is a key greenhouse gas (GHG) indicator.
Example sources: Some are indirect; e.g., ozone is formed by the reaction of precursors, VOCs and nitrogen oxides (such as from vehicle exhaust) in sunlight;
similarly, photochemical oxidation of hydrocarbon combustion products forms formaldehyde. See Table 3-5 for supporting data and information sources. Note
that many of these pollutants are also emitted in indoor environments (e.g., homes and workplaces), in some cases in higher concentrations than in outdoor air.
3-8
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31 October 2013
TABLE 3-5 Example Airborne Concentrations and Emission Sources for the Study Pollutants3
Pollutant
(concn unit)
National Ambient
Air Quality Standards
Averaging Time
Concn
Illustrative Concentrations
Concn
Context
Example
Emission Sources
Selected
Information Resources
Criteria Pollutants
Carbon
monoxide
(ppm
by volume)
Lead
(Ijg/m3)
8hr
(not to be exceeded
more than once a yr)
1 hr
(not to be exceeded
more than once a yr)
3 mo
(rolling average)
(not to be exceeded)
9
35
0.15
0.12
0.04
5
0.5-5
5-15
0.015
<0.05
0.11
Northern hemisphere
Southern hemisphere
Busy traffic conditions
Home without gas stove
Home with gas stove
2010 per annual maximum
3-month average
2002 ambient air
Mean for indoor air,
per EPA Region 5 survey
Vehicle exhaust (dominant
source), with smaller amounts
possibly from petroleum
refineries, petrochemical
plants, gas and coal-burning
power plants, coke oven
plants, biomass combustion,
wildfires and controlled burns
(incomplete combustion)
Lead smelting and refining
facilities, steel welding and
cutting operations, battery
manufacturing plants, radiator
repair shops, rubber and
plastic production facilities,
printing facilities, waste
incinerators, leaded gasoline
combustion, firing ranges,
and construction activities
ATSDR(2012)(ToxGuide)
http://www.atsdr.cdc.qov/toxquides/toxquide-
201.pdf
ATSDR (2012) (Toxicological
Profile)
http://www.atsdr.cdc.qov/ToxProfiles/tp201-
c6.pdf
EPA (2000) (Air Quality Criteria)
http://cfpub.epa.qov/ncea/cfm/recordisplav.cf
m?deid=18163
EPA (2012) (Air Trends)
http://www.epa.qov/airtrends/lead.html
ATSDR (2007) (ToxGuide)
http://www.atsdr.cdc.qov/toxquides/toxquide-
13.pdf
ATSDR (2007) (Toxicological
Profile)
http://www.atsdr.cdc.qov/toxprofiles/tp13-
c6.pdf
3-9
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31 October 2013
Pollutant
(concn unit)
Nitrogen
dioxide
(ppb)
Ozone
(ppm)
PM2.5
(Ijg/m3)
PMio
(Ijg/m3)
National Ambient
Air Quality Standards
Averaging Time
Annual (mean)
1 hr
(98* percentile
averaged over 3 yr)
8hr
(annual 4th highest
daily maximum 8-hr
concn averaged over
3 yr)
Annual
(primary and
secondary means,
averaged over 3 yr)
24 hr
(98* percentile.
averaged over 3 yr)
24 hr
(not to be exceeded
more than once a yr
on average over 3 yr)
Concn
53
100
0.075
12
15
35
150
Illustrative Concentrations
Concn
10
530
0.07
10
11.5
51
63
Context
2012 annual mean
Busy traffic conditions;
hourly average
2010 mean, per
annual 4th maximum
8-hr average
2010 mean,
per seasonally weighted
annual average
2006-2008 mean,
per annual average
2009 mean,
24- hr average
2010 mean, per annual 2nd
maximum
24- hr average
Example
Emission Sources
Vehicle exhaust, coal-burning
power plants, petroleum and
metal refining facilities, wood
burning
Vehicle exhaust, power
plants, refineries, chemical
plants, industrial boilers
(formed by reaction of volatile
organic compounds and
nitrogen oxides with sunlight)
Vehicle exhaust, fossil fuel
combustion, industrial
processes, wood burning,
agricultural operations,
construction and demolition
activities
Selected
Information Resources
EPA (2012) (Air Trends)
http://www.epa.aov/airtrends/nitroaen.html
EPA (2012) (Our Nation's Air)
http://www.epa.qov/airtrends/201 1/
EPA (2008) (Integrated Science
Assessment for Oxides of
Nitrogen)
http://www.epa.qov/ncea/isa/
EPA (2012) (Ground Level Ozone
FAQ)
http://www.epa. qov/qlo/faq.html#where
EPA (2012) (Air Trends)
http://www.epa.qov/airtrends/ozone.html
EPA (201 2) (Air Trends)
http://www.epa.qov/airtrends/pm.html
EPA (2010) (Report on the
Environment)
http://cfpub.epa. qov/eroe/index.cfm?fuseacti
on=detail.viewlnd&lv=list.listbvalpha&r=2313
31&subtop=341
EPA (2004) (Looking at Trends)
http://www.epa. qov/air/airtrends/aqtrnd04/p
mreportOS/pmlooktrends 2405.pdf
3-10
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31 October 2013
Pollutant
(concn unit)
Sulfur
dioxide
(ppb)
National Ambient
Air Quality Standards
Averaging Time Concn
1 hr
(99* percentlle of 1-hr 75
daily maximum concn
averaged over 3 yr)
3hr
(not to be exceeded 500
more than once a yr)
Illustrative Concentrations
Concn
2.2
600-3,000
Context
2010 mean,
annual arithmetic average
Breathing zone of
forest fires
Example
Emission Sources
Power generation, fuel
combustion, industrial
processes such as refining
and smelting, volcanoes
Selected
Information Resources
EPA (2012) (Air Trends)
http://www.epa.aov/airtrends/sulfur.html
ATSDR (1998) (Toxicological
Profile)
http://www.atsdr.cdc.aov/ToxProfiles/tp116-
c5.pdf
Additional Study Pollutants
Acetalde-
hyde
(ppb)
Acrolein
Not applicable
Not applicable
0-0.8
1.6-2.8
2.8 (5 |jg/m3)
32
3-15
113
28-3,600
(0.05-6.4
mg/m3)
780-4,900
(1.4-8.8
mg/m3)
0.5-3.2
Rural regions (Point
Barrow, Alaska; Whiteface
Mountain, New York)
South Coast Air Basin
(California), 24-hr sample
averages
Ambient air, average
Los Angeles, ambient
Indoor, California
Indoor, with smokers,
California
Diesel exhaust
Gasoline exhaust
Outdoor air
Cigarette smoke, incomplete
combustion processes
(tailpipe exhaust and fires),
photochemical oxidation of
hydrocarbons, agricultural
burning, wildfires, fireplaces,
woodstoves, cooking, building
materials, nail polish remover
Vehicle exhaust, biomass
CalEPA Air Resources Board
(1993)
http://www.oehha.ora/air/toxic contaminants
/html/acetaldehvde.htm
NTP (2011) (Report on
Carcinogens)
http://ntp.niehs.nih.aov/ntp/roc/twelfth/profile
s/Acetaldehvde.pdf
EPA (1992, 2000) (Hazard
Summary)
http://www.epa.aov/ttnatw01/hlthef/acetalde.
html
ATSDR (2007) (ToxGuide)
3-11
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31 October 2013
Pollutant
(concn unit)
(ppb)
Ammonia
(ppb)
Benzene
(ppb)
1,3-
Butadiene
(ppb)
National Ambient
Air Quality Standards
Averaging Time Concn
Not applicable
Not applicable
Not applicable
Illustrative Concentrations
Concn
<0.02-12
0.3-6
0.58
0.36-1.4
0.04-1
0.1
Context
Indoor residential air
Global average
Metropolitan areas
Ambient outdoor air,
annual mean, 2009
(10th-90th percentile range)
Cities and suburban areas
Outdoor air (Texas 2003
annual average; excluding
point source
downwind monitors)
Example
Emission Sources
conversion and biodiesel
production facilities, heating
fats, tobacco smoke
Fertilizer production plants,
household cleaning products
(and production facilities),
animal excreta and decaying
organic matter, volcanoes
Vehicle exhaust, gas stations,
tobacco smoke, natural gas
development
Vehicle exhaust,
manufacturing plants, burning
rubber or plastic, burning
wood, forest fires, tobacco
smoke
Selected
Information Resources
http://www.atsdr.cdc.qov/toxquides/toxquide-
124.pdf
ATSDR (2004) (ToxGuide)
http://www.atsdr.cdc.qov/toxquides/toxquide-
126.pdf
ATSDR (2007) (ToxGuide)
http://www.atsdr.cdc.qov/toxquides/toxquide-
3.pdf
ENVIRON (2012) (Hydrofracking:
Air Issues and Community
Exposure)
http://lawweb.colorado.edu/law/centers/nrlc/
events/hottopics/Kaden%20PPT%20(1-27-
12 .pdf
ATSDR (201 2) (ToxGuide)
http://www.atsdr.cdc.qov/toxquides/toxquide-
28.pd
Texas (2007)
http://www.ncbi.nlm.nih.qov/pubmed/170115
34
3-12
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31 October 2013
Pollutant
(concn unit)
National Ambient
Air Quality Standards
Averaging Time
Concn
Illustrative Concentrations
Concn
Context
Example
Emission Sources
Selected
Information Resources
Formalde-
hyde
(ppb)
Not applicable
0.4
Mean, natural background
0.2-6
Rural and suburban areas
1-20
Urban areas
21-98
California,
monitored classrooms
(mean-max)
20-4,000
Indoor air
83
(100ug/m3)
Conventional homes
(mean, Southern
California)
0.8(0.5,0.1)
Odor threshold (lower
reflects sensitive noses)
9.1
Heavy traffic, inversions
(short-term peaks)
45
(<83)
Manufactured homes
(mean, mobile homes)
<100
(mean <50;
25-60 ug/m3)
Homes without
urea-formaldehyde foam
insulation (UFFI)
>300
Homes with substantial
pressed wood products
Photochemical oxidation of
hydrocarbon combustion
products (88%), power plants,
incinerators, unvented gas or
kerosene heaters, carpets and
permanent press fabrics,
wood product manufacturing
(plywood, furniture),
automobile exhaust, cigarette
smoke, latex paints,
varnishes, fingernail polish
and remover, preserved
specimens
ATSDR (1999) (Toxicological
Profile)
http://www.atsdr.cdc.gov/toxprofiles/tp111 .p
df
EPA (2007) (Technology Transfer
Network, Air Toxics Web site)
http://www.epa.gov/ttnatw01/hlthef/formalde.
html#ref1
CalEPAOEHHA(2001)
(Children's Environmental Health
Protection Act)
http://www.oehha.org/air/toxic contaminants
/pdf zip/formaldehyde final.pdf
EPA (2012) (Introduction to Indoor
Air Quality)
http://www.epa.gov/iaa/formaldehvde.html
WHO (2001) (Air Quality
Guidelines)
http://www.euro.who.int/ data/assets/pdf fil
e/0014/123062/AQG2ndEd 5 SFormaldehv
de.pdf
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31 October 2013
Pollutant
(concn unit)
Hydrogen
sulfide
(ppb)
Methane
(ppm)
National Ambient
Air Quality Standards
Averaging Time Concn
Not applicable
Not applicable
Illustrative Concentrations
Concn
0.11-0.33
<1
0.4-2.4
>90
2.2
600,000
Context
Ambient air
Urban air
Homes near concentrated
animal feeding operations
(CAFOs)
Homes near industrial
facilities
Natural atmosphere
Unlined landfills (average)
Example
Emission Sources
Pulp and paper mills,
petroleum refineries, natural
gas production plants,
geothermal power plants,
coke oven plants, iron
smelters, food processing
plants, swine containment
facilities and other CAFOs,
manure handling, wastewater
treatment facilities, swamps,
stagnant water, volcanoes,
natural gas development
Fossil fuel mining and
distribution (including natural
gas and petroleum systems),
livestock, landfills, biomass
burning (including wildfires),
wetlands, rice cultivation,
stationary and mobile
combustion, natural gas
development
Selected
Information Resources
ATSDR (2006) (ToxGuide)
http://www.atsdr.cdc.aov/toxauides/toxauide-
114.pdf
ENVIRON (2012) (Hydrofracking:
Air Issues and Community
Exposure)
http://lawweb.colorado.edu/law/centers/nrlc/
events/hottopics/Kaden%20PPT%20(1-27-
Purdue Extension (2007) (CAFOs)
http://www. extension. purdue. edu/extmedia/l
D/cafo/l D-358-W.pdf
New England Waste Services of
Vermont, Inc. (2006)
http://www.anr.state.vt.us/DEC/wastediv/soli
d/documents/NEWSVTAttachF.pdf
Alberta Environmental Protection,
CH2M Gore and Storrie Limited
(1999)
http://www.environment.aov.ab.ca/info/librar
v/5847.pdf
a These illustrative concentrations represent data from an online search to indicate the variety of concentrations to which people could be exposed
in different types of settings and over different time frames (e.g., short-term peaks to annual averages). The values in this table are generally
rounded to two significant figures. Similar summaries of example concentrations in air for selected pollutants are provided in Appendix D. Those
tables illustrate how data inputs from EPA Regional staff and others interested in region- and setting-specific compilations could be organized to
inform community-based initiatives. National Ambient Air Quality Standards (NAAQS) are only established for six criteria pollutants. Values for
lead, nitrogen dioxide (annual mean), ozone, PPvh.5 (24-hour), and PMio are joint primary-secondary standards. Carbon dioxide, nitrogen dioxide
(1-hour), PM25 (annual mean of 12 ug/m3), and sulfur dioxide (1-hour) are primary standards, while PPvbs (annual mean of 15 ug/m3), and sulfur
dioxide (3-hour) are secondary standards. Primary standards are defined to protect public health, including sensitive populations; secondary
standards protect public welfare, including protecting against decreased visibility and damage to animals, crops, vegetation and buildings. The
NAAQS entries (concentrations and averaging times) do not correspond to the example ambient levels in the adjacent column. Additional
resources for methane include: EPA (2013) (Methane Emissions); http://epa.gov/climatechange/ghgemissions/gases/ch4.html, and EPA (2013)
(Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2010); http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html.
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As evident from Table 3-5, selected pollutant measurements are available for a number of
settings and time periods, including for areas with common ambient emission sources (e.g.,
vehicle exhaust) as well as emergency response-related settings (e.g., breathing zone of forest
fires). Given the variety of community interests across different regions, companion tables are
provided in Appendix D to facilitate the organization of pollutant measurement data for locations
of interest to specific communities.
Also included in Table 3-5 are the regulatory standards for the criteria pollutants, as points of
comparison for their example measurements. As described in the preceding section, like
ambient measurements, the NAAQS and other exposure benchmarks provide practical context
for assessing sensor detection capabilities. Both benchmark concentrations and example
concentrations in air are plotted with sensor detection levels in the graphical arrays for each
study pollutant presented in Section 3.5, to facilitate comparisons and frame the evaluation of
gaps and opportunities for different settings and situations.
3.4 SENSOR TECHNOLOGIES AND TECHNIQUES
3.4.1 Sensing Categories
The sensors highlighted in this report are organized into three main categories based on the
underlying detection technique: chemical interaction (hereafter referred to as chemistry or
chemical techniques), spectroscopy, and ionization. A brief description of these categories and
underlying sensing principles follows, additional information is provided in Appendix F. The
numbers of sensors identified in these categories from the literature reviewed is presented in
Table 3-6.
Chemistry
This sensing technique involves contact-based chemical interactions. This category includes
solid-state sensors with a chemical film that reacts with a gas to produce a signal, e.g., due to a
change in mass or electrical properties. That signal is then analyzed to indicate the presence
and/or concentration of the given pollutant. Sensors in this category are divided into four groups
in this report to help highlight active research areas:
• Electrochemical sensors - typically an electrochemical cell with a solid or liquid
electrolyte; the chemical reaction of an incoming substance at the working electrode
creates an electrical potential difference between that electrode and the reference
electrode. Metal oxide semiconductors (MOS), also referred to as chemiresistors, and
electrochemical membranes account for most entries in this category.
• Nanotechnology-based sensing materials - including nanocrystalline metal oxides,
carbon nanotubes (CNTs), and organic nanocomposites; these materials can be used as
stand-alone sensing films and can also be incorporated into other sensor systems.
• Polymer films - including hybrid films, with thin-film organic polymers providing
conductive or fluorescent surfaces.
• Surface acoustic wave (SAW) sensors - with a chemical film that selectively adsorbs a
gas, producing a change in mass, detected by a change in surface-propagating waves.
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31 October 2013
TABLE 3-6 Technologies/Techniques Reflected in Research Sensors and Systems3
Technology/Technique
Chemistry
Electrochemical
(notably MOS and
membrane sensors)
Nanotechnology-
based
Polymer film
Surface acoustic wave
Spectroscopy
Absorption
Emission
Laser absorption
LIDAR
Light scattering
lonization
Mass spectrometry
Gas chromatography
Other
Number
73
30
28
10
5
57
19
18
15
4
1
5
3
2
3
Pollutants
Acetaldehyde, acetone, alcohols, ammonia, benzene, CO,
carbon dioxide, formaldehyde, NO2, NOx, phenolate, PM, VOCs
Acetaldehyde, ammonia, benzene, CO, carbon dioxide,
formaldehyde, hydrogen sulfide, methane, NO2, oxygen, SO2,
VOCs
Acetaldehyde, ammonia, CO, formaldehyde, trinitrotoluene
Hydrogen sulfide, NOx
Ammonia, benzene, CO, NO2, SO2, explosives
Acrolein, ammonia, hydrogen sulfide, SO2, VOCs
Acrolein, BTEX, CO, NO2, PM, VOCs
Ammonia, hydrogen sulfide, NO2, Os, PM
PM
PM-ultrafine particles (UFPs), VOCs
BTEX, other VOCs
PM
a The numbers in bold font represent the totals for each of the four categories. Counts include duplicates because
some devices/technologies contain components that cross multiple categories. Also, these values reflect a number
of pollutants/parameters reported in the literature reviewed, not just those comprising the study set; for example,
trinitrotoluene (TNT) and others are included n this summary. Sensors using the LIDAR technique were found as
part of the literature review and are included in this table; however, they are not generally considered to be portable
and are not included in subsequent count figures. Note that metal oxide semiconductors (MOS) and
electrochemical membrane sensors dominate within the electrochemical group.
Spectroscopy
Spectroscopic techniques rely on chemical-specific emission and/or absorption spectra that
result from molecules interacting with an energy source such as light. Commonly referred to as
optical sensors, these devices identify pollutants based on their absorption or emission
characteristics. These sensors can be further grouped as follows:
• Absorption Spectroscopy - based on absorption peak patterns and intensities using
infrared (IR), ultraviolet (UV), or visible light.
• Laser absorption Spectroscopy - including quantum cascade lasers (QCLs), tunable
diode lasers, and organic microlasers that operate at a specific light frequency range.
• Emission Spectroscopy.
• Light scattering, or nephelometry.
Light detection and ranging (LIDAR) is another spectroscopic technique identified in the
literature search; this sensing technique is based on back-scattered light from interaction with a
laser or other light source. Although LIDAR detectors are not currently used in mobile sensing
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31 October 2013
(thus are not reflected in the accompanying figure), they could potentially represent an
opportunity for future research. Laser-induced breakdown spectroscopy (LIBS) is another
technique that might represent a future opportunity, although it was not reflected as such in the
literature reviewed. This technique uses a high-power, pulsed laser beam to induce a plasma
that vaporizes, atomizes, and excites the sample gas, and the identity and concentration of the
substance are then determined from the intensities of the resulting atomic emissions.
lonization
This sensing technique involves identifying pollutants on the basis of their ions. A gas
chromatograph (GC) is frequently placed in front of ionization detectors for selective separation,
and some mass spectrometers also use front-end chromatography to achieve selection. Three
groups of sensing techniques within this category are:
• Photoionization detection - which measures the ionization potential (IP) of gases at or
below the frequency of light emitted from an ultraviolet (UV) lamp. Concentrations are
determined by the extent of ion deposition on the collecting electrode resulting from
photon absorption. Photoionization detectors (PIDs) often serve as detectors in GC
systems. (Note that PIDs, including commercial sensors, are not reflected in the
sensors highlighted in Table 3 6.)
• Flame ionization detection - which involves mixing a sample gas with hydrogen then
introducing a flame, and collecting the released electrons at electrodes where the energy
is converted to electrical output signals. Flame ionization detectors (FIDs) commonly
serve as detectors in GC systems.
• Mass spectrometry - which consist of an ion source, analyzer, detector, and data
recorder; several aspects of these systems can be changed to address the specific
chemical species to be measured. Major ion formation techniques include electron
impact ionization, chemical ionization, fast atom bombardment, electroscopy ionization,
and matrix-assisted laser desorption ionization. Analyzers include magnetic,
electrostatic, quadrupole, ion trap, time of flight, and Fourier transform ion cyclotron
resonance. Detector components include secondary electron multipliers,
photomultipliers, and multi-channel plates.
3.4.2 Technology/Technique Trends for Mobile Sensors
Of the 138 studies reviewed that focus on research sensors: spectroscopic and chemical
techniques dominate for the selected set of pollutants (see Table 3-6 and Figure 3-2). Chemical
techniques are reflected in 73 studies while 57 reflect spectroscopy, five reflect ionization, and
three use other methods such as microelectromechanical systems (MEMS). Note that nano-
electromechanical systems are also being developed. Counts for subcategories within the three
main categories are also included in Figure 3-2. Electrochemical and nanotechnology-based
sensors (grouped with chemical techniques) lead the subcategories. Chemical techniques are
most common across all measurands and also for the study set, as shown in Figure 3-3.
Insights from this limited set indicate no broad-spectrum sensor exists, i.e., no single
technology/technique can detect all 14 (see Figure 3-3). However spectroscopic techniques are
reported for all but one (lead), and chemical techniques apply to all but three (acrolein, lead, and
ozone). Many commercial sensors are available for ozone, including electrochemical sensors;
but this review focuses on research sensors and novel systems, and these were not found for
ozone. Three pollutants are sensed by all three techniques: PM, benzene, and 1,3-butadiene.
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31 October 2013
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FIGURE 3-3 Detection Techniques Reflected in Sensors and Systems for the Study Pollutants
3-19
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31 October 2013
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2010
2011
2012
FIGURE 3-4 Technology/Technique Counts by Year (2010 to early 2012)
3-20
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31 October 2013
To assess recent trends, the number of research studies distributed across the three main
sensing techniques is plotted by year in Figure 3-4. Publications for sensors focusing on
chemical techniques increased substantially in 2011 and have continued to increase since then.
Note that this figure only reflects publications from the beginning of 2012; subsequent checks of
selected literature through 2012 confirm this trend, with nanotechnology also continuing to play
a prominent role. Similar results are indicated for spectroscopic techniques. Most of the recent
research focuses on refining existing technologies and techniques to gain improvements such
as smaller size, greater sensitivity, and lower power consumption, rather than developing wholly
new approaches. For example, nanoparticle coatings are being added and light frequencies are
being altered to increase the sensitivity of various devices, and many spectroscopic sensors are
incorporating nanotechnology to improve portability.
3.5 DETECTION CAPABILITIES
From the literature reviewed, nearly 170 sensors were reported to detect at least one of the
Hair pollutants studied. These include research sensors as well as novel systems that
incorporate commercial sensors. Summary information for these sensors and systems is
tabulated in Appendix E (Tables E-1 and E-2), which also includes reference levels for
commercial sensors that were selected to represent standard sensors for those pollutants. Of
these sensors, more than 70% (118 of 166) identify the sensing technique. About half (87)
report a lower detection limit (LDL) or similar value (i.e., minimum tested concentration), and
less than a quarter (37) include an upper limit of quantitation or maximum tested concentration.
The sensing techniques for each pollutant are summarized in Table 3-7 together with the LDLs
where available. The lowest (most protective) benchmark concentration is included with the
pollutant (in the first column) as context for this overview of potential detection capabilities.
(Note this lowest benchmark is also included with others in graphical arrays to facilitate
comparisons, as described in Section 3.5.1.) The LDLs that achieve this lowest benchmark are
shaded green, the others are shaded blue. When no LDL was identified, an 'x' is used to signify
that technique is reported for the given pollutant. A blank cell indicates the technique was not
identified for that pollutant (e.g., see the row for lead, for which no research sensors were
found). The only commercial sensors included in this table are those used in novel sensor
systems. Information from studies that only addressed sensor architectures and infrastructures
is presented in Section 3.6 (also see Raymond et al. [2013]).
3.5.1 Graphical Arrays of Exposure Benchmarks and Reported Sensor Detection Levels
Graphical arrays have been created for each of the study pollutants to compare reported sensor
detection levels with established exposure benchmarks. Array elements are described below.
Selected Benchmarks
Benchmarks have been established by EPA and other agencies to protect the health and safety
of the general public and workers under various conditions. The selected benchmarks
presented in the arrays are organized according to four time periods (labeled at the top of each
array) to reflect the exposure durations addressed: acute, short term, subchronic, and chronic.
The symbols plotted on these arrays identify the concentration of the benchmark (y-axis) for the
given exposure duration (x-axis). Shading indicates the benchmark category: (1) rose signifies
emergency response levels for the general public, only lasting up to a day (primarily to 8 hr);
(2) green signifies continuous ambient exposures for the general public, extending over a
lifetime; and (3) blue indicates occupational noncontinuous exposures for adult workers over a
working life.
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31 October 2013
TABLE 3-7 Detection Capabilities for Selected Sensing Technologies/Techniques
(concentrations are ppm for gases, fjg/m3 for particles)3
Pollutant
(concentrations
are as ppm
except as noted,
for lead and PM)
Acetaldehyde
RBC: 0.000278
Acrolein
RfC: 8. 73E-7
Ammonia
MRL chronic: 0. 1
Benzene
RBC: 0.0004
1,3-Butadiene
RBC: 1.36E-5
Carbon monoxide
NAAQS (8-hr av): 9.0
Formaldehyde
RBC: 6.5E-5
Hydrogen sulfide
RfC: 0.001
Lead
CalEPA IUR/RBC:
0.0833 jjg/m3
Methane
MEG-negligible effect
(1 hr): 2,830
Nitrogen dioxide
NAAQS (as annual
avg): 0.053
Ozone NAAQS
(as 8-hr avg): 0.075
Participate matter
NAAQS (annual
avg): 12 pg/m3
Sulfur dioxide
NAAQS (as 1-hr avg):
0.075
Sensing Technology/Technique
Chemistry
Electro-
chemi-
cal
X
X
X
0.2
0.05
0.02
X
Nano-
based
0.01
3.1
0.05
9.5
100
0.01
0.002
125
0.01
0.01
Poly-
mer
film
0.025
20
17100
80
X
SAW
X
lonization
Mass
spec-
trometry
X
GC
ele-
ment
0.1
0.5
Spectroscopy
Absorp-
tion
1
0.001
0.0095
X
0.106
X
5
X
0.4
Emis-
sion
0.25
X
X
0.001
0.001
0.001
0.001
Laser
absorp-
tion
X
0.003
0.00235
10
0.12
X
LIDAR
X
X
0.001
0.001
X
Light
scat-
tering
X
This table reflects LDLs reported for these techniques and pollutants in the research literature reviewed. (While
commercial sensors may have lower LDLs, this report focuses on research sensors so data for commercial sensors
alone were not pursued for this compilation.) The lowest exposure benchmark is included for comparison (the IRIS
RBCs correspond to the 10'6 risk level; concentrations are ppm for gases and mass-based for lead and PM. The
LDLs that achieve this lowest benchmark are shaded green, the others are shaded blue. An x (with yellow shading)
indicates the technique has been reported to detect the pollutant but the concentration was not identified. An empty
cell indicates the technique has not been identified forthat pollutant (e.g., none were found for lead in air).
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Gas chromatography (GC) is reflected as a selective element of the sensor system; the main detector could be a
photoionization, flame ionization or mass spectrometry detector. LDLs shown here for benzene and 1,3-butadiene
are for a system that uses GC to separate the two gases, which are then detected by a commercial MOS sensor.
It is important to clarify that this report focuses on research sensors and novel systems, rather than commercial
sensors. Thus, many commercial sensors exist that are not included in the figures and tables of this report. For
example, a variety of commercial sensors are available for ozone, including a number of electrochemical sensors,
yet the chemistry entries for ozone are blank in this table. This simply means no research and development sensors
or novel sensor systems were found for ozone in the literature review.
The graphical arrays that compare exposure benchmarks to detection levels are presented for
the pollutants in alphabetical order in Figures 3-5 through 3-18. An interpretation guide for the
symbols and shading used in these arrays is provided in Table 3-8. The symbol color indicates
the effect severity for benchmarks with tiered effects (e.g., acute exposure guideline levels,
AEGLs). A dashed line indicates the benchmark concentration applies across that indicated.
Where a series of concentrations have been established for different exposure durations for a
given benchmark (e.g., 10-min, 30-min, 1-hr, 4-hr, and 8-hr values for certain emergency
response values), the corresponding symbols are connected by a line of the same color.
TABLE 3-8 Guide to Distinguishing Types of Benchmarks in the Graphical Arrays
Shape
O
AO
Shading-
Outline
O +
O
O
A
O
Target Group and Setting
General public, emergency response
General public, routine ambient
Adult worker, occupational
Health Effect Level / Nature
Above this level, effect could be severe
Above this, effect could be serious, irreversible
Above this, effect could be mild, reversible
Occupational, OSHA regulatory standard
Occupational, other/recommended limit
Level considered safe for the general public
Nature of Exposure
Discrete exposures lasting up to 8 hr
Continuous exposures, to lifetime (70 yr)
Noncontinuous exposures (work shifts over
working life (e.g., 25+ yr)
Effect Notes
Examples: AEGL-3, IDLH
Example: AEGL-2
Example: AEGL-1
Not all permissible exposure limits (PELs)
are strictly health based (some reflect
technical feasibility)
Includes downward adjustments to assure
safe margin of exposure across general
population including sensitive subgroups
To illustrate how this is represented on a graphical array, see the lines connecting multiple-
duration benchmark concentrations for acetaldehyde AEGL values in Figure 3-5. The AEGL-1
values remain constant across these 5 time intervals, as seen from the flat gold line through the
gold diamonds. In contrast, while the AEGL-2 and AEGL-3 values are the same for the first two
intervals (as indicated by the flat orange line between the orange diamonds plotted at 10 min
and 30 min, and the flat red line between the parallel red diamonds), the benchmark
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31 October 2013
concentrations for the AEGL-2 and AEGL-3 decrease over the next three intervals as indicated
by the angled line connecting those respective diamonds. Not all guidelines are shown on
these graphical arrays. For example, additional guidelines exist for many of the pollutants,
notably OELs established for military personnel and specialized exposure settings (e.g.,
spacecraft and submarines). Those benchmarks are only tapped for methane because the
more common health-based benchmarks (available for the other pollutants) have not been
established for this compound. (See the introductory text of Appendix B for further information
regarding the methane benchmarks.) Complementary figures are provided in Section 3.5.2 to
compare sensor detection levels with illustrative concentrations in air for eleven study pollutants.
Few example concentrations were found for the other three - ammonia, benzene, and
1,3-butadiene. Thus, these examples are included in the graphical arrays for exposure
benchmarks (Figures 3-7 through 3-9) to streamline those presentations.
Selected Sensors
The sensors listed in the graphical arrays are primarily from studies that focus on sensing
technology/technique; a few are from studies that focus on the architecture/infrastructure
approach and incorporate a commercial sensor in a novel system. In addition, one or two
commercial sensors (depending on the pollutant) that are considered to represent a standard
accepted sensor for that pollutant are included on the arrays as points of reference, to guide the
assessment of needs and opportunities. (For example, if a commercial detector already cost-
effectively measures CO at the concentration of interest, or if another commercial sensor can
measure a nuisance indicator at the odor threshold, then research investments would not be
expected to target sensor development for those pollutants.) Further details about the sensors
shown on these arrays are presented in Appendix E (Table E-1). A brief interpretation guide for
the sensors plotted on the graphical arrays is provided below.
• Each sensor is identified with a small label. This label lies on a relatively thick horizontal
line, which represents the reported LDL or minimum tested concentration, where reported.
Its position is not related to the data recording time or instrument response time.
• The vertical line extending up from this label indicates the reported range of detection
(where available). This vertical line ends at the upper limit of quantification or greatest
tested concentration, which is represented by a short horizontal bar.
• The identifier number in the sensor label corresponds to the number in the summary table in
Appendix E (Table E-1), where further information is provided for each plotted sensor.
• The font and line colors for the research sensors are darker than those for systems involving
commercial sensors, so these primary sensors will stand out on the arrays.
• Devices that involve use of a commercial sensor in a novel system are distinguished by a
lower-case "c" next to the identifier number. The font and lines used for these devices are a
bit lighter than for the research sensors.
• Standard commercial sensors are also included in most arrays to provide further context for
assessing detection gaps and opportunities. These reference sensors are denoted with an
upper-case "C" next to the identifier number, as well as a star in that sensor box; the font
and line colors for these standard commercial sensors are the lightest of all the sensors
plotted on the arrays.
• An asterisk following the technique type or device name denotes the sensors for which the
response time is reported to be 5 minutes or less. (See Appendix E for additional
information regarding specific response times, where available.)
3-24
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31 October 2013
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-D-EPA IRIS RBC (10'6)
CalEPA REL (chronic)
O ACGIH (ceiling)
• NIOSH IDLH(SOmin)
-fc-OSHA PEL (8-hr TWA)
0.1
10
100
1,000
10,000 100,000 1,000,000
Duration Addressed by Exposure Benchmark (hours)
FIGURE 3-5 Acetaldehyde: Comparison of Detection Levels to Exposure Benchmarks
a)
(A
I
0)
0)
E
LU
Q_
"TO
0)
c
0)
(D
TO
O
is
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3-25
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31 October 2013
100 q
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6 1
§
c
:§ 0.1
2
+j
a)
c 0.01
o
O
\\ 1b. Intra-pulsed QCL, DFB configuration* f
•5 0.001
b
0.0001
0.00001 :
0.000001
0.0000001
Acute
»» •>
Short Term
Subchronic
C3. FTIR
x-
Chronic
a. Inter-pulsed QCL, DFB configuration*
—•—AEGL-3
—•—AEGL-2
AEGL-1
O ERPG-3
O ERPG-2
ERPG-1
CalEPA REL (1, 8-hr)
ATSDRMRL(1-14d)
—X- ATSDR MRL (14 d - 1 y)
-m- EPA IRIS RfC (chronic)
CalEPA REL (chronic)
—*— OSHA PEL (8-hr TWA)
—•—NIOSH REL (STEL)
O NIOSH IDLH (30 min)
—•— NIOSH REL (8-hr TWA)
—0—ACGIH TLV (ceiling)
0 1 10 100 1,000 10,000 100,000
Duration Addressed By Exposure Benchmark (hours)
FIGURE 3-6 Acrolein: Comparison of Detection Levels to Exposure Benchmarks
1,000,000
a)
I/)
I
o
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o
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.a
if
E
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3-26
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31 October 2013
IU,UUU ;
1,000
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=ted at 20 and 10
Term
c
e
/>
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3
3
O
Subchronic
6. PAAEMA latex
thin films, UV-vis*
[tested ranqel
red
Dsite*
0 ppm]
in
i_
re
£
t-
C1 4. TOLAS
(LGA-4000)*
Chronic
in
o
h.
5. PANi/SWCNT film t
[tested at 35 ppm]
i path fiber optic, UV abs L
(Optran UVNS)* \
/
X
!
—•—AEGL-3
—•—AEGL-2
AEGL-1
O ERPG-3
O ERPG-2
ERPG-1
-CalEPAREL(l-hr)
- ATSDRMRL(1-14d)
-•O- EPA IRIS RfC (subchronic)
i/)
I
in
0)
U
0)
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0)
HI
3
Q.
-•+- EPA PPRTV RfC (subchronic)
-'X- ATSDR MRL (chronic)
CalEPA REL (chronic)
—O—NIOSH REL (STEL)
—0—ACGIH TLV (STEL)
O NIOSH IDLH (30 min)
—*— OSHA PEL (8-hr TWA)
—O— NIOSH REL (8-hr TWA)
—0— ACGIH TLV (8-hr TWA)
(0
Q.
3
U
U
O
0.1
000,000 Note. Purple bandjndjcates giobai
average concentration
1 10 100 1,000 10,000 100,000
Duration Addressed by Exposure Benchmark (hours)
FIGURE 3-7 Ammonia: Comparison of Detection Levels to Exposure Benchmarks and an Example Concentration
3-27
-------
31 October 2013
100,000
1
c
o
•£
Q>
C
O
o
a
10,000
1,000
100
10
0.1 =
0.01
0.001
Short Term
s
3. Nanosensor with SnO2,
Au, and PPY on SWCNTs
[tested range: 13-65 ppm]
2. Colorimetry array
with preoxidation tube
* S-
0.0001
0.00001
0.000001
8. Hollow fiber detection cell
[tested range: 0-10 ppb]
9. Optical fiber with
PMTFPS polymer film
c13. SnO2 semiconductor with ceramic base
(ARPOL mobile system, Figaro TGSS23)
Subchronic
Chronic
4. Coral-shaped SnO2 nanostructures
[tested range: 50-150 ppm]
c14. MOS, with micro-GC preconcentrator
(Figaro-TGS 800) [tested range: 2-10 ppm]
^
US ambient
(10-90% range, 2009)
C15. GC-PID
(Synspec GC 955-603)
C16. GC-PID
(Synspec GC 955-601)
O
o
o
m
0.1
10
100
1,000
10,000
AEGL-3
AEGL-2
AEGL-1
ERPG-3
ERPG-2
ERPG-1
CalEPA REL (6-hr)
-ATSDR MRL(1-14d)
X ATSDR MRL(14d-1 y)
- • - EPA PPRTV RfC (subchronic)
EPA IRIS RfC (chronic)
EPA IRIS RBC(10-4)
D EPA IRIS RBC(10-6)
- X - ATSDR MRL (chronic)
CalEPA REL (chronic)
A OSHA PEL (STEL)
—O—NIOSH REL (STEL)
—©—ACGIH TLV (STEL)
O NIOSH IDLH (30 min)
OSHA PEL (action level)
OSHA PEL (8-hr TWA)
NIOSH REL (8-hr TWA)
ACGIH TLV (8-hr TWA)
100,000 1,000,000 Note: Purple band indicates US
ambient concentrations from 339
monitoring sites in 2009.
Duration Addressed by Exposure Benchmark (hours)
0)
(A
I
tn
0)
a:
5x
O
a)
O)
a>
LU
3
a.
a)
0)
TO
o
is
a.
3
O
o
O
Green line indicates concentration in
urban/metropolitan areas.
FIGURE 3-8 Benzene: Comparison of Detection Levels to Exposure Benchmarks and Example Concentrations
3-28
-------
31 October 2013
I,UUU,UUU
100,000
10,000
Q. 1,000
c
.2 100
£
g 10
0
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0
£
DO 0 01
0.001 =
0.0001
0 00001 --
0 000001
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4-
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A A
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A A
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&
to
3
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(Syntech Spectras GC 955 811)
C4.
Short Term
30 days
Subchronic
GC-PID
(Syntech Spectras GC 955 604)
c2 MOS wit
(
to
TO
0)
r-
Chronic
to
TO
0
i micro-GC preconcentrator
Figaro-TGS 800)
1
mposite film
-
)
r
1
• AEGL-3
•— AEGL-2
•- AEGL-1
O ERPG-3
O ERPG-2
ERPG-1
o
o:
a)
5
LU
EPA IRIS RfC (chronic)
a
3
D.
TO
5
0)
O
0.1
1 10 100 1,000 10,000 100,000 1
Duration Addressed by Exposure Benchmark (hours)
•«- EPA IRIS RBC(10-4)
•«0 EPA IRIS RBC (10-6)
CalEPA REL (chronic)
O NIOSH IDLH (30 min)
A OSHA PEL (action level) .2
1
—A—OSHA PEL (8-hr TWA)
o
O
O ACGIH TLV (8-hr TWA)
„„„ Note: Orange band indicates outdoor
concentrations in citites and suburban
areas.
Green bar indicates ambient outdoor
annual average for Texas in 2003.
FIGURE 3-9 1,3-Butadiene: Comparison of Detection Levels to Exposure Benchmarks and Example Concentrations
3-29
-------
31 October 201'3
-1 n nnn
1,000
Q^
Q^
o 100
4J
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rbon Monoxid<
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re '
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0.1-
Acute
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BBB
^
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' c14. (Mobile-DAQ)*
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(Sonfay GS
4
24 hours
4—
Short Term
I 3. MOS: WO3
^l [tested at 30 ppm]
|2. Potentiometric*!
-S-CM;*
C22. Non-dispersive UV absorption
(Ecotech EC9830)
to
re
o
Subchronic
c20.
(Hanwei
4. PANi/SWCNT film
[tested at 80 ppm]
SnO2 thin film
Electronics MQ-7)
.MOS
(e2v_MiCS5521)
1. MOS: WO3ser
sor with ITC
to
re
5. Sr
Chronic
Ox'
/vith I
odes*L
to
o
c13. SnO2 thin film
(Figaro TGS 2442)*
. .
c12. Electrochemical
(Langan T15n)
-•-AEGL-3
-•-AEGL-2
o
Q.
O ERPG-3 £
5*
o
ERPG-2 |
o
E
ERPG-1 u
CalEPA REL (1-hr)
ro o
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A OSHA PEL (8-hr TWA)
0 NIOSH REL (ceiling)
0 NIOSH IDLH (30 min) ro
g
-0-NIOSH REL (8-hr TWA)a
§
-e-ACGIHTLV(STEL)
-0-ACGIH TLV (8-hr TWA)
Note: NAAQS values address
chronic exposures; the position
10
100
1,000
10,000 100,000 1,000,000 measurement averaging times
(1 hour and 8 hours), with the
Duration Addressed by Exposure Benchmark (hours) dashedune extending to the
chronic exposure duration.
FIGURE 3-10 Carbon Monoxide: Comparison of Detection Levels to Exposure Benchmarks
3-30
-------
31 October 2013
1,000
8. Coral-shaped SnO2 nanostructures
^n inn anrM^n nnm
5.100 urn PDMS, Pt electrode
4. ZnO nanorods, Uv light-assisted
3. Capacltatlve electronic chip, MWCNT*
9. MIR, colorlmetry
rested at 0.106 and 0.691 ppm
2. MOS, La-Sr-FeO3
coated ceramic tube*
Tested Ranae: O-Soom
1.SWCNT sensor array* |
8. Nanopourous matrix w;
Fluoral-P (Colorimetry kit)
Potential Range
C12. (400 Series Portable Analyzer)*
many ranges available,
LDLis 1% full scale
0.000001
—•—AEGL-3
—•—AEGL-2
-AEGL-1
O ERPG-3
O ERPG-2
ERPG-1
CalEPAREL(1, 8-hr)
-ATSDR MRL(1-14d)
X ATSDR MRL(14d-1 y)
—•—EPA IRIS RBC(10-4)
D EPA IRIS RBC(10-6)
- X - ATSDR MRL (chronic)
CalEPAREL (chronic)
—A— OSHA PEL (STEL)
—O—NIOSHREL (ceiling)
—9—ACGIHTLV (ceiling)
O NIOSH IDLH(30min)
—A—OSHA PEL (8-hr TWA)
—O— NIOSH REL (8-hr TWA)
10
100
1,000
10,000
100,000 1,000,000
Duration Addressed by Exposure Benchmark (hours)
FIGURE 3-11 Formaldehyde: Comparison of Detection Levels to Exposure Benchmarks
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31 October 2013
1 ,UUU
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re
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C7. Gold film
( lornmo lfifl<;i*
^^ 3. Non-pulse UV fluorescence
1
^
/N
Subchronic
vs
/^
C6. Electrochemical
in
re
Chronic
U)
1
o
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C8. Semiconductor
(Aeroqual Series 500)*
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,
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^^^^ ALUL-J
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a.
— •— AEGL-2
(£
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0)
P
CalEPAREL(l-hr) ^
LLI
ATSDRMRL(1-14d)
o
-X- ATSDRMRL(14d-1 y) %
0.
— D— IRIS RfC (chronic)
0)
c
0)
CalEPA REL (chronic)
A OSHA PEL (ceiling)
A OSHA PEL (8-hr TWA)
re
c
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re
Q.
— 9— ACGIH TLV (STEL)
O
O NIOSH IDLH (30 min)
— 0— ACGIH TLV (8-hr TWA)
10 100 1,000 10,000 100,000
Duration Addressed by Exposure Benchmark (hours)
1,000,000
FIGURE 3-12 Hydrogen Sulfide: Comparison of Detection Levels to Exposure Benchmarks
3-32
-------
31 October 2013
100,000
10,000
1,000
-^ 100
CO
1
~"I -. «
<± 10
c
o
Hi 1
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c 01
o
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8 0.01
—1
0.001
0.0001
0.00001
0.000001
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0
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C2. XRF
(Xact1 625 Monitoring System)*
Short Term
S.
re
•O
O
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Subchronic
U)
1
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Chronic
S2
(0
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D NAAQS
.0
S
CalEPA CAAQS (30 day)
—
CalEPA IUR/RBC(10-4)
t n
C3
X CalEPA IUR/RBC(10-6)
A OSHA PEL (action level)
A OSHA PEL (8-hr TWA)
g
O NIOSHIDLH(30min)
§
o
0 NIOSH REL (8-hr TWA)
O ACGIHTLV (8-hr TWA)
Note: NAAQS values address chronic
exposures; the position of this symbol
indicates the measurement averaging
time (3 months), with the dashed line
extending to the chronic exposure
duration.
10 100 1,000 10,000
Duration Addressed by Exposure Benchmark (hours)
100,000 1,000,000
FIGURE 3-13 Lead: Comparison of Detection Levels to Exposure Benchmarks
3-33
-------
31 October 2013
;
100,000 :
_^ 10,000
S
t
c
o
| 1,000
Methane Concen
->. o
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1 -.
n 1 •
Acute
in
0
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4. Comb filter-based fiber optic
^m
^H
9C. Cavity ring-down spectroscopy
(PICARRO G2204)*
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Short Term
&
re
•D
O
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X i
Subchronic
S2
re
h.
Chronic
70 years
5c. (GJ4-2000, Integrated
' sensor alerting system)
(alarm concentration)
, 1 MOS, SnO2 nannrnrU* ^_
(varies by temperature)
3. MIR, elipsoid gas cell*
(tested up to 1,000 ppm)
0
-•-PAC-3
Q.
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0
-•-PAC-2 >
u
0
PAC-1
UJ
D MEG -critical (1-h)
• MEG - marginal (1-h) w
0
MEG - negligable (1-h, 8-h, 14 d)|
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o
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i
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"a
* IDLH-type
n
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0.1
1
10
100
1'000
10-000
100,000 1,000,000 * Note on p/cARRO sensor 3 ppm is
detectable with guaranteed specifications.
Duration Addressed by Exposure Benchmark (hours) ° 20 ppm is °Peratin9 ™ge.
FIGURE 3-14 Methane: Comparison of Detection Levels to Exposure Benchmarks
3-34
-------
31 October 2013
c
o
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Short Term
re
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AI2O3 ceramic substra
Subchronic
in
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geometry with
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jlti-pass cell^B
MLIAS
—
°" | | c13. (Mobile-DAQ) |
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0
C17. Chemiluminescence
(EcoTech: Serinus 40)
re
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1 10 100 1,000 10,000 100,000 1,OOC
Duration Addressed by Exposure Benchmark (hours)
0)
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o
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O NIOSH IDLH (30 min)
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Note: NAAQS values address
chronic exposures; the position
of this symbol indicates the
measurement averaging times
) ooo ^ hour and 1 year)' w!th the
' dashed line extending to the
chronic exposure duration.
FIGURE 3-15 Nitrogen Dioxide: Comparison of Detection Levels to Exposure Benchmarks
3-35
-------
31 October 2013
1,000
100
10
.2 1
4-1
n
o
o
o
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0.01
0.001
0.0001
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Short Term
01
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^m
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Chronic
a>
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(2B Technologies: Model 211)*
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CalEPAREL(l-hr) 01
NAAQS
WWOSHA PEL (8-hr TWA)
O NIOSH REL (ceiling)
NIOSH IDLH(SOmin) _
CO
o
7=
CO
a.
•ACGIH TLV (8-hr TWA) o
Heavy Work o
ACGIH TLV (8-hr TWA)
Medium Work
ACGIH TLV (8-hr TWA)
Light Work
-0-ACGIH TLV (<2-hr TWA)
0 1 10 100 1,000 10,000
Duration Addressed by Exposure Benchmark (hours)
FIGURE 3-16 Ozone: Comparison of Detection Levels to Exposure Benchmarks
Note: The NAAQS address
chronic exposure durations;
positions of those symbols
100,000 1,000,000 indicate the averaging time for
these measurements
3-36
-------
31 October 2013
1,000,000
C10. Nepnelometnc
(pDR-1500)*
C12. Beta attenuation, gravimetric
(OPSIS SM200)
c6. Electrochemical
(Real time remote
monitoring system)
C9. FDMS, TEOM mass sensor
(TEOM 1405-DF)
INAAQS [PM10]
!NAAQS(1°and2°,24-hr) [PM25]
.o
3
3
Q.
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31 October 2013
de Concentration (ppm)
o i
->•->• o o c
X
0
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(0.3 ppb) .
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(EcoTech: Serinus 50)*
9 AECL 3
— •— AEGL-2
>
— •— AEGL-1
D)
CalEPAREL(1 hr) E
— •- NAAQS(1°, 1-hravg) o
A
3
r; NAAQS (2°, 3-hr avg) ^
5
- ATSDR MRL(1-14d) J
— A— OSHAPEL(STEL)
— 6- NIOSH REL (8-hr TWA) ro
o
—O— NIOSH REL (STEL)
u
— 0— ACGIH TLV (STEL)
O NIOSH IDLH (30 min)
ACGIH TLV (8-hr TWA)
Note: The NAAQS address chronic
exposure durations; positions of
those symbols indicate the
0 1 10 100 1,000 10,000 100,000
Duration Addressed by Exposure Benchmark (hours)
FIGURE 3-18 Sulfur Dioxide: Comparison of Detection Levels to Exposure Benchmarks
1,000,000
primary and secondary standard,
respectively.
3-38
-------
31 October 2013
From the information evaluated in this report, it appears that the criteria pollutant gases can be
detected across the range of existing health-based benchmarks. Some electrochemical
sensors are appearing to show promise for some gases, such as ozone and nitrogen dioxide, at
the part per billion (ppb) level. However, rarely do current technologies achieve concentrations
in that range (or in the ppm range for carbon monoxide), and that capability has long been a
critical need for epidemiologists, in order to reduce some of the high uncertainty in the exposure
estimates used for those studies. The same need to characterize individual exposures also
applies to emerging personalized medicine studies.
Beyond the challenge of reliably detecting low concentrations, interferences also represent a
difficult issue for gas sensors. Almost all will respond to interferences, and in some cases the
response can be quite dramatic. Thus, not only do emerging sensors need to demonstrate
detection capabilities in the low ppb range and lower, they must also demonstrate specificity, to
be able to detect the pollutant gas of interest over a wide range of temperatures and
environmental conditions, including in the presence of interferences. Many sensors have
difficulty separating the target pollutant from interfering gases, which can result in high readings.
See Chapter 4 for additional discussion of interferences.
Similar to the needs described above for the criteria pollutant gases, lead poses a substantial
gap, and detection capabilities for this pollutant are quite limited. Thus, opportunities exist for
developing smaller, less expensive, reliable, accurate sensors for all criteria pollutants over a
range of temperatures and environmental conditions, including under high humidity and in the
presence of interferences such as other gases. Because of the concern that even very low
concentrations of lead can produce adverse health effects, research and development needs for
sensors that can detect lead at sufficiently low levels are particularly pressing.
Several other pollutants may not be detectable across the range of health-based benchmarks,
particularly at concentrations established for lifetime exposures. Only three pollutants were
reported to be detected by a research or commercial sensor at the concentration corresponding
to an incremental lifetime cancer risk of 10~4 and/or reference concentration (RfC) that
represents a safe level for noncancer effects. These pollutants are benzene and 1,3-butadiene
(HAPs) and hydrogen sulfide (indicator pollutant). (Regarding 1,3-butadiene, CalEPA [2013]
recently proposed new reference exposure levels, notably 297 ppb for acute (1-hr) exposures,
13 ppb for 8-hr exposures, and 3 ppb for chronic exposures, which is lower than the extant
value shown in Figure 3-9.)
Four pollutants - acetaldehyde, acrolein, ammonia, and formaldehyde - are potentially
detectable at concentrations corresponding to 10'4 risk but not at the lower concentration
corresponding to an incremental risk of 10~6 (point of departure) and/or at the RfC established to
protect against adverse noncancer effects. (As a note for formaldehyde, the commercial sensor
reflected on the graphical array can detect concentrations ranging from 0 to 20, 200, or
2000 ppm, with a minimum of 1.0% full scale).
Regarding benchmarks for higher concentrations, such as emergency response or occupational
levels, it appears that these levels could potentially be detected but this cannot actually be
determined without further information in some cases. Sensor response time is an important
aspect of sensing capabilities, and that is not accounted for in the arrays; thus, the "effective
detection capability" for short-duration benchmarks is not known. For example, even if a sensor
could detect a concentration at or below a level established for the 10-minute emergency
response level or a 15-minute ceiling value for the workplace, the sensor response and recovery
time may not be sufficient for that concentration to be measured during the target time interval.
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31 October 2013
To illustrate, if the response time were 20 minutes or the device needed 20 minutes to refresh
between sampling, then the pollutant could easily be missed during that measurement period
even if the concentration were high.
A second important qualification for the initial findings is that for some sensors, because of
incomplete reporting, it is not known whether certain pollutant concentrations could be detected
or not. That is, the detection range is missing from many research publications, and in others
the range only reflects the predetermined concentrations tested. Furthermore, many of the
sensor capabilities reported in these research highlights have not been validated, including
many LDLs, detection ranges, and accuracies. Without that information, no definitive statements
can be made regarding sensor capabilities, including whether a given sensor can detect
relatively high concentrations such as for emergency response situations. In fact, for a number
of sensing technologies/techniques, high concentrations could potentially overwhelm the
sensing substrate (such as a film), thus making it difficult to assure that the pollutant would be
detected during the short intervals addressed by those guidelines.
3.5.2 Plots of Example Concentrations and Sensor Detection Levels
As complements to the graphical arrays (which compare reported sensor detection levels to
exposure benchmarks), additional plots were developed to compare reported detection levels to
example pollutant concentrations listed in various studies, to help frame the evaluation of gaps
and opportunities. These further practical comparisons are shown in Figures 3-19 through 3-30.
(As noted in Section 3.5.1, because little information was found to suggest illustrative
concentrations for ammonia, benzene, and 1,3-butadiene, the example concentrations for these
pollutants are included in their graphical arrays, see Figures 3-7 through 3-9.)
The following observations are offered from the comparison of reported sensor detection
capabilities for the study pollutants and example concentrations across a variety of settings, as
illustrated in Figures 3-19 through 3-30.
Acetaldehyde: Sensors can potentially detect the higher concentrations shown in
Figure 3-19, such as those identified for gasoline and diesel exhaust, ambient Los Angeles
air; and smoking households. However, the example ambient average and illustrative
concentrations for the California air basin and regions in New York and Alaska fall below the
reported detection levels, so many concentrations across the U.S. may not be addressed.
Acrolein: The sensors identified (inter- and intra-pulsed QCL) might be able to detect the
higher example concentrations shown for indoor and outdoor air, but they would not be able
to detect other concentrations shown in this range. In any case, even the concentrations
reported here must be considered somewhat uncertain, given the continuing difficulties
involved in accurately measuring this chemical in air.
Ammonia: Several research sensors can potentially detect the concentration range
indicated for the global ambient average (see Figure 3-7).
Benzene: Two commercial sensors (GC-PIDs) and a novel system that incorporates a
commercial sensor can potentially detect benzene concentrations in the example range
represented on Figure 3-8 (U.S. ambient levels for 2009). However, the detection levels
reported for the sensors found for this report lie above that range.
3-40
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31 October 2013
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FIGURE 3-19 Acetaldehyde: Comparison of Detection Levels to Example Concentrations
3-41
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31 October 2013
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FIGURE 3-20 Acrolein: Comparison of Detection Levels to Example Concentrations
3-42
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31 October 2013
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0.01
"All values are representative of the United States unless otherwise stated.
Sensor numbers correspond to entries in the supporting sensor table.
NAAQS(l-hr)
Home with gas stove,
poorly adjusted
Home with gas stove,
properly adjusted
NAAQS (8-hr)
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(annual mean, 2010)
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(annual 24-hr avg, 2000)
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(annual avg, 2009)
Southern hemisphere
(annual avg, 2009)
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CO concentrations are variable
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when inversion conditions are
more frequent
FIGURE 3-21 Carbon Monoxide: Comparison of Detection Levels to Example Concentrations
(Note that the Langan T15n can reasonably detect concentrations of 0.1 ppm, but values below that level are considered uncertain.)
3-43
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31 October 2013
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3 All values are representative of the United States unless otherwise stated
Sensor numbers correspond to entries in the supporting sensor table.
blndoor residential concentrations vary
widely due to differences in room sizes, air
exchange rates, and emission sources (such
as gas space heaters, gas stoves with or
without pilot lights, and fireplaces).
FIGURE 3-22 Formaldehyde: Comparison of Detection Levels to Example Concentrations
3-44
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31 October 2013
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Ambient
(2006)
aAII values are representative of the United States unless otherwise stated.
Sensor numbners correspond to entries in the supporting sensor table.
FIGURE 3-23 Hydrogen Sulfide: Comparison of Detection Levels to Example Concentrations
3-45
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31 October 2013
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' Busy traffic conditions
(annual avg, 2001-2010)
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(annual max, 3-mo avg, 2010)
FIGURE 3-24 Lead: Comparison of Detection Levels to Example Concentrations
3-46
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(Range from 0 ppm)1
FIGURE 3-25 Methane: Comparison of Detection Levels to Example Concentrations
31 October 2013
Explosive range,100% LEL - UEL
(5-15% volume in air)
.Atmosphere (natural),
0.00022% by volume
13 ppm is detectable with guaranteed
specifications; 0-20 ppm is operating range.
3-47
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31 October 2013
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sources (such as gas space heaters,
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range from -15-400 ppb in certain rooms,
with peak concentrations exceeding
1,000 ppb.
a All values are representative of the United States unless otherwise stated.
Sensor numbers correspond to entries in the supporting sensor table.
FIGURE 3-26 Nitrogen Dioxide: Comparison of Detection Levels to Example Concentrations
(Note that some electrochemical sensors appear to show promise at the ppb level for some gases, like NO2 and ozone.)
3-48
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31 October 2013
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(8-hravgmax, 2006-2008)
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(min-max range, 2004)
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includes natural background
contributions from throughout
the globe and emissions of
anthropogenic pollutants
contributing to global O3 from
countries outside North America
aAII values are representative of the United States unless otherwise stated.
Sensor numbers correspond to entries in the supporting sensor table.
FIGURE 3-27 Ozone: Comparison of Detection Levels to Example Concentrations
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31 October 2013
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(data range, 2003-2005)
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(annual avg, 2009)
Chicago ambient
(24-hr, 3-yravg, 2005-2007)
'All values are representative of the United States unless otherwise stated.
Sensor numbers correspond to entries in the supporting sensor table.
FIGURE 3-28 PIVh.s: Comparison of Detection Levels to Example Concentrations
3-50
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31 October 2013
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(2nd max 24-hr avg, 2010)
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(1-hr& 24-hr, 3-yravg, 2005-2007)
aAII values are representative of the United States unless otherwise stated.
Sensor numbers correspond to entries in the supporting sensor table.
FIGURE 3-29 PM™: Comparison of Detection Levels to Example Concentrations
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31 October 2013
100
2. Non-pulse
UV-fluorescence
(0.3 ppb)^
0.0001
600-3,000 ppm
Breathing zone of
forest fires
NAAQS, secondary
(3-hr averaging time)
NAAQS, primary
(1-hr averaging time)
Indoor, kerosene heater
(1994-1996)
Chicago ambient
(range of mean, 2003-2005)
Ambient
(annual mean, 2010)
Indoor, no secondary heating
appliance (1994-1996)
C8. UV fluorescent radiation
(EcoTech: Serinus 50)*
"All values are representative of the United States unless otherwise stated.
Sensor numbers correspond to entries in the supporting sesnor table.
FIGURE 3-30 Sulfur Dioxide: Comparison of Detection Levels to Example Concentrations
3-52
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31 October 2013
1,3-Butadiene: Two commercial sensors (GC-PID and GC-FID) can detect concentrations
in the range identified for outdoor concentrations for cities and suburban areas, as well as
the outdoor annual average for Texas reported for 2003 (see Figure 3-9). The reported
detection level for one novel system that uses a commercial sensor (MOS with micro-GC
pre-concentrator) also lies within the range for cities and urban areas. However, it is not
clear whether the composite film research sensor can detect those levels (the LDL lies
below that ambient range but no detection range is identified). A companion research
sensor based on the same technique lies well above the example concentration range for
cities and urban areas.
Carbon monoxide: Commercial sensors can detect the example concentrations reported
for this compound, as can a novel system that uses a commercial electrochemical sensor.
Note that this sensor (Langan) can detect concentrations to about 1 ppm, but below that
level the values are highly uncertain. As with many sensors and systems being developed
or refined, the device can report a value, but whether it should (per validity) is a different
matter. Some research sensors could potentially detect the higher example concentrations
shown. They might also be able to detect some of the lower concentrations (the LDL lies
below those ranges), but this is not known because the detection range was not reported.
Formaldehyde: Reported detection levels for several research sensors lie within the
example concentration ranges identified for several settings, including residential homes
and classrooms (California), as well as in urban areas, heavy traffic zones, and the upper
range of rural and suburban outdoor concentrations.
Hydrogen sulfide: Commercial sensors can potentially detect this pollutant at the example
concentrations shown. One research sensor (based on gold-functionalized nanoparticles
with carbon nanotubes) might also be able to detect concentrations represented by the
general ambient U.S. level from 2006. It is not clear whether a second research sensor
(non-pulse UV fluorescence) can also potentially measure that level, because the reported
LDL is much lower and no detection range was reported for that sensor.
Lead: The one commercial sensor identified for lead in air can detect the example
concentrations shown, including the ambient U.S. concentration from 2010.
Methane: The reported LDLs of the research sensors for methane are higher than the
natural atmospheric concentration, but least two could potentially detect this compound
below its alarm concentration and explosive level. A novel system that uses a commercial
sensor can also potentially detect methane at the alarm level.
Nitrogen dioxide: Research sensors can potentially detect this compound at the example
ambient concentrations reported both for Chicago and the United States; the same finding
applies for a commercial sensor that uses chemiluminescence and a novel sensor system
that uses a commercial electrochemical sensor.
Ozone: A research sensor (using a tunable LIDAR technique) can potentially be used to
measure ozone at the example concentrations reported, and the same finding applies for a
commercial sensor that uses UV absorption. It is not clear if the novel sensor system that
uses a UV photometric commercial sensor can detect these ambient levels, because the
reported LDL lies below the example concentrations and no detection range was reported
for that sensor.
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31 October 2013
Particulate matter: Commercial sensors (gravimetric and mass-based) are reported to
detect PM at the example concentrations identified, and a novel system using a commercial
(electrochemical) sensor designed for real-time remote monitoring can also potentially
detect those levels.
Sulfur dioxide: Sensors can potentially detect indoor concentrations reported for homes
with certain heating appliances; however, they may not be well suited to detecting the high
example concentrations reported, such as in the breathing zone of forest fires.
3.6 ARCHITECTURE AND INFRASTRUCTURE APPROACHES AND APPS
The status of architecture and infrastructure for research sensors covers all stages of research
and development, extending to patent applications, and commercially available products. The
architectures/infrastructures can be organized into three general categories: (1) physical
devices, (2) user interface design/programming, and (3) other approaches - such as data
broadcasting and distributed data processing. These categories are further subdivided and
described in the following sections. The number of sensing devices that reflect these
architecture/infrastructure categories is summarized in Table 3-9 and illustrated in Figure 3-31.
Note that not all subgroups in this section directly apply to sensors and apps for air pollutants.
Nevertheless, having been included in relevant workshops and meetings (notably for ubiquitous
computing), they provide a glimpse of related aspects of this active research area that may offer
"cross-training" insights for the development and implementation of citizen sensor systems.
3.6.1 Physical Devices
Cell phone-based sensing (27) - The cell phone group of devices utilize mobile phones as
mobile sensing units. This group includes sensors mounted directly in/on mobile phones,
downloadable apps, user interfaces, and imbedded processing technology. In general, these
devices are typically used to collect data and/or detect environmental pollutants; transmit and
share data across wireless networks; and process and analyze data, match outcomes, and
interpret results from either a source database or sensor network within the user's proximity to
enable real-time monitoring. Location is also commonly recorded by cell phone devices. The
few detection methods or techniques that were identified for the cell phone-based sensor group
include: colorimetrics, vibration, ultrasound, global positioning system (GPS), participatory
sensing, user generated content, classification algorithms, and signal processing. The devices
cover most stages of development from the research phase through commercial availability. Air
pollutants detected by these mobile devices, as identified in the records, primarily include
vehicle emissions and greenhouse gases, such as CO, CO2, 02, NOX, SOX, and PM. Other
chemicals and parameters that can be measured include: formaldehyde, black carbon,
temperature, humidity, UV radiation, activity, noise, location, and weeds.
Embedded/integrated sensor (7) - This group of sensors if embedded or integrated in
something another object for a new or novel application, such as a musical instrument for
respiratory therapy, thin flexible substrate applied to a floor to detect the presence and
whereabouts of users using electromagnetic radiation sensing, animated fabric for multi-sensory
communication, or cell phone for smart home management. In general, these devices detect
sound, gestures, and movement, including pressure, vibration, acceleration, and gyroscope
data. However, another device in this group combines multiple separate gas sensors into a
single integrated sensor for detection of airborne gases, including CO, CO2, NO2, HbS, and Chk
These embedded or integrated sensors cover most stages of development from the research
phase through design prototype.
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31 October 2013
TABLE 3-9 Categories and Counts for Architecture/Infrastructure3
Category for Architecture/Approach
Number
Physical Device
Cell phone-based sensing
Embedded/integrated sensor
Fixed/semi-portable sensor unit
Handheld sensor
Mountable sensor
Nanoscale technology
Robotics
Vehicle-mounted unit
Wearable sensor
27
7
66
51
59
50
8
44
23
User Interface Design/Programming
Activity/speech recognition
Algorithm/modeling
Ambient intelligence
Augmented reality
Context awareness
Database/data mining
Location awareness
Mobile sensing
Multi-sensor system
Participatory/citizen sensing
Remote sensing/monitoring
Sensor calibration
Social networking/computing
Ubiquitous computing
Virtual reality sensing
Visual sensing
Web-based system
Wireless sensor network (WSN)
16
38
6
5
10
5
23
11
9
23
11
1
13
14
5
11
10
54
Other
Data broadcasting
Distributed data processing
Economic tradeoffs
Exposure assessment
Prediction service
Radar system
3
1
2
8
1
1
1 These entries are organized alphabetically within each of the three general groups. See
Figure 3-31 for the display organized by total counts. Note that these categories and
subgroups are not exclusive, so the numbers include duplicate or multiple counts.
3-55
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31 October 2013
70 n
66
Architecture/Approach
FIGURE 3-31 Number of Sensors Using Selected Architecture/Infrastructure Approaches
3-56
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31 October 2013
Fixed/semi-portable sensor unit (66) - The primary purposes identified for the fixed/semi-
portable sensor units are environmental pollutant detection, air quality monitoring, emissions
identification, concentration estimation, and data collection. A number of these devices also
have the capability to simultaneously measure for multiple pollutant gases, store data, compare
measurements against standards, perform modeling, and output data and information to a
display unit. In addition, some units have the potential for wireless communication and
Bluetooth network connection. These devices range from shoebox, to suitcase, to table/bench
top, to refrigerator size; they cover most stages of development from the research phase
through commercial availability.
In general, sampling is done a variety of ways, including automated active air sampling,
continuous monitoring, and ambient air diffusion, and measure pollutants with and without filter
collection. The detection methods or techniques identified for the fixed/semi-portable sensor
group include: colorimetrics, photonics, UV fluorescence, laser-induced fluorescence,
spectroscopy, refractometry, diffuse illumination, IR, optics, thermography, ion chromatography,
gravimetrics, capacitance, semiconductance, resonance, potentiometrics, acoustics,
photoacoustics, olfactometry, biosensing, immunoassay, polymerase chain reaction, differential
absorbance, optical absorbance, UV absorbance, participatory sensing, and accelerometry.
Some examples of applications in which fixed/semi-portable sensor units have been used
include: traffic and vehicle emissions studies; filtering and removal of pollutants; electronic nose
for detecting odorous gases; pollution hotspots identification and dispersion modeling of
pollution clouds; alarms in industrial settings to warn workers when potentially dangerous of
unusual situations are detected; soil gas and penetration studies to detect emission from soil to
the atmosphere or leaks from underground storage facilities; monitoring practices at landfills;
and earthquake location and magnitude detection.
Fixed/semi-portable sensor units are used to detect numerous greenhouse and exhaust gases,
including: CO, CO2, O3, NO2, N2O, NH3, H2S, SO2, SOX, CH4, VOCs, HCs, PM, PM™, PM2.5,
and PMi. They are also identified in the records as being able to detect a wide variety of other
chemicals: acrolein, formaldehyde, acetone, BTEX, chloroform, p-dichlorobenzene, ethanol,
ethyl acetate, MEK, phenol, phenolate, isopropanol, styrene, 1,1,1-trichloroethane,
1,2,4-trimethylbenzene, water pollutants, pesticides, hormone residue, phosphate, chemical
and biological agents, and chemical explosives. Many of the devices can also measure
temperature, pressure, UV, humidity, odors, acceleration, power consumption, and movement.
Handheld sensor (51) - In general, the handheld devices detected or monitored for air
pollutants, chemicals, environmental contaminants, and/or other various compounds (e.g.,
explosives, allergens, E. coli bacteria). Some of the sensor units identified the potential for
wireless/Bluetooth connectivity. Where identified, these devices ranged from the size of a thumb
to that of a shoe box. The devices cover most stages of development from the research phase
through commercial availability.
The detection methods or techniques identified for the handheld sensor group include:
colorimetrics photonics, photoionization, spectroscopy, thermography, IR, optics,
interferometry, electrochemistry, electromagnetism, conductance, resonance, harmonics,
olfactometry, bioluminescence, gas chromatography, and absorbance.
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31 October 2013
The handheld sensors primarily measure airborne pollutants, including: CO, CC>2, 02, Os, NO,
NO2, NOX, NH3, SO2, SOX, H2, H2S, VOCs, hydrocarbons (HCs), and UFPs (ultrafine particles).
Other chemicals specifically identified include: CI2, COCI2, methylene chloride, black carbon,
formaldehyde, chloroform, 1,3-butadiene, n-butanol, alcohol, ethylene, methanol, acetic acid,
acrylic acid, dichloromethane, pentane, propane, styrene, hydrogen cyanide (HCN), phosphine
(PHs), pentachlorophenol (PCP), perchloryethylene, trichloroethylene, solvent fumes (BTEX,
chlorinated VOCs, solvents used in adhesives, paint and paint strippers, and varnishes), Cr(VI),
explosives (e.g., TNT and dinitrotoluene, DNT), amine, odorant, and chemical warfare agents
(such as sarin, soman, or GF). Other parameters measured include: temperature, humidity,
noise, odors, blood sugar, allergens, and molds.
Mountable sensor: micro- and miniature-scale platforms (59) - Sensors in the mountable
micro- and miniature-scale platform group have been used to sense environmental pollutants,
including to monitor air quality and detect nanoparticles. The traditional (primary) application
has been in vehicles (e.g., within automobile exhaust systems), for which a wide variety of
electrochemical sensors has been used. A subgroup of these devices can detect motion and
position to enable learning, track movement, and facilitate augmented reality. Micro-scale
detectors and monitoring devices commonly use sensor arrays, remote sensing, WSNs, and
service-oriented architecture (SOA).
These sensors are generally smaller than the handheld sensors but larger than the nano-scale
ones, and are commonly designed to be affixed to a mobile device (e.g., chip or circuit board
size), such as MP3 players and cell phones, for personal monitoring. Some benefits identified
related to micro- and miniature-scale sensor units include: small, inexpensive, low power,
sustainable (e.g., could use solar cells or power-scavenging and energy-harvesting techniques).
The devices cover most stages of development from the research phase through prototype
development and testing. In general, research continues to improve sensor detection range,
efficiency, baseline stability, sensing properties, and understanding of the sensing mechanism.
The detection methods or techniques identified for the mountable micro- and miniature-scale
sensor group include: photonics, luminescence, fluorescence, spectroscopy,
spectrophotometry, diffuse illumination, thermography, thermoelectrics, optics, X-ray diffraction,
energy dispersive X-ray analysis, scanning electron microscope (SEM), optoelectrics,
electrochemistry, magnetics, electrometry, resistance, conductance, semiconductance,
capacitance, impedance, thick and thin film microelectrics, voltammetry, acoustics, olfactometry,
GPS, gas chromatography, amperometry, absorbance, accelerometry, radio frequency
identification (RFID), and classification algorithms.
Micro- and miniature-scale mountable sensors are commonly used to detect air pollutants and
components of exhaust gas, including: CO, CO2, O2, NO2, NOX, NH3, H2S, SOX, CH4, VOCs,
HCs, PM, and PMi. Other chemicals also identified in the records include formaldehyde,
benzaldehyde, acetaldehyde, decane, isobutene, acetone, BTEX, chloroform, amine,
trimethylamine, alcohol, methanol, ethanol, isopropanol, benzo[a]pyrene (B[a]P),
trichloroethylene, and trace explosives. In addition, many of these sensors can also measure
temperature, humidity, odors, biomarkers (e.g., glucose, blood gas, pH, and electrolytes),
sound, voice, position, and movement.
Nanoscale technology (50) - In general, the purposes of nanoscale sensors are to detect a
specific chemical or environmental pollutant, notably with higher sensitivity (i.e., lower detection
limits). The detection methods or techniques identified for the nanosensor group include:
colorimetrics, photonics, spectroscopy, optics, chemiluminescence, electrochemistry,
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resistance, chemoresistance, conductance, transconductance, capacitance, resonance,
amperometry, absorbance, and adsorbance.
For the sensor element architecture, nanoparticles are typically made into nano-sized crystals,
tubes, wires, thin films, porous matrixes, sensor pads, or sensor arrays, which are used as the
detector element. These nanotechnology-based sensor elements are then typically integrated
into wearable, handheld, or mobile detectors and monitoring devices. Nanotechnology is
reflected in a number of sensors in the research, prototype development, and testing phases.
The nanoscale sensors identified in this literature review are able to detect a wide variety of air
pollutants, including: CO, CO2, O2, NO, NO2, NH3, H2S, SO2, VOCs, HCs, PM, PMi, and
nanoparticles. These technologies are also identified as capable of detecting many other
chemicals, such as: acrolein, formaldehyde, decane, hexane, styrene, MEK, acetone, ethyl
acetate, BTEX, chloroform, phenol, ethanol, isopropanol, diethyl ether, organophosphates,
p-dichlorobenzene, 1,1,1-trichloroethane, 1,2,4-trimethylbenzene, mercury vapor (elemental
Hg), Cr(VI), chemical warfare agents (e.g., sarin, soman, or GF), explosives (e.g., TNT and
DNT), and microbes such as E. coli. Such sensors have also been designed to measure light
and odors.
Robotics (8) - Robots are used in conjunction with sensor systems to collect data and/or carry
out specific tasks based on the results of the data collection and monitoring. The robotic units
found in the records are identified as being able to detect air pollutants, such as CO, PM,
natural gas, liquid petroleum gas, smoke, and RFID tags. For example, an autonomously
navigating robot used for disaster management that is equipped with air quality sensors and the
ability to search for disaster survivors; a robot vacuum cleaner linked to a monitoring unit that is
wirelessly signaled to turn on when cleaning is deemed necessary; robots outfitted with an
electronic noses to collect environmental data while completing urban hygiene tasks (e.g.,
cleaning pedestrian areas and collecting garbage); or an autonomous robot capable of
searching an area for RFID-tagged items and uploading the location information into a
database. These units cover most stages of development from the research phase through
design prototype and patent application.
Vehicle-mounted unit (44) - This group includes sensors mounted on baby carriages,
shopping carts, bicycles, cars, airplanes, and public transportation vehicles (e.g., buses).
Position of these units generally includes placement in or near a: bicycle basket, bicycle
helmet, car window control unit, vehicle exhaust system, and vehicle roof. The devices are
generally used to measure, collect, monitor, and analyze real-time environmental pollutant and
related parameter data (e.g., emissions levels, temperature, humidity, wind speed, and traffic
density) inside and outside the vehicle. Some units are used to filter air for the user (e.g. baby in
a carriage). The detection methods or techniques identified for vehicle mounted sensor units
include: spectroscopy, IR, electrochemistry, selective catalytic reduction, impedance,
microwaves, ultrasound, pressure, depth, weight, GPS, participatory sensing, and user
generated content.
Information gathered is commonly linked to spatial attribute data (e.g., time, date, and GPS
coordinates) that can be combined with web-based applications to track and map results over
specific areas. Many of these vehicle mounted units use wireless sensor networks (WSNs) to
facilitate information exchange between users; transmit data to a central database for querying,
processing, and storage; and allow online access by the public. The devices cover most stages
of development from the research phase through commercial availability.
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Vehicle mounted sensors primarily measure airborne pollutants, including vehicle exhaust and
many greenhouse gases, as identified: CO, CO2, O3, NO2, NOX, NH4, NH3, SO2, SOX, H2S, CH4,
VOCs, non-methane HCs, black carbon, PMio, PM2.s, PMi, and UFPs. Other chemicals and
parameters specifically identified include: benzene, formaldehyde, natural gas, propane,
temperature, pressure, humidity, wind speed, noise, location, and traffic density.
Wearable sensor (23) - In general, the purposes of wearable sensors are for monitoring
personal air quality, location, and/or movement. Sensors are typically placed in or on wearable
items, such as a shirt, mask, hat, eyeglasses, necklace, or ring. The devices cover most stages
of development from the research to prototype phase.
The wearable sensors identified primarily measure airborne pollutants, including CO, CO2, Os,
NO2, NOX, SOX, H2S, CH4, VOCs, PM, and UFPs (ultrafine particles). Other chemicals and
parameters specifically identified include metal oxide gas, black carbon, contagious viruses,
temperature, pressure, humidity, light, UV radiation, location, and movement.
3.6.2 User Interface Design and Programming
Activity/speech recognition (16) - Systems that use activity and/or speech recognition detect
a wide variety of information. Some of this data are physical parameters, such as motion,
gestures, pressure, vibration, electromagnetic radiation, speech, and audio level; other
information is virtual, such as computer activity and application use. Applications include:
activity and sound recognition; location detection; position classification; proximity sensing;
occupancy sensing and prediction; eye movement analysis; gesture recognition and interaction;
speech and gesture enabled multi-modal user interface; 3-D cell phone interaction; object
manipulation; availability sharing systems; cell phone ringtone interaction system.
Algorithm/modeling (38) - Records in the algorithm/modeling group cover research and
developed applications that define, build, and present various algorithms and models for: data
tracking, management, processing and exchange; information extraction, retrieval, and
presentation; image analysis; data mining and fusion; location identification; outcomes
prediction; voice, speech, and sound detection; optimization analysis; and data visualization and
mapping. Some specific examples include: air quality analysis and monitoring;
pollutant/contaminant detection, source identification, concentration distribution, and dispersion;
and multi-sensor data systems control. In general, applications utilize both collected and historic
data, and can leverage WSNs, Bluetooth, and data acquired from smartphones.
Algorithms specifically identified include: heuristic algorithms; classification algorithms for
pattern matching and voice activator detection; authentication and encryption algorithms to
protect communication over wireless networks; change detection algorithms to identify
suspicious user activity and potential threats; dimensionality reduction algorithm to retrieve and
present information. Models specifically identified include the: integer programming model;
two-dimensional stochastic model; two-level hybrid fusion model; spatiotemporal model; three-
dimensional mapping; multi-component model; user activity model; and relevant context mode.
Ambient intelligence (6) - In general, ambient intelligence refers to electronic environments
and/or devices that are sensitive and responsive to the presence of people and work in concert
to support people in carrying out their everyday life. Examples identified include: a lambent
display on a shopping trolley intended to "nudge" (or induce) people when choosing what to
buy; a digital appliance that encourages the act of smiling; a way to communicate with the public
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beach conditions; an item recommendation based on customer interests and past behavior; a
way to inform and communicate with field security and law enforcement personnel.
Augmented reality (5) - Augmented reality (AR) is a live, direct or indirect, view of a physical,
real-world environment whose elements are augmented by computer-generated sensory input
such as sound, video, graphics or GPS data. The records identified in this research area
specifically discuss: a multi-level visualization system based on the distance between the user
and sensor nodes, to support end-user management of WSN status information; a novel
terminal (e.g., eyeglasses) that can supply AR service and allow users to carry on hand-free
operations (e.g., cell phone, or wheelchair control); a system that connects people in different
locations through enriched multi-sensory communication using gesture-based screen
interaction, ambient pictures on an animated tablecloth, and message transmission; a system
that expands projected augmentations in wide area by using visible light communication and
wireless RFID technology; and how and where to use sensors to maximize their efficiency and
ensure only the most relevant data are used and presented visually.
Context awareness (10) - In general, context awareness in computing can be defined as the
use of information that characterizes a situation related to the interaction between humans,
applications, and the surrounding environment, and provide task-relevant information and/or
services to a user. The efforts in this area leverage smartphones, Bluetooth technologies, and
WSNs. Context-aware applications and research included: user grouping methods; contextual
and personalized recommendations without any explicit user input; activity and device position
recognition; online/offline tracking and integration; location detection; usage pattern tracking;
creation of narrative events for status updates; adaptive duty-cycling of the sensor activity;
investigation of intelligibility for uncertain context-aware applications; user activity modeling; aid
to field security and law enforcement personnel; and travel assistant application for special
needs passengers in public transit environments.
Database/data mining (5) - The database/data mining group contains records that involve the
compilation, organization, analysis, or extraction of data. In general, the databases presented
are designed to be user-friendly, making it easy for users to access, locate, and share of data.
Location awareness (23) - Location awareness refers to devices, such as cell phones, that
can passively or actively determine their location. The location data, which is also often link to
time, is then commonly correlated with air pollution data, environmental conditions (e.g. to
estimate exposure), geographical context (e.g., maps), traffic patterns and density, and social
networks. Other research in this area relates to privacy protection and control of a user's
location data.
Mobile sensing (11) - Mobile sensing involves the use of mobile devices (e.g., cell phones or
vehicles) to measure, gather, process, characterize, and communicate information. The data
gathered by these devices relates to a variety of matters, including: location, traffic patterns,
activity, motion, sound, social mechanisms, interpersonal relationships, query history, and
privacy protection.
Multi-sensor system (9) - Multi-sensor systems combine two or more senor technologies in a
single device to collect data, monitor conditions, and potentially feed into intelligent control
systems. A few examples that use these types of systems, as identified: intelligent
environmental control system for a greenhouse that collects and stores real-time parameters
that can be fused with historical data; air quality monitoring system for an automobile that
correlates the impact of gas temperature and flow rate on SC>2 monitoring; intelligent indoor air
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quality control system for health care centers that allows for continuous monitoring of air in
multiple rooms and is capable of detecting hazardous gas leaks; human lifestyle indicators
recorder that collects and compiles data (e.g., temperature, humidity, illumination intensity,
presence of people, lights on or off, and open or closed doors) to identify wasted resources;
urban scanning systems that acquire mass quantities of real-time information (e.g., traffic
estimation data, real-time energy efficiency, calculation of optimal travel routes, and pollution
maps) from multiple sources of different types of data and make it available on a cloud network;
soil erosion and non-point source pollution monitoring system for a reservoir area; and
heterogeneous long-term water monitoring system that collects and stores real-time water
quality parameters such as pH, temperature, conductivity, turbidity, and dissolved oxygen.
Participatory/citizen sensing (23) - Participatory sensing is the concept of using members of
the public to collect data and information, typically through the use of mobile devices, to form a
body of knowledge. The identified records indicated numerous topics for which of information
gathered: pollutants; air and water quality; environmental conditions (e.g., temperature,
humidity); energy consumption; galaxies; plant phenology; and weeds.
Remote sensing/monitoring (11) - Remote sensors and/or monitoring involve the acquisition
of data and information about an object or phenomenon, without making physical contact with
the object or area being investigated. Research and applications utilizing this type of sensing, as
identified in the records, include: air pollution monitoring; vehicle emissions analysis; and facility
monitoring (e.g., gases released from CAFOs).
Sensor calibration (1) - The single record that discussed sensor calibration addresses the
question of whether low-cost sensors can be calibrated to provide sufficiently accurate
information about levels of pollution to support further scientific investigation.
Social networking/computing (13) - In general, social computing can be broadly defined as
computational facilitation of social studies and human social dynamics, as well as the design
and use of information and communication technologies that consider social context (e.g., social
networks). The records identified in this group discuss a variety of applications, including:
research on energy-use and energy conservative behaviors of consumer individuals; mobile-
phone based social and behavioral sensing system that combines extremely rich data collection
in terms of signals, dimensionality, and throughput, together with the ability gain insights on
intricate social mechanisms and to conduct targeted experimental interventions (e.g., increasing
physical activity) with study populations. Also included are mobile social network aggregators
that integrate social networking services into the mobile device user interface and recommends
new content that is likely to be interesting to the user; mobile social service for an office
environment that is used to record a user's position and allow users to efficiently manage office
resources and connect other colleagues; and a smart makeup system that helps users find new
makeup methods for use with their daily cosmetics. Additional topics include location-based
information fusion for mobile navigation that leverages static public online information with
users' location-based social network resources to provide real-time exploration in novel
environments; a study of three visualization types (text-, map-, and time-based) for social
sharing of past locations; research related to targeted location-sharing privacy attacks; and
modeling social and geographical context based on co-location networks in human mobility
datasets. Further applications include use of context-based awareness cues in status updates,
especially in SNSs (Social Networking Services); new method for public communication about
ambient water conditions through investigation of novel ways to measure ambient quality and
make this information available as a public resource; and research exploring which features of
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interpersonal relationships influence personal information sharing with friends and social
groups.
Ubiquitous computing (14) - Ubiquitous computing (ubicomp) is a method of information
processing based on the fact that computers have been thoroughly integrated and embedded
into everyday objects and activities, making them effectively invisible to the user, but able to
collect and communicate information. The research in this area leverages smartphone and
Bluetooth technologies. Specific research and applications identified in the records that utilize
ubicomp involve: new perspective on everyday biomarkers that utilize the lens of organic and
non-digital sensing to reflect on current sensing paradigms; the design of personal informatics
tools that effectively assists people's reflection on collected data; a zero-configuration spatial
localization system for networked devices based on ambient sound sensing; mapping
interaction qualities identified in private and work contexts; transferring information from mobile
devices to personal computers by using vibration and an accelerometer; supplying AR service
and allow users to carry on hand-free operations; a line of prototypical products for future
homes that simulate and stimulate emotion; using a mobile application to assist in the capture of
the contextual information for diabetics using self-care devices; the design and evaluation of
school-based ubicomp that treats the school as a social institution; exploration on the extent that
data, recorded by automated fare collection (AFC) systems of public transport authorities, offers
the possibility to both build and measure future of travel-based ubicomp applications; research
to establish the theoretical foundations for the design of mechanisms for forgetting and develop
and evaluate the framework and its interfaces for users to control these mechanisms; the design
systems that rely on sensing and recording, but also account for privacy concerns of users; a
persuasive sleep application that involves self-monitoring and feedback features to help people
be aware of their sleep habits; and new modes of collaborative human navigation.
Virtual reality sensing (5) - Virtual reality (VR) is a term that applies to computer-simulated
environments that can simulate physical presence in places in the real world, as well as in
imaginary worlds. The records identified in this research area specifically discuss: the creation
and study of haptic (i.e., tactile feedback) devices for both blind and sighted people that provide
sensory augmentation; the electrical stimulation of the gustation (e.g., sense of taste) for a
human; a technique that enables controlling of CD ratio by finger height and movement above
the touch surface for multi-scale navigation tasks; and an availability sharing system designed
to balance the costs and benefits between the interrupter and the interruptee.
Visual sensing (11) - Visual sensing involves the use of visual images to extract, characterize,
and interpret information and data about the three-dimensional world. Applications utilizing this
type of sensing, as identified in the records, include: real-time roadway emissions estimation
using visual traffic measurements; airflow and light monitoring devices; air quality evaluation
and modeling using surveillance data; colorimetric detection of a contaminant, which can
activate an alert system; smile recognition to encourage the act of smiling; use of thermal
imaging to track disaggregated appliance usage; and item location finder.
Web-based system (10) - The web-based category contains records that use an accessible
web portal to submit, access, manage, analyze, and monitor data and results. Areas that were
identified as using a web interface: air quality, emissions, mapping and visualization, route
planning, personal health, water consumption, plant phenology, and web history.
Wireless sensor network (WSN) (54) - Wireless sensor networks (WSNs) generally consist of
a collection of spatially distributed autonomous sensor nodes for data acquisition, transmission,
and distribution, which is monitored and controlled at a central management point. These
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networks are used in and for a variety of applications, including and as identified in the records:
air, noise, and water pollution monitoring; traffic conditions indication; travel route calculation;
weather and monitoring of environmental conditions; hazard identification; exposure; asthma
management; data visualization and identification of gaps; geographic location and mapping;
jamming attack model; energy consumption; power charging; parking management; and voice
activation.
3.6.3 Other Systems and Data Features
Data broadcasting (3) - In general, data broadcasting is the transmittal and distribution of data
over a wide area by means of digital signals (e.g. radio waves). The records in this category
discuss applications for digital signage systems and mobile phones.
Distributed data processing (1) - The single record for distributed data processing discussed
a tool that boosts the data handling and trial-and-error process of the signal processing.
Economic tradeoffs (2) - The studies in this group discuss a link between sensor use and
money. One of the studies uses sensor technology to investigate the value of sensor
information enhancing the control of perishable goods to decrease waste, thus increasing profit
(and consequently reducing greenhouse gases). The other study evaluated economic micro-
incentive compensation for participants wearing sensors in high-burden studies (e.g. extended
durations) and how these strategies affect compliance with study protocol, data quality, and
participant retention.
Exposure assessment (8) - A number of research studies looked at the use of portable and
personal monitors to measure and monitor pollutant concentrations, as well as to address
exposure. The devices in this category are located outside and inside of schools, at bus stops,
on and along roadways, and on individual persons. Specific pollutants detected by this group of
devices include: UFP, CO, CO2, Os, PM, vehicle exhaust, benzene, black carbon, nicotine,
cotinine, and PAHs.
Prediction service (1) - The single record included in this category described a service that
provides household water usage data, consumption projection, and regional demand
forecasting for both short-term and med-term.
Radar system (1) - The research under this category involves space-based surveillance and
detection and sensor development for missile defense that focuses on electronic attach and
protection techniques, tracing and sensor fusion, vulnerability analysis, space-time adaptive
processing.
3.7 HIGHLIGHTS OF RECENT FEDERAL RESEARCH ACTIVITIES
Sensor research is ongoing at a number of federal agencies and national laboratories.
Especially significant is the Exposure Biology Research Program in the National Institutes of
Health (NIH), National Institute of Environmental Health Sciences (NIEHS). This $20 million
research effort is pursuing technologies and assays to precisely measure human exposures and
modifying factors, toward producing sensitive, high-throughput, potentially portable systems that
can measure exposures to environmental agents and their impacts on human biology. (See
http://www.niehs.nih.gov/research/supported/dert/sphb/programs/sbir/topics/ebp/index.cfm.) On
a much smaller scale, highlights of selected recent research at several laboratories and other
agencies are presented in Tables 3-10 and 3-11.
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TABLE 3-10 Highlights of Recent Sensor Research Led or Funded by Federal Agencies
Agency
Research
(with publication year or weblink)
Selected Highlights
DOE Office
of Biological
and
Environ-
mental
Research
Self-calibrating Balloon-Borne Methane Gas Sensor (Southwest
Sciences, Inc., 2011, grant renewed 2013)
http://www. sbir. gov/sbirsearch/detail/3 73544
Research to develop a self-calibrating, low mass, greenhouse
gas sensor to be deployed on meteorological balloons to cover
wide geographic areas; a similar methane-only sensor is also
being developed.
Diode laser sensor for methane detection on an unmanned aerial
vehicle (Southwest Sciences, Inc., 2013)
http://science.energy.gOv/~/media/sbir/pdf/awards%20abstracts/fy1
3/FY13-Phase-1-Release-1Final.pdf
Research to develop a small, light-weight diode laser sensor
for methane (3300 nm range) that can be deployed on an
unmanned aerial vehicle.
Isotopic CO2 Instrumentation for UAV Measurements (Southwest
Sciences, Inc., 2013)
http://science.energy.gOv/~/media/sbir/pdf/awards%20abstracts/fy1
3/FY13-Phase-1-Release-1Final.pdf
Research for rapid and precise measurement of isotopic
carbon dioxide for unmanned aerial vehicles.
Lightweight Integrated Optical Sensor for Atmospheric
Measurements on Mobile Platforms (Physical Sciences Inc., 2013)
http://science.energy.gOv/~/media/sbir/pdf/awards%20abstracts/fy1
3/FY13-Phase-1-Release-1Final.pdf
Research to develop a sensor for materials of national security
interest, environmental monitoring, and industrial
manufacturing.
Airborne Sensor for Aerosol Precursors (Vista Photonics, Inc.,
2013)
http://science.energy.gOv/~/media/sbir/pdf/awards%20abstracts/fy1
3/FY13-Phase-1-Release-1Final.pdf
Research to develop an airborne sensor for monitoring
ammonia at atmospheric concentrations.
Infrared Laser Direct Absorption Spectroscopy for Carbon Isotope
Measurements from UAVs
(Aerodyne Research, Inc., 2013)
http://science.energy.gOv/~/media/sbir/pdf/awards%20abstracts/fy1
3/FY13-Phase-1-Release-1Final.pdf
Research to develop light-weight infrared laser spectrometer
to measure isotopologues of carbon dioxide and methane.
Highly sensitive, low-power, and low-weight gas analyzer for UAVs
(Mesa Photonics, Lie, 2013)
http://science.energy.gOv/~/media/sbir/pdf/awards%20abstracts/fy1
3/FY13-Phase-1-Release-1Final.pdf
Research to develop a light-weight, compact sensor for
greenhouse gases that can be deployed on unmanned aerial
vehicles.
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Agency
Research
(with publication year or weblink)
Selected Highlights
DOE Office
of Biological
and
Environ-
mental
Research
(cont'd.)
Compact QCL spectrometer for carbon isotopologue
measurements from Small UAVs (Physical Sciences Inc., 2013)
http://science.energy.gOv/~/media/sbir/pdf/awards%20abstracts/fy1
3/FY13-Phase-1-Release-1Final.pdf
Research to develop a highly sensitive sensor for monitoring
stable isotopes of carbon dioxide.
Low Cost Small Sample Volume High Precision Carbon Dioxide
Analyzer (Li-cor Biosciences, 2013)
http://science.energy.gOv/~/media/sbir/pdf/awards%20abstracts/fy1
3/FY13-Phase-1-Release-1Final.pdf
Research to develop a high-precision, low-cost sensor for
carbon dioxide in the atmosphere.
National
Aeronautics
and Space
Administra-
tion
DISCOVER -AQ(2011),
http://www.nasa. gov/mission_pages/discover-aq/news/DA Q-
20110622.html
Research to improve methods for aerial pollution monitoring
via aircraft; ideally, the data collected will be combined with
ground-collected measurements to provide a more complete
picture of air quality.
TEMPO (Tropospheric Emissions: Monitoring of Pollution) (2012),
http://www.nasa.gov/home/hqnews/201'2/nov/HQ_1'2-
390 TEMPO lnstrument.html
Research in collaboration with the Smithsonian Astrophysical
Observatory, to accurately measure tropospheric pollution
concentrations of ozone, nitrogen dioxide, sulfur dioxide,
formaldehyde, and aerosols with high resolution in North
America using a space-based instrument attached to a
satellite. (Completion of the structure is expected in 2017.)
JPL E-nose (2013),
http://www.nasa.gov/mission_pages/station/research/experiments/3
2.html
Research to train this device to identify and quantify various
pollutants at concentrations from one-third to three times the
24-hr spacecraft maximum allowable concentration value.
National
Science
Foundation
Various projects funded in 2013 (these and others can be found via
the award search engine using keyword "sensor")
Research themes include mobile sensors, carbon dioxide
sensors, sensors for combustion applications, data quality and
cloud infrastructures, health monitoring, radiation monitoring,
and energy harvesting. Awards since 2010 include
miniaturization attempts, sensor network development,
greenhouse gas detection, data analysis methods, wearable
sensors, and sensors aimed to improve fuel efficiency.
U.S. Depart-
ment of
Agriculture
Development of optical fiber sensors and sensor array for
continuous monitoring of ammonia spatial distribution in animal
feedlots (West Texas A&M University, 2013)
Research to develop optical chemical sensor (evanescent
wave absorption) networks for in-situ, real-time, long-term
continuous monitoring for ammonia distribution in CAFOs, to
inform air quality research as well as site emission control and
measurement activities.
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TABLE 3-11 Highlights of Selected Research Activities at DOE National Laboratories
Laboratory
Ames
Argonne
Brookhaven
Idaho
Lawrence
Berkeley
Research (publication year or weblink)
Microwave and terahertz sensing using
slab-pair-based metamaterials (2012)
Sensors and materials research
(http://www. anl. gov/security/sensors-
materials)
Photoacoustic spectroscopy (PAS) system
for remote detection of explosives, chemi-
cals, and special nuclear materials (2012)
NOx/O2 Sensors for High Temperature
Applications
(http://web.anl.gov/eesa/pdfs/success_storie
s/49 NOX-O2 Sensors v4.pdf)
In-situ spectra-microscopy on organic films:
Mn-phthalocyanine on Ag(100) (2013)
Instrumentation, control, and intelligent
systems; sensors
(https://inlportal.inl.gov/portal/server.pt?ope
n=514&objlD=5325&mode=2&)
Low power, fast, selective nanoparticle-
based hydrogen sulfide gas sensor (2012)
Available technologies for licensing and
further R&D
(http://www. Ibl. gov/Tech-
Transfer/techs/lbnH 850.html;
http://www. Ibl. gov/Tech-
Transfer/techs/lbnl2689. htm;
http://www. Ibl. gov/Tech-
Transfer/techs/lbnl2349. html)
Selected Highlights
Testing metamaterial structure that can be used to measure gas concentrations by
evaluating the electrical permittivity of the material between a pair of metamaterial
slabs. Tested materials include silicon, and low-density polyethylene.
Mobile sensors to detect nuclear and radiological materials, chemical and biological
agents, and explosives. Recent developments include millimeter-wave systems that
track biometrics and detect chemicals, gases, and radiation as well as carbon-based
nanomaterials and nanostructures that enhance the performance of nanoscale
devices.
PAS can be used to remotely sense chemicals in an open environment; gases can be
detected several meters from the target.
Simultaneously measures NOx and 62 in vehicle engines; the sensor has a self-
contained reference gas system, is compact, cheap, and easy to produce.
Research on metal phthalocyanines for potential use in chemical sensors.
Research on sensors that use advanced mass spectrometry and ion mass
spectrometry modeling software to detect trace explosives, as well as sensors for
high-temperature tests, and portable isotopic neutron spectroscopy used for handheld
detection of concealed bulk explosives as part of munitions inspections.
Small, low-cost, low-power, nanomaterial-based gas sensor with high selectivity for
hydrogen sulfide and no significant cross sensitivity for hydrogen, water, or methane.
Miniature airborne particle mass monitor: power need of <100 mW, can measure
picogram of material, prototype costs $100. Compact microchip gas sensor:
hydrogen microchip sensor for real-time detection and analysis of hazardous gas
levels.
Membrane and receptors for highly selective gas-phase sensing: insensitive to
humidity changes, portable and low cost.
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Laboratory
Lawrence
Livermore
Los Alamos
Oak Ridge
Pacific
Northwest
Sandia
Savannah
River
Research (publication year or weblink)
NOx sensor development (2012)
A block-based MC-SURE algorithm for
denoising sensor data streams (2012)
MotionCast for mobile wireless networks
(2073;
Sensors: Theory, algorithms, and
applications (2072;
Routing for wireless multi-hop networks
(2073;
Sensors and controls research
(http://www. ornl. gov/sci/ees/mssed/sst/proje
cts.shtml)
NOA: A scalable multi-parent clustering
hierarchy for WSNs (2072;
Microelectronics, sensors, MEMS, and
photonics research
(https://ip. sandia.gov/category. do/category/
D=20)
Sensors development research
(http://srnl.doe.gov/sens_dev.htm)
Selected Highlights
Impedance metric sensor to measure NOx in vehicle exhaust at <5-1 00 ppm; low
cost, close to commercial development, licensed by EmiSense Technologies.
Algorithm to denoise real sensor streaming data; enables blind optimization of the
denoising parameter of a wide class of filters.
Research on mobile ad-hoc networks for use used when infrastructure for
communication are limited (e.g., for mobile air quality sensors carried by citizen
scientists); the emphasis is on capacity and connectivity.
Highlights recent advances, current work, and future needs; investigates information
patterns obtained by sensor measurements, mathematical approaches encompassing
dynamic systems and statistical techniques, application specific approaches, and
other related research.
Generic routing model to use as a foundation for wireless multi-hop routing protocol
analysis and design.
Very low power, wireless microsensor array (to measure CO2, humidity, temperature,
occupancy); also, in-situ fuel cells to measure temperature and humidity, vehicle
exhaust (ammonia, nitrogen oxides, and oxygen), harsh environment sensing
(inductive noise thermometry), and others; recent research is on perovskite oxides for
use in electrochemical sensors.
Tool to reduce the amount of data sent to sinks, which reduces the cost of overhead
by reducing the cost of network setup.
Research on microsystems and constituent components, including chemical sensors,
microelectronics, displacement sensors, hybrid microsystems, and others.
Research emphasizes modifying available instruments for particular situations and
building robust systems that can operate for years in harsh environments.
Technology research areas include fiber-optic sensors, remote chemical analysis, Sol-
Gel indicator sensors, remote robotic sensor systems, and environmental sensors
which are used to measure environmental pollutants and various chemical
concentrations or physical properties including ammonia, hydrogen, temperature, and
humidity.
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4 GAPS AND OPPORTUNITIES
Gaps and opportunities for mobile sensors and apps have been identified from a review of
recent literature, illustrated by a targeted evaluation of fourteen representative air pollutants.
These gaps and opportunities are organized into two main topics: (1) sensing technologies and
techniques; and (2) architecture and infrastructure approaches, including software applications.
4.1 SENSOR TECHNOLOGIES AND TECHNIQUES
4.1.1 Specific Pollutants
Most portable sensors are designed for gases, so particles represent a substantial gap. No
mobile research sensors were found for lead. Techniques for this metal and others are primarily
lab-based, with associated time lags and costs. LIBS represents an opportunity technique for
lead, but the ability to detect low concentrations is not known. Similar constraints apply for PM,
and current methods are expensive ($1,000 to $20,000 and higher). Furthermore, they are non-
specific (e.g., mass-based via filtration), so chemical identities are not determined. A MEMS
based on the acoustic-wave microbalance sensing principle represents an opportunity area.
The cost might be less than $100.
Among the gases, acrolein represents a key gap for mobile sensors. Few were found to
address this HAP, which is a national risk driver per NATA and is also associated with emerging
emission sources such as biodiesel production. Health-based concentrations established for
lifetime exposures are difficult to reliably measure, even with large, fixed systems. Thus, further
research and development to produce mobile sensors for this compound would be useful. A
second gas that could benefit from targeted research and development is 1,3-butadiene.
Relatively few sensors (and only one research sensor initiative) are identified for this chemical.
Given its status as a national risk contributor per NATA and the nature of its emission sources
(including from emerging natural gas development), further research to pursue mobile sensors
for this compound could also be broadly beneficial.
A practical needs assessment to prioritize pollutant targets, seeking inputs from parties ranging
from agencies to industry and citizen groups, could help guide investments for targeted
research. Cross-walking the results with ongoing research collaborations to identify leveraging
opportunities would expedite progress. A good example of a needs assessment can be found
in the recent EPA NCEA initiative for nanotechnology/nanomaterials risk assessment. Using a
comprehensive environmental assessment (CEA) approach, knowledge gaps and research
needs have been identified and prioritized with interesting tools that may be useful, particularly
structured techniques for soliciting and incorporating input from experts with diverse
backgrounds. (See http://cfpub.epa.gov/ncea/CFM/nceaQFind.cfm?keyword=Nanomaterials.)
4.1.2 Detection Levels
Gaps in sensor detection levels compared with exposure benchmarks are summarized in
Table 4-1. (A blank cell indicates the benchmark has not been established; an "x" indicates the
concentration may be detected; a "?'" indicates it is not known if the benchmark concentration
can be detected, e.g., absent a reported detection range; and dark shading indicates a gap for
that detection capability.) This summary indicates that gaps may exist for concentrations
established as safe for continuous lifetime exposures for four of the fourteen study pollutants.
Three are HAPs - acetaldehyde, acrolein, and formaldehyde, and the fourth is an indicator
pollutant - ammonia.
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TABLE 4-1 Reported Ability to Detect Exposure Benchmark Concentrations3
Emergency
Response
Occupational
Pollutant
1,3-Butadiene
Carbon monoxide
Formaldehyde
x*
Hydrogen sulfide
Lead
Methane
Nitrogen dioxide
Ozone
Particulate matter
Sulfur dioxide
An empty cell indicates the benchmark is not available. Dark (red) shading indicates a benchmark exists but the concentration has not been reported as
detected. An 'x' in green shading indicates the concentration could potentially be detected by a research or commercial sensor either at a reported LDL or within
the reported detection range. The "?" in yellow shading indicates it is not clear from the information reviewed whether the concentration can be detected or not;
for example, if an LDL is below the benchmark but the detection range is not identified, higher concentrations/interference may limit detection at the higher level.
Risk-based concentrations (RBCs) correspond to the target risk levels shown. Note the CalEPA RBC for lead (not shown here) can be detected. For exposure
durations, general public: ac = acute, chr = chronic, subc = subchronic. Note the acute MRL which extends multiple days, is grouped with "emergency" values
on the arrays. Lead and PM entries reflect commercial sensors only. For methane, the entries reflect protective action criteria; the entries for other pollutants in
the first three columns reflect AEGLs. For ozone, 8-hr TLVs (as TWA) are available for light, medium, and heavy work; a 2-hr TWA is also available.
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An underlying gap involves basic data reporting. That is, a number of research studies indicate
a given pollutant is sensed by the technology/technique but do not report a detection level or
range. Thus, some gaps could be addressed by improving the rigor of scientific reporting.
Detection capability gaps are not limited to mobile sensors, as even fixed commercial systems
can experience difficulties in measuring health-based concentrations identified as protective for
continuous, chronic exposures. To illustrate, in aiming to detect a much higher concentration
than the RfC, the NIOSH method for acrolein notes the accuracy limitation (i.e., it does not meet
the criterion for a valid method; note the NIOSH REL-TWA is 0.1 ppm and the STEL is
0.3 ppm). Similarly, the standard OSHA method targets a detection level that is orders of
magnitude higher than the RfC. Further monitoring context can be gained by considering
chemical fate in air. With a half-life of less than a day (and fate products including CO and
formaldehyde, also in the pollutant study set), real-time monitoring would be useful, and the
nature of the emissions would help guide the appropriate detection target (e.g., ongoing
emissions would align with continuous chronic exposures). Integrating fate context in such a
way provides an opportunity to guide multi-pollutant sensing considerations (see Section 4.2).
Opportunity areas for acrolein involve techniques that move beyond the standard
absorbent-desorption to gas chromatograph method, such as:
• Proton-transfer reaction linear ion trap (PTR-LIT) mass spectrometer.
• Fluorescence, using polyfluorophore sensors built on a DMA scaffold.
4.1.3 Response Time
Response time affects utility of mobile sensors for real-time data collection and has an impact
on power consumption. Use of nanomaterials has helped improve response time, as has the
addition of pre-concentration techniques to GC systems, for example, as sensors generally
respond faster when concentration is higher. However, this improvement may be offset by the
lag time for the pre-concentrator to gather enough of the pollutant to trigger a response (as an
example, the concentration time identified for a spectroscopic sensor was three minutes).
Benefits of a faster response time include:
• Lower power consumption (e.g., a quick response would allow MOS sensors that require
internal heating devices to operate for short intervals, thus conserving energy).
• Ability to capture conditions in real time, particularly important for dynamic conditions.
4.2 ARCHITECTURE AND INFRASTRUCTURE APPROACHES AND AIR QUALITY APPS
4.2.1 Size/Mobility
Relatively little quantitative information was found for the size of research sensors and systems.
Although specific measurements are provided for a handful of devices, in most cases the
characterization is qualitative (e.g., "small" or "very small"); for those, photos or other images
were pursued to provide more descriptive information where possible. Reported and inferred
sizes range from nanoscale; to micro and miniature scale; to thumb, bar of soap, shoe box, and
suitcase-sized; to refrigerator and mobile laboratory-sized.
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In terms of implications for sensing techniques, research efforts in each of the three main
technique categories acknowledge the need for mobile sensors with a similar degree of
sensitivity and reliability as established fixed sensing units. Sensors based on chemical
techniques, especially those using nanomaterials, are particularly amenable to small sensor
size. For a sensor to be practical, the smallest sensing material size has been estimated to be
about 1 cubic centimeter (cm3) (Honicky 2011). However, the decreased size of these sensing
materials corresponds to a smaller reactive surface area which results in a lower sensitivity.
Nearly two-thirds of the literature reviewed reflects research and/or development for handheld or
very small sensing units, or the use of nanoscale materials to increase sensitivity.
Spectroscopy-based sensors are also affected by size constraints. Even with the use of
reflective mirrors, detection cell paths may need to be at least a few centimeters in length to
ensure proper laser pathlines (Honicky 2011). Similar to chemical absorption- or reaction-based
sensors, the decreased size results in higher LDL values (i.e., decreased sensitivity). About
one-third of this literature reviewed reports advancements in developing small handheld devices
and/or use of nanoscale materials with this sensing technique.
4.2.2 Power Requirements
Power consumption is a key contributor to sensor costs, so energy conservation is an active
research area. The driver for power consumption is the length of time the monitoring will be
performed. For example, if only spot measurements were needed throughout the day (e.g.,
totaling less than 4 to 8 hours), then existing batteries such as a common cell phone ion battery
would be more than adequate. However, if 24-hour continuous measurements were needed,
then an alternative power supply would be required or additional capacity (more batteries) would
need to be added to the system (and regularly replaced).
The substantial power requirements of electrochemical techniques are widely recognized,
including the high operational temperatures required for MOS sensing. For this reason, a
number of researchers are working to develop techniques that would allow room-temperature
operation, in order to reduce power consumption and also potentially reduce response time.
These techniques include doping existing MOS sensing films with functional layers and
developing novel polymer films operable at room temperature (Nomani et al. 2011; He et al.
2012). Note that rapid response would also allow for a very brief "on" period, resulting in less
energy consumption; it would facilitate periodic (pulse, burst, or intermittent) sampling that could
be optimized for the setting, target pollutant, and energy use. As another example, UV-induced
room-temperature sensing has been found useful for low-power operation, longer sensor
lifetime, and fast on/off capabilities (Aluri et al. 2011).
Emerging opportunities for novel power sources range from photovoltaic films to biological and
mechanical sources, including energy transfer from humans (e.g., footsteps and heartbeat).
That is, in addition to pursuing low power-consumption components, opportunities are being
pursued for novel power sources and self-powered devices. Recent nanotechnology research
efforts have illustrated the potential to successfully harvest ambient energy from solar,
mechanical, biomechanical, and thermal sources. "Nanogenerators" that can convert these
sources of energy to electrical energy in real time have been used to power small LEDs or
LCDs. Hybrid nanogenerators capable of harvesting energy from more than one source to
increase the feasibility and reduce environmental limitations are being pursued, as is the
development of self-charging power cells in which the nanogenerator and battery are hybridized
into a single component (Wang et al. 2012).
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Air velocity or wind is one of the sources of ambient energy being studied for harvesting to
power mobile sensors. Research on a self-powered, battery-free, air velocity and temperature
sensing system indicated such a system could be used for short-range monitoring, with airflow
velocities as low as 3 m/s in an air duct (Sardini and Serpelloni 2011). These systems could
potentially be modified for mobile or mounted sensing units (e.g., on a bicycle or vehicle). A key
condition for self-powered units is that the power needed to run all individual units must be less
than the power harvested from the environment, which drives the need for low-power
components.
4.2.3 Sensitivity, Selectivity, and Multiple-Pollutant Sensing
To offset the reduced sensitivity that accompanies a smaller sensor size, researchers have
been investigating methods for increasing sensor surface area. These include adding nanoscale
materials (e.g., nanoparticles and SWCNTs) that can react with the desired pollutant to a
previously established sensing film. Recent research activities pursuing electrochemical
techniques that can detect components of a gas mixture have identified modified polymer films
as potential candidates for select chemicals; however the overviews of two studies on this topic
did not appear to include concentration measurements (Yang 2010; Kim et al. 2010). Thus, this
area constitutes both a gap and opportunity.
The development of customizable sensor arrays (sometimes referred to as electronic noses or
e-noses) is also an active research area. Selecting specific, small gas sensors that can be
operated in a single handheld device would make it possible to detect a range of gases,
customized to a given location and pollutants suite. While this would be more convenient, one
challenge is assuring sound mechanisms for addressing interference to prevent false readings.
To illustrate, selected examples of pre-concentration techniques that facilitate detection at lower
concentrations, as well as potentially boosting selectivity (based on media used) include:.
• Spectroscopic technique with pre-concentration and thermal desorption; concentration
cells: Materials are selected for placement into a separate cell into which the sample air
is initially drawn; these materials are specifically chosen to promote selective adsorption.
After some interval (e.g., three minutes), the selected gas is desorbed by a heating
process and travels to the detection cell. Because all the target gas is released at once,
the concentration measured in the detection cell at any one time is increased.
• Chemical technique, semiconductor: pre-oxidation tube increases sensitivity to
formaldehyde.
4.2.4 Cost
Bridging the gap between traditional, very expensive pollutant monitors to widely affordable,
portable sensors is central to the current sensor initiative. Striking progress has already been
made in producing mobile sensors for reasonable cost. For example, simple systems can be
easily assembled for on the order of $100 or less using materials readily available from local
hardware stores and online. (Examples include the Air Quality Egg, light-up detectors built into
$4 weather balloons, and more.)
Replacing expensive system components with affordable alternatives is an ongoing research
theme. As an example, researchers at the Korea Institute of Science and Technology found that
using conductive electrodes in MOS sensors in place of the traditional platinum sensors
increased sensor response and decreased fabrication costs (Shim et al. 2011). Others have
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investigated using absorption at low-irradiation light intensities to address the issue of increased
cost of photoelectric gas sensor (Peng et al. 2011).
Although MOS sensors are relatively inexpensive, this generally translates to lower sensitivity
than alternate spectroscopy or ionization techniques (which are typically more bulky and costly).
Batch and microfabrication and 3-D printing are among the opportunity areas being pursued to
improve sensing capabilities while reducing costs.
Some sensor systems, such as those using GC-PID, are sensitive to external environmental
factors including relative humidity and temperature. Required climate control features add both
capital costs and operation and maintenance costs to the sensor system, including related to
power consumption. One aim of the current initiative is to identify opportunities for developing
lower-cost, reliable mobile sensors and systems that do not require such infrastructure support,
by limiting the control elements needed without a corresponding power drop.
4.2.5 Field Performance
Gap areas and opportunities include:
• Reliability and durability of sensors and systems in field conditions - with opportunities
including replacement, regeneration and reuse of sensor components, and extension of
maintenance schedules.
• Long-term stability and autocalibration represent current gaps - with opportunities
including self-healing and autocalibration networks, such as via Web4.0/lnternet of
Things, and tapping physical infrastructure for joint fixed-mobile sensor calibration hubs.
• Operator training - mobile sensing provides an opportunity for a new "operator corps" to
effect widespread participatory sensing with much more user-(operator-)friendly
monitoring devices; in tandem with greatly reduced operating complexity compared to
traditional monitoring systems, resources such as e-learning, social media, educational
programs (e.g., in the GO3 model), and DIY networks create a strong opportunity for
addressing the "trained operators" issue for mobile sensing.
Technology/technique and architecture/infrastructure gap and opportunity areas discussed in
Sections 4.1 and 4.2 are highlighted in Tables 4-2 and 4-3, with an illustration comparing
commercial and research sensors for carbon monoxide presented in Table 4-4. Supporting
context for Tables 4-2 through 4-4 is provided as follows.
Cost: Depending on the monitoring needs for a given situation (including detection level),
relatively cheap technologies are increasingly available, such as metal oxide versus
spectroscopic sensors. Incorporating novel sensor components into existing hardware
(such as smartphones) to harness associated data analysis and transmission capabilities
can reduce system costs.
Mobility: Trade-offs between sensor mobility-size and accuracy-precision are common.
Automatic calibration networks and integrated/hybrid systems represent opportunity areas
for research. Hybrid systems could involve joint fixed and mobile sensors (including sensors
affixed to existing infrastructure); they could also involve handheld portable sensors
combined with vehicle-mounted larger devices with higher precision/accuracy, such as
BikeNet (sensors mounted on bicycles), CommonSense (sensors mounted on street
sweepers), or sensors mounted on taxi cabs.
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TABLE 4-2 Highlights of Sensor Gaps/Limitations and Opportunities3
Feature
Gaps
Potential Solutions / Advances
Issues / Research Opportunities
Cost
Affordability for widespread
use
Pursue cheaper technologies (e.g., metal oxide
vs. spectroscopic sensors).
Use less expensive materials (e.g., coated glass
electrodes vs. platinum electrodes; low vs. high
light irradiation sources).
Mass produce to reduce cost; note the sensors
should not require expensive infrastructure to
function properly.
Increase sensing capabilities of cheaper
technologies.
Could potentially reduce sensing capabilities.
Mass production could follow public demand
for personal exposure information, for
example (especially if easy to use, e.g.,
integrated into popular devices such as a
mobile phones).
Mobility
Easy to carry
Pursue portable/handheld devices, recognizing
some technologies are more amenable to
miniaturization than others (e.g., chemical vs.
spectroscopic and ionization sensors).
Leverage for hybrid systems, supplement with
larger mounted sensors (e.g., on cars, bikes).
Typical trade-off between size and sensing
capabilities.
Not amenable to indoor applications, location
limitations for vehicle-mounted sensors.
Energy
consumption
Sustained energy
conservation (e.g., to
increase battery life between
charges)
Reduce sampling time, reduce warmup period.
Non-continuous sampling: select times for discrete
vs. continuous sampling (e.g., when air quality is
expected to be poorer or conditions change, or
when in transit vs. stationary); can be useful when
pollutant patterns are known (e.g., when
concentrations are likely elevated).
Delay data upload (GPS/concentration) to
optimize energy-efficiency (e.g., upload while
charging the device).
Decrease operational temperature (e.g., for MOS).
Use passive vs. active sensing to reduce energy
associated with drawing air into the sensor.
Integrate energy-producing devices that use
textile fibers to harvest mechanical, vibrational
and hydraulic energy and convert to electrical
energy to power nanodevices.
Capture real-time, daily air pollutant
concentration spectrum, optimize pulse vs.
continuous sampling based on pollutant and
setting characteristics and data use/decision
needs and objectives.
Facilitate real-time data upload (applies to
charged units, not battery-powered devices).
Nanomaterials can continue to be explored
(e.g., the operational temperature was
reduced to 300°C in one case, still much
higher than room temperature operation for
polymer-film sensors).
Tap ongoing innovative research on novel
energy sources and energy harvesting.
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TABLE 4-2 Highlights of Sensor Gaps/Limitations and Opportunities3
Feature
Gaps
Potential Solutions / Advances
Issues / Research Opportunities
Sensitivity-
selectivity
High sensitivity and
selectivity is a current gap
for lower-cost sensors
Interference control
Sensor drift
Add hardware and software to account for
interferences (including humidity, temperature, air
flow, barometric pressure).
Choose pre-concentration materials chosen based
on composition for selectivity of target substances
and ability to deliver molecules to sensing
elements in more concentrated bursts to increase
sensitivity.
Use multiple sensing elements (e.g., sensor
arrays, e-noses).
Pursue humidity-assisted gas sensing to improve
selectivity.
Use chemical coatings that only allow certain
particles (based on electric potentials) to reach the
sensing device.
Use front-end filter/screen interferences.
Harness super-sampling and automated sensor
calibration networks; software tools include
CaliBree, Quintet, and Halo.
Increase desorption rate of chemicals and
interferents such as water molecules from reactive
chemical films.
Additional components typically increase cost
and energy consumption; might be
accommodated by mass fabrication.
Time to reach desired pre-concentration rates
may be increased if the concentration is low,
resulting in increased sampling and response
times.
Possible increased maintenance needs, cost.
Has been applied to SCb/NCb differentiation.
More research is needed for application to
other pollutants of interest.
Adding components typically increases cost
and energy consumption; a possible cost
reduction from mass fabrication would be at
least somewhat offset by additional software.
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TABLE 4-2 Highlights of Sensor Gaps/Limitations and Opportunities3
Feature
Gaps
Potential Solutions / Advances
Issues / Research Opportunities
Response,
recovery,
and analysis
time
Identification of exposure
context (e.g., environmental
setting, concurrent activity)
Response time
Recovery time
Include technology that "hears" ambient noise and
classifies its spectral features to identify the
measurement environment. Include an
accelerometer.
Enhance software, e.g., building on Intel's Place
Lab or MIT's Cricket Indoor Locator (note Place
Lab requires a radio beacon to estimate position,
so although this example is not practical for
general use it illustrates the basic concept).
Depends on specific application needs,
constraints, and preferences. Can Increase
reactive surface area in chemical sensors; could
be achieved using nanoparticles.
Expose to UV light or heating elements.
Increase chemical desorption rates.
Continued research and development is
indicated for such technologies; adding
components to sensor systems can increase
cost and/or energy consumption, depending
on the component (note a common 3-axis
accelerometer is about $10).
Typically governed by energy constraints,
sensor technology and sampling preferences.
Hazardous and unreliable purification
techniques (dissolving in solutions such as
dichlorobenzene) for CNTs results in
increased cost of production. Use of other
materials has been successful. Increasing
reactive surface typically greatly increases
recovery time. Added cost is also an issue.
Climate
control
Reliable field operation
(without a relatively large
climate control
infrastructure)
Pursue technologies that do not require climate
control and/or include components that account
for environmental factors (e.g., sensors in which
the current in metal oxide filaments is altered per
temperature and humidity; such as for the Air
Quality Egg).
Technology changes to improve field
operations typically come at the expense of
other capabilities; additional components can
increase cost and/or energy consumption.
Information resources include:
Place Lab: Intel, http://www.intel-research.net/Publications/Seattle/100220061038_340.pdf: estimates location (ubiquitously) by scanning for fixed radio beacons
(802.11 access points [WiFi or Bluetooth] and GSM cell towers). Works indoors and outdoors, and can run on laptops, PDAs, and cell phones. Privacy
addressed by not needing any network connection or server-based infrastructure. Less accurate than GPS (can estimate location within 15 to 20 meters if three
distinct beacons are seen in a 10-s window), but can cover nearly all user locations. Developed in tandem BeaconPrint, a program used for place learning (the
location cannot be estimated without a beacon database).
Cricket: MIT, http://cricket.csail.mit.edu/.
Air quality egg: http://airqualitveqq.wikispaces.com/AirQualityEqq.
CNT hazards and costs: http://scitechdailv.com/mit-team-uses-carbon-nanotubes-to-draw-qas-sensors/: http://www.azonano.com/article.aspx?ArticlelD=3107.
Quintet and Halo: Eisenman (2008): People-Centric Mobile Sensing Networks; http://www.ists.dartmouth.edu/library/440.pdf.
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TABLE 4-3 Highlights of Advantages and Limitations for Selected Technologies-Techniques3
Sensing
Technology/
Technique
Advantages
Limitations
Electrochemical
1. User-friendly
2. Requires very little power to operate; operates at ambient
temperature
3. Linear response over a wide concentration range
4. Relatively sensitive (very sensitive to diverse VOCs)
5. Very fast sampling
1. Noise and drift in electronics can impact accuracy and
precision of measurements
2. Smallest practical sensor = 1 cm3
3. Larger = more precise and accurate
4. Can be sensitive to temperature and extreme humidity
5. Have short life spans (1 -2 years)
Spectroscopic
1. Laser can be tuned to measure one or more specific
gases
2. Accuracy can be very high
3. Can detect relatively inert compounds (does not rely on
chemical reactions)
1. Typically at least a few centimeters long (minimum distance
required for laser), and difficult to improve upon existing
detection limits (limited by optical cross section, absorption
cross section, and other factors)
2. Components are typically more expensive for spectroscopic
sensors than electrochemical and MOS sensors
Metal oxide
1. Available off the shelf
2. Low cost
3. Very small
4. Good for identifying relative concentration changes
1. Better for reasonably reactive gases, not for inerts
2. Requires high power to heat metal oxide (250-500°C)
3. Very sensitive to environmental factors
4. Requires extensive calibration
5. Have short life spans (1 -2 years)
MEMS
with resonator
technology
(e.g., FBAR)
1. Detects PM
2. Very, very small
3. Manufacturing process is already tuned for high volume,
low cost; currently used in some mobile phones (note
FBAR represents the technique whereby mechanical
stress is converted to an electrical signal)
1. Still under research (one feature under study is high power
air pump for controlled flow rate resulting in high energy
consumption)
2. Does not detect gases
MEMS: microelectromechanical systems; FBAR is a thin-film, bulk acoustic resonator technology.
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TABLE 4-4 Example Comparison of Limitations and Opportunities for Three CO Sensors3
Feature
Cost
Opportunity
Mobility
Opportunity
Energy consumption
Opportunity
Sensitivity-selectivity
Opportunity
Analysis/response time
Opportunity
Other operating factors
Opportunity
Operator type
Opportunity
Commercial Sensor System:
Ecotech Serinus 30
Relatively high
Could potentially lower, e.g., via
large-scale manufacturing-marketing
Yes, but -40 Ibs
Reduce system size (to handheld)
99-132 VAC, 198-264 VAC 47-63 Hz
Decrease, optimize
0.04-200 ppm (LDL 20 ppb) -
highly selective for CO
Increase sensitivity
Response time: 60 seconds
Reduce warmup, response times
0-40°C; regular calibration
Decrease maintenance requirements
Trained
Make user friendly for general public
R&D Sensor System:
Solid electrolyte sensor
(Not identified)
< to household sensor price
Setup could be miniaturized
Reduce system size
(Not identified)
10-500 ppm (detection limit:
0.5 ppm); also detects VOCs &
hydrocarbons; high selectivity
Increase sensitivity
Response time: <1 min; continuous
Reduce response/warmup times
(noncontinuous)
Pursue autocalibration (e.g., via
network)
Trained
Make user friendly for general public
Household Sensor:
Kidde Digital CO
~$30
Decrease cost
Mounted in outlet; 13 in. width
Pursue portability, reduce size
120 VAC
30-999 ppm (+/- 20%); also
detects smoke
Continuous sampling/readout
7-year life
General public
3 The first two sensors have networking capabilities and concentration displays; the household sensor includes the latter but no networking.
R&D Sensor-System: Information highlighted from Kida et al. (2010). Application of a Solid Electrolyte CO2 Sensor for the Analysis of Standard Volatile Organic
Compound Gases. Analytical Chemistry. 82(8):3315-3319, http://pubs.acs.org/doi/full/10.1021/ac100123u (Kyushu University, Fukuoka Japan; Dept. Energy
and Materials Sciences). Note: this represents a relatively early stage of research so information for some table entries is not yet known; it has only been tested
in the laboratory (not the field).
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Energy consumption: Reducing sample acquisition time can significantly reduce energy
consumption. Moving from continuous sampling to discontinuous (pulse) sampling within
longer time intervals or during specific times of the day could conserve energy.
Opportunities for energy conservation include reducing the duration of each measurement
and decreasing warmup times. Some sensor technologies such as those based on metal
oxides require a high power heater for operation. Transmitting collected data from the
sensor to the network database also consume energy. Delaying data upload until the sensor
is being charged can help reduce that consumptions (e.g., for situations where real-time
data are not needed).
Sensitivity-selectivity/interferences: Interferences are a common problem for research
sensors, including from other chemicals, humidity, temperature, barometric pressure, and air
flow rate. Performance can be enhanced by either keeping key interferents constant or
otherwise accounting for variability. Hardware options exist to address some sources of
interference, but at a cost. Precision can also be increased by super-sampling approaches,
which would involve deploying a large number of mobile sensors (e.g., hot spots where
people frequently congregate would offer an opportunity for super-sampling and collective
calibration).
Analysis/response time: Because mobile sensors take measurements in many places,
context needs to be attributed to each sample for data to be useful. Research opportunities
being pursued include incorporating capabilities into the device that provide this context
across multiple setting. For example, audiosensing for ambient noise with classification of
the spectral features is being used to indicate the sampling environment. Accelerometers
are being used to indicate the level of activity during data collection (common for sensors
embedded in cell phones). Response time is related to energy consumption; warmup times
differ by technology, and different sensors take samples over different intervals. Note the
emphasis for response time is not so much taking as many samples as quickly as possible
but rather what type of sampling regimen is well suited and most efficient for purpose of the
given monitoring. It is very important for sensor readouts to place the concentration in
context of the exposure benchmark. For example, a 1-minute value that exceeds an 8-hour
or 24-hour standard might not produce adverse health effects and could cause unnecessary
concern if the readout is separated from that key information context.
Other operating factors: Calibration/data quality is one of the most important issues for
mobile sensors. Minimal drift over a long time is desired. For some situations, such as
super-sampling (in which sensors are close to each other), tools exist for manually
calibrating the sensors within a large network so sampling results can be compared across
sensors and drift can be assessed (e.g., with CaliBree). Also described in research
literature are tools to tap other sensing resources from nearby devices and to rendezvous
with static infrastructure, such as Quintet and Halo, respectively (Eisenman 2008). Because
ambient concentrations are typically low, even a slight drift can produce unreliable results.
Software advancements are needed to detect malfunctioning sensors to avoid throwing off
the automatic calibration system (or including those data). Also, incorporating hardware that
can account for changes in humidity and temperature reduces the extensive climate-control
infrastructure needs. Sensors need to have the ability to be interference-free or take such
changes into account, including changes in humidity and temperature.
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4.2.6 Mobile Applications
An overview of several online air quality resources (including EPA 2012b) and free mobile
applications is presented in Appendix G, together with a demonstration of selected Android
apps (Temple [2012]). Also included in Appendix G are user comments that have been posted
online for three apps, reflecting inputs from more than 60 users reported from April 2012 to
February 2013 (Table G-1, tapping Aesthetikx [2012], ALA [2013], and EPA [2013c]). These
comments are ordered within three main topic areas (value, data coverage, and user interface),
first in order of the user rating (e.g., comments rated highest are grouped first within a category),
and then in order of the comment date. (Note the date provides some context for progressive
versions of the apps, as the version and the mobile device used are not reported in all cases.)
The example demonstration of air quality apps in Appendix G indicates several issues for
current mobile apps:
• Data coverage: Limited to a fixed monitoring station from the nearest urban center, and
data are only available for major cities. Without further information to fill in current spatial
gaps, it cannot be known if such data are representative across community, neighborhood,
and individual scales.
• Pollutant coverage: Limited to two pollutants: PlVb.s and ground-level ozone. The air
quality index (AQI) map application does not differentiate between the two. (Note in most
cases, the air quality is represented by a number associated with the AQI.)
• Update frequency: The frequency of AQI updates varied for certain areas and monitors.
Forecasting is affected in those areas for which data are updated less frequently.
• User interface, and inconsistencies.
Online reviews and ratings indicate many of the same concerns. The value of the app concept
is highly regarded (collective rating of 4.5 out of 5), which affirms that mobile apps represent a
clear opportunity area. The main gaps are associated with data coverage (collective rating of
1.4) and the user interface (collective rating of 2.3).
4.2.7 Data Quality and Data Management
Quality and quantity are key issues for data collected by mobile sensors, in terms of both
practical utility and the management approaches and systems needed for the enormous
amounts of data generated. Regarding the first issue, data quality objectives are key - as the
purpose for which the data are collected drives the quality expectations (to assure they are "fit
for purpose"). Regulatory requirements and guidance exist for data quality for specific
enforcement or compliance purposes, including guidance for ambient air quality data. For
example, the recent list of designated reference and equivalent methods for criteria pollutants
(EPA 2012a) emphasizes the importance of using each method "in strict accordance with its
associated operation or instruction manual and with applicable quality assurance procedures."
Recognizing that substantial amounts of data are used for many other purposes beyond
regulatory programs, it is useful to consider the nature of the data warranted for various uses.
For example, the quality of data needed to guide an individual's plan for outdoor activities (e.g.,
to avoid strenuous activities when a given pollutant concentration is high) would be much
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different than that for reporting facility emissions and fenceline concentrations under specific
state or federal compliance programs. In many cases, qualitative or semiquantitative data (such
as may result from a low-cost colorimetric approach) will be sufficient for the intended purpose.
Thus, tiered approaches (or "banding") for indicating appropriate data quality and management
standards and tools are expected to play an important roles in addressing air quality for data
from mobile sensors. It is vital to assure that readouts are compared to appropriate benchmarks
so that measured values are not compared to benchmarks with much different averaging times.
Big data represents an emerging research area, as a number of agency, national laboratory,
industry, and other research efforts aim to develop and implement effective ways of dealing with
the explosion of data being generated (e.g., as illustrated by the open government initiative, see
data.gov). With continuing advances in high-performance computing, a new breed of smart
supercomputers is being tapped to tackle the enormous amounts of data being generated; for
air quality alone, the volume of data (which is already extremely large) will markedly increase
under the vision of widespread mobile sensing. New statistical approaches including scalable
and parallelizable numerical algorithms are being developed to address the much more complex
data analytics challenges created by massive data sets. Parallel advancements in data storage
and access approaches and systems, such as cloud computing, also provide opportunities for
addressing the vast scale of data to be generated by mobile sensors.
In the age of ubiquitous computing, ongoing internet advances provide a strong opportunity for
addressing the huge amounts of air quality data envisioned. In less than twenty years, the
evolution of the world wide web has been remarkable - beginning with the "web of cognition"
(1.0) and morphing to a web of communication (2.0), cooperation (3.0), and integration (4.0)
(Aghaei et al. 2012). As described by Larson (2012), Web 1.0 was content creation by the few,
with software on local machines and reliance on desktop computers. The Web 2.0 era saw
content creation by the many, emergence of social technologies and both local and web-based
software, and the use of mobile phones and tablets. With Web 3.0, content was being created
by the majority, web participation was common, and software was in the cloud. In the current
era, Web 4.0, meaning is being created by the majority, operating systems are in the cloud,
desktop computers, mobile phones, and tablets have been joined by iTV, and augmented data
layers are common. Sustained advancements will continue to provide strong opportunities for
uploading, distributing, sharing, visualizing, storing, and maintaining data from mobile sensors.
A further important consideration for citizen-based sensing relates to privacy/ethics, such as
issues related to location tagging and human subject constraints. Approaches for addressing
these issues include tools that can decouple, aggregate, anonymize and otherwise transform
data to provide appropriate protection.
4.3 PARTNERSHIPS
4.3.1 Funding Sources: Leveraging
Many U.S. and international organizations have funded research relevant to mobile sensors and
apps for air pollutants. Funding agencies include the U.S. Department of Agriculture
(USDA); Department of Defense (DoD), including the Air Force Office of Scientific Research
and Army Research Office, Defense Advanced Research Projects Agency (DARPA), Defense
Threat Reduction Agency (DTRA); Department of Energy (DOE); Department of Homeland
Security (DHS); Department of Transportation (DOT), including Federal Aviation Administration
(FAA); EPA, National Aeronautics and Space Administration (NASA); Department of Health and
Human Services (DHHS), including NIH, NIEHS ; National Science Foundation (NSF) and other
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foundations; and U.S. Department of Commerce, National Institute of Standards and
Technology (NIST). For example, EPA ORD has been very active in promoting and leveraging
partnerships, including through several recent workshops in Research Triangle Park, NC, and
Washington, DC; NIEHS has also hosted workshops and webinars to convene researchers
active in this area, and both agencies are collaborating on these efforts.
Private-sector funding sources include Motorola, Exxon Mobil, Microsoft, Nokia, Intel, and
others. As an indication of new opportunities, individual citizens are also playing a role in
funding mobile sensor initiatives, including via crowdsourcing platforms such as Kickstarter.
Leveraging is being pursued at multiple levels including via agency work group, researcher
collaborations, research-community initiatives, and community-centered projects with DIYers.
Programs such as AIRNow and the Weather Underground serve as examples for expanded
collaboration, including internationally. Topics being addressed include harmonization and open
source approaches for standards, protocols, platforms, and networks.
4.3.2 Education and Participation Initiatives
Student-centered programs have played an important role in promoting awareness of mobile
sensing for air pollutants that is expected to continue and increase. Programs such as the G03
project (focused on schoolchildren awareness of atmospheric pollution) and Air Quality Egg
provide a strong foundation for related initiatives. Similar successes include the Bucket Brigade
(developed to assist fenceline communities), Common Sense, and AIR by Preemptive Media.
Such community-centered sensing projects have demonstrated extensive networking
capabilities with associated integration and visualization of air quality data contributed by citizen
users.
The DIY community is increasingly active. A number of online resources are available to
facilitate mobile sensing projects. For example, step-by-step instructions can be found for a
wide variety of projects via Explore lnstructables.com and SparkFun.com. Tutorials and product
information can be found for an extensive suite of DIY projects, including air and weather
sensors. The SparkFun Inventor's Kit for Arduino is an example of a resource that guides
beginners through the construction of 14 basic circuits and can be modified to include individual
pollutant sensors. Basic materials for DIYers include an Arduino board (a platform compatible
with a number of sensor inputs) and small sensor units that can be purchased preassembled or
in parts for hands-on activities. Individual "plug-and-play" pollutant-specific sensors can be
purchased through SparkFun or directly from standard manufacturers such as Hanwei and
Figaro. The nonprofit AirCast initiative is a nice example of active engagement, with citizens
building their own sensor devices using information and materials from a common source for the
DIY community, including (remote) international participants.
Opportunities for increased awareness and creative citizen involvement are tapped via open
challenges (e.g., My Air, My Health; C3: Collect, Construct, Change) and gamifying approaches.
A key aim of the mobile sensors and apps initiative is to facilitate participatory sensing, and a
number of successful programs serve as examples, from Project BudBurst to NightSky.
Additional lessons can be learned from citizen-based sensing associated with incidents such as
Fukushima. Insights gained from a wide variety of recent participatory sensing projects frame
opportunities for enhancing the citizen involvement element of the current EPA initiative for
next-generation air monitoring.
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5 SUMMARY
This report highlights recent research relevant to mobile sensors and apps for air pollutants.
Study Pollutants
Fourteen pollutants (two solids and twelve gases) provided context for practical applications:
• Criteria pollutants: Carbon monoxide, lead, nitrogen dioxide, ozone, particulate matter,
and sulfur dioxide.
• HAPs: Acetaldehyde, acrolein, benzene, 1,3-butadiene, and formaldehyde.
• Indicators: Ammonia, hydrogen sulfide, and methane.
Sensor Targets
• Most mobile sensors detect gases; criteria pollutant gases appear to be adequately
covered.
• No mobile sensors were found for lead; particulate matter (PM) options are also limited.
• Research sensors or novel systems with commercial sensors exist for the indicator
pollutants and most of the HAPs, but sensors for acrolein and 1,3-butadiene are limited.
Sensing Technology/Technique
• Technique: These can be grouped into three main categories: chemical, spectroscopic,
and ionization techniques (in order of prominence per the selected literature reviewed); the
first two are most active and growing.
• Technology: Nanotechnology is a dominant theme in recent air pollutant sensor research.
Detection Capabilities
• Health-based guidelines: Most levels appear to be detectable based on reported sensor
capabilities, except for the lower (protective) concentrations for four pollutants:
acetaldehyde, acrolein, ammonia, and formaldehyde.
• Ambient measurements: Acrolein, lead, and methane are among the pollutants for which
common ambient levels could be difficult to detect.
• Commercial sensors and novel systems that use commercial sensors dominate over
strictly research sensors for many of the chemicals.
Architecture/Infrastructure and Apps
• Architecture: Portable, handheld and vehicle-mounted architectures are relatively
common; less common are wearable sensors. The trend is increasingly small and mobile.
• Infrastructure: Sensor components are increasingly integrated with mobile phones,
tapping Bluetooth/wireless network links. User interface design and programming
components are highly active research areas, and a limited set of air quality apps exist.
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Gaps
• Particle sensors, including chemical-specific sensors, are generally lacking; multipollutant
sensors in a single small, affordable system (rather than modular plug-ins) are needed.
• Detection capabilities are not sufficiently low to address the full range of benchmarks and
example air concentrations for the pollutants studied (or others of interest to communities).
• Field reliability and durability of sensors/systems are not yet assured over a range of
environmental conditions (including less expensive measures for addressing drift,
interferences and local climatic conditions).
• Algorithms and approaches are needed for consistent data processing, quality assurance
and control, and data scrubbing, transformation, integration, visualization, and analysis.
• Effective and efficient data and knowledge management approaches are needed,
including standards and protocols, infrastructure, and software for reporting, accessing,
sharing, storing/archiving, and maintaining data, considering both raw and transformed
data and topical syntheses.
• Apps that provide high-resolution spatial coverage for community, local, and individual
scales are needed, with displays for a full suite of pollutants. Also needed are reliable
user interfaces across multiple devices, extending beyond mobile phones to include
tablets and other emerging systems, as well as supporting content for data interpretation
(e.g., links to health-based standards and other measurements relevant to that setting).
Opportunities
• Nanotechnology-based advancements in sensing technologies/techniques and sensor
systems provide a strong opportunity for increasingly mobile sensors.
• Integrated sensor arrays for multi-pollutant sensing, as well as links to biosensors (e.g.,
personalized medicine applications) combined with insights from sensors for other
measurands, offer opportunities for multipurpose systems.
• Novel energy sources (including human) and optimized sampling regimens (e.g., super
sampling and targeted sampling guided by pollutant behaviors and fate) offer opportunities
for lower power use and more affordable systems.
• Hybrid fixed-mobile systems that leverage existing infrastructure represent opportunities
for practical field applications.
• Greater automation and expanding networks also offer opportunities for higher
performance, including autocalibration and self repair (e.g., Internet of Things/Web 4.0).
• Real-time data collection, upload, integration, distribution, display, and interpretation via
user-friendly apps represent opportunities for platforms that extend beyond smartphones.
Ongoing advances in cloud computing and related systems offer opportunities for more
efficient data sharing and management.
• Leveraging systems, organizational resources, and citizen capital represents an
opportunity for achieving the goal of nationwide air quality data coverage and context for
guiding environmental health management measures from personal to regional and
national scales. Collateral programmatic benefits including intermediate baselining for
climate change and adaptation planning, increased awareness of personal exposure and
health, and other related environmental health and impact analyses programs.
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6 ACKNOWLEDGEMENTS
The authors wish to acknowledge the valuable contributions of U.S. EPA ORD colleagues,
including in particular Stacey Katz, Gail Robarge, and Eben Thoma.
This report was prepared by Argonne National Laboratory as part of Collaborative Interagency
Agreement (IAG) No. DW8992268301-0 between EPA ORD and DOE.
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7 SELECTED INFORMATION RESOURCES
(A number of information resources are also provided in individual tables and supporting notes.)
ACGIH (American Conference of Governmental Industrial Hygienists) (2012). 2012 TLVs and
BEIs: Based on the Documentation of the Threshold Limit Values for Chemical Substances and
Physical Agents & Biological Exposure Indices, Cincinnati, OH.
Aesthetikx (2012). Air Quality, Android Application;
https://plav.google.com/store/apps/details?id=com.aesthetikx.airquality&feature=search result#?t
=W251bGwsMSwxLDEslmNvbS5hZXNOaGVOaWt4LmFpcnF1YWxpdHkiXQ (page indicates last
update was Feb. 22, 2012; online reviews were posted April 12 through Aug. 9, 2012; last
accessed Feb. 18, 2013).
Aghaei, S., M.A. Nematbakhsh, and H.K. Farsani (2012). Evolution of the World Wide Web:
from Web 1.0 to Web 4.0. International Journal of Web & Semantic Technology, 3(1): 1-10
(Jan.); http://airccse.org/iournal/ijwest/papers/3112ijwestQ1 .pdf.
ALA (American Lung Association) (2013). State of the Air, Android Application;
http://www.lung.org/healthy-air/outdoor/state-of-the-air/app.html:
https://play.google.com/store/apps/details?id=com.reddeluxe.sota
(page indicates last update was Oct. 24, 2012; online reviews were posted June 18 through
Dec. 8, 2012; last accessed May 22, 2013).
Aluri, G.S., A. Motayed, A.V. Davydov, et al. (2011). Highly Selective GaN-Nanowire/
TiC-2-Nanocluster Hybrid Sensors for Detection of Benzene and Related Environment Pollutants.
Nanotechnology, 22. Retrieved from http://iopscience.iop.org/0957-
4484/22/29/295503/pdf/0957-4484 22 29 295503.pdf.
ATSDR (Agency for Toxic Substances and Disease Registry) (2013). Minimal Risk Levels
(MRLs). (Jan.); http://www.atsdr.cdc.gov/mrls/index.asp (page last updated Mar. 6; last
accessed May 22).
Beirne, S., B.M. Kieman, C. Fay, C. Foley, B. Corcoran, A.F. Smeaton, and D. Diamond (2010).
Autonomous Greenhouse Gas Measurement System for Analysis of Gas Migration on Landfill
Sites. 2010 IEEE Sensors Applications Symposium Proceedings. Accessed from IEEE Xplore
at http://ieeexplore.ieee.org/xpls/abs all.jsp?arnumber=5439422&tag=1.
Caland, F., S. Miron, D. Brie, and C. Mustin (2011). A Candecomp/Parafac Approach to the
Estimation of Environmental Pollutant Concentrations Using Biosensors. IEEE Statistical
Processing Workshop. Retrieved from
http://ieeexplore.ieee.org/stamp/stamp.isp?tp=&arnumber=5967826&isnumber=5967628.
CalEPA (California EPA) (2012a). All OEHHA Acute, 8-hour and Chronic Reference Exposure
Levels (chRELs) as on February 2012. Office of Environmental Health Hazard Assessment
(OEHHA), Sacramento, CA (February 9); http://www.oehha.org/air/allrels.html (page last
accessed May 22, 2013).
CalEPA (2013). Proposed Reference Exposure Levels for 1,3-Butadiene [09/11/12]. OEHHA,
Sacramento, CA; http://oehha.ca.gov/air/hot spots/106990 2012.html (draft last revised on
March 1; page last accessed May 22).
7-1
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Yang, C.H. (2010,). Development ofNanosensorto Detect Mercury and Volatile Organic Vapors.
Retrieved from
http://dukespace.lib.duke.edU/dspace/bitstream/handle/10161/3060/D Yang Chang%20Heng
a 201Q.pdf?sequence=1.
Eisenman, S.B. (2008). People-Centric Mobile Sensing Networks;
http://www.ists.dartmouth.edu/library/440.pdf.
ERG (Eastern Research Group) (2011). Advancements in Air Monitoring using Fence Line and
Network Sensors: Currently Available and Promising Technologies. Prepared for E. Thoma,
U.S. EPA, Air Pollution Prevention and Control Division, ORD National Risk Management
Research Laboratory, Research Triangle Park, NC (Rev. 0, Oct. 31).
EPA: see U.S. EPA.
Guan, J. and S. Wang. (2009). Application of Integrate Sensor in Gas Alert System of Coal
Mine. Accessed from IEEE Xplore at
http://ieeexplore.ieee.org/stamp/stamp.isp?tp=&arnumber=5072745&isnumber=5072599.
He, J., T.-Y. Zhang, and G. Chen (2012). Ammonia Gas-Sensing Characteristics of
Fluorescence-based Poly(2-(acetoacetoxy)ethyl methacrylate) Thin Films. Journal of Colloid
and Interface Science, 373 (1): 94-101. Retrieved from
http://www.sciencedirect.com/science/article/pii/S0021979711014512.
Honicky, R.E. (2011). Towards a Societal Scale, Mobile Sensing System;
http://www.eecs.berkelev.edu/Pubs/TechRpts/2011/EECS-2011-9.pdf (UCB/EECS-2011-9).
Kida, T., M.-H. Seo, S. Kishi, et al. (2010). Application of a solid electrolyte CC-2 sensor for the
analysis of standard volatile organic compound gases. Analytical Chemistry, 82(8): 3315-3319.
Kim, I., K.-Y. Dong, B.-K. Ju, et al. (2010). Gas Sensor for CO and NH3 Using Polyaniline/CNTs
Composite at Room Temperature. Proceedings of 10th IEEE International Conference on
Nanotechnology Joint Symposium with Nano Korea. Retrieved from http://ieeexplore.ieee.org/
stamp/stamp.isp?tp=&arnumber=5697782&isnumber=5697724&tag=1.
Larson, L. (2012). Web 4.0: The Era of Online Customer Engagement (Jan. 5);
http://www.business2communitv.com/online-marketing/web-4-0-the-era-of-online-customer-
engagement-0113733 (page last accessed May 22, 2013).
Lenntech (2012). Water Treatment Solutions, Calculators, Parts per Million (ppm) Converter.
http://www.lenntech.com/calculators/ppm/converter-parts-per-million.htm
(last accessed May 22, 2013).
Lozenko, S., M. Lebental, J. Lautru, et al. (2011). Specific (bio-)chemical Sensing with Organic
Microlasers. CLEO EUROPE/EQEC, 12th European Quantum Electronics Conference.
Retrieved from
http://ieeexplore.ieee.org/stamp/stamp.isp?tp=&arnumber=5943699&isnumber=5942420.
7-2
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Mariano, S., W. Wang, G. Brunelle, Y. Bigay, and T.-H. Tran-Thi (2010). Color/metric Detection
of Formaldehyde: A Sensor for Air Quality Measurements and a Pollution-warning Kit for
Homes. 2010 First International Conference on Sensor Device Technologies and Applications.
http://ieeexplore.ieee.org/stamp/stamp.isp?tp=&arnumber=5632112&isnumber=5632098&tag=1
NIOSH (National Institute for Occupational Safety and Health) (2010). NIOSH Pocket Guide to
Chemical Hazards; http://www.cdc.gov/niosh/npg/default.html (page last reviewed Jan. 30,
2012; accessed May 22, 2013).
Nomani, Md.W.K., D. Kersey, J. James, etal. (2011). Highly Sensitive and Multidimensional
Detection of NO2 using 1^03 Thin Films. Sensors and Actuators B, 160: 251-259. Retrieved
from
http://pdn.sciencedirect.com/science? ob=MiamilmageURL& cid=271353& user=1722207& pi
i=S0925400511006861 & check=v& origin=browse& zone=rslt list item& coverDate=2011-
12-15&wchp=dGLzVlk-zSkzV&md5=a9efb7781a0624784753f0979694f695/1-s2.0-
S0925400511006861-main.pdf.
NRC (National Research Council). (2013). Acute Exposure Guideline Levels Program, via
http://dels.nas.edu/global/best/AEGL-Welcome (last accessed May 22, 2013).
Peng, L, et al. (2011). Improvement of Formaldehyde Sensitivity ofZnO Nanorods by Modifying
with Ru(dcbpy)2(NCS)2. Sensors and Actuators B, 160(1):39-45 (Dec).
Raymond, M., D. Wyker, T. Raymond, M. Finster, B. Temple, and M. MacDonell (2013).
Literature Highlights Relevant to Mobile Sensors and Apps for Air Pollutants. (Complementary
table to this report.) (Feb. 19).
Sardini, E., and M. Serpelloni (2011). Self-Powered Wireless Sensor for Air Temperature and
Velocity Measurements with Energy Harvesting Capability. IEEE Transactions on
Instrumentation and Measurement, 60(5): 1838-1844 (May).
Sekine, Y. and R. Katori (2009). Indoor Air Quality Monitoring via IT Network. ICROS-SICE
International Joint Conference 2009, Fukuoka International Congress Center, Japan.
http://ieeexplore.ieee.org/stamp/stamp.isp?tp=&arnumber=5333246&isnumber=5332438&tag=1
Shim, Y.-S., et al. (2011). Transparent Conducting Oxide Electrodes for Novel Metal Oxide Gas
Sensors. Sensors and Actuators B, 160(1):357-363 (Dec.)
Temple, B. (2012). Air Quality Monitoring: Citizen Sensing Initiative. Prepared in fulfillment of
the requirement of the Office of Science, Department of Energy's Science Undergraduate
Laboratory Internship, Environmental Science Division, Argonne National Laboratory (Aug.).
U.S. EPA (U.S. Environmental Protection Agency) (2008). Risk-Based Criteria to Support
Validation of Detection Methods for Drinking Water and Air, EPA/600/R-08/021 (Oct.);
http://cfpub.epa.gov/si/si public record report.cfm?address=nhsrc/&dirEntryld=188648 (page
last updated and accessed May 22, 2013).
U.S. EPA (2011). National-Scale Air Toxics Assessment, and Summary of Results for the 2005
National-Scale Assessment; http://www.epa.gov/ttn/atw/nata2005/05pdf/sum results.pdf,
http://www.epa.gov/nata2005/ (page last updated May 21, 2012; last accessed May 22, 2013).
7-3
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U.S. EPA (2012a). AirData; http://www.epa.gov/airdata/ (page last updated Sep. 27, 2012; last
accessed May 22, 2013).
U.S. EPA (2012b). List of Designated Reference and Equivalent Methods (Dec. 17);
http://www.epa.gov/ttnamti1/files/ambient/criteria/reference-equivalent-methods-list.pdf, via
http://www.epa.gov/ttnamti1/methods.html (page last updated Dec. 20, 2012; last accessed
May 22, 2013).
U.S. EPA (2013a). Integrated Risk Information System (IRIS); online database; National Center
for Environmental Assessment, Washington, DC; http://www.epa.gov/IRIS (chemical-specific
pages last updated and accessed May 22, 2013).
U.S. EPA (2013b). Provisional Peer-Reviewed Toxicity Values for Superfund (PPRTV), online
resource of the National Center for Environmental Assessment, Washington, DC, hosted by
Oak Ridge National Laboratory; http://hhpprtv.ornl.gov/ (page not dated; last accessed May 22).
U.S. EPA (2013c). AIRNow, Android Application;
https://play.google.com/store/apps/details?id=com.saic.airnow (page indicates last update was
March 23, 2012; most recent comment posted Apr. 24, 2013; last accessed May 22).
Wang, Z. L, G. Zhu, Y. Yang, S. Wang., and C. Pan (2012). Progress in Nanogenerators for
Portable Electronics. Materials Today, 15(12): 532-543 (Dec.).
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APPENDIX A:
SUPPORTING DETAILS FOR THE LITERATURE SEARCH APPROACH
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APPENDIX A:
SUPPORTING DETAILS FOR THE LITERATURE SEARCH APPROACH
A.1 INITIAL FRAMING ACTIVITIES
A.1.1 Pollutants
As part of initial framing for the literature search, early inputs from EPA Program and Region
staff regarding pollutants of interest were compiled in Table A-1.
TABLE A-1 Program and Regional Inputs to the List of Candidate Pollutants3
Air Pollutant
Acrolein
Benzene
1,3-Butadiene
Carbon monoxide (CO)
1,1-Dichloroethylene
Ethyl benzene
Formaldehyde
Hexabromocyclododecanes (HBCDs)
Hydrogen sulfide
Lead
Mercury
Methane
Nitrogen oxides/dioxide (NOx/NCb)
Ozone
Participate matter (PM)
Perchlorate
Perfluorocarbons (PFCs)
Phthalates
Polybrominated diphenyl ethers (PBDE)
Polychlorinated biphenyls (PCBs)
Sulfur oxides/dioxide (SOx/SO2)
Toluene
Trichloroethylene
Xylene
Basis
National Air Toxics Assessment (NAT A), national hazard driver
NATA regional risk driver
NATA national risk contributor
Criteria pollutant
Risk driver, soil vapor intrusion
Risk driver, soil vapor intrusion; NATA national risk contributor
NATA national risk driver
Children's health program, flame retardant
Emissions indicator
Children's health program; criteria pollutant
Children's health program
Emissions indicator
Criteria pollutant
Criteria pollutant
Criteria pollutant; from NATA: diesel PM, coke oven emissions
Children's health program
Children's health program
Children's health program
Children's health program, flame retardant
Children's health program
Criteria pollutant
Children's health program
Risk driver, soil vapor intrusion
Children's health program
3 This list includes inputs provided by EPA Program and Regional staff to the ORD innovation team for
air pollutant sensors and apps. In addition to these candidates, other hazardous air pollutants (HAPs)
are also expected to be of interest across multiple programs and projects. Supporting context provided
with selected Regional input for several compounds, included risk-based concentrations in parts per
billion by volume (ppbv) corresponding to a 10~5 risk (probability of getting cancer over a lifetime, per an
assumed continuous exposure using current toxicity values and default residential exposure
assumptions for that screening evaluation). Those concentrations were considered as part of the
evaluation of sensor detection levels presented in Chapters.
A-3
-------
31 October 2013
A. 1.2 Search Terms
Early framing searches were conducted to identify terms for the broader literature search.
Candidate terms identified at this early stage are presented below (Table A-2). Core terms
include air, quality, toxics, contaminant, hazardous, pollutant/pollution, HAP, measurement,
monitoring, citizen, community; mobile, portable, miniature, sensor; technology, smartphone.
TABLE A-2 Candidate Search Terms
Actuator(s)
Aerosol (sensor)
Air/emissions
Air pollutant/pollution
Air quality
Air toxic(s)
Airborne contaminant
Ambient air monitoring
Ambient air quality
Analyzer (portable)
Android
App, application
Architecture (mobile
sensor, access point,
back end)
A uto m ate (d)/a utom ati c
Autonomous (semi-)
Background (noise)
Badge (sensor)
Biosensor
Blackberry
Browser
BZ (breathing zone)
monitor
Chemical (pollutant,
sensor)
Chip
Citizen science/scientist
Cloud (computing,
services)
Colorimetric (array)
Commercial sensor
Commercial product
Communication
Community
Community-ledAbased
Compact
Components (sensor)
Computer (-aided,
-assisted)
Configuration
Criteria air pollutant(s)
Crowd source(d)/sourcing
Cumulative exposure
Cumulative risk
Data access
Data aggregation
Data analysis
Data collection
Data curation/curator
Data delivery
Data distribution
Data download
Data flow
Data format
Data infrastructure
Data management
Data processing
Data push
Data quality
Data refresh/reload
Data sharing
Data storage/storing
Data stream(ing)
Data structure
Data update (updating)
Data upload(ing)
Data validation
Data verification
Data visualization
Demonstration/demo
Deploy/deployment
Detection level/limit
Detector
Device (end device)
Digital (imaging)
Distributed control
Distributed network
Download (app, data)
Dynamic
Electronic
Embed(ded)
ENS/embedded network
sensing
Engineer/ed/ing
Environment
Environmental app
Environmental contaminant
Environmental
contamination
Environmental data
Environmental informatics
Environmental measurement
Environmental monitoring
Environmental
observation(s)
Environmental pollutant
Environmental pollution
Equipment
Eye on earth
Fenceline community
Fenceline monitor
Fenceline sensor
Field demonstration
Field deployment
Field detection/detector
Field implementation
Field measure/ment
Field monitor/ing
Field sensing/sensor
Film (sensor)
Filter (noise)
Fixed sensor
Flash
Fluorescent (fluorescence)
Gamify
Gas (sensor)
Geoprocessing
Geo-reference(d)
GeoRSS feed
A-4
-------
31 October 2013
(TABLE A-2, Cont'd.)
Geospatial
Geostatistics
GIS/geographic information
system(s)
GO3
Google earth
GPS/global positioning
system
Graphics
Handheld (hand-held)
Hardware
HAP(s)
Hazardous air pollutant(s)
Hazardous chemical(s)
ICT/ information and
communication
technology(ies)
Image/imaging
Industry/industrial
Informatics
Infrastructure
Innovation/innovative
Install(ation) (app)
Interface (graphical, web)
Internet
Interoperability
lonization (ionizer)
Iphone
Laser
Localized algorithm(s)
Locational
Luminescent,
luminescent(nce)
Maintenance
Manufacture(r)(ing)
Map(s)
Mashup
MEMS
/microelectricomechanical
Micro
Microsensor
Miniature/miniaturized
Mobile app
Mobile aware
Mobile measurement
Mobile monitor(ing)
Mobile phone
Mobile sensor
Monitoring data
Multiple air pollutants
Multiple exposures
Multiple sensors
Multiple toxics
NAAQS/ National Ambient
Air Quality Standard(s)
Nanosensor
Network sensing
Noise (filter)
Observations &
measurements (O&M)
Open data (linked)
Open database license
Open platform, system
Operating/operations)
Optimum/optimize
Palm
Participation/participatory
(sensing)
Personal device
Personal/personnel monitor
Personal exposure
assessment
Personal exposure
monitoring
Photo (electric, ionization)
Pilot (project, study)
Platform (cross-, hardware)
Plug-in
Pollutant(s)/pollution
Portable
Position(al) (sensors)
Product, production (cost)
Prototype
QA, QA/QC
Quality assurance
Quality control
R&D/research &
development
Rapid (analyzer, analysis)
Raw data
Recognizer (recognition,
voice)
Record
Real-time (data collection)
Real-time (feedback)
Real-time
(monitor/monitoring)
Replacement
Self-configuring/ed (system)
Sensing/sensor
Sensor accuracy
Sensor array
Sensor
capability/capabilities
Sensor component
Sensor cost
Sensor deployment
Sensor-enabled
Sensor performance
Sensor platform
Sensor precision
Sensor requirements
Sensor sensitivity
Sensor size
Sensor system
Sensor technology
Sensor validation
Shake (function)
Signal
Signal (processing)
Small-scale
Smartphone
Smart sensors
Social media
Software
Spatial data
Spectroscopy/spectroscopic
Tag(ging)
Technology(ies)
Test (demonstration, field)
TIC(s)/toxic industrial
chemical(s)
Toxic(s)/toxicant
Trace(r)
Track(er)(ing)
Unified (sensor)
Untethered (wireless)
Usablenet
A-5
-------
31 October 2013
(TABLE A-2, Cont'd.)
VGI/volunteered geographic
information
Visualization/visualize
Wearable (sensor, monitor)
Web 2.0/3.0
Web app
Web-based information
system(s)
Web-enabled
Web processing service
Web service interface
Wireless
A-6
-------
31 October 2013
A.2 PHASE I SEARCH
A broad online search of recent literature was initiated in late 2011 and continued into early
2012. Its focus was mobile sensor technologies and associated architecture/infrastructures and
applications.
A.2.1 Scope
The search focused on the following considerations:
• Sensor size: Small, handheld
• Portability: Mobile / easy to carry
(Note: Fixed sensors are considered when part of a combined system or if
transition to a portable system is indicated in the near term.)
• Stage: Research and development, to prototype
(Note: Commercial sensors are included when they are part of a novel
sensing device or system.)
• Cost: Low
(Note: Per the research and development focus, it is recognized that this
information will not be available in many cases; ultimate goal: $10 or less.)
• Detection limit: ppb or lower (recognizing many will be ppm)
• Date: 2010-2012
• Language: English preferred (others not excluded)
A.2.2 Online Resources
A number of standard searches were conducted during this initial phase, tapping online
databases via Web of Science, Web of Knowledge, PubMed, and others, using tools such as
RefMan. Further searches ranged from general Google searches (including for paper retrievals)
to targeted searches of specific journals, institutions, and researchers as identified from initial
reviews.
INSPEC/Physics Abstracts
This heavily indexed database covers the fields of physics, electrical engineering and
electronics, computers and control, information technology and mechanical and production
engineering, as well as cross-disciplinary subjects such as materials science and
nanotechnology. Two initial searches conducted in Phase I (not limited by language) were:
1. Classification Codes covering "sensor" AND indexed keywords for air pollution; search
limited to records from 2010-present.
A-7
-------
31 October 2013
Year Published=(2010-2012) AND Classification=(B7230 OR B6250K OR A0670D OR
A8280T OR C3240 OR A8780B) AND Topic=((environment* or (air pollution) or (air
quality) or (air toxic*) or (air and emission*) or (airborne contaminant*) or (air pollutant*)
or (air monitor*))) AND (Uncontrolled lndex=((environment* or (air pollution*))) OR
Controlled lndex=((environment* or (air pollution*))))
2. Classification Codes covering "air pollution" AND indexed keywords for sensors; this
search was limited to records from 2010-present.
Year Published=(2010-2012) AND Classification=(A8670L OR A9260T OR B7720 OR
C3310G) AND Topic=(((sensor or sensors or microsensor* or nanosensor* or biosensor*
or detector*)))
Results of Set 1 and Set 2 were combined and screened for those with words and
synonyms for portable/miniature.
Topic=((portable or mobile or (low cost) or (small size*) or (miniature*) or (micro-
portable) or wireless or handheld or (hand held) or personal or (small scale) or
smartphone or app or wearable or (citizen science) or (citizen scientist*) or fenceline))
These results were further limited by keywords in the controlled index or uncontrolled
index fields:
Ul=((air pollution) or (air quality) or (air toxic*) or (air and emission*) or (airborne
contaminant*) or (air pollutant*) or (air monitor*) or (toxic gas*)) OR CIX=((air pollution)
or (air quality) or (air toxic*) or (air and emission*) or (airborne contaminant*) or (air
pollutant*) or (air monitor*) or (toxic gas*))
Ul = Uncontrolled Index
CIX = Controlled Index
Classification codes considered include:
A0670D Sensing and detecting devices
A8280T Chemical sensors
A8670L Measurement and control techniques and instrumentation in environmental
science
A8780B Biosensors
A9260T Air quality and air pollution
B6250K Wireless sensor networks
B7230 Sensing devices and transducers
B7720 Pollution detection and control
C3240 Transducers and sensing devices
C3310G Pollution control
Chemical Abstracts, Scifinder
A general search for air pollution sensors was conducted for the past two years. Scifinder
identifies relevant Chemical Abstracts records, including journal articles, worldwide patents
and patent applications.
A-8
-------
31 October 2013
Web of Science
The Web of Science (WoS) / Science Citation Index covers all the core peer-reviewed
scientific literature. Because the indexing is minimal, records appear quickly in the WoS
database. This database was searched for relevant 2011-2012 records as a check, to catch
papers that may have been missed by the INSPEC and Chemical Abstracts searches.
The WOS does not include all the further indexing available in some other databases so
records can be incorporated into the database much more quickly than other database
publishers. (This search duplicates some results from Physics and Chemical Abstracts
searches.)
Full publications were pursued via the Argonne library system (access to most IEEE articles
and AIP conference papers.
Patent Searches
Additional searches were conducted to complement the Chemical Abstracts search using
free tools available via the internet, including the Google database and others highlighted
below. (Further patent data could be obtained by a patent searcher tapping commercial
patent databases.)
1. WIPO - Patentscope, www.wipo.int. This resource was searched for mentions of air
pollution sensor in patent documents and international patent applications (PCT) for
2011.
2. US Patent & Trademark Office USPTO. This resource was searched for recent patents
and patent applications for air pollution sensors.
3. Espacenet. This resource includes European patents, WIPO patents and Japanese
patents. It was searched using "smart search" for recent patents and patent applications
for air pollution sensors.
Additional Conference Searches
Simple searches were conducted for "portable or mobile or handheld or miniature" and " air
pollution" and "sensors." Engineering Index results were reasonably productive, as those
keywords were included in the controlled vocabularies.
Results included abstracts from a search on the Engineering Index platform, and a
compilation with links and references to presentations at recent (2009-2011) professional
society conferences.
In many cases, relevant abstracts or session descriptions were culled from the websites
because formal proceedings volumes were not available. (Note some conferences
encourage participants to submit their research to peer-reviewed journals, so some of these
presentations are expected to be addressed via other searches designed to tap journals.)
A-9
-------
31 October 2013
A.3 PHASE II SEARCH
Results of the broad initial search served as the foundation for the more extensive search and
retrieval activities in Phase II. These searches followed a similar approach as for Phase I,
tapping similar databases and other online resources.
A.3.1 Targeted Searches
Phase II involved a number of targeted searches, including pollutant-specific searches that
focused on the study set determined from collaborator inputs and insights from Phase I.
Searches also targeted specific authors, organizations, journals, conferences, and projects that
were identified during Phase I as particularly promising.
A.3.2 Secure Searches
Active research is being conducted in support of homeland security-related applications, to
support intelligence, security, and defense applications. A targeted search was conducted in a
secure facility at Argonne. Results were similar to those from the search of the open literature
so they are not discussed further in this report.
A.3.3 Expert Contacts
Colleagues and other experts were contacted during this phase to obtain selected materials not
available online (including several conference presentations and proceedings). For example,
electronic and hard copy proceedings were obtained for recent ICT conferences (e.g., Ubicomp
and Pervasive) to support the compilations and evaluations for research on
architecture/infrastructure and application.
A.4 RETRIEVAL, EXTRACTION, AND DATA COMPILATION
Information from the literature search was reviewed, research reports and other publications
retrieved, and summary data extracted. The data were then organized and compiled into
summary tables to facilitate topical searches and sorts (e.g., by pollutant or by sensing
technology/technique).
To illustrate, an example table compiled early in Phase II from journals and organizational
websites is provided as Table A-3. Shading indicates results for a pollutant-specific search of to
identify sensors for benzene. An example data summary from a patent search prior to
compilation in a summary table is presented in Table A-4.
The combined iterative searches and data reviews, extractions, and compilations produced a
master sensor table (Raymond et al. 2013) and topical subsets, including those prepared from
pollutant-specific sorts. As an example, a subset table is presented in Appendix E to provide
summary details for the sensors considered in creating the graphical arrays presented in
Section 3.5.
A similar process of search, retrieval, extraction, and compilation was followed to prepare the
companion summary table for architecture/infrastructure including software apps.
A-10
-------
31 October 2013
TABLE A-3 Preliminary Data Compilation for Research Sensors Highlighting the Subset for Benzene3
#
20
29
Research
Organization
(authors, funding)
Duke University
Yang, Chang-
Heng
(Masters thesis,
funding source
not identified but
refers to "NASA
needs')
Guangzhong
University,
Environmental
Science and
Engineering
Institute (China)
Cao, X, Tao, Y.,
Li, L, Liu, Y.,
Peng, Y., Li, J.
(Funding:
National Natural
Science
Foundation of
China, Natural
Science
Foundation of
Guandong
Province, Science
and Technology
Project
Foundation of
Guangdong
Province)
Weblink/
Citation, Year
httD://dukescace.lib
.duke.edu/dscace/
bitstream /handle/1
0161/3060/D Yana
Chana%20Hena
a 2010.Ddf?seaue
nce=1
[July 2010]
httD://onlinelibrarv.
wilev.com/doi/10.1
002/bio.1174/full
[2009,
Luminescence,
26:5-9]
Detection
Technique
Nanosensors
made of
conducting
polypyrrole
(PPy) and tin
dioxide (SnO2)
on single-walled
nanotubes
(SWNTs)
Cataluminescen
ce (CTL, type of
chemolumi-
nescence
produced by
catalytic
oxidation
reactions on
surface of solid
catalyst)
Size
5 mm
ceramic
heating tube
within
12 mm
quartz tube
Stage
Re-
search
Re-
search
Cost
($K)
Device
Y2O3
nanoparticle
sensor
(Y2Os coated
on ceramic
heating tube;
catalytic
reaction when
exposed to
ethyl acetate,
resulting CTL
intensity is
measured)
Network
Capa-
bility
Not
indicated
Auto-
mated
Yes
Pollutant/
Parameter
VOCs:
benzene,
MEK,
hexane,
xylene
Ethyl
acetate
Detection/
Sensitivity
500 ppb
Operation-
Application
Notes
Fast and
sensitive for
individual
chemicals,
but not found
in this study
to be
successful for
mixtures.
Sensitivity in
presence of
other vapors;
interference
caused by
formic acid,
n-hexane,
toluene,
acetic acid,
benzene,
formaldehyde,
and ethanol at
respective %
levels of 0.52,
5.75, 8.63,
0.46^ 2.8li
1.03, 21.1.
A-11
-------
31 October 2013
#
74
Research
Organization
(authors, funding)
University of
Bahkesir (Turkey)
Departments of
Physics and
Chemistry
Acikbas, Y.,
Capan, R.,
Erdogan, M., and
Yukruk, F.
Weblink/
Citation, Year
http://pdn.sciencedi
rect. com/science ?_
ob=MiamilmageUR
L& cid =2713538,
user=1722207& pi
i=S092540051100
6551 &_check=y&_
origin=browse&_zo
ne=rslt_l istjtem &_
coverDate=201 1 -
12-
15&wchp=dGLzVlk
zSkzV&md5=0388
683803395c4afcf8
70fed42e0795/1-
s2.0-
S092540051 10065
51-main.pdf
[2011, Sensors
and Actuators B,
160:65-71]
Detection
Technique
Fluorescence
(mass change
using a quartz
crystal
microbalance
[QCM]
measurement
system during
influence of gas
tnnl&fN I|QQ
1 1 IUICUUICO
adsorbed on or
diffusing into a
thin film
surface,
deposited on
glass or quartz
crystal
substrate by
Langmuir-
Blodgett [LB]
thin film
deposition
technique)
Size
Stage
Re-
search
Cost
($K)
Device
Solid-state
fluorescence
sensing device
with perylene
bisimides
acting as a
fluorescence
probe, using a
novel perylene
molecule
(perylene-
dimide[FY1]
material)
Network
Capa-
bility
Auto-
mated
Pollutant/
Parameter
Chloroform,
isopropyl al-
cohol
Also tested:
benzene,
toluene, and
ethyl alcohol
Detection/
Sensitivity
Chloroform
3.9 x10-4
Hz/ppm
(at1.5x104
ppm)
Operation-
Application
Notes
Large, fast,
reproducible
results for
these two
chemicals.
A-12
-------
31 October 2013
#
94
Research
Organization
(authors, funding)
University of
Illinois at Urbana-
Champaign
Henowei, L.,
Jang, M., Suslick.,
K.S.
(Funding: NIH
(^£*t~l£}&
OC//CO,
Environment, and
Health Initiative)
Weblink/
Citation, Year
http://vwvw.scs.illi
nois.edu/suslick/
documents/iacs.
2011.Dreox.pdf
[Oct. 2011]
Detection
Technique
"Optoelectronic
nose" using
disposable
colorimetric
sensor array
and pre-
oxidation tube
packed with
chromic acid on
silica
(nanoporous
pigments with
pre-oxidation
technique)
Size
Bench-top
(tube and
array);
flatbed
scanners
used for
imaging;
prototype
handheld
array is
under
develop-
ment
Stage
Re-
search
Cost
($K)
Device
Laboratory
(in addition to
work on a
handheld
prototype,
work is under
way on a
wearable
device for fast,
cheap, highly
sensitive
personal
monitoring of
VOC vapors)
Network
Capa-
bility
Auto-
mated
Yes
Pollutant/
Parameter
Phenol,
others
20 VOCs
tested,
including
acetone,
BTEX,
chloroform,
p-dichloro-
benzene,
ethanol,
ethyl
acetate,
formalde-
hyde, MEK,
phenol,
isopropanol,
styrene,
1,1,1-tri-
chloro-
ethane,
1,2,4-tri-
methyl-
benzene
Detection/
Sensitivity
Limits of
detection
improved to
an average
1.4% of
respective
permissible
exposure
limit concen-
trations
(improved
sensing of
less-reactive
VOCs)
Operation-
Application
Notes
Color
changes of
array are
concentration-
dependent,
provides
semiquantita-
tive analysis;
changes in
relative
humidity (a
problem for
prior
electronic
nose
technologies)
do not
generally
affect the
response
even at low
analyte
concentra-
tions.
A-13
-------
31 October 2013
#
103
Research
Organization
(authors, funding)
University of
Michigan,
Department of
Environmental
Health Sciences
Kim, S.K., Chang,
H., Zellers, E.T.
(Funding: DoD
ESTCPandNSF
Engineering
Research Centers
Program; devices
fabricated in Lurie
Nanofabrication
Facility, part of
A/A// Network
supported by
NSF)
Weblink/
Citation, Year
http://vwvw.chem.
ntnu.edu.tw/~che
mweb/download/
ac201788a.pdf
[2011, Analytical
Chemistry,
83(1 8):71 98-720
6]
Detection
Technique
Gas chroma-
tography (field
microsystem)
Size
Portable
Stage
Re-
search
proto-
type
Cost
($K)
Device
Microsensor
array
Network
Capa-
bility
Autono-
mous
operation
with
laptop
Auto-
mated
Via
laptop
Pollutant/
Parameter
Trichloro-
ethylene
among 11
VOCs tested
Also shows
promise for
BTEX (with
modification
of pretrap
for transfer
r i^%
from
atmosphere
to sampler)
Detection/
Sensitivity
11 ppbTCE
Operation-
Application
Notes
A pump
brings in and
scrubs air,
which is then
heated and
transferred to
two micro-
columns for
analyte
separation
and VOC
detection by
microsensors.
A-14
-------
31 October 2013
#
109
Research
Organization
(authors, funding)
University of
Sheffield,
Department of
Physics and
Astronomy
AIQahtani, Had/,
and colleagues
King Saud
University (Saudi
Arabia),
Department of
Physics (AIQ, H.)
(Funding: King
Saud University,
UK Engineering
and Physical
Sciences
Research Council
[doctoral training
and post-doctoral
fellowship], and
European
Commission
Experienced
Research Marie
Curie fellowship
under the
'FlexSmel'
Training Network)
Weblink/
Citation, Year
http://pdn.scienc
edirect.com/scie
nee? ob=Miamil
maaeURLS cid=
271353& user=1
722207& Dii=SO
9254005110072
22& check=v&
oriqin=browse&
zone=rslt list ite
m& coverDate=
2011-12-
15&wchp=dGLz
Vlk-
zSkzV&md5=a9
b2a8baaf63a476
C64f548981a8d0
9d/1-s2.0-
S092540051100
7222-main.pdf
[2011, Sensors
and Actuators B,
160:399-404]
Detection
Technique
Resistance
(with resulting
current driven
into a virtual
ground by a
current/voltage
converter)
Size
Stage
Cost
($K)
Device
Gold core/shell
nanoparticle
(Au-CSNP)
film on glass
substrate
(processed by
the Langmuir-
Schafer [LS]
printing
method)
Network
Capa-
bility
Auto-
mated
Pollutant/
Parameter
Decane
(for odor
detection
relative to
the lower
explosive
limit, LEL)
(the
response is
weaker but
still
significant
for toluene
and xylene)
Detection/
Sensitivity
15 ppm
(about
1 /600th the
LEL of
8,000 ppm)
Operation-
Application
Notes
Resistance is
weakly
dependent on
temperature
A-15
-------
31 October 2013
#
1
Research
Organization
(authors, funding)
Ben-Gurion
University of the
Negev (Israel)
Eltzov, £.,
Pavluchkov, V.,
Burstin, M.,
Marks, R.S.
Weblink/
Citation, Year
http://pdn.scienc
edirect.com/scie
nee? ob=Miamil
maaeURLS cid=
271353& user=1
722207& Dii=SO
9254005110008
9X& check=v&
oriqin=article& z
one=toolbar& co
verDate=20-Jul-
2011&view=c&or
iainContentFamil
v=serial&wchD=d
GLbVlk-
zSkzS&md5=6c5
90f4c870fff04f6f
44961 ad25eab1/
1-S2.0-
S092540051100
089X-main.pdf
[2011, Sensors
and Actuators B,
155:859-867]
Detection
Technique
Bioluminescenc
e (bacteria on
end face of fiber
optic)
Size
Field bio-
sensor
Stage
Re-
search
Cost
($K)
Device
Biolumi-
nescent
bacteria,
£. co// strains
(notably
TV1061)
immobilized on
the end face of
a portable fiber
optic
biodetector
Network
Capa-
bility
Online
capabiliti
es
Auto-
mated
Pollutant/
Parameter
Chloroform
Toluene was
also tested
Detection/
Sensitivity
6.65 ppb
chloroform
Operation-
Application
Notes
Bacteria are
immobilized in
alginate
layers on the
fiber optic.
A-16
-------
31 October 2013
#
14
Research
Organization
(authors, funding)
Chinese Academy
of Sciences,
Research Center
for Biomimetic
Functional
Materials and
Sensing Devices,
Institute of
Intelligent
Machines
Meng, Fan-Li,
Huang, Zing-Jiu,
and colleagues,
also at Anhui
Polytechnic
University
(Funding: One
Hundred Person
Project of the
Academy,
National Natural
Science
Foundation of
China, National
Basic Research
Program of China,
and Anhui
Provincial Natural
Science
Foundation)
Weblink/
Citation, Year
http://pdn.scienc
edirect.com/scie
nee? ob=Miamil
maaeURLS cid=
271353& user=1
722207& Dii=SO
9254005100081
8X& check=v&
oriqin=search&
zone=rslt list ite
m& coverDate=
2011-03-
31&wchp=dGLz
VBA-
zSkzV&md5=ffO
be4989bfbde8f2
8e9d5daeb7bfae
9/1-S2.0-
S092540051000
818X-main.pdf
[2011, Sensors
and Actuators B,
153:103-109]
Detection
Technique
Capacitance
response;
chip with a
dielectric
medium (silica
and gases) and
two electrodes,
one of Si and
gold (Au) and
the other of
MWCNT and
Au
Size
Very small
Stage
Re-
search
Cost
($K)
Device
Paper film-
based
capacitive
electronic chip
by spray-
casting
MWCNT
suspension;
also with self-
oriented
carbon
nanotube on
top surface of
gold electrode
Network
Capa-
bility
Not
indicated
Auto-
mated
Pollutant/
Parameter
Formalde-
hyde
ammonia
toluene
Detection/
Sensitivity
Formalde-
hyde:
300 ppb
ammonia:
3.1 ppm
toluene:
7.4 ppm
Operation-
Application
Notes
Liquid
samples
placed in
sample
chamber,
nitrogen used
as carrier gas
to flow
through the
chamber.
A-17
-------
31 October 2013
#
72
17
Research
Organization
(authors, funding)
Universiti Putra,
Malaysia
Electrical and
Electronic
Department
Abadi, M.H.S.,
H ami don, M.N.,
Shaari, A.M.,
Abdullah, N.,
Wagiran, R.,
Misron, N.
FLIR Systems,
Inc.
Weblink/
Citation, Year
[201 1, Sensors &
Transducers
Journal,
125(2):76-88]
http://vwvw.eDa. a
ov/etv/vt-
ams.html
(Nov. 2010)
Detection
Technique
Metal oxide
semicon-
ductance
IR imaging
(temperature/
emissivity
differences
between natural
IR radiation and
thermal
emission or
absorption of
leaking gas)
Size
Portable
camera
Stage
Re-
search
Avail-
able
Cost
($K)
65-80
Device
Array gas
sensor formed
by semicon-
ductor oxides;
SnO2
nanopower
with different
weight %
platinum
powders
FLIR
GasFindIR
Midwave (MW)
Camera
(passive IR
camera)
Network
Capa-
bility
Not
indicated
Not
indicated
Auto-
mated
Pollutant/
Parameter
Alcohols:
ethyl,
isopro-
panol, and
methanol;
hydro-
carbons
(xylene,
isobutene),
acetone;
exhaust gas
com-
ponents
1,3-buta-
diene
acetic acid
acrylic acid
benzene
methylene
chloride
(dichloromet
hane)
ethylene
methanol
pentane
propane
styrene
Detection/
Sensitivity
Sensitive to
sub ppm of
applied
species,
very
sensitive to
gases above
10 ppm
Minimum,
g/hr:
1,3-buta-
dienel.3;
acetic acid
<0.02;
acrylic acid
0.92;
benzene
0.35;
ethylene
0.35;
methanol
0.28;
MeCI4.9;
pentane
<0.28;
propane
<0.44;
styrene 0.35
Operation-
Application
Notes
ETV field test
conducted in
Freeport TX
A-18
-------
31 October 2013
#
18
Research
Organization
(authors, funding)
Gas Imaging
Technologies,
LLC
Weblink/
Citation, Year
http://vwvw.epa. q
ov/etv/vt-
ams.html
(Nov.
2011)
Detection
Technique
Imaging
spectrometry
Size
Portable
camera
Stage
Avail-
able
Cost
($K)
89
Device
Sherlock VOC
(passive IR
camera)
Network
Capa-
bility
Not
indicated
Auto-
mated
Pollutant/
Parameter
As above
Detection/
Sensitivity
Minimum,
g/hr:
1,3-buta-
diene 8.0;
acetic acid
1.7;
acrylic acid
0.92;
benzene
3.2;
ethylene
3.3;
methanol
2.1;
methyleneCI
>70;
pentane
0.83;
propane
0.88;
styrene 15
Operation-
Application
Notes
A-19
-------
31 October 2013
#
12
Research
Organization
(authors, funding)
Drager Safety Inc.
(Gas Detection
Systems)
505 Julie Rivers
Suite 150
Sugarland, TX
784-78-28471
800-230-5029
Weblink/
Citation, Year
http://www.draeq
er.us/Publishinql
maaes/Dr%c3%
a4aer%20Bio-
Check%20Form
aldehyde/indoor
pollutants br 90
44567 en.pdf
Detection
Technique
Collection tube
(sample sent to
lab for analysis)
Size
Small tube
Stage
Avail-
able
Cost
($K)
Device
Drager Bio-
Check
Solvents
Network
Capa-
bility
Not
indicated
(personal
home
use)
Auto-
mated
Pollutant/
Parameter
Solvents:
"Different
solvents can
be detected
simultane-
ously."
Overview
infers BTEX
(petrol
stations or
traffic
fumes),
chlorinated
VOCs (dry
cleaners),
as well as
solvents
used in
adhesives,
paint and
paint
strippers,
and
varnishes.
Detection/
Sensitivity
Operation-
Application
Notes
Collection
tube sent to
Drager for lab
analysis.
A-20
-------
31 October 2013
TABLE A-4 Example Data Retrieval: Selected Results of a Targeted Patent Search (prior to compilation in a summary table)
A hybrid separation and detection device for chemical detection and analysis
By: Tao, Nongjian; Forzani, Erica; Iglesias, Rodrigo; Tsow, Francis; Assignee: Arizona State University, USA; Patent Information Mar. 17, 2011, WO
2011031500, A2; Application: Aug 25, 2010, WO 2010-US46702; Priority: Sep 14, 2009, US 2009-242256P; Source: PCT Int. Appl., 39pp., Patent, 2011,
CODEN: PIXXD2 Accession Number: 2011:327719, CAN 154:342741, CAPLUS; Language: English
Abstract
The present invention provides a device that makes it possible to perform real-time detection and anal, of BTEX components in real samples using an
inexpensive and miniaturized hybrid specific binding-sepn. device.
The device may be used in occupational health and safety applications as well as for toxicol. population studies to del the presence of org. volatile
components in an air sample. Priority Application: US 2009-24225 6P
A portable sensor system for air pollution monitoring and malodours olfactometric control
Suriano, D.1; Rossi, R.1; Alvisi, M.1; Cassano, G.1; Pfister, V.1; Penza, M.1; Trizio, L.2; Brattoli, M.2; Amodio, M.2; De Gennaro, G.2 Source: Lecture Notes in
Electrical Engineering, v 109 LNEE, p 87-92, 2012, Sensors and Microsystems, AISEM 2011 Proceedings; ISSN: 18761100, E-ISSN: 18761119; ISBN-13:
9781461409342; DOI: 10.1007/978-1-4614-0935-9_15; Conference: 16th Conference on Italian Association of Sensors and Microsystems, AISEM 2011,
February 7, 2011 - February 9, 2011; Publisher: Springer Verlag; Author affiliations: 1 ENEA, Brindisi Technical Unit for Technologies of Materials, Brindisi, Italy;
2 Department of Chemistry, University of Bari, Lenviros Sri, Bari, Italy
Abstract: A portable sensor-system based on solid-state gas sensors has been designed and implemented as proof-of-concept for environmental air-monitoring
applications and malodours olfactometric control. Commercial gas sensors (metal-oxides, n-type) and nanotechnology sensors (carbon nanotubes, p-type) are
arranged in a configuration of array for multisensing and multiparameter devices. Wireless sensors at low-cost are integrated to implement a portable and mobile
node, that can be used as early-detection system in a distributed sensor network. Real-time and continuous monitoring of hazardous air-contaminants (e.g., NO2,
CO, SO2, BTEX, etc.) has been performed by in-field measurements. Moreover, monitoring of landfill gas generated by fermentation of wastes in a municipal site
has been carried out by the portable sensor-system. Also, it was demonstrated that the sensor-system is able to assess the malodours emitted from a municipal
waste site and remarkably compared to the olfactometry method based on a trained test panel. © 2012 Springer Science+Business Media, LLC. (12 refs.);
Database: Compendex
Title: Gas Nanosensor Design Packages Based on Tungsten Oxide: Mesocages, Hollow Spheres, and Nanowires
Author(s): Nguyen Due Hoa; El-Safty, S.A.
Source: Nanotechnology Volume: 22 Issue: 48 Pages: 485503 (10 pp.) Published: 2 Dec. 2011
Treatment: Practical, Experimental
Abstract: Achieving proper designs of nanosensors for highly sensitive and selective detection of toxic environmental gases is one of the crucial issues in the
field of gas sensor technology, because such designs can lead to the enhancement of gas sensor performance and expansion of their applications. Different
geometrical designs of porous tungsten oxide nanostructures, including the mesocages, hollow spheres and nanowires, are synthesized for toxic gas sensor
applications. Nanosensor designs with small crystalline size, large specific surface area, and superior physical characteristics enable the highly sensitive and
selective detection of low concentration (ppm levels), highly toxic NO 2 among CO, as well as volatile organic compound gases, such as acetone, benzene, and
ethanol. The experimental results showed that the sensor response was not only dependent on the specific surface area, but also on the geometries and crystal
size of materials. Among the designed nanosensors, the nanowires showed the highest sensitivity, followed by the mesocages and hollow spheres-despite the
fact that mesocages had the largest specific surface area of 80.9 m 2 g -1, followed by nanowires (69.4 m 2 g -1 ), and hollow spheres (6.5 m 2 g -1). The nanowire
sensors had a moderate specific surface area (69.4 m 2g '1) but they exhibited the highest sensitivity because of their small diameter (~5 nm), which
approximates the Debye length of WO 3. This led to the depletion of the entire volume of the nanowires upon exposure to NO 2, resulting in an enormous increase
in sensor resistance.
A-21
-------
31 October 2013
Controlled Indexing: air pollution; gas sensors; nanosensors; nanowires; organic compounds; porous materials; toxicology; tungsten compounds
Uncontrolled Indexing: Debye length; nanowire sensor; ethanol; benzene; acetone; organic compound gas; low concentration detection; specific surface area;
crystalline size; porous nanostructure; environmental toxic gas sensor application; hollow sphere; mesocages; gas nanosensor design packaging; WO 3
Classification Codes: A8280T Chemical sensors; A0710C Micromechanical and nanomechanical devices and systems; B7230L Chemical sensors; B7230M
Microsensors and nanosensors; B0580 Powders and porous materials (engineering materials science); B7720 Pollution detection and control
Chemical Indexing: WO3/int O3/int O/int W/int WO3/bin OS/bin O/bin W/bin
International Patent Classification: B82B1/00
Author Address: Nguyen Due Hoa; El-Safty, S.A.; Nat. Inst. for Mater. Sci. (NIMS), Tsukuba, Japan.
Publisher: IOP Publishing Ltd., UK
Title: Mesoporous SnO(2) sensor prepared by carbon nanotubes as template and its sensing properties to indoor air pollutants
Author(s): Li, HH (Li, Huihua); Meng, FL (Meng, Fanli); Sun, YF (Sun, Yufeng); Liu, JY (Liu, Jinyun); Wan, YT (Wan, Yuteng); Sun, B (Sun, Bai); Liu, JH (Liu,
Jinhuai)
Editor(s): Li M; Yu D
Source: 2010 SYMPOSIUM ON SECURITY DETECTION AND INFORMATION PROCESSING Book Series: Procedia Engineering Volume: 7 Pages: 172-
178 DOI: 10.1016/j.proeng.2010.11.026 Published: 2010
Cited Reference Count: 23
Abstract: An effort has been made to develop a kind of mesoporous SnO(2) gas sensor for detecting indoor air pollutants such as ethanol, benzene, meta-
xylene. Mesoporous SnO(2) material has been prepared by sol-gel method joined into multiwall carbon nanotubes as template. The field emission scanning
electron microscope (FSEM) was used to characterize the samples, by which the mesoporous structure of SnO(2) was obviously observed. The investigation
results suggest that the as-prepared mesoporous SnO(2) has a good response and reversibility to indoor environmental air pollutants. At last, the selectivity of the
mesoporous sensor was investigated.
Document Type: Proceedings Paper
Conference: 2010 Symposium on Security Detection and Information Processing PEOPLES R CHINA 2010
Conference Title: 2010 Symposium on Security Detection and Information Processing
Conference Location: PEOPLES R CHINA
Author Keywords: mesoporous SnO(2); gas sensor; indoor air pollutants
Keywords Plus: NANOWIRES; GROWTH; NANOPARTICLES; BATTERY
Addresses: [Li, HH; Meng, FL; Sun, YF; Liu, JY; Wan, YT; Sun, B; Liu, JH] Chinese Acad Sci, Inst Intelligent Machines, Res Ctr Biomimet Funct Mat & Sensing
Devices, Hefei 230031, Peoples R China
Subject Category: Engineering
IDS Number: BTK27
ISSN: 1877-7058
29-char Source Abbrev.: PROCEDIA ENGINEER
A-22
-------
31 October 2013
APPENDIX B:
OVERVIEW OF EXPOSURE BENCHMARKS
B-1
-------
31 October 2013
B-2
-------
31 October 2013
APPENDIX B:
OVERVIEW OF EXPOSURE BENCHMARKS
This appendix supplements the information given in Section 3.2. Exposure benchmarks are
standards and guidelines for chemicals in air that have been established by a number of
organizations for specific health and safety programs. These concentrations serve as useful
points of comparison for the detection levels reported for research sensors. A number of
benchmarks were compiled to serve as a practical foundation for assessing sensor gaps and
opportunities. An overview description of these benchmarks is provided in Table B-1, together
with the set of study pollutants for which they have been established (as reflected in the
graphical arrays in Section 3.5).
The primary sources of health-based benchmarks are EPA databases, including toxicity values
for continuous exposures from the Integrated Risk Information System (IRIS) database (EPA
2013a), and related benchmarks in the Provisional Peer Reviewed Toxicity Value (PPRTV)
database (EPA 2013b). Similar benchmarks for the general public such as minimal risk levels
(MRLs) established by the Agency for Toxic Substances and Disease Registry (ATSDR 2013)
and reference exposure levels from California Office of Environmental Health Hazard
Assessment (OEHHA) (CalEPA 2012) are also reflected in the tables and figures in Chapter 3.
Additional health-based values include concentrations defined for acute and short-term
exposures to guide emergency response measures for the general public. Such values include
acute exposure guideline levels (AEGLs) established by the National Research Council (NRC).
Further health-based guides recommended by the NRC for continuous exposures over weeks to
months include spacecraft maximum allowable concentrations (SMACs) as well as continuous
exposure guideline levels (CEGLs) for submarines. Benchmark values recommended the NRC
and the chemicals for which they have been developed are summarized in Table B-2.
Additional information is included at the end of that table for occupational benchmarks
established by the National Institute for Occupational Safety and Health (NIOSH) and
U.S. Army; information is also provided for selected CalEPA reference exposure levels
established for the general public.
Occupational exposure levels (OELs) also provide useful context for assessing sensor detection
capabilities and potential opportunities. (For example, such limits were considered in assessing
health risks associated with the World Trade Center collapse, notably for pollutants for which
other reference values were not available.) Table B-3, which was prepared during the initial
phase of this project when the candidate set was still being developed, highlights OELs for
selected pollutants and illustrates the range of limits available within this benchmark category
alone. Illustrative bar graphs of benchmarks for the general public and for workers established
for arsenic and benzene are presented in Figures B-1 and B-2.
For methane, standard health-based guidelines have not been developed for the general public
because it is biologically inert, although it can be an asphyxiant as well as explosive at high
concentrations. Loss of consciousness can result when methane concentrations are high
enough to displace oxygen in air below a certain level. Military guidelines for confined spaces
are based on oxygen displacement and explosive characteristics, while ACGIH classifies
methane as a simple asphyxiant and does not establish a TLV. In evaluating methane for the
SMAC, the NRC suggests 5,300 ppm based on 0.1% of the lower explosive limit and the
expectation that consciousness would be maintained at oxygen levels above 18%. To displace
oxygen below that threshold, methane would need to be 14.3% by volume in air, or
143,000 ppm; this concentration is used as an IDLH-equivalent value on the graphical array.
B-3
-------
31 October 2013
TABLE B-1 Overview of Exposure Benchmark Bases and Selected Pollutants
Benchmark
Organization
Definition
Duration
Study Pollutants
Reference
General Public: Emergency Response, Acute Exposures (up to 8 hours)
AEGL:
Acute Exposure
Guidance Level
ERPG:
Emergency
Response Planning
Guideline
PAC:
Protective Action
Criteria
National
Research Council
(NRC), in
coordination with /
U.S. EPA and
U.S. Department
of Defense (DoD)
Emergency
Response
Planning
Committee of the
American
Industrial Hygiene
Association
(AIM A)
U.S. Department
of Energy (DOE);
Subcommittee on
Consequence
Assessment and
Protective Actions
(SCAPA)
Threshold exposure concentrations for rare, emergency exposures
to airborne chemical releases, divided into three levels of increasing
severity for each exposure time duration.
1 = Non-disabling and reversible mild discomfort, irritation, or
asymptomatic nonsensory effects.
2 = Irreversible or serious long lasting adverse health effects.
3 = Life-threatening health effects or death.
Maximum airborne concentration to which the general public could
be exposed for 1 hour in rare, emergency releases without
experiencing or developing the following (based on severity level) :
1 = Adverse health effects.
2 = Irreversible or serious health effects
3 = Life-threatening effects.
PACs are reported as AEGL, ERPG, or TEEL values (hierarchy
preference order), as available and appropriate. The three
benchmark levels are defined as followed, each associated with
increasing severity of health effects and higher levels of exposure.
1 = Mild, transient health effects
2 = Irreversible or serious health effects which may impair ability to
take protective action measures (e.g., escape)
3 = Life-threatening effects
10 min
30 min
1 hr
4hr
8 hr
1 hr
1 hr
Acrolein, ammonia,
benzene,
1,3-butadiene,
carbon monoxide,
formaldehyde,
hydrogen sulfide,
nitrogen dioxide,
sulfur dioxide
Acrolein,
ammonia, benzene,
1,3-butadiene,
carbon monoxide,
formaldehyde,
Methane
NRC 2009
(and
others)
AIHA2011
DOE 2012
(PAC
Database,
27)
B-4
-------
31 October 2013
Benchmark
Organization
Definition
Duration
Study Pollutants
Reference
General Public: Ambient Exposures (continuous, 1 day to a lifetime)
NAAQS:
National Ambient
Air Quality
Standards
EPA IRIS RBC:
Risk-Based
Concentration
EPA IRIS RfC:
Reference
Concentration
EPA PPRTV RfC:
Provisional Peer-
Reviewed Toxicity
Value RfC
ATSDR MRL:
Minimal Risk Level
CalEPA REL:
Reference
Exposure Level
U.S. EPA
U.S. EPA
U.S. EPA
U.S. EPA
Agency for Toxic
Substance and
Disease Registry
(ATSDR)
CalEPA Office of
Environmental
Health Hazard
Assessment
(OEHHA)
Outdoor air concentrations not to be exceeded for six criteria
pollutants.
Primary standards are meant for public health protection including
for sensitive populations.
Secondary standards are meant for protection of public welfare,
including against decreased visibility and damage to animals, crops,
vegetation, and buildings.
Based on cancer risk: Concentration associated with a target risk
level of 10'4or 10'6 for carcinogenic effects (calculated from the
inhalation unit risk, IUR).
Based on noncancer effects: An estimate (with uncertainty
spanning perhaps an order of magnitude) of a continuous inhalation
exposure to the human population (including sensitive subgroups)
that is likely to be without an appreciable risk of deleterious,
noncarcinogenic (for most substances) effects during a lifetime.
As described above for the IRIS RfC. (PPRTVs reflect a similar
derivation process as the IRIS values, while the extent of external
peer review differs.)
Estimate of daily human exposure likely to be without appreciable
risk of adverse non-cancer effect over given exposure duration,
based on target organ/most sensitive effect; set below levels that
might cause adverse health effects in people most sensitive to such
substance-induced effects, based on current information; intended
as screening levels for hazardous waste sites, for target organ/most
sensitive effect.
Concentration at or below which no adverse health effects are
anticipated; based on most sensitive, relevant, adverse effect
reported in medical and toxicological literature; designed to protect
the most sensitive individuals in the population by inclusion of
margins of safety; since margins of safety are incorporated to
address data gaps and uncertainties, exceeding the REL does not
automatically indicate an adverse health impact.
Chronic
(averaging
times include:
1 hr, 2 hr, 3
hr, 8 hr, 24
hr, 3 mo,
annual)
Chronic
(7 yr-lifetime)
Subchronic
(2-7 yr)
Chronic
(7 yr-lifetime)
As above
Acute
(1-14 d)
Intermediate
( 1 5-365 d)
Chronic
(>365 d)
1 hr
6hr
8hr
Chronic
Carbon monoxide,
lead, nitrogen
dioxide, ozone,
particulate matter
(PMlO, PM2.5),
sulfur dioxides
Benzene,
1,3-butadiene,
formaldehyde
Acrolein, ammonia,
benzene,
1,3-butadiene,
hydrogen sulfide
Ammonia, benzene
Acrolein, ammonia,
benzene,
formaldehyde,
hydrogen sulfide
Acrolein, ammonia,
benzene,
1,3-butadiene,
formaldehyde,
hydrogen sulfide,
ozone
U.S. EPA
2012 (last
updated)
U.S. EPA
2013, IRIS
database
U.S. EPA
2013, IRIS
database
U.S. EPA
2013
(PPRTV
database)
ATSDR
2013, MRL
list
CalEPA
OEHHA
2013
B-5
-------
31 October 2013
Benchmark
CAAQS
California Ambient
Air Quality
Standard
Organization
CalEPA, OEHHA
Definition
Maximum allowable concentration in ambient air without threatening
the health of the public, including sensitive populations.
Duration
Similar to
NAAQS
Study Pollutants
Lead, PM-io, PlVh.s
Reference
CalEPA
ARE 2009
Occupational: Workplace Exposures (noncontinuous, work shins extending over a work life)
PEL:
TWA, STEL, C, AL
Permissible
Exposure Limit:
Time-Weighted
Average, Short-
Term Exposure
Limit, Ceiling, and
Action Level
REL:
TWA, STEL, C
Recommended
Exposure Limit
(same categories as
for the PEL above)
IDLH:
Immediately
Dangerous to Life or
Health
Occupational
Safety and
Health
Administration
(OSHA)
National Institute
for Occupational
Safety and
Health (NIOSH)
NIOSH
The TWA is a time-weighted average concentration for an 8-hour
workday during a 40-hour workweek. (Levels must not be exceeded
during any 8-hour workshift during a 40-hour workweek.)
A STEL is a 15-minute TWA exposure and should not be exceeded
at any time during a work day.
A ceiling should not be exceeded at any time during a work day.
As described above for the PELs, except that the REL TWA is a
time-weighted average concentration for up to a 10-hour workday
during a 40-hour workweek.
(A STEL is a 15-minute TWA exposure and should not be exceeded
at any time during a work day.
A ceiling REL should not be exceeded at any time.)
Exposure level below which a person should be able to escape
without loss of life or accrual of delayed irreversible health effects,
including impairment of vision which may prevent or delay escape.
This value was established to ensure employee escape given failure
of a respiration device. IDLH values have been established for
85 substances meeting the OSHA definition of a potential
occupational carcinogen.
8hr
10 hr
10 min
"1 ^ min
\ \J 1 1 Ml 1
30 min
Acrolein, ammonia,
benzene,
1,3-butadiene,
carbon monoxide,
formaldehyde,
hydrogen sulfide,
lead, ozone, PM-io,
PlVh.s (and black
carbon), sulfur
dioxide
Acrolein, ammonia,
benzene, carbon
monoxide,
formaldehyde,
hydrogen sulfide,
lead, nitrogen
dioxide, ozone,
black carbon (PM),
sulfur dioxide
Acrolein, ammonia,
benzene,
1,3-butadiene,
carbon monoxide,
formaldehyde,
hydrogen sulfide,
lead, nitrogen
dioxide, ozone,
black carbon (PM)
OSHA
2006
NIOSH
1994
NIOSH
2010
B-6
-------
31 October 2013
Benchmark
TLV:
TWA, STEL, C
Threshold Limit
Value
Organization
American
Conference of
Governmental
Industrial
Hygienists
(ACGIH)
Definition
Time-weighted average concentration to which most workers may
be exposed daily without adverse health effects. For ozone, values
are available for three work activity levels: light, medium, and
heavy.
Duration
2hr
8hr
15 min
Study Pollutants
Acrolein, ammonia,
benzene,
1,3-butadiene,
carbon monoxide,
formaldehyde,
hydrogen sulfide,
lead, methane
(representing
alkanes), nitrogen
dioxide, ozone,
PM-io, PM2.5, black
carbon, sulfur
dioxide
Reference
ACGIH
2011
Occupational: Special Environments (Army Field Deployment, Submarine, and Spacecraft)
MEG
Military Exposure
Guideline
EEGL, CEGL
Emergency and
Continuous
Exposure Guidance
Levels
SMAC
Spacecraft
Maximum Allowable
Concentration
US Army Public
Health Command
(USAPHC)
National
Research
Council
(National
Academies)
NRC/ National
Aeronautics and
Space
Administration
(NASA)
Concentration for intakes in moderate and arid climates, with the
lowest guidelines designed to result in negligible/minimal or no
adverse effects for deployed military personnel.
Exposure levels for confined work space, for exposures continuing
1 day to 3 months, for submarines.
Limit for traces of substance in the closed-loop atmosphere of a
spacecraft cabin, continuous exposure; to not compromise
performance of specific tasks during emergency conditions (short
term: 1 to 24 hr) or cause serious or permanent toxic effects, for
continuous exposures extending 7 to 180 d (long term).
1 hr
8hr
14 d
1yr
24 hr
90 d
1 hr
24 hr
7d
30 d
180 d
Methane
Methane
Methane
USAPHC
2010
NRC 1984
NRC 1994
' The "study pollutants" column identifies those pollutants for which the indicated benchmark has been established (and is reflected in the graphical array.
Note typical health-based benchmarks are not available for methane, so Army and other benchmarks for the specialized occupational environments are included
for this compound, as well as the PAC value for emergency response (because no AEGLs have been established).
B-7
-------
TABLE B-2 Exposure Benchmark Sources
31 October 2013
I. National Research Council
(Nationaj Academies of Science)
A. AEGLs
Volume 1 (2000)
http://books.nap.edu/cataloq.php7record id=10043
Aniline
Arsine
Monomethylhydrazine
Dimethylhydrazine
Volume 2/2002;
http://books.nap.edu/cataloq.php7record id=10522
Phosgene
Propylene glycol dinitrate
1,1,1,2-Tetrafluoroethane (HFC-134A)
1,1 -Dichloro-1 -fluoroethane (HCFC-141B
Hydrogen cyanide
Volume 3 (2003;
http://books.nap.edu/cataloq.php7record id=10672
Nerve agents GA, GB, GD, GF, and VX
Sulfur mustard
Methyl isocyanate
Diborane
Volume 4 (2004;
http://books.nap.edu/cataloq.php7record id=10902
Chlorine
Hydrogen chloride
Hydrogen fluoride
Toluene 2,4- and 2,6-diisocyanate
Uranium hexafluoride
Volume 5 (2007;
http://books.nap.edu/cataloq.php7record id=11774
Chlorine dioxide
Chlorine trifluoride
Cyclohexylamine
Ethylenediamine
Hydrofluoroether-7100 (H 7100)
(40% methyl nonafluorobutyl ether,
60% methylnonafluoroisobutyl ether)
Tetranitromethane
Volume 6 (2008;
http://www.nap.edu/cataloq.php7record id=12018
Allylamine
Ammonia
Aniline
Arsine
Crotonaldehyde, trans and cis-trans
Dimethylhydrazine
Iron pentacarbonyl
Monomethylhydrazine
Nickel carbonyl
Phosphine + 8 metal phosphides
Volume 7 (2009;
http://books.nap.edu/cataloq.php7record id=12503
Acetone cyanohydrin
Carbon disulfide
Monochloroacetic acid
Phenol
Volume 8 (2010)
http://books.nap.edu/cataloq.php7record id=12770
Acrolein
Carbon monoxide
1,2-Dichloroethene (cis, trans, cis-trans)
Ethyleneimine
Fluorine
Hydrazine
Peracetic acid
Propylenimine
Sulfur dioxide (subsequently revisited)
Volume 9 (2010)
http://books.nap.edu/cataloq.php7record id=12978
Bromine
Ethylene oxide
Furan
Hydrogen sulfide
Propylene oxide
Xylenes (m-, o-, p-)
PBPK modeling white paper
Volume 10(2011)
http://books.nap.edu/cataloq.php7record id=13247
N,N-Dimethylformamide
Jet propellant fuels 5 and 8
Methyl ethyl ketone
Perchloromethyl mercaptan
Phosphorus oxychloride
Phosphorus trichloride
Sulfuryl chloride
B-8
-------
TABLE B-2 (Cont'd.)
(I. NRC, A. AEGLs, Cont'd.)
31 October 2013
Volume 11 (2012)
http://books.nap.edu/cataloq.php7record id=13374
bis-Chloromethyl ether
Chloromethyl methyl ether
Selected chlorosilanes
Nitrogen oxides: Nitric oxide
Nitrogen dioxide
Nitrogen tetroxide
Vinyl chloride
Volume 12 (2012)
http://www.nap.edu/cataloq.php7record id=13377
Butane
Chloroacetylaldehyde
Chlorobenzene
Chloroform
Methyl bromide
Methyl chloride
Propane
Metal phosphides (see Volume 6):
Aluminum phosphide
Calcium phosphide
Magnesium phosphide
Magnesium aluminum phosphide
Potassium phosphide
Sodium phosphide
Strontium phosphide
Zinc phosphide
Chlorosilanes (see Volume 11):
Monochlorosilanes:
Dimethyl chlorosilane
Methyl chlorosilane
Trimethyl chlorosilane
Dichlorosilanes:
Dichlorosilane
Diethyl dichlorosilane
Dimethyl dichlorosilane
Diphenyl dichlorosilane
Methyl dichlorosilane
Methylvinyl dichlorosilane
Trichlorosilanes:
Allyl trichlorosilane
Amyl trichlorosilane
Butyl trichlorosilane
Chloromethyl trichlorosilane
Dodecyl trichlorosilane
Ethyl trichlorosilane
Hexyl trichlorosilane
Methyl trichlorosilane
Nonyl trichlorosilane
Octadecyl trichlorosilane
(AEGLs Volume 12, Cont'd.)
Octyl trichlorosilane
Propyl trichlorosilane
Trichloro(dichlorophenyl)silane
Trichlorophenylsilane
Trichlorosilane
Vinyl trichlorosilane
Tetrachlorosilane
Volume 13 (2013)
http://www.nap.edu/cataloq.php7record id=15852
Boron trifluoride
Bromoacetone
Chloroacetone
Hexafluoroacetone
Perchloryl fluoride
Piperidine
Trimethoxysilane and tetramethoxysilane
Trimethylbenzenes
Volume 14 (2013)
http://www.nap.edu/cataloq.php7record id=18313
Agent BZ (3-quinuclidinyl benzilate)
Ethyl phosphorodichloridate
n-Hexane
Methanesulfonyl chloride
Nitric acid
Propargyl alcohol,
Vinyl acetate
(Note: Additional chemicals and updated
information fora number of those listed above
can be found in interim reports via the National
Academies website, e.g., see links via:
http://dels.nas.edu/Report/Acute-Exposure-
Guidelines-Levels-Selected/12018. The
National Academies website can also be
searched, e.g., for "interim report of committee
on acute exposure guideline levels")
B-9
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31 October 2013
TABLE B-2 (Cont'd.)
I. (NRC, Cont'd.)
B. SMACs
Volume 1 (1994)
http://www.nap.edu/cataloq.php7record id=9062
Acetaldehyde (75-07-0)
Ammonia (7664-41-7)
Carbon monoxide (630-08-0)
Formaldehyde (50-00-0)
Freon 113(76-13-1)
Hydrogen (1333-74-0)
Methane (74-82-8)
Methanol (67-56-1)
Octamethyltrisiloxane (107-51-7)
Trimethylsilanol (1066-40-6)
Vinyl chloride (75-01-4)
Volume 2 (1996)
http://www.nap.edu/catalog.php7record id=5170
Acrolein (107-02-08)
Benzene (71-43-2)
Carbon dioxide (124389)
2-Ethoxyethanol (110-80-5)
Hydrazine (302-01-2)
Indole (120-72-9)
Mercury (7439-97-6)
Methylene chloride (75-09-2)
Methyl ethyl ketone (78-93-3)
Nitromethane (75-52-5)
2-Propanol (67-63-0)
Toluene (108-88-3)
Volume 3 (1996)
http://www.nap.edu/catalog.php7record id=5435
Bromotrifluoromethane (75-63-8)
1-Butanol(71-36-3)
tert-Butanol (75-65-0)
Diacetone alcohol (123-42-2)
Dichloroacetylene (7572-29-4)
1,2-Dichloroethane (ethylene diCI) (107-06-2)
Ethanol (64-17-5)
Ethylbenzene(100414)
Ethylene glycol (107-21-1)
Glutaraldehyde (111-308)
Trichloroethylene (79-01-6)
Xylene (95476)
Volume 4 (2000)
http://www.nap.edu/catalog.php7record id=9786
Acetone (67641)
C3 to C8 Aliphatic saturated aldehydes
- Propanal (propionaldehyde) (171426-73-6)
- Butanal (n-butyraldehyde) (171339-76-7)
(SMACs Volume 4, cont'd.))
- Pentanal (n-valeraldehyde) (110-62-3)
- Hexanal (caproaldehyde) (66-25-1)
- Heptanal (n-heptaldehyde) (111-71-7)
- Octanal (caprylaldehyde (124-13-0)
Hydrogen chloride (7647-01-1)
Isoprene (78-79-5)
Methylhydrazine (60-34-4)
Perfluoropropane/other aliphatic
perfluoroalkanes
- Perfluoropropane (PFA3) (76-19-7)
Polydimethylcyclosiloxanes
- Hexamethylcyclotrisiloxane (541-05-9)
- Octamethylcyclotetrasiloxane (556-67-2)
- Decamethylcyclopentasiloxane (541-02-6)
Dichlorofluoromethane (Freon 21) (75-43-4)
Chlorodifluoromethane (Freon 22) (75-45-6)
Triichlorofluoromethane (Freon 11) (75-69-4)
Dichlorodifluoromethane (Freon 12) (75-71-8)
4-Methyl-2-pentanone (108-10-1)
Chloroform (67-66-3)
Furan (110-00-9)
Hydrogen cyanide (74-90-8)
Volume 5 (2008)
http://books.nap.edu/catalog.php7record id=125
29
Acrolein (107-02-08) (Vol 2)
C3 to C8 Aliphatic saturated aldehydes (Vol 4)
Ammonia (7664-41-7) (Vol 1)
Benzene (71 -43-2) (Vol 2)
n-Butanol (71-3Q-3) (Vol 3)
C2-C9 Alkanes
- Ethane (C2) (78-84-0)
- Propane (C3) (74-98-6)
- Butane (C4) (106-97-8)
- Pentane(C5) (109-66-0)
- Hexane(C6) (110-54-3)
- Heptane (C7) (142-82-5)
- Octane (C8) (111-65-9)
- Nonane(C9) (111-84-2)
Carbon dioxide (124389) (Vol 2)
Carbon monoxide (630-08-0) (Vol 1)
1,2-Dichloroethane (EDC) (107-06-2) (Vol 3)
Dimethylhydrazine (57-14-7)
Ethanol (64-17-5) (Vol 3)
Formaldehyde (50-00-0) (Vol 1)
Limonene (5989-27-5)
Methanol (67-56-1) (Vol 1)
Methylene chloride (75-09-2) (Vol 2)
Propylene glycol (57-55-6)
Toluene (108-88-3) (Vol 2)
Trimethylsilanol (1066-40-6) (Vol 1)
Xylenes (Vol 1)
B-10
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31 October 2013
TABLE B-2 (Cont'd.)
I. (NRC, Cont'd.)
C. EEGLs and CEGLs
(Emergency and Continuous Exposure Guidance Levels)
Volume 1 (2007)
http://books.nap.edu/catalog.php7record id=11170
Acrolein (107-02-8)
Carbon dioxide (124-38-9)
Carbon monoxide (630-08-0)
Formaldehyde (50-00-0)
Hydrazine (302-01-2)
Methanol (67-56-1)
Monoethanolamine (141-43-5)
Nitric oxide (10102-43-9)
Nitrogen dioxide (10102-44-0)
Oxygen (7782-44-7)
Volume 2 (2008)
http://books.nap.edu/catalog.php7record id=12032
Ammonia (7664-41-7)
Benzene (71-43-2)
2,6-Di-tert-butyl-4-nitrophenol (728-40-5)
Freon 12 (difluorodichloromethane) (75-71-8))
Freon 114 (dichlorotetrafluoroethane)
(76-14-2)
Hydrogen (1333-74-0)
2190 Oil mist/turbine oil (2190 TEP)
(64742-54-7)
Ozone (7782-44-7)
Surface lead (7439-92-1)
Toluene (108-88-3)
Xylene (1330-20-7)
- m-Xylene (108-38-3)
- o-Xylene (95-47-6)
- p-Xylene (106-42-3)
Volume 3 (2009)
http://books.nap.edu/catalog.php7record id=12741
Acetaldehyde (75-07-0)
Hydrogen chloride (7647-01-0)
Hydrogen fluoride (7664-39-3)
Hydrogen sulfide (7783-06-4)
Propylene glycol dinitrate (6423-43-4)
(Additional information resources for exposure benchmarks are listed on the following page.)
B-11
-------
31 October 2013
TABLE B-2 (Cont'd.)
II. NIOSH
A. Emergency Response Safety and Health Database (includes AEGLs and OELs)
http://www.cdc.gov/niosh/ershdb/about.html (page last updated February 2012)
Includes:
Aluminum phosphide (7803-51-2)
Ammonia (7664-41-7)
Arsenic oxide (1303-28-2)
Benzene (71-43-2)
BZ (6581-06-2)
Chlorine (7782-50-5)
Hydrogen cyanide (AC) (74-90-8)
Hydrogen fluoride (7664-39-3)
Mercury (elemental) (7439-97-6)
Methyl alcohol (67-56-1)
Tear gas (2-chloroacetophenone) (532-27-4)
B. NIOSH Pocket Guide (includes PELs, RELS, and IDLHs)
http://www.cdc.gov/niosh/npg/default.html (page last reviewed January 2012)
Includes an extensive number of chemicals:
I. U.S. Department of Defense (DoD)/Army
Technical Guide 230, Environmental Health Risk Assessment and Chemical Exposure
Guidelines for Deployed Military Personnel (July 2010)
http://phc.amedd.army.mil/PHC%20Resource%20Librarv/TG230.pdf
Includes a considerable number of chemicals
IV. Ca/EPAOEHHA
Reference Exposure Levels (RELs)
http://oehha.ca.gov/air/allrels.html
Includes:
Acetaldehyde (75-07-0)
Acrolein (107-02-8)
Acrylic acid
Acrylonitrile (107-13-1)
(and many others)
B-12
-------
31 October 2013
TABLE B-3 Example Occupational Exposure Limits for Selected Pollutants of Interest3
Pollutant
Arsenic (As,
inorganic, including
trivalent)
Benzene
Carbon dioxide
Carbon monoxide
Chlorine
1,2-Dichloroethane
(ethylene dichloride)
CAS RN
7440-38-2
71-43-2
124-38-9
630-08-0
7782-50-5
107-06-2
Concentration
Limit (ijg/m3
or as listed)
10
2
10
1.1
3,200
320
1,600
8,000
55
9,000,000
54,000,000
14,000,000
2,200,000
55,000
40,000
29,000
140,000
7,000
3,000
1,500
2,900
290
4
200,000
40,000
4,000
Benchmark Type
PEL
REL-C
TLV
MEG air, 1 yr
PEL
REL
TLV
TLV-STEL
MEG air, 1 yr
PEL
REL
TLV
TLV-STEL
CEGL, 90 d
MEG air, 1 yr
PEL
REL
TLV
CEGL, 90 d
MEG air, 1 yr
PEL-C
REL-C
TLV
TLV-STEL
MEG air, 14 d
MEG air, 1 yr
PEL
TLV
REL
Notes
Ceiling value; NIOSH also indicates a half-mask air-purifying respirator with high-
efficiency filter, or a half-mask supplied air respirator, is needed at 100 ug/m3
0.003 ppm
1 ppm
0.1 ppm (conversion 3.19 mg/m3); identified as one of the RELs based primarily on
technical factors (feasibility, detection limit)
0.5 ppm (half the PEL, 5 times the REL)
2.5 ppm
5,000 ppm (0.5%); note EPAOPP (1991) indicated chronic intermittent exposures to
1.08% or 1,000 ppm for brewery workers, compared to the natural concentration of
0.03% or 300 ppm
30,000 ppm
50 ppm
35 ppm (given as 40 mg/m3, with a conversion factor of 1.15 mg/m3)
25 ppm (half the PEL and 5/7 the REL)
1 ppm (3 mg/m3)
0.5 ppm (half the PEL ceiling)
1 ppm
50 ppm
10 ppm
1 ppm (4 mg/m3)
B-13
-------
31 October 2013
TABLE B-3 Example Occupational Exposure Limits for Selected Pollutants of Interest3
Pollutant
1,2-Dichloroethane
(ethylene dichloride)
cont'd.
Diesel engine
exhaust (for some
as carbon black, with
PAHs; see notes)
Ethylbenzene
Ethylene oxide
(Oxirane)
Formaldehyde
n-Hexane
CAS RN
1333-86-4
100-41-4
75-21-8
50-00-0
110-54-3
Concentration
Limit (ijg/m3
or as listed)
8,000
180
50
3,500
100
3.4
440,000
87,000
545,000
100,000
2,100
500,000
1,800
180
9,000
48
2,000(1,600)
440
900
20
360
2,500
140,000
25
1,800,000
180,000
1,400
Benchmark Type
REL-STEL
MEG air, 1 yr
TLV
PEL
TLV
REL
MEG air, 1 yr
PEL, REL
TLV
REL-STEL
TLV-STEL-C
MEG air, 1 yr
MEG air, 1 hr
PEL
REL
PEL-STEL
MEG air, 1 yr
TLV
MEG air, 14 d
PEL
REL
TLV-C
PEL-STEL
CEGL, 90 d
MEG air, 1 yr
PEL
REL
TLV
MEG air, 1 yr
Notes
2 ppm
As inhalable fraction and vapor
For carbon black
C black in the presence of PAHs
For diesel engine emissions
100 ppm (435 mg/m3), rounded to two significant figures here
20 ppm
125 ppm
For aerosol
Minimal effect (negligible value)
1 ppm; conversion given as 1.8 mg/m3
0.1 ppm; REL is identified as one of those based primarily on technical factors
(feasibility, detection limit)
5 ppm
1 ppm, 10 times the 0.1 ppm PEL, REL (at 1.55 mg/m3 per ppm)
0.75 ppm
0.016 ppm; one of those based primarily on technical factors (feasibility, detection
limit)
Short-term exposure limit ceiling is 0.3 ppm (40% of the PEL)
2 ppm (2.46 mg/m3), rounded
500 ppm (10 times the REL and TLV)
50 ppm (180 mg/m3)
B-14
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31 October 2013
TABLE B-3 Example Occupational Exposure Limits for Selected Pollutants of Interest3
Pollutant
Hydrogen chloride
(HCI)
Hydrogen sulfide
Mercuric chloride, as
mercury (inorganic)
(for some, CAS is
7439-97-6, as metal)
Nitrogen dioxide
(N02)
Nitrogen (nitric) oxide
(NO)
Ozone
Polychlorinated
biphenyls (PCBs),
as Aroclors 1242 and
1254)
CAS RN
7674-01-0
7783-06-4
7487-94-7
10102-44-0
10102-43-9
10028-15-6
53469-21-9
Concentration
Limit (ijg/m3
or as listed)
7,000
3,000
14
400
1,500
30,000
15,000
1,400
7,000
14
100
25
9,000
1,800
380
860
940
30,000
3,700
3,700
200
100
39
1,000
1
340
Benchmark Type
PEL-C, REL-C
TLV-STEL-C
MEG air, 1 yr
TLV
MEG air, 14 d
PEL-C
REL-C
TLV
TLV-STEL
MEG air, 1 yr
PEL-C
REL-C
TLV
PEL-C
REL-STEL
TLV
CEGL, 90 d
MEG air, 1 yr
PEL, REL, TLV
CEGL, 90 d
MEG air, 1 yr
PEL
REL-C
TLV
MEG air, 1 yr
PEL
TLV
REL
MEG air, 1 yr
Notes
Ceiling values, 5 ppm (7 mg/m3); conversion given as 1.49 mg/m3
2 ppm; short-term exposure limit ceiling
As F, given as 0.5 ppm (1/6 the PEL and REL of 3 ppm)
20 ppm is a ceiling value, as a 10-min maximum peak
10 ppm is a ceiling value, as a 10-min maximum peak
listed as (10 ppm), which is the same as the REL-C1 ppm
5 ppm
Ceilings are 0.1 mg/m3 (the REL-TWA for mercury vapor is half that, at 0.05 mg/m3)
For elemental and inorganic forms, as mercury
5 ppm; ceiling
1 ppm; short-term exposure limit
0.2 ppm
25 ppm
Same value for 14 d
0.1 ppm (note (the TLV below is half the PEL, also as a TWA)
Ceiling value
Listed is the lowest value, 0.05 ppm for heavy work; TLV is 0.2 ppm for 2 hr or less
REL also applies to other PCBs, including Aroclor 1254 (below)
Same as the PEL, for chlorodiphenyl, 42% chlorine (skin)
B-15
-------
31 October 2013
TABLE B-3 Example Occupational Exposure Limits for Selected Pollutants of Interest3
Pollutant
PCBs (cont'd.)
Sulfur dioxide
Sulfuric acid
Toluene
Xylenes
CAS RN
11097-69-1
53469-21-9
7446-09-5
7664-93-9
108-88-3
1330-20-7
Concentration
Limit (ijg/m3
or as listed)
500
1
170
340
13,000
5,000
650
520
1,000
3,000
200
49
750,000
380,000
75,000
560,000
3,400
440,000
651,000
270
Benchmark Type
PEL
TLV
REL
MEG air, 1 yr
MEG air, 1 yr
PEL
REL
TLV-STEL
MEG air, 14 d
PEL, REL
PEL-STEL
TLV
MEG air, 1 yr
PEL
REL
TLV
REL-STEL
MEG air, 1 yr
PEL, REL
TLV
TLV-STEL
MEG air, 1 yr
Notes
Same as the PEL, for chlorodiphenyl, 54% chlorine
REL also applies to other PCBs, including Aroclor 1254
Aroclor 1254
Aroclor 1242
5 ppm
2 ppm
0.25 ppm
For sulfuric acid as the thoracic fraction
Same value for 14 d
200 ppm (750 mg/m3)
100 ppm (375 mg/m3, rounded here)
20 ppm
150 ppm
PEL and REL are 100 ppm (435 mg/m3, rounded here) for m-, o-, p-xylene
(CAS 108-38-3, 95-47-6, and 106-42-3, respectively)
100 ppm (for combined and the 3 isomers)
150 ppm
For mixed o-, m-, and p-xylenes, and individually
a This table was developed at an early stage of the evaluation process to provide an example compilation for selected pollutants from
the candidate set. See Table 3-2 for additional information regarding these benchmarks, including the application context.
C = ceiling; CAS RN = Chemical Abstracts Service Registry Number; CEGL = continuous exposure guidance level; MEG = military
exposure guideline; NIOSH = National Institute for Occupational Safety and Health; PEL = permissible exposure limit;
REL = recommended exposure limit; STEL = short-term exposure limit; TLV = threshold limit value.
B-16
-------
31 October 2013
ro
I
C,
_g
+•«
5
+•«
c,
0)
u
c
o
O
o
'c
0)
(2
1000000
100000
10000
1000
100 -
10 -
Emergency Response Values
100,000
Occupational Values
0.1 -
0.01 -
0.001 -
0.0001
Value Type
FIGURE B-1 Example Display of Selected Exposure Benchmark Values for Arsenic in Air
B-17
-------
31 October 2013
10000 -
1000 •
100 -
,..
0.1 -
0.01 -
0.001
n nom
10 nin I SOmin 1h 4h IShj 8h If
Publ c: Acute
(Emergency Response)
.— .
—
-
-
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-
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Public:
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n
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n
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:
-
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o
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FIGURE B-2 Example Display of Benchmarks for Exposures to Benzene in Air
B-18
-------
31 October 2013
SELECTED REFERENCES
ACGIH (American Conference of Governmental Industrial Hygienists) (2011). 2011 TLVs and
BEIs: Based on the Documentation of the Threshold Limit Values for Chemical Substances and
Physical Agents & Biological Exposure Indices, Cincinnati, OH.
AIHA (American Industrial Hygiene Association) (2011). ERPG/WEEL Handbook.
ATSDR (2012). Minimal Risk Levels (MRLs) List, http://www.atsdr.cdc.gov/mrls/index.asp (page
last reviewed Mar. 6, 2013; last accessed May 22, 2013).
CalEPA ARE (California Environmental Protection Agency, Air Resources Board) (2009).
California Ambient Air Quality Standards (CAAQS).
http://www.arb.ca.gov/research/aaqs/caaqs/caaqs.htm (page last reviewed Nov. 24, 2009; last
accessed May 22, 2013).
CalEPA OEHHA (Office of Environmental Health Hazard Assessment) (2012). Acute, 8-hour
and Chronic Reference Exposure Levels (chRELs) (as of February 2012).
U.S. DOE (2012). Protective Action Criteria (PAC): Chemicals with AEGLs, ERPGs, and
TEELS. 27th Revision. http://www.atlintl.com/DOE/teels/teel.html (page last accessed May 22,
2013).
NIOSH (National Institute for Occupational Safety and Health) (2010). NIOSH Pocket Guide to
Chemical Hazards, http://www.cdc.qov/niosh/npq/default.html; (page last reviewed
January 2012; last accessed May 22, 2013).
NRC (National Research Council) (1994). Spacecraft Maximum Allowable Concentrations for
Selected Airborne Contaminants, Volume 1. http://www.nap.edu/cataloq.php7record id=9062
NRC (1984). Emergency and Continuous Exposure Limits for Selected Airborne Contaminants
Volume 1. http://books.nap.edu/cataloq.php7record id=689.
OSHA (Occupational Safety and Health Administration) (2013). Table Z-1 Limits for Air
Contaminants. 71 FR 12819.
http://www.osha.gov/pls/oshaweb/owadisp.show document?p table=standards&p id=9992
(page last accessed May 22, 2013).
USAPHC (U.S. Army Public Health Command) (2010). Environmental Health Risk Assessment
and Chemical Exposure Guidelines for Deployed Military Personnel.
http://phc.amedd.armv.mil/PHC%20Resource%20Library/TG230.pdf
U.S. EPA (U.S. Environmental Protection Agency) (2013). Integrated Risk Information System
http://cfpub.epa.qov/ncea/iris/index.cfm?fuseaction=iris.showSubstanceList (page last updated
and accessed May 22, 2013).
U.S. EPA (2012). National Ambient Air Quality Standards (NAAQS).
http://www.epa.gov/air/criteria.html (last updated Dec. 14, 2012; last accessed May 22, 2013).
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31 October 2013
APPENDIX C:
CONTEXT FOR CHEMICAL FATE IN AIR
C-1
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31 October 2013
C-2
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31 October 2013
APPENDIX C:
CONTEXT FOR CHEMICAL FATE IN AIR
Chemical fate in air plays an important role in pollutant detection, for both discrete and
continuous releases. Understanding this role can help guide research activities toward practical
mobile sensors for air pollutants, including integrated multi-pollutant sensor systems.
Supporting context is provided in this appendix.
Many chemicals (including a number of organic compounds) transform to other chemicals when
released to air, in some cases within minutes to hours. As an indicator of how long a parent
chemical would be expected to remain following a release, persistence is represented by its
atmospheric half-life (the time it takes an initial amount to decrease by half).
Persistence can be affected by many factors, including physical state (e.g., gas, liquid, or
particulate), chemical and physical reaction mechanisms, and setting-specific physical-chemical
conditions such as temperature, relative humidity, sunlight, and the presence of other
substances, including hydroxyl radicals (per their oxidizing potential). In many cases, different
persistence intervals have been identified for a given chemical; these reflect the range of
conditions addressed. For this reason, the example persistence intervals in this appendix are
generally presented as ranges (e.g., "seconds to hours" or "days to weeks").
In addition to persistence, ambient conditions affect specific fate processes. Common
processes include hydroxyl radical reaction and wet and dry deposition. The latter are the
processes by which species are removed from the atmosphere and deposited on the Earth's
surface. In wet deposition, chemical species are scavenged by hydrometeors, such as rain, fog,
cloudwater, snow, and sleet. Dry deposition is primarily affected by the level of atmospheric
turbulence, the chemical properties of the depositing species, and the nature of the surface itself
(Seinfeld and Pandis 1996). For wet and dry deposition, where quantitative data were not found
for this illustrative compilation, a general default of days is indicated. Depending on the
compound and local/regional conditions, this could be hours (e.g., for highly reactive VOCs and
short-chain alkanes) or weeks (for longer-chain alkanes). Dry deposition is much slower than
wet, so the latter is more important for semivolatile compounds. Basic physicochemical
properties considered in evaluating environmental fate (including volatilization from surface
water to air) are described as follows.
• Molecular weight (Ml/I/) is the sum of the weights of the atoms present in a given
compound, based on its formula. For this study, the MW (and formula) indicates the size
(and complexity) of a given compound and can also indicate other properties such as
vapor pressure (VP) and physical state at ambient temperatures, when those more
specific measures are not known. The MW is also used to convert from parts per million
(ppm) to mg/L or ug/L, or to mg/m3 or ug/m3, as indicated. For example, a value given
as ppm in air can be multiplied by the MW and divided by 24.5 to convert it to mg/m3
under standard temperature (25°C) and pressure (1 atmosphere) conditions. (Note that
gases are presented as volume per volume of air, e.g., ppm, and particles are presented
as mass per volume of air, e.g., ug/m3).
• Henry's law constant (Kn) provides a measure of how a chemical partitions between the
liquid and gas phases at equilibrium in a closed system (ratio of to water solubility to a
chemical's volatility). A chemical with a high Henry's law constant has a low tendency to
volatilize and will stay in the water phase (when units are expressed as water
C-3
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31 October 2013
concentration over air concentration; note that KH can also be expressed with the inverse
units). The KH indicates how much of a chemical may be released into the gas phase
above the water compared with the amount dissolved in the aqueous phase. Even
though this constant applies to a closed system at equilibrium, it can offer useful context
for understanding the tendency of a chemical to evaporate, particularly in an open
system, such as a water treatment basin, or in a home when the faucet is turned on.
The KH can be used to compare this tendency of a chemical to volatilize from water and
to model partitioning for static or dynamic conditions. Chemicals with a KH less than
1 x 10'5 atm-m3/mole (or greater than 100 mole/L-atm) and a MW above 200 g/mole are
considered unlikely to pose an inhalation hazard as a result of volatilization from drinking
water in a residential setting (Andelman 1990).
• Vapor pressure (VP) is the pressure exerted by a chemical's vapor in equilibrium with its
solid or liquid form at a given temperature. This can be used to calculate how fast the
chemical will volatilize from the water surface or to estimate a Henry's law constant for
chemicals that are not very soluble in water. The higher the VP, the greater the tendency
for the chemical to form a gas. The VP can offer useful context regarding the tendency
of a given chemical to volatilize from water to air. (Note that the KH is a more useful
value to predict partitioning from water to air.)
• Atmospheric OH rate constant is an indicator of the tropospheric lifetime of a given
chemical (ic). It is controlled by reaction with the hydroxyl (OH) radical, ic= (konfOH])'1,
where kon is the first-order rate constant for the reaction of the OH radical with the
chemical of interest, and [OH] is the concentration of the OH radical. Chemicals that
react more rapidly with the OH radical have a larger atmospheric OH rate constant and a
shorter lifetime in the troposphere. Chemicals with smaller kon values would have a
longer atmospheric residence time than those with higher values.
These parameters are illustrated in Tables C-1 through C-3. Table C-2 illustrates values for
selected physicochemical properties for the study pollutants, and Table C-3 illustrates the range
of values for other chemicals.
TABLE C-1 General Categories for Parameters that Can Influence Chemical Fate in Air3
Parameter
Octanol-water partition coefficient: Kow
Henry's constant: KH (mole/L-atm)
Vapor pressure: VP (mmHg)
Solubility product: Ksp
Water solubility: Sw (ppm)
Vapor pressure: VP (mmHg)
Melting point : MP (°C)
Boiling point: BP (°C)
General Categories and Examples
Low
<100
<0.01 to 1
<0.001
<1 x 10-50
<10
<0.001
<0
<50
Medium
100 to 10,000
1 to 1000
0.001 to 1
1 x 10-10to1 x 10-50
10 to 1000
0.001 to 1
Oto 100
50 to 300
High
>10,000
>1000
>1
>1 x 10-10
>1000
>1
>100
>300
a Kow is the partition constant between water and octanol, which represents a generic "organic" phase; this coefficient
is commonly used for organic chemicals (i.e., those containing carbon).
C-4
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31 October 2013
KH is the distribution constant for a chemical between air and water phases, based on the partial pressure of the
gas above the solution to its dissolved concentration; the extent to which a given gas dissolves in solution (here,
water) is proportional to its pressure (Henry's law), and KH is the proportionality constant for this relationship.
KSp is the solubility product of inorganic compounds, which describes the equilibrium between the (excess) solid
form and dissolved (or solvated) ions; it is used to determine if a solid is readily soluble in water and is a function of
the water solubility, Sw.
VP is the pressure exerted by a vapor in equilibrium with its solid or liquid phase, typically used for a vapor in
contact with its liquid (so it would represent the vapor-phase pressure of the pure liquid).
BP and MP, the boiling and melting points, are simple physical constants.
The values shown here simply illustrate the principles outlined above; other values could also be applied (e.g.,
example, a Ksp of 10~5 could be used as a delineator for "readily soluble" for one-molar electrolyte solutions, while
formal water solubilities <0.003 mole/liter could indicate the compound is "not readily soluble").
TABLE C-2 Illustrative Parameter Values for the Study Pollutants3
Chemical
Acetaldehyde
Acrolein
Ammonia
Benzene
1,3-Butadiene
Carbon monoxide
Formaldehyde
Hydrogen sulfide
Lead
Methane
Nitrogen dioxide
Ozone
Sulfur dioxide
Kow
(unitless)
0.457
0.977
1.698
134.9
97.7
60.256
2.239
1.698
12.303
0.263
0.135
0.0063
KH
(mole/L-atm)3
15
8.197
62.5
0.182
0.0135
9.62x1 0'4
3,058
0.12
1.52x10-3
0.01
0.0113
1.235
Sw
(ppm)3
1x1Q6
2.12x105
310,000
1,880
735
23,000 @20°C
4x105@20°C
4,100@20°C
Insoluble
22
Reacts with H2O
f(P,T,pH); 1,060 at
0°CandpH3.5
1.13x105@20°C
VP
(mmHg)3
902
274
7,752
94.8
2,110
1.55x108
3,883
15,600
1.77@1000°C
4.66x105
900
41,257@-12°C
3,000
MP
(°C)
-123
-87.7
-77.7
5.5
-108.97
-205
-92
-85.49
327.5
-182.4
-9.3
-193
-72.7
BP
(°C)
20.1
52.6
-33.35
80.1
-4.5
-191.5
-19.1
-60.33
1,740
-161.5
21.15
-111.9
-10
3 These values are at 25°C (77°F) unless otherwise noted. Shading indicates the entry is not applicable/not
available.
Particulate matter is not included in this table because it consists of a many species from both natural and
anthropogenic sources, thus has no fixed physical or chemical properties (i.e., PM composition and associated
properties vary by location and time).
Sources: ATSDR (2013); Kroschwitz (2004); NIH (2013); NOAA (2013); Seinfeld and Pandis (1996); SRC Inc.
(2013).
C-5
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31 October 2013
TABLE C-3 Illustrative Parameter Values for Other Example Chemicals3
Chemical
Benzo(a)pyrene
DiOXin (2,3,7,8-TCDD)
Lead chloride fpfccy
Mercury (Hg)
Mercury sulfide (HgS)
PCBs
Pentachlorophenol
Phenol
Toluene
Trichloroethylene
Kow
(unitless)
1,300,000
6,300,000
4.2
12,600,000
132,000
29
540
260
KH
(mole/[L-atm])
2,200
20
0.12
2.4
40,800
3,000
0.15
0.1
KSp
(unitless)
1 .6 x 1 0-5
1.6x 10-52
Sw
(ppm)
0.001
0.0002
3,300
0.06
2x 10-21
0.7
14
83,000
526
1,280
VP
(mmHg)
5x 10-9
1 .5 x 1 0-9
0.002
0.0005
0.0001
0.35
28
69
MP
(°C)
176.5
305
-39
174
40.9
-95
-84.7
BP
(°C)
311
357
309
182
111
87.2
Shading indicates the entry is not applicable/not available. Values are
specific information should be used to determine the appropriate value
taken from various data sources; setting-
fora given assessment.
Three additional tables are included to illustrate how physicochemical properties and other
technical information can be used to guide monitoring and detection efforts to address pollutant
releases to air. Table C-4 highlights key fate processes and chemical products that could form
in air following a given release. This table and the two that follow illustrate fate context for
example chemicals beyond the initial study set. The initial entry for each parent chemical
indicates the interval during which the initial fate process is expected, such as hydrolysis or
hydroxyl radical oxidation. Subsequent intervals capture further fate processes as indicated.
When the half-life information shown represents only a removal process (such as wet or dry
deposition from air) rather than a change from the parent, that process is indicated without a
fate product.
When no transformation is identified for a given chemical, removal is generally assumed to
occur by deposition with a half-life of days to weeks; the actual time frame would depend on the
physicochemical characteristics of the compounds and the particles onto which they sorb
(including size), as well as location-specific meteorology and other factors. In many cases, the
nature of the reaction is known but the specific fate product is not (common for many hydroxyl
radical reactions), so fate products are not always available. Finally, for some chemicals, the
hydrolysis half-life is similar to that for persistence in air. Thus in certain cases, hydrolysis
products are included to indicate fate products that could be formed in moist (humid) air.
An overview of the fate and persistence context for these example chemicals in air is also
illustrated in Table C-5, without the time component. This information can be combined with
qualitative health information to provide context for detection levels (e.g., for mobile sensors)
considering different methods and time frames, as illustrated in Table C-6. (The relative toxicity
indicated in the right-most column of this table is based on EPA chronic toxicity values.)
C-6
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31 October 2013
TABLE C-4 Highlights of Persistence and Fate per Time Frame for Example Chemicals3
Contaminant
Arsenic (inorganic)
Benzene
Chlorine
1,2-Dichloroethane
Diesel engine exhaust
Ethylbenzene
Ethylene oxide (Oxirane)
Formaldehyde
Hydrogen chloride (HCI)
Hydrogen sulfide (hhS)
Mercuric chloride
Nitric acid
Nitrogen oxides, as NO2
Polychlorinated biphenyls
(PCBs)
Sulfur dioxide
Sulfuric acid
Toluene
Xylenes
Persistence (Half-Life) and Key Fate/Removal Processes
Seconds-Hours
Photooxidation:
phenol, nitrophenols,
nitrobenzene,
formic acid,
peroxyacetyl nitrate
Atomic chlorine, hydrogen
chloride, chlorinated
hydrocarbons; and in moist
air: hydrochloric and
hypochlorous acids
Days-Weeks
Deposition
(arsenate, arsenite)
Hydroxyl radical
oxidation;
deposition
Deposition
Deposition
Weeks-Months
Hydroxyl radical
oxidation
Months-Years
Arsenic (inorganic)
Minutes to extended periods (e.g., years), depending on specific compound
Oxidation
Formic acid, hydrogen gas,
carbon monoxide and
dioxide, H radical,
HCO radical
In moist air: hydrogen and
nitrate ions, NCband NO
Toluene-O2 complex,
ozone, nitrotoluene,
peroxyacetylnitrate,
peroxybenzoylnitrate,
cresol, benzaldehyde,
simple hydrocarbons
Glyoxal, methylglyoxal,
other hydroxyl radical
oxidation products
Oxidation and
deposition
In moist air:
ethylene glycol
Deposition
Deposition
Sulfur dioxide,
sulfates; deposition
Deposition
Deposition
Hydroxyl radical
oxidation,
deposition
Sulfurous acid,
sulfur trioxide,
sulfuric acid, and
associated salts
Deposition
Same as first entry
(at left), deposition
Deposition
Hydroxyl radical
oxidation
Nitrous and nitric
acids, nitrite and
nitrate salts
Hydroxyl radical
oxidation,
deposition
Same as first entry
Multiple
compounds
Sulfuric acid,
sulfate
C-7
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TABLE C-5 Summary of Key Fate Products and Processes for Example Chemicals3
31 October 2013
Chemical
Arsenic (including trivalent)
Benzene
Carbon monoxide
Chlorine
1 ,2-Dichloroethane (ethylene dichloride)
Ethylbenzene
Ethylene oxide (Oxirane)
Formaldehyde
n-Hexane
Fate Products
Arsenate, arsenite
Phenol, nitrophenols, nitrobenzene, formic
acid, and peroxyacetyl nitrate
Carbon dioxide
(note CO also accelerates the rate of ozone
formation and the oxidation of nitric oxide to
nitrogen dioxide)
Hydrochloric acid
Hypochlorous acid
Atomic chlorine
Hydrogen chloride
Chlorinated hydrocarbons
Formyl and monochloroacetyl chloride
Hydrogen chloride
Carbon monoxide
Carbon dioxide
Ethylphenols, benzaldehyde, acetophenone,
and m- and p-nitroethylbenzene
Methane, ethane, water, carbon dioxide,
simple aldehydes
1 ,2-Ethanediol (ethylene glycol)
Hydrogen (gas)
Carbon monoxide (CO)
Hydrogen radical (H-)
Formyl radical (HCO-)
Carbon dioxide (CO2 from CO)
Formic acid
Peroxyl radical
Aldehydes, ketones, nitrates
Fate Processes
Deposition
Hydroxyl radical oxidation,
reaction with ozone and nitrate radicals
Deposition (including wet)
Hydroxyl radical oxidation
Hydrolysis
(in moist air)
Photolysis
Reactions with atomic chlorine
Hydroxyl radical oxidation
Reaction with hydroxyl radicals and nitrogen
radicals; deposition, notably wet/precipitation
Hydroxyl radical oxidation
Hydrolysis (in moist air)
Photolysis
Oxidation
Hydroxyl radical, nitrate oxidation
Hydroxyl radical reaction
(also reaction with nitrate radicals)
C-8
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31 October 2013
Chemical
Hydrogen chloride
Hydrogen sulfide (H2S)
Mercuric chloride
Nitrogen oxides
Ozone
Polychlorinated biphenyls (PCBs)
Sulfate (also see sulfur oxides, sulfuric acid)
Sulfur oxides, sulfur dioxide
(also see above and next entry)
Sulfuric acid/hydrogen sulfate
(also see sulfate, sulfur oxides)
Toluene
Xylenes
Fate Products
Hydrochloric acid
Sulfur dioxide
Sulfates
Elemental mercury
Nitric oxide
Nitrogen dioxide (NO2)
Oxygen
2-hydroxybiphenyl intermediate
Chlorinated benzoic acid
Various sulfate ions
Sulfur trioxide (combines with water to form
sulfuric acid), sulfurous acid
Sulfuric acid, sulfate
Hydrogen ion, bisulfate, sulfate ions
Cresol, benzaldehyde
Ring cleavage to simple hydrocarbons
Nitrotoluene, benzyl nitrate, ozone,
peroxyacetylnitrate, and
peroxybenzoylnitrate
Glyoxal (C2H2O2) and methylglyoxal
Carbon dioxide and water (ultimately)
Fate Processes
Incorporated in cloud, rain, and fog water
Deposition (wet and dry)
Hydroxyl radical oxidation
Exchange reaction
Reduction (in moist air), deposition
Deposition, photooxidation, natural cycling
Natural decay, chemical instability, oxidation with
organic materials, reaction with bacteria
Hydroxyl radical oxidation
Deposition
Deposition (wet, dry)
Deposition (wet, dry)
Photooxidation, e.g., in presence of hydrocarbons
Catalytic oxidation in presence of PM containing
iron or manganese compounds
Reaction with ammonia
Dissociation
Deposition
Hydroxyl radical reaction
Reaction with peroxy radicals (alkyl, aryl groups),
ozone, and atomic oxygen
Deposition
Photolysis/reaction with NOx in air
Photooxidation, hydroxyl radical reaction
Successive transformation products are indicated by indenting. The identities of specific fate products are often not available (e.g., disappearance is noted,
rather than new chemical produced).
C-9
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31 October 2013
TABLE C-6 Combined Persistence, Fate, and Toxicity Indicators for Example Chemicals3
Chemical
Arsenic, inorganic
Benzene
Carbon monoxide
Chlorine
1,2-Dichloroethane
Diesel engine exhaust
Ethylbenzene
Ethylene oxide (Oxirane)
Formaldehyde
Gasoline (CAS is for vapors)
n-Hexane
Hydrogen chloride
Hydrogen sulfide
Mercuric chloride
Methanol
Nitrogen dioxide
Ozone
Polychlorinated biphenyls
Sulfate
Sulfur dioxide
Sulfuric acid/hydrogen sulfate
Toluene
Xylenes
CASRN
7440-38-2
71-43-2
630-08-0
7782-50-5
107-06-2
Multiple
100-41-4
75-21-8
50-00-0
8006-61-9
100-54-3
7647-01-0
7783-06-4
7487-94-7
67-56-1
10102-44-0
10028-15-6
1336-36-3
14808-79-8
7446-09-5
7664-93-9
108-88-3
1330-20-7
Formula or Symbol
As
CeHe
CO
CI2
C2H4Cl2
Various
CsHio
C2H4O
CH2O
Cs to Ci2 hydrocarbons
CeHi4
HCI
H2S
HgCI2
CH4O
NO2
03
from Ci2HgCI to Ci2Cho
so4-2
SO2
H2SO4
C7H8
CsHio
Molecular
Weight
74.9
78.1
28.0
70.9
99.0
Various
106.2
44.1
30.0
(72.1 to 170.3)
86.2
36.5
34.1
271.5
32.0
46.0
48.0
186.7 to 498.7
96.1
64.1
98.1
92.1
106.2
General
Persistence
Days
Hours to days
Weeks
Sec to minutes
Weeks to months
Minutes to years
Hours to days
Weeks to months
Hours to days
Hours to months
Days
Days
Days
Days
Days to weeks
Days to weeks
Minutes
Days to months
Days
Days
Days
Hours to days/mo
Hours
Key Fate Process
Deposition
Hydroxyl radical oxidation,
nitrate and ozone radical oxidation,
photooxidation, deposition
Oxidation; also naturally cycles
Chemical reactivity
Hydroxyl radical oxidation
Multiple (deposition, oxidation)
Oxidation and deposition
Hydroxyl radical oxidation
Photolysis, hydroxyl radical oxidation,
deposition
(Depends on specific compound)
Hydroxyl radical oxidation
Deposition
Hydroxyl radical oxidation, deposition
Deposition
Hydroxyl radical oxidation, deposition
Deposition, oxidation
Conversion, oxidation
Deposition, hydroxyl radical oxidation
Deposition
Photooxidation, deposition
Dissociation, deposition
Hydroxyl radical oxidation, deposition
Free radical oxidation
Toxicity
Indicator
Very high
Moderate
Very low
High
Moderate
Moderate
Low
Moderate
Moderate
Low
Low
Moderate
High-mod
High
Very low
Mod-low
Mod-low
High-mod
Moderate
Mod-low
High-mod
Very low
Mod-low
C-10
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31 October 2013
Notes for Table C-6:
3 This table highlights fate and persistence information for selected contaminants (including fate products) for which
risk-based concentrations were identified from benchmarks relevant to chronic public exposures. The molecular
weight represents the weight of one mole in grams (for compounds, this is the sum of the atomic weights of their
components). The general formula for PCBs is Ci2Hio-xClx; the molecular weight shown here is from a
representative range for these compounds.
For persistence and fate context, see companion tables. For some chemicals, several persistence values were
found, reflecting differences in the conditions assessed by the underlying studies. In those cases, a range is given
for the general persistence indicator (e.g., days to weeks). Long-term persistence indicators for compounds that
are part of a natural biogeochemical cycle (e.g., nitrogen dioxide) are represented as years.
Qualitative toxicity indicators are included to indicate how fate, persistence, and toxicity can be integrated to help
identify priority pollutants to be considered for air quality monitoring in a given setting; the relative toxicity context is
based on EPA chronic toxicity values (see EPA 2013a, 2013b).
Additional important context for mobile sensing of air pollutants are seasonal and diurnal
patterns. These patterns could be used to help guide planning for discrete vs. continuous
sampling designed to reduce overall power consumption, by considering periods when pollutant
concentrations are anticipated to be higher or to vary substantially. General information
regarding seasonal and diurnal patterns for selected pollutants is offered below.
Gaseous air pollutants consist of primary and secondary pollutants. Primary pollutants are those
released directly from a source into the air; carbon monoxide (e.g., from vehicle exhaust) is one
example. In contrast, secondary pollutants are formed through chemical reactions of primary
and/or other secondary pollutants in the atmosphere; ozone (formed by photochemical reaction
with nitrogen oxides and VOCs) is an example. Spatial and temporal distributions of ambient
concentrations depend on various factors such as the location and emission strength of the
sources, chemical reactivity, wet and dry deposition, and meteorological conditions, such as
wind speed and mixing heights. In most cases, ambient concentration levels tend to be
proportional to the distance from the emission source. For example, emissions from onroad
traffic such as carbon monoxide or benzene are higher along the roadways, and concentrations
of sulfur dioxide and nitrogen oxides are higher around and downwind of coal-fired power
plants.
Air pollutants with low or medium chemical reactivity are ubiquitous, i.e., they can be found near
and far from their sources. As a rough guide, if emission strengths and depositions are constant
diurnally and seasonally, ambient concentrations are a function of the variation in mixing height.
Higher concentrations occur at night when a temperature inversion prevails, while lower
concentrations occur during the day when vigorous mixing occurs up to the higher elevation.
Typically, mixing height is highest in the early afternoon and lowest around sunrise.
Emissions and related ambient concentrations for most gaseous air pollutants are closely linked
to energy use by the transportation sector, which peaks around early morning, decreases as the
day proceeds, and peaks again in late afternoon. This behavior leads to the most commonly
observed pattern of high ambient concentrations in the morning and evening hours for primary
pollutants, and high concentrations during the afternoon hours for secondary pollutants, which
are photochemically produced in the atmosphere from the primary pollutants.
Ozone is commonly known as a summertime pollutant. Seasonal and diurnal patterns of ozone
are distinct compared with other air pollutants. The conditions conducive to high ozone
concentrations typically include high temperature, low wind speed, intense solar radiation, and
an absence of precipitation (NRC 1992). However, high temperature is not a necessary
C-11
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31 October 2013
condition, as evidenced by high-ozone episodes in winter.1 In most central or downtown urban
settings, ozone concentrations have been found to vary over a diurnal cycle with a minimum in
the early morning hours before dawn and a maximum in the afternoon (typically from noon to
5 p.m.), but areas downwind of those settings have experienced daily maximum concentrations
late afternoon and in the evening on occasion. At night, air pollutants are trapped in the shallow
mixing layer caused by ground-based temperature inversion. In areas near large sources of
nitrogen oxide, the nighttime minimum for ozone can be quite pronounced because of the rapid
titration reaction between ozone and nitrogen oxides. In many urban areas, the nitrogen oxide
source is strong enough to cause the complete nighttime disappearance of ozone, while
concentrations close to background levels of 20 to 40 ppb have been reported for remote areas
without nitrogen oxide emissions.
In contrast, carbon monoxide is a typical winter pollutant. Atmospheric levels are typically
highest during the winter, because vehicles burn fuel less efficiently in cold weather, and with
longer nighttime hours, a stronger inversion layer develops in the atmosphere that traps
pollutants near the ground and prevents mixing with cleaner air above. Accordingly, the
overnight buildup of carbon monoxide combined with morning rush-hour traffic emissions often
result in exceedance (violations) of the ambient air quality standard in a limited area near
intersections with heavy traffic volumes during the winter months.
Methane can be a pollutant of particular interest in certain locations, such as near concentrated
animal feeding operations or areas of natural gas development. On the national to global scale,
methane is the second most prevalent greenhouse gas among anthropogenic emissions, and
pound for pound, it is estimated to be about 21 times more potent at warming the atmosphere
than carbon dioxide. Its chemical lifetime in the atmosphere is estimated to be about 12 years.
Methane sources are primarily continental (rather than oceanic), and because most of the
world's land mass is in the northern hemisphere, the largest sources are there. Due to its
relatively long atmospheric lifetime, methane has a nearly constant mixing ratio around the
globe, with only a slight change in abundance across the intertropical convergence zone (ITCZ)
near the equator.
1 Recently, high ozone concentrations have been observed in several western rural areas during winter months,
even when temperatures are below freezing. Sublette County, Wyoming, is the area in which wintertime high
ozone levels were first identified. Daily maximum 8-hour ozone levels there have frequently exceeded the
NAAQS level of 0.075 ppm in winter, mostly during January to March. The number of Os exceedance days is
comparable to or higher than those in major U.S. metropolitan areas. These wintertime high ozone occurrences
have been found at high-elevation monitoring sites, such as in southwest Wyoming, northeast Utah, and
northwest of Colorado. Air quality modeling indicated that these high-ozone incidents during wintertime result
from several factors: high solar radiation due to high elevation enhanced by high albedo caused by snow cover;
shallow mixing height below temperature inversion; no or few clouds; stagnant or light winds; and abundant
ozone precursors (such as NOX and VOCs) from existing oil and gas development activities (Kotamarthi and
Holdridge 2007; Morris et al. 2009). In particular, snow cover plays a crucial role in ultraviolet reflection and
insulation from the ground, which reduces the surface heating that promotes the breakup of temperature
inversions. Recent observations corroborate that the level of snow cover is closely related to high-ozone
occurrences. Interestingly, ozone exceedances during the summer ozone season (lasting from spring to early
fall) are rare in this area.
C-12
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31 October 2013
SELECTED REFERENCES
ATSDR (Agency for Toxic Substances & Disease Registry) (2013). Toxicological Profiles;
http://www.atsdr.cdc.gov/toxprofiles/index.asp (last accessed April 17).
Andelman, J.B. (1990). Total Exposure to Volatile Organic Compounds in Potable Water,
Chapter 20, in: Significance and Treatment of Volatile Organic Compounds in Water
Supplies, N.M. Ram, R.F. Christman, and K.P. Cantor (Eds.), Lewis Publishers, Chelsea, Ml.
EPA (2013a). Integrated Risk Information System (IRIS); online database; National Center for
Environmental Assessment, Washington, DC; http://www.epa.gov/IRIS (page last updated May
8; last accessed May 22).
EPA (2013b). Provisional Peer-Reviewed Toxicity Values for Superfund (PPRTV), online
resource of the National Center for Environmental Assessment, Washington, DC, hosted by
Oak Ridge National Laboratory; http://hhpprtv.ornl.gov/ (page not dated; accessed May 22).
Kotamarthi, V.R., and D.J. Holdridge (2007). Process-Scale Modeling of Elevated Wintertime
Ozone in Wyoming, ANL/EVS/R-07/7, prepared by Environmental Science Division, Argonne
National Laboratory, for BP America (Dec.); http://www.ipd.anl.gov/anlpubs/2008/01/60757.pdf.
Kroschwitz, J.I. (2004). Kirk-Othmer Encyclopedia of Chemical Technology, Fifth Edition, John
Wiley & Sons, Inc., Hoboken, NJ.
Morris, R.E., et al. (2009). Simulation of Wintertime High Ozone Concentrations in
Southwestern Wyoming, presented at the 8th Annual CMAS Conference, Chapel Hill, NC (Oct.);
http://www.cmascenter.org/conference/2009/abstracts/morris simulation wintertime 2009.pdf.
NIH (National Institutes of Health) (2013). TOXNET (Toxicology Data Network), Hazardous
Substances Data Bank (HSDB); http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen7HSDB (last
modified December 10, 2011; accessed April 17).
NOAA (National Oceanic and Atmospheric Administration) (2013). Ozone, CAMEO Chemicals
Version 2.4; http://cameochemicals.noaa.gov/chemical/5102 (last accessed April 17).
NRC (National Research Council) (1992). Rethinking the Ozone Problem in Urban and Regional
Air Pollution, National Academy Press, Washington, DC.
Seinfeld, J.H., and S.N. Pandis (1996). Atmospheric Chemistry and Physics, from Air Pollution
to Climate Change, John Wiley & Sons, Inc., New York, NY.
SRC Inc. (2013). PHYSPROP; http://www.srcinc.com/what-we-do/product.aspx?id=133 (last
accessed April 17).
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APPENDIX D:
EXAMPLE CONCENTRATION SUMMARIES TO GUIDE REGIONAL AND LOCAL INPUTS
D-1
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D-2
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31 October 2013
APPENDIX D:
EXAMPLE CONCENTRATION SUMMARIES TO GUIDE REGIONAL AND LOCAL INPUTS
Summary tables are presented in Chapter 3 for example concentrations of the study pollutants
in air, to provide a practical basis for assessing reported sensor detection levels.
Complementary tables are presented in this appendix, organized to facilitate data inputs by EPA
Regional scientists and others interested in compiling selected location- or setting-specific
summaries.
Example summaries for criteria pollutants are provided in Table D-1, including EPA Region-
specific summaries for carbon monoxide, nitrogen dioxide, ozone, and particulate matter (both
PMio and PlVb.s). Example summaries for acrolein, ammonia, benzene, 1,3-butadiene, and
hydrogen sulfide are provided in Table D-2.
D-3
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31 October 2013
TABLE D-1 Illustrative Concentrations of Criteria Pollutants in Aira
Information Basis
(location, time frame)
Criteria Pollutant (concentration unit)
CO
(ppm)
EPA Regions
Region 1
Region 2
Region 3
Region 4
Region 5
Region 6
Region 7
Region 8
Region 9
Region 10
1.8
1.3
1.7
1.7
1.4
1.7
1.6
2.2
1.9
2.0
Lead
(|jg/m3)
NO2
(PPb)
15
17
12
8
13
11
8
16
16
Os
(PPb)
81
76
77
74
73
75
65
73
77
60
PMio
(|jg/m3)
35
52
42
PM2.5
(|jg/m3)
25
(9.4)
28
(11)
30
(12)
•« 26
36 (11)
41
53
41
76
63
30
(12)
22
(10)
25
(11)
28
(8.5)
33
(11)
51 31
51 (8.8)
SO2
(PPb)
Notes
For EPA Region-specific data: CO: annual average for 2009; NCb:
annual average for 2009; Os: 3-yr average for 2007-2009; PMio:
24-hour, annual average for 2009; PlVh.s: 24-hour, 3-year average
for 2007-2009 (with annual average concentrations in
parentheses)
Other examples
Ambient, annual means:
2012
2010
1.6
(0.8-2.5)
0.11
(0-0.37)
10
(3.4-18)
72
(62-81)
63
(31-90)
10
(6.8-13)
2.2
(0.44-
4.7)
From EPA reports: Air Trends and Report on Environment
CO: annual 2nd max, 8-hravg; Pb: per annual max, 3-month avg;
NO2: annual avg; Os: per annual 4th max 8-hravg; PMio: per
annual 2nd max 24-hr avg; PIvh.s: seasonally weighted annual avg;
SO2. annual avg
D-4
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31 October 2013
Information
(location, tin
Basis
le frame)
2009
2007-2009
2006-2008
2002
Regional
Mid-Atlantic
Midwest
Northeast
Southeast
Annual
24-hour concentration
8-hour concentration
1-hour concentration
Northern hemisphere
Southern hemisphere
North
America
background
<1,500 m
>1,500 m
Criteria Pollutant (concentration unit)
CO
(ppm)
1.8
(1.0-2.6)
0.12
0.04
Lead
(ug/m3)
0.09
(0.01-
0.18)
<0.05
NO2
(PPb)
14
(7-22)
03
(PPb)
75
(64-85)
29
(14-45)
41
(23-60)
29
(6-51)
29 ±8
40 ±8
PMio
(ug/m3)
51
(28-80)
25
(16-35)
26
(9-46)
PM2.5
(ug/m3)
11.5
(7.8-15)
12
(8-16)
12
(4-23)
27 10
(6-51) (2-21)
SO2
(PPb)
3.3
2.3
1.2
1.3
4
(1-8)
4
(1-10)
4
(1-10)
Notes
CO: annual avg; Pb: annual avg; NCb: annual avg;
PMio: annual mean per 24-hr avg
03'. 3-yr avg
PM2.s: 3-yr avg
Pb: from 2007 ATSDR ToxGuide
SOi. mean ambient 2003-2005
SO2. mean ambient 2003-2005
SOi. mean ambient 2003-2005
SOi. mean ambient 2003-2005
PMio: 24-hr and 1-hr data, 3 yr avg period, 2005-2007, no
seasonal weighting; PIVh.s: 24-hr, 3-yr avg, 2005-2007, no
seasonal weighting; SCb: mean, metropolitan area, with 2003-2005
averages
Os: mean, 24-hr avg, 2007-2009; PMio: 24-hr and 1-hr data, 3-yr
avg, 2005-2007, no seasonal weighting; PIVh.s: 3-yr avg, 2005-
2007, no seasonal weighting; SOz. mean, metropolitan area, with
2003-2005 avgs
Os: mean, 8-hr daily max, 2007-2009
Os: mean, 1-hr avg, 2007-2009; PMio: 1-hr data, 3-yr avg period,
2005-2007, no seasonal weighting; PIVh.s: 3-yr avg, 2005-2007, no
seasonal weighting; SO2: mean, metropolitan area, with 2003-2005
avgs
Os: March-August 2006-2008, per daily 8-hr avg, max (U.S. -only
average is indicated as about 1 to 3 ppb higher)
D-5
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31 October 2013
Information Basis
(location, time frame)
Geothermal
Forest fires
Urban background
Example city (urban)
Rural ambient
Busy traffic
Tunnel
Airport
Neighborhood adjacent
to airport
Downwind smelter
Indoor air
Schools
Single-family home
Multi-family home
Home with electric stove
Criteria Pollutant (concentration unit)
CO
(ppm)
5
Lead
(ug/m3)
0.01
0.02
0.03
0.03-
0.05
0.63-
0.88
0.11
NO2
(PPb)
6-30
40-70
1,500
10-30
15.5
10
23
9
03
(PPb)
37
(19-57)
47
(<1500m
)
60
(>1500m
)
15
(7.3-19)
PMio
(ug/m3)
26
(11-45)
PM2.5
(ug/m3)
14
(6-25)
54
(44-60)
20
(6.8-75)
SO2
(PPb)
3.9
600-
3,000
0.34-2
2.4-6.7
0.08-0.88
Notes
SOz. mean concentration in geothermal area among regions of
metropolitan Taipei (low traffic density areas)
SOz. breathing zone of forest fires
SO2. Alberta communities, 7-day avg range, 5-wk sampling period
NO2: Chicago, 3-yr mean (2008); Os: Chicago, mean, 8-hr daily
max, 2007-2009; PMio: 24-hr and 1-hr data, 3 yr avg period, 2005-
2007, no seasonal weighting; PIVh.s: 24-hr, 3-yr avg, 2005-2007,
no seasonal weighting; SOz. Chicago, range of mean, 2003-2005
Os: Great Smoky Mountain NP, median;
SOz. Alberta communities, 7-day avg range, 5-wk sampling period
Pb: 2001-2010 annual avg; NCb: Los Angeles freeway,
nonconsecutive 4-day avg; Os: Toronto roadway, 1-wk, August
2004, mean [min-max]; PM2s: freeway 1-71 OS, mean, 4 days in
spring, 2003 [avg interquartile range]
NO2: NOx, San Francisco tunnel exit, 1999
Pb: 10-day avg, Canadian airport, 2000 (0.30 max)
Pb: Santa Monica neighborhood, 155 m downwind
Pb: 2001 , mean from EPA Region 5 survey; NO2: 24-hr avg;
PM2.s: two consecutive 3- to 4-day measurements collected
biannually from urban homes (lower socioeconomic status) [data
range]
NO2: Australia, 12 wks, 6-hr/day, winter, electric orflued heater
NO2: 2-wk mean
NO2: 2-wk mean
NO2: 2-wk mean
D-6
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31 October 2013
Information Basis
(location, time frame)
Criteria Pollutant (concentration unit)
CO
(ppm)
Lead
(|jg/m3)
NO2
(PPb)
03
(PPb)
PMio
(|jg/m3)
PM2.5
(|jg/m3)
SO2
(PPb)
Notes
Indoor air, residential
(by heating source)
Kerosene heater
Gas space heater
Fireplace
Wood stove
Without heaters
above
Home without gas stove
Home with proper gas
stove
Home with improper
stove
0.5-5
5-15
>30
17.7
(3.3-84)
54.8
(13-147)
9.3
(2.8-80)
11.2
(2.2-52)
13.5
(2.7-41)
26
6.4
(0.0-44)
0.9
(0.0-9.1)
0.4
(0.0-7.2)
0.3
(0.0-16)
0.2
(0.0-1.3)
NO2, SO2. median, one heating season (October-April) between
1994-1996, w/out heaters 1, 2-wk sampling period, w/ heaters
3-6, 2-wk sampling periods
CO: gas stove properly adjusted; NO2: 2-wk mean, does not
indicate if stove was properly adjusted
Gas stove not properly adjusted
Standards comparison
National Ambient Air
Quality Standard
measurement averaging
time
9
8hr
35
1 hr
0.15
rolling 3
mo
53
ann
mean
100
1 hr
0.075
8hr
150 15
24 hr
ann
mean
3-yr
35
24
hr
75
1 hr
3 This table was prepared to support regional and community inputs, e.g., to facilitate data compilation by EPA Regional staff and others (via entries provided in the
upper portion of the table, with illustrative context for further comparisons provided in the rest of the table). CO = carbon monoxide; NO2 = nitrogen dioxide;
03 = ozone; PMio = particulate matter <10-micron in diameter; PlVh.s = PM <2.5-micron in diameter; SO2 = sulfur dioxide.
The examples concentrations shown here represent U.S. values unless otherwise indicated; most ambient annual averages from the EPA Air Trends report are
rounded to two significant figures; values in parentheses represent the 90th percentile range unless otherwise stated.
National Ambient Air Quality Standards are provided at the end of this table for comparison with the example concentrations shown for these criteria pollutants.
The standards listed here reflect primary standards established for human health protection (averaging times for the measurements are given in parentheses
beneath the concentration value); in some cases the primary standard also applies as the secondary standard - i.e., the NAAQS for lead, ozone, PM, and the
D-7
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31 October 2013
annual mean for nitrogen dioxide are joint primary-secondary standards. Not shown here is the separate secondary standard for sulfur dioxide (500 ppb);
secondary standards are established to protect public welfare, including protecting against decreased visibility and damage to animals, crops, vegetation and
buildings. Selected information sources are indicated below and in the companion Table D-2 (which presents example concentrations in air for selected
pollutants beyond the six criteria pollutants); also see Table 3-5.
Carbon monoxide (CO)
ATSDR (2009). ToxGuide (http://www.atsdr.cdc.qov/toxquides/toxquide-201.pdf).
ATSDR (2009). Toxicological Profile (Draft) (http://www.atsdr.cdc.gov/ToxProfiles/tp201-c6.pdf).
EPA (2009). Report on the Environment, Ambient Concentrations of Carbon Monoxide
(http://cfpub.epa. qov/eroe/index.cfm?fuseaction=detail.viewlnd&lv=list.listbyalpha&r=231329&subtop=341).
EPA (2000). Air Quality Criteria (http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=18163).
Lead (Pb)
EPA (2012). Integrated Science Assessment: Lead (Draft) (http://cfpub.epa.gov/ncea/isa/recordisplav.cfm?deid=235331).
Levin, R., M.J. Brown, M.E. Kashtock, D.E. Jacobs, E.A. Whelan, et al. (2008). Lead Exposures in U.S. Children, 2008: Implications for Prevention. Environ
Health Perspect 116(10)1285-1293 (http://ehp03.niehs.nih.qov/article/info%3Adoi%2F10.1289%2Fehp.11241).
Also: Choel et al. (2006); Sabin et al. (2006).
Nitrogen dioxide (NCfe)
EPA (2008). Integrated Science Assessment: Oxides of Nitrogen (http://www.epa.gov/ncea/isa/).
EPA (2009). Report on the Environment, Ambient Concentrations of Nitrogen Dioxide
(http://cfpub.epa.qov/eroe/index.cfm?fuseaction=detail.viewlnd&ch=46&subtop=341&lv=list.listByChapter&r=231330)
Triche, E., K. Belanger, etal. (2005). Indoor Heating Sources and Respiratory Symptoms in Nonsmoking Women. Epidemiology. 16(3):377-384.
Also: Westerdahl et al. (2005); Pilotto et al. (2004); Belanger et al. (2006); Strien et al. (2004).
Ozone (Os)
EPA (2012). Integrated Science Assessment: Ozone (Draft) (http://www.epa.qov/ncea/isa/ozone.htm).
Also: Beckerman et al. (2008); Zhang et al. (2011).
Particulate matter (PM)
EPA (2009). Report on the Environment, Ambient Concentrations of Particulate Matter
(http://cfpub.epa.qov/eroe/index.cfm?fuseaction=detail.viewlnd&ch=46&subtop=341&lv=list.listByChapter&r=231331).
EPA (2009). Integrated Science Assessment: Particulate Matter (http://cfpub.epa.qov/ncea/isa/recordisplav.cfm?deid=216546).
Baxter, L. K. (2007). Predicting Residential Indoor Concentrations of Nitrogen Dioxide, Fine Particulate Matter, and Elemental Carbon Using Questionnaire and
Geographic Information System Based Data. Atmospheric Environment, 41(31):6561-6571
(http://www.sciencedirect.com/science/article/pii/S1352231007003718).
Fruin, S. (2008). Measurements and Predictors of On-Road Ultrafine Particle Concentrations and Associated Pollutants in Los Angeles. Atmospheric
Environment, 42(2):207-219 (http://www.sciencedirect.com/science/article/pii/S135223100700859X).
Sulfur dioxide (SO2)
EPA (2008). Integrated Science Assessment: Sulfur Oxides - Health Criteria (http://www.epa.gov/ncea/isa/soxnox.htm).
D-8
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31 October 2013
Kindzierski and Sembaluk (2001). In: Review of the Health Risks Associated with Nitrogen Dioxide and Sulfur Dioxide in Indoor Air, Report to Health Canada
(https://circle.ubc.ca/bitstream/handle/2429/938/IAQNO2SO2full.pdf?sequence=8).
Triche, E., Belanger, K., et al. (2005). Indoor Heating Sources and Respiratory Symptoms in Nonsmoking Women. Epidemiology. 16(3):377-384.
D-9
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31 October 2013
TABLE D-2 Illustrative Concentrations of Five Hazardous Air Pollutants (ppb)a
Concentration Context
Acrolein
Ammonia
Benzene
1,3-
Butadiene
Hydrogen
sulfide
Notes
Area of interest
EPA Region
Community-based area
Example Concentrations
Ambient/outdoor air
Urban/metropolitan areas
Ambient, cities & suburbs
Homes nearCAFOs
Geothermal area
Indoor residential air
Near landfill
Near industrial facilities
Global average
0.5-3.2
<0. 02-12
0.3-6
0.85
(0.36-1.4)
0.58
0.1
0.04-1
3.1
0.11-0.33
<1
0.4-2.4
8.4
400
1-14
>90
Benzene: ug/m3, annual mean, 2009 (10th-90th percentile range);
1.3-Butadiene: 2003 annual avg, Texas; excludes monitors downwind of
point sources
Hydrogen sulfide: 2006, time-weighted avg (8.42 ppb), dominated by
swine containment areas
Hydrogen sulfide: mean concentration in geothermal area among regions
of metropolitan Taipei
Hydrogen sulfide: Maximum 30-min rolling avg range, 2002 (2-wk, 15-min
avg measurements) near Norridgewock landfill, Maine
1,3 -Butadiene: 5-yravg, 1999-2003, downwind industrial point source,
Milby Park monitor in Houston, Texas
3 This table was prepared to facilitate inputs from EPA Region staff and other interested parties to assess regional or local air quality data, with open entries at the
top and example comparison values below. Values are as ppb and reflect U.S. data except as otherwise indicated. Selected information resources include:
Hydrogen sulfide (H2S): Iowa (2006), Modeling Hydrogen Sulfide Concentrations near CAFOs (8.42ppb) (http://www.public-
health. uiowa.edu/icash/research/H2S-near-CAFOs.html): Maine (2006), Ambient Air Guidelines for Hydrogen Sulfide (near landfill).
(http://www.maine.gov/dep/waste/publications/documents/ambientairquidelines.pdf): Taipei (2010), Geothermal: sulfur-rich geothermal emissions elevate acid
aerosol levels in metropolitan regions (http://www.ncbi.nlm.nih.gov/pubmed/20561610): California (1999), Odor detection threshold, geometric mean, 8 ppb
(geometric standard deviation of 4) (Cal EPA).
1,3-Butadiene: Texas (2007), ambient and downwind of point source (http://www.ncbi.nlm.nih.gov/pubmed/17011534). (For others, also see Table 3-5.)
D-10
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D-11
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31 October 2013
APPENDIX E:
SUMMARY OF SENSORS REPRESENTED ON THE GRAPHICAL ARRAYS
E-1
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31 October 2013
E-2
-------
31 October 2013
APPENDIX E:
SUMMARY OF SENSORS REPRESENTED ON THE GRAPHICAL ARRAYS
The plots in Chapter 3 include small boxes listing example sensors that indicate where their
detection levels fall compared with exposure benchmarks and illustrative concentrations in air
for each pollutant. Supporting information for these sensors is summarized in Table E-1, as
excerpted from the much larger master table that includes additional pollutants and other
parameters (see Raymond et al. 2013).
Because gas and particle sensor technologies and techniques differ, the first level of
organization for Table E-1 is by pollutant type: first gases, then particles. Within the gases, the
criteria pollutants are listed first (carbon dioxide, nitrogen dioxide, ozone, and sulfur dioxide),
followed by the other eight gases (acetaldehyde, acrolein, ammonia, benzene, 1,3-butadiene,
formaldehyde, hydrogen sulfide, and methane). The two particulate criteria pollutants (PM and
lead) are presented at the end.
Within each pollutant, the research sensors with new or modified detection techniques are listed
first, grouped by detection principle (e.g., chemical, spectroscopy, and ionization), beginning
with the lowest reported lower detection level (LDL) within each of these categories.
Where relevant, the research sensors that reflect a sensor system architecture incorporating a
commercial sensor are listed next, again ordered by increasing reported LDL. The commercial
sensors are listed last within each pollutant section.
The identifier (ID) listed in the first column corresponds to the numbers listed in the sensor
boxes on the graphical arrays. An "np" in the ID column indicates that sensor was not plotted on
the graphical array because no detection level or range was reported. A "c" indicates a
commercial sensor used in a novel detection system; a C denotes a standard commercial
sensor included for comparison.
A subsequent targeted literature search for chemical-based sensors conducted in early 2013
produced results summarized in Table E-2. This table is organized in a similar manner as
Table E-1.
A number of acronyms and abbreviations are defined within the tables; the notation section at
the front of this report includes additional definitions.
E-3
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31 October 2013
TABLE E-1 Detectors Displayed on Graphical Arrays for Detection Levels, Benchmarks, and Ambient Levels8
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pnllntant/
miiuidnif
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Carbon Monoxide
Detection Technique: Chemistry
1
2
3
Korea Institute of
Science and
Technology
(Republic of Korea)
Shim, Y.-S., Yoon,
S.-J., et al.
(Funding: Core
Technology
Materials Research
& Development
Program of Korea
Ministry of
Intelligence and
Economy, andKIST
Research Program)
Kyushu University,
Fukuoka Japan
Department of
Energy and
Materials Sciences
Kida, T., et al.
University College
London
Varsani, P., et al.
University of
Auckland (New
Zealand)
Binions R
(Funding: EPSRC,
Wolfson Trust, Royal
Society, Dorothy
Hodgkin fellowship)
Transparent conducting
oxide electrodes for
novel metal oxide gas
sensors
[Aug. 201 1 , Sensors and
Actuators B, 160:357-
363]
http://pdn.sciencedirect.com/
science? ob=Miamilmagell
RL& cid=271353& user=17
22207&_pii=S092540051 1 00
71 55&_check=y&_origin=bro
wse& zone=rslt list item& c
overDate=2011-12-
15&»chp=dGLzVlk-
zSkzV&md5=96ca5099839c
Oba48523ae38a59c54ac/1 -
S2.0-S092540051 10071 55-
main.pdf
Application of a solid
electrolyte CO? sensor
for the analysis of
standard volatile organic
compound gases
[2010, Analytical
Chemistry, 82(8):3315-
331 9]http://pubs. acs.org/doi/
full/10.1021/ac100123u
Zeolite-modified WOi
gas sensors - Enhanced
detection ofNO2
[2011, Sensors and
Actuators B,
160:475-482]
http://pdn.sciencedirect.com/
science? ob=Miamilmagell
RL& cid=271353& user=17
22207&_pii=S092540051 1 00
7374&_check=y&_origin=bro
wse& zone=rslt list item& c
overDate=2011-12-
15&»chp=dGLzVlk-
zSkzV&md5=da1 6648f4b925
33eb1 7ae36c6fe2cOad/1 -
S2.0-S092540051 1007374-
main.pdf
Metal
oxide
semi-
conductor
(MOS)
Electro-
chemical,
potentio-
metric
MOS
Thin film with trans-
parent conducting
oxide electrodes,
tungsten trioxide
(WO3) and tin
dioxide (SnO2) thin-
film (200 nm) sen-
sors with indium-tin
oxide (ITO) and Al-
doped zinc oxide
(AZO) films
(200 nm) on glass
substrates, 20-|jm
spacing of interdigi-
tated electrodes
Solid electrolyte
CO2 sensor using
NASICON
(Na3Si2Zr2PO4; Na+
conductor) as the
solid electrolyte and
binary carbonate
(Li2CO3-BaCO2) as
the sensing layer
Solid-state metal
oxide screen printed
WO3 sensors
modified by addition
of acidic and
catalytic zeolite
layers
CO
VOCs:
ethanol,
formalde-
hyde, and
toluene),
CO, and
hydro-
carbons
NO2;
also
tested:
CO,
mixture of
NO2 and
CO,
acetone
at o ppm
Sub-ppm,
limit 200 ppb
(high
transmit-
tance,
exceeds 75%
of sensors at
visible
wavelengths)
Measures
10-500 ppm,
standard
gases;
detection
limited to
around
0.5 ppm CO2
CO tested at
30 ppm
-240
sec
<1 min
30-min
re-
sponse
with 1-hr
recov-
ery
Very
small
Not indicated
(This column also
includes brief
notes related to
the general
sensor category,
e.g., mountable.)
Computer-
recorded
electrometer
readings
(operating
temperature of the
CO2 sensor)
(Mountable)
(Mountable)
Designed to replace traditional
platinum (Pt) electrodes. Required
annealing time of 53.3 hours; ln2O3
and others doped with other ions
(e.g., aluminum-doped zinc oxide
and ITO) reduce cost. The work
functions of Pt electrodes and ITO
were 5.65 electron volts (eV) and
4.6 eV, respectively, with operating
temperatures of 200-500°C.
VOCs are decomposed to CO2 due
to catalytic combustion, and the
CO2 is then detected using a solid-
state CO2 sensor; the operating
temperature is 500°C.
Highly selective to NO2, including
in the mixture; potential for use in
electronic nose technology for
environmental monitoring. Zeolite
layers were added to increase
selectivity. The sensor operates at
350°C.
E-4
-------
31 October 2013
ID
4
5
6
np
7
np
Organization
Author (Funding)
Shinwoo Electronics
Co., Ltd.
Kim,l.
Korea University
(S. Korea)
Dong, K.Y.,Ju,B.K.
Yonsei University
(S. Korea)
Choi, H.H.
VLSI Technology
Laboratory, National
Cheng Kung
University (Taiwan)
Juang, F.-R., Chiu,
H.-Y., Fang, Y.K.,
Cho, K.-H., and
Chen, Y.C.
(Funding: National
Science Council)
University of
California San Diego
Sailor, M.J.
(Funding: NSF and
Air Force Office of
Scientific Research)
Solapur University,
Materials Research
Laboratory (India)
Bhabha Atomic
Research Centre
(India)
Pawar, S.G.,
Chougule, M.A.,
Patil, V.B.
Abbreviated Citation
Gas sensor for CO and
WHs using
polyaniline/CNTs
composite at room
temperature
[2010, IEEE,
International Conference
on Nanotechnology Joint
Symposium with Nano
Korea]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5697782&isnumber=56977
24
An n-SnOJi-diamond/p-
diamond diode with
nanotip structure for
high-temperature
CO sensing applications
[2012, IEEE]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=6183505
[Feb. 2003]
http://sailorgroup.ucsd.edu/re
search/gassensors. html
Development of
nanostructured
polyaniline-titanium
dioxide gas sensors for
ammonia recognition
[2012, Journal of Applied
Polymer Science,
125(2): 1418-1424],
http://onlinelibrary.wiley.com/
doi/1 0. 1 002/app.35468/full
Sensor Technology
Type
Polymer
film,
organic
Nano-
based,
chemical
vapor
depo-
sition
(CVD)
Nano-
based
Polymer
film,
hybrid
Description
(Name)
Polyaniline
(PANi)/single-walled
carbon nanotube
(SWCNT) film
dispersed in sodium
dodecyl sulfate and
applied over
titanium/gold (Ti/Au)
electrodes of an
interdigitated
electrode (IDE) by
photolithography
Palladium (Pd)/n-tin
oxide (SnOx)/ i-dia-
mond/p-diamond
diodes prepared by
field-enhanced hot-
wire CVD
(FEHWCVD)
system on silicon
substrate with nano-
tip structures on
SnOx sensing layer
Crystal made from
nano-structured
porous silicon (Si)
film, detects shifts in
Fabry-Perot fringes
or resonance; nano-
structured porous Si
sensor pad
Nanostructured
PANi-TiO2 thin film
deposited on glass
substrate (1 mm-
wide strips)
Pnllntant/
miiuidnif
Parameter
CO, NH3;
also
benzene
and NO2
CO;
also CO2,
ethanol,
NO
Chemical
warfare
agents,
VOCs,
CO, CO2,
O2, and
hydro-
carbons
NH3, CO
Reported
Detection
Capability
Indicates
ppm levels
(CO tested at
80 ppm, NH3
at 35 ppm)
100 ppm
ambient
(91%),
saturated
above
1,200 ppm
Re-
sponse
Time
Fast re-
sponse
and re-
covery
~2sec
41 sec
Size
5 mm x
17mm,
480 |jm
thick
Portable,
quarter-
sized
sensor
pad for
multiple
chemical
detection
Automation and
Network
Capability
(Handheld)
(Fixed/semi-
portable)
Application and Operation Notes
Demonstrates the use of PANi/
SWCNT composite-based sensor
for mixed gas detection. The
change in resistance of the sensor
determines the presence of a
single gas or mixture of gases.
This composite has a large
surface-to-volume ratio which
makes it a good candidate for new
gas sensors. The sensor operates
at room temperature.
Addresses emerging diamond-
filmed nanostructures for more
sensitivity to CO in ambient
conditions under high
temperatures. The p-i-n diamond
diode was reported to have more
sensing applications. The use of
diamond tips instead of MOS
sensors allows for lower operation
temperatures (material band gap of
5.47 eV).
Porous Si sensor pad engineered
to be green in presence of air then
turn red in presence of the target
chemical. Developer envisioned
producing thousands of miniature
sensor pads on a silicon chip the
size of a quarter to quickly identify
a wide range of chemical toxins.
No response at room temperature
for 20 ppm NH3 using TiO2
nanoparticles alone; needed to be
operated at 200° C. Pure PANi
composite showed little response,
while the PANi-TiO2 composite
was the most responsive at room
temperature. Response time
decreased as NH3 concentration
increased, but so did recovery time
(possibly due to lower desorption
rate).
E-5
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Detection Technique: Spectroscopy
8
9
np
10
np
Yanshan University
(China)
Zhang, J., Cao, S.,
Guan, L.
Tianjin University
(China)
Nan, G., Zhen-hui,
D., Xue-hong, Z.,
Van, W.
(Funding: National
High Technology
Research and
Development
Program of China
and the Natural
Science Foundation
of Tianjin)
Imperial College
London
Polak, J., and others
(including at
Universities of
Cambridge, Leeds,
Newcastle, and
Southampton)
(Funding:
Engineering and
Physical Sciences
Research Council
and Department of
Transport)
Carbon monoxide gas
sensor based on cavity
enhanced absorption
spectroscopy and
harmonic detection
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=523031 5&isnumber=52300
50[2009, IEEE]
Tunable diode laser
absorption spectroscopy
for sensing CO and CO2
of vehicle emissions
based on temperature
tuning
[2011]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=591 4237&isnumber=591 42
33
[2009]
http://www.wired.co.uk/news/
archive/2009-
06/30/pedestrians-and-cars-
turned-into- pollution-sensors
Laser
absorp-
tion
Laser
absorp-
tion
Ultraviolet
absorp-
tion
(UV abs)
Harmonic detection,
cavity enhanced
absorption
spectroscopy
(CEAS); absorption
detected at 1567 nm
for CO
Tunable diode laser
absorption
spectroscopy
(TOLAS), 1580nm
distributed feedback
(DFB) laser with
thermoelectric
cooler.
Supported by
ultrasound
technology for traffic
count
CO
CO, CO2
(vehicle
emissions)
NOX, SOX,
CO (up to
5 traffic
pollutants)
10 ppm
5-hr
cycle
detec-
tion
5-sec
interval
Pocket
size
(Handheld)
(Remote sensor)
Data transmitted
to base using
mobile phones,
locations tagged
using Google
Maps
(Wearable and
vehicle-mounted
units)
Uses enhanced absorption
spectroscopy and harmonic
detection techniques in a fiber CO
gas sensor. This system increases
the distance of interaction between
light and CO from that typically
seen in a standard gas cell.
Possible applications include
increased sensing capabilities in
mines, industrial settings, and
homes. Reported power usage is
3.63 decibel-milliwatt (dBm).
Explores a broad-spectrum
temperature tuning method by
controlling the thermoelectric
cooler in the laser diode.
Temperature ranged from -7.6°C to
30.8°C, allowing a wavelength
range of 4 nm. This method
increased response speed and
allows for the simultaneous
detection of CO and CO2.
Applicable for remote detection of
vehicle emissions.
12 static and 6 mobile sensors,
3 of which were on vehicles and
3 were carried by pedestrians.
E-6
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Novel Sensor Systems Using Commercial Sensors
c
1 1
c
12
c
13
Jackson State
University
Anjaneyulu, Y.
Jawaharlal Nehru
Technological
University
Jayakumar, /., Bindu,
\/ i-i
V.n.
Andhra Pradesh
Pollution Control
Board
Ramani, K.V.
Spectrochem
Instruments
Rao, T.H.
University of
Canterbury (New
Zealand)
Pattinson, W.
American University
of Sharjah, Sharjah,
UAE
AIM, A.R., Zual-
kernan, /., Aloul, F.
(Funding/resources:
Computer Science
and Engineering
Department,
American University
of Sharjah, UAE)
Real time remote
monitoring of air
pollutants and their
online transmission to
the web using internet
protocol
[2007, Environ Monit
Assess, 124:371-381]]
http://cardiff.academia.edU/S
AGARESWARGUMMENENI/
Papers/922566/
Cyclist exposure to traffic
pollution: microscale
variance, the impact of
route choice and
comparisons to other
modal choices in two
New Zealand cities
[2009]
http://ir.canterbury.ac.nz/han
die/10092/3687;
http://ir.canterbury.ac.nz/bitst
ream/1 0092/3687/1 /thesisju
lltextpdf
A mobile GPRS-sensors
array for air pollution
monitoring
[2010, IEEE Sensors
Journal,
10(10):1666-1671]
http://vwvw.aloul.net/Papers/f
aloul_sensors10.pdf
Electro-
chemical
various
commer-
cial
Commer-
cial
sensors
Sensor
array
(assume
commer-
cial)
(Real Time Remote
Monitoring System)
Mobile sampling
using 4 portable kits
with sampling
instruments; logging
software Specifically
used: LanganT15n
for CO; GRIMM
Dust Monitor (1.1 01,
1ft r\—7 ft ft r\n\ f—~
.1U/, 1.1 Oo) tor
PM; TSI 2007
Condensation
Particle Counter for
UFP, and Kestrel
4500 for
meteorological data
24 sensors and
10 routers in a
single-chip
microcontroller
(GPRS-sensors
array)
SO2, NO,
NO2, CO,
03, H2S,
PM10,
PM2.5,
hydro-
carbons,
mercap-
tans
CO, PM10,
PM2.5,
PM-, and
UFP
CO, NO2,
SO2
Varies per
pollutant,
within
0-200 ppm,
or 0-50 |jg/m3
(mercaptans)
CO:
Range:
0-1 00 ppm
CO:
range:
0-200 ppm
resolution:
0.05 ppm
CO:
range:
0-1 ,000 ppm
resolution:
<1.5 ppm
30 or
60 min
<25 sec
Shoebox
Ethernet network
module, uploading
to webpage
(Remote sensor)
(Portable, vehicle-
mounted units)
Online
capabilities, single
chip microcon-
troller and global
position system
(GPS) to locate
air pollution
readings on
Google Maps.
(Vehicle-mounted
unit)
This device is a remotely
monitored detection system that
can be run on a 12-volt (12V)
battery. The sensors can be set to
collect data every 30 or
60 minutes, depending on user
preference and weather conditions.
Also measures environmental
parameters (including temperature
and humidity).
This outdoor study examined
exposure to pollutants in various
settings. The focus was on cars
and bikes in urban settings. The
study assessed cyclist exposures,
with the kits in the front baskets of
the bikes.
Proposes an integrated system
composed of a single-chip
microcontroller, sensors for CO,
NO2, and SO2, a general packet
radio service (GPRS)-modem, and
a GPS module. Public mobile
networks are used to upload data
to the Pollution Server, which is
interfaced with Google maps; was
deployed on the American
University of Sharajah campus.
E-7
-------
31 October 2013
ID
c
14
c
15
c
16
Organization
Author (Funding)
Bharath University
GOT, R.K.
Universidade da
Coruria (Spain)
Lopez-Pena, F.,
Varela, G.,
Paz-Lopez, A., Duro,
R.J.
Universidad de Vigo
(Spain)
Gonzalez-Castano,
F.J.
(Funding: Ministerio
de Fomento of
Spain)
University of South
Florida and
Universidad de
Oviedo (Spain)
Mendez, D., et al.
Abbreviated Citation
An Itinerant GPRS-GPS
and sensors integration
for atmospheric
effluence screening
[2011JJTES
2(2):152-157]
http://vwvw.sensaris.com/wp-
content/uploads/old/201 1/09/
India-bus-GPRS-GPS- Air-
sensing. pdf
Public transportation
based dynamic urban
population monitoring
system
[2010, Sensors &
Transducers, 8:13-25]
http://www.sensorsportal.co
m/HTML/DIGEST/february 2
010/P_SI_100.pdf
(CO sensor link:
http://mkwheatingcontrols.co.
uk/download/GS-S-CM.pdf)
P-Sense: A participatory
sensing system for air
pollution monitoring and
control
[2011, IEEE Work in
Progress Workshop at
PerCom2011]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp— &arn umber
=5766902
(Commercial sensor link:
http://www.figarosensor.com/
products/5042pdf.pdfj
Sensor Technology
Type
Sensor
array
(assume
commer-
cial)
Commer-
cial
sensors
(by
Sontay,
GS-S-CM
CO
sensor)
Electro-
chemical
(by Figaro
and
Sensirion)
Description
(Name)
Several air pollution
sensors (Mobile-
DAQ)
(P-Sense)
Pollutant/
r LHIUldlllf
Parameter
CO, NO2,
SO2
CO2, CO,
NO2, SO2,
temp and
relative
humidity
CO2, CO,
combus-
tible
gases, air
quality;
also: temp
a no
relative
humidity
Reported
Detection
Capability
CO:
range:
0-1 ,000 ppm
resolution:
<1.5 ppm
CO: sub-
ppm (ppb)
Sontay:
operating
range: 0-100
or 1,000 ppm
resolution:
O.Sppm
CO:
range:
Oto
10,000 ppm
baseline
offset:
+/- 1 0 ppm
Re-
sponse
Time
<25 sec
<30 sec
<60 sec
Size
Mobile-
DAQ unit
roughly
book
size,
sensor is
20mm
diameter
and
server is
a laptop
Desktop
(from
photos)
Automation and
Network
Capability
Public mobile
network; fixed
internet-enabled
monitoring server
(pollution server)
is interfaced to
Google Maps to
display real-time
pollutant levels
and locations in a
metropolitan area
(24 hr/7 d) (Vehi-
cle-mounted unit)
Bluetooth GPS;
mobile sensing
network includes
distributed
software to
acquire, integrate,
and geolocate
sensor data ;
linked to Google
Maps application
programming
interface (API)
(WSN) (Vehicle-
mounted unit)
Data transmitted to
first-level integra-
tor device,
P-Sense;
integrates digital
and analog
sensors with Blue-
tooth-capable
AVR-based board;
WSN, mobile
sensing network
includes applica-
tion server and
PRO200 Sanyo
cellular phone with
GPS, Bluetooth.
Application and Operation Notes
System is composed of a single-
chip microcontroller, air pollution
sensors for CO, NO2, and SO2,
GPRS-Modem, and GPS device.
Data is transmitted to the Pollution
Server, which is interfaced with
Google Maps. Device was
mounted on a bus, which was
driven around the Bharath
University of Chennai campus.
Public transportation buses were
used as mobile sensing units to
measure urban pollution; the
device runs on two 1 2V batteries at
an operating temperature between
-30 and 60°C, with a life
expectancy of 2 years. This
research reflects the second stage,
which follows the single sensor
pilot study (using a car-mounted
sensor) in Vigo.
Potential for citizen sensing
applications. Challenges noted for
broader implementation include:
privacy (protection), security (need
simple and energy-efficient
mechanism), data validity (e.g.,
user could fake reading to avoid
fine), data visualization (with gaps
in location and time; consider
Gandin Interpolation or optimal
interpolation technique), and
incentives (why participate?).
Output current of 1 .2~2.4 nA/ppm
and an operating temperature
range between -40 and 70°C.
E-8
-------
31 October 2013
ID
c
17
c
18
c
19
np
Organization
Author (Funding)
University of
Cambridge
Kanjo, E.
(Sponsors: C>2,
Nokia, Symbian,
Outspoken,
Cambridge city
Council, Thales)
Vanderbilt
University, Institute
for Software
Integrated Systems
Volgyesi, P., et al.
Northwestern
University
Otto, J.S., Rula, J.P.,
Bustamante, F.E.
Abbreviated Citation
Mobile phones as
sensors
(presentation)
http://vwvw.admin.cam.ac.uk/
offices/research/documents/I
ocal/events/downloads/sw/4.
2-Kanjo.pdf
Air quality monitoring
with sensorMap(200S,
IEEE, and Int'l Confc on
Info Processing in
Sensor Networks)
http://vwvw.google.com/url7s
a=t&rct=j&q=microsoft%20re
search%20sensors%20air%
20pollutant&source=web&cd
=8&ved=OCFOQFjAH&url=htt
p%3A%2F%2Fciteseerx.ist.p
su.edu%2Fviewdoc%2Fdow
nload%3Fdoi%3D10.1. 1.157.
1 544%26rep%3Drep1 %26ty
pe%3Dpdf&ei=DOYgT7aCBa
N2gXbtZWBDw&usg=AFQjC
NFgTjs-go6sStpL48YV-
RuSBOqEMg
[April 2009]
http://www.aqualab.cs.no
rthwestern.edu/publicatio
ns/JOtto09C3R-TR.pdf
C3R-Participatory urban
monitoring from your car
[April 2009]
http://www.eecs. northwester
n.edu/docs/techreports/2009
_TR/NWU- EECS-09-10.pdf
(MQ7 CO gas sensor
man ufacturer page:
http://hwsensor.en.alibaba.co
m/product/285416574-
209771 1 1 0/MQ7_CO_carbo
n_monoxide_gas_sensor.ht
mlj
Sensor Technology
Type
MOS
MOS
(Sn02)
Description
(Name)
(MobGeoSen)
Commercial
MiCS-5521 analog
sensor
(MAQUMON-mobile
air quality
monitoring network)
Modified Hanwei
Electronics MQ-7
CO sensor (C3R)
Pollutant/
Parameter
CO2, CO,
noise
O3, NO2,
and
CO/VOC
Also:
temp and
relative
humidity
CO, temp,
humidity.
Reported
Detection
Capability
CO:
indicated
limit: 0.5 ppm
CO:
indicated
range: 1 to
1,000 ppm
CO:
indicated
range:
A f\
1U-
10,000 ppm
(unmodified
sensor)
Re-
sponse
Time
90 sec
Size
Cell
phone
Hand-
held
(infer
from
picture)
Shoebox
Automation and
Network
Capability
Wireless
communication
network uses
GPS coordinates
(Cell phone-
based)
Measurements
uploaded to
server when car is
in a WiFi hotspot,
processed, then
published on
SensorMap portal
(as contours, with
time series data
for given sensor
and/or location);
integrated
Bluetooth module
provides wireless
interface for
laptops or PDAs.
(WSN) (Vehicle-
mounted unit)
Communicates
with other
vehicles and
agencies; each
C3R node
maintains a
detailed air quality
map that is
shared with the
driver, other
vehicles, and
public agencies.
(Vehicle-mounted
unit, participatory/
citizen sensing)
Application and Operation Notes
This system uses mobile
technology to obtain accurate
readings both indoors and
outdoors. It would run while the
mobile device was active and could
show the user their exposure as
they traveled, via a Google Earth
overlay.
This vehicle-mounted sensor takes
measurements every minute while
the car is in motion (less often
when stationary). Battery lasts a
few hours, but the system can be
constantly powered by the
cigarette lighter when the car is
moving, and power-intensive
elements such as GPS, Bluetooth,
and gas sensors can be turned off
when the 2-axis MEMS
accelerometer indicates the system
is not in motion.
This is an outdoor vehicular
networking system. This system
uses 760 mW of power mostly for
heating the sensing device.
Interference may occur if wind or
other factors cool the sensor below
a necessary temperature. It takes
48 hr for the sensor to warm up,
and it runs on a 90-sec cycle, with
intermitted 60-sec purge cycles.
E-9
-------
31 October 2013
ID
c
20
c
21
np
Organization
Author (Funding)
Amrita University
(India)
Freeman, J.D.,
Omanan, V.,
Ramesh, M. V.
Sungkyunkwan
University
Foundation for
Corporate
Collaboration
Kim, W.J.
Abbreviated Citation
Wireless integrated
robots for effective
search and guidance of
rescue teams
[2011, IEEE]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=587291 9&isnumber=58729
11
Bicycle navigation
having air pollution
measurement sensors, a
method for storing the air
pollution measurement
(and more)
http://vwvw.wipo.int/patentsco
pe/search/en/detail.jsf?docld
=KR1 0525661 &recNum=1 &d
ocAn=1 020090005097&quer
yString=FP:(1 020090005097
)&maxRec=1
Sensor Technology
Type
Infrared
(IR) (by
Hanwei
Elec-
tronics
Co. Ltd.)
Description
(Name)
(MQ-7, MQ-4,
MQ-2, MQ-6 (all
manufactured by
Hanwei Electronics
CO., Ltd.)
Bicycle navigation
system with air
pollution measure-
ment sensors, a
method for storing
the measurement,
and a method for
locating the road, to
enable a mobile
equipment user to
find less polluted
route (patent appl.)
Pollutant/
Parameter
CO,
natural
gas, liquid
petroleum
gas,
smoke
CO, NO2,
SO2
Reported
Detection
Capability
CO:
indicated
range:
20-2000 ppm
Re-
sponse
Time
Size
Mobile
device
Automation and
Network
Capability
Wireless sensor
network, (WSN)
precise location
(GPS)
Robot platform:
ATMEGA328P
microcontroller
(Robotics)
Unit transmits the
measured data
and GPS position
to a storage
server.
(Vehicle-mounted
unit)
Application and Operation Notes
Explores the application and
technology required for a wireless
sensor network for disaster
management by use of
autonomously navigating robots
equipped with air quality sensors
and the ability to search for
disaster survivors. The search
team's network is formed between
two fixed nodes.
A GPS navigation system for
bicycles with embedded sensors
that measure and transmit CO,
NO2, SO2 concentrations along
with GPS data.
Reference Commercial Sensor
c
22
Ecotech
http://www.ecotech.com/gas-
analyzers/co-analyzer
Non-
disper-
sive UV
abs)
(EC9830 CO
analyzer)
CO
Indicated
range:
0-200 ppm;
indicated
limit: SOppb
Data access via
RS232, USB
interface, or
Ethernet
connector
Optional internal 12V direct current
(DC) power supply allows sensor
to be operated from a battery- or
solar-powered source.
Nitrogen Dioxide
Detection Technique: Chemistry
1
Sungkyunkwan
University (Korea)
Yao, F., Lee, Y.H., et
al.
(Funding: STAR-
f acuity program and
World Class
University program
through KRF funded
by MEST)
Humidity-assisted
selective reactivity
between NO2 and SO2
gas on carbon
nanotubes
[2011, J. Materials
Chemistry,
21:4502-4508]
http://nanotube.skku.ac.kr/da
ta/paper/Humidity-
assisted_Fei%20Yao. pdf
Nano-
based
Random-network
SWCNTs in
electrodes made
with dip-pen
method, with
dichloroethane
solution on Pt IDEs.
Varying humidity
level allows for
selective gas
detection
NO2, SO2
0.01 ppm
10min
Small
Cites earlier
results:
NO2100ppt with
polymer-coated
CNT
SO210ppb with
SWCNT, spectro-
scopic analysis)
Pollutant diluted with other ambient
air, added humidity, and
transported to testing chamber. A
0.1V current is passed through the
device. Sensor has an operating
temperature of 150°C.
E-10
-------
31 October 2013
ID
2
3
4
Organization
Author (Funding)
Silpakorn University
(Thailand), and
others
Robert, OP.; others
(Funding: Thailand
Research Fund; also
supported by Office
of the Higher
Education
Commission and
others; e.g., Petro-
Instruments Corp.,
Ltd. for sensor
calibrations; NASA)
University of South
Carolina,
Nomani, Md.W.K.,
etal.
SENS4, LLC
James, J.
(Funding: NSF,
Army Research
Office, and SENS4,
LLC [through an
NSF grant])
University College
London
Varsani, P., etal.
University of
Auckland (New
Zealand) Queen
Mary, University of
London Binions, R.
(Funding: EPSRC,
Wolfson Trust, Royal
Society, Dorothy
Hodgkin fellowship)
Abbreviated Citation
SnO2 gas sensors and
geo-informatics for air
pollution monitoring
[2011, JOAM
(J Optoelectronics and
Advanced Materials),
13(5):560-564]
http://modis.gsfc.nasa.gov/sc
i team/pubs/abstract.php?id
=04314;
joam.inoe.ro/download.php?i
du=2778
Highly sensitive and
multidimensional
detection ofNO2 using
InOi thin films
[Aug. 201 1 , Sensors and
Actuators B,
160:251-259]
http://pdn.sciencedirect.com/
science? ob=Miamilmagell
RL& cid=271353& user=17
22207&_pii=S092540051 1 00
6861&_check=y&_origin=bro
wse&_zone=rslt_list_item&_c
overDate=2011-12-
15&wchp=dGLzVlk-
zSkzV&md5=a9efb7781 a062
4784753109796941695/1-
s2 0-S092540051 1006861-
main.pdf
Zeolite-modified WO3
gas sensors - Enhanced
detection ofNO2
[2011, Sensors and
Actuators B,
160:475-482]
http://pdn.sciencedirect.com/
science? ob=Miamilmagell
RL& cid=271353& user=17
22207&_pii=S092540051 1 00
7374&_check=y&_origin=bro
wse& zone=rslt list item& c
overDate=2011-12-
15&wchp=dGLzVlk-
zSkzV&md5=da1 6648f4b925
33eb1 7ae36c6fe2cOad/1 -
S2.0-S092540051 1007374-
main.pdf
Sensor Technology
Type
Nano-
based
MOS
MOS
Description
(Name)
Nanotechnology-
based SnO2 gas
sensor.SnO2 thick
film with alumina
substrate from E2V
Technologies;
Pt electrodes
Fabricated sensor
chip using ln2O3thin
films (from
Thinfilms, Inc.) as
functionalization
layers coated on an
AI2O3 ceramic
substrate,
interdigitated metal
fingers, 100 |jm
apart of Ti (5 nm)/
Au (50 nm)
deposited atop the
thin film
Solid-state metal
oxide screen printed
WO3 sensors
modified by addition
of acidic and
catalytic zeolite
layers
Pollutant/
Parameter
NO2
NO2;
also
assessed:
mixture of
NO2, NH3
NO2;
also
tested:
CO,
30 ppm,
and
mixture of
NO2 and
CO,
acetone
at 5 ppm
Reported
Detection
Capability
Sensitivity
O.05-5 ppm
(85%
accuracy)
20ppb
(20%
reduction in
conductivity
at this level)
NO2:
indicated
range:
200-300 ppb
(tested
50-400 ppb)
in dry air
Re-
sponse
Time
A few
sec
30- min
re-
sponse
with 1-hr
re-
covery
Size
Automation and
Network
Capability
Data transferred
to pocket PC
linked via
Bluetooth and
GPS. Minnesota
Mapserver 2009
used to monitor,
view, retrieve NO2
concentrations in
real time.
(Wearable,
participatory/
citizen sensing)
(Mountable)
(Mountable)
Application and Operation Notes
Research goal is low-cost, simple,
reliable, portable, real-time air
pollution monitoring system for
central Bangkok area.
20% reduction in conductivity at
20 ppb, normal temperature
(20°C); in a vacuum, ln2O3
nanowires indicated sensitivity of
5 ppb; per an average ambient/
background level of 1 1 ppb, the
concentration differential of 9 ppb
is identified as responsible for the
conduction change (indicating even
more impressive sensitivity to the
authors); the system can monitor
minute deviations from the general
average background value (30-50
mV) at a frequency of 1 kHz, at an
operating temperature of >150°C.
Highly selective to NO2, including
in the mixture; potential for use in
electronic nose technology for
environmental monitoring. Zeolite
layers were added to increase
selectivity. Sensor operates at
350°C.
E-11
-------
31 October 2013
ID
5
np
6
np
7
np
Organization
Author (Funding)
Korea Advanced
Institute of Science
and Technology
(Republic of Korea)
Cho, N.G., Kim, I.-D.
(Funding: Korea
Ministry of Research,
Israel Ministry of
Science &
Technology)
Brno University of
Technology (Czech
Republic)
Hrdy, R.,
Vorozhtsova, M.,
Drbohlavova J
Prasek, J., Hubalek,
J.
(Funding: Czech
Ministry of Education
and Grand Agency
of Czech Republic)
Shinwoo Electronics
Co., Ltd.
Kim, I.
Korea University
(S. Korea)
Dong, K.Y.,Ju,B.K.
Yonsei University
(S. Korea)
Chni H H
O//U/, it. it.
Abbreviated Citation
WO2 gas sensing
properties of amorphous
lnGaZnO4 submicron-
tubes prepared by
polymer fiber templating
route
[Aug. 201 1 , Sensors and
Actuators B, 160:499-
504]
http://pdn.sciencedirect.conV
science? ob=Miamilmagell
RL&_cid=271 353&_user=1 7
22207&_pii=S092540051 1 00
7404& check— y& origin— bro
wse& zone=rslt list item& c
overDate=2011-12-
15&»chp=dGLzVlk-
zSkzV&md5=7220341 a4a8ff
1059a32e198b06fec69/1-
S2.0-S092540051 1007404-
main.pdf
Electrochemical
transducer utilizing
nanowires surface
[2010, IEEE, 33rd Int.
Spring Seminar on
Electronics Technology]
http://ieeexplore.ieee.org
/stamp/stamp.jsp?tp=&ar
number=5547340&isnu
mber=5547245
Gas sensor for CO and
NH3 using
polyaniline/CNTs
composite at room
temperature
[2010, IEEE,
International Conference
on Nanotechnology Joint
Symposium with Nano
Korea]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5697782&isnumber=56977
24
Sensor Technology
Type
Nano-
based
Nano-
based
Polymer
film
Description
(Name)
Semiconducting
amorphous
lnGaZnO4
submicron, thin wall
hollow tubes
TiO2 modified Au
nanowires applied
to surface of
sensing electrode
PANi/SWCNTs film
dispersed in sodium
dodecyl sulfate and
applied over Ti/Au
electrodes of an IDE
by photolithography
Pollutant/
Parameter
NO2
CO2, NO2,
02, NH3
CO, NH3
(also
benzene
and NO2)
Reported
Detection
Capability
100, 200,
400, 800 ppb
(and higher)
Re-
sponse
Time
"Varies"
Fast re-
sponse.
and re-
covery
Size
Very
small
5 mm x
17 mm,
480 |jm
thick
Automation and
Network
Capability
Not indicated
(Mountable)
Application and Operation Notes
Higher sensitivity may be due to
much reduced interface area
between sensing film and
substrate, enhanced surface-
depletion areas, and effective gas
diffusion through porous tube
networks; simple and versatile
synthesis method indicates
opportunities to control tube
morphology, for a new class of
building blocks for these gas
sensors. 20 kV was applied to the
sensor, which operates at 300°C.
Operated at 5-30 keV in high
vacuum mode. Sensor operates in
a range between 300 and 400°C.
This research demonstrates the
use of PANi/SWNTs composite-
based sensor for mixed gas
detection. The changes in
resistance of the sensor determine
the presence of a single gas or
mixture of gases. This composite
has a large surface-to-volume ratio
which makes it a good candidate
for new gas sensors.
E-12
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Detection Technique: Spectroscopy
8a
8b
9
np
10
np
11
np
Adelphi University
Roa, G.N., Karpf, A.
University of
Mississippi
Uddin, W.
(Funding: DOT)
Bogor Agricultural
University
(Indonesia) Rustami,
E., Azis, M., Maulina,
W., Rahmat, M.,
Alatas, H., Seminar,
K.B.
(Funding: Beasiswa
Unggulan Terpadu -
Education Ministry of
Republic of
Indonesia, Bogor
Agricultural
University)
Bogor Agricultural
University, Bogor
Indonesia Maulina,
W., Rahmat, M.,
Rustami, E., et al.
(Funding: Beasiswa
Unggulan Terpadu -
Education Ministry of
Republic of Indone-
sia, Bogor Agricul-
tural University
_ . . .
Departments)
A trace gas sensor at
ppb sensitivity based on
multiple line integration
spectroscopy techniques
[2010, IEEE]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5499863&isnumber=54994
82
[2001]
http://vwvw.ncrste.msstate.ed
u/archive/projects/um/UM-
CAIT-Laser-Year-1 -Report-
Final. pdf
An integrated optical
instrumentation for
measuring NO2 gas
using one-dimensional
photonic crystal
[2011, IEEE,
International Conference
on Instrumentation,
Communication,
Information Technology,
and Biomedical
Engineering, (Indonesia)]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=61 08658&isnumber=61 085
78&tag=1
Fabrication and
characterization ofNO2
gas sensor based on
one-dimensional photonic
crystal for measurement
of air pollution index
[2011, IEEE, International
Conference on Instru-
mentation, Communica-
Engineering (Indonesia)]
ittp://ieeexplore. ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=61 08657&isnumber=61 0857
8&tag=1
Laser
absorp-
tion
multiple-
line inte-
grated
absorp-
tion spec-
troscopy
(MLIAS)
Light
detection
and
ranging
(LIDAR)
UV-visible
(UV-vis)
absorp-
tion
UV-vis
absorp-
tion
MLIAS with a multi-
pass cell (12.5 cm
and others) and
pulsed quantum
cascade laser
(QCL) (from
Daylight Solutions)
Tunable LIDAR
measure
wavelengths
between 2,500-
11, 000 Angstroms
Integrated optical
sensor with 1-D
photocrystal, LED,
and photodiode
operating in NO2
absorption range
Photonic crystal
fabricated by
electron beam
evaporation with
photonic pass band
(PPB) at 533 nm
wavelength
NO2
O3 or NO2
NO2
NO2
(dissolved
in
reagent)
8a:
530 ppt
(200-m path)
8b:
120 ppb
(0.88-m path)
1-1 00 ppb, at
a range of
several km
Compact,
portable
(Mountable)
(Remote sensing)
(Mountable)
(Fixed/semi-
portable unit)
Technology designed to be
compact and portable, easy to
operate, and low cost. Detector
can monitor in real time. Explores
use of QCLs to enhance detection
sensitivity, varied cell sizes, and
multi-pass geometry to increase
sensitivity of NO2 gas detection.
In this study, the system was
mounted in a plane and used to
analyze traffic pollution in different
areas of Mississippi.
A signal conditioning circuit
consisting of a current to voltage
converter, voltage follower, active
low pass filter, and instrumentation
amplifier was designed to better
measure the output signal.
Millivolt range
Device may be deployed as a
sensor in an air pollution index
measurement system. Sensor has
an operating temperature of 300°C.
E-13
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Novel Sensor Systems Using Commercial Sensors
c
12
c
13
c
14
Universidade da
Corufia (Spain)
Lopez-Pena, F.,
Varela, G., Paz-
Lopez, A., Duro,
R.J.,
Universidad de Vigo
(opainj
Gonzalez-Castano,
F.J.
(Funding: Ministerio
de Fomento of
Spain)
American University
of Sharjah, Sharjah,
UAE
Al AH, A.R.,
Zualkernan, /., Aloul,
F.
Bharath University,
Department of
Computer Science
and Engineering
GOT, R.K.
Public transportation
based dynamic urban
population monitoring
system
[2010, Sensors &
Transducers, 8:13-25]
http://vwvw.sensorsportal.co
m/HTML/DIGEST/february_2
01 0/P SI 1 00 odf
(CO sensor link:
http://mkwheatingcontrols.co.
uk/download/GS-S-CM.pdf)
A mobile GPRS-sensors
array for air pollution
monitoring
[2010, IEE Sensors
Journal,
10(10):1666-1671]
http://vwvw.aloul.net/Papers/f
aloul sensors10.pdf
An itinerant GPRS-GPS
and sensors integration
for atmospheric
effluence screening
[2011, IJTES
2(2):152-157]
http://vwvw.sensaris.com/wp-
content/uploads/old/201 1/09/
India-bus-GPRS-GPS- Air-
sensing. pdf
Com-
mercial
sensors
(by
Sontay,
GS-S-CM
CO
sensor)
Sensor
array
(assume
commer-
cial)
Sensor
array
(assume
commer-
cial)
24 sensors and
10 routers in a
single-chip
microcontroller
(GPRS-sensors
array, Mobile-DAQ)
Several air pollution
sensors
CO2, CO,
NO2,
SO2;
also temp
and
relative
humidity
CO, NO2,
SO2
CO, NO2,
SO2
NO2:
0-10ppm
(O.02 ppm)
NO2:
range;
0-20 ppm
limit:
O.02 ppm
NO2:
range;
0-20 ppm
resolution:
O.02 ppm
<30 sec
Desktop
(inferred
from
photos)
Shoebox
Mobile
DAQ:
book
size,
sensor:
20 mm
Bluetooth GPS;
mobile sensing
network includes
distributed
software to
acquire, integrate,
and geolocate
data from
sensors; linked to
GoogleMaps API
for data
visualization.
(WSN) (Vehicle-
mounted unit)
Online
capabilities, uses
a single chip
microcontroller
and a GPS
system to locate
air pollution
readings on
Google Maps.
(Vehicle-mounted
unit)
Public mobile
network; fixed
internet-enabled
monitoring server
(pollution server)
is interfaced to
Google Maps to
display real-time
pollutant levels
and locations in a
metropolitan area
(24 hr/7 d) (Vehi-
cle-mounted unit)
Public transportation buses were
used as mobile sensing units to
measure urban pollution; the
device runs on two12V batteries
and operates between -30 and
60°C. The device is reported to
have a life expectancy of 2 years.
This research reflects the second
stage, which follows the single
sensor pilot study (using a car-
mounted sensor) in Vigo.
Proposes an integrated system
composed of a single-chip
microcontroller, air pollution
sensors for CO, NO2, and SO2, a
GPRS-modem, and a GPS
module. Public mobile networks
are used to upload data to the
Pollution Server, which is
interfaced with Google maps; was
deployed on the American
University of Sharajah campus.
System is composed of a single-
chip microcontroller, air pollution
sensors for CO, NO2, and SO2,
GPRS-Modem, and GPS device.
Data is transmitted to the Pollution
Server, which is interfaced with
Google Maps. Device was
mounted on a bus, which was
driven around the Bharath
University of Chennai campus.
E-14
-------
31 October 2013
ID
c
15
np
c
16
np
Organization
Author (Funding)
Sungkyunkwan
University
Foundation for
Corporate
Collaboration
Kim, W.J.
Jackson State
University
Anjaneyulu, Y.
Jawaharlal Nehru
Technological
University
Jayakumar, /., Bindu,
V.H.
Andhra Pradesh
Pollution Control
Board
Ramani, K.V.
Spectrochem
Instruments
Rao, T.H.
Abbreviated Citation
http://vwvw.wipo.int/patentsco
pe/search/en/detail.jsf?docld
=KR1 0525661 &recNum=1 &d
ocAn=1 020090005097&quer
yString=FP:(1 020090005097
)&maxRec=1
Real time remote
monitoring of air
pollutants and their
online transmission to
the web using internet
protocol
[2007, Environ Monit
Assess, 124:371-381]]
http://cardiff.academia.edU/S
AGARESWARGUMMENENI/
Papers/922566/
Sensor Technology
Type
Electro-
chemical
various
commer-
cial
sensors
Description
(Name)
Bicycle navigation
system (GPS) with
air pollution
measurement
sensors
(Real Time Remote
Monitoring System)
Pollutant/
Parameter
CO, NO2,
SO2
SO2, NO,
NO2, CO,
O3, H2S,
PM10,
PM2.5,
hydro-
carbons,
mercap-
tans
Reported
Detection
Capability
Varies per
pollutant,
within
0-200 ppm,
or 0-50 |jg/m3
(mercaptans)
Re-
sponse
Time
30 or
60m in
Size
Mobile
device
Automation and
Network
Capability
Unit transmits the
measured data
and GPS position
to a storage
server.
(Vehicle-mounted
unit)
Ethernet network
module, uploading
to webpage
(Remote sensing)
Application and Operation Notes
A bicycle GPS navigation system
with embedded sensors that
measure and transmit CO, NO2,
SO2 concentrations along with
GPS data.
This device is a remotely
monitored detection system. It can
be run on a 1 2V battery and
operates between 55 and 125°C.
The pollution sensors can be set to
collect data every 30 or 60 minutes
depending on user preference and
weather conditions. Also
measures environmental
parameters such as temperature
and humidity, as well as sound.
(Meteorological monitoring system,
Bruel and Kajaer sound level
measurement system.)
Reference Commercial Sensor
c
17
EcoTech
http://www.ecotech.com/gas-
analyzers/nox-analyzer
Chemi-
lumines-
cence
(EcoTech: Serinus
40)
NO, NO2,
NOX
Indicated
range:
0-20 ppm
LDL:
O.4 ppb
Indicates several system
components to improve system
integrity, power usage, and carbon
footprint (99-132 volts alternating
current (AC), 198-264 VAC 47-63
Hz).
Ozone
Detection Technique: Spectroscopy
1
np
University of
Mississippi, Center
for Advanced
Infrastructure
Technology
Uddin, W.
(Funding: DOT)
[2001]
http://www.ncrste.msstate.ed
u/archive/projects/um/UM-
CAIT-Laser-Year-1 -Report-
Final. pdf
LIDAR
Tunable LIDAR can
measure
wavelengths
between 2,500-
11, 000 Angstroms
O3 or NO2
1-100ppb, at
a range of
several km
System
was
mounted
in plane
(Remote sensing,
vehicle-mounted
unit)
Analyzed traffic pollution in
different areas of Mississippi.
E-15
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Novel Sensor Systems Using Commercial Sensors
c2
c3
np
Pennsylvania State
University
Cazorla, M., Brune,
W.H.
Jackson State
University
Anjaneyulu, Y.
Jawaharlal Nehru
Technological
University
Jayakumar, /., Bindu,
V.H.
Andhra Pradesh
Pollution Control
Board
Ramani, K.V.
Spectrochem
Instruments
Rao, T.H.
Measurement of ozone
production sensor
[2009, Atmospheric
Measurement
Techniques Discussion,
2:3339-3368]
http://atmos-meas- tech-
discuss. net/2/3339/2009/amt
d-2-3339-2009.pdf
Real time remote
monitoring of air
pollutants and their
online transmission to
the web using internet
protocol
[2007, Environ Monit
Assess, 124:371-381]]
http://cardiff.academia.edU/S
AGARESWARGUMMENENI/
Papers/922566/
UV photo-
metric,
commer-
cial
sensor
Electro-
chem,
various
commer-
cial
11.3LUV-transmit-
ting Teflon film
chambers, NO2to
O3 converting unit,
and modified ozone
analyzer (Thermo
Scientific, Model 49i
with ozone scrubber
removed and
temperature
stabilization)
(MOPS: Measure-
ment of Ozone
Production Sensor)
Tapered element
oscillating
microbalance
(Real Time Remote
Monitoring System)
03
(measures
production
rate, can
also study
sensitivity
ofO3
production
to NO,
using the
NO
converter)
SO2, NO,
NO2, CO,
O3, H2S,
PM10,
PM2.5,
hydro-
carbons,
mercap-
tans
Indicated limit
0.67 ppb/hr
fora 10-min
average
measurement
Ozone:
Indicated
range:
0-10ppm;
varies per
pollutant,
within
0-200 ppm
30 to 60
min
Not indicated
(Fixed/semi-
portable unit)
Ethernet network
module, uploading
to webpage
(Remote sensing)
Compares O3 production in one
chamber with ambient air/normal
conditions, and in a second
chamber with O3 production
inhibited.
This device is a remotely
monitored detection system that
can be run on a 1 2V battery. The
pollution sensors can be set to
collect data every 30 minutes or
every 60 minutes depending on
user preference and weather
conditions. Operates between -55
and 125°C. (Also measures
temperature, pressure, rainfall,
relative humidity, wind speed and
direction, and sound; per
meteorological monitoring system,
Bruel and Kajaer sound level
measurement system.)
Reference Commercial Sensor
C4
2B Technologies
(an InDevR
company)
http://vwvw.twobtech.com/mo
dei_106.htm
UVabs
Absorbs at 254 nm
(Ozone monitor
model 106-L)
03
Indicated
range
0-1 00 ppm;
resolution:
1 ppb
precision and
accuracy:
higher of
2 ppb or 2%
of reading
10 sec
Shoebox,
laptop
(4.5 Ib)
USB and RS-232
output of time/
date, O3 concen-
tration, internal
temperature,
pressure; internal
data logger, on-
board microproc-
essor. (Fixed/
semi-portable unit)
Intended for industrial settings, was
used in the GO3 project. Unit
requires 12V (500 mA, 6 W) of
power (can be battery operated)
and has a long-life pump
(15,000 hr). Data can be averaged
over 1 min, 5 min, and 1 hr.
Available in standard or NEMA
enclosure; components mounted
on one printed circuit board.
E-16
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Sulfur Dioxide
Detection Technique: Chemistry
1
Sungkyunkwan
University (Korea)
Yao, F., Lee, Y.H., et
al.
(Funding: STAR-
f acuity program and
World Class
University program
through KRF funded
by MEST)
Humidity-assisted
selective reactivity
between NO2 and SO2
gas on carbon
nanotubes
[2011, J. Materials
Chemistry,
21:4502-4508]
http://nanotube.skku.ac.kr/da
ta/paper/Humidity-
assisted_Fei%20Yao. pdf
Nano-
based
Random-network
SWCNTs in
electrodes made
with dip-pen
method, with
dichloroethane
solution on Pt IDEs;.
varying humidity
level allows for
selective gas
detection
NO2, SO2
SO2:
indicated
range:
0.01-10 ppm
4 sec
Small
Cites earlier
results:
NO2100ppt with
polymer-coated
CNT
SO2 10 ppb with
SWCNT,
spectroscopic
analysis)
Pollutant diluted with other ambient
air, added humidity, and
transported to testing chamber. A
0.1V current is passed through the
device and the sensor operates at
150°C.
Detection Technique: Spectroscopy
2
3
Wuhan Huali
Environment
Protection Science
Technology Co., Ltd.
Wan, Y., Dai, B.
Harbin Institute of
Technology (China)
Lou, XT.,
Somesfalean, G.,
Zhang, Z.G.,
S van berg, S.
Atmospheric pollution
monitoring gas sensor
using non-pulse
ultraviolet fluorescence
method
rom m
[^U I UJ
http://WDrldwide.espacenet.c
onVpublicationDetails/biblio?
FT=D&date=201 01 208&DB=
worldwide, espacenet. co m&lo
cale=en EP&CC=CN&NR=2
01 666873U&KC=U&ND=4
Sulfur dioxide
measurements by
correlation spectroscopy
using an ultraviolet light-
emitting diode
[2009, IEEE]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=51 96268&isnumber=51 91 4
47
Fluores-
cence
Spectros-
copy
(correla-
tion
spectros-
copy, CO-
SPEC)
Non-pulse UV
fluorescence
method
LED and UV range
absorption
H2S, SO2
SO2
1 ppb as
lowest level
of detection
0.4 ppm
(Fixed/semi-
portable unit)
(Fixed/semi-
portable unit)
Continuous monitoring of real-time
concentrations of H2S and SO2 in
air. Sensor reduces noise and has
high anti-interference capability,
which greatly improves the
measurement accuracy and leads
to more stable data.
Demonstrates the use of LEDs with
structureless emission in the UV
region as sources for gas
absorption measurements when
combined with the COSPEC
technique. Tested system is
immune from interfering gases,
pressure variations, and light
intensity fluctuations.
E-17
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Novel Sensor Systems Using Commercial Sensors
c4
c5
np
c6
np
Jackson State
University
Anjaneyulu, Y.
Jawaharlal Nehru
Technological
University
Jayakumar, /., Bindu,
\/ i-i
V.n.
Andhra Pradesh
Pollution Control
Board
Ramani, K.V.
Spectrochem
Instruments
Rao, T.H.
Bharath University,
Department of
Computer Science
and Engineering
GOT, R.K.
American University
of Sharjah, Sharjah,
UAE
Al AH, A.R.,
Zualkernan, /., Aloul,
F.
(Funding/resources:
Computer Science
and Engineering
Department,
American University
of Sharjah, UAE)
Real time remote
monitoring of air
pollutants and their
online transmission to
the web using internet
protocol
[2007, Environ Monit
Assess, 124:371-381]]
http://cardiff.academia.edU/S
AGARESWARGUMMENENI/
Papers/922566/
An itinerant GPRS-GPS
and sensors integration
for atmospheric
effluence screening
[2011JJTES
2(2):152-157]
http://www.sensaris.com/wp-
content/uploads/old/201 1/09/
India-bus-GPRS-GPS- Air-
sensing. pdf
A mobile GPRS-sensors
array for air pollution
monitoring
[2010, IEEE Sensors
Journal,
10(10):1666-1671]
http://w/vwaloul.net/Papers/f
aloul_sensors10.pdf
Electro-
chemical
various
commer-
cial
Sensor
array
(assume
commer-
cial
sensors)
Sensor
array
(assume
commer-
cial)
Tapered element
oscillating
microbalance
(Real Time Remote
Monitoring System)
24 sensors and
10 routers in a
single-chip
microcontroller
(GPRS-sensors
array)
SO2, NO,
NO2, CO,
O3, H2S,
PM10,
PM2.5,
hydro-
carbons,
mer-
captans
CO, NO2,
SO2
CO, NO2,
SO2
SO2:
indicated
limit:
0.05 ppm
SO2:
indicated
range:
0-20 ppm
resolution:
O.02 ppm
SO2:
indicated
range:
0-20 ppm
(O.1 ppm)
30 to 60
min
Mobile-
DAQ unit
is book
size,
sensor is
20mm
diameter
and
server is
a laptop
Shoebox
Ethernet network
module, uploading
to webpage
(Remote sensing)
Public mobile
network; fixed
internet-enabled
monitoring server
(pollution server)
is interfaced to
Google Maps to
display real-time
pollutant levels
and locations in a
metropolitan area
(24 hr/7 d) (Vehi-
cle-mounted unit)
Online
capabilities, uses
a single chip
microcontroller
and a GPS
system to locate
air pollution
readings on
Google Maps.
(Vehicle-mounted
unit)
This device is a remotely
monitored detection system that
can be run on a 1 2V battery. The
pollution sensors can be set to
collect data every 30 minutes or
every 60 minutes depending on
user preference and weather
conditions. Operates between -55
and 125°C. (Also measures
temperature, pressure, rainfall,
relative humidity, wind speed and
direction, and sound, per
meteorological monitoring system,
Bruel and Kajaer sound level
measurement system.)
System is composed of a single-
chip microcontroller, air pollution
sensors for CO, NO2, and SO2,
GPRS-Modem, and GPS device.
Data is transmitted to the Pollution
Server, which is interfaced with
Google Maps. Device was
mounted on a bus, which was
driven around the Bharath
University of Chennai campus.
Proposes an integrated system
composed of a single-chip
microcontroller, air pollution
sensors for CO, NO2, and SO2, a
GPRS-modem, and a GPS
module. Public mobile networks
are used to upload data to the
Pollution Server, which is
interfaced with Google maps; was
deployed on the American
University of Sharajah campus.
E-18
-------
31 October 2013
ID
c7
np
Organization
Author (Funding)
Xi'an University of
Posts and
Telecommunications
(China)
Xiaoquiang, Z.,
Zuhou, Z.
Abbreviated Citation
Development of remote
waste gas monitor
system
[2010, International
Conference on
Measuring Technology
and Mechanics
Automation]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5460346&isnumber=54584
85
Sensor Technology
Type
Electro-
chemical
Description
(Name)
Solid electrolyte
layer, Au
electrodes, Pt, Pb,
heater, porcelain
tube, double-layer
steeless (steel) net,
Ni- and Cu-plated
ring, Bakelite, Ni-
and Cu-plated pin.
Pollutant/
Parameter
CO2, SO2,
Reported
Detection
Capability
SO2:
indicated
range;
<1 -2,000
ppm (<0.1)
sensitivity:
400-
700 nA/ppm
in 10 ppm
SO2
Re-
sponse
Time
Size
Automation and
Network
Capability
GPS, GSM,
Global System for
Mobile
Communications
wireless
transmission,
secure digital
(SD) memory
(Remote sensing)
Application and Operation Notes
This research explores a system in
which chimneys are monitored for
several pollutant outputs and
collected data are used to alert
users/monitors of unhealthy
emission levels. Collected data
would be compared with national
standards and alerts would go out
if emission levels exceeded
warning levels.
Reference Commercial Sensor
C8
EcoTech
http://pdf.directindustry.com/
pdf/ecotech/serinus-50-
sulfur-dioxide-
analyzer/501 78-1 43935. html
UV
fluores-
cent
radiation
(Serinus 50 SO2
Analyzer)
SO2
Indicated
range:
0-20 ppm
LDL:
<0.3 ppb
60 sec
to 95%
440 x
178 x
620 mm,
bench
18.1 kg,
rack
13.1 kg
USB port, data
logging
This system requires 99-1 32 VAC,
198-264 VAC 47-63 Hz and can
operate in 0-40°C range. The
sample flow rate is 675 cc/min, and
the user can select the displayed
concentration units.
Acetaldehyde
Detection Technique: Chemistry
1
University of Ferrara
(Italy)
Giberti, A., Carotta,
M.C., Fabbri, B.,
Gherardi, S., Guidi,
V., Malagu, C.
(Funding//support:
Programma
Operative FESR,
Spinner Regione
Emalia-Romagna)
High-sensitivity detection
of acetaldehyde.
[2012, Sensors and
Actuators B,
174:402-405]
Nano-
based
Set of
nanostructured
single and mixed
metal oxides,
including ZnO, SnO,
TiO, and W3O-SnO
type. Created on
alumina plates with
gold contacts as a
heater
Acetalde-
hyde
0.1 -10 ppm
LDL: 10 ppb
(0.010 ppm)
2 mm x
2 mm,
20-30|jm
thick
Sensors were tested in both dry
and wet air at various temperatures
to determine optimal operating
conditions. Optimal range was
450-500°C for all sensors, with
ZnO performing the best. Wet air
was reported to negatively affect
reactions between acetaldehyde
and sensing material. Sensors
consume 0.6-1 .0 W at working
temperature and have the potential
to be operated by grid connection
or alkaline batteries. Each unit was
tested 3 times and was reported to
have good reproducibility.
E-19
-------
31 October 2013
ID
2
3
4
Organization
Author (Funding)
National Institute of
Advanced Industrial
Science and
Technology (Japan)
/toh, T., Matsubara,
/., Shin, I/I/., Izu, A/.,
Nishibori, M.
(Funding: New
Energy and
Industrial
Technology
Development
Organization)
University of Tehran
(Iran)
Ahmadnia-
Feyzabad, S.,
Khodadadi, A.A.,
Vesali-Naseh, M.,
Mortazavi, Y.
Chonbuk National
University (South
Korea)
Rai, P., Yu, Y.-T.
(Funding:
Post-BK21 Program
Ministry of Education
and Human-
Resource
Development and
the National
Research
Foundation)
Abbreviated Citation
Preparation of layered
organic-inorganic
nanohybrid thin films of
molybdenum trioxide
with polyaniline
derivations for aldehyde
gases sensors of several
tens ppb level.
[2008, Sensors and
Actuators B,
128:512-520]
Highly sensitive and
selective sensors to
volatile organic
compounds using
MWCNTs/SnO2
[2012, Sensors and
Actuators B,
166-167:150-155]
Citrate-assisted
hydrothermal synthesis
of single crystalline ZnO
nanoparticles for gas
sensor applications
[2012, Sensors and
Actuators B, 1 73:58-65]
Sensor Technology
Type
Polymer
film,
hybrid
Nano-
based
Nano-
based
Description
(Name)
MoO3 with PANi and
MoO3 with PoANIS
(poly(o-anisidine)
thin film organic-
inorganic hybrid on
LaAIO3/SiO2/Si
substrate with gold
comb-type
electrodes
0.05 wt% and 0.10
wt% MWCNTs on
SnO2, screen
printed on the
surface of alumina
substrate with gold
electrodes
ZnO nanoparticles
synthesized via
hydrothermal
method
Pnllntant/
miiuidnif
Parameter
Acetalde-
hyde,
formalde-
hyde
Acetalde-
hyde,
acetone,
ethanol,
toluene,
TCE
Acetalde-
hyde, CO,
NO2,
ethanol
Reported
Detection
Capability
LDL:
25-400 ppb,
(0.025-0.4
ppm
Up to 7 ppm
(acetalde-
hyde)
All tested at
300 ppm,
acetalde-
hyde also
tested:
0.2-5 ppm
Tested
ranges:
CO: 10-1000
ppm
ethanol and
acetaldehyde
25-250 ppm
NO2:
5-1 00 ppm
Re-
sponse
Time
Size
~1 cm
Automation and
Network
Capability
Application and Operation Notes
Study aim is to create a sensing
film appropriate for applications to
ventilation systems in buildings that
can detect at ppb level (instead of
ppm). (PANi) MoO3 film was
reported to have the strongest
response to formaldehyde, while
(PoANIS)MoO3 showed equal
response to both. Baseline drift
due to difficulties of gas desorption
remained a problem, especially
with acetaldehyde; the authors aim
to address this limitation in future
research.
Gases were tested over a range of
temperatures at 300 ppm. Optimal
response for acetaldehyde was
reported to be at 200°C. The
addition of MWCNTs increased
sensor selectivity by 2.4 times for
acetaldehyde with significant
responses to sub-ppm
concentrations.
Gases were tested over a range of
temperatures (27-250°C) and
concentrations. The maximum
response for acetaldehyde was
recorded at 250 ppm and 400°C.
Response decreased with
decreasing temperature. Recovery
time for ethanol and acetaldehyde
was poor when compared to that of
CO.
E-20
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Detection Technique: Spectroscopy
5
np
6
np
7
np
Institute of
Environmental
Pollution and Health,
School of
Environmental and
Chemical
Engineering,
University Shanghai
Huang, J, Feng, Y L,
Xiong, B, Fu, J.M.,
Sheng, G. Y.
National Research
Council of Canada,
Institute for
Research in
Construction
(Canada)
Deore B Diaz-
Quijada, A., Wayner,
D. D.M., Stewart, D.,
Won.D.Y., Waldron,
p.
(Funding: NRCof
Canada, ICT sector)
Science and
Engineering services
Inc.,
Prasad, C.R., Lei, J.
Mass Tech Inc.
Shi, I/I/., Li, G.
Pranalytica Inc.
Dunayevskiy, /.,
Pat el, C.K.N.
Ambient levels of
carbonyl compounds in
Shanghai, China
[2009]
http://www.ncbi. nlm.nih.g
ov/pbumed/1 9927828
An electronic nose for
the detection of carbonyl
species
[20 11, ECS
Transactions,
35(7):83-88]
http://www.nrc-
cnrc.gc.ca/obj/irc/doc/pu
bs/nrcc54483.pdf
Laser photoacoustic
sensor for air toxicity
measurements
[2012, Advanced
Environmental,
Chemical, and Biological
Sensing Technologies
IX, Society of
Photoelectric engineers]
http://proceedings.spiedi
gitallibrary.org/mobiie/pr
oceeding.aspx?articleid=
1353801
High-
perfor-
mance
liquid
chroma-
tography
(HPLC)-
UV
Lumines-
cence,
polymer
Spec-
troscopy,
laser
Solution casting
polymer (polyaniline
and polypyrrole
derivatives) at a
given doping level
on a glass substrate
with or without four
Au lines (electrodes)
with respective pads
to align with
commercial probe
head)
Laser photoacoustic
spectroscopy
(LPAS) sensor,
broadband
measurement with
one or more tunable
laser sources,
infrared (IR) QCL,
external cavity
grating tuner
Formalde-
hyde,
acetalde-
hyde,
acetone,
2-
butanone
Formalde-
hyde;
also
tested
acetalde-
hyde
HAPS
(benzene,
formalde-
hyde,
acetalde-
hyde)
simultan-
eously
250 ppb
formalde-
hyde at 44%
relative
humidity
("real world"
conditions)
A few ppb
5-60 sec
3 min
(Multisensor
system)
Not indicated
(Mountable)
Not indicated
HPLC-UV was used to separate
acrolein from acetone
Polyaniline and polypyrrole
derivatives interact with a carbonyl
group to produce measureable
changes in resistivity, which can be
used for e-nose (electronic nose)
sensing of carbonyls (reaction
between carbonyl and nitrogen
spurs molecular recognition).
This technology is reported as
being capable of simultaneously
measuring several HAPs (per
preliminary spectral pattern
recognition algorithm).
E-21
-------
31 October 2013
ID
8
np
Organization
Author (Funding)
Yong-Hui, L, Xiao-
An, C., Fu-Gao, C.,
etal.
Abbreviated Citation
A gaseous acrolein
sensor based on
cataluminescence using
ZrOMgO composite
[2011, Chinese Journal
of Analytical Chemistry,
39(8):1213-1217]
Sensor Technology
Type
Catalumi-
nescence
(CTL),
nano-
com-
posite
Description
(Name)
Nanosized
ZrO2/MgO
composite
Pollutant/
Parameter
Acrolein
Also
tested:
acetalde-
hyde,
methanol,
benzene,
toluene,
dimethyl-
benzene
Reported
Detection
Capability
Indicated
limit:
1.65mL/m3
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Optimal temperature was reported
to be 269°C. Optimal gas flow was
found to be 200 mL/min. Optimal
wave length was reported to be
425 nm.
Reference Commercial Sensor
9C
Teledyne
Technologies
http://vwvw.teledyne-
ai.com/products/4060.asp
Gas
chromato
graph-
flame
ionization
detector
(GC-FID)
(4060/FID)
Acetalde-
hyde
Indicated limit
(per vendor
input): 10 ppb
Real time data including
chromatograms which can be
superimposed to verify
repeatability. Gas chromatography
allows sensing of single or multiple
components from an air stream
which may include interferents.
Acrolein
Detection Technique: Spectroscopy
1a
1b
University of Alberta,
Edmonton (Canada).
Department of
Chemistry,
Department of
Electrical and
Computer
Engineering
Marine, J., Lim, A.,
Tulip, J., Jager, W.
(Funding: Canada
Foundation for
Innovation, Natural
sciences and
Engineering
Research Council of
Canada, and an
Alberta Ingenuity
Postdoctoral
Fellowship)
Sensitive detection of
acrolein and acrylonitrile
with a pulsed quantum
cascade laser
[2011, Applied Physics
B., 107: 441-447]
http://vwvw.springerlink.eom/c
Ontent/tk0w6v2k3v3t0208/full
text.pdf
QCL,
inter- and
intra-
pulsed,
spectros-
copy
Inter-pulse:
5-1 Ons,
2.3 cm'1 frequency
scan
Intra-pulse: up to
500 ns, 2.2 cm"1
spectral window;
multipass Herriot
cell configuration
with a 250-m path
length
Acrolein,
acrylo-
nitrile
Inter-pulsed:
acrolein limit:
3 ppb;
intra-pulsed:
acrolein limit:
6 ppb
-10 sec
(Fixed/semi-
portable unit)
Uses a pulsed, distributed
feedback QCL to detect acrolein.
Using a room-temperature
mercury-cadmium-telluride
detector resulted in a cryogen-free
spectrometer.
E-22
-------
31 October 2013
ID
2
np
Organization
Author (Funding)
Yong-Hui, L, Xiao-
An, C., Fu-Gao, C.,
etal.
Abbreviated Citation
A gaseous acrolein
sensor based on
cataluminescence using
ZrOMgO composite
[2011, Chinese Journal
of Analytical Chemistry,
39(8):1213-1217]
Sensor Technology
Type
Lumines-
cence
Description
(Name)
Nanosized
ZrO2/MgO
composite
Pollutant/
Parameter
Acrolein
and
others
Reported
Detection
Capability
Indicated
limit:
1.65mL/m3
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Optimal temperature was reported
to be 269°C. Optimal gas flow was
reported to be 200 mL/min.
Optimal wave length was reported
to be 425 nm.
Reference Commercial Sensor
C3
Industrial Monitor
and Control
Corporation
http://vwvw.ftirs.com/Products
Services/ExtractiveMonitorin
g.aspx
Extractive
Fourier
transform
(FT) IR
cell
100 meter cell
Acrolein
and
others
Acrolein:
limit: 7 ppb
100m
(towed
behind
trailer)
(Fixed/semi-
portable unit)
Cells can be heated to >200°C;
intended for industrial monitoring
and process control; may be
customized per consumer needs.
Ammonia
Detection Technique: Chemistry
1
2
Chinese Academy of
Sciences
Meng, F.-L., Huang,
Z.-J., and
colleagues, also at
Anhui Polytechnic
University
(Funding: One
Hundred Person
Project of the
Academy, National
Natural Science
Foundation of China,
and others)
Georgia Tech (GT)
University
Tentzeris, M.
Naishadham, K.
(Funding: NSF)
Electronic chip based on
self-oriented carbon
nanotube microelectrode
array to enhance the
sensitivity of indoor air
pollutants capacitive
detection
[2011, Sensors and
Actuators B, 153:103-
109]
http://pdn.sciencedirect.com/
science? ob=Miamilmagell
RL& cid=271353& user=17
2220~7&_pii=S092540051 000
81 8X&_check=y&_origin=se
arch&_zone=rslt_list_item&_
coverDate=201 1 -03-
31&wchp=dGLzVBA-
zSkzV&md5=ffObe4989bfbde
8f28e9d5daeb7bfae9/1 -s2.0-
S092540051 00081 8X-
main.pdf
http://vwvw.gtri.gatech.edu/ca
sestudy/paper-based-
wireless-sensors;
http://www.gtri.gatech.edu/ca
sestudy/polymer-coated-
optical-sensor-explosives-
detection
Nano-
based
Nano-
particle
Compares
nanotube-based
electronic chip (Si
dielectric medium,
Au and Si
electrodes) with
self-oriented
MWCNTs
microelectrode array
Sonicated ink
containing
functionalized CNTs
with silver (Ag)
nanoparticles in
emulsion on paper
via InkJet printer
Formalde-
hyde
ammonia
toluene
NH3 (for
trace
explo-
sives,
chemical
is attracted
to polymer
coating)
Ammonia:
3.1 ppm
Indicated
limit: 5 ppm
<10sec
Very
small
Compact
Not indicated
(Mountable)
(Mountable)
Response time is limited by the
time it takes to load and remove
analyte from the chip. Liquid
samples placed in sample
chamber, nitrogen used as carrier
gas to flow through the chamber.
Can potentially be used to detect
explosives at distance. Wireless
sensor nodes need relatively little
power; could use thin-film
batteries, solar cells or power-
scavenging and energy-harvesting
techniques; GT Research Institute
is investigating passive operation
with no power consumption.
E-23
-------
31 October 2013
ID
3
4
5
6
Organization
Author (Funding)
Hanyang University,
(South Korea)
Nguyen, T.-A.; Kim,
Y. S.; and others,
including at Pusan
National University
Solapur University,
Materials Research
Laboratory (India)
Bhabha Atomic
Research Centre
(India)
Pawar, S.G.,
Chougule, M.A.,
D^+II \/ D
Par//, V.D.
Shinwoo Electronics
Co., Ltd.
Korea University
/O Kr\ra^\
(o. r\oreaj
Dong, K.Y.,Ju,B.K.
Yonsei University
/c kWo^
^O. rMJlcdJ
Choi, H.H.
The Hong Kong
University of Science
and Technology
(China),
He, J., Zhang, T-Y.,
Chen, G.
Abbreviated Citation
Polycrystalline tungsten
oxide nanofibers for gas-
sensing applications
[Aug. 201 1 , Sensors and
Actuators B 160:39-45]
http://pdn.sciencedirect.com/
science? ob=Miamilmagell
RL& cid=271353& user=17
22207& pii=S092540051100
751 9&_check=y&_origin=bro
wse&_zone=rslt_list_item&_c
overDate=2011-12-
15&wchp=dGLzVlk-
zSkzV&md5=220df24b26528
d3d69cd1fb06c53dc98/1-
S2.0-S092540051 100751 9-
main.pdf
Development of
nanostructured
polyaniline-titanium
dioxide gas sensors for
ammonia recognition
[2012, Journal of Applied
Polymer Science,
125(2): 1418-1424],
http://onlinelibrary.wiley.com/
doi/10.1002/app.35468/fullpd
f
Gas sensor for CO and
WHs using
polyaniline/CNTs
composite at room
temperature
[2010, IEEE,
International Conference
on Nanotechnology Joint
Symposium with Nano
Korea]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5697782&isnumber=56977
24
Ammonia gas-sensing
characteristics of
fluorescence-based
poly(2-(acetoacetoxy)-
ethyl methacrylate) thin
///ms[2012, Journal of
Colloid and Interface
Science, 373:94-101]
Sensor Technology
Type
Nano-
particle
Polymer
film,
hybrid
Polymer
film
Organic
polymer
film (also
spectros-
copy:
fluores-
cence)
Description
(Name)
WO3 nanofibers
(40-nm diameter) Si
wafer stuck to Al foil
with pair of comb-
shaped
(interdigitated)
Au electrodes
formed on SiO2/Si
substrate, sensing
area is3x 10mm2
with electrode gap
of 300 |jm
Nanostructured
0-50% PANi-TiO2
(fabricated by spin
coating technique)
thin film deposited
on glass substrate
(1 mm wide strips)
PANi/SWCNTs film
dispersed in sodium
dodecyl sulfate and
applied over Ti/Au
electrodes of an IDE
by photolithography
PAAEMA latex thin
films cast onto
quartz discs; UV-Vis
spectra emitted
(275 nm)
Pnllntant/
miiuidnif
Parameter
NH3
NH3,
ethanol,
methanol.
NO2, H2S
CO, NH3
(also
benzene
and NO2)
NH3
Reported
Detection
Capability
Indicated
limit: 10 ppm
NH3: 20 ppm
and 100 ppm
tested
Others tested
at 100 ppm
ppm level
(CO tested at
80 ppm, NH3
at 35 ppm)
Tested
range:
54-540 ppm
Re-
sponse
Time
>1 hr
72 sec
at
20 ppm
NH3;
41 sec
at 100
ppm
NH3
Fast re-
sponse
and
recov-
ery
80 sec
at
54 ppm
<30 sec
at 540
ppm
Size
Stick of
gum
5 mm x
17mm,
480 |jm
thick
Automation and
Network
Capability
Not indicated
(Fixed/semi-
portable unit)
Application and Operation Notes
Study analyzed the practicality of
electrospun tungsten oxide
nanofibers for NH3 detection. It
was reported that the optimal
operating temperature for this
device is 300°C because at this
temperature the response times
are at their lowest and sensing is
most accurate.
No response at room temperature
for 20 ppm NH3 using TiO2
nanoparticles alone; needed to be
operated at 200° C. Pure PANi
composite showed little response,
while the PANi-TiO2 composite was
the most responsive at room
temperature. Response time
decreased as NH3 concentration
increased, but so did recovery time
(possibly per lower desorption rate).
Demonstrates the use of
PANi/SWNTs composite-based
sensor for mixed gas detection.
The changes in resistance of the
sensor determine the presence of
a single gas or mixture of gases.
This composite has a large
surface-to-volume ratio which
makes it a good candidate for new
gas sensors.
Response time increases as
concentration of NH3 decreases.
Operates at room temperature.
E-24
-------
31 October 2013
ID
7
np
8
np
9
np
Organization
Author (Funding)
University of South
Carolina,
Nomani, Md.W.K., et
a/.
SENS4, LLC
James, J.
(Funding: NSF,
Army Research
Office, and SENS4,
LLC [through an
NSF grant])
University of
Bayreuth
Schonauer, D.;
Sichert, /., Moos, R.
Brno University of
Technology (Czech
Republic)
Hrdy, R.,
Vorozhtsova, M.,
Drbohlavova, J.,
Prasek, J., Hubalek,
J. (Funding: Czech
Ministry of Education
and Grand Agency
of Czech Republic)
Abbreviated Citation
Highly sensitive and
multidimensional
detection ofNO2 using
InOi thin films
[Aug. 201 1 , Sensors and
Actuators B,
160:251-259]
http://pdn.sciencedirect.conV
science? ob=Miamilmagell
RL& cid=271353& user=17
222CJ7&_pii=S092540051 1 00
6861&_check=y&_origin=bro
wse& zone=rslt list item& c
overDate=2011-12-
15&»chp=dGLzVlk-
zSkzV&md5=a9efb7781 a062
4784753f0979694f695/1 -
S2.0-S092540051 1006861-
main.pdf
Vanadia doped-titania
SCR catalysts as
functional materials for
exhaust gas sensor
applications
[2011, Sensors and
Actuators B,
155:199-205]
http://pdn.sciencedirect.conV
science? ob=Miamilmagell
RL& cid=271353& user=17
22207&_pii=S092540051 000
91 59&_check=y&_origin=sea
rch&_zone=rslt_list_item&_c
overDate=201 1 -07-
05&»chp=dGLbVIV-
zSkzS&md5=5e39e5ec9429
6bcd2c21 77384bd57895/1 -
S2.0-S092540051 00091 59-
main.pdf
Electrochemical
transducer utilizing
nanowires surface
[2010, IEEE, 33rd Int.
Spring Seminar on
Electronics Technology]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5547340&isnumber=55472
45
Sensor Technology
Type
MOS
MOS
Nano-
based
Description
(Name)
Interdigitated metal
fingers, 100 |jm, a
part of Ti (5nm)/
Au (50 nm)
deposited on ln2O3
thin film (from
Thinfilms Inc.)
coated on AI2O3
ceramic substrate
Selective catalytic
reduction catalyst;
materials are based
on vanadia-doped
tungsten-titania gas
sensing films
TiO2 modified Au
nanowires applied
to surface of
sensing electrode
Pollutant/
Parameter
NO2;
also
assessed:
mixture of
NO2, NH3
NH3
(tested
with gas
containing
NOX)
CO2, NO2,
02, NH3
Reported
Detection
Capability
Re-
sponse
Time
A few
sec
Size
Small
Automation and
Network
Capability
(Mountable)
Not indicated
(Mountable)
(Mountable)
Application and Operation Notes
20% reduction in conductivity at
20 ppb, normal temperature
(20°C); in a vacuum, ln2O3
nanowires indicated sensitivity of
5 ppb; per an average ambient/
background level of 1 1 ppb, the
concentration differential of 9 ppb
is identified as responsible for the
conduction change (indicating even
more impressive sensitivity to the
authors); the system can monitor
minute deviations from the general
average background value (30-50
mV) at a frequency of 1 kHz, at an
operating temperature of >150°C.
Reported to provide accurate
results for ammonia detection
around 500°C. NO2 sensitivity is -
17%; in the presence of CO and
H2, 8% sensitivity; when water
concentration increases from
0-3%, 3% sensitivity
Operated at 5-30 keV in high
vacuum mode with an operating
temperature range between 300
and 400°C.
E-25
-------
31 October 2013
ID
10
np
Organization
Author (Funding)
Linkoping University
Pearce, R.,
Soderlind, F.,
Hagelin, A., Kail,
P.O., Yakimova, R.,
Spetz, A.L.
Chalmers University
of Technology
Becker, E.,
Skoglundh, M.
Abbreviated Citation
Effect of water vapour on
gallium doped zinc oxide
nanoparticle sensor gas
response
[2009, IEEE Sensors,
Conference]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5398276&isnumber=53981
21
Sensor Technology
Type
Nano-
based
Description
(Name)
Gallium (Ga)-doped
ZnO nanoparticle
sensor, resistor type
Pollutant/
Parameter
O2, NO,
NO2, H2,
CO, NH3
Reported
Detection
Capability
Re-
sponse
Time
Size
~10 mm
(per
image)
Automation and
Network
Capability
Application and Operation Notes
Investigated effects of background
water vapor and oxygen on the
response of nanoparticle Ga-doped
ZnO resistive sensors in high
temperature applications to detect
O2, NO, NO2, H2, CO, and NH3.
The presence of water vapor
increased the response and
recovery rates and improved
baseline stability. Responsiveness
was reported to decrease in humid
environments as the gas
concentration increased, thought to
be caused by competing reaction
mechanisms. These mechanisms
require more studies using DRIFT
spectroscopy to determine surface
species. Operates at 500°C.
Detection Technique: Spectroscopy
11
12
University of
Limerick (Ireland)
O'Keeffe, S., Manap,
H., Dooly, G., Lewis,
E.
University of
Limerick (Ireland)
Manap, H., Dooly,
Q Muda, R.,
O'Keeffe, S., Lewis,
.
(Funding: Higher
Education Authority,
PRTLI cycle 4 for
Environmental and
Climate change,
University of
Malaysia, Ministry of
Higher Education)
Real-time monitoring of
agricultural ammonia
emissions based on
optical fibre sensing
technology
[2010, IEEE Sensors
Conference]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5690821
Cross sensitivity study
for ammonia detection in
ultra violet region using
an optical fibre sensor
[2009, Third International
Conference on Sensor
Technologies and
Applications]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=521 0951 &isnumber=521 08
28
UV
absorp-
tion
UV
absorp-
tion
2 ultraviolet
nonsolarizing
(UVNS) optical
fibers (644 nm),
60-cm stainless
steel gas cell,
HR2000
spectrometer
(Ocean Optics)
200-230 nm UV
range
NH3
NH3
Indicated
range:
1-50 ppm
Indicated
limit: 7 ppm
(estimated)
4 sec
<3sec
(Fixed/semi-
portable unit)
(Fixed/semi-
portable unit)
This system is used in confined
areas to determine the amount of
ammonia in the space with
real-time measurements by using
an optical fiber sensor system that
detects in the UV absorption
region.
Investigates the use of UV light
sources to detect NH3 in an optical
fiber sensor. Using UV as a light
source reduces sensor sensitivity
to other gases (namely water
vapor) in the ambient air. A
computer using SpectraSuite
software enables acquisition of
spectrometer data in real time.
Future studies on this technique
plan to include finding the lowest
concentration detectable by the
sensor and the integration of a
commercial NH3 sensor for more
accurate concentration verification.
E-26
-------
31 October 2013
ID
13
np
Organization
Author (Funding)
Utah State
University, University
of Iowa; (Iowa);
Space Dynamics
Laboratory (SDL)
(Utah) Hipps, L,
Silva, P., (UT State);
Zavyalov, V.V.,
Wllkerson, T.,
Bingham, G.E.
(SDL), others
(Funding: USDA)
Abbreviated Citation
http://vwvw.sdl.usu.edu/progr
ams/aglite;
http://vwvw.ars.usda.goV/is/A
R/archive/aug06/ames0806.
pdf
Sensor Technology
Type
LIDAR
Description
(Name)
Scanning 3-color
LIDAR
FTIR
(Aglite)
Pollutant/
Parameter
PMand
gases,
including
NH3, H2S,
NOX
Reported
Detection
Capability
Re-
sponse
Time
Size
Large
suitcase
Automation and
Network
Capability
(Remote sensing)
Application and Operation Notes
Has been used to monitor an entire
CAFO facility (e.g., swine finishing)
and other sources, including
multiple diffuse source dairy, cotton
gin, and almond harvesting. The
goal is to measure amount of CO2,
CH4, N2O, and other greenhouse
gases released from soil into the
atmosphere and determine how
different crop- and soil-
management methods affect these
exchanges.
Reference Commercial Sensor
c
14
Teledyne
Technologies
http://vwvw.teledyne-
ai.com/pdf/lga-4000.pdf
Laser
absorp-
tion,
TOLAS
Transmitter: diode
laser, laser driver,
HMI modules,
realizing diode laser
driving, spectrum
data processing;
receiver: photo-
electric sensor,
signal processing
and purge control
modules (Process
LaserGas Analysis
System, Model
LGA-4000)
O2, CO,
CO2,
water,
H2S, HF,
HCI,
HCN,
NH3, CH4,
C2H2,
C2H4
NH3:
Indicated
limit: 0.4 ppm
Indicated
range:
0-40 ppm
(other
pollutant info
provided via
link)
<1 sec
RS485/GPRS/
Bluetooth digital
interface
(Fixed/semi-
portable unit)
Useful in almost any environment,
including high temperatures,
pressures, dust densities, and
corrosion; requires a 24 volts direct
current (VDC) (220 VAC optional,
<20 W), as well as 0.3-0.8 MPa,
99.99% N2 purging gas; is
operable between -30 and 60°C
(ambient temp); calibration and
maintenance recommended
-------
31 October 2013
ID
2
3
4
Organization
Author (Funding)
University of Illinois
at Urbana-
Champaign
Hengwei, L, Jang,
M., Suslick., K.S.
(Funding: NIH
Genes, Environment,
and Health Initiative)
Duke University
Vann (^ t-f
j any, o.-n.
Chinese Academy of
Sciences
Y. Wan, H. Li, J. Liu,
F. Meng, Z. Jin, L.
Kong, and J. Liu
(Funding: "973"
State Key Project of
Fundamental
Research for Nano
science and
Nanotechnology, the
National Natural
Science Foundation
of China, and others)
Abbreviated Citation
Preoxidation for
colorimetric sensor array
detection of VOCs
[2011, JACS,
133:16786-16789]
http://vwvw.scs.illinois.edu/su
slick/documents/jacs.201 1 .pr
eox.pdf
Development of
nanosensorto detect
mercury and volatile
organic vapors
[July 2010, thesis]
http://dukespace.lib.duke.edu
/dspace/bitstream/handle/1 0
161/3060/D_Yang_Chang%2
OHeng a 2010.pdf?sequenc
e=1
Sensitive detection of
indoor air contaminants
using a novel gas sensor
based on coral-shaped
tin dioxide
nanostructures
[2012, Sensors and
Actuators, 165:24-33]
http://ac.els-
cdn.com/S092540051 2001 0
62/1 -S2.0-
S092540051 2001 062-
main.pdf? tid=601aeea1c25
ade4743a4b1 04952fcec2&ac
dnat=1339604316 9f65193c
a1 a1 71 af77fb721 afc87e2dc
Sensor Technology
Type
Pre-
oxidation
Nano-
based
(various)
Nano-
based
Description
(Name)
Vapor stream
passes through
chromic acid and
silica-coated pre-
oxidation tube
before colorimetric
array
Nanosensor with
SnO2, Au and
polypyrrole (PPy) on
SWCNTs
Coral-shaped SnO2
nanostructures,
prepared by
hydrothermal/
annealing
processes coated
on AI2O3 tubes,
Ni-Cr heater wire
Pollutant/
Parameter
Phenol
and
others,
including
benzene;
20 VOCs
Hg,
VOCs:
benzene,
methyl
ethyl
ketone
fMFKI
^ivi^rxy ,
hexane,
xylene
Benzene,
formal-
dehyde,
toluene
and
acetone
Reported
Detection
Capability
Benzene:
0.20 ppm
within 1 .4%
of OSHA PEL
Indicated as
ppb;
benzene'
sensitivity
-2% in
13-65 ppm
rsnos
Benzene:
...
testec range.
50-150 ppm
Re-
sponse
Time
Size
Benchtop
Automation and
Network
Capability
(Fixed/semi-
portable unit)
Application and Operation Notes
Optimum response was reported
using a disposable 2-cm by 3-mm
i.d. Teflon tubing with 30 mg of
chromic acid on silica. The pre-
oxidation agent must be newly
prepared for each testing cycle.
VOC vapors were produced by
bubbling nitrogen through the pure
compound. Color changes of the
array are concentration-dependent
and provide semi-quantitative
analysis; changes in relative
humidity were not reported to
generally affect the response even
at low analyte concentrations.
Fast and sensitive for individual
chemicals, but not found in this
study to be successful for mixtures.
Real-time gas sensing used to
detect contaminants in indoor air;
coral-shaped receptacles gave
faster results. Response to gases
is increased when particle size is
reduced. Operates between 350
and 400°C.
E-28
-------
31 October 2013
ID
5
6
np
7
np
Organization
Author (Funding)
University of
Bahkesir,
Department of
Physics and the
Department of
Chemistry (Turkey)
Acikbas, Y., Capan,
R., Erdogan, M.,
Yukruk, F.
Dalian University of
Technology (China)
Wang, J.; Wu, I/I/.;
and colleagues
(Chen, X.R., Yao,
P.J., Ji, M., Qi, J.Q.)
(Funding: National
Natural Science
Foundation of China)
Shinwoo Electronics
Co., Ltd.
Kim, 1.
Korea University
(S. Korea)
Dong, K.Y.,Ju,B.K.
Yonsei University
(S. Korea)
Choi, H.H.
Abbreviated Citation
Thin film characterization
and vapor sensing
properties of a novel
perylenediimide material
[2011, Sensors and
Actuators B, 160(1):65-
71]
http://ac.els-
cdn.com/S092540051 1 0065
51/1-S2.0-
S092540051 1006551-
main.pdf? tid=2d70fc366b81
a2841 361 4eOef1 42fb98&acd
nat=1338392430 f688fab1cb
35779ecbff302d5582f45f
Detection of indoor
formaldehyde
concentration using
LaSrFeOs-doped SnO2
gas sensor
[2010, Key Engineering
Materials, 437:349-353]
http://vwvw.ets. ifmo.ru:8101/t
omasov/konferenc/AutoPlay/
Docs/Volume%203/6_51 .pdf
Gas sensor for CO and
WHs using
polyaniline/CNTs
composite at room
temperature
[2010, IEEE,
on Nanotechnology Joint
Symposium with Nano
Korea]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5697782&isnumber=56977
24
Sensor Technology
Type
Polymer
thin film
MOS
Polymer
film
Description
(Name)
IB thin film-based,
N,N'-(glycine t-
butylester)-3,4,9,10-
perylendediimide)
thin film deposition,
QCM, Au electrodes
MOSSnO2(2wt%
doped with La-Sr-
FeO3 as ceramic
tube coating,
electrodes and
sensors then affixed
PANi/SWCNTs film
dispersed in sodium
dodecyl sulfate and
applied over Ti/Au
electrodes of an IDE
by photolithography
Pollutant/
Parameter
Chloro-
form,
benzene,
toluene,
ethyl
alcohol
_._ j
ana
isopropyl
alcohol
Formalde-
hyde
(also
tested:
ethanol,
methanol,
and
benzene)
CO, NH3
(also
benzene
and NO2)
Reported
Detection
Capability
Chloroform:
Indicated
limit:
1 5,300 ppm;
benzene:
Indicated
limit:
17,100 ppm;
toluene:
Indicated
limit:
18,600 ppm
Re-
sponse
Time
3 sec re-
sponse,
4 sec
recov-
ery
1 20 sec
Fast re-
sponse
and
recov-
ery
Size
Very
small
5 mm x
17 mm,
480 |jm
thick
Automation and
Network
Capability
(Mountable)
Not indicated
(Mountable)
Application and Operation Notes
Requires a 4-sec recovery period.
Absorbance was measured over
many Langmuir-Blodgett (LB) thin
films. Operates at room
temperature.
Evaluated with N vapor in testing
chamber. Highest response
occurred when the sensor reached
a temperature of 370°C. The
response of this sensor to
alcoholate gases was higher than
the response to formaldehyde and
will be improved by integrating
sensor arrays and neural networks.
For the described experiment the
sensors were aged at 5V for 240 hr
and operate at 370°C.
This research demonstrates the
use of PANi/SWNTs composite-
based sensor for mixed gas
detection. The changes in
resistance of the sensor determine
the presence of a single gas or
mixture of gases. This composite
has a large surface-to-volume ratio
which makes it a good candidate
for new gas sensors.
E-29
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Detection Technique: Spectroscopy
8
9
10
NIT Microsystem
Integration lab,
Microsensor
Research Group
(Japan)
Camou, S., Horiuchi,
T., Haga, T.
University of Aveiro
(Portugal) and
ISEITA/iseu-lnstituto
Piaget, Estrada do
Alto do Gaio
(Portugal)
Silva, L.I.B., Rocha-
Santos TAP
Duarte, A.C.
(Funding: FCT
[Portugal] for project,
and Ph.D. grant)
Georgia Institute of
Technology
Young, C.R.,
Menegazzo, N., et
al.
from Exxon Mobile
and the University of
Ulm (Germany)
(Funding/support:
Exxon Mobil and
Engineering
Company, Exxon
Mobil Biomedical
Sciences, Inc.)
Ppb level benzene gas
detection by portable
BTX sensor based on
integrated hollow fiber
detection cell
[2006, IEEE Sensors]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?arnumber=041
78603
Remote optical fibre
microsensor for
monitoring BTEX in
confined industrial
atmospheres [2009,
Talanta, 78(2):548-552]
http://ac.els-
cdn.com/S003991 40080088
86/1 -S2.0-
S003991 4008008886-
main.pdf? tid=123fc547c2b1
754bf3971 075f3dOba6e&acd
nat=1337968655 7377646d
ee760377c1 4ed1 7841 fa094b
Infrared hollow
waveguide sensors for
simultaneous gas phase
detection of benzene,
toluene, and xylenes in
field environments
[2011, Anal. Chem.,
83:6141-6147]
http://pubs.acs.org/doi/pdfplu
s/10.1021/ac1 031034
UV-vis
absorp-
tion
Laser
absorp-
tion
(optical
fiber)
Thermal
desorp-
tion (TD)-
FTIR-
hollow
wave-
guide
(HWG)
SBA16 mesoporous
silica as pre-
concentrator, two
pumping systems,
Al-coated hollow
fiber inside glass
cell, with optical
fibers
Monomode optical
fiber coated with
nanometric
poly[methyl(3,3,3-
trifluoropropyl)siloxa
ne] (PMTFPS)
polymer film
TD as a pre-
concentration step,
HWG to propagate
mid-infrared (MIR)
radiation from light
source and serves
as a miniaturized
cell.
Benzene,
BTX
(benzene,
toluene,
xylene)
Benzene,
BTEX
(benzene,
toluene,
ethyl ben-
zene,
xylene)
BTX
Benzene:
Indicated
limit: 1 ppb
(concentra-
tion range
tested:
0-10 ppb)
Indicated
limit:
0.00235 ppm
Indicated
limit: 5 ppb
-20 min
(until
precon-
centra-
tor
satura-
tion)
A few
sec per
sub-
stance;
9 min
total for
all
-43 sec
Portable
Portable,
compact
(Handheld)
(Fixed/semi-
portable unit)
Benzene is diluted in nitrogen
carrier gas. Originally tested for
indoor usage so the benzene could
be isolated. It is not known if it will
work with other ambient particles in
the air.
Applied to air monitoring in a
confined industrial environment at
150°C. Allows for in situ and real-
time remote (60-m maximum)
monitoring. Performance compared
to that of a GC-FID device.
Direct and selective
real-time detection of BTX using
thermal desorption. Results
validated by GC-FID and
compared to commercially
available prototypes.
E-30
-------
31 October 2013
ID
11
np
12
np
Organization
Author (Funding)
Arizona State
University
Tsow, F., Forzani,
£., Rai, A., Wang,
R., and others
(Funding: NIH/NCI,
Motorola, Arizona
State University)
Yong-Hui, L., Xiao-
An, C., Fu-Gao, C.,
etal.
Abbreviated Citation
A wearable & wireless
sensor system for real-
time monitoring of toxic
environmental volatile
organic compounds
[2009, IEEE Sensors
Journal, 9(12):1734-
1740]
http://ieeexplore.ieee.org/xpl
slabs all.jsp?arnumber=529
1983
A gaseous acrolein
sensor based on
cataluminescence using
ZrOMgO composite
[2011, Chinese Journal
of Analytical Chemistry,
39(8):1213-1217]
Sensor Technology
Type
Laser
absorp-
tion
(tuning
fork)
Cata-
lumines-
cence
Description
(Name)
Array of polymer-
modified quartz
crystal tuning fork,
custom built filter,
frequency change
detection circuit
Nanosized
ZrO2/MgO
composite
Pollutant/
Parameter
VOCs,
BTEX
Acrolein
Akn
rtloU
tested:
acetalde-
L-, ,,J _
hyde,
methanol,
benzene,
toluene,
dimethyl-
benzene
Reported
Detection
Capability
"Beyond
OSHA's
requirement
for benzene"
Re-
sponse
Time
Size
Cell
phone
Automation and
Network
Capability
Bluetooth
technology,
wireless, graphic
user interface
software with
Visual Studio
(Microsoft) in cell
phone
(Wearable)
Application and Operation Notes
Uses sensor cartridge, sample
delivery and conditioning
components, electronic circuits for
signal processing, and wireless
communication chip; allows for
upgrades and easy disposal of old
tuning forks. Studies were
performed to demonstrate
capabilities with interfering
chemicals such as perfume and a
BTEX gas mixture; 5-minute
exposure cycles require 10- to
1 5-minute purging periods.
Optimal temperature was reported
to be 269°C. Optimal gas flow was
reported to be 200 mL/min.
Optimal wave length was reported
to be 425 nm.
Novel Sensor Systems Using Commercial Sensors
c
13
Gdansk University of
Technology, with
ARMAAG
Foundation (Poland)
Kaulski, R., et. al
(Funding:
Environmental
Protection and Water
Management Fund
of Gdansk Province)
Mobile system foron-
road measurements of
air pollutants
[2010, AIP Review of
Scientific Instruments,
81(4)]
http://rsi.aip.Org/resource/1/rs
inak/v81 /i4/p0451 04_s1 ?vie
w=fulltext
MOS (by
Figaro)
SnO2 MOS with
ceramic base in
ARPOL system
Benzene,
NO2, NOX,
CO, CO2
Benzene:
Indicated
limit:
0.0125ppb
Mobile
Uploads to
webpage
(Vehicle-mounted
unit)
Results collected over several
24-hour cycle test periods.
E-31
-------
31 October 2013
ID
c
14
Organization
Author (Funding)
Universitat Rovira I
Virgili (Spain),
Universite de
Franche Comte
(France), Universite
de Lorraine Lahlou
(France), and the
Gas Sensors Group
with the National
Centre of
Microelectronics
(Spain)
Lahlou, H., Sanchez,
J.B., Mohsen, Y.,
Vilanova, X., et. Al
Abbreviated Citation
A planar micro-
concentrator/injector for
low power consumption
microchromatographic
analysis of benzene and
1,3-butadiene
[2012, Microsyst
Technol,18: 489-495]
Towards a GC-based
microsystem for benzene
and 1,3-butadiene
detection: Pre-
concentration
characterization
[201 1 , Sensors and
Actuators B, 156: 680-688]
Sensor Technology
Type
GC, pre-
concen-
trator
Description
(Name)
Commercial sensor
with low power
consumption GC
microsystem
Pollutant/
Parameter
Benzene,
1,3-
butadiene
Reported
Detection
Capability
Benzene
Indicated
limit:
0.1 ppm;
1,3-butadiene
Indicated
limit:
0.5 ppm;
tested range:
2-10 ppm
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
This p re-concentrator was tested in
the presence of benzene and
1,3-butadiene concentrations
normally too low for the
commercial sensor to detect.
1 .02 mW/°C power consumption
level. Operates at 25°C.
Reference Commercial Sensors
c
15
C
16
Synspec
Synspec
http://www.synspec.nI/pdf/G
C955-600_Bu_Be. pdf
http://www.synspec.nI/pdf/G
C955-600_BTX.pdf
Photoio-
nization
detector
(PID), GC
PID, GC
(GC 955-603
Benzene and 1,3-
butadiene sensor)
(GC 955-601
Benzene/BTEX
analyzer)
Benzene
and 1 ,3-
butadiene
Benzene,
BTEX
Benzene
indicated
range:
9.4 ppb to
0.3 ppm
Indicated
range:
0.032 ppb to
0.3 ppm
15-min
cycle
time
15-min
cycle
time
(F/S-PSU)
(Fixed/semi-
portable unit)
Meant for ambient air sensing with
a 15-minute cycle time (220 VAC,
100 volt-amperes (VA); 1 10 VAC
available).
Meant for ambient air sensing with
a 15-minute cycle time (230 VAC,
100VA; 115 VAC available).
1,3-Butadiene
Detection Technique: Electrochemistry
1a
1b
University of
Michigan, Ann Arbor
Rowe, M.P.,
Steinecker, W.H.,
Zellers, E.T.
Exploiting charge-
transfer complexation for
selective measurement
of gas-phase olefins with
nanoparticle-coated
chemiresistors
[2007, Analytical
Chemistry, 79: 1164-
1172]
http://pubs.acs.org/doi/pdfplu
Sfull/10.1021/ac061305k
Nano-
based,
chemo-
resistor,
polymer,
UV-vis
Charge-transfer
mediated olefin
selective sensing
system with
chemiresistors
coated in composite
films of C8-MPN
and PtCI2
complexes.
Ethane,
ethylene,
n-butane,
1,3-
butadiene,
ethyl-
benzene,
styrene,
n-octane,
1 -octene
a. C8-PBP :
Indicated
limit 9.5 ppb
b. C8-MPN:
Indicated limit
24,000 ppb
5 min
expo-
sure
period
I MACC Software
Suite includes
FTIR control and
data server,
scripting engine
and editor, quanti-
zation method
development
tools, configurable
FTI R monitor Ul,
and a synthetic to
background tool.
5-minute exposure period is
mentioned (see article for more
detection capabilities) Additional
modules are available to integrate
data and alarms to external
systems.
E-32
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Novel Sensor Systems Using Commercial Sensors
c2
Universitat Rovira I
Virgili (Spain),
Universite de
Franche Comte
(France), Universite
de Lorraine Lahlou
(France), and the
Gas Sensors Group
with the National
Centre of
Microelectronics
(Spain)
/-/., Sanchez, J.B.,
Mohsen, Y.,
Vilanova, X., Berger,
F., et. al.
A planar micro-
concentrator/injector for
low power consumption
microchromatographic
analysis of benzene and
1,3-butadiene
[2012, Microsyst
Technol,18: 489-495]
Towards a GC-based
microsystem for benzene
and 1 ,3-butadiene
detection: Pre-
concentration
characterization
[2011, Sensors and
Actuators B, 156: 680-
688]
http://vwvw.springerlink.eom/c
ontent/428336485jlm3822/fu
ltext.pdf
Com-
mercial
sensor,
GC
micro-
system
Planar micro-
concentrator/
injector
Benzene,
1,3-
butadiene
Benzene:
Indicated
limit:
0.1 ppm;
1,3-butadiene
Indicated
limit:
0.5 ppm;
tested:
2.5-10 ppm
This p re-concentrator was tested in
the presence of benzene and
1,3-butadiene concentrations
normally too low for the
commercial sensor to detect;
requires low amounts of power.
1 .02 mW/°C power consumption
level. Operates at 400°C.
Reference Commercial Sensors
C3
C4
Synspec
Synspec
http://vwvw.synspec.nI/pdf/G
C955-600-800_POCP. pdf
http://www.synspec.nI/pdf/G
C955-600_Bu_Be. pdf
FID, PID
PID, GC
(GC 955-811 Ozone
Precursors Fraction
C2-C5)
(GC 955-603
Benzene and 1,3-
butadiene sensor)
1,3-
butadiene
and
others
Benzene
and 1 ,3-
butadiene
1,3-
butadiene:
Indicated
range:
1-300 ppb
rr
1,3-
butadiene:
Indicated
range:
9.05 ppt to
0.3 ppm
Semi-
con-
tinuous
30-min
cycle
j *"*•*
15-min
cycle
Chromatograms
are stored on PC
hard disk and can
be transferred by
network and
modem
connection.
Output options
are available for
communication
with other data
logging systems.
(Fixed/semi-
portable unit)
(Fixed/semi-
portable unit)
This sensor can be used to
analyze hydrocarbons emitted by
traffic as well as those in industrial
or household processes.
Hydrocarbons are concentrated on
a cooled trap, allowing low
detection levels, and the PID
detects unsaturated compounds
while the FID detects saturated
compounds (220 VAC, 200 VA;
110 VAC available).
Meant for ambient air sensing with
a 15-minute cycle time (220 VAC,
100VA; 110 VAC available).
E-33
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Formaldehyde
Detection Technique: Chemistry
1
2
3
NASA Ames
Research Center
(CA)
Lu, Y., Meyyappan,
M., Li, J.
(Funding: NASA)
Dalian University of
Technology (China)
Wang, J.; Wu, Wei;
and colleagues
(Chen, X.R., Yao,
P.J., Ji, M., Qi, J.Q.)
(Funding: National
Natural Science
Foundation of China)
Chinese Academy of
Sciences
Meng, F.-L, Huang,
Z.-J., and
colleagues, also at
Anhui Polytechnic
University
(Funding: One
Hundred Person
Project of the
Academy, National
Natural Science
Foundation of China,
National Basic
Research Program
of Chtnd 3nd Anhui
Provincial Natural
Science Foundation)
A carbon-nanotube-
based sensor array for
formaldehyde detection
[2011, Nanotechnology
22(5)]
http://iopscience.iop.org/095
7-
4484/22/5/055502/pdf/0957-
4484_22_5_055502. pdf
Detection of indoor
formaldehyde
concentration using
LaSrFeOs-doped SnO2
gas sensor
[2010, Key Engineering
Materials, 437:349-353]
http://vwvw.ets. ifmo.ru:8101/t
omasov/konferenc/AutoPlay/
Docs/Volume%203/6_51 .pdf
Electronic chip based on
self-oriented carbon
nanotube microelectrode
array to enhance the
sensitivity of indoor air
pollutants capacitive
detection
[2011, Sensors and
Actuators B, 153:103-
109]
http://pdn.sciencedirect.com/
science? ob=Miamilmagell
RL& cid=271353& user=17
22207&_pii=S092540051 000
81 8X&_check=y&_origin=se
arch&_zone=rslt_list_item&_
coverDate=201 1 -03-
31&»chp=dGLzVBA-
zSkzV&md5=ffObe4989bfbde
8f28e9d5daeb7bfae9/1 -s2.0-
S092540051 00081 8X-
main.pdf
Nano-
based
MOS
Nano-
based
32 sensor array of
SWCNTs including
Pd-doped, Rh-
loaded, ZnO- and
nanoAu-coated, and
polyethylene-imine
functionalized
MOSSnO2(2wt%
doped with La-Sr-
FeO3 as ceramic
tube coating,
electrodes and
sensors then affixed
Compares
electronic chip (Si
dielectric medium,
Au and Si
electrodes) with
self-oriented
MWPMTc
IVIW VOI N I o
microelectrode array
Formalde-
hyde
Formalde-
hyde
(also
tested
alco-
holates:
ethanol,
methanol,
and
benzene)
Formalde-
hyde
ammonia
toluene
10ppb
Formalde-
hyde
indicated
limit: SOppb
(tested in
range of 0-5
ppm)
(limits not
given for
others)
Formalde-
hyde:
300 ppb
18 sec
120 sec
<10sec
Very
small
Very
small
Not indicated
Not indicated
(Mountable)
Not indicated
(Mountable)
Response time of seconds, room
temperature operation; research to
create an e-nose for crew in the
International Space Station. 200 to
400°C operating temperature.
Evaluated with N vapor in testing
chamber. Response was highest
when the sensor reached 370°C.
The response of this sensor to
alcoholate gases was higher than
the response to formaldehyde and
will be improved by integrating
sensor arrays and neural networks.
For the described experiment the
sensors were aged at 5V for 240 hr
and operate at 370°C.
Response time is limited by the
time it takes to load and remove
analyte from the chip. Liquid
samples placed in sample
chamber, nitrogen used as carrier
gas to flow through the chamber.
E-34
-------
31 October 2013
ID
4
5
Organization
Author (Funding)
Hunan Cultural
University (China),
and others
Peng, Liang, and
others
(Funding: National
Natural Science
Foundation of China,
Start Foundation of
Hunan Agricultural
University, National
Science and
Technology Major
Projects, and
National
Environmental
Protection Public
Welfare Program)
Gdansk University of
Technology,
Gdansk, Poland
Gebicki, J.
Abbreviated Citation
Improvement of
formaldehyde sensitivity
ofZnO nanorods by
modifying with
Ru(dcbpy)2(NCS)2
[2011, Sensors
and Actuators B, 160:39-
45]
http://pdn.sciencedirect.com/
science? ob=Miamilmagell
RL& cid=271353& user=17
22207&_pii=S092540051 1 00
6381 &_check=y&_origin=bro
wse&_zone=rslt_list_item&_c
overDate=2011-12-
15&wchp=dGLzVlk-
zSkzV&md5=6a80f667c84fe
21 eeeOfefa4be2ff8d3/1 -s2.0-
S092540051 1006381-
main.pdf
A prototype of
electrochemical sensor
for measurements of
carbonyl compounds in
air
[2011, Electroanalysis,
23(8):1958-1966]
http://onlinelibrary.wiley.conV
doi/1 0. 1 002/elan.201 1 001 64/
abstract
Sensor Technology
Type
Nano-
based
Electro-
chemical,
square
wave
voltam-
metry
(SWV)
Description
(Name)
UV light-assisted
gas sensor made of
ZnO nanorods
dispersed in ETOH
and dripped on ITO,
modified with RuN3
Composed of Pt and
Au electrodes, ionic
liquid 1-hexyl,
3-methylimidazolium
bis(trifluoromethane
sulfonyl)imide as an
electrolyte and a
PDMS membrane
Pollutant/
Parameter
Formalde-
hyde; also
tested:
ethanol,
diethyl
ether;
responses
were
lower,
indicating
more
acces-
sible
oxidation
/-if
OT
formalde-
hyde
Benzalde-
hyde,
formalde-
hyde
Reported
Detection
Capability
5 ppm
LOQ (200 urn
thickness, Pt
and Au
electrodes
respectively)
= 37 and 61
ppm
LOQ (100 urn
thickness, Pt
and Au
electrodes,
respectively)
= 29 and 40
ppm
Limit of
Quantification
(LOQ) = 3
LOD
Re-
sponse
Time
Size
Automation and
Network
Capability
Not indicated
(Mountable)
Application and Operation Notes
Ambient (room temperature)
conditions.
Response decreases with
increasing relative humidity.
(Absorption is at low irradiation
light intensity; addresses the issue
of increased cost of the
photoelectric gas sensor with high
irradiation light intensity.)
Prototype of an electrochemical
sensor for measuring selected
VOCs; square wave voltammetry
using a low quantification limit due
to elimination of capacity current.
50 mV amplitude, 10 Hz frequency
and a scan step of 5 mV. Sensor
materials functioned for 3 months
in a reproducible manner with a 5%
standard deviation in results.
E-35
-------
31 October 2013
ID
6
7
np
Organization
Author (Funding)
Chinese Academy of
Sciences
Y. Wan, H. Li, J. Liu,
F. Meng, Z. Jin, L.
Kong, and J. Liu
(Funding: "973"
State Key Project of
Fundamental
Research for Nano
Science and
Nanotechnology, the
National Natural
Science Foundation
of China, and others)
Beijing Yadu Air
Pollution Tre
Feng Jiang',
Dongfang Liu;
Xiaocong Ma
Abbreviated Citation
Sensitive detection of
indoor air contaminants
using a novel gas sensor
based on coral-shaped
tin dioxide
nanostmctures
[2012, Sensors and
Actuators, 165:24-33]
http://ac.els-
cdn.com/S092540051 2001 0
62/1 -S2.0-
S092540051 2001 062-
main.pdf? tid=601aeea1c25
ade4743a4b1 04952fcec2&ac
dnat=1339604316 9f65193c
a1 a1 71 af77fb721 afc87e2dc
Patent application for a
formaldehyde gas
sensor
[Application number:
CN2101104419
20100201]
http://WDrldwide.espacenet.c
om/publicationDetails/biblio?
FT=D&date=201 0071 4&DB=
worldwide, espacenet. co m&lo
cale=en EP&CC=CN&NR=1
01 776640A&KC=A&ND=4
Sensor Technology
Type
Nano-
based
Electro-
chemical
Description
(Name)
Coral-shaped
nanostructures,
prepared by
hydrothermal/
annealing
processes coated
on AI2O3 tubes,
Ni-Cr heater wire.
Energy bandgap of
3.62 eV at 300°K
Composed of gas
inlet shell, filter
layer, supporting
shell, MEC, and
electrodes.
Pollutant/
Parameter
Benzene,
formalde-
hyde,
toluene
and
acetone
Formalde-
hyde
Reported
Detection
Capability
Tested at 50,
100, and
150 ppm
Re-
sponse
Time
<30 sec
for
50 ppm
Size
Automation and
Network
Capability
(Fixed/semi-
portable unit)
Application and Operation Notes
Real-time gas sensing used to
detect contaminants in indoor air;
coral-shaped receptacles gave
faster results. Response to gases
is increased when particle size is
reduced. Operates between 350
and 400°C (optimal temperature of
200°C). Smaller particulate size
was reported to be correlated with
higher sensor response.
Sensor detects formaldehyde gas
under the normal temperature,
while reducing the affection of
other gases in the air, so the
detection result is more accurate.
Detection Technique: Spectroscopy
8
Francis Perrin
Laboratory (France)
Mariano, S., Wang,
I/I/., Brunelle, G.,
Tran-Thi, T.H.,
Start-up Ethera,
Minatec Enterprises
(France)
Bigay, Y.
Colorimetric detection of
formaldehyde: sensor for
air quality measurements
and a pollution-warning
kit for homes
[2010, IEEE, 1st
International Conference
on Sensor Device
Technologies and
Applications]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=56321 1 2&isnumber=56320
98&tag=1
Colorim-
etry,
electro-
chemical
Colorimetry kit with
nanoporous matrix
doped with
Fluoral-P
Formalde-
hyde
Potentially
1-250ppb
zxposed
or 10-30
nin
12x5x2
mm
(Visual)
Sensor could be applied to a wide
range of concentrations by varying
the exposure flux (to be addressed
in further studies). For a duration of
1620 minutes, only 0.068% of
Fluoral-P (4-amino-3-penten-2-
one) was consumed, meaning a
few hundred measurements could
be obtained with starting materials.
E-36
-------
31 October 2013
ID
9
10
11
np
Organization
Author (Funding)
Tokai University
(Japan)
Sekine, Y., Katori, R.
National Research
Council of Canada,
Institute for
Research in
Construction
(Canada)
Deore, B., Diaz-
Quijada, A., Wayner,
D. D.M., Stewart, D.,
Won, D.Y., Waldmn,
p.
(Funding: NRC of
Canada, ICT sector)
Universiti Teknologi
Mara (Selangor)
Masrie, M., Adnan,
.
Universiti Industry
Selangor (Selangor)
Ahmad, A.
Abbreviated Citation
Indoor air quality
monitoring via it network
color/metric monitoring of
formaldehyde in indoor
environment using image
transmission of mobile
phone
[2009, ICROS-SICE
International Joint
Conference (Japan)]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5333246&isnumber=53324
38
An electronic nose for
the detection ofcarbonyl
species
[20 11, ECS
Transactions,
35(7):83-88]
http://vwvw.nrc-
cnrc.gc.ca/obj/irc/doc/pubs/nr
cc54483.pdf
A novel integrated
sensor system for indoor
air quality measurement
[2009, IEEE, 5th
International Colloquium
on Signal Processing
and Its Applications]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5069260&isnumber=50691
67
Sensor Technology
Type
MIR,
Colorim-
etry
detection
Lumin-
escence,
polymer
Spectros-
copy, IR
Description
(Name)
Reagent grade HCI,
NaOH, ZnO, KIO4,
AHMT, HCHO
solution and agar
powder
Solution casting
polymer (polyaniline
and polypyrrole
derivatives) at a
given doping level
on a glass substrate
with or without four
Au lines (electrodes)
with respective pads
to align with
commercial probe
head
3 MIR LEDs,
detection by
photodiode (lambda
3600 nm) and
photoresistor
(4300 nm)
Pollutant/
Parameter
Formalde-
hyde
Formalde-
hyde
also
tested
acetalde-
hyde
CO2, CO,
formalde-
hyde
Reported
Detection
Capability
Tested at
0.85 and
0.13mg/m3
(0.691 and
0. 106 ppm,
respectively)
250 ppb
formalde-
hyde at 44%
relative
humidity
("real world"
conditions)
Abstract
states 1 ppm ,
but detailed
results given
only for CO2
Re-
sponse
Time
De-
creases
when
co nee n-
:ration in-
creases
5-60 sec
Size
-Size of
a quarter
(per
photo)
Automation and
Network
Capability
Image
transmission via
mobile phone
(Visual, cell
phone-based)
Not indicated
(Mountable)
(Embedded/
integrated sensor)
Application and Operation Notes
Colorimetric detection of
formaldehyde expresses
concentrations as variances in
degree of color change of the
colorimetric reagent. These results
may be interpreted differently from
user to user and leads to
uncertainty and inconsistent data
collection. This project investigates
a system in which a mobile phone
may be used to take and send
pictures of the color change to a
laboratory where a laboratory
operator would determine the
proper interpretation of colorimetric
test results.
Polyaniline and polypyrrole
derivatives interact with a carbonyl
group to produce measureable
changes in resistivity, which can be
used for e-nose (electronic nose)
sensing of carbonyls (reaction
between carbonyl and nitrogen
spurs molecular recognition).
Used for indoor air quality
measurement.
E-37
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Reference Commercial Sensors
c
12
InterScan
Corporation
http://vwvw.gasdetection.com
/wp-
content/uploads/hcho_monit
oring_instruments_and_syst
ems.pdf
400 Series Portable
Analyzer
Formalde-
hyde
Minimum:
1.0% of full
scale
Accuracy:
±1.0% of full
scale
Range
options (for
portable
analyzers):
0-1999ppm,
0-1 99.9 ppm,
0-1 9.99 ppm,
and
0-0.5 ppm.
Lag
time:
>1 sec
30 sec
to 90%,
8 sec to
50% of
final
value.
30 sec
to 10%
original
value
Portable
analyzer:
7"x
8.875" x
4"
(4.5 Ibs)
Full data
acquisition,
archiving, and
reporting
capabilities
available
Company claims this unit can
withstand the toughest of field
conditions and is "reliable for
decades." Many different variations
are available. Flexible features
include: detection range,
continuous monitoring, multi-point
systems, rack mounting, alarm
systems, and more. The company
also provides a chart detailing
estimated concentrations of
interfering gases required to cause
a 1 ppm deflection on the analyzer.
Sensor drift reported as less than
±2.0% of full scale (24 hr).
Sampling rate is adjustable from
once per second to once every
10 hours.
Hydrogen Sulfide
Detection Technique: Chemistry
1
University of
California-
Riverside
Mubeen, S. et a/.
(Funding: NIH
Genes
Environmental and
Health Initiative, and
the Fundamental
R&D Program for
Core Technology of
Materials funded by
the Ministry of
Knowledge
Economy, Republic
of Korea)
Gas sensing mechanism
of gold nanoparticles
decorated single walled
carbon nanotubes
[2011, Electroanalysis,
23(11): 2687-2692.]
http://deshusses.pratt.duke. e
du/files/deshusses/u31/pdf/ja
87.pdf
Nano-
based
Hybrid
Au-functionalized
SWCNT
nanostructures on
gold electrode
(working electrode),
Pt wire and
chlorinated Ag wire
as reference
electrode, 100 nm
thick SiO2 as
dielectric layer
H2S
Indicated
range:
2-200 ppb
Sensitivity for H2S depends on the
number of gold nanoparticles;, the
sensing function was independent
of the extra nanoparticles.
E-38
-------
31 October 2013
ID
2
Np
Organization
Author (Funding)
University of Malaya
(Malaysia)
Moghavvemi, M.,
Attaran, A.
Abbreviated Citation
Design of a low voltage
0.18 um CMOS surface
acoustic wave gas
sensor
[2011, Sensors and
Transducers,
125(2):22-29]
http://vwvw.sensorsportal.co
m/HTML/DIGEST/february 2
011/P_747.pdf
Sensor Technology
Type
Surface
acoustic
wave
(SAW)
Description
(Name)
IDT antennas
coated with thin film
WO3. Frequency
shifts from
300-500 MHz
Pollutant/
Parameter
H2S
Reported
Detection
Capability
Potentially
detects H2S
change at a
100-12g/cm2
change in
mass
Re-
sponse
Time
Size
Shoebox
Automation and
Network
Capability
(Mountable)
Application and Operation Notes
Two interdigitated transducers
(IDT) on a substrate, which
determines the wavelength. 1.8V
power requirement.
Detection Technique: Spectroscopy
3
4
np
Wuhan Huali
Environment
Protection Science
Technology Co., Ltd.
Y. Wan; B. Dai
Utah State
University, University
of Iowa;
US Department of
Agriculture/Agricultur
e Research Service,
National Soil Tilth
Laboratory (Iowa);
Space Dynamics
Laboratory (SDL)
(Utah)
Hipps, L, Silva, P.,
(UT State);
Zavyalov, V.V.,
Wllkerson, T.,
Bingham, G.E.
(SDL), et al.
(Funding: USDA)
Atmospheric pollution
monitoring gas sensor
using non-pulse
ultraviolet fluorescence
method
[2010, Patent
application]
http://worldwide.espacenet.c
om/publicationDetails/biblio?
FT=D&date=201 01 208&DB=
worldwide, espacenet. co m&lo
cale=en EP&CC=CN&NR=2
01666873U&KC=U&ND=4
http://vwvw.sdl.usu.edu/progr
ams/aglite;
http://www.ars.usda.goV/is/A
R/archive/aug06/ames0806.
pdf
UV fluor-
escence
Spectros-
copy
Gas sensor optical
platform, electronic
measuring control
system, gas
reforming device,
peripheral interface
Scanning 3-color
LIDAR
Fourier Transform
Spectrometer
(Aglite)
\' >y |i%-'/
H2S, SO2
PMand
gases,
including
NH3, H2S,
NOX
H2S indicated
LDL:
1 ppb
Vehicle-
portable,
(from
photos,
main
elements
appear
to be
roughly
the size
of a
large
suitcase)
(Fixed/semi-
portable unit)
(Remote sensing)
Continuous monitoring of real-time
concentrations of H2S and SO2 in
air. Sensor reduces noise and has
high anti-interference capability,
which greatly improves the
measurement accuracy and leads
to more stable data.
Has been used to monitor entire
CAFO facility (e.g., swine finishing)
and others, including multiple
diffuse source dairy, cotton gin,
and almond harvesting.
(Goal is to measure amount of
CO2, CH4, N2O, and other
greenhouse gases released from
soil into the atmosphere and
determine how different crop- and
soil-management methods affect
these exchanges.)
E-39
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Novel Sensor Systems Using Commercial Sensors
c5
Jackson State
University
Anjaneyulu, Y.
Jawaharlal Nehru
Technological
University
Jayakumar, /., Bindu,
\/ i-i
V.n.
Andhra Pradesh
Pollution Control
Board
Ramani, K.V.
Spectrochem
Instruments
Rao, T.H.
Real time remote
monitoring of air
pollutants and their
online transmission to
the web using internet
protocol
[2007, Environ Monit
Assess, 124:371-381]]
http://cardiff.academia.edU/S
AGARESWARGUMMENENI/
Papers/922566/
Various
commer-
cial
Tapered element
oscillating
microbalance
(Real Time Remote
Monitoring System)
SO2, NO,
NO2, CO,
O3, H2S,
PM10,
PM2.5,
hydro-
carbons,
mer-
captans
H2S:
0-1 Oppm
30 to
60 min
Ethernet network
module, uploading
to webpage
(Remote sensing)
This device is a remotely
monitored detection system. It can
be run on a 1 2V battery. The
pollution sensors can be set to
collect data every 30 minutes or
every 60 minutes depending on
user preference and weather
conditions. Also measures
environmental parameters such as
temperature and rainfall, and
sound, per meteorological
monitoring system, Bruel and
Kajaer sound level measurement
system.)
Reference Commercial Sensors
C6
C7
Aeroqual
Arizona Instrument
LLC
http://vwvw.gas-
sensing.com/aeroqual-
hydrogen-sulfide-sensor-0-
10-ppm-eh.html
http://vwvw.azic.com/downloa
ds/brochures/Jerome®%20J
605%20Brochure.pdf
Electro-
chemical
Electro-
chemical
Gas sensitive
electrochemical
(Aeroqual Hydrogen
Sulfide Sensor
Head 0-1 Oppm)
Gold film
(Jerome J605)
H2S
H2S
Indicated
range:
0-1 Oppm,
minimum:
0.01 ppm,
max: 20 ppm;
res: 0.01 ppm
Indicated
range:
3
ppb-10 ppm,
resolution:
20 ppt
<60 sec
Varies,
12-52
sec
Hand-
held
11 ftx
6ft x
6.5ft
(Handheld)
On-board data
logging, USB
interface, data
storage capability
of 20,000 samples
Uses diffusion sampling method
and operates in the temperature
range of -20-40° C. Sensor head is
compatible with any Series-200,
-300 or -500 Aeroqual monitor.
This device has an internal battery
(rechargeable NiMH), AC power
supply/charger, and external
battery pack or car accessory
cable of 12 VCD; JEROME
requires a 0-40° C operational
environment free of condensation
and explosives. The company
recommends annual factory
calibrations and intermittent user
checks with the functional test
module.
E-40
-------
31 October 2013
ID
C8
Organization
Author (Funding)
Aeroqual
Abbreviated Citation
http://vwvw.gas-
sensing.com/aeroqual-
hydrogen-sulfide-sensor-0-
50-ppm-ht.html
Sensor Technology
Type
Semicon-
ductor
Description
(Name)
Gas sensitive
semiconductor
(Aeroqual Hydrogen
Sulfide Sensor
Head 0-50 ppm)
Pollutant/
Parameter
H2S
Reported
Detection
Capability
Indicated
range:
0-50 ppm,
minimum:
0.05 ppm,
maximum:
100 ppm;
res: 0.01 ppm
Re-
sponse
Time
<60 sec
Size
Hand-
held
Automation and
Network
Capability
Data interface to
PC/ onboard data
logging and direct
to PC
(Handheld)
Application and Operation Notes
Uses diffusion sampling method
and operates in the temperature
range of -20-40°C. Sensor head is
compatible with any Series-200,
-300 or -500 Aeroqual monitor.
Methane
Detection Technique: Chemistry
1
2
np
NASA Glenn
Research Center
Biaggi-Labiosa, A.,
Lebron-Colon, F.,
Evans, L.J.,Xu, J.C.,
Hunter, G.W.,
Berger, G.M.,
Gonzalez, J.M.
Shanzt University,
Tatyuan (China),
Hong Kong Baptist
University
Li, Z., Zhang, J.,
Zhou, Y., Shuang,
S., Dong, C., Choi,
M.M.F
(Funding: Hundred
Talent Programme of
Shanxi Province,
Shanxi International
S&T Cooperation
Program , National
Natural Science
Foundation, Shanxi
Scholarship Council)
A novel methane sensor
based on porous SnO2
nanorods: room
temperature to high
temperature detection.
[Oct. 2012,
Nanotechnology, 23]
Electrodeposition of
palladium nanoparticles
on fullerene modified
glassy carbon electrode
for methane sensing
[May 2012,
'
Nano-
based
Nano-
based
Porous SnO2
nanorods (~5 nm)
synthesized using
MWCNTs as
templates
Nanocomposite of
Pd nanoparticles-
modified fullerene
by electrodeposition
on a glassy carbon
electrode
CH4
CH4
0.25% CH4
in air,
125-2500
ppm
(depends on
temperature
of operation.
100-500°C
capable of
entire range,
25°C capable
of 2500 ppm
only)
j i
0.19-0.55%
5-50
sec
Note re-
sponse
time de-
creases
as con-
centra-
tion in-
creases
Micro-
sensor
3 mm
diameter
glassy
carbon
elec-
trode
Authors consider this the first of its
type for methane; operated at room
temperature and exhibited a wide
temperature range (25-500°C).
Optimal sensitivity reported at
300°C. Authors note sensor may
be used for personal health and
environmental monitoring. Sensor
reported to have low power
consumption, be easy to use and
cheap to produce in batch
fabrication. (Sensitivity: ratio of
sensor conductance in presence of
the gas minus the baseline
conductance measured in air.)
Reported to have higher sensitivity
and selectivity compared to bare
glassy carbon electrodes, pristine
C60 modified glassy carbon
electrodes, and palladium
nanoparticle-modified glassy
carbon electrodes. Stable and
demonstrated reproducible results.
Operation at room temperature is
preferred, to prolong sensor life
and minimize explosion risk. CO
and CO2 were not found to
interfere with measurements, while
H2 and NH3 did interfere slightly.
E-41
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Detection Technique: Spectroscopy
3
4
Jilin University, State
Key Laboratory on
Optoelectronics,
College of Electronic
Science and
Engineering, College
of Material Science
and Engineering
Zheng, C-T, Wang,
Y-D., et al.
(Funding: Science
and Technology
Council of China,
National Natural
Science Foundation
of China, Science
and Technology
Department of Jilin
Province)
Huazong University
of Science and
Technology (China),
Nanyang
Technological
University
(Singapore)
Liu, D., Fu, S., Tang,
M., Shum, P.
Performance
enhancement of a mid-
infrared CH4 detection
sensor by optimizing an
asymmetric ellipsoid
gas-call and reducing
voltage-fluctuation:
Theory, design and
experiment
[Aug. 201 1 , Sensors and
Actuators B, 160(1):389-
398]
http://www.sciencedirect.com
/science/artide/pii/S0925400
511007210
Comb filter-based fiber-
optic methane sensor
system with mitigation of
cross gas sensitivity
[Oct. 2012, IEEE,
30(19):3103-3109]
Spectros-
copy,
MIR,
asym-
metric
ellipsoid
gas cell
Spectros-
copy, gas
cell
Asymmetric ellipsoid
light-collector gas
cell (ALCGC) as
absorption pool,
light collector, MIR
wire source, and
dual-channel
detector (multi-pass)
Polarization-
maintaining
photonic crystal
fiber-based Sagnac
loop filter, gas cell
with multiple
reflections, p-i-n
photodetector; fiber
optical sensor
CH4
CH4
5 ppm
minimum
detection
limit
6-7 ppm
sensitivity
range under
1 ,000 ppm
concentration
level
Improvement
s are
expected to
lower this to
1 ppm and
below
450 ppm
<6sec
Sensing
3.8 cm /
1.5cm
Range of
compon
ent sizes
(largest
is DVD
player
sized)
(Mountable)
Designed to remotely monitor
methane in a coal mine, the sensor
system has three parts: host
machine with all signal processing
functions, gas cell, and alarm
device. An optical fiber links the
host machine to the remote gas
cell. The alarm device is activated
when CH4 reaches explosive levels.
Novel Sensor Systems Using Commercial Sensors
5c
Changzhou Institute
of Technology
(China)
Guan, J., Wang, X
Application of integrated
sensor in gas alert
system of coal mine
[2009, IEEE]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5072745&isnumber=50725
99
Integrated sensor
alerting system
(MSP430)
JHAT-FM-CO
JHAT-FM- H2S
3011-CO2
GJ4-2000-CH4
Chip: 430149
CO, H2S,
CO2, CH4,
NO2, and
marsh
gas
Voice alerts
are set at:
CH4: 500
DDm
rr
H2S: 50 ppm
CO2: 500
ppm
Small, fit
in mining
helmet
(Embedded/
integrated sensor)
Design combines 4 separate gas
sensors into single integrated
sensor small enough to be installed
in a mining helmet for real-time
monitoring (deployed in coal mine).
When concentrations of any
monitored gas exceeds specified
levels, the wearer would hear a
voice alert reminding of proper
safety precautions and procedures.
Sensor contains a location device
as a further safety element.
E-42
-------
31 October 2013
ID
6c
np
7c
np
Organization
Author (Funding)
Polytechnic
University of
Bucharest
(Romania)
Tudose, D.S.,
Patrascu, T.A.,
Voinescu, A.,
Tataroiu, R., Tapus,
N.
Dublin City
University (Ireland)
Beirne, S., Kiernan,
B., Fay, C., Foley,
C., Corcoran, B.,
Smeaton, A.F.,
Diamond, D.
(Funding:
Environmental
Protection Agency,
Ireland, and SFI)
Abbreviated Citation
Mobile sensors in air
pollution measurement
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5961035&isnumber=59609
99
Autonomous greenhouse
gas measurement
system for analysis of
gas migration on landfill
sites
[2009.IEEE]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=5439422&isnumb4:er=5439
372
Sensor Technology
Type
Thick film
metal
oxide
semi-
conductor
sensors
(from
Figaro)
Spectros-
copy, IR
Description
(Name)
(Mobile Unit)
Sensors from
Dynament Ltd.
CO2 = IRCEL-CO2,
CH4 = IRCEL-CH4,
Humidity sensor
from Honeywell
(HIH-4000-001),
Temperature sensor
from Thermometrics
(DKF103N5)
Pollutant/
Parameter
CO, NOX,
hydrocarb
ons, NH4,
H2S,
gasoline
and diesel
exhaust,
natural
gas,
propane,
CH4, CO2
CH4, CO2
Reported
Detection
Capability
CO2:
350-10,000
ppm
(1.5min)
NOx: 0.3-10
ppm (30 sec)
CO, HC:
10-10,000
ppm (30 sec)
NH4:
50-300 ppm
(2m in)
0-100%
volume
Re-
sponse
Time
30-120
sec
Size
Portable,
car
interface
Automation and
Network
Capability
Users are able to
select different
gases and view
concentrations
overlaying a map
of the city.
Publicly
accessible
through on-line
web interface.
GPRS connection
for computer
transfer. GPS
capable.
Temporary
memory buffer
periodically
relayed to a
central on-line
repository. Real-
time web-interface
(Vehicle-mounted
unit)
Fully automated.
Short range:
communication to
laptop
Long range:
Bluetooth.
Data are sent in
SMS format
Yec
I Co
(Fixed/semi-
portable unit)
Application and Operation Notes
This unit is meant to be embedded
in a car and relies on the car's
power supply. Information on
current pollution readings could be
provided to the driver. Various
sensor 'plugs' would be available to
attach to the device based on user
preference. Device can be
connected to any commercially
available memory card via a SD
card Interface.
Investigates an autonomous gas
sensing platform prototype for
monitoring gases as an alternative
to current manual monitoring
practices at landfills. IR gas
sensors integrated into a bespoke
platform are fully automated and
take measurements twice daily
from borehole wells. The sampling
chamber contains four sensors: IR
gas sensors for CO2 and CH4, a
humidity sensor, and a
temperature sensor. The system
was not yet optimized for energy
efficiency. Because of the
connection to Bluetooth network,
landfill operators can be alerted if
CH4 or CO2 concentration flares
occur. Powered by a 1 2V 7Ah lead
acid battery (system sustained for
7 wks, with 2 sampling cycles/day).
E-43
-------
31 October 2013
ID
8c
np
Organization
Author (Funding)
Dong-Eui University
Industry-Academic
Cooperation
Foundation
Yun Sik Yu; Jae
Cheon Sohn
Abbreviated Citation
Apparatus for Measuring
Methane Gas Emission
Amount in Case of
Ruminant Respiration
http://vwvw.wipo.int/patentsco
pe/search/en/detail.jsf?dodd
=KR30060407&recNum=1&d
ocAn=1 0200901 04099&quer
yString=FP:(1 0201 1 0047462
)&maxRec=1
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
CH4
Reported
Detection
Capability
Re-
sponse
Time
Size
Mask
Automation and
Network
Capability
(Wearable)
Application and Operation Notes
Mask comprised of a methane gas
detection module and a volume
meter for measuring the amount of
air emitted during ruminant
respiration. Detection module is
comprised of an inlet and outlet,
with a gas sensor mounted in
between.
Reference Commercial Sensor
9C
Picarro
https://picarro. box. com/share
Cavity
ring-down
spec-
troscopy
PICARRO G2204
Methane and
Hydrogen Sulfide
Analyzer
CH4, H2S
(requires
air-like
matrix)
Precision
(5 0 sV 2 ppb
Range:
0-3 ppm
specifications
guaranteed;
0-20 ppm
operating
range
Interval:
5 sec
Rise/
fall-
<5 0 sec
1 7" w x
7"hx
17.5"d,
46lbs
and
separate
external
pump:
7 5" w x
11" d
10lbs
Delivers real-time
data to Google
Earth plume
maps. Reported
to identify plume
origins down to
specific buildings.
This sensor can be benchtop or
rack-mounted. Operational
temperature is 10-35°C, and
storage temperature is -10-50°C.
Sensor power requirements are as
follows: 10-240 VAC, 47-63 Hz
(auto-sensing), <260 w start-up
(total); 1 10 W (analyzer),
35 W (pump) at stead state.
Maximum drift over 8 hours is
reported as <4 ppb. Reported to
take continuous readings at
65 mph with 30 readings per
minute.
Lead
Novel Sensor System Using Commercial Sensor
1
National Institute for
Occupational Safety
and Health (NIOSH)
Harper, M., Pacolay,
B Hintz P Bartley
D.L., Slaven, J.E.,
Andrew, M.E.
http://www.cdc.gov/niosh/minin X-ray
g/pubs/pdfs/pxaoo.pdf fluores-
cence
(XRF)
X-ray fluorescence
analyzer
Pb
0-5,000
|jg/m3
4 min
Portable
Airborne lead is collected on
sample filters that are then
presented to the XRF analyzer; this
system has been tested in
industrial environments, including
mining, manufacturing, and
recycling.
Reference Commercial Sensor
C2
Pall Corporation
http://www.pall. com/main/OEM- Ree|-
Materials/Product.page?id=547 lo"reel
09 (RTR),
XRF
(Xact 625
Monitoring System -
Fence-Line Monitor
(FLM))
Pb
10pg/m3to
57|jg/m3
-20 sec
2ftx
2 ftx
4ft
(Fixed/semi-
portable unit)
Continuous sensing, designed for
use near fencelines of industrial
facilities and in complex urban
environments; power requirement:
120 VAC/60 Hz at 20 amps.
E-44
-------
31 October 2013
ID
Organization
Author (Funding)
Abbreviated Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
Application and Operation Notes
Particulate Matter (PM)
Detection Technique: Chemistry
1
np
United States Patent
Application
Publication
US 201 2/0059598
A1
Yokoi, S., Sakurai, T.
Particulate matter detection Electro-
device cnemi-
[2012, Patent application cal,
http://www.freepatentsonline.co poten-
m/y201 2/0059598. html *:„
metric
Measuring ion
current of charged
particulate matter
using a pair of
electrodes by
applying voltage
signal and
measuring electric
characteristics
(Particulate Matter
Detection Device)
PM
Detects particulate matter in
air/exhaust.
Detection Technique: Nanoparticle Condensation
2
np
University of
Cincinnati
Son, S.Y.
Water vapor uptake on
aerosol particles -
determination of
condensation coefficient
of water on nanoparticles
under forced convection
conditions
[20111
L*-\J i ij
http://aaarabstracts.com/201
1/viewabstract.php?paper=5
68
Nano-
particles
Promotes droplet
growth by
heterogeneous
condensation on
nanoparticles.
PM.,
Detection Technique: Spectroscopy
3
np
University of Nevada
- Reno, College of
Science; with Desert
Research Institute;
commercialized by
Droplet
Measurement
Technology of
Boulder, CO
Arnott, P., Arnold, 1.
TTO licenses Arnott's
next-gen air quality
monitor
[March 2011]
http://www.unr.edu/nevada-
today/news/201 1/tto-
licenses-arnotts-next-gen-air-
quality-monitor
Photo-
acoustic
System includes
lasers, mirrors,
flexible tubes, and
wires
(Photoacoustic
Extinctiometer
/DA VU
(PAXJJ
PM,
aerosols
relevant
for climate
change
and
visibility
Suitcase
size,
20 Ib
(down
from
80 Ib)
Not indicated
(Mountable)
Water kept at an elevated
temperature forms supersaturated
conditions inside a porous
chamber, where an air stream
carries through nanoparticles. The
nanoparticles are exposed to the
supersaturated conditions and
grow, and can then be accounted
for using numerical investigations.
(Fixed/semi-
portable unit)
Beta-versions are in use by
researchers at LBL and Bay Area
Air Quality District, Max Planck
Institute for Chemistry in Europe,
and Mexico City; Droplet aims to
produce many more that will be a
fraction of the cost to users.
(Researchers are working on
developing a "truly miniature
device that may find use as an on-
board sensor for real-time black
carbon air pollution emission
control".)
E-45
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31 October 2013
ID
4
np
5
np
Organization
Author (Funding)
Utah State
University, University
of Iowa; Space
Dynamics
Laboratory (SDL)
(Utah)
Hipps, L, Silva, P.,
(UT State);
Zavyalov, V.V.,
Wllkerson, T.,
Bingham, G.E.
(SDL), and others
(Funding: USDA)
Thermo Scientific
K. Goohs, J. Hiss,
M. Rossmeisl,
D. Kita
Abbreviated Citation
http://vwvw.sdl.usu.edu/progr
ams/aglite;
http://vwvw.ars.usda.goV/is/A
R/archive/aug06/ames0806.
pdf
A hybrid method for PM
OEMS
AAAR Conference 2009
(presentation)
http://stratusllc.com/uploads/
PM_CEMS_White_Paper.pdf
Sensor Technology
Type
LIDAR
Light
scattering
Description
(Name)
Scanning 3-
colorlidar LIDAR
FTIR
(Aglite)
Pollutant/
Parameter
PMand
gases,
including
NH3, H2S,
NOX
PM
Reported
Detection
Capability
Re-
sponse
Time
Size
Suitcase
Automation and
Network
Capability
(Remote sensing)
(Fixed/semi-
portable unit)
Application and Operation Notes
Has been used to monitor an entire
CAFO facility (e.g., swine finishing)
and others sources, including
multiple diffuse source dairy, cotton
gin, and almond harvesting. The
goal is to measure the amount of
CO2, CH4, N2O, and other
greenhouse gases released from
soil into the atmosphere and
determine how different crop- and
soil-management methods affect
these exchanges.
Introduces a light-scattering
configuration with continuous mass
referencing for source emissions
and ambient air monitoring
applications. Light scattering
demonstrates potential for
continuous PM monitoring because
it can configure to measure an
angle relative to the applicable light
source and it is sensitive enough to
measure most size particles within
a plume.
Novel Sensor Systems Using Commercial Sensors
c6
Jackson State
University
Anjaneyulu, Y.
Jawaharlal Nehru
Technological
University
Jayakumar, /., Bindu,
V.H.
Andhra Pradesh
Pollution Control
Board
Ramani, K.V.
Spectrochem
Instruments
Rao, T.H.
Real time remote
monitoring of air
pollutants and their
online transmission to
the web using internet
protocol
[2007, Environ Monit
Assess, 124:371-381]]
http://cardiff.academia.edU/S
AGARESWARGUMMENENI/
Papers/922566/
Electro-
chemical,
PM
analyzer
various
commer-
cial
R&P series 1400a
TEOM
(Real Time Remote
Monitoring System)
SO2, NO,
NO2, CO,
O3, H2S,
PM10,
PM2.5,
hydro-
carbons,
mercap-
tans
Varies per
pollutant,
within
0-200 ppm,
or 0-50 |jg/m3
(mercaptans)
CO:
Indicated
range:
0-1 00 ppm
30 or
60m in
Ethernet network
module, uploading
to webpage
(Remote sensing)
This device is a remotely
monitored detection system. It can
be run on a 1 2V battery. The
pollution sensors can be set to
collect data every 30 or 60 minutes
depending on user preference and
weather conditions. Also
measures environmental
parameters such as temperature
and humidity.
E-46
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31 October 2013
ID
c7
c8
np
c9
np
Organization
Author (Funding)
Bogor Agricultural
University
(Indonesia)
Azis, M., Rustami,
£., Maulina, I/I/.,
Rahmat, M., Alatas,
H., Seminar, K.
(Funding/support:
Beasiswa Unggulan
Terpadu - Education
Ministry of Republic
of Indonesia, and
various Departments
in Bogor Agricultural
University)
NASA-Glenn
Research Center
Greenberg, P.S.,
H\/att M 1
nydll, lvl.\J.
Stanford University
Acevedo-Bolton, V.
Klepeis, N.E., Jiang,
R. Cheng, K., Ott,
W.R., Hildemann,
L.M.
Abbreviated Citation
Measuring air pollutant
standard index (ISPU)
with photonics crystal
sensor based on
wireless sensor network
(WSN)
[2011, IEEE,
International Conference
on Instrumentation,
Communication,
Information Technology,
and Diomedical
Engineering, (Indonesia)]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=61 08656&isnumber=61 085
78
Instrumentation and
sensor technologies for
the measurement and
detection of lunar dust
[2009, IEEE]
http://ieeexplore.ieee.org/sta
mp/stamp.jsp?tp=&arnumber
=4839566&isnumber=48392
94&tag=1
Employee and patron
exposure to pollutants in
a Northern California
casino
ISES 2009 (poster)
Sensor Technology
Type
Optical,
photonic
crystal
Description
(Name)
(Sensor developed
from previous
research at this
institution)
Stationary sensors
and portable
sensors place on
different employees
Pollutant/
Parameter
CO, SO2,
NO2, O3,
PM10
PM
..
(lunar
dust)
PM2.5,
real-time
PM2.5,
nicotine,
cotinine,
PAH,
ultrafine
particles,
CO2
Reported
Detection
Capability
Re-
sponse
Time
Size
Automation and
Network
Capability
WSN, desktop
and web
applications to
display data in
real time and non-
real time.
(Fixed/semi-
portable unit)
(Mountable)
Application and Operation Notes
Investigates use of information
systems of the air pollutant
standard index and photonics
crystal sensors to monitor air
pollution and collect data by
wireless communication.
Study conducted because
appropriate sensor and detection
technologies for lunar dust are
unavailable.
Sensors tracked the amount of
contaminants in the air of a
California casino and used
gathered data and patron counts to
estimate the air change rates over
time. A mass balance model was
used to evaluate how the data on
nicotine correlated with the indoor
PM2.5 measurements.
Reference Commercial Sensors
c
10
Thermo Fisher
Scientific
http://thermoscientific.com/ec
omm/servlet/productsdetail
11152 L11036 89583 1196
1321_-1
Gravi-
metric
(pDR-1500,
personal DataRAM
Aerosol Monitor)
PM
particle
size of
maximum
response
0.1-10 |jm
Indicated
range: 0.001
to 400 mg/m3
1 sec
Shoebox
(Fixed/semi-
portable unit)
Requires 70 to 450 mA in run
mode, 32 mA in ready mode.
E-47
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31 October 2013
ID
c
11
c
12
Organization
Author (Funding)
Thermo Scientific
Enviro Technology
Services pic
Abbreviated Citation
http://vwvw.thermo.com/eThe
rmo/CMA/PDFs/Product/prod
uctPDF_3275.pdf
http://vwvw.et.co.uk/products/
air-quality-
monitoring/particulate-
monitoring/opsis-sm200-
beta-attenuation-particulate-
monitor-gravimetric-sampler/
Sensor Technology
Type
Filter
dynamics
measure-
ment
system
(FDMS),
tapered
element
oscillating
micro-
balance
(TEOM)
mass
sensor
Geiger
counter
Description
(Name)
(Thermo Scientific
Ambient Particulate
Monitor
TEOM® 1405-DF)
(OPSIS SM200
Beta-attenuation
Particulate Monitor/
Gravimetric
Sampler)
Pollutant/
Parameter
PMio and
PM2.5
PM10and
PM2.5
Reported
Detection
Capability
Oto
1,000,000
|jg/m3
resolution:
0.1 |jg/m3,
precision:
2.0 |jg/m3
(1-hr avg),
1.0 |jg/m3
(24-hour
ava)
y/»
accuracy for
mass
measurement
0.75%
0.5 |jg/m3to
1,000 |jg/m3
Re-
sponse
Time
6 min
1-24hr
Size
17 in. x
19 in. x
55 in.
Automation and
Network
Capability
(Fixed/semi-
portable unit)
(Fixed/semi-
portable unit)
Application and Operation Notes
This device is made for indoor and
commercial readings of ambient
PM concentrations even in the
presence of volatile materials. It
requires between 47 and 63 Hz of
power to operate.
Primarily used for modern
monitoring stations. Uses 800 W of
power and has a long cycle period
of sampling.
This table highlights sensor technologies/techniques reported in selected conference proceedings, poster abstracts, peer-reviewed journals, and university and other organization
(e.g., company) web pages; at the time of the publications reviewed, these sensors were in the research and development stage. Also included are two types of commercial
sensors: (a) those that are part of a novel sensing system ("Sensor Systems Using Commercial Sensors" as appropriate), and (b) representative standard sensors that serve as
points of comparison ("Reference Commercial Sensor"). Research focused only on networks, architecture, or mobile applications for mobile sensors that do not identify a specific
sensor or technology/technique are not included in this table.
The table is organized by pollutant, starting with the four criteria pollutant gases (shaded green), followed by four additional gases (shaded pink), and then the two particulate criteria
pollutants (shaded yellow). This organization results in some sensors being repeated because they can detect multiple pollutants. Within each pollutant section, the entries are
further grouped by general detection technique, in order of decreasing sensitivity (i.e., most sensitive listed first). Sensors without reported detection capabilities are listed at the end
of the technology category.
The ID (identifier) in the left column corresponds to the sensor boxes plotted on the benchmark-sensor arrays. Note that only sensors with reported detection levels appear on those
arrays; np = not plotted, c = commercial sensor used in a novel detection system; C = standard commercial sensor included for comparison.
A number of acronyms and abbreviations are defined within the entries; others are provided in the notations section at the front of this report.
E-48
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TABLE E-2 Additional Recent Literature for Mobile Sensors for Air Pollutants3
#
Sort
Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Acetaldehyde
Chemical
ac
eta
Ide
hy
de
C
Universidade de
Sao Paulo
(Brazil)
Li, R.W.C., etal.
(FAPESP and
CNPq)
Low cost selective
sensor for carbonyl
compounds in air
based on a novel
conductive
poly(p-xylene)
derivative
[2009, Materials
Science and
Engineering C.
29(1):426-429]
Conduc-
ting
polymer
Poly(p-xylene)
(PPX) doped
with CSA
(camphor
sulfonic acid)
Acetaldehyde,
benzaldehyde,
acetone,
butanone
(shows good
discrimination)
Response:
2 sec
recovery:
<10 sec
No significant drift of the background conductance after
several exposures. Power consumption: <1 |jW.
Demonstrated good reproducibility over 20 repetitive
exposure/recovery cycles. Sensors have been tested for
more than 3 months and still respond well. No change in
conductance in the presence of humidity (due to
hydrophobic characteristics of the polymer). Total sensor
cost: <$1. Operates at room temperature.
E-nose
ac
eta
Ide
hy
de
e-
nose
Semiconductor
Physics Institute
(Lithuania),
University of
Brescia (Italy)
Setkus, A., et al.
(WOUNDMONIT
OR)
Analysis of the
dynamic features
of metal oxide
sensors in
response to SPME
fiber gas release
[2010, Sensors
and Actuators B.
146(2): 539-544]
MOS
Metal oxide
(MOx) sensor
array
VOCs,
specifically
those emitted
from infected
wounds
(acetone,
acetic acid,
acetaldehyde,
and butyric
acid)
3-4 ppm
Time-dependent release of VOCs from a solid phase
micro-extraction (SPME) fiber.
Acetic acid
Chemical
ac
eti
c
aci
d
C
Ehime University
(Japan), National
Institute for
Materials Science
(Japan)
Mori, M., et al.
(Japan Science
and Technology
Agency)
Detection ofsub-
ppm level of VOCs
based on a
Pt/YSZ/Pt
potentiometric
oxygen sensor with
reference air
[2009, Sensors
and Actuators B.
143(1):56-61]
Potentio-
metric
Pt/YSZ/Pt
(platinum/
yttria-
stabilized
zirconia/
platinum)
structure
VOCs,
specifically
acetic acid,
methyl ethyl
ketone,
ethanol,
benzene,
toluene, o- and
p-xylene
Sub-ppm
Ethanol
response:
4-204 sec
(quickest
for 0.5 ppm
at 500° C)
recovery:
190-480
sec
(quickest
for 1 ppm
at 500° C)
400-500°C, highest response at 400°C. Increased
temperatures decreased sensitivity but modified response
and recovery times. Analysis of multidimensional plots of
sensor output at various temperatures suggests possible
selective sensing of different VOCs.
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#
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
E-nose
ac
eti
c
aci
d
e-
nose
Semiconductor
Physics Institute
(Lithuania),
University of
Brescia (Italy)
Setkus, A., et al.
(WOUNDMONIT
OR)
Analysis of the
dynamic features
of metal oxide
sensors in
response to SPME
fiber gas release
[2010, Sensors
and Actuators B.
146(2): 539-544]
Metal
oxide
MOx sensor
array
VOCs, emitted
from infected
wounds
(acetone,
acetic acid,
acetaldehyde,
and butyric
acid)
3-4 ppm
Time dependent release of VOCs from a SPME fiber.
Acetone
Chemical
ac
eto
ne
C
Donghua
University
(China), Leibniz
Institute of
Polymer
Research
Dresden
(Germany)
Fan, Q., et al.
(Shanghai
Pujiang Program,
State Key
Laboratory of
Chemical Fibers
and Polymer
Materials,
Doctorate
Innovation
Foundation of
Donghua
University, and
the National
Natural Science
Foundation for
Distinguished
Young Scholar of
China)
Vapor sensing
properties of
thermoplastic
polyurethane
multifilament
covered with
carbon nanotube
networks
[201 1 , Sensors
and Actuators B.
156(1):63-70]
Nano-
materials
Thermoplastic
polyurethane
multifilament -
carbon
nanotubes
(TPU-CNTs)
composite
(conductive
polymer)
VOCs,
specifically
benzene,
toluene, CHCI3,
THF, ethanol,
acetone, and
methanol
Response:
1 0 sec to
50% max
relative
resistance
recovery:
begins
3-5 sec
after
injection of
dry air
E-50
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#
Sort
Codes
P
ac
eto
ne
ac
eto
ne
ac
eto
ne
T
c
c
C
A
Organization
Author (Sponsor)
Isalamic Azad
University (Iran),
K. N. Toosi
University of
Technology (Iran)
Amini, A.,
Ghafarinia, V.
Shivaji University
(India), Chonnam
National
University (South
Korea), Solapur
University (India)
Pawar, R.C.,
et al. (University
Grants
Commission,
New Delhi)
University of
Massachusetts
Lowell
Li, X., et al.
(National Science
Foundation)
Abbreviated
Citation
Utilizing the
response patterns
of a temperature
modulated
chemoresistive gas
sensor for gas
diagnosis
[2011,IOPConf.
Ser.: Mater. Sci.
Eng. 17012408]
Surfactant assisted
low temperature
synthesis of
nanocrystalline
ZnO and its gas
sensing properties.
[2010, Sensors
and Actuators B.
151(1):212-218]
Fabrication and
integration of metal
oxide nanowire
sensors using
dielectrophoretic
assembly and
improved post-
assembly
processing
[2010, Sensors
and Actuators B.
148(2):404-412]
Sensor Technology
Type
Commer-
cial
(modified)
Nano-
materials
MOS
nanowires
Description
(Name)
Staircase
heating
voltage
waveform
applied to
micro-heater
for SnO2 gas
sensor.
Vertically
aligned ZnO
nanorods on
glass (MOS)
Metal oxide
nanowires
(indium, tin,
and
indium-tin).
Dielectro-
phoretic (DEP)
assembly onto
interdigitated
micro-
electrodes
Pollutant/
Parameter
Acetone,
1-butanol,
ethanol,
methanol
Acetone,
ammonia,
liquefied
petroleum gas
(LPG), ethanol
Acetone,
chloroform,
ethanol,
methanol,
propanol, and
benzene.
Reported
Detection
Capability
Tested:
50-1700
ppm
Tested:
2000 ppm
1 ppm
(potentially
ppb)
Response/
Recovery
Time
Response:
10 sec
recovery:
8-10 min
Application and Operation Context
Operation between 50 and 400°C yielded unique vectors
for methanol, ethanol, 1-butanol, and acetone, suggesting
potential for selectivity. The sensor was exposed for five
40-sec segments.
High sensitivity for acetone. Low operating temperature
(tested 200-450°C). Optimal sensitivity and response time
at 275°C.
Good repeatability, however, recovery time increases
when exposed to greater concentrations of substance. No
response to 1500 ppm was observed below 200°C;
performance was enhanced when 440°C was reached.
Determined that further optimization of ITO composition
may improve sensitivity and selectivity of the sensors.
Oxygenated organic compounds cause a higher response
than aromatic or chlorinated compounds.
E-51
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31 October 2013
#
Sort
Codes
P
ac
eto
ne
T
c
A
Organization
Author (Sponsor)
Universidade de
Sao Paulo
(Brazil)
Li, R.W.C., etal.
(FAPESP and
CNPq)
Abbreviated
Citation
Low cost selective
sensor for carbonyl
compounds in air
based on a novel
conductive poly(p-
xylene) derivative
[2009, Materials
Science and
Engineering C.
29(1):426-429]
Sensor Technology
Type
Conduc-
ting
polymer
Description
(Name)
Poly(p-xylene)
(PPX) doped
with CSA
Pollutant/
Parameter
Acetaldehyde,
benzaldehyde,
acetone,
butanone
(shows good
discrimination)
Reported
Detection
Capability
Response/
Recovery
Time
Response:
2 sec
recovery:
<10 sec
Application and Operation Context
No significant drift of the background conductance was
observed after several exposures. Power consumption:
<1 |JW. Good reproducibility over 20 repetitive exposure/
recovery cycles. Sensors have been tested for more than
3 months and still respond well. No change in
conductance in the presence of humidity (due to
hydrophobic characteristics of the polymer). Total sensor
cost: <$1. Operates at room temperature.
E-nose
ac
eto
ne
ac
eto
ne
e-
nose
e-
nose
Saratov State
Technical
University
(Russia),
Southern Illinois
University at
Carbondale,
Northeastern
University
Sysoev, V. V., et
al.
(Fullbright
scholarship and
RFBR grant and
NSF)
Semiconductor
Physics Institute
(Lithuania),
University of
Brescia (Italy)
Setkus, A., et al.
(WOUNDMONIT
OR)
The electrical
characterization of
a multi-electrode
odor detection
sensor array based
on the single
SnO2 nanowire
[2011, Thin Solid
Films.
520(3): 898-903]
Analysis of the
dynamic features
of metal oxide
sensors in
response to SPME
fiber gas release
[2010, Sensors
and Actuators B.
146(2): 539-544]
e-nose
e-nose
SnO2
wedge-like
nanowire,
multielectrode
odor detection
sensor array;
functionalized
by deposition
ofPd
na no particles
MOx sensor
array
Acetone,
2-propanol,
CO, and H2
VOCs,
specifically
those emitted
from infected
wounds
(acetone,
acetic acid,
acetaldehyde,
and butyric
acid)
3-4 ppm
Fabricating metal oxide single crystals in shape of
nanowires is cost effective.
Time-dependent release of VOCs from a SPME fiber.
E-52
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#
Sort
Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Ammonia
Chemical
NH
3
NH
3
NH
3
c
c
c
ENEA (Italy)
Penza, M., et al.
Hanoi University
of Science and
Technology
Van Quy, A/., et
al
dl.
Kyushu
University
(Japan), Japan
Society for the
Promotion of
Sciences (Japan)
Plashnitsa, V.V.,
etal.
(MEXT, The
Grant-in- Aid for
Scientific
Research on
Priority Area,
Nanoionics)
Functional
characterization of
carbon nanotube
networked films
functionalized with
tuned loading of Au
nanoclusters for
gas sensing
applications
[2009, Sensors
and Actuators B.
140(1): 176-1 84]
Gas sensing
properties at room
temperature of a
quartz crystal
microbalance
coated with ZnO
nanorods
[201 1 , Sensors
and Actuators B.
153(1): 188-1 93]
Zirconia-based
electrochemical
gas sensors using
nano-structured
sensing materials
aiming at detection
of automotive
exhausts
[2009,
Electrochimica
Acta.
54(25): 6099-61 06]
Chemi-
resistor
Nano-
materials
Potentio-
metric
Gold
functionalized
CNTs
Quartz crystal
microbalance
coated with
ZnO nanorods
YSZ-based
planar sensors
using nano-
structured
sensing
electrodes
NO2, NH3, CO,
N2O, H2S, SO2
Ammonia
Ammonia, NO2,
CO, CH4, C3H8,
C3H6, NO
Sub-ppm:
NO2, H2S
and NH3
9DD nnh MD
^UU |-'|-'[-' |NW2
Negligible
response for
CO, N2O,
and SO2
Tested at
50, 100, and
200 ppm
Highly
selective at
20-200 ppm
Response:
226-239
sec
recovery:
294-398
sec
Operational temperature range is 20-250°C.
High selectivity to NH3 over LPG, N2O, CO, NO2, and CO2.
Mechanism appears reversible when flushed with air.
Good reproducibility (same response over 3 cycles) and
high stability are reported.
This sensor may be appropriate for harsh environments
(600°C) such as automotive exhausts. Sensing
characteristics (sensitivity, response time, recovery time)
are dependent on sputtering time of Au sensing
electrodes.
E-53
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#
Sort
Codes
P
NH
3
NH
3
T
C
C
A
Organization
Author (Sponsor)
Shivaji University
(India), Chonnam
National
University (South
Korea), Solapur
University (India)
Pawar, R.C.,
et a/. (University
Grants
Commission,
New Delhi)
Solid State
Physics
Laboratory (India)
Raj, V.B., etal.
(University
Grants
Commission,
DST and Defense
Research and
Development
Organization)
Abbreviated
Citation
Surfactant assisted
low temperature
synthesis of
nanocrystalline
ZnO and its gas
sensing properties.
[2010, Sensors
and Actuators B.
151(1):212-218]
Cross-sensitivity
and selectivity
studies on ZnO
surface acoustic
wave ammonia
sensor
[2010, Sensors
and Actuators B.
2(3): 51 7-524]
Sensor Technology
Type
Nano-
materials
SAW
Description
(Name)
Vertically
aligned ZnO
nanorods on
glass (MOS)
ZnO coated
one-port SAW
Pollutant/
Parameter
Acetone,
ammonia,
LPG, ethanol
Ammonia
(cross
sensitivity
analyzed for
VOCs)
Reported
Detection
Capability
Tested:
2000 ppm
Tested:
590-120,000
ppm
Response/
Recovery
Time
90%
response in
20 sec
Application and Operation Context
High sensitivity for acetone. Low operating temperature
(tested 200-450°C). Best sensitivity and response
achieved at 275°C.
Ammonia can be distinguished from other tested gases by
examining the direction of frequency shift (positive for
ammonia and negative for the rest of the test gases).
Good sensitivity, selectivity, reversibility, and repeatability.
Tested sensor detected ammonia in humid environments
without degrading sensor performance.
E-nose
NH
3
enos
e
Chinese
Academy of
Sciences (China),
Anhui Polytechnic
University (China)
Meng, F.-L., et al.
(Chinese
Academy of
Sciences
National Natural
Science
Foundation of
China, National
Basic Research
Program of
China, Anhui
Provincial
Natural Science
Foundation)
Electronic chip
based on self-
oriented carbon
nanotube
microelectrode
array to enhance
the sensitivity of
indoor air
pollutants
capacitive
detection
[201 1 , Sensors
and Actuators B.
153(1): 103-1 09]
e-nose
Electronic chip
with self-
oriented CNT
microelectrode
array
Formaldehyde
(best
response),
toluene (lowest
response),
ammonia
Response:
"tens of
seconds"
rscovsry .
slowest for
toluene
Use of self-oriented CNTs reduces noise and less
response to water (weak adsorption between CNTs and
water molecules.
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Codes
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T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Benzene
Chemical
be
nz
en
e
be
nz
en
e
c
c
Donghua
University
(China), Leibniz
Institute of
Polymer
Research
Dresden
(Germany)
Fan, Q., et al.
(Shanghai
Pujiang Program,
State Key
Laboratory of
Chemical Fibers
and Polymer
Materials,
Doctorate
Innovation
Foundation of
Donghua
University, and
the National
Natural Science
Foundation for
Distinguished
Young Scholar of
China)
Ehime University
(Japan), National
Institute for
Materials Science
(Japan)
Mori, M., et al.
(Japan Science
and Technology
Agency)
Vapor sensing
properties of
thermoplastic
polyurethane
multifilament
covered with
carbon nanotube
networks
[201 1 , Sensors
and Actuators B.
156(1):63-70]
Detection ofsub-
ppm level of VOCs
based on a
Pt/YSZ/Pt
potentiometric
oxygen sensor with
reference air
[2009, Sensors
and Actuators B.
143(1):56-61]
Nano-
materials
Potentio-
metric
Thermoplastic
polyurethane
multifilament -
carbon
nanotubes
(TPU-CNTs)
composite
(conductive
polymer)
Pt|YSZ|Pt
structure
VOCs,
specifically
benzene,
toluene, CHCI3,
THF, ethanol,
acetone, and
methanol
VOCs,
specifically
acetic acid,
methyl ethyl
ketone,
ethanol,
benzene,
toluene, o- and
p-xylene
Sub-ppm
Response:
1 0 sec to
50% max
relative
resistance
rppn\/prv
i cuuvci y .
begins
3-5 sec
after
injection of
dry air
Ethanol
response:
4-204 sec
(quickest
for 0.5 ppm
at 500° C)
recovery:
190-480
sec
(quickest
for 1 ppm
at 500° C)
400-500°C, highest response at 400°C. Increased
temperatures decreased sensitivity and modified response
and recovery times. Analysis of multidimensional plots of
sensor output at various temperatures suggests possible
selective sensing of different VOCs.
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Codes
P
be
nz
en
e
T
C
A
Organization
Author (Sponsor)
University of
Massachusetts
Lowell
Li, X., et al.
(National Science
Foundation)
Abbreviated
Citation
Fabrication and
integration of metal
oxide nanowire
sensors using
dielectrophoretic
assembly and
improved post-
assembly
processing
[2010, Sensors
and Actuators B.
148(2):404-412]
Sensor Technology
Type
MOS
nanowires
Description
(Name)
Metal oxide
nanowires
(indium, tin,
and
indium-tin).
Dielectro-
phoretic (DEP)
assembly onto
interdigitated
micro-
electrodes
Pollutant/
Parameter
Acetone,
chloroform,
ethanol,
methanol,
propanol, and
benzene.
Reported
Detection
Capability
1 ppm
(potentially
ppb)
Response/
Recovery
Time
Response:
10 sec
recovery:
8-10 min
Application and Operation Context
Good repeatability, however, recovery time increases
when exposed to greater concentrations of substance. No
response to 1500 ppm was observed below 200°C;
performance was enhanced when 440°C was reached.
Further optimization of ITO composition may improve
sensitivity and selectivity of the sensors. Oxygenated
organic compounds cause a higher response than
aromatic or chlorinated compounds.
Benzaldehyde
Chemical
be
nz
aid
eh
yd
e
C
Universidade de
Sao Paulo
(Brazil)
Li, R.W.C., etal.
(FAPESP and
CNPq)
Low cost selective
sensor for carbonyl
compounds in air
based on a novel
conductive poly(p-
xylene) derivative
[2009, Materials
Science and
Engineering C.
29(1):426-429]
Conduc-
ting
polymer
Poly(p-xylene)
(PPX) doped
with CSA
Acetaldehyde,
benzaldehyde,
acetone,
butanone
(shows good
discrimination)
Response:
2 sec
recovery:
<10 sec
No significant drift of the background conductance after
several exposures. Power consumption: <1 |jW. Good
reproducibility over 20 repetitive exposure/ recovery
cycles. Sensors have been tested for more than 3 months
and still respond well. No change in conductance in the
presence of humidity (due to hydrophobic characteristics
of the polymer). Total estimated sensor cost: <$1.
Operates at room temperature.
1-Butanol
Chemical
1-
but
an
ol
C
Isalamic Azad
University (Iran),
K. N. Toosi
University of
Technology (Iran)
Amini, A.,
Ghafarinia, V.
Utilizing the
response patterns
of a temperature
modulated
chemoresistive gas
sensor for gas
diagnosis
[2011,IOPConf.
Ser.: Mater. Sci.
Eng. 17012408]
Commer-
cial
(modified)
Staircase
heating
voltage
waveform
applied to
micro-heater
for SnO2 gas
sensor.
Acetone,
1-butanol,
ethanol,
methanol
Tested:
50-1700
ppm
Operation between 50 and 400°C yielded unique vectors
for methanol, ethanol, 1-butanol, and acetone, suggesting
potential for selectivity. The sensor was exposed for five
40-sec segments.
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Butanone
Chemical
but
nn
e
C
Universidade de
Sao Paulo
(Brazil)
Li, R.W.C., etal.
(FAPESP and
CNPq)
Low cost selective
sensor for carbonyl
compounds in air
based on a novel
conductive polyfp-
xylene) derivative
[2009, Materials
Science and
Engineering C.
29(1):426-429]
Conduc-
ting
polymer
Poly(p-xylene)
(PPX) doped
with CSA
Acetaldehyde,
benzaldehyde,
acetone,
butanone
(shows good
discrimination)
Response:
2 sec
recovery:
<10 sec
No significant drift of the background conductance after
several exposures. Power consumption: <1 |jW. Good
reproducibility over 20 repetitive exposure/ recovery
cycles. Sensors have been tested for more than 3 months
and still respond well. No change in conductance in the
presence of humidity (due to hydrophobic characteristics
of the polymer). Total sensor cost: <$1 . Operates at room
temperature.
Butyric acid
E-nose
but
yn
n
aci
d
e-
Semiconductor
Physics Institute
(Lithuania),
University of
Brescia (Italy)
Setkus, A., et al.
(WOUNDMONIT
OR)
Analysis of the
dynamic features
of metal oxide
sensors in
response to SPME
fiber gas release
[2010, Sensors
and Actuators B.
146(2): 539-544]
Metal
oxide
(MOx)
MOx sensor
array
VOCs,
specifically
those emitted
from infected
wounds
(acetone,
acetic acid,
acetaldehyde,
and butyric
acid)
3-4 ppm
Time-dependent release of VOCs from a SPME fiber.
Carbon monoxide
Chemical
C
C
ENEA (Italy)
Penza, M., et al.
Functional
characterization of
carbon nanotube
networked films
functionalized with
tuned loading of Au
nanoclusters for
gas sensing
applications
[2009, Sensors
and Actuators B.
140(1): 176-1 84]
Chemi-
resistor
Gold
functionalized
CNTs
NO2, NH3, CO,
N2O, H2S, SO2
Sub-ppm:
NO2, H2S
and NH3
200 ppb NO2
negligible
response for
CO, N2O,
and SO2
Operational temperature: 20-250°C.
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Codes
P
c
o
c
o
c
o
T
c
c
c
A
Organization
Author (Sponsor)
Korea University
(Republic of
Korea)
Kim, Y.-S., etal.
Kyushu
University
(Japan), Japan
Society for the
Promotion of
Sciences (Japan)
Plashnitsa, V.V.,
etal.
(MEXT, The
Grant-in- Aid for
Scientific
Research on
Priority Area,
Nanoionics)
Rosemount
Analytical Inc.,
Kurt-Schwabe
Institute for
Measuring
Sensor
Technology
(Germany)
Shuk, P., et al.
(German Federal
Ministry of
Economics and
Rosemont
Analytical Inc.)
Abbreviated
Citation
CuO nanowire gas
sensors for air
quality control in
automotive cabin
[2008, Sensors
and Actuators B.
135(1):298-303]
Zirconia-based
electrochemical
gas sensors using
nano-structured
sensing materials
aiming at detection
of automotive
exhausts
[2009,
Electrochimica
Acta.
54(25): 6099-61 06]
New advanced in
situ carbon
monoxide sensor
for the process
application
[2009, International
Journal of Ionics.
15(2):131-138]
Sensor Technology
Type
Nano-
materials
Potentio-
metric
Potentio-
metric
Description
(Name)
CuO
nanowires
grown by
thermal
oxidation of
Cufoil. P-type
oxide
semiconductor
YSZ-based
planar sensors
using nano-
structured
sensing
electrodes
Mixed
potential solid
electrolyte with
sensing
electrodes
based on
composite with
various semi-
conducting
oxides
Pollutant/
Parameter
CO, NO2
Ammonia, NO2,
CO, CH4, C3H8,
C3H6, NO
CO
(interferents
analyzed: CO2,
H2O, O2, SO2)
Reported
Detection
Capability
Tested: 10,
50, 100ppm
CO;
1-5 ppm, 10,
50 and
100 ppm
NO2
Highly
selective at
20-200 ppm
~5 ppm
Response/
Recovery
Time
Response:
25-30 sec
at 550°C;
1 5-20 sec
at 650° C
Application and Operation Context
Sensor resistance was reported to decrease with NO2
concentrations between 30 and 100 ppm, and increase
with NO2 concentrations between 1 and 5 ppm.
Resistance increased with exposure to 10, 50, and 100
ppm of CO. Sensor was tested at 300°C and 370°C, using
300 mW and 400 mW of power, respectively.
This sensor may be appropriate for harsh environments
(600°C) such as automotive exhausts. Sensing
characteristics (sensitivity, response time, recovery time)
seem to be dependent on sputtering time of Au sensing
electrodes.
Good reproducibility and stability in hazardous combustion
environment (tested at power plant). Sensor demonstrated
little to no cross sensitivity to H2O, and no cross sensitivity
to CO2; however, there was cross sensitivity to
O2 +/-15 ppm. After exposure to 1 ,000 ppm SO2, CO
sensor sensitivity increased by 50%, but response was
stable and reproducible.
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Codes
P
c
o
c
o
T
c
c
A
Organization
Author (Sponsor)
Shiraz University
(Iran), Persian
Gulf University
(Iran)
Javadpour, S., et
al.
Universitat de
Barcelona
(Spain), Ecole
Nationale
Superieure de
Mines de
Saint-Etienne
(France),
Proprietes et
Moderations
des Solides
(France)
Morata, A., et al.
Abbreviated
Citation
Morpholine doped
poly (3,4-
ethylenedioxy)
thiophene-
poly(styrene-
sulfonate) as a low
temperature and
quick carbon
monoxide sensor
[2009, Sensors
and Actuators B.
142(1): 152-1 58]
Development and
characterisation of
a screen-printed
mixed potential gas
sensor
[2008, Sensors
and Actuators B.
1 30(1 ):561 -566]
Sensor Technology
Type
Polymer
film
Potentio-
metric
Description
(Name)
Poly(3,4-
ethylenedioxy)
thiophene-
poly(styrene-
sulfonate)
(PEDOT/PSS
thin films). Fe,
Al, and
morpholine
were added
Screen
printing
Pollutant/
Parameter
CO
CO (in the
presence of O2
and NO2)
Reported
Detection
Capability
Tested: 10,
15,50,100,
150, and
200 ppm for
CO;
3, 5, 7, and
10 ppm for
NO2
Response/
Recovery
Time
Response:
5 sec
0-3000 sec
depending
on
concentra-
tion
Application and Operation Context
This thin film was reported to produce a better response
and reversibility time than other tested polymer sensing
films (PANi or polypyrrole). Un-doped sensor responds
more to general air than it does to just CO (50% vs. 2%).
Mixture of air and CO results in a better response, but
doping appears to improve the sensing capabilities.
Combining the polymer with the Fe-AI-morpholine as a
doping compound reduced the effect of moisture and
improved response to CO (only 5% unstable increase in
the resistance).
Baseline drifts slightly for lower working temperatures.
Results show that NO2 has a very small effect on the
sensors response to CO when both species are introduced
together. Sensor response does not vary appreciably after
20 hours of continuous operation. Tested at temperatures
ranging from 530 to 580°C.
E-nose
c
o
e-
nose
CNR-IMM-
Istituto per la
Microelettronica
ed i Microsistemi,
University
Campus (Italy),
ITC-irst -
Microsystems
Division, (Italy)
Francioso, L, et
al.
Linear temperature
microhotplate gas
sensor array for
automotive cabin
air quality
monitoring
[2008, Sensors
and Actuators B.
134(2): 660-665]
e-nose
MOS, MEMS
CO, NO2, SO2
Investigates a temperature gradient electronic nose for
increasing sensitivity. Total power consumption below
130 mW with power supplied to voltage heater.
Temperature was increased in a 100°C-wide temperature
window. Sensor exhibited faster response times for higher
temperatures (300-400°C) and higher gas concentrations.
Signal was lost when sensor was operated below 280°C.
Sensor was exposed to injected gas for 30 minutes,
followed by 90 minutes of recovery in dry air. Principal
component analysis (PCA) was used to identify and
analyze patterns in data.
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Codes
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CO
T
e-
nose
A
Organization
Author (Sponsor)
Saratov State
Technical
University
(Russia),
Southern Illinois
University at
Carbondale,
Northeastern
University
Sysoev, V. V., et
al.
(Fullbright
scholarship and
RFBR grant and
NSF)
Abbreviated
Citation
The electrical
characterization of
a multi-electrode
odor detection
sensor array based
on the single
SnO2 nanowire
[2011, Thin Solid
Films.
520(3): 898-903]
Sensor Technology
Type
e-nose
Description
(Name)
SnO2
wedge-like
nanowire,
multielectrode
odor detection
sensor array;
functionalized
by deposition
ofPd
nanoparticles
Pollutant/
Parameter
Acetone,
2-propanol,
CO, and H2
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Fabricating metal oxide single crystals in shape of
nanowires may be cost effective.
Carbonyl sulfide
Chemical
car
bo
nyl
sul
fid
e
c
Matheson
Tri-Gas Inc.
Chase, D., et al.
Pyrolysis-
electrochemical
sensor for
monitoring
carbonyl sulfide
levels in ambient
air
[July 1,2010]
http ://www.electroiq .c
om/articles/sst/print/v
olume-53/issue-
7/features/ehs/pyrolys
is-electrochemical-
sensor.html
Commer-
cial
Honeywell
Analytics
Carbonyl
sulfide
Tested:
50-100ppm
Typical response ranged from 12-14% of the gas
depending on humidity. 15% increase in response was
observed as relative humidity increased from 1 to 100%.
This commercial sensor is typically used to monitor
process tools, gas cabinets, valve manifold boxes,
ambient air and other areas where gases are generated.
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P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Chloroform
Chemical
chl
oro
for
m
chl
oro
for
m
c
c
Donghua
University
(China), Leibniz
Institute of
Polymer
Research
Dresden
(Germany)
Fan, Q., et al.
(Shanghai
Pujiang Program,
State Key
Laboratory of
Chemical Fibers
and Polymer
Materials,
Doctorate
Innovation
Foundation of
Donghua
University, and
the National
Natural Science
Foundation for
Distinguished
Young Scholar of
China)
University of
Massachusetts
Lowell
Li, X., et al.
(National Science
Foundation)
Vapor sensing
properties of
thermoplastic
polyurethane
multifilament
covered with
carbon nanotube
networks
[201 1 , Sensors
and Actuators B.
156(1): 63-70]
Fabrication and
integration of metal
oxide nanowire
sensors using
dielectrophoretic
assembly and
improved post-
assembly
processing
[2010, Sensors
and Actuators B.
148(2):404-412]
Nano-
materials
MOS
nanowires
Thermoplastic
polyurethane
multifilament -
carbon
nanotubes
(TPU-CNTs)
composite
(conductive
polymer)
Metal oxide
nanowires
(indium, tin,
and
indium-tin).
Dielectro-
phoretic (DEP)
assembly onto
interdigitated
micro-
electrodes
VOCs,
specifically
benzene,
toluene, CHCI3,
THF, ethanol,
acetone, and
methanol
Acetone,
chloroform,
ethanol,
methanol,
propanol, and
benzene.
1 ppm
(potentially
ppb)
Response:
1 0 sec to
50% max
relative
resistance
rppn\/prv
i cuuvci y .
begins
3-5 sec
after
injection of
dry air
Response:
10 sec
rscovsry .
8-10 min
Good repeatability, however, recovery time increases
when exposed to greater concentrations of substance. No
response to 1500 ppm was observed below 200°C;
performance was enhanced when 440°C was reached.
Further optimization of ITO composition may improve
sensitivity and selectivity of the sensors. Oxygenated
organic compounds seem to cause a higher response than
aromatic or chlorinated compounds.
E-61
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Ethanol
Chemical
eth
an
ol
eth
an
ol
C
c
Donghua
University
(China), Leibniz
Institute of
Polymer
Research
Dresden
(Germany)
Fan, Q., et al.
(Shanghai
Pujiang Program,
State Key
Laboratory of
Chemical Fibers
and Polymer
Materials,
Doctorate
Innovation
Foundation of
Donghua
University, and
the National
Natural Science
Foundation for
Distinguished
Young Scholar of
China)
Ehime University
(Japan), National
Institute for
Materials Science
(Japan)
Mori, M., et al.
(Japan Science
and Technology
Agency)
Vapor sensing
properties of
thermoplastic
polyurethane
multifilament
covered with
carbon nanotube
networks
[201 1 , Sensors
and Actuators B.
156(1):63-70]
Detection ofsub-
ppm level of VOCs
based on a
Pt/YSZ/Pt
potentiometric
oxygen sensor with
reference air
[2009, Sensors
and Actuators B.
143(1):56-61]
Nano-
materials
Potentio-
metric
Thermoplastic
polyurethane
multifilament -
carbon
nanotubes
(TPU-CNTs)
composite
(conductive
polymer)
Pt|YSZ|Pt
structure
VOCs,
specifically
benzene,
toluene, CHCI3,
THF, ethanol,
acetone, and
methanol
VOCs,
specifically
acetic acid,
methyl ethyl
ketone,
ethanol,
benzene,
toluene, o- and
p-xylene
Sub-ppm
Response:
1 0 sec to
50% max
relative
resistance
rppn\/prv
i cuuvci y .
begins
3-5 sec
after
injection of
dry air
Ethanol
response:
4-204 sec
(quickest
for 0.5 ppm
at 500° C)
recovery:
190-480
sec
(quickest
for 1 ppm
at 500° C)
400-500°C, highest response at 400°C. Increased
temperatures decreased sensitivity but modified response
and recovery times. Analysis of multidimensional plots of
sensor output at various temperatures suggests possible
selective sensing of different VOCs.
E-62
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Codes
P
eth
an
ol
eth
an
ol
eth
an
ol
T
C
c
C
A
Organization
Author (Sponsor)
Isalamic Azad
University (Iran),
K. N. Toosi
University of
Technology (Iran)
Amini, A.,
Ghafarinia, V.
Shivaji University
(India), Chonnam
National
University (South
Korea), Solapur
University (India)
Pawar, R.C.,
et al. (University
Grants
Commission,
New Delhi)
University of
Massachusetts
Lowell
Li, X., et al.
(National Science
Foundation)
Abbreviated
Citation
Utilizing the
response patterns
of a temperature
modulated
chemoresistive gas
sensor for gas
diagnosis
[2011,IOPConf.
Ser.: Mater. Sci.
Eng. 17012408]
Surfactant assisted
low temperature
synthesis of
nanocrystalline
ZnO and its gas
sensing properties
[2010, Sensors
and Actuators B.
151(1): 21 2-21 8]
Fabrication and
integration of metal
oxide nanowire
sensors using
dielectrophoretic
assembly and
improved post-
assembly
processing
[2010, Sensors
and Actuators B.
148(2):404-412]
Sensor Technology
Type
Commer-
cial
(modified)
Nano-
materials
MOS
nanowires
Description
(Name)
Staircase
heating
voltage
waveform
applied to
micro-heater
for SnO2 gas
sensor.
Vertically
aligned ZnO
nanorods on
glass (MOS)
Metal oxide
nanowires
(indium, tin,
and
indium-tin).
Dielectro-
phoretic (DEP)
assembly onto
interdigitated
micro-
electrodes
Pollutant/
Parameter
Acetone,
1-butanol,
ethanol,
methanol
Acetone,
ammonia,
LPG, ethanol
Acetone,
chloroform,
ethanol,
methanol,
propanol, and
benzene.
Reported
Detection
Capability
Tested:
50-1700
ppm
Tested:
2000 ppm
1 ppm
(potentially
ppb)
Response/
Recovery
Time
Response:
10 sec
recovery:
8-10 min
Application and Operation Context
Operation between 50 and 400°C yielded unique vectors
for methanol, ethanol, 1-butanol, and acetone, suggesting
potential for selectivity. The sensor was exposed for five
40-sec segments.
High sensitivity for acetone. Low operating temperature
(tested 200-450°C). High sensitivity and fast response at
275°C.
Good repeatability, however, recovery time increases
when exposed to greater concentrations of substance. No
response to 1500 ppm was observed below 200°C;
performance was enhanced when 440°C was reached.
Further optimization of ITO composition may improve
sensitivity and selectivity of the sensors. Oxygenated
organic compounds seem to cause a higher response than
aromatic or chlorinated compounds.
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
E-nose
eth
an
ol
C
University of
Southern
California
Chen, P.-C., et al.
(National Science
Foundation)
A nanoelectronic
nose: a hybrid
nanowire/carbon
nanotube sensor
array with
integrated
micromachined
hotplates for
sensitive gas
discrimination
[2009,
Nanotechnology.
20(12)]
e-nose
Chemical
sensor array
composed of
individual
ln2O3
nanowires,
SnO2
nanowires,
ZnO
nanowires,
and SWCNTs
with integrated
micro-
machined hot
plates for
sensitive gas
discrimination
H2, ethanol,
NO2
Sensor was exposed to three gas injection pulses of
different concentrations and compositions, both at room
temperature and at 200°C (avoids complications due to
moisture interference). The bending energy induced by
adsorption is different for different materials, which could
allow for gas discrimination in an e-nose system. Sensor
behavior was reproducible with small (<1 %) error bars.
Formaldehyde
E-nose
for
ma
Ide
hy
de
enos
e
Chinese
Academy of
Sciences (China),
Anhui Polytechnic
University (China)
Meng, F.-L., et al.
(Chinese
Academy of
Sciences
National Natural
Science
Foundation of
China, National
Basic Research
Program of
China, Anhui
Provincial
Natural Science
Foundation)
Electronic chip
based on self-
oriented carbon
nanotube
microelectrode
array to enhance
the sensitivity of
indoor air
pollutants
capacitive
detection
[201 1 , Sensors
and Actuators B.
153(1): 103-1 09]
e-nose
Electronic chip
with self-
oriented CNT
microelectrode
array
Formaldehyde
(best
response),
toluene (lowest
response),
ammonia
Response:
"tens of
seconds"
rscovsrv
slowest for
toluene
Use of self-oriented CNTs reduces noise and less
response to water (weak adsorption between CNTs and
water molecules.
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Hydrogen
Chemical
H2
C
Los Alamos
National
Laboratory, ESL
ElectroScience
Sekhar, P.K.,
etal.
(DOE office of
Vehicle
Technologies,
DOE Hydrogen
Fuel Cell and
Infrastructure
Programs)
Application of
commercial
automotive sensor
manufacturing
methods for
NOx/NH3 mixed
potential sensors
for on-board
emissions control
[2010, Sensors
and Actuators B.
144(1): 112-119]
Potentio-
metric
ITO/YSZ/Pt
configuration
(indium tin
oxide/yttria-
stabilized
zirconia/
platinum)
H2
Tested:
1,000-
20,000 ppm
Response:
3-7 sec
Responds in real-time to varying concentrations of H2
(1000-20,000 ppm). Cross sensitivity to C3H6 (propylene),
but not to NO, NO2, NH3, or CO. Lower power
consumption, compact, simple operation, fast response,
direct voltage read out, conducive to commercialization.
Sensitivity varied between 0.135 and 0.1 67V. Baseline
signal ranged from 0-0.04 V.
E-nose
H2
e-
nose
Saratov State
Technical
University
(Russia),
Southern Illinois
University at
Carbondale,
Northeastern
University
Sysoev, V. V., et
al.
(Fullbright
scholarship and
RFBR grant and
NSF)
The electrical
characterization of
a multi-electrode
odor detection
sensor array based
on the single
SnO2 nanowire
[2011, Thin Solid
Films.
520(3): 898-903]
Nano-
materials
SnO2
wedge-like
nanowire,
multielectrode
odor detection
sensor array;
functionalized
by deposition
ofPd
nanoparticles
Acetone,
2-propanol,
CO, and H2
Fabricating metal oxide single crystals in shape of
nanowires may be cost effective.
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Codes
P
H2
T
c
A
Organization
Author (Sponsor)
University of
Southern
California
Chen, P.-C., et al.
(National Science
Foundation)
Abbreviated
Citation
A nanoelectronic
nose: a hybrid
nanowire/carbon
nanotube sensor
array with
integrated
micromachined
hotplates for
sensitive gas
discrimination
[2009,
Nanotechnology.
20(12)]
Sensor Technology
Type
e-nose
Description
(Name)
Chemical
sensor array
composed of
individual
ln2O3
nanowires,
SnO2
nanowires,
ZnO
nanowires,
and SWCNTs
with integrated
micro-
machined hot
plates for
sensitive gas
discrimination
Pollutant/
Parameter
H2, ethanol,
NO2
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Sensor was exposed to three gas injection pulses of
different concentrations and compositions, both at room
temperature and at 200°C (avoids complications due to
moisture interference). The bending energy induced by
adsorption is different for different materials, which could
allow for gas discrimination in an e-nose system. Sensor
behavior was reproducible with small (<1 %) error bars.
Hydrogen sulfide
Chemical
H2
S
H2
S
c
c
ENEA (Italy)
Penza, M., et al.
JiLin University
Liang, X., et al.
(Natural Science
Foundation of
China and
National Science
Fund for
Distinguished
Young Scholars
of China)
Functional
characterization of
carbon nanotube
networked films
functionalized with
tuned loading of Au
nanoclusters for
gas sensing
applications
[2009, Sensors
and Actuators B.
140(1): 176-184]
Solid-state
potentiometric H2S
sensor combining
NASICON with
Pr6Olrdoped
SnO2 electrode
[2007, Sensors
and Actuators B.
125(2): 544-549]
Chemi-
resistor
Potentio-
metric
Gold
functionalized
CNTs
Sodium super
ionic
conductor
(NASICON)
andPr6O11-
doped SnO2
sensing
electrode
NO2, NH3, CO,
N2O, H2S, SO2
H2S
Sub-ppm:
NO2, H2S
and NH3
9DDnnh MD
^UU|J|JU |NW2
Negligible
response for
CO, N2O,
and SO2
5-50 ppm at
200-400 C
Response:
4-8 sec
recovery:
1 2-30 sec
Operational temperature: 20-250 °C.
This sensor demonstrated good linear relationship
between EMF and the logarithm of H2S concentration. The
results were an improvement over those obtained for pure
SnO2. Sensor demonstrates selectivity against SO2, NO2,
CH4, and CO.
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Codes
P
H2
S
T
c
A
Organization
Author (Sponsor)
Tongji University
(China), Hunan
University (China)
Zhang, F., et al.
Abbreviated
Citation
CuO nanosheets
for sensitive and
selective
determination of
H2S with high
recovery ability
[2010, J. Phys.
Chem.
114:19214-19219]
Sensor Technology
Type
Nano-
materials
Description
(Name)
CuO leaf-like
nanosheet
(p-type
semiconductor
properties)
Pollutant/
Parameter
H2S
Reported
Detection
Capability
LOD: 2 ppb
Linear
Range:
20 ppb —
1.2 ppm
Response/
Recovery
Time
Response:
4 sec
recovery:
9 sec
Application and Operation Context
Results suggest that sensitivity and recovery time are
highly dependent on the working temperature (optimum
reported to be 240°C). Negligible responses reported for
100-fold higher concentrations of N2, O2, NO, CO, NO2,
and H2.
Liquid petroleum gas
Chemical
LP
G
c
Shivaji University
(India), Chonnam
National
University (South
Korea), Solapur
University (India)
Pawar, R.C.,
et al. (University
Grants
Commission,
New Delhi)
Surfactant assisted
low temperature
synthesis of
nanocrystalline
ZnO and its gas
sensing properties
[2010, Sensors
and Actuators B.
151(1):212-218]
Nano-
materials
Vertically
aligned ZnO
nanorods on
glass (MOS)
Acetone,
ammonia,
LPG, ethanol
Tested:
2000 ppm
Demonstrated good sensitivity for acetone at a low
operating temperature (tested 200-450°C). Best sensitivity
and fast response reported at 275°C.
Methane
Chemical
me
tha
ne
C
Beijing University
of Chemical
Technology
(China), Case
Western Reserve
University Chen,
A., et al. (National
Natural Science
Foundation of
China and Beijing
Natural Science
Foundation)
Methane
gas-sensing and
catalytic oxidation
activity of
SnO2-ln2O3
nanocomposites
incorporating TiO2
[2008, Sensors
and Actuators B.
135(1):7-12]
Chemi-
resistive
lnO1.5-SnO2
nanocompo-
sites
incorporating
TiO2
methane
Concluded that the selectivity of the MOS may have been
aided by addition of more metal oxides. Overall
performance depended on composition and operational
temperatures. Addition of TiO2 was reported to improve
the response to methane and better select against CO.
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Codes
P
me
tha
ne
me
tha
ne
T
c
C
A
Organization
Author (Sponsor)
Jadavpur
University (India)
Basu, P.K., etal.
Kyushu
University
(Japan), Japan
Society for the
Promotion of
Sciences (Japan)
Plashnitsa, V.V.,
etal.
(MEXT, The
Grant-in-Aid for
Scientific
Research on
Priority Area,
Nanoionics)
Abbreviated
Citation
Low temperature
methane sensing
by electro-
chemically grown
and surface
modified ZnO thin
films
[2008, Sensors
and Actuators B.
135(1): 81 -88]
Zirconia-based
electrochemical
gas sensors using
nano-structured
sensing materials
aiming at detection
of automotive
exhausts
[2009,
Electrochimica
Acta.
54(25): 6099-61 06]
Sensor Technology
Type
Thin film
Potentio-
metric
Description
(Name)
Planar
resistive and
metal-
insulator-metal
sensors using
electro-
chemically
grown nano-
crystalline-
nanoporous
ZnO thin films
modified by
dipping in an
aqueous
solution of
PdCI2
YSZ-based
planar sensors
using nano-
structured
sensing
electrodes
Pollutant/
Parameter
Methane
Ammonia, NO2,
CO, CH4, C3H8,
C3H6, NO
Reported
Detection
Capability
1% methane
in nitrogen
and1%
methane in
air
Highly
selective at
20-200 ppm
Response/
Recovery
Time
Response:
planar:
5 sec,
metal-
insulator-
metal
(MIM):
2.7 sec
recovery:
planar:
22.7 sec,
MIM: 16 sec
Application and Operation Context
Operational temperatures were reduced to 70 and 100°C
for each of the two configurations.
This sensor is appropriate for harsh environments (600°C)
such as automotive exhausts. Sensing characteristics
(sensitivity, response time, recovery time) are dependent
on sputtering time of Au sensing electrodes.
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Methanol
Chemical
me
tha
nol
me
tha
nol
c
c
Donghua
University
(China), Leibniz
Institute of
Polymer
Research
Dresden
(Germany) Fan,
Q., et al.
(Shanghai
Pujiang Program,
State Key Lab of
Chemical Fibers
and Polymer
Materials,
Doctorate
Innovation
Foundation of
Donghua
University, &
National Natural
Science
Foundation for
Distinguished
Young Scholar of
China)
Isalamic Azad
University (Iran),
K. N. Toosi
University of
Technology (Iran)
Amini, A.,
Ghafarinia, V.
Vapor sensing
properties of
thermoplastic
polyurethane
multifilament
covered with
carbon nanotube
networks
[201 1 , Sensors
and Actuators B.
156(1):63-70]
Utilizing the
response patterns
of a temperature
modulated
chemoresistive gas
sensor for gas
diagnosis
[2011,IOPConf.
Ser.: Mater. Sci.
Eng. 17012408]
Nano-
materials
Commer-
cial
(modified)
Thermoplastic
polyurethane
multifilament -
carbon
nanotubes
(TPU-CNTs)
composite
(conductive
polymer)
Staircase
heating
voltage
waveform
applied to
micro-heater
for SnO2 gas
sensor
VOCs,
specifically
benzene,
toluene, CHCI3,
THF, ethanol,
acetone, and
methanol
Acetone,
1-butanol,
ethanol,
methanol
Tested:
50-1700
ppm
Response:
1 0 sec to
50% max
relative
resistance
Recovery:
begins
3-5 sec
after
injection of
dry air
Operation between 50 and 400°C yielded unique vectors
for methanol, ethanol, 1-butanol, and acetone, suggesting
potential for selectivity. The sensor was exposed for five
40-sec segments.
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Codes
P
me
tha
nol
T
c
A
Organization
Author (Sponsor)
University of
Massachusetts
Lowell
Li, X., et al.
(National Science
Foundation)
Abbreviated
Citation
Fabrication and
integration of metal
oxide nanowire
sensors using
dielectrophoretic
assembly and
improved post-
assembly
processing
[2010, Sensors
and Actuators B.
148(2):404-412]
Sensor Technology
Type
MOS
nanowires
Description
(Name)
Metal oxide
nanowires
(indium, tin,
and
indium-tin).
Dielectro-
phoretic (DEP)
assembly onto
interdigitated
micro-
electrodes
Pollutant/
Parameter
Acetone,
chloroform,
ethanol,
methanol,
propanol, and
benzene
Reported
Detection
Capability
1 ppm
(potentially
ppb)
Response/
Recovery
Time
Response:
10 sec
recovery:
8-10 min
Application and Operation Context
Good repeatability, however, recovery time increases
when exposed to greater concentrations of substance. No
response to 1500 ppm was observed below 200°C;
performance was enhanced when 440°C was reached.
Further optimization of ITO composition may improve
sensitivity and selectivity of the sensors. Oxygenated
organic compounds cause a higher response than
aromatic or chlorinated compounds.
Methyl ethyl ketone
Chemical
M
EK
c
Ehime University
(Japan), National
Institute for
Materials Science
(Japan)
Mori, M., et al.
(Japan Science
and Technology
Agency)
Detection ofsub-
ppm level of VOCs
based on a
Pt/YSZ/Pt
potentiometric
oxygen sensor with
reference air
[2009, Sensors
and Actuators B.
143(1):56-61]
Potentio-
metric
Pt|YSZ|Pt
structure
VOCs,
specifically
acetic acid,
methyl ethyl
ketone,
ethanol,
benzene,
toluene, o- and
p-xylene
Sub-ppm
Ethanol
response:
4-204 sec
(quickest
for 0.5 ppm
at 500° C)
recovery:
190-480
sec
(quickest
for 1 ppm
at 500° C)
400-500°C, highest response at 400°C. Increased
temperatures decreased sensitivity but modified response
and recovery times. Analysis of multidimensional plots of
sensor output at various temperatures suggests possible
selective sensing of different VOCs.
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P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
NOx
Chemical
N
Ox
c
The Ohio State
University
Yang, J.-C.,
Dutta, P.K.D.
(DOE NETL)
Promoting
selectivity and
sensitivity for a
high temperature
YSZ-based
electrochemical
total NOx sensor
by using a Pt-
loaded zeolite Y
filter
[2007, Sensors
and Actuators B.
125(1):30-39]
Potentio-
metric
Study
examines Pt
electrode
covered with
Pt containing
zeolite Y (PtY)
and WO3 as
electrode
materials
NOx
(interferents:
CO, propane,
NH3, CO2, O2,
and H2O)
WO3 is used as sensing electrode due to its poor reactivity
with NOx (assumes that and unmodified NOx species will
reach the WOx/YSZ to produce a more sensitive
response).
Nitrogen dioxide
Chemical
N
O2
N
O2
c
c
Achhab, M.,
Shanack, H.,
Schierbaum, K.
Chiang Mai
University
(Thailand),
National
Electronics and
Computer
Technology
Center (Thailand)
Kruefu, V., et al.
NO2 sensing
properties of
WO3 nanorods
grown on mica
[201 1 , Physica
Status Solid! A.
208(6): 1229-1 234]
Selectivity of
flame-spray-made
Nb/ZnO thick films
towards NO2 gas
[201 1 , Sensors
and Actuators B.
156(1):360-367]
Nano-
materials
Nano-
materials
One-
dimensional
W03
nanostructures
with gold
electrodes
Unloaded ZnO
and niobium
(Nb)/ZnO
na no particles
for the
detection of
NO2
(semiconducti
ng
gas-sensing
behavior)
NO2
NO2
-0.1 ppm
(transient
W03
needles)
Tested:
0.1-4 ppm
Limit: 20ppb
(est.)
Response:
"quick"
rppn\/prv
i cuuvci y .
slow,
10, 000 sec
using mica
substrate
Response:
27 sec with
4 ppm NO2
at 300° C
Sensor is more sensitive towards NO2 with increased
humidity, up to 40%. Maximum operation temperature was
reported to be 260°C, due to limited gold contact stability.
A drift was observed at concentrations over 0.4 ppm which
may be attributed to temperature effects from the internal
heater.
Detection limit is estimated to be 20 ppb, however testing
is limited to 100 ppb due to gas mixing capability of the
system. Sensor recovers to within 10% of baseline value
after several NO2 exposures. Selectivity to NO2 was tested
with C2H3OH, CO, and acetone. Authors reported good
selectivity to 4 ppm NO2 concentration at 350°C. Niobium
enhances NO2 adsorption reaction.
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Codes
P
N
O2
N
O2
N
O2
N
O2
T
c
c
c
c
A
Organization
Author (Sponsor)
ENEA (Italy)
Penza, M., et al.
Korea University
(Republic of
Korea)
Kim, Y.-S., etal
Kyushu
University
(Japan), Japan
Society for the
Promotion of
Sciences (Japan)
Plashnitsa, V.V.,
etal.
(MEXT, The
Grant-in- Aid for
Scientific
Research on
Priority Area,
Nanoionics)
University of
Leeds
Xiong, I/I/., and
Kale, G.M.
Abbreviated
Citation
Functional
characterization of
carbon nanotube
networked films
functionalized with
tuned loading of Au
nanoclusters for
gas sensing
applications
[2009, Sensors
and Actuators B.
140(1): 176-1 84]
CuO nanowire gas
sensors for air
quality control in
automotive cabin
[2008, Sensors
and Actuators B.
135(1):298-303]
Zirconia-based
electrochemical
gas sensors using
nano-structured
sensing materials
aiming at detection
of automotive
exhausts
[2009,
Electrochimica
Acta.
54(25): 6099-61 06]
Electrochemical
NO2 sensor using a
NiFei,9Alo.iO4 oxide
spinel electrode
[2007, Anal.
Chem..
79(10:3561-3567]
Sensor Technology
Type
Chemi-
resistor
Nano-
materials
Potentio-
metric
Solid
state
Description
(Name)
Gold
functionalized
CNTs
CuO
nanowires
grown by
thermal
oxidation of
Cufoil. P-type
oxide
semiconductor
YSZ-based
planar sensors
using nano-
structured
sensing
electrodes
(Sc2O3)0.08
(ZrO2)0.92
(ScSZ)
electrolyte
f*rtl!*J ~i«*J
solid and
NiFe1.9AI0.1)
4 oxide spinel
electrode
Pollutant/
Parameter
NO2, NH3, CO,
N2O, H2S, SO2
CO, NO2
Ammonia, NO2,
CO, CH4, C3H8,
C3H6, NO
NO2
Reported
Detection
Capability
Sub-ppm:
NO2, H2S
and NH3
9DD nnh MD
^UU |-'|-'U |NW2
Negligible
response for
CO, N2O,
and SO2
Tested: 10,
50, 100ppm
CO;
1-5 ppm, 10,
50 and
100 ppm
NO2
Highly
selective at
20-200 ppm
Response/
Recovery
Time
Response:
8 sec
recovery:
10 sec
Application and Operation Context
Operational temperature: 20-250°C.
Sensor resistance was reported to decrease with NO2
concentrations between 30 and 100 ppm, and increase
with NO2 concentrations between 1 and 5 ppm.
Resistance increased with exposure to 10, 50, and
100 ppm of CO. Sensor was tested at 300°C and 370°C,
using 300 mW and 400 mW of power, respectively.
This sensor is appropriate for harsh environments (600°C)
such as automotive exhausts. Sensing characteristics
(sensitivity, response time, recovery time) are dependent
on sputtering time of Au sensing electrodes.
Sensor response is rapid, reproducible at 703°C and
740°C (appropriate for automobile applications), and
selective against O2, CO, and CH4.
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Codes
P
N
O2
T
c
A
Organization
Author (Sponsor)
University of
Massachusetts
Lowell
Surwade, S.P., et
al.
(University of
Massachusetts
Lowell, MTC-
funded
Nanomanufac-
turing Center for
Excellence, NSF)
Abbreviated
Citation
Nitrogen dioxide
vapor detection
using poly-o-
toluidine
[2009, Sensors
and Actuators B.
143(1): 454-457]
Sensor Technology
Type
Conduct-
ing
polymer
film
Description
(Name)
Doped thin
film
conducting
polymer (poly-
o-toluidine)
deposited on
plastic
substrates
Pollutant/
Parameter
NO2
Reported
Detection
Capability
Tested:
10-100ppm
Response/
Recovery
Time
Application and Operation Context
Reaction on polymer film can be reversed with UV
irradiation for <2 minutes if exposed to concentrations
between 10 and 100 ppm. Without UV exposure, recovery
took 45-90 minutes.
E-nose
N
O2
N
O2
e-
nose
C
CNR-IMM-
Istituto per la
Microelettronica
ed i Microsistemi,
University
Campus (Italy),
ITC-irst -
Microsystems
Division, (Italy)
Francioso, L, et
al.
University of
Southern
California
Chen, P.-C., et al.
(National Science
Foundation)
Linear temperature
microhotplate gas
sensor array for
automotive cabin
air quality
monitoring
[2008, Sensors
and Actuators B.
134(2): 660-665]
A nanoelectronic
nose: a hybrid
nanowire/carbon
nanotube sensor
array with
integrated
micromachined
hotplates for
sensitive gas
discrimination
[2009,
Nanotechnology.
20(12)]
e-nose
e-nose
MOS, MEMS
Chemical
sensor array
composed of
individual
ln2O3
nanowires,
SnO2
nanowires,
ZnO
nanowires,
and SWCNTs
with integrated
micro-
machined hot
plates for
sensitive gas
discrimination
CO, NO2, SO2
H2, ethanol,
NO2
Investigates a temperature gradient electronic nose for
increasing sensitivity. Total power consumption below
130 mW with power supplied to voltage heater.
Temperature was increased in a 100°C-wide temperature
window. Sensor exhibited faster response times for higher
temperatures (300-400°C) and higher gas concentrations.
Signal was lost when sensor was operated below 280°C.
Sensor was exposed to injected gas for 30 minutes,
followed by 90 minutes of recovery in dry air. PCA was
used to identify and analyze patterns in data.
Sensor was exposed to three gas injection pulses of
different concentrations and compositions, both at room
temperature and at 200°C (avoids complications due to
moisture interference). The bending energy induced by
adsorption is different for different materials, which could
allow for gas discrimination in an e-nose system. Sensor
behavior was reproducible with small (<1 %) error bars.
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Nitric oxide
Chemical
N
O
N
O
N
O
c
c
c
Florida
International
University, Inje
University
(Republic of
Korea)
Verma, V.P., et
al.
(Dissertation
Year Fellowship
from University
Graduate School,
Florida
International
University)
-Shou University
(Taiwan)
Wang, S.-H., et
al.
(National Science
Council)
Kyushu
University
(Japan), Japan
Society for the
Promotion of
Sciences (Japan)
Plashnitsa, V.V.,
etal.
(MEXT, The
Grant-in-Aid for
Scientific
Research on
Priority Area,
Nanoionics)
Nitric oxide gas
sensing at room
temperature by
functionalized
single zinc oxide
nanowire
[2010, Materials
Science and
Engineering.
171 (1-3): 45-49]
A nitric oxide gas
sensor based on
Rayleigh surface
acoustic wave
resonator for room
temperature
detection
[201 1 , Sensors
and Actuators B.
156(2): 668-672]
Zirconia-based
electrochemical
gas sensors using
nano-structured
sensing materials
aiming at detection
of automotive
exhausts
[2009,
Electrochimica
Acta.
54(25): 6099-6106]
Nano-
materials
SAW
Potentio-
metric
Cr
na no particle
decorated
single ZnO
nanowire
sensor
Rayleigh SAW
resonator with
polyaniline/
tungsten oxide
na no-
composite thin
film
YSZ-based
planar sensors
using nano-
structured
sensing
electrodes
NO
NO (selective
against NO2
and CO2)
Ammonia, NO2,
CO, CH4, C3H8,
C3H6, NO
46%
sensitivity for
10 ppm.
LOD:
1.5 ppm
1.2ppm
frequency
shift when
exposed to
138ppb
Highly
selective at
20-200 ppm
Recovery:
20 sec
using UV
light
Response
and
recovery:
20-80 sec
In this system, Cr particles act as a catalyst to transform
NO into NO2, which can then be detected at room
temperature. The sensor is reported to be selective for
NO when tested with N2, CO and CO2 gases. Sensor
functioned well even after 10 days.
Exhibited reversibility and repeatability. Room
temperature operation.
This sensor is appropriate for harsh environments (600°C)
such as automotive exhausts. Sensing characteristics
(sensitivity, response time, recovery time) are dependent
on sputtering time of Au sensing electrodes.
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
N2O
Chemical
N2
C
ENEA (Italy)
Penza, M., et al.
Functional
characterization of
carbon nanotube
networked films
functionalized with
tuned loading of Au
nanoclusters for
gas sensing
applications
[2009, Sensors
and Actuators B.
140(1): 176-184]
Chemi-
resistor
Gold
functionalized
CNTs
NO2, NH3, CO,
N2O, H2S, SO2
Sub-ppm:
NO2, H2S
and NH3
200ppb NO2
Negligible
response for
CO, N2O,
and SO2
Operational temperature: 20-250 °C.
Octane
Chemical
oct
c,
Institute de Fisica
Aplicada, CSIC
(Spain) and
Fundacion
Inasmet,
Mikeletegi
(Spain)
Sayago, /., et al
(Spanish MEC)
Surface acoustic
wave gas sensors
based on
polyisobutylene
and carbon
nanotube
composites
[201 1 , Sensors
and Actuators B,
1(10): 1-5]
SAW
Octane,
toluene, H2,
CO, NO2, NH3
(selective for
VOCs)
Response:
30 sec
recovery:
30 sec
Sensor was tested in a controlled environment (temp
23°C, dry air, constant flow rate) and was reported to be
reversible over the course of the 45 day continuous testing
period. Addition of nanotubes in higher percentages was
not seen to increase response, and made the response
worse in most cases. Sensor response frequency was not
reported to be modified by the presence of NO2, NH3, H2,
or CO, indicating a sensor selective for VOCs.
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#
Sort
Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Propanol
Chemical
pro
pa
nol
c
University of
Massachusetts
Lowell
Li, X., et al.
(National Science
Foundation)
Fabrication and
integration of metal
oxide nanowire
sensors using
dielectrophoretic
assembly and
improved post-
assembly
processing
[2010, Sensors
and Actuators B.
148(2):404-412]
MOS
nanowires
Metal oxide
nanowires
(indium, tin,
and
indium-tin).
Dielectro-
phoretic (DEP)
assembly onto
interdigitated
micro-
electrodes
Acetone,
chloroform,
ethanol,
methanol,
propanol, and
benzene.
1 ppm
(potentially
ppb)
Response:
10 sec
recovery:
8-10 min
Good repeatability, however, recovery time increases
when exposed to greater concentrations of substance. No
response to 1500 ppm was observed below 200°C;
performance was enhanced when 440°C was reached.
Determined that further optimization of ITO composition
may improve sensitivity and selectivity of the sensors.
Oxygenated organic compounds cause a higher response
than aromatic or chlorinated compounds.
2-Propanol
E-nose
2-
pro
pa
nol
e-
nose
Saratov State
Technical
University
(Russia),
Southern Illinois
University at
Carbondale,
Northeastern
University
Sysoev, V. V., et
al.
(Funding:
Fullbright
scholarship and
RFBR grant and
NSF)
The electrical
characterization of
a multi-electrode
odor detection
sensor array based
on the single
SnO2 nanowire
[2011, Thin Solid
Films.
520(3): 898-903]
e-nose
SnO2
wedge-like
nanowire,
multielectrode
odor detection
sensor array;
functionalized
by deposition
ofPd
na no particles
Acetone,
2-propanol,
CO, and H2
Fabricating metal oxide single crystals in shape of
nanowires may be cost effective.
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Sulfur dioxide
Chemical
so
2
SO
2
c
c
ENEA (Italy)
Penza, M., et al.
JiLin University
(China)
Liang, X., et al.
(Natural Science
Foundation of
China)
Functional
characterization of
carbon nanotube
networked films
functionalized with
tuned loading of Au
nanoclusters for
gas sensing
applications
[2009, Sensors
and Actuators B.
140(1): 176-1 84]
Solid-state
potentiometric
SO2 sensor
combining
NASICON with
V2O5-doped
TiO2 electrode
[2008, Sensors
and Actuators B.
134(1):25-30]
Chemi-
resistor
Potentio-
metric
Gold
functionalized
CNTs
based on
NASICON
(sodium super
ionic
conductor)
and V2O5-
doped TiO2
sensing
electrode
NO2, NH3, CO,
N2O, H2S, SO2
SO2
Sub-ppm:
NO2, H2S
and NH3
200 ppb NO2
Negligible
response for
CO, N2O,
and SO2
1-50 ppm at
200-400 C
Response:
10 sec
recovery:
35 sec
Operational temperature: 20-250 °C.
A small amount of V2O5 doping may promote catalytic
activity of sensing electrode and increases sensitivity
(using more increased response time and decreased the
catalytic ability, >10% weight). Appears to be selective
against NO, NO2, CH4, CO, NH3, and CO2.
E-nose
N
O2
e-
nose
CNR-IMM-
Istituto per la
Microelettronica
ed i Microsistemi,
University
Campus (Italy),
ITC-irst -
Microsystems
Division, (Italy)
Francioso, L, et
al.
Linear temperature
microhotplate gas
sensor array for
automotive cabin
air quality
monitoring
[2008, Sensors
and Actuators B.
134(2): 660-665]
e-nose
MOS, MEMS
CO, NO2, SO2
Investigates a temperature gradient electronic nose for
increasing sensitivity. Total power consumption below
130 mW with power supplied to voltage heater.
Temperature was increased in a 100°C-wide temperature
window. Sensor exhibited faster response times for higher
temperatures (300-400°C) and higher gas concentrations.
Signal was lost when sensor was operated below 280°C.
Sensor was exposed to injected gas for 30 minutes,
followed by 90 minutes of recovery in dry air. PCA was
used to identify and analyze patterns in data.
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Tetrahydrofuran
Chemical
TH
F
c
Donghua
University
(China), Leibniz
Institute of
Polymer
Research
Dresden
(Germany)
Fan, Q., et al.
(Shanghai
Pujiang Program,
State Key
Laboratory of
Chemical Fibers
and Polymer
Materials,
Doctorate
Innovation
Foundation of
Donghua
University, and
the National
Natural Science
Foundation for
Distinguished
Young Scholar of
China)
Vapor sensing
properties of
thermoplastic
polyurethane
multifilament
covered with
carbon nanotube
networks
[201 1 , Sensors
and Actuators B.
156(1):63-70]
Nano-
materials
Thermoplastic
polyurethane
multifilament -
carbon
nanotubes
(TPU-CNTs)
composite
(conductive
polymer)
VOCs,
specifically
benzene,
toluene, CHCI3,
THF, ethanol,
acetone, and
methanol
Response:
1 0 sec to
50% max
relative
resistance
recovery:
begins
3-5 sec
after
injection of
dry air
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
Toluene
Chemical
tol
ue
ne
tol
ue
ne
c
c
Donghua
University
(China), Leibniz
Institute of
Polymer
Research
Dresden
(Germany)
Fan, Q., et al.
(Shanghai
Pujiang Program,
State Key
Laboratory of
Chemical Fibers
and Polymer
Materials,
Doctorate
Innovation
Foundation of
Donghua
University, and
the National
Natural Science
Foundation for
Distinguished
Young Scholar of
China)
Ehime University
(Japan), National
Institute for
Materials Science
(Japan)
Mori, M., et al.
(Japan Science
and Technology
Agency)
Vapor sensing
properties of
thermoplastic
polyurethane
multifilament
covered with
carbon nanotube
networks
[201 1 , Sensors
and Actuators B.
156(1):63-70]
Detection ofsub-
ppm level of VOCs
based on a
Pt/YSZ/Pt
potentiometric
oxygen sensor with
reference air
[2009, Sensors
and Actuators B.
143(1):56-61]
Nano-
materials
Potentio-
metric
Thermoplastic
polyurethane
multifilament -
carbon
nanotubes
(TPU-CNTs)
composite
(conductive
polymer)
Pt|YSZ|Pt
structure
VOCs,
specifically
benzene,
toluene, CHCI3,
THF, ethanol,
acetone, and
methanol
VOCs,
specifically
acetic acid,
methyl ethyl
ketone,
ethanol,
benzene,
toluene, o- and
p-xylene
Sub-ppm
Response:
1 0 sec to
50% max
relative
resistance
rppn\/prv
i cuuvci y .
begins
3-5 sec
after
injection of
dry air
Ethanol
response:
4-204 sec
(quickest
for 0.5 ppm
at 500° C)
recovery:
190-480
sec
(quickest
for 1 ppm
at 500° C)
400-500°C, highest response at 400°C. Increased
temperatures decreased sensitivity but modified response
and recovery times. Analysis of multidimensional plots of
sensor output at various temperatures suggests possible
selective sensing of different VOCs.
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#
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Codes
P
tol
ue
ne
T
c,
SAW
A
Organization
Author (Sponsor)
Institute de Fisica
Aplicada, CSIC
(Spain) and
Fundacion
Inasmet,
Mikeletegi
(Spain)
Sayago, /., et al
(Spanish MEC)
Abbreviated
Citation
Surface acoustic
wave gas sensors
based on
polyisobutylene
and carbon
nanotube
composites
[201 1 , Sensors
and Actuators B,
Sensor Technology
Type
SAW
Description
(Name)
Pollutant/
Parameter
Octane,
toluene, H2,
CO, NO2, NH3
(selective for
VOCs)
Reported
Detection
Capability
Response/
Recovery
Time
Response:
30 sec
recovery:
30 sec
Application and Operation Context
Sensor was tested in a controlled environment (temp
23°C, dry air, constant flow rate) and was reported to be
reversible over the course of the 45-day continuous testing
period. Addition of nanotubes in higher percentages was
not reported to increase response, rather diminished the
response in most cases. The sensor response frequency
was not found to be modified by the presence of NO2, NH3,
H2, or CO, indicating a sensor selective for VOCs.
E-nose
tol
ue
ne
enos
e
Chinese
Academy of
Sciences (China),
Anhui Polytechnic
University (China)
Meng, F.-L, et al.
(Chinese
Academy of
Sciences
National Natural
Science
Foundation of
China, National
Basic Research
Program of
China, Anhui
Provincial Natural
Science
Foundation)
Electronic chip
based on self-
oriented carbon
nanotube
microelectrode
array to enhance
the sensitivity of
indoor air
pollutants
capacitive
detection
[201 1 , Sensors
and Actuators B.
153(1): 103-1 09]
e-nose
Electronic chip
with self-
oriented CNT
microelectrode
array
Formaldehyde
(best
response),
toluene (lowest
response),
ammonia
Response:
"tens of
seconds"
recoverv
slowest for
toluene
Use of self-oriented CNTs reduces noise and less
response to water (weak adsorption between CNTs and
water molecules.
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Codes
P
T
A
Organization
Author (Sponsor)
Abbreviated
Citation
Sensor Technology
Type
Description
(Name)
Pollutant/
Parameter
Reported
Detection
Capability
Response/
Recovery
Time
Application and Operation Context
VOCs
E-nose
vo
Cs
VO
Cs
e-
nose
e-
nose
University of
Nevada, Reno
Je, C.-H., et al.
University of
Pune (India)
Botre, B.A., et al.
Development and
application of a
multi-channel
monitoring system
for near real-time
VOC measurement
in a hazardous
waste
management
facility
[2007, Science of
the Total
Environment.
382(2-3):364-374]
Embedded
electronic nose
and supporting
software tool for its
parameter
optimization
[2010, Sensors
and Actuators B.
146(2): 453-459]
e-nose
e-nose
Array of PI D
sensors
MOS sensors
by Figaro
VOCs
VOCs
Resolution:
250 ppm ;
Alcohol ID =
100%,
concentratio
n estimation
98%,
mixture
analysis
95%
"Real time"
Setting: walk-in hood in a hazardous waste management
facility. System consists of an array of PID sensors and a
networked control program providing operational
schematic diagrams, performs data analysis, and
illustrates real-time graphical displays. Shows that real-
time monitoring system may be effective for early warning
detection of hazardous chemicals and for predicting the
performance of adsorption filters used for VOC removal.
Supporting software extracts unique characteristics from
sensor array response patterns to various odors and
allows for easy identification. The data acquisition virtual
instrument allows user to specify specific sensor
parameters (e.g., heater, on/off period, odor pulse period,
selection of number of gas sensors in the array, sampling
rate, and data acquisition time). The VOCs tested in this
study were alcohols, separately and mixed with water.
Xylenes
Chemical
xyl
en
es
C
Ehime University
(Japan), National
Institute for
Materials Science
(Japan)
Mori, M., et al.
(Japan Science
and Technology
Agency)
Detection ofsub-
ppm level of VOCs
based on a
Pt/YSZ/Pt
potentiometric
oxygen sensor with
reference air
[2009, Sensors
and Actuators B.
143(1):56-61]
Potentio-
metric
Pt|YSZ|Pt
structure
VOCs,
specifically
acetic acid,
methyl ethyl
ketone,
ethanol,
benzene,
toluene, o- and
p-xylene
Sub-ppm
Ethanol
response:
4-204 sec
(quickest
for 0.5 ppm
at 500° C)
recovery:
190-480
sec
(quickest
for 1 ppm
at 500° C)
400-500°C, highest response at 400°C. Increased
temperatures decreased sensitivity but modified response
and recovery times. Analysis of multidimensional plots of
sensor output at various temperatures suggests possible
selective sensing of different VOCs.
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This table highlights sensor technologies/techniques reported in selected conference proceedings, poster abstracts, peer-reviewed journals, and university and other organization
(e.g., company) web pages; at the time of the publications reviewed, these sensors were in the research and development stage. The publications reflected in this table were
gathered as part of a later literature review conducted in early 2013 that specifically targeted chemical detection techniques and electronic noses (e-noses).
The table is organized by pollutant in alphabetical order with the targeted study subset shaded green and additional pollutants shaded grey. Sensors reported to detect more than
one pollutant are repeated across the respective pollutant sections. The entries are further grouped by sensing technique, and then by research institution (in alphabetical order).
E-82
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APPENDIX F:
OVERVIEW OF SENSING TECHNOLOGIES/TECHNIQUES
F-1
-------
31 October 2013
F-2
-------
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APPENDIX F:
OVERVIEW OF SENSING TECHNOLOGIES AND TECHNIQUES
Brief descriptions of selected sensing technologies and techniques discussed in this report are
presented in this appendix, together with associated architecture/infrastructure approaches from
the recent literature. These technologies and techniques are grouped according to three basic
sensing principles: chemistry, ionization, and spectroscopy.
F.1 CHEMISTRY
Chemical sensors typically contain a sensing substrate, usually a metal or polymer film, that
interacts with a pollutant to produce measurable changes in physical properties of the substrate
(such as electrical resistivity or mass). Several types of chemical sensors are highlighted below.
F.1.1 Electrochemical Gas Sensors
Electrochemical sensors are typically composed of an electrochemical cell containing an
electrolyte (solid or liquid) and electrodes, one referred to as the working electrode and the
other as the reference or counter electrode. An incoming gas reacts at the working electrode
and creates a measurable difference in electrical potential between the working and
reference/counter electrodes proportional to the target gas concentration (Capone et al. 2003).
Recent research in this area includes the use of nanoscale materials. A common research
focus is on improving sensitivity, selectivity, and cost efficiency of sensors by varying electrolyte
or electrode composition.
Architecture and infrastructure approaches reported for these electrochemical sensors include
fixed/semi-portable units and mountable sensors with micro- and miniature-scale platforms.
F.1.2 Metal Oxide Semiconductors (MOS)
These metal oxide sensors, also frequently referred to as chemiresistors, typically consist of an
n-type orp-type oxide thick, thin or porous film, metal electrodes, and an internal heating device
to increase the reaction rate. The incoming gas or pollutant adsorbs to the film, typically
transition or heavy metal oxides such as SnC>2, WOs, or ZnO (research summaries also note
ln2Os, BaTiOs-CuO, TiC>2 and others) deposited on a thin layer of silicon, where it then
undergoes catalytic oxidation. The oxidation produces a change in resistance of the sensing
material, which is generally proportional to the concentration of the gas or pollutant present
(Capone et al. 2003).
Recent research indicates active investigation of various n-type or p-type oxide film
compositions, electrode composition, and internal heating temperature to create more sensitive,
responsive and cost-effective sensing systems. Nanoscale materials represent an active
research area, using wires, rods, particles, and carbon nanotubes (CNTs) with sensing
substrates in order to increase reactive surface area, thus increasing sensitivity and decreasing
reaction time. Room temperature operation is also being studied for MOS sensors (operational
temperatures for these sensors have typically ranged from 200 to 500°C).
Architecture and infrastructure approaches reported for MOS sensors include fixed/semi-
portable sensor units, mountable sensors with micro- and miniature-scale platforms, and
wearable sensors for participatory sensing.
F-3
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31 October 2013
F.1.3 Polymer Films (organic and hybrid)
Organic polymer films are being used as sensing materials, with thin-film polymers providing
conductive or fluorescent surfaces that are often highly sensitive. Advantages of using organic
films over inorganic films include the ability to operate at room temperature. Thin-film polymers
identified from the review of recent sensor literature include poly(2-(acetoacetoxy)ethyl
methacrylate) (PAAEMA), polyaniline (PANi), polypyrrole (PPy), and N,N'-(glycine t-butylester)-
3,4,9,10-perylendediimide. These films are sometimes modified with nanomaterials such as
single walled carbon nanotubes (SWCNTs) to increase sensitivity.
As grouped for this report, this category also includes hybrid films composed of inorganic oxides
and organic materials. For these hybrids, the limited chemical reactivity of inorganic oxides is
balanced by the high specific reactivity of the organic substances. Similarly, the limited thermal
stability of the organic material is balanced by the thermally stable inorganic oxides. Hybrid films
have many additional advantages, including ease of fabrication, controllable porosity and
surface characteristics, high thermal stability, and good flexibility in sensor reactivity and
specificity (Bescher 1999).
Architecture and infrastructure approaches reported for polymer film sensors include mountable
sensors with micro- and miniature-scale platforms, fixed/semi-portable units, handheld devices,
and visual sensing systems.
F.1.4 Surface Acoustic Wave
These sensors contain a chemical film that selectively adsorbs the gaseous analyte to produce
a measurable change in mass, which is detected by change in surface propagating waves
(Kryshtal and Medved 2002). Recent research for these piezoelectric-type sensors has focused
on the use of various sensing materials and methods to reduce power consumption.
Architecture and infrastructure approaches reported for these sensors include mountable
systems with micro- and miniature-scale platforms.
F.1.5 Nanotechnology-Based Sensors
While this category is cross-cutting, it is grouped here because many of the sensors that use
nanotechnology involve chemical techniques.
Nanotechnology advances are prominent in recent sensor research, as nanocrystalline metal
oxides, carbon nanotubes (CNTs), organic nanocomposites, and other nanomaterials and
coatings are being used to develop increasingly small-scale sensors. Some nanoscale materials
are being tapped as stand-alone sensing films, while others are incorporated into integrated
systems to improve sensing characteristics via increased surface area.
A variety of architectures and approaches are reported for sensor systems using
nanotechnology, as reflected in the other summaries within this appendix.
F.2 IONIZATION
These sensors have traditionally existed as fixed, non-portable sensors; however, some
sensors such as the photoionization detector and mass spectrometer are being modified to
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make them more portable and cost efficient. Research highlights relevant to portable systems in
which ionization is a primary detection principle are presented below.
F.2.1 Mass Spectrometry
Mass spectrometers consist of an ion source, analyzer, detector, and data recorder. Several
elements of these systems can be changed to address specific chemical species. Major ion
formation techniques include electron impact ionization, chemical ionization, fast atom
bombardment, electroscopy ionization, and matrix-assisted laser desorption ionization.
Analyzers include magnetic, electrostatic, quadrupole, ion trap, time of flight, and Fourier
transform ion cyclotron resonance. Detector components include secondary electron
multipliers, photomultipliers, and multi-channel plates. Architecture and infrastructure
approaches reported for sensors that use mass spectrometry include handheld and other
portable devices.
F.2.2 Gas Chromatography
In traditional gas Chromatography (GC) systems, the gas sample (pure or mixed) is injected into
a chromatograph where it is selectively dissolved or absorbed in a solid or syrup-like substrate-
lined flow column. The components of the gas sample interact with the absorbing agent at
different rates as they travel through the column, allowing for selective separation. A computer
chart is generated depicting the rate at which various components of the gas sample exit the
column. The general rate peaks are associated with specific chemicals or chemical species. In
systems that use GC for selective separation, photoionization and flame ionization detectors
(PIDs and FIDs) and mass spectrometers are the detectors typically used.
Recent research relevant to portable sensors involves the addition of gas pre-concentrators to
improve detection levels. Architecture and infrastructure approaches reported for gas
Chromatography detection techniques include handheld and larger portable systems.
F.2.3 Photoionization Detector (PID)
Photoionization detectors capitalize on substance-specific ionization potentials (IP). The core
component of this system is an ultraviolet (UV) lamp which emits a specific light frequency
(measured in electron volts, eV). Gases with IPs less than or
equal to that of the lamp are detectable. Concentrations of these
gases are measured based on the amount of ions (resulting from
absorption of photons from the UV lamp) deposited on a collecting
electrode. Nearly twenty years ago, PIDs were identified as 'go
to' devices for detecting VOCs (EPA 1994). Note that costs can
be relatively high (e.g., several thousand dollars).
Image source: http://www.intlsensor.com/pdf/photoionization.pdf.
F.2.4 Flame Ionization Detector (FID)
Flame ionization detectors are commonly part of gas Chromatography systems. Incoming gas
samples are mixed with hydrogen before being introduced to a flame, which causes the release
of electrons. The electrons are collected at electrodes and converted into electrical output
signals. Sensitivity is influenced by gas flow rates (before and after flame ionization), flame jet
exit diameter, positioning of components, and detector temperature (Hinshaw 2005).
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F.3 SPECTROSCOPY
Spectroscopic techniques involve examining the substance-specific emission and/or absorption
spectra resulting from the interaction between molecules and an energy source, such as light.
This technique operates on the principle that chemical substances absorb light at specific
frequencies. This category includes, and is often referred to as, optical-based sensing
techniques.
F.3.1 Broadband Molecular Absorption Spectroscopy
Molecular absorption spectroscopy techniques function on the principle that bonds of organic
compounds absorb different frequencies of light. Currently in broadband molecular absorption
spectroscopy, infrared, UV, and visible light sources can be used to selectively detect some
specific chemicals. A light source is directed at the target gas sample and resulting light
absorption patterns are observed. Absorption peak patterns are analyzed to identify of the
compound, while the intensity of the peaks are analyzed to identify the concentration of the
compound. Research in this area includes nanoscale technologies and multiple-line integrated
absorption spectroscopy (MLIAS), a technique that combines many different spectroscopic
readings to obtain a greater degree of sensitivity and accuracy.
Architecture and infrastructure approaches reported for sensors using absorption spectroscopy
include fixed/semi-portable units, handheld sensors, mountable sensors with micro- and
miniature-scale platforms, wearable sensors, and vehicle-mounted units.
F.3.2 Laser Absorption Spectroscopy
This method covers a variety of lasers, including quantum cascade lasers (QCLs), tunable diode
lasers, and organic micro-lasers adjusted to operate at, or within, a specific light frequency
range. Thus, pollutant-specific sensors can be fabricated based on the knowledge of
corresponding light absorption spectra. Some devices using this technology may also include
optical fibers, a detection path or cell, and a photodetector. One investigated component of
these systems is the tuning fork. Efforts are focused on developing more responsive polymer
coatings and polymer nanowires in order to increase sensitivity of sensors. Research is also
focused on the development and optimization of laser systems, as well as increased portability
of sensors. Specific techniques under further development include cavity enhanced absorption
spectroscopy (CEAS) and tunable diode laser absorption spectroscopy (TOLAS).
Architecture and infrastructure approaches reported for sensors using laser absorption
spectroscopy include fixed/semi-portable units, handheld devices, mountable sensors with
micro- and miniature-scale platforms, remote sensor/monitoring units, wearable sensors, and
wireless sensor networks.
F.3.3 Molecular and Atomic Emission Spectroscopy
Luminescence
Luminescence is a form of 'cool body' radiation, meaning reactions are not induced by a heat
source. Emissions occur after a sample gas has absorbed energy from a source (such as
radiation or chemical reaction), leaving it in an unstable excited state. As the substance
undergoes the transition back to the preferred ground state, the absorbed energy is released as
light. Because only a small percentage of reacting molecules emit light, these sensors can be
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chemical specific. The techniques in this category include bioluminescence, cataluminescence,
chemiluminescence, and fluorescence.
Architecture and infrastructure approaches reported for these sensors include fixed/semi-
portable units, handheld devices, mountable sensor with micro- and miniature-scale platforms,
and visual sensing systems.
Laser-Induced Breakdown Spectroscopy (LIBS)
This technique uses a high-power, pulsed laser beam to produce laser-induced plasma which
then vaporizes, atomizes, and excites the atoms of the gas sample. The resulting emission
intensities and frequencies are used to determine the identity and concentration of the
substance.
Architecture and infrastructure approaches reported for LIBS sensors include fixed/semi-
portable units, handheld units, and vehicle mounted units
F.3.4 Light Scattering (Nephelometry)
Light scattering or nephelometric techniques measure the irradiance of scattered light resulting
from contact with particulates within a specified volume or area defined by an intersection of a
light beam and the field of view of an optical detector. The resulting electrical signal is
proportional to the concentration of particulates present. This technique is very responsive to
rapid changes in particulate concentrations, but it is only responsive to substances with constant
optical properties.
Architecture and infrastructure approaches reported for light-scattering sensors include
fixed/semi-portable sensor systems.
F.3.5 Light Detection and Ranging (LIDAR)
This detection technique can be applied to multiple spectroscopy sensing systems, including
laser absorption and fluorescence emission. A laser or other light source is used to illuminate
the target gas sample. The backscattered light is recorded and analyzed to determine sample
composition and concentration. Infrastructure requirements are substantial (and these translate
to substantial costs).
Architecture and infrastructure approaches reported for LIDAR sensors include mounted
sensors with micro- and miniature-scale platforms, remote sensing/monitoring, and vehicle
mounted units.
SELECTED REFERENCES
Bescher, E., and J.D. Mackenzie. (1999). Hybrid Organic-Inorganic Sensors. Materials Science
and Engineering; C, 6 (2-3);
http://www.sciencedirect.com/science/article/pii/S0928493198000393
(last accessed May 22, 2013).
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Capone, S., A. Forleo, L. Francioso, R. Rella, P. Siciliano, J. Spadavecchia, D.S. Presicce, and
A.M. Taurino (2003). Solid State Gas Sensors: State of the Art and Future Activities. Journal of
Optoelectronics and Advanced Materials, 5(5): 1335-1348.
Chu, S., Graybeal, J.D., Hurst, G.S., and Stoner, J.O. (undated/2013). Spectroscopy. In
Encyclopedia Britannica Online:
http://www.britannica.com/EBchecked/topic/558901/spectroscopy (last accessed May 22,
2013).
Clark, J. (2000). The Mass Spectrometer;
http://www.chemguide.co.uk/analvsis/masspec/howitworks.html (last accessed May 22, 2013).
EPA (1994). Photoionization Detector (PID) HNU. SOP#2114, Rev. 0.0;
http://www.dem.ri.gov/pubs/sops/wmsr2114.pdf.
Goohs, K. (2011). Thermo Scientific Hybrid PM OEMS Development Update. White Paper
AQIPMCEMS 05.11: http://stratusllc.com/uploads/PM GEMS White Paper.pdf.
Gundermann, K.-D. (2013/undated). Luminescence. In Encyclopedia Britannica Online:
http://www.britannica.com/EBchecked/topic/351229/luminescence (last accessed May 22,
2013).
Hinshaw, J.V. (2005). The Flame lonization Detector. ;
http://www.chromatographyonline.com/lcgc/GC/The-Flame-lonization-
Detector/ArticleStandard/Article/detail/254500 (last accessed May 22, 2013).
Honicky, R.E. (2011). Towards a Societal Scale, Mobile Sensing System;
http://www.eecs.berkelev.edu/Pubs/TechRpts/2011/EECS-2011-9.pdf (UCB/EECS-2011-9).
Kryshtal, R.G., and A.V. Medved (2002). New Surface Acoustic Wave Gas Sensor of the Mass-
Sensitive Type Sensitive to the Thermal Properties of Gases. In Sensors 2002. Proceedings of
IEEE. 1:372-375; http://ieeexplore.ieee.org/xpls/abs all.isp?arnumber=1037119.
Sheffield Hallam University (2013). Chromatography Introductory Theory; Department of
Biosciences and Chemistry; http://teaching.shu.ac.uk/hwb/chemistrv/tutorials/chrom/chrom1.htm
(last accessed May 22, 2013).
Physical Sciences Inc. (2013). About TDLAS: Overview; http://www.tdlas.com/
(last accessed May 22, 2013).
Plodinec, J. (1998). Technology Demonstration and Verification Research. DE-FT26-
98FT40395, Diagnostic Instrumentation and Analysis Laboratory prepared for the
U.S. Department of Energy (Oct.)
UCLA (University of California Los Angeles). (2013). Introduction to IR Spectra;
http://www.chem.ucla.edu/~webspectra/irintro.html (last accessed May 22, 2013).
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APPENDIX G:
EVALUATION OF SELECTED AIR QUALITY APPS
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APPENDIX G:
EVALUATION OF SELECTED AIR QUALITY APPS
Several organizational websites were reviewed to evaluate air quality resources available to the
general public relevant to mobile phone apps and data representations. The objective was to
assess gaps underlying existing apps, to frame potential opportunities for investments in this
area. The online resources considered include:
• AIRNow, AirData, and Envirofacts (EPA)
• State of the Air (American Lung Association)
• Air Quality Forecast (National Weather Service, National Ocean and Atmospheric
Administration (NOAA)
• The Weather Channel
• Power Plant Pollution Risk (Clean Air Task Force)
A number of free mobile phone apps compatible with Android smartphones were found. Of
these, the AIRNow and State of the Air mobile phone apps were selected for a brief
comparison. Screen captures were taken for both apps every morning for two weeks; examples
are shown in Figures G-1 and G-2.
Today's Air Quality
The Air Quality Index (AQI) for
Chicago
Current
7/5/2012
2:00 PM CST
Pollutant:
PM2.5
Current
7/5/2012
2:00 PM CST
Pollutant:
OZONE
96
Moderate
Unhealthy for
Sensitive Groups
FIGURE G-1 AIRNow Mobile Phone App,
Screen Capture for Chicago, 7-5-12
(Source: Temple 2012.)
Metropolitan
^Washington. DC
FIGURE G-2 State of the Air Mobile
Phone App, Screen Capture for
Washington DC, 7-6-12
(Source: Temple 2012.)
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The average rating from 36 reviews posted online for the State of the Air App is 3.4. Twelve
gave it 1 star, and seventeen gave it 5 stars, reflecting different expectations and indicating
opportunities for improvement.
The air quality data collected from these two mobile phone applications were plotted to illustrate
how conditions in Chicago and Washington, DC, could be compared. The PlVb.s comparison is
presented in Figure G-3, and the ground-level ozone comparison is presented in Figure G-4.
Morning PM2.5
•Chicago
DC
4-Jul 6-Jul 8-Jul 10-Jul 12-Jul 14-Jul 16-Jul 18-Jul 20-Jul
FIGURE G-3 Morning AQI Data for Ozone, 4-20 July 2012 (Source: Temple 2012.)
60
50
40
30
20
10
Morning Ozone
•Chicago
DC
4-Jul 6-Jul 8-Jul 10-Jul 12-Jul 14-Jul 16-Jul 18-Jul 20-Jul
FIGURE G-4 Morning AQI Data forPM2.5, 4-20 July 2012 (Source: Temple 2012.)
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A third mobile phone app, Air Quality by Aesthetikx, also provides information for these criteria
pollutants. Data representations include color-coded overlays on geographic maps, as
illustrated in Figure G-5. Note that the average rating from nine online reviewers was 3.2 out of
5, which indicates an opportunity for improvement. (Three reviewers gave this app 1 star and
one reviewer gave it a rating of 2, while four gave it 5 stars and one person who gave it 4 stars.
Only 5 reviews are available online; the reviewer who gave the 4-star rating indicated the map
was "awesome" but they wished for more detailed descriptions.)
USA Hourly AQI
Moderate
Unhealthy for Sensitive Groups
Unhealthy
\/ery Unhealth]1
Hazardous
AP Environmental Science
FIGURE G-5 Air Quality Mobile Phone App.
Screen Capture of U.S. AQI Map, 7-5-12
(Source: Temple 2012.)
A variety of air quality data and resources are available online. The EPA AirData website (EPA
2012) provides links to resources and data summaries, including daily AQI plots, time series
concentration plots, an interactive map, and AQI reports. The link for "monitor values"
(http://www.epa.gov/airdata/ad rep mon.html) provides access to a tool for user-selected
reports for data from monitoring stations available in the user-selected area. Additional
information resources available through AirData include educational materials for school
children.
The findings of this focused review of air quality apps are highlighted as follows. Of ten air
quality-related mobile apps initially identified, three provide relevant air quality monitoring data.
Both EPA's AIRNow app and the American Lung Association's (ALA) State of the Air app supply
hourly and forecasted AQI for PlVb.s and ozone at the nearest urban center. The State of the Air
app also provides air quality alerts as issued.
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Gaps identified from the evaluation of selected mobile phone apps include the following.
• Data coverage
Limited to a fixed monitoring station from the nearest urban center, and data are only
available for major cities. Such data are clearly not representative across community,
neighborhood, and individual scales.
• Pollutant coverage
Essentially limited to just two: PlVh.s and ground-level ozone. The AQI map application
does not differentiate between the two. (Note in most cases, the air quality is represented
by a number associated with the AQI.)
• Update frequency
The frequency of AQI updates varied for certain areas and monitors. Forecasting is
affected in those areas for which data are updated less frequently.
• User interface, and inconsistencies
Air Quality by Aesthetikx supplies animated AQI maps of the U.S. and local areas. The
animation characteristics depend on the data connection speed for the phone. Using a 3G
Android smartphone with limited service in many areas led to restricted performance of the
app. With limited data connection, the app provided an AQI map of the requested area
taken many hours earlier (12:20 that morning). Presumably, this feature would work better
with a 4G connection and/or greater data coverage. (This issue resulted in
inconsistencies due to data access, coverage, and other limitations.)
Other user reviews for the three free apps evaluated here have been posted online. Comments
and overall ratings that reflect inputs from more than 60 users are presented in Table G-1.
(Note the inputs address progressive versions of the apps in some cases, so the comments are
ordered within each main topic first in order of rating and then in order of date posted.) The
comments are grouped by main category, and topical ratings are provided for those with
multiple comments, per app. (Ratings are on a scale of 1 to 5, in order of increasing
satisfaction.) Overall ratings were modest, ranging from 3.1 (AIRNow) to 3.4 (State of the Air).
Most users commented on the ALA State of the Air app - more than double the number
commenting on EPA's AIRNow app, and four times the number commenting on the Air Quality
app by Aesthetikx. High regard for the concept (collective rating of 4.5) affirms that mobile apps
represent a clear opportunity area. The main gaps are associated with data coverage
(collective rating of 1.4) and the user interface (collective rating of 2.3).
The data coverage limitations (locations and pollutants) reflect limitations in the data available,
not in the apps themselves. That underlying data limitation represents one of the drivers for this
initiative, toward facilitating participatory sensing that can contribute to the overall state of
knowledge for air quality. The insights from existing free apps can help frame research and
development investments toward future mobile apps that enhance citizen involvement.
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TABLE G-1 Online User Reviews of Three Mobile Apps for Air Quality3
App / Issue
Air Quality
(Aesthetikx)
Concept value
Online Reviewer Comments
(Overall rating from 9 user reviews; 5 available online)
Awesome. The map is awesome. I wish there were more detailed descriptions. (V1.0, Motorola DroidX2; April 12, 2012)
Rating
3.2
4
Data coverage
Not helpful. Only informative if you are looking for one of the major cities. The US map time laps goes too fast.
Uninstalling, useless. (HTC Thunderbolt; Aug. 9, 2012)
1
User interface
(1.3)
No N.M. Radar. It force-closes when you turn phone sideways. (V1.0; June 2, 2012)
No map. Maps not showing, useless. Uninstalling. (Samsung Galaxy S; July 15, 2012)
Lol. Closes if I turn my phone. (Galaxy S2 Skyrocket; July 16, 2012)
2
1
1
AIRNow (EPA)
Concept value
Data coverage
(1.8)
(Overall rating from 16 user reviews, 10 available online)
Thank you! Helps me monitor air in Nashville. (V. 1.0; June 14, 2012)
Nah. Gives air quality for white plains, 30 minutes away, instead of the bronx, which is 2 blocks from my house. (VI. 0,
HTC G2; June 13, 2012)
Needs more features. It's a decent app for finding out what air quality is like in areas around you but because of a lack of
air quality reporting in many areas, the results are generally irrelevant. It would be better for the app to let the user know
that there is little to no air quality information for their specific location and then show information for nearby areas.
Searching by city name would also be nice to have. (Galaxy S2 Skyrocket; Jan. 24, 2013)
Great idea. Needs more sources, push an air quality add on national Weather Service stations. (V1.0, Samsung Galaxy
S3; Sep. 27, 2012)
Won't locate. App can't display anything about my zipcodes or Geo location. (V1.0, Samsung Galaxy Nexus; July 8,
2012)
Coverage sucks. Doesn't work in my area. (Samsung Galaxy S; Sep. 25, 2012)
Vog. Nothing on vog on Oahu. (Samsung Galaxy Nexus; Oct.4, 2012)
3.1
5
3
3
2
1
1
1
User interface
(1.7)
Good but needs favorite locations! This is more convenient than the website. It needs a favorites list to save time. A
way to search by city would be great. VI. 0; July 8, 2012)
Worthless. I use web page all the time but can't get this to work for gsp area of sc which is a metro area (HTC
Thunderbolt; July 1, 2012)
Doesn't work. I have a droid and whenever I type in my zipcode it won't show me any results. Highly disappointed.
(Feb. 7, 2013)
3
1
1
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App / Issue
State of the
Air (ALA)
Concept value
(4.5)
Online Reviewer Comments
(Overall rating from 36 user reviews, 26 available online)
Great. Have little kids, air quality is essential. (HTC Thunderbolt; June 20, 2012)
Love it! It's definitely the best air quality alert app out there. It helps give me an idea of whether I should run outside or
hit the gym. (V1.0, Motorola DroidX2; June 18, 2012)
Great app! Helps protect my kids. This is a very useful app to monitor air quality in my community. Helps me make sure
I know if it's safe for my kids to go outside. (V1.0, Motorola DroidX; June 18, 2012)
Works on Galaxy Sll. Wonderful app I will use to schedule my outdoor activities. Adding an hourly forecast would raise
this to 5 stars. (Samsung Galaxy S2; June 23, 2012)
State of the air. Only app w air pollution forecast. Includes ozone. Needs to show alert time window. (V1.3, Samsung
Infuse 4G; Aug. 3, 2012)
Hi. It's good to be able to tell when to breath outside. (V1.3, HTC Sensation 4G; Aug. 16, 2012)
Rating
3.4
5
5
5
4
4
4
Consistency
with other
information
(4.0)
Does exactly what it says, accurately! Thank you! I see a number of reviewers complaining about inaccurate
information in this app. When I compare this app's data to actual readings in my community, they are dead on. You can
get confused if you expect the apps real time readings to match the daily FORECAST - they don't. Real time readings are
not the same as the forecast (much like the weather.) To see real time readings, I go into my state's Department of
Environmental Quality site and go to the MONITORING page, NOT the forecast page. That shows me a map of several
monitoring sites, some monitoring particulates (PM2.5), some monitoring ozone, and some monitoring carbon monoxide
(CO). The readings vary slightly by site, but I have figured out which sites are the local sources of this app's readings, and
IT IS VERY ACCURATE. Right now, the air quality forecast for today is 50 for PM2.5, but the reading at the monitoring
site is only 28, which is exactly what is displayed on this app on my phone. Of course, it can only report data from
WHERE THERE ARE MONITORING SITES. No sites, no data. Before you criticize this app for being inaccurate, educate
yourself! This app does exactly what it claims. Those who say it doesn't are confused. (V1.3, Sep. 7, 2012)
Spot on for Cincinnati's air quality. (V1.3, HTC myTouch 4G; Dec. 8, 2012)
Not sure if this works. Just downloaded, live in sic we have lots of smog. Wanted to see what the air quality was right
now because i wanted to go on a Bike ride, tons of smog probably half visibility but the app says its green and low
pollution. Hard to trust that. (V1.3, Motorola Droid RAZR; Sep. 17, 2012)
5
5
2
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App / Issue
Online Reviewer Comments
State of the Air (Cont'd.)
Data coverage
(1.1)
No data for my zipcode. Lame. (Samsung Galaxy Note; Aug. 29, 2012)
Uninstall. The app gives me data of a location that's 46 miles away. What good is that for me? (V1.3, HTC Droid
Incredible; June 21, 2012)
This app is useless. I live in Long Beach NOT South Coastal LA. Only ... This app is useless. I live in Long Beach
NOT South Coastal LA. Only gives you a general area that spans 50-100 miles and several climates. (V1. 1, HTC
myTouch 3G Slide; June 21, 2012)
Data not available. I don't live near a big city so this is worthless. Neat idea but only ideal if you live in a major city.
(Droid Bionic; June 23, 2012)
Almost worthless, info given does not seem to be zip code specific but regional for a metro area. 30 miles away in the
burbs same information. (V1. 1, HTC Evo 4G; June 24, 2012)
This is the worst! The app is not accurate. Asthmatics need to know what the air quality is each day. Having a mobile
app that shows the air quality using GPS is a great idea. But the worst thing that could happen is an air quality app that
says the air quality is Green when it is Yellow or some other color. I checked their website for my location and it shows
the Ozone = Yellow but the app shows the Ozone = Green. How sad, how irresponsible. (V1.3; July 9, 2012)
Doesn't locate. When i typed in my zip it put a city that's over four hours away. How can it say that's my areas air quality
when it doesn't even locate my area within a reasonable distance. Also i know where i live has horrid air quality. I live in
factory district. This app needs more pinpoint accuracy. (LG Optimus One; Sep. 21, 2012)
Needs more coverage. Doesn't cover where I live. (Samsung Galaxy S; Sep. 25, 2012)
Rating
2
1
1
1
1
1
1
1
User interface
(2.8)
Thank you! This is so helpful and easy to use. (V1.1, Samsung Galaxy S2; June 19, 2012)
Very useful app!! The air alerts and ability to contact Congress are great! (June 19, 2012)
You Need This App. I plan to use this app every morning as part of my daughter's asthma management plan. So glad
the Lung Association has made this tool so easy to use. (June 19, 2012)
Great. I downloaded the same app for my ipod and my android phone and it works great. (June 29, 2012)
Doesn't work. After the opening screen, it goes blank/dark and does nothing. (VI. 1, HTC Rezound; June 20, 2012)
Doesn't Work for Me. It opens to a black screen and does nothing. (HTC Rezound, June 20, 2012)
Disappointed Force closes at opening every time. Was excited about it too as my one yr old has severe lung disease,
PH and BPD. (V1.2, Samsung Sidekick 4G; June 22, 2012)
Force closes. Not working. Infuse. (Samsung Infuse 4G; June 23, 2012)
Ergh. This app has never worked for me. Even after refreshing and reopening, still gives me nothing. (V1.3, Samsung
Galaxy S; Sep. 9, 2012)
5
5
5
5
1
1
1
1
1
3 Sources: Aesthetikx (2012), ALA (2013), EPA (2013). ALA = American Lung Association; "V" indicates the app version, where reported; the
mobile device is also identified where reported. (A few comments reflect minor editing, e.g., for spelling.) Topical ratings are shown in column 1.
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SELECTED REFERENCES
Aesthetikx (2012). Air Quality, Android Application;
https://plav.google.com/store/apps/details?id=com.aesthetikx.airquality&feature=search result#?t
=W251bGwsMSwxLDEslmNvbS5hZXNOaGVOaWt4LmFpcnF1YWxpdHkiXQ (page indicated last
update was Feb. 22, 2012; online reviews continued to later that year; last accessed April 4,
2013).
ALA (American Lung Association) (2012). State of the Air, Android Application;
http://www.lung.org/healthy-air/outdoor/state-of-the-air/app.html:
https://play.google.com/store/apps/details?id=com.reddeluxe.sota
(page indicated last update was Oct. 24, 2012; online reviews continued to later that year; last
accessed May 22, 2013).
Temple, B. (2012). Air Quality Monitoring: Citizen Sensing Initiative. Prepared in fulfillment of
the reguirement of the Office of Science, Department of Energy's Science Undergraduate
Laboratory Internship, Environmental Science Division, Argonne National Laboratory (Aug.).
U.S. EPA (U.S. Environmental Protection Agency) (2012). Air Data; http://www.epa.gov/airdata/
(page last updated Sep. 27. 2012; last accessed May 22, 2013).
EPA (2013). AIRNow, Android Application;
https://play.google.com/store/apps/details?id=com.saic.airnow (page indicated last update was
March 23, 2012; most recent comment was posted in April 2013; last accessed May 22).
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