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emissions. Area combustion sources showed little variation
between the IP sites and non-IP sites (x = 11 for IP sites and 10
for non-IP sites). Point sources emissions around non-IP sites
were significantly higher, averaging 77 and 92 percent more than
the IP sites for TSP and PM... emissions. A closer examination of
the point source difference shows that among IP sites, only the
El Paso (six sources <500 tons/yr = 1852 tons/yr) and Magna
(Kennecott 2000 tons/yr) sites had large point source emissions.
Among non-IP sites, four sites were influenced by major point
sources totaling 1394 to 3911 tons per year of TSP. Because the
non-IP sites had both higher average area and point source emis-
sions, total emissions for non-IP sites averaged 55 and 66 per-
cent higher for TSP and PM1Q compared with the IP sites.
Overall, 50 percent of the TSP emissions and 38 percent of
the PM10 emissions calculated for the 20 sites were attributable
to fugitive dust sources. Less than 1 percent of the emissions
for both TSP and PM.. were due to area source combustion emis-
sions. The remainder were attributable to point sources (49 and
61 percent). For the IP sites the fugitive dust contributions of
the total emissions were 54 and 44 percent for TSP and PM.
Equivalent values for the non-IP sites were 48 and 36 percent.
In all cases, the fraction of the PM.n emissions attributable to
fugitive dust was lower than that for TSP. Conversely, the
fraction attributable to point sources was higher for pM10-
A comparison of TSP area source fugitive dust categories is
shown in Table 8. The percentage of total emissions rows for
each category indicates that the percentage of clean street
emissions at IP sites (38.3 percent) is significantly greater
than the 10.7 percent for non-IP sites, which demonstrates a
generally more urban exposure for IP sites. Conversely, 40
percent of the fugitive dust source emissions from non-IP sites
were from cleared areas, versus only 3.0 percent at IP sites.
Non-IP sites also had a higher percentage of emissions from
unpaved roads and alleys, whereas the IP sites had a higher
26
-------
percentage of construction emissions. In summary, emission types
from IP sites are more indicative of more fully developed urban-
ized areas with a greater percentage of paved roads and fewer
cleared areas.
Comparison of PM..-/TSP Emission Ratios at IP and Non-IP Sites
The PM10/TSP ratios calculated for all 20 sites (Table 7)
showed the values were remarkably close, with similar ratios
(0.63, 0.64) and similar standard deviations (0.10, 0.15). The
non-IP site ratios, however, had a higher range, as they were
influenced by the point sources near the Fontana (Kaiser Steel),
Oildale (Chevron Oil, Getty Oil, Shell Oil), and Hurley (Kenne-
cott Copper Smelter) sites.
The nearly identical ratios are partly a function of the
calculation methodology. For fugitive dust sources, the PM.. _
fraction used varied from only 0.50 to 0.59 of TSP emissions
(excluding agricultural tilling). The point sources also had a
narrow PM,n fraction band, generally from 0.85 to 1.00. In
reality, particle size distributions may not be in two such
homogenous groupings; however, relatively little data are avail-
able for calculating PM,Q from TSP emissions.
Comparison of Emissions PM.-/TSP Ratio With Ambient Ratio
Converting IP monitored values to PM.. . (see Section 2) made
it possible to compare the microinventory-derived PM-.../TSP emis-
sions ratio with the monitored PM,Q/TSP ratio. This comparison
was made for five sites (1, 3, 7, 13, 19). The monitored ratio
of 0.46 was lower than the microinventory emissions ratio of 0.60
for the sites. There are several possible explanations for this
factor. Assuming the monitored values are correct, possible
theoretical explanations are:
1) The microinventories resulted in an underestimation of
fugitive area source TSP emissions, which would result
in an underestimate of the TSP value for the denom-
inator. A higher value would decrease the ratio. For
these five sites, the PMlf.-to-TSP ratio for fugitive
dust was 0.56.
27
-------
2) The influence of PM-,0 emissions from point sources in a
5-mile radius of the site may not be as direct as
assumed, as emissions from point sources generally have
a higher PM,0/TSP ratio. For the three sites with
point source emissions, the PM1f./TSP ratio
was 0.69. This could result in an overestimation of the
numerator value.
3) The conversion from TSP emissions to PM,0 emissions for
area and point sources could be inaccurate with an
unknown positive or negative impact. In other words,
there could be an error in the basic emission factor
equations.
Assuming the microinventories are correct, possible explan-
ations are:
1) At sites heavily impacted by fugitive dust, the high-
volume sampler (hi-vol) may not have a D-fi of 30 mi-
crometers; it may be higher. This would nave the
impact of increasing the denominator and reducing the
ratio.
2) The dichotomous samplers used for the IP monitoring may
not have a Dcr( of 15 micrometers; it may be lower.
This would decrease the numerator and thereby reduce
the ratio.
Tc investigate the theoretical explanations, data were
obtained relevant to particle collection efficiency of the hi-vcl
and dichotomous samplers (TSP and PM.. Q measurement devices). Two
findings about the hi-vol are of interest. First, preliminary
data about the collection efficiency of a hi-vol indicate that
the Dj-n particle size cut of 30 micrometers decreases with in-
creasing windspeed. The exact function will not be established
until additional research with dry particles such as those found
in the Southwest are completed (Rhodes 1984) . Of greater signif-
icance are preliminary results from EPA's PM1Q Monitor Intercom-
parison Study. In this study a wide range aerosol classifier
(WRAC) was collocated with a hi-vol and PM1Q measurement device.
This device consists of a group of high-volume single-stage
impactors arranged in parallel. Each sampler has a different
particle size cutpoint for the impactor stage. Based on WRAC
28
-------
results and data from Rubidoux, California, and Phoenix, Arizona,
the hivol monitors of these two locations appear to have a DSO of
about 50 micrometers under ambient monitoring conditions (Rhodes
1984). Other sampler data on the dichotomous sampler indicate
that the D-^ of an IP dichotomous sampler also decreases with
windspeed, with the 15 micrometer cut achieved at about 9 miles
per hour. Average windspeed for the monitor locations used in
this study was 7.2 miles per hour. At this windspeed, the di-
chotomous sampler D_ particle collection efficiency would be
about 16.5 micrometers, a relatively small error considering the
particle size distribution of ambient air.
Although the windspeed-induced errors in the dichotomous
sampler are relatively small, the finding of the D^ at 50 mi-
crometers for the hi-vol is significant. For direct comparison
of the emissions ratio with the ambient ratio, it is necessary to
inflate the emissions estimate by the ratio of D50 of 50 microm-
eters over the D,-,, of 30 micrometers. This ratio affects pri-
marily the area fugitive sources. The five particle size distri-
butions from AP-42 used with the fugitive sources were examined.
The use ol a lognormal interpolation between Total Particulate
and 30 micrometers showed that the ratio of emissions less than
50 micrometers to emissions less than 30 micrometers was 1.12.
The point source emissions could have been similarly modified to
represent a Dj-n of 50 micrometers, but they were not because the
PM,_/TSP ratios were generally in the range of 0.85 to 1.0 for
individual processes. Any change due to a modification would
have been slight. Modifying the area source emissions in Table
8 for the five IP sites results in a change of the emissions
ratio from 0.60 to 0.54. This ratio is still greater than 0.46,
the ambient ratio. This difference in the ratios must be attri-
butable to one of the other explanations, a combination of the
theoretical explanations just listed, or to an unknown variable.
29
-------
Comparison of Study Ambient Ratio With National Ambient Ratio
The study ambient PM,Q/TSP ratio is 0.46. The national
ambient PM,Q/TSP ratio from the IP network is also 0.46. Conven-
tional wisdom would hold that the ratio in the Southwest should
be lower than the national average, because the West has a lower
background concentration (particles <1Q micrometers), more fugi-
tive dust (particles >10 micrometers), and fewer point sources
(particles <10 micrometers) than the East. The conventional
wisdom requires further consideration.
Regarding background concentrations, a comparison of eastern
and western sites in the IP network (PEDCo 1984b) at background
monitors indicates that the PM... average concentration was 21.2
yg/m3 in the East, and 24.5 yg/m3 in the West, i.e. the conven-
tional wisdom is contradicted by these monitoring data.
There probably are more fugitive emissions in the West than
in the East; however, the emissions from these sources are not as
coarse as commonly assumed. This greater amount of fugitive
emissions increases both the denominator and the numerator of the
ratio. Because the PM.. n ratio for fugitives is roughly 0.5, the
ratio for all emissions will stay near 0.5 when fugitive emis-
sions are added to the ambient air. For example, if the PH.,.
ratio was near 0.5 and the TSP value was 60 yg/m3, 30 yg/m3 would
be PM.. n. Adding 10 yg/m3 of TSP emissions to the ambient air of
fugitive emissions would change the TSP value to 70 yg/m3 and the
PM10 value to 35 yg/m3, again resulting in a ratio of 0.5.
Regarding point sources, 13 of 20 monitors were impacted by
point source emissions of greater than 200 tons of TSP per year.
Seven of 20 monitors were impacted by point source emissions of
greater than 1400 tons of TSP per year.
In conclusion, it is clear that some of the conventional
wisdom is in error. First, PM,-, background concentrations are
slightly higher in the West than in the East. Because fugitive
30
-------
emissions have a PM..- ratio of about 0.5, the addition of fugi-
tives does not change the overall PM10 ratio very much. Western
monitors are also impacted by point sources. These three factors
combined make it likely that the ambient ratio for the Southwest
should be similar to the national average ratio.
31
-------
SECTION 4
EVALUATION OF MEASURED AIR QUALITY AND
METEOROLOGICAL VARIABLES
It has been hypothesized that high monitored particulate
concentrations in the Southwest may be due, at least in part, to
the hot and dry conditions in the region. This section presents
an analysis of the impact of specific meteorological variables,
both on an annual and a daily basis. The final subsection ana-
lyzes the relationship between windstorms and measured concen-
trations .
ANNUAL METEOROLOGICAL VARIABLES
The data base developed for the analysis of annual mete-
orological variables includes 50 TSP, 8 IP, and 50 calculated
PM-i Q annual arithmetic means; number of samples for each air
quality mean value; mean annual wind direction (10-degree sec-
tors; 00-36) and windspeed; peak windspeed; and annual precipita-
tion. These data are summarized in Table 9. Derivation of the
air quality values was described in Section 2.
Most of the meteorological data were obtained from data
summaries of the National Weather Service (NWS) station nearest
to the site. The exception was the precipitation data. In cases
where local precipitation records were available for sites closer
to the monitor than the NWS station, these local data superseded
the NWS data.
The data in Table 9 were input to the MLR program, and the
outputs were examined for significant variables. For PM10 the
results were as follows:
32
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Variable
(in order of output)
Mean wind direction
Peak windspeed
Precipitation
Mean windspeed
R2
Significance
0.21
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Similarly, for TSP the results are:
Variable
Step (in order of output)
1 Mean wind direction
2 Mean windspeed
3 Precipitation
4 Peak windspeed
R2 Significance
0.18 0.032 0.232
0.19 0.038 0.627
0.23 0.054 0.397
0.24 0.059 0.658
As is evident from the results, this analysis did not pro-
duce any meaningful insights into the relationship between annual
air quality and meteorological parameters. There is some differ-
ence in the results of the two analyses because not all of the
PM , data were calculated from TSP data. None of the variables
evaluated (singly or in combination) explain a significant por-
tion of the variance in measured air quality at the sites.
DAILY METEOROLOGICAL VARIABLES
The previous analyses used annual average air quality data
with various independent variables in an attempt to describe the
variation in measurements between sites. The results were not
meaningful. In this section, daily air quality and meteoro-
logical variables were examined to see whether variations in
day-to-day meteorological conditions could be used to explain the
variation in measured concentrations.
The 20 sites that were microinventoried were divided into
two subsets. The first subset contained data for the eight IP
network sites. The second subset consisted of the remaining 12
sites. The air quality data used for the first subset consisted
of paired TSP and IP readings over the period May 1979 to
35
-------
December 1982. For each day that paired data were available, a
number of meteorological parameters were extracted from NWS Local
Climatological Data Summaries of the nearest reporting station.
It should be noted that the large distance between a monitor site
and the nearest meteorological site may, in some instances,
result in meteorological data that are not representative of the
conditions at the monitor. The data extracted" from the summaries
included:
1. Resultant wind direction
2. Resultant windspeed
3. Average windspeed
4. Peak windspeed
5. Precipitation
6. Number of days since rain exceeding 0.01 in.
7. Number of days since rain exceeding 0.10 in.
8. Number of days since rain exceeding 0.25 in.
For the last three data items, the maximum number of days since
rain was set at 7, as it was assumed that any effects of precipi-
tation would be negligible after 7 days. One final variable
compiled for each site was whether the sample was collected on a
weekday or a weekend. For each IP measurement, an equivalent
PM.. . measurement was calculated in the MLR program by. multiplying
the IP readings by 0.77 (Subsection 2.4). A similar data compila-
tion was prepared for the other 12 sites; however, in this sum-
mary, only TSP data were used.
The concept behind this division of data sets was twofold.
With the first subset, it would be possible to evaluate the
relationship between air quality measurements and meteorological
variables for both TSP and PM.... In addition, the ratio of
PMin/TSP could be evaluated with the same variables. If a con-
.1 U
sistent relationship could be established for the first subset of
TSP and PM.. . data, it might be possible to expand the relation-
ship to the sites where only TSP data were available.
36
-------
Multiple Regression Analyses
Individual Site Regressions for IP Sites--
The data for each IP site were first input to the MLR pro-
gram. For each site, a regression was performed and then the
results were examined. The next step was to perform the eval-
uations for the entire data set. The third step involved an
evaluation of the PM..-/TSP ratio for six subgroups. These sub-
groups were based on three levels of either mean windspeed or
peak windspeed.
The results of the regressions performed with data from the
IP network sites are shown in Table 10 as linear equations. Both
TSP and PM... results are included in the table. The data base
used included only days when both TSP and IP measurements were
available. The IP concentrations were converted to PM.. n concen-
trations by multiplying them by the 0.77 factor discussed in
Subsection 2.4.
For this analysis the following abbreviated variable de-
scriptors were used:
1. RESDIR - resultant wind direction, 10° sectors
2. RESWS - resultant windspeed, mi/h
3. AVGWS - average windspeed, mi/h
4. PEAKWS - peak windspeed, mi/h
5. PRECIP - precipitation amount, in.
6. DSR01 - number of days since rain, 0.01 or more in.
7. DSR10 - number of days since rain, 0.10 or more in.
8. DSR25 - number of days since rain, 0.25 or more in.
9. WDWE - weekday/weekend; weekday = 1, weekend = 0
These abbreviations are used in Table 10 and in the following
discussions. The data in Table 10 do not include all of the
program results. Only those variables that are significant at
the 0.05 level or better are included. The remaining variables
will not significantly improve the correlations. In addition,
the R values have been eliminated from these summaries; however,
they can easily be calculated given the R2 value.
37
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As shown in Table 10, only two of the equations have sig-
nificant variables that explain over 50 percent of the variance
in the data (Site 1-TSP, Site 7-PM-.). It is interesting to note
that all of the sites have DSR01 as a significant variable with a
positive coefficient. This result suggests the fugitive nature
of the sources impacting the monitors. Rainfall generally sup-
presses fugitive dust emissions on the day of precipitation, and
the emissions then increase for a few days until the material is
once again dry. The TSP coefficients range from 4.6 to 16.1, and
the PM1Q coefficients range from 2.1 to 8.8. Sites 1 and 14 also
exhibited a decrease in concentration on days of precipitation.
Site 1 (Carefree) is located near a dirt taxiway at a rural
airport. Site 7 (Bayard) is located in an unpaved school bus
parking lot. Both locations would have lower localized emissions
on the days with precipitation. RESDIR was a significant vari-
able at four sites. At three of the sites the coefficient is
positive, and at the fourth it is negative. This suggests that
the highest concentrations at the first three sites are asso-
ciated with a particular wind direction and that the lowest
concentrations at the fourth site are associated with a par-
ticular direction. If pollution roses were developed for these
sites, they might indicate the direction of the significant
emission points.
A similar dichotomy exists for the several measurements of
windspeed. In some cases the coefficients are positive, in
others they are negative. A problem arises when attempting to
correlate windspeed to TSP because windspeed has a positive and
negative effect on TSP. As windspeed increases, suspension of
particles increases, which tends to increase concentrations.
Greater dispersion of particulate matter also occurs with in-
creasing windspeed, which decreases concentrations. Under high
wind conditions the increased suspension of particles is often
overshadowed by the effects of dispersion, but when 24-hour mean
windspeed is addressed, the two consequences of windspeed cannot
be separated.
39
-------
The relationship between concentrations and the various
meteorological variables is not straightforward. It is inter-
esting to note that from 18 to 56 percent of the variance in the
air quality data can be explained by just the meteorological
variables at any one site. The relationships shown in Table 10
do not include the other interrelationships that exist near a
monitor: source-receptor geometry, and meteorological variables
versus source and receptor locations.
One final analysis was performed with these data. The daily
PM..Q/TSP ratio was calculated for each data point and regressed
against the meteorological variables, both as a whole and with
specific attention to three ranges of peak windspeed and average
windspeed. No significant variables were identified during the
regression analysis.
Non-IP Sites
For the 12 sites that were not a part of the IP Network,
only TSP air quality data were available. For these sites, the
MLR program was exercised with the daily TSP values and their
associated meteorological parameters. No PM1, conversions were
performed because no reasonable method of converting over 1200
daily TSP values to equivalent PM.n values could be devised other
than by multiplying each by an identical constant.
The results of these analyses are shown in Table 11. These
results explain less than 40 percent of the variance in the
measured data at each site. Similar to the results for the IP
sites, DSR01 is a significant variable at 11 of the 12 sites. At
site 8 (Brawley) the lack of significant relationships may be a
function of the limited data base for that site. At site 6
(Yuma) the coefficient (-298) for PRECIP suggests the extreme
reduction in fugitive dust emissions realized by rainfall in this
arid location. Five of the sites (5, 6, 9, 15, 18) show a posi-
tive coefficient for PEAKWS, which suggests a possible impact of
duststonns or wind-erosion emissions under high wind conditions;
however, the simple correlations between peak windspeed and
40
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concentration are rather low. As discussed earlier, windspeed
may have both a positive and negative effect on measured concen-
trations. Further, the results support this concept because no
definitive relationship was established.
Further analyses of this particular observation were per-
formed by grouping the entire data base (20 sites) into three
subgroups of average windspeed and three subgroups of peak wind-
speed. None of the regression analyses explained over 7 percent
of the variance in the data.
None of the regression analyses in this section revealed any
definitive relationships between measured concentrations and
daily meteorological variables. Previous studies performed by
PEI for TSP nonattainment sites impacted by fugitive dust sources
have shown equally variable results (PEDCo 1978, 1983). By their
nature, the ubiquitous fugitive dust sources are intermittent
sources of particulate and are not easily explained by regression
analyses.
RELATIONSHIP BETWEEN DUSTSTORMS AND MONITORED TSP CONCENTRATIONS
Those familiar with the southwest know that the periodic
duststorms that occur in this region can reduce visibility to
almost zero. During these storms, which last from minutes to
hours, particulate concentrations are extremely high as a result
of natural as opposed to man-made phenomena.
Duststorm Events
As a means of examining the frequency of duststorms near the
eight IP microinventoried monitors, data from the nearest NWS
station (sometimes as much as 100 miles away) were examined for
the parameter, "Weather ConditionDuststorm." This parameter is
defined as a condition of less than 5/8 mile visibility for any
duration of time. This level of dust in the air would be indi-
cative of concentrations in the several hundreds of yg/m3. A
42
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total of 40 windstorm events were recorded at the NWS stations
closest to the eight IP monitors over the approximately 3-year
period. This factor in itself suggests that the definition used
by the NWS to qualify for a windstorm event is quite restrictive.
Of the 40 windstorm events, only 5 days corresponded with days
having measured air quality at four sites. This limited data
set, which is shown in Table 9, is inconclusive because of the
small number of cases and the discrepancy of results. In two
cases, the monitored value on the duststorm day was less than the
annual mean. On 3 days, the reverse was true.
The NWS data were only of limited utility for this exercise.
Because of the criteria used by the NWS to define a duststorm
event, few days with duststorms are reported. No indication is
given in the NWS summaries as to the duration and location of the
event. For the four monitoring sites shown in Table 12, it is
impossible to determine whether the event was also occurring at
the monitor site. In the case of the Bayard site, it is unlikely
that conditions observed at the NWS site were also prevalent at
the monitor 132 miles away. For the other sites, the distance of
3 to 16 miles separating the monitors and the NWS locations could
also be a significant factor. Further, NWS data cannot be used
to define more localized phenomena such as the
familiar "dust-devils."
High-Volume Sampler Cut-Point
As noted in Subsection 3.4, the collection efficiency of a
hi-vol changes from a D_n of 30 micrometers to a D^ of a smaller
particle size under high wind conditions. The change in particle
size collection efficiency with windspeed has not been fully
characterized; however, this change would have the effect of
measuring a lower concentration than was actually present in the
ambient air. Although the magnitude of the duststorm phenomenon,
and the partially off-setting change in particle size collection
are not known, it is believed that the dust storm phenomenon
would predominate.
43
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Theoretical Approach
Duststorms can be a single hour to multiple hours to days in
duration. Assuming that an average hourly concentration prior to
a windstorm was 60 yg/m3 and that the concentration during a
windstorm hour was 500 yg/m3, a windstorm of one hour's duration
would increase the 24-hour value from 60 yg/m3 to 78 yg/m3. If
the storm were 2 hours in duration, the value would be 97 yg/m3;
3 hours in duration, 115 yg/m3; etc. If the TSP 24-hour standard
value of 260 yg/m3 is considered, the above assumptions would
show that a duststorm of 11 hours' duration would produce a 24-
hour concentration in excess of 260 yg/m3.
This theoretical approach would tend to indicate that dust-
storms would need to be quite lengthy to cause a violation of the
24-hour standard.
45
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SECTION 5
POTENTIAL REGULATORY APPROACHES
Three potential regulatory approaches to remedy a nonattain-
ment situation should be considered. Whereas each would have
some impact if adopted in isolation, maximum potential benefit
would be achieved with all three programs in place.
SITING CRITERIA
Existing TSP monitor siting criteria (Federal Regulations
121:2234) state the following:
2.3 Spacing from roads
"...Except, for situations where the monitoring objec-
tive is to determine the impact of a single source,
ambient monitors should not be located sc as to measure
the plume of a single source ... For TSP, it is appro-
priate that ambient monitors be located beyond the
concentrated particulate plume generated from traffic,
and not so close that the roadway totally dominates the
measured ambient concentration...
2.4 Other Considerations
"Stations should not be located in an unpaved area
unless there is vegetative ground cover year round so
that the impact of reentrained or fugitive dusts will
be kept to a minimum."
As shown in previously cited Table 7, 11 of the 2C microinventory
sites (2 IP sites, 9 non-IP sites) had siting issues because they
were located very near either unpaved roads (a possible violation
of Section 2.3 of the siting criteria) or exposed areas with
little or no vegetative cover (a possible violation of Section
2.4 of the siting criteria). Several of the drive-by sites were
46
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also very close to unpaved roads and exposed areas. Siting of
these monitors in other locations would significantly improve
measured air quality.
Even in areas where fugitive dust sources are pervasive, the
issue of the proximity of a monitor to a specific source is
subject to interpretation. In this report, a number of monitor
locations were identified where it is believed that the monitor
could have been sited differently to provide results more repre-
sentative of a given area.
This concept of source proximity is still a subject of
debate. The proposed revisions to the NAAQS for PM Q will in-
clude further guidance for monitor siting in the vicinity of
specific sources. At this time, it is premature to speculate on
the exact form of the final recommendations; however, as evi-
denced by the discussion in this report, monitor siting is one of
the critical aspects that need to be addressed for PM,- monitor-
ing .
MODIFICATION OF MEASURED VALUES
The periodic duststorms that occur in the Southwest can
reduce visibility to almost zero. During these storms, which
range in duration from minutes to hours, particulate concentra-
tions are extremely high as a result of natural as opposed to
man-made phenomena. Because local agencies cannot be expected to
control duststorms, the concept of removing monitored values
influenced by duststorms was explored.
Representatives of Texas, New Mexico, California, and Ari-
zona air agencies were contacted (Butts 1984, Shankar 1984, Cook
1984, Guyton 1984). None of the States currently remove any data
from their data base (although some local agencies may), even if
they know it was influenced by a duststorm. Only Texas has a
system whereby they flag "duststorm" influenced data. Although
most were favorable to the idea, the Arizona representative noted
47
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that many monitors are in remote locations and no hard data on
windstorm occurrences are available.
Unfortunately, the data on windstorms and measured concen-
trations presented in Section 4.0 were inconclusive because of
the small data base. In addition, the distance between the NWS
station and the monitor may confound the results. Further, the
phenomenon may be confounded by the changing collection effi-
ciency of a hi-vol in high winds. In spite of these facts, it is
still believed that windstorms are responsible for some periods
of high measured values and might strongly influence attainment
of 24-hour standards.
REGULATION OF FUGITIVE DUST SOURCES
The influence cf fugitive dust sources near monitors has
been noted throughout the report. Also, as noted in Section 2,
the microinventory results indicated the presence of a large
number of exposed areas, unpaved roads, unpaved alleys, and
unpaved parking lots. Fugitive dust sources are directly regu-
lated by local and state governments as opposed to indirect
regulation by the Federal government through SIP action. Many of
the locations visited had vast areas of cleared exposed areas (as
opposed to sparse natural vegetation) that have never been
treated with a dust suppressant or revegetated, and also miles
and acres of unpaved roads, alleys, and parking lots that had
never been treated. Even though fugitive dust control is indeed
more difficult in the semi-arid and arid Southwest, application
of some dust-control methods is clearly possible.
Many locations have instituted all or some of the following
controls (EPA 1978; EPA 1979b; EPA 1979c; EPA 1979d; EPA 1979e;
Ohio EPA 1980):
(1) Paving of unpaved roads or stabilization with a chem-
ical dust suppressant.
(2) Paving of unpaved parking lots or stabilization with a
chemical dust suppressant.
48
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(3) Revegetation or chemical stabilization of exposed
areas.
(4) Removal of spills from streets.
(5) Control of trackout from construction sites.
(6) Covering of truck loads.
(7) Promoting agricultural practices that minimize dust
generation.
Particularly at the Arizona, Texas, and New Mexico sites,
the existence of such dust control measures was not evident. Not
all of the control measures outlined above may be appropriate in
all areas; however, fugitive-dust-control plans should include
consideration of these possibilities.
FURTHER REGULATION OF POINT SOURCES
The sites impacted by major point sources were Hayden,
Fontana, Oildale, Hurley, and Magna. Point source emissions of
TSP at individual facilities ranged from 846 to 2940 tons per
year. Control systems at individual plants were not reviewed as
part of this study; however, visible observations made during the
microinventories suggest that the potential exists for improve-
ment of air quality from additional point source controls at 5 of
20 sites studied.
49
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SECTION 6
SUMMARY AND CONCLUSIONS
The purpose of this project was to answer the following
questions:
(1) What are the sources that are expected to contribute to
high PM,Q concentrations in the southwest?
(2) To what extent does meteorology play a role in high
Total Suspended Particulate (TSP) concentration (e.g.,
high windspeed and lack of rainfall).
(3) What are potential remedies to a nonattainment situa-
tion at these sites?
Subsections 6.1 through 6.3 summarize the findings of previous
report sections in meeting the three study objectives.
SOURCES THAT ARE EXPECTED TO CONTRIBUTE TO HIGH PM . CONCENTRA-
TIONS IN THE SOUTHWEST
A total of 20 monitoring sites were microinventoried (Ap-
pendix C). When the Empirical Model described in Appendix E was
used with data from 15 of these sites, less than 35 percent of
the variance in the TSP and PM,Q concentration data was explained
by the model. In contrast, the model as originally developed
explained 77 percent of the variance in the measured data. The
model was developed with microinventory and air quality data from
primarily urban monitoring sites in Kansas City, St. Louis,
Birmingham, and Portland. The fact that the model explained such
a small amount of the variance for the Southwest sites investi-
gated in this study indicates that TSP air quality in the South-
west is influenced by different factors than elsewhere. .
50
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Using the same procedures as were used to develop the orig-
inal Empirical Model and the Southwest microinventory/air quality
data base, new regression equations were developed for TSP and
PM1n in the Southwest. A comparison of the results (Table 13)
with those from the original model development showed that the
LOCAL and VISDT terms for the new equations had much higher
coefficients. These two variables combined explained 42 percent
of the variance in the TSP data and 40 percent of the variance in
the PM,Q data for the 15 sites evaluated (Subsection 3.2.3). At
the 20 sites microinventoried, 14 monitors were within 200 feet
of a roadway (the LOCAL term), and 7 of these roadways had vis-
ible plumes from passing traffic (the VISDT term). The AREA term
in the new equations explained 10 and 8 percent of the variance
in the TSP and PM.. data, and the POINT term explained 3 and 2
percent of the variance.
In contrast, in the original equation, the AREA term ex-
plained 38 percent of the variance; POINT, 20 percent; LOCAL, 4
percent; and VISDT, 5 percent. Overall, the original model
explained 77 percent of the variance in the data; whereas the new
forms explained 55 and 50 percent of the variance for TSP and
PM10'
These results from the two analyses (original model and the
forms of the model developed for the Southwest) indicate the
greater importance of nearby streets (paved or unpaved) on mea-
sured air quality in the Southwest compared with the areas for
which the model was originally developed. (LOCAL and VISDT
entered the MLR equations before the AREA and POINT terms.)
Beyond the 200-foot boundary for the LOCAL term, specific
source categories impacting the study monitors were identified
from the microinventory data. At the 20 monitoring sites eval-
uated, 50 percent of the total calculated TSP emissions were
attributed to fugitive dust sources. These emissions were from
sources within 1 mile of the monitor compared with the point
source emissions, which were compiled out to 5 miles. Of the
51
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TABLE 13. COMPARISON OF COEFFICIENTS FROM ORIGINAL
EMPIRICAL MODEL AND SW EMPIRICAL MODEL
Term
AREA
POINT
LOCAL
VISDT
Percent explained by
Original Model3
38
20
4
5
SW TSP Model b
10
3
19
23
Developed from data derived from Kansas City, St. Louis, Birmingham, and
Portland
Developed from data derived from cities in California, Arizona, New Mexico,
and Texas
52
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fugitive dust sources, 20 percent of the emissions were from
clean streets, 15 percent were from unpaved roads/alleys, 28
percent were from cleared areas, 8 percent were from construc-
tion, and 22 percent were from agriculture.
The point source inventories included many types of pro-
cesses. The monitoring sites that were impacted by point sources
with large emission estimates included Magna, Hayden, and Hur-
leysmelters; Fontanasteel mill; Oildalecrude oil burning to
generate steam for injection wells.
A comparison was also made between IP sites and non-IP sites
to determine if both groups of sites were influenced by the same
types of sources. If the same source types were present, the
sites should have similar PM.-/TSP ratios. Given similar ratios
at the two groups of sites, the use of the Probability Guidelines
at non-IP network sites would be reinforced.
As shown in Table 13, it was found that point source emis-
sions represented 45 percent of the total TSP emissions at IP
sites compared with 51 percent at the non-IP sites. Within the
fugitive dust categories, predominate sources between IP and
non-IP sites differed considerably (Table 14). For IP sites,
cleared paved streets accounted for 22.1 percent of total emis-
sions; whereas for non-IP sites, the value was 5.2 percent. For
IP sites, cleared areas accounted for 1.7 percent of emissions;
or non-IP sites, the corresponding value was 19.6 percent. Other
lesser differences are evident. In spite of the source differ-
ences between the two groups of sites, the calculated PM-../TSP
emission ratios for the sources in the inventories were almost
identical (0.63 vs. 0.64). This phenomenon, however, is par-
tially due to the relatively homogenous PM,n/TSP ratios assumed
for the different fugitive dust source types (0.50 to 0.59). For
a five-site subset of the IP network group, a troubling aspect of
the results was the difference between the PMin/TSP emission
ratio (0.60) and the ratio of ambient concentrations for PM,n and
TSP (0.46). This difference can be partially explained by the
variation in particle size collection efficiency of the hi-vol
53
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TABLE 14. COMPARISON OF SOURCE CATEGORIES
FROM IP AND NON-IP SITES
Category
Point
Fugitive Dust
Railroads
Clean Paved Streets
Unpaved Roads/Alleys
Cleared Areas
Construction
Agricul ture
Storage Areas
Unpaved Parking Lots
Percent of total emissions
IP sites
44.8
3.0
22.1
4.0
1.7
9.4
14.6
0.2
0.2
Non-IP sites
51.1
3.6
5.2
9.2
19.2
1.5
9.6
0.0
0.2
54
-------
with windspeed (see Subsection 3.4.3). This does not totally
explain the observed difference, however.
THE EXTENT TO WHICH METEOROLOGY PLAYS A ROLE IN HIGH TSP
CONCENTRATIONS
Annual meteorological variables were regressed against
annual air quality data. The results were not statistically
significant.
Subsequently, daily meteorological variables were regressed
against daily air quality. The 20 sites that were microinven-
toried were divided into subsetsIP sites and non-IP sites. The
meteorological variables used were wind direction; resultant,
average, and peak windspeed; days with precipitation; and three
indicators of days since precipitation.
Meteorological variables explained 18 to 56 percent of the
variance at individual IP sites. The most significant variable
in each site equation was days since rain (DSR01). Daily mea-
surements of particulate matter exhibited a positive correlation
with the variable.
A dichotomy exists for the several measurements of wind-
speed. In some cases, a direct relationship was found between
daily air quality measurements and windspeed; in other cases, an
inverse relationship was found. The difficulty in attempting to
correlate windspeed to measurements of TSP relates to the dual
role played by windspeed. As windspeed increases, suspension of
particles, from unpaved surfaces increases, which tends to in-
crease measured concentrations; however, increasing windspeed
also tends to dilute the emissions from a point source or fugi-
tive dust source as a result of the greater volume of air passing
the source, which tends to decrease ambient concentrations.
Under high wind conditions the increased suspension of particles
is often overshadowed by the effects of dispersion. When average
windspeeds and average concentrations are evaluated, the two
aspects of windspeed cannot be separated. This situation is
further compounded in the case of 24-hour hi-vol measurements.
55
-------
Because the D - of a hi-vol changes as a function of windspeed,
the TSP measurements reflect different composite size distribu-
tions depending on the windspeed. Consequently, it is difficult
to make a direct comparison of concentration measurements taken
during different windspeed conditions.
It would be desirable to perform a regression analysis to
determine the combined effect of particulate emissions and daily
meteorological variables on 24-hour TSP measurements. This
analysis was not feasible, however, because the emission inven-
tories reflect annual conditions and cannot be reliably converted
to a 24-hour basis.
An analysis was performed to determine if windstorms re-
sulted in increase measured concentrations. Although the results
were inconclusive, it is believed that such a relationship does
exist.
In summary, a definitive link between meteorological vari-
ables and measured concentrations was not found. The study was
hampered by the fact that meteorological and air quality measure-
ments were not made ar the same location; however, this situation
seldom occurs except in special detailed monitoring studies.
Additional analysis of data collected with collocated meteorolog-
ical and air quality measurement devices from such a special
study might yield more significant results.
POTENTIAL REMEDIES TO A NONATTAINMENT SITUATION AT THESE SITES
Four potential remedies were sited in Section 5. Each would
individually reduce monitored particulate levels. The applica-
tion of all four measures would be additive. Suggested control
remedies are:
1. Evaluation of adherence to monitor siting criteria
Monitor siting regulations require that monitors not be
placed so as to be overly influenced by a single source,
and not be placed in exposed or unpaved parking/driving
areas. Eleven of 20 monitors may be in violation of
siting criteria. Nine of the 11 monitors were non-IP
sites.
56
-------
2. Development of procedures to flag monitored values
influenced by windstormsAlthough the limited data
collected as part of this study do not conclusively
show the impact of duststorms on measured values,
intuitively, duststorms are believed to have an effect.
3. Control of fugitive dust sourcesAt all microinventory
sites except those east of Los Angeles and Bakersfield,
many common dust-control techniques were not being
practiced. Such common measures include use of chem-
ical dust suppressants or paving highly traveled un-
paved roads and parking lots, stabilization or revege-
tation of cleared areas (as opposed to sparse vegeta-
tion) , cleanup of spills, and trackout control near
construction sites. Whereas it is acknowledged that
fugitive dust may be more difficult to control in the
Southwest, at some locations, little effort in this
area was observed.
4. Control of point sourcesFive of 20 sites were in-
fluenced by major point sources with emissions ranging
from 850 to 3000 tons per year of TSP. Control systems
on individual plants were not reviewed; however, the
general level of visible emissions suggests the poten-
tial for increased control.
57
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REFERENCES
Butts, L. 1984. Telephone conversation with S. Lueck, July 26,
1984.
Cook, J. 1984. Telephone conversation with S. Lueck, July 31,
1984.
Draper, N. R., and H. Smith. 1965. Applied Regression Analysis.
John Wiley and Sons, New York, New York.
Guyton, J. 1984. Telephone conversation with S. Lueck, July 31,
1984.
Nie, N. H., et al. 1975. Statistical Package for the Social
Sciences, Second Edition. McGraw-Hill, Inc., New York.
Ohio Environmental Protection Agency. 1980. Reasonably Avail-
able Control Measures for Fugitive Dust Sources. Office of Air
Pollution Control, Columbus, Ohio.
PEDCo Environmental, Inc. 1978. Particulate Analyses in Iowa
Nonattainment Areas. Prepared for U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina, and Region VII,
Kansas City, Missouri.
PEDCo Environmental, Inc. 1979a. TSP Source Inventory Around
Monitoring Sites in Selected Urban Areas, Kansas City. Prepared
for U.S. Environmental Protection Agency, Research Triangle Park,
North Carolina.
PEDCo Environmental, Inc. 1979b. TSP Source Inventory Around
Monitoring Sites in Selected Urban Areas, Portland. Prepared for
U.S. Environmental Protection Agency, Research Triangle Park,
North Carolina.
PEDCo Environmental, Inc. 1979c. TSP Source Inventory Around
Monitoring Sites in Selected Urban Areas, St. Louis. Prepared
for U.S. Environmental Protection Agency, Research Triangle Park,
North Carolina.
PEDCo Environmental, Inc. 1979d. TSP Source Inventory Around
Monitoring Sites in Selected Urban Areas, Birmingham. Prepared
for U.S. Environmental Protection Agency, Research Triangle Park,
North Carolina.
58
-------
REFERENCES (cont.)
PEDCo Environmental, Inc. 1979e. TSP Source Inventory Around
Monitoring Sites in Selected Urban Areas, Philadelphia. Prepared
for U.S. Environmental Protection Agency, Research Triangle Park,
North Carolina.
PEDCo Environmental, Inc. 1979f. Validation of Empirical Tech-
nique for Estimating TSP Annual Concentrations. Prepared for
U.S. Environmental Protection Agency, Research Triangle Park,
North Carolina.
PEDCo Environmental, Inc. 1983. Filter Analysis and Particulate
Identification. Prepared for U.S. Environmental Protection
Agency, Region VII, Kansas City, Missouri.
PEDCo Environmental, Inc. 1984a. Generic Particle Size Distri-
butions for Use in Preparing Particle-Size-Specific Emission
Inventories. Prepared for U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina.
PEDCo Environmental, Inc. 1984b. Estimating PM-,0 and FP Back-
ground Concentrations from TSP and Other Measurements. Prepared
for U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina.
Rhodes, C. 1984. Personal communication with T. Pace, EPA
Project Officer, September 17, 1984.
Shankar, A. 1984. Telephone conversation with S. Lueck, July
26, 1984.
Snedecor, G. W. 1946. Statistical Methods. Fourth Edition.
The Iowa State College Press, Ames, Iowa. Chapter 13, Multiple
Regression and Convariance.
U.S. Environmental Protection Agency. 1978. Potential TSP
Control Measures for Grand Junction. Prepared by PEDCo Environ-
mental, Inc., Kansas City, Missouri, for Region VIII, Denver,
Colorado, and Colorado Department of Health, Denver, Colorado.
U.S. Environmental Protection Agency. 1979a. An Empirical
Approach for Relating Particulate Microinventory Emissions Data,
Monitor Siting Characteristics and Annual TSP Concentrations.
Research Triangle Park, North Carolina. EPA-450/4-79-012.
U.S. Environmental Protection Agency. 1979b. Assessment of Six
Particulate Control Measures in Omaha and Council Bluffs. Pre-
pared by PEDCo Environmental, Inc., Kansas City, Missouri, for
Region VII, Kansas City, Missouri; Iowa Department of Environ-
mental Quality, Des Moines, Iowa; and Nebraska Department of
Environmental Control, Lincoln, Nebraska.
59
-------
REFERENCES (cont.)
U.S. Environmental Protection Agency. 1979c. Demonstration of
Nonpoint Pollution Abatement Through Improved Street Cleaning
Practices. Prepared by Woodward-Clyde Consultants, San Fran-
cisco, California, for Municipal Environmental Research Labora-
tory, Cincinnati, Ohio.
U.S. Environmental Protection Agency. 1979d. Particulate Con-
trol Measures for Colorado Springs, Colorado. Prepared by PEDCo
Environmental, Inc., Kansas City, Missouri, for Region VIII,
Denver, Colorado.
U.S. Environmental Protection Agency. 1979e. Support Document
for Kansas and Missouri TSP SIP Revisions for the Kansas City
Nonattainment Area. Prepared by PEDCo Environmental, Inc.,
Kansas City, Missouri, for Region VII Kansas City, Missouri;
Missouri Department of Natural Resources, Jefferson City, Mis-
souri; Kansas Department of Health and Environment, Topeka,
Kansas.
U.S. Environmental Protection Agency. 1984. Procedures for
Estimating Probability of Nonattainment of a PK.. Q NAAQS Using
Total Suspended Particulate or Inhalable Particulate Data.
Draft. Thompson G. Pace, Neil H. Frank, Research Triangle Park,
North Carolina.
60
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APPENDIX A
MICROINVENTORY PROCEDURES
The microinventory technique described herein was developed
over the past five years as part of the particulate analyses in
several regional areas. The procedures followed are not
necessarily technically superior to alternative methods that
might be used. They have evolved as a result of this past
experience, however, and are recommended for succeeding
inventories, primarily to maintain consistency in data collection
and to minimize the time required in the field.
The survey procedure employed was to ground-survey, via
automobile, the sampler stations and all probable sources of
particulate emissions within a 1-mile radius of each sampling
site. This method enabled the survey team to make the best
observation of overall site exposure and to identify the sources
with primary impact on the samplers. The 1-mile radius around
each sampler was specified because that is approximately the
range to which theoretical treatments (dispersion modeling) show
that low-level sources will have a significant impact.
Sources of fugitive dust and conventional point and area
sources were located on maps during the ground survey and labeled
with pertinent data such as size and boundaries, observed
activity and operational characteristics, and estimated emission
rates. Two observers independently estimated and agreed upon
parameters observed at each site to increase accuracy and
completeness.
PREPARATORY WORK
Point source and air quality data were obtained from the
state air pollution control agency and U.S. EPA. Traffic volume
A-l
-------
maps were obtained from the State and local traffic departments.
Two other types of maps were also utilized during the microinven-
tories. USGS 7-1/2 minute quadrangle maps were used to locate
sampler sites and point sources by UTM coordinates. They were
also a convenient size and scale (1 in. = 2000 ft) for covering
the impact area1-mile radius for area sources and a 5-mile
radius for point sources. A city street map was used for plan-
ning the survey route, navigating the survey route, and locating
sources in the field.
FIELD WORK
The actual inventory procedure followed was to begin at the
sampler site and survey the 1/4-mile radius area first. Photo-
graphs were taken of the sampler location. Monitor height and
placement were recorded on an auxiliary data sheet. Any physical
interference that could bias the air quality readings was also
noted. The site code (if available), street address, and dis-
tances of the nearest intersecting streets or localized sources
were determined and recorded.
The field team then observed each street within the 1/4-mile
radius and noted the location of all pertinent sources on the
map. Closer inspections of the sources were made as necessary.
All important parameters were independently estimated and then
agreed upon by the two observers. For area sources, the type,
dimensions, and activity rate were recorded. For point sources,
the company name and source types were noted. This information
was usually recorded on an auxiliary data sheet.
The general condition of paved streets within the area was
described on the 1/4-mile sketch by indicating whether streets
were curbed or uncurbed. Surface conditions and dirt loadings
were described as clean or dirty.
The above steps were repeated with little modification for
the rest of the 1-mile radius survey area. In many cases, it was
possible to inventory this area by driving across alternate
streets, in a Crosshatch pattern, covering each zone completely.
A-2
-------
In this way, all intersections and sources were observed from a
maximum one-block distance. All sources noted were directly
examined. Locations of point and area sources were noted on the
USGS map; descriptions of the sources were recorded on the
data sheets.
Station Type
The purpose of this category is to describe the area
surrounding the sampler. This is accomplished by use of the
following descriptors:
1. Type of area - urban, suburban, center-city, rural.
2. Land use - commercial, residential, etc.
3. Topography - elevated receptor or no special terrain.
Measuring Distances of Sources
For purposes of determining the distance from a source to
the sampler, the following rules were observed:
Within 200 ft of the - determine distance to with-
monitor in 5 percent during visit
to the site
From 200 ft to 1/4 mile - scale off the aerial
photo map
From 1/4 to 1 mile - scale off either the
aerial photo map or
the USGS map
Distances to sources within 200 ft of the monitor were
recorded during the survey. Distances beyond 200 ft were scaled
from a map and recorded on the data sheet at a later date,
after completion of the field work.
Defining Sources
So that all inventories were done with the same degree of
detail, it was necessary to establish exactly what sources were
to be included. If a stack emission appeared to involve an
industrial process not included in the point source listing, the
source was noted if the facility appeared to be operating at the
time of the survey.
A-3
-------
Defining what constitutes a fugitive dust source was
somewhat more difficult; however, sources of fugitive dust would
include at least the following activities:
Excavating
Grading
Detonation of explosives
Earth moving
Construction
Demolition
Site preparation
Sand blasting
Mining
Quarrying
Processing of sand, gravel,
and rock
Crushing
Screening
Drying
Athletic fields
Handling and transporting of
materials
Barren storage areas or
storage piles
Vehicle movement (reentrain-
ment)
Unpaved roads and alleys
Unpaved parking lots
Agricultural tilling
Wind erosion of exposed
surfaces
Feedlots
Campgrounds
Fairgrounds
Playgrounds
Unpaved driveways or unpaved road shoulders were not in-
cluded unless they were common to all the blocks in a particular
segment. The presence of these sources often resulted in in-
creased street dirt loadings, however, and those streets were
noted to be dirty.
A significant factor in determining which temporary sources
to consider is the time period for which the source is expected
to be active. For example, a construction site should be in-
cluded as a fugitive dust source only if the air quality data are
for the period of time the site is active.
A checklist for the procedures discussed above is presented
in Table A-l. This list should facilitate both the planning and
the execution of the field surveys so that consistent data collec-
tion is achieved for succeeding microinventories.
A-4
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TABLE A-l. CHECKLIST OF STEPS IN MICROINVENTORY
Preparatory work
1. Identify sampling sites to be inventoried: record SAROAD code and
street address
2. Obtain U.S. Geological Survey maps of the area (from either USGS or
a local engineering supply store) or other suitable map
3. Obtain traffic data (from local COG, or city or state transportation
agency)
4. Obtain recent air quality data (local or state air pollution control
agency)
5. Obtain gridded inventory of area source emissions (local agency) if
available
6. Obtain point source inventory including annual particulate emissions
and UTM coordinates (local agency)
7. Obtain aerial photographs (from local COG, city engineering
department, or federal agency)
8. Prepare maps for field survey
Field work
1. Determine distances to localized sources within 200 feet and sampler
height
2. Note possible interferences or biases
3. Take photographs of site and surrounding area
4. Describe land use and topography around sampler
5. Survey 1/4-mile area: locate all sources on 1/4-mile sketch,
describe on data sheet
6. Note condition of streets
7. Survey remainder of one-mile radius areas: locate sources on map,
describe on same data sheet as above
Calculations
1. Allocate area source emissions (from agency inventory) to one-mile
radius area and to subareas
2. Calculate emissions for discrete fugitive dust sources; assign to
appropriate sectors
3. Calculate vehicle-related emissions by sector
4. Prepare table of point sources in order of increasing distance from
site
5. Compare listing with point sources identified in field; add to list
any source missed
A-5
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APPENDIX B
DEVELOPMENT OF EMISSION FACTORS
Reentrained Dust from Paved Streets
The equation used to calculate emissions from paved road is
(EPA 1983) :
where E = emission factor, Ib/VMT
K = particle size multiplier, TSP = 0.86, PM. _ = 0.51
I = industrial augmentation factor, 1=1
m = number of traffic lanes, m = 4
S = surface material silt content, S = 10%
L = surface dust loading, L = 160 Ib/mile
W = average vehicle weight, W = 3 tons
Using the above equation and the independent variable values
indicated, the calculated emission factors were 0.012 Ib/VMT for
TSP and 0.0073 for PM.. . . The testing on which the above equation
was based did not differentiate between reentrained dust and the
exhaust tire-wear emission component. Consequently, the
calculated emissions from paved streets are assumed to include
these components.
The emission factors for this source category reflect
average dry day conditions. During periods of rainfall or snow
cover, reentrainnent of dust should be negligible. After the
rain or snow has ended, however, the emission may be temporarily
increased as a result of trackout of material from unpaved areas
adjacent to the streets or from the application of sand or salt
for snow control. As this deposited material dries, it may
become entrained by passing vehicles. Therefore, the 0.012
B-l
-------
Ib/VMT and 0.0073 Ib/VMT values represent reasonably good
averages.
UNPAVED ROADS AND PARKING LOTS
Many different groups have investigated emissions from
unpaved roads. All the studies show emission rates usually in
the range of 1 to 20 Ib/VMT, and show that the rate is highly
dependent on vehicle speed. After reviewing these studies, EPA
selected the following emission factor for publication in AP-42.
This factor is appropriate for vehicle speeds between 13 and 40
mph (EPA 1983) .
E - K (5 o)
E - K (5..)
where E = emission factor, Ib/VMT
K = particle size multiplier, TSP = 0.80, PK1Q = 0.45
s - silt content of road surface material, s = 12%
S - mean vehicle speed, roads = 25 mi/h, parking lots =
10 mi/h, industrial roads - 35 mi/h
W = mean vehicle weight, road and parking lots = 3 tons,
industrial = 35 tons
w = mean number of wheels, residential = 4, industrial =
6
P = mean number of days with at least O.C1 inch of preci-
pitation per year
The percent silt on gravel road surfaces is about 12 percent
based on data presented in the EPA publication, Development of
Emission Factors for Fugitive Dust Sources (EPA 1974) . No
aggregate material is applied to the road bed of graded and
drained road surfaces. It is composed of compacted native soil.
The fine material originally on the surface is probably rapidly
removed by turbulence from passing vehicles or by wind and water
erosion forces. The remaining stable surface is composed of
sand- and pebble-sized particles; dust is generated primarily by
the continuing mechanical breakdown of these particles as a
result of traffic. It is assumed that the percent of silt-sized
particles on a seasoned dirt road surface is approximately the
same as that for gravel, or 12 percent. The mean number of days
B-2
-------
with 0.01 in. or more of rainfall for the sites surveyed ranged
from 16 to 89.
Based on other information on the relationship between
q
emissions and vehicle speed, the :rr term in the equation was
s ^
modified to -=-JT for the unpaved parking lots. Further, it was
assumed that 40 cars would be on each acre of parking (GCA 1978)
and that the average distance traveled by a given vehicle would
be the length of the longest side of the lot (h the distance into
the lot, ^ the distance exiting the lot).
For unpaved industrial roads it was assumed that a
combination of controls would yield a 50% control efficiency.
The range of calculated values for the three sources, then, were:
Emission factor, Ib/VMT
Source TSP PM10
Unpaved industrial roads 14.2-18.0 8.0-10.1
Other unpaved roads 3.0-3.8 1.7-2.1
Unpaved parking lots 0.4-0.5 0.22-0.28
AGRICULTURE
Total emissions from agriculture are calculated from the sum
of tilling and wind erosion emissions. The emission factor for
tilling from AP-42 (EPA 1983) is as follows:
E = K(538) (S)°*6 (Eq. 3)
where E = emission factor, Ib/ac
K = particle size multiplier, TSP = 0.33, PM1Q = 0.21
S = surface silt content of surface soil, s = 15 to 30
percent
Using this equation and the appropriate variable values,
calculated emissions range from 901 to 1366 Ib/acre for TSP and
574 to 870 for PM1Q.
For wind erosion, an adaptation of the U.S. Department of
Agriculture's wind erosion was used. The modified equation
estimates annual suspended perticulate emissions due to wind
erosion from exposed soil surfaces (EPA 1974) :
B-3
-------
E = a I K C L1 V (Eq. 4)
where E = emission factor, tons/acre per year
a = portion of total wind erosion losses that would be
measured as suspended particulate, estimated to be
0.025
I = soil credibility, tons/acre per year
K = surface roughness factor
C = climatic factor
L1 = unsheltered field width factor
V = vegetative cover factor
In this equation, K, C, L', and V are all dimensionless. All of
the variables in the equation except "C" and "a" are dependent on
the crop type grown on each field. The crop types were
determined by visual observation during the microinventory. The
"C" factors were calculated from the following equation:
C = 0.345 (£|)2 (Eq. 5)
where C = climatic factor
W = mean annual wind velocity in mi/h at 30 feet
PE = Thcrnthwaite's precipitation evaporation index
Calculated C values ranged from 0.05 to 4.55 for the sites
inventoried. The final calculated emission factor ranges for the
five crop types identified are as follows:
Crop Emission factor, tens/acre
Corn 0.005 to 2.01
Cotton 0.015 to 1.80
Beets 0.020 to 2.57
Wheat 0
Alfalfa 0
A PMin/TSP ratio of 0.5 was used to calculate PM10 emissions
from the TSP emissions. This ratio represents the median Pr-i1Q/TSP
ratio for fugitive dust sources for which AP-42 includes particle
size multipliers. Because of the approximate nature of this
factor, it was rounded to one significant digit.
RAILROAD YARDS
Most of the emissions in train yards appear to be from wind
erosion rather than from train movement. Therefore, the wind
B-4
-------
erosion equation described earlier was used as the basis for
deriving emission factors for railroad yards. An average
erodibility (I) of 86 tons/acre per year was assumed to be
applicable for the soil in the railroad yards (EPA 1974). The
area around railroad yards generally has a fairly smooth surface,
which is equivalent to a K value of 1.0. The climatic factor for
the Iowa sites ranged from 0.07 to 0.18. An average width of
1000 ft was assumed for railroad yards, since this is not a
critical variable in the equation. The L1 value equivalent to L
= 1000 ft was determined from a nomograph in which soil type is
also a variable 0.84. Because there is no vegetation in rail
yards, V = 0 and V = 1.0. Also, these areas are generally
treated with oil or chemicals to reduce dust and weed problems.
Therefore, the emissions have been estimated based on the above
values and the following assumptions:
0 No vegetation
0 Half the area is stabilized, with an emission rate of
20 percent of the unstabilized area
0 Train traffic increases the wind erosion emissions by
20 percent
Therefore, the resulting emission factor is:
E= (0.025) (86) (1.0) (C) (0.84) (1.0) (X +2°'2) (1.2) (Eq. 6)
= 0.07 to 5.12 tons/acre-year
For PM,Q the 0.5 factor for the PM..-/TSP ratio described above
was applied to the calculated results.
CLEARED OR EXPOSED AREAS
The modified wind erosion equation described earlier was
used for estimating emissions from cleared or exposed areas. An
average erodibility (I) of 47 or 86 tons/acre per year was
assumed for the soil types common to these areas (EPA 1974). The
cleared areas are generally left with a fairly smooth surface,
which is equivalent to a K value of 1.0. The climatic factor for
B-5
-------
the study areas ranged from 0.05 to 4.55. An average field width
of 1000 ft was assumed for all the cleared areas, since this is
not a critical variable in the equation. The L1 value equivalent
to L = 1000 ft was determined from a nomograph in which soil type
is also a variable. Because there is no vegetation on cleared
areas, V = 0 and V = 1.0.
Based on these input data, the average emission rate from
cleared areas is between 0.09 and 8.22 tons/acre-year. For PM10
the 0.5 factor was multiplied by the calculated TSP emission to
obtain estimated PM.,- emissions.
CONSTRUCTION
The emission factor presented in AP-42 (EPA 1983) of 1.2
tons/acre per month is corrected for the climatic conditions at
each site by use of the equation:
E = (PE/50)2 (Eq* 7)
where PE = varies from 6 to 39 at sites
E = 1.78-83.3 tons/acre per month
These values are appropriate for residential, commercial,
and highway construction with excavation and regrading. Con-
struction projects are assumed to be fugitive dust sources for 3
months a year if they are residential and 6 months a year if
commercial, industrial, or highway. The PM.. emissions were
obtained by multiplying the calculated TSP emissions by 0.5.
UNPAVED STORAGE AREAS
No emission factor was found for this source category. By
comparison with other source categories, it was estimated that
annual emissions per acre for this source were less than either
cleared areas or railroad yards. Consequently, it was estimated
that emissions from storage areas would be 25 percent of those
for a cleared area. The PK../TSP factor of 0.5 was applied to
the calculated TS? emissions to obtain the PM-,,- emissions.
B-6
-------
REFERENCES FOR APPENDIX B
Environmental Protection Agency. 1974. Development of Emission
Factors for Fugitive Dust Sources. U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina. Publication No.
EPA-450/3-74-037.
Environmental Protection Agency. 1983. Compilation of Air
Pollutant Emission Factors. 3d ed. (including supplements 1-14).
AP-42.
GCA Corporation. 1978. Inventory Development for Evaluation of
Measures for the Control of Nontraditional Sources of
Particulates in Southern New England. (Draft final report). GCA
Corporation, GCA/Technology Division, Bedford, Massachusetts.
B-7
-------
APPENDIX C
MICROINVENTORY RESULTS
C-l
-------
DESCRIPTION OF SITE (Number 1)
SAROAD code - 0304400C6
Location - Maricopa CountyCarefree Airport
UTM coordinates - 416.96 3742.15
Monitor height - monitor no longer active
Topography - desert landscape
Localized sources, within 200 ft of monitor -
Source
Unpaved area
Distance
Adjacent to monitor site
Description
Little or no
traffic
Air quality data -
Monitor Year
Annual arithmetic mean, yg/m3
TSP IP PM,,
No. of samples
TSP IP PM
AC7
A07
A07
1980
1981
1982
30.0
43.0
46.0
0
25.7
21.2
iu
0
0
0
21
3C
43
0
37
49
0
0
0
10
C- 2
-------
One mile radius around Maricopa County, Carefree Airport. (Site 1)
C-3
-------
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DESCRIPTION OF SITE (Number 2)
SAROAD code - 030300001
Location - Hayden Jail, on Canyon Drive one block west of Utah Avenue, Hayden,
Arizona
UTM coordinates - 520.0 3651.7
Monitor height - 18 ft., on roof of building
Topography - complex terrain
Localized sources, within 200 ft of monitor -
Source
Canyon Drive
Distance
24 ft.
Description
Paved 2000 ADT
Air quality data -
Monitor Year
Annual arithmetic mean, yg/m3
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No. of samples
TSP IP PM,
F02
F02
1982
1983
172.3
119.2
0
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51
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DESCRIPTION OF SITE (Number 3)
SAROAD code - 030600002
Location - 1845 Roosevelt St., Phoenix Arizona
UTM coordinates - 403.2 3702.4
Monitor height - 30 feet
Topography - no special terrain
Localized sources, within 2CC ft of monitor -
Source
I9th Street
Garfield Street
Roosevelt Street
Paved Parking
L istance
75 feet
93 feet
157 feet
24-33 feet
Description
Paved, 2000 ADT
Paved, 8000 ADT
Paved, 12200 ADT
N and S of monitor
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Monitor Year
Annual arithmetic mean, yg/m3
TSP IP PM,,
No. of samples
TSP IP PM
A07
A07
A07
G01
G01
1980
1981
1982
1982
1983
121
108
101.1
95
106
0
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54
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43
53
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DESCRIPTION OF SITE (Number 4)
SAROAD code - 030600013
Location - 4732 S. Central Avenue, Phoenix, Arizona
UTM coordinates - 400.2 3696.5
Monitor height - 30 feet
Topography - no special terrain
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Central Avenue
Tamarisk Avenue
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Distance
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175 feet
70 feet
Description
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Monitor Year
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1983
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DESCRIPTION OF SITE (Number 5)
SAROAD cede - 030680001
Location - County Jail, 521 10th Avenue, Safford, AZ
UTM coordinates - 618.8 3633.5
Monitor height - 12 ft., on roof of building
Topography - No special terrain
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source
10th Street
unnamed road
Pi stance
105 feet
20 feet
Description
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Unpaved, 100 ADT
Air quality data -
Monitor Year
F01 1982
FC1 1983
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TSP IP PM10
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110
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TSP IP PM
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59
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DESCRIPTION OF SITE (Number 6)
SAROAD code - 030960002
Location - 201 South 2nd Avenue, Yuma, AZ
UTM coordinates - 722.9 3622.9
Monitor height - 20 feet
Topography - no special terrain
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F01
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1983
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C-27
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C-31
-------
DESCRIPTION OF SITE (Number 7)
SAROAD code - 05052CG03
Location - 225 Chester Avenue
UTM coordinates - 316.6 3914.4
Monitor height - 35 feet
Topography - no special terrain
Localized sources, within 20C ft of monitor -
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Chester Avenue
2nd Street
Distance
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175 feet
Description
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Air quality data -
Monitor Year
Annual arithmetic mean, yg/m3
TSP IP PM,,
No. of samples
TSP IP PM
F01
F01
A07
A07
A07
1982
1983
1980
1981
1982
125.9
103.2
221.5
118.2
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C-32
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DESCRIPTION OF SITE (Number 8)
SAROAD code - 050840003
Location - 401 Main Street, Brawley, CA
UTM coordinates - 637.1 3649.6
Monitor height - 26 feet
Topography - no special terrain
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Main Street
Fourth Street
Distance
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147 feet
Description
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101
101
1982
1983
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DESCRIPTION OF SITE (Number 9)
SAROAD code - 051295001
Location - Tower Road, N. of China Lake, CA
UTM coordinates - 442.2 3951.8
Monitor heiaht - 6 feet
Topography - dry lake bed
Local-'zed sources, within 200 ft of monitor -
Source Distance Description
Tower Road 120 feet Paved, 100 ADT
Exposed ground Completely sur- Dry lake bed;
rounding monitor no vegetation
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Monitor Year TSP IP PM.n TSP IP PM
101
101
1982
1983
25
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DESCRIPTION CF SITE (Number 10)
SAROAD code - 052240001
Location - 935 Broadway, El Centro, CA
UTM coordinates - 634.6 3629.1
Monitor height - 25 feet
Fopography - no special terrain
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Source
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9th Street
Main Street
Distance
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195 feet
200 feet
Description
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Paved, 1000 ADT
Paved, 9140 ADT
Air quality data -
Monitor Year
101 1982
101 1983
Annual arithmetic mean, yg/m3
TSP IP PMIO
122.6
88.0
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TSP IP PM
10
42
19
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C-47
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DESCRIPTION OF SITE (Number 11)
SAROAD code - 05268C001
Location - 14838 Foothill Blvd., Fontana, CA
UTM coordinates - 455.8 3773.1
Monitor height - monitor no longer in place
Topography - no special terrain
Localized sources, within 200 ft of monitor -
Source
Foothill Blvd.
Unnamed road
Unnamed road
Distance
117 feet
72 feet
72 feet
Description
Paved, 13COO ADT
Unpaved
Unpaved
Air qua!ity data -
Monitor Year
Annual arithmetic mean, yg/m3
TSP IP PM,,
No. of samples
TSP IP PM
101
101
1980
1981
129.3
142.9
0
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51
56
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DESCRIPTION OF SITE (Number 12]
SAROAD code - 055330231
Location - 3021 Manor Street, Oil Dale, CA
UTM coordinates - 408.1 3921.6
Monitor height - monitor has been removed
Topography - no special terrain
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Source
Manor Street
Al 1 ey
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125 feet
20 feet
Description
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Pcved, 25 ADT
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Monitor Year
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No. of samples
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F01
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1983
107
108
0
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49
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DESCRIPTION OF SITE (Number 13)
SAROAD code - 056535001
Location - 5888 Mission Blvd., Rubidoux, CA
UTM coordinates - 461.8 3762.2
Monitor height - 7 feet
Topography - no special terrain
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Source
Parking lot
Distance
Adjacent
Description
Paved
Air qua!ity data -
Monitor Year
Annual arithmetic mean, yg/m3
TSP IP PM,,
No. of samples
TSP IP PM
101
101
A07
A07
A07
1982
1983
1980
1981
1982
117.8
146.9
160.8
126.7
130.4
0
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28
38
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C-65
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C-66
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DESCRIPTION OF SITE (Number 14)
SARCAD code - 320090001
Location - Cobre Consolidated School Supply, near corner of North East and
Mayo Streets, Bayard, NM
UTM coordinates - 768.8 3628.1
Monitor height - 12 ft., on instrument platform
fopography - no special terrain
Localized sources, within 200 ft cf monitor -
Source
Nortn East Street
Mayo Street
Distance
106 feet
28 feet
Description
Feved, 100 ADT
Paved, 1000 ADT
Air quality data -
Monitor Year
Annual arithmetic mean, yg/m3
TSP IP PM,,
No. of samples
fSP IP PM
F01
F01
A07
A07
A07
1982
1983
1980
1981
1982
132.2
115.8
142.0
122.0
110.0
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48
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37
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C-72
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C-76
-------
DESCRIPTION OF SITE (Number 15)
SAROAD code - 320240001
Location - Anthony Elementary School, Anthony, NM
UTM coordinates - 348.7 3541.8
Monitor height - 15 ft., on roof of building
Topography - no special terrain
Localized sources, within 200 ft of monitor -
Source
Incinerator
Playground
Distance
90 feet
Adjacent
Description
Small garbage
incinerator
Unpaved
Air quality data -
Monitor Year
F02 1982
F02 1983^
Annual arithmetic mean, yg/m3
TSP IP P_MIO
130.8
115.6
0
0
0
0
No. of samples
TSP IP PM
10
57
60
0
0
0
0
C-77
-------
One mile radius around Anthony School, Dona Ana County. (Site 15)
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DESCRIPTION OF SITE (Number
SAROAD code - 320440001
Location - Grant County Water Dept. Bldg., near corner of A and First Streets,
Hurley, NM
UTM coordinates - 769.5 3621.4
Monitor height - 12 ft., on roof of building
Topography - no special terrain
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Distance Descriptor
Air qua!ity data -
Unnamed street 24 feet Unpaved
Annual arithmetic mean, ug/m3 No. of samples
Monitor Year TSP IP P_MIO TSP IP PM1(,
F02 1982 111.5 0 0 57 0 0
F02 1983 98.1 0 C 59 0 0
C-82
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One mile radius around Grant County, Hurley. (Site 16)
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DESCRIPTION OF SITE (Number 17)
SAROAD code - 451700002
Location - Tillman Health Center, 222 South Campbell Street, El Paso, TX
UTK coordinates - 359.8 3514.4
Monitor height - 35 ft., OR roof of building
Topography - no special terrain
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Source
Distance
Description
Florence Street
First Avenue
Campbell Street
Parking lot
Air qual ity date -
Monitor Year
F01 1982
F01 1983
A07 1980
A07 1981
A07 1982
Annual
TSP
99.2
95.5
125.3
115.7
164.9
144 feet
84 feet
120 feet
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Paved, 9710 ADT
Paved, adjacent
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south sides of
building
No. of samples
TSP IP PM
52 0 0
51 0 0
43 27 0
20 52 0
450
10
C-87
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DESCRIPTION OF SITE (Number 18)
SARCAD code - 451700031
Location - 2616 W. Paisano, El Paso, TX
UTM coordinates - 355.3 3517.3
Monitor heiaht - 10 feet
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source Distance Description
Air auality data -
Unpaved Road Adjacent Adjacent to east
side of monitor
Annual arithmetic mean, ug/m3 No. of samples
Monitor Year TSP IP PM1Q TSP IP PM10
602 1982 169.9 0 0 56 0 0
G02 1983 139.2 0 0 57 0 0
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DESCRIPTION OF SITE (Number 19)
SAROAD code - 451710004
Location - Clint High School, Highway 20, Clint, TX
UTM coordinates - 383.4 3495.3
Monitor height - monitor no longer located at site
Topography - no special terrain
Localized sources5 within 200 ft of monitor -
Source
Highway 20
Distance
Unknown
Paved, 3100 ADT
Air quality data -
Monitor Year
Annual arithmetic mean, yg/m3
TSP IP PM,,
No. of samples
rsp IP PM
A07
A07
A07
1980
1981
1982
91.4
67.0
89.0
57.0
46.1
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DESCRIPTION OF SITE (Number 20)
SAROAD code - 060520001
Location - 2935 South, 8560 West, Kagna, Utah
UTM coordinates - 407.6 4506.6
Monitor height - 25 feet
Topography - no special terrain
Local'zee sources, within 20C ft of monitor -
raved pc'-k"inc 20 feet North of site
Ai r Qua!i ty data -
Annual arithmetic mean, vg/m3 No. of samples
Monitor Year TSP IP PMin TSP IP PM
F02
F02
£07
A07
407
1982
1983
1980
1981
1982
52.2
55. 1
87.0
69.0
53.6
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C-106
-------
APPENDIX D
STEPWISE MULTIPLE LINEAR REGRESSION
Multiple linear regression (MLR) is a statistical technique
for estimating expected values of a dependent variable, in this
case particulate emission rates, in terms of corresponding values
of two or more other (independent) variables. This technique
uses the method of least squares to determine a linear prediction
equation from a set of simultaneously obtained data points for
all the variables. The equation is of the form:
Concentration = B-x.. + B»x0 +...+ B x + constant
11 22 n n
where x1 to >: = concurrent quantitative values for each
of the independent variables
B, to B = corresponding coefficients
The coefficients are estimates of the rate of change in
emission rates produced by each variable. They can be determined
easily by use of an MLR computer program or with a programmed
calculator. Other outputs of the MLR program are:
1. A correlation matrix. It gives the simple correlation
coefficients of all of the variables (dependent and
independent) with one another. It is useful for identifying
two interdependent (highly correlatedeither positive or
negative) variables (two variables that produce the same
effect on emission rates), one of which should be eliminated
from the analysis.
2. The multiple correlation coefficient (after addition of each
independent variable to the equation). The square of the
multiple correlation coefficient is the fraction of total
variance in emission rates that is accounted for by the
variables in the equation at that point.
3. Residual coefficient of variability. This is the standard
deviation cf the emission rates predicted by the equation
(with the sample data set) divided by the mean of the
D-l
-------
predicted emission rates, expressed as a percent. If a
variable eliminates some sample variance, it will reduce the
standard deviation and hence the relative coefficient of
variability.
4. Significance of regression as a whole. This value is calcu-
lated from an F test by comparing the variance accounted for
by the regression equation with the residual variance. A
0.05 significance level is a 1 in 20 chance of the correla-
tion being due to random occurrence.
5. Significance of each variable. This is a measure of whether
the coefficient (B) is different from 0, or that the rela-
tionship with the dependent variable is due to random occur-
rence. Variables that do not meet a prespecified signif-
icance level may be eliminated from the equation.
6. Constant in the equation.
The multiple correlation coefficient, unlike the simple
correlation coefficient, is always positive and varies from 0 to
1.0. A value of zero indicates no correlation and 1.0 means that
all sample points lie precisely on the regression plane. Because
cf rand err. fluctuations in -he data and inability to identify ail
the factors affecting concentrations, the multiple coefficient is
never 1.0, even when concentrations trac.-c known variables VPI"
closely. Therefore, it is important to test for statistical
significance.
The form of MLR in the program used in this study was step-
v.-ise MLR. Variables were added to the equation in order of
greatest increase in the multiple correlation coefficient, and
the concentrations were then adjusted for that variable and
regressed against the remaining variables again. The procedure
can be ended by specifying a maximum number cf variables or a
minimum F value in the significance test.
A statistically significant regression relationship between.
independent variables and concentration is no indication that the
independent variables cause the observed changes in concentra-
tion, as both may be caused by a neglected third variable.
D-:
-------
APPENDIX E
DESCRIPTION OF EMPIRICAL PARTICULATE MODEL
The empirical particulate model represents an approach to
quantifying the relationship between particulate emissions and
ambient concentrations. It was developed primarily by EPA, and
is based on the correlation previously observed between measured
particulate concentrations and emission density within a 1-mile
radius surrounding the sampler. The technique for inventorying
particulate sources within this area has evolved to its present
state over a period of about 6 years. The technique, commonly
referred to as a microinventory, is described in detail ir.
Appendix A.
FORM OF EMPIRICAL MODEL
The form of the model is a multiple linear regression
equation with annual geometric mean particulate concentration as
the dependent variable and seven independent variable terms. The
independent variables are annual area source emissions within 1
mile, annual point source emissions within 5 miles, local sources
(particularly traffic-related), presence of dirt on the nearest
street, and three city-specific adjustments to the background
concentration. These variables were determined by screening
approximately 30 potential variables by simple linear regression
and selecting the independent ones with the highest partial
correlations. Stepwise multiple regression was then employed to
derive the coefficients for each of the terms in the equation and
to obtain the statistical values used to judge the accuracy of
the regression equation.
The data base for developing the model was generated at "?9
sites in four ci-ies. Each site had measured concentrations,
E-l
-------
rnicroinventory results, and other information such as surrounding
land use and distances to the sources. The multiple correlation
(R) with the seven variables was 0.876, based on the use of all
79 data sets; none was eliminated as outliers. Therefore, the
seven variables explained 0.767 of the variance (R2) in measured
concentrations.
The resulting general equation is as follows:
AVGAQ = 0.00451 (AREA) + 0 . 000 96 (POINT) --
50.5 (LOCAL) + 18.6(VISDT) +
(DPT, DSC, D3, or 0) + 57.2
where AVGAQ = Predicted annual geometric mean, mo/m3
Al } (_25\ . (A2-5\ + (A6-9
G.0324/ VEGT/ ' vo.ie/ T \o.6084
A. = Total area source emissions in sector i, ton/yr
1 (i = 1 to 9)
HGT = Height of sampler, ft
PS EH.
POINT --= £ ^- (WWF)
-: _" "> ^
n = Number of ocir.t sources within 5 rri
FSEM.. = Emissions from point source i, ton/yr
D7 = Distance to point source i, mi (lower limit of
- n : JT n 9 r \
LJ _. v . £ ~ \
vrv.'F = Wind weigrtina factcr computed 2s T,'"ird rfrecuencw
(*) in quadrant where source is iccatef divic^c
by 25, dimensionless. (The frequency of winds
was determined from._NV,"S data taken at the nearest
station or a compilation of the two nearest
stations' data.)
In ADT. In ADT0
LOCAL = *
VHGT2+DIS12 VHGT2
ADT. = Average daily traffic on^nearby road i, vein/day
DIS1 = Distance to road i, ft (upcer limit of DIS. is
1 200) * x
VISDT = 0 if no visible plume results from passing
traffic, 1 if there is a plume
DPT, DSL, DB = Dummy variables to account for different back-
ground concentrations in each of the cities
relative to Kansas City:
DPT = -21.6 (Portland), DSL = -6.6 (St. Louis),
DB = -6.9 (Birmingham)
Corresponding multiple regression equations with different
coefficients and only four variables can be developed by using
E-2
-------
the smaller data set from any one city. For example, the equa-
tion derived with just the 32 Kansas City sites would be:
AVGKC = 0.0054(AREA) + 0.00057(POINT) +
55.2(LOCAL) + 10.6(VISDT) + 57.6
The equation using the 23 St. Louis sites would be:
AVGSL = 0.0019 (AREA) -I- 0 . 0020 (POINT) +
45.9(LOCAL) + 25.0(VISDT) + 56.0
EVALUATION OF MODEL
Three independent evaluations of the model were performed:
one from the statistics provided by the stepwise multiple regres-
sion program, the second by testing stability of the coefficient,
and the third by testing the model's predictions with data from
32 additional sites in three other cities.
The summary statistics from the stepwise multiple regression
analysis with 79 data sets are presented in Table E-l. Prior to
the regression analysis, a correlation matrix was prepared to
determine whether intercorrelations between any of the variables
could bias the results. The variables were all found to be
independent. The F rest was used to determine whether each of
the individual variables was significant. The critical F value
of 2.87 at the 99 percent level for seven variables and 79 obser-
vations, compared with the F values in Table E-l, indicated that
all the variables except the city-specific adjustments to back-
ground were highly significant.
The standard errors for the variable coefficients were
(again, with the exception of the city-specific variables) only
about 20 percent of the coefficient values. The standard error
of the regression equation was 13.6 pg/m3, about 17 percent of
the average measured concentration. The relative errors for all
but 3 of the 79 predicted concentrations were less than 28 per-
cent. It should be emphasized again that none of the data sets
from the four cities was removed before performing the regression
analysis, although some were suspected to contain anomalies.
E-3
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The stability or sensitivity of coefficients in the equation
was tested by running two additional regression analyses, each
with 75 percent of the sites (selected at random) from the
initial analysis. The AREA and POINT coefficients were found to
be very stable, the LOCAL coefficient fairly stable, and the
VISDT coefficient fairly sensitive to the specific sites included
and excluded. It was determined that a few influential sites
could affect the VISDT coefficient by as much as 15 percent.
Comparison of model-predicted and measured concentrations at
28 additional sites in three cities showed that, with
consideration of city-specific background concentrations, the
predicted values were quite good:
City
Philadelphia
Pittsburgh
Boston-
V.'orchester
If linear regression for a particular area yields a good
correlation but the model consistently over- or ur.derpredicts
concentrations, the accuracy of the predictions can be improved
by calculating the variable (e.g., DPT, DSL, DB) for the area
being analyzed.
This variable is simply the average residual for the
samplers in the study area. The average residual is readily
computed with the equation:
n MC.-PC.
R = ^ X 1
No. of
sites
10
7
11
r
0.95
0.94
0.93
2
0.90
0.88
0.86
Slope
1.16
1.21
1.03
Intercept
-12.7
-19.9
-6.5
1 1 n
where R = average residual
MC. = measured concentration at site i, ug/m3
PC. = predicted concentration at site i, yg/m3
n = number of samplers
The average residual is, effectively, a correction to the
57.2 constant tern in the general form of the empirical model.
E-5
-------
APPLICATION OF MODEL
The empirical model is or.ly moderately data-intensive. It
can be used for participate air quality analyses ranging from
neighborhood to regional scales. The primary purpose of the
model is to estimate the relative impact of different sources and
source categories on ambient concentrations. It cannot properly
be used to predict concentrations at specified locations in areas
other than those for which it is specifically developed, because
of its means of derivator, ar.d the magr.itude of the standard
error associated with the regression equation. It is very useful
in interpreting microinve::tory data by estimating the relative
impacts of various sources and source categories at specific
sites in other areas.
At each site, the measured concentration provides a check on
the accuracy of the model's prediction. The only limitation to
self-validatin feature is that several sites in a city must
t.~e curccse cr this stucv tne sac,-;arc inc. .~ve_s were estimatec
E-6
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TECHNICAL REPORT DATA
(Please read Instrjcrions on the reverse before completing)
1. REPORT NO.
E^A-450/4-85-008
3. RECIPIENT'S ACCESSION NO.
ugi'ti've Dust in the SouthwestAmbient Impact,
5. .HE.P CRT-DATE
July 1985
Sources and Remedies
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
PEI Associates, Inc.
S. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
PEI Associates, Inc.
14060 Denver West Parkway
Golden, Colorado 80401
TO. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-02-3512
12. SPONSORING AGENCY NAME AND ADDRESS
U. S. EPA
Office of Air Quality Planning and Standards, MDAD, AMTB
(MD-14)
Research Triangle Park, N. C. 27711
13. TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
EPA Project Officer: Thompson G. Pace, III
U.S.
16. ABSTRACT
A study of possible PMin non-attainment sites was conducted by Pace and Hanks of the
EPA in 1984. This investigation showed that about half of these sites are in arecs
having less than 20 inches of precipitation per year. Also, 10 percent of the sites were
in dry rural areas with no apparent' traditional sources nearby. The purpose of this pro-
ject is 1) determine the nature of the sources having an impact on these sites, 2) deter-
mine to what extent meteorology plays 3 role in high measured particulate matter concen-
trations, and 3) develop possible remedies to a potential non-attainment situation in
areas with little rainfall.
This report presents the results of three analyses that were designed to answer
these questions. A rnicroi inventory was performed on 20 sites. These sites were then
compared to sites in the US EPA's IP Network and similarities and differences were
analyzed. An evaluation of the relationship between meteorological variables and
measured concentrations was made. The report discusses the results of thes. analyses
and alternate control remedies to high concentrations in fugitive dust areas.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COSAT1 Field/Group
PM
TSP
n
Fugitive Dust
is. DISTRIBUTION STATEMENT
19. SECURITY CLASS (This Report I
PAGES
20. SECURITY CLASS (Thispage)
22. PRICE
EPA Form 2220-1 (Re«r. 4-77) PREVIOUS EDITION IS OBSOLETE
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