EPA-460/3-81-016
Statistical Analysis of the Effect of
Inspection and Maintenance on Carbon Monoxide
Air Quality in Portland, Oregon
By
G.C. Xiao, J. Ledolter and G. Hudak
May 15, 1981
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Final Report
on
Statistical Analysis of the Effect of Inspection
and Maintenance on Carbon Monoxide
Air Quality in Portland, Oregon
by
G.C. Tiao
J. Ledolter
G. Hudak
EPA Contract 68-03-2931
May 15, 1981
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hPA Sur -miry and .interpretat i i o Con rac t i 68032931
STATISTICAL ANAlYSIS CF J F!E FFFECT OF
INSPECTION AND MAINTErANCE ON CARBON
NONOXIDE AIR QUALITY IN PORTLAND, OI ECON
This study, conducted for EPA by Dr. George Tiao of the University of
Wisconsi.n and his associates Johannes Ledolter and Gregory Iludak, compared CO
air quality trends in Portland and Eugene, Oregan. Statistical models which
accounted for local meteorological, traffic and air quality mocitoring effects
were constructed to determine if the biennial inspection and maintenance (I/N)
program in Portland, which became mandatory in July of 1975, caused a
significant improvement in CO air quality for the years 1975 through 1979.
Because Eugene did not have an I/ 1 program and because it was geographically
close to Portland, it was used as a comparison site for studying the effect of
the I/ i program.
As explained below, and in detail in the report, the study found a CO air
quality improvement attributable to the biennial I/N program in Portland. In
the y . ars when most Portland vehicles received their inspections, ambient CO
concenf:rations were 8% to 15% less than they would have been without the 1/%
.prograv. Based on thts finding of the study, EPA calculates that. the ae ent
CO ir ,;ovcpent due to a annual I/ -ogram that inspected every su!ject
vehici e eh year would have been 10% to 19% .
Three statistical approaches were used to analyze CO data for years 1970
through 1979 from four Portland monitoring sites and one Eugene site. The
first approach related daily average ambient CO levels to wind speed and
traffic counts in order to determine the true trend in CO concentrations over
time. !ind speed and traffic counts were used because these variables
resulted in the most successful fit to the CO data. The model shows an
average annual reduction in CO concentrations of 3.4% to 7.3% per year for the
Portland sites. [ 101* A comparable number for Eugene could not be found
because th CO inoa tor in Eugene was altered during 1975, making it undesir-
able to use the s; ie type of analysis as used for the Portland sites. This
first approach was a preliminary step and by itself does not tell anything
about the effect of the Portland I/N progr :n.
The second approach related monthly averages of CO concentrations to monthly
averages of traffic volume and relative humidity, CO sampling probe chances at
certain monitors, disruptions in traffic patterns, and a long term trend due
to the Federal vehicle emission standards. The effect of I/N is represented
* Numbers in brackets refer to page numbers or tables in the report.
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i c t he model by i va nab i.e which aCCO;h! .; for fliOi U i I y inspect: ion voluicc in
years 1.975 through 1979. This is done by us ng the sum of the monthly
I nspcction VOlUli 3 for the Current nd 1 )rc CeC!i ng months, except that the
volteaes for preceding months are progressively cU.scotinted to account for
enission deterioration after repair. This model shows that an average annual
rec!uction in CC) concentrations of 5% over the entire 1970 through 1979 period
can he attributed to the Federal vehicle emission standards at one Portland
site (cAMS) [ 29]. In addition, a sizable CO benefit can he attributed to the
I/ 1 prograa at that site. Because of the T/M program, the average CO
concentratl.on over the 1975 through 1979 period was 6% to 12% less than it
would have been otherwise [ 30J. The I/i! benefit was greater than this average
in evennumbered years and lower in oddnumbered years, due to the biennial
nature of the Ifli program. For example, the estimate of the I/N benefit is 8%
to 15% for both 1976 and 1978, which are the years in which inspection volumes
were highest [ Table 4.4]. None of the other Portland sites shows evidence of
a CO reduction due to I/N. However, the report notes that the CA 1S site had
more complete CO data for 1970 through 1979 than any other Port]afld site arid
therefore the results from this site can be considered the most reliable. EPA
notes that only one of the three other sites is in downtown Portland, and that
the CANS site generally has the highest CO readings of all four sites.
The analysis of the Eugene data showed no evidence of an I/N effect on CO
levels in Eugene; this was expected since Eugene does not have an I/N progran
and supports the vail dity of the n odeiing approach used to determine iikC
impact of i/U in Portland.
The third approach related averages of hourly CO observations over threehour
periods a the CANS site for the peak weekday traffic hours in sumn:er and
winter (69 a.rn. and 36 p.m. , JuneSeptember and NovemberFebruary) to
traffic and meteorological observations. As in the second model, variables
were included to account for probe changes and traffic disruptions. The 3:/U
program was again rCi)reSeflted by the discounted total of monthly inspection
volumes. The third approach demonstrated that an average reduction in CO
levels of 8% to 15% can be attributed to the Portland I/N program for years
1975 through 1979 140]. The estititate of i/N benefit for 1976 is from 11% to
20% and for 1978 is from 12% to 217., due to higher inspection volumes during
these years as e iained above. Those findings confirm the results of the
second appreach. Other Portland sites were not examined using the third
approach because the necessary traffic observations iere not avai labia.
As mentioned ri-eviously, one setback for this study was the fact that no
morei.tori.ng site had complete data for years 1970] 979, and that several sites
bad large gaps in the data. Another prohi em was that CO sampling probe
changes occurred at s-.:veral monitoring sites and a disruption in traffic
occurred at one site (CAMS). The range. of benefits listed above reflects the
inclusion or exclusion of indicator variables for probe changes and traffic
disruJ)t ions.
The 1976 a un 1978 benefit cst imates tere not 1. i s ted in the report , but wore
calculated us i ng the proce dure & sc ribe d on page 40 of the report: , as
nec oi :icn d c c i by hr. 3. Le do] ten, aTi a ssoc in te an thor of the reJ)onE:
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a d. tion, it must: b rai ivbcred that tliO cstjprit:es of j/ bijefit ii stcd in
the s L udy i -c fhc t CO air quail ty improvcm -n [ : from Porti an d s bi I f
program. Ocher I/ progr:u18 will have annual inspections and therefore t:hc
air quality bcn fit from these programs will he greater than that of the
Portland program. I ascc1 on the report t s benefit estimates for 1976 and 1978,
EPA has estimated what the air quality benefit would have been from an annual
I/M program by accounting for increased inspection volume* in an annual
program:
Tiao Estimated EPA Estimated
CO Ai.r iality Benefit For CO/dr Quality BeueEit For
Year Biennial I/M Annual
1976 8%15% 10%19%
1978 8Z15% 1o%19%
This 10% to 19% estimate of the CO air quality benefit for an annual I/N
program can he coc mpared to the benefit predicted by NOJIILE2, EPAs mobile
source emission factor model.. MOBILE2 predicts that an annual I/ 1 program,
similar to the Portland I/N program in other respects, would have produCed a
CO air quality benefit of 11,% in 1976 and 28% in 197$. These :O3Il.F:2
predictions compare favorably with the estimates based on actual CO air
quality data.
* L sLecf below in the first column are the actual Portland annual inspection
voJu : es (including initial test and retest) from Table 1.2 of the report. The
SeCond column gives an estimate of the number of Initial inspections if the
program had been annual, as provided by Bill Jasper of Oregon PF.Q. The third
Co1 .imn adjusts the second column by i:niltip!ying by 1.4, to stake the thIrd
column coniparable with the first by accounting for the 40 percent failure rate
on the initial test. -
Ac tual Number Nuirhcr of reitial 1 umher of Inspections
of Inspect:ions Inspections Assuming and Reinspections
Year and ileinspect ions an . or ial Fro ram A tmmninc an Annual Proeram
1976 589,lmO5 5183000 725,200
1978 577,022 527,000 737,600
Second approach.
MO! ILE2 input: parameters: I/N started on July 1, 1975; 40% Stringency
factor; all, model years subject to inspection ; and no mcchan Ic traini tig
y tog r a ut.
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1. Introduction and Description of Data Base
1.1 Principal objectives and sim mary of findings
This report presents a statistical analysis of carbon monoxide concen-
trations over the period 1970 to 1979 at five air monitoring sites in the
state of Oregon. Four of the sites (CAMS, Hollywood, Lloyd, and Alder) are
located in Portland where the car inspection and maintenance (I/F l) program
has been In effect since 1975, and the r naining site is located in Eugene
which is not covered by this program. The principal objectives of this study
are to assess the trend in the CO concentrations which can be associated with
the Federal enission standards and to determine whether or not the I/Fl program
in the Portland area has any effect on the concentration level.
A description of the carbon monoxide, traffic and meteorological data
used in the analysis is given in this section. Section 2 presents a prelim-
inary exploratory analysis of the data including various suimiary statistics
and graphical presentations of the main features of the variables. In
Section 3 diurnal models relating CO to traffic and meteorological factors
are constructed. Such models serve to identify the major exogenous variables
affecting CO. In Section 4, intervention time series models for monthly means
of CO readings are given. Traffic volume adjusted by relative humidity (a proxy
for mixing height) is used as an exogenous variable, and appropriate nonlinear
functional forms to model the effects of Federal emission standards and the I/M
program are constructed. Finally in Section 5, a linear regression trend analysis
of peak hour (6-9 am and 3-6 pm) CO concentrations with exogenous traffic and
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meteorological variables is given.
Our principal findings are as follows:
(1) At all Portland sites, one can observe a reduction in ambient CO
concentrations over the 1970-79 period, with the average reduction ranging
from 3.4% to 7.3% per year. The reduction in Eugene is much less, the average
being 1.9% per year.
(ii) Based on the models for monthly means of CO at the CAMS site in
Portland and the control site at Eugene, an estimated reduction in CO
enisslons (and, therefore, CO concentrations) of 5% per year can be attributed
to the Federal euisslon standards. In addition, there is evidence to Indicate
that the 1/14 prΰgrajn has had an additional beneficial effect In reducing CO
concentrations. During the period In which the I/M program was In effect,
one can attribute to the program an average annual reduction of 6 to 12
percent of what the CO ambient levels would have been without the program.
This period includes years In which few vehicles were Inspected. In 1976 and
1978, years In which most vehicles in the biennial I/N program were inspected,
the reductiofl attributable to the program is estimated to range from 8 to 15
percent.
(lii) The regression analysis of peak hour CO readings at CAMS and
Eugene indicates a higher annual reduction associated with the Federal
standards. The estimated average yearly benefit of the I/M program, ranging
from 8% tO 15%, is In good agreenent with the findings based on models for the
monthly means.
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1.2 The Oregon car inspection and maintenance program
The United States Clean Air Act Amendments of 1977 require that certain
states implement car inspection and maintenance programs (I/M programs) in cer-
tain of their major cities to reduce hydrocarbon (HC) and carbon monoxide (CO)
emissions from light duty vehicles. The Oregon Department of Environmental
Quality (DEQ) initiated in 1974 on a trial basis a motor vehicle inspection
program within the boundaries of the Metropolitan Service District which
includes the city of Portland. This I/M program became mandatory in July,
1975. By state law, vehicles registered within this area must comply with
the state emissions control standards for HC and CO and obtain a certificate
of compliance prior to registration renewal-see Rutherford and Waring (1980).
Since car registrations in Oregon are valid for a two year period
(starting with January 1974), the CO and HC emissions inspection is required
every two years. Data on the annual number of inspections, the number of
rejections and failure rates obtained from the Oregon DEQ are given in
Table 1.1 and plotted in Figure 1.1. Due to the biennial registration
system, initially more cars are inspected in even years as compared with
odd years. In 1976, for example, all cars which were licensed in 1974 for
a two year period, had to be inspected. Testing volume will gradually
stabilize as the vehicles involved in the initial testing become a smaller
proportion of the fleet. It is expected that by 1983 approximately the
same number of cars will be inspected in each year. Excluding the initial
year of operation (1975) the failure rates among all inspections have been
fairly constant between 21 and 23 percent. However, these failure rates
included initial inspection and reinspection tests. Failure rates on the
initial test were at a level of approximately 40 percent (James Rutherford,
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personal coninunication).
For more detailed Information about the Portland I/M program, monthly
inspection figures have also been obtained from the Oregon DEQ. These are
given In Table 1.2 and are plotted in Figure 1.2.
1.3 Carbon monoxide data
The data consists of hourly CO concentrations recorded at 4 Portland
sites and. one Eugene site. The Portland CO measurement locations are: CAMS
(at 718 West Burnside in downtown Portland), Alder (at the intersection
of 4th and Alder), (at the Intersection of 41st and Sandy Blvd.),
and Lloyd Center (at 1420 NE Halsey). The Eugene station Is at 11th and
Willamette in downtown Eugene. It was chosen as a control site since cars
in Eugene are not subject to the Portland 1/14 program. The locations of
the CO measurement sites are indicated on the maps of Portland (Figure 1.3)
and Eugene (Figure 1.4).
The hourly data onCO concentrations at these 5 stations vary in length:
CAMS (1011-79/12), Hollywood (73/179/12); Alder (75/979/12), Lloyd (75/11-
79/12) and Eugene (71/5-79/12). Missing data occur at all 5 sites, but the
worst is at Euqene where several months of data are completely missing.
1.4 TraffIc
Many studies have shown that ambient CO concentrations are approximately
proportiona1 to traffic, (Tiao, Box and Hanining (1975), Tiao and Hilimer (1978),
Ledolter and Tiaa (1979b)). It is therefore necessary to incorporate possible
changes in traffic into the trend analysis. Ideally, one would want to use
traffic data recorded at the CO measurement sites. Unfortunately, apart from
a 14 month period (from 1978/2-1979/4) of hourly traffic counts at the Eugene
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statIon (11th and Willamette), such data are not available.
Thus, traffic data had to be obtained from other locations in the
Portland and Eugene area which could be judged representative of the local
traffic at the CO receptors. As traffic indicator for the Eugene station
at 11th and Willamette, we used a monthly average of daily traffic counts
recorded at the state operated Franklin station (at US 99 Pacific Highway
West, approximately .02 miles NW of 11th Avenue). A previous analysis
by the Oregon Department of Transportation of overlapping traffic
counts at the 11th and Willamette, and the Franklin station has shown
that they are highly correlated (R 2 = .78). For the Portland CO stations,
we used the monthly average of daily traffic counts on I 80N, Columbia
River Highway at NE 21st Avenue, which is a major tributary to the downtown
Portland area. For the location of the traffic counters in relation to the
CO receptors see Figures 1.3 and 1.4.
1.5 Meteorological data
Apart from traffic, variations in the CO concentrations are to a large
extent affected by the meteorological conditions. Meteorological data were
obtained from the National Climatic Center (NCC) for monitoring stations at
the Portland and Eugene atrports. Data on wind speed, wind direction, temper-
ature, relative humidity and precipitation frequency were obtained for the
time period 1970/1-1979/12. In addition to the NCC data which are recorded
at three hour intervals (midnight, 3 am, 6 am, 9 am, noon, 3 pm, 6 pm, and
9 pm) we used hourly data on wind speed and wind direction supplied by the
Oregon Department of Air Quality. In downtown Portland, wind speed and
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direction were measured at the Hughes Building (1970/12-1977/2) and at the
Federal Building (1977/4-1979/12). These stations are in closer proximity
to the downtown CO receptors (especially at CAMS and Alder) than the NCC sta-
tion at the Portland airport. For Eugene the Oregon DEQ supplied wind speed
and direction data for a station at Oakway Mall. However due to frequent
gaps in the data, they are not used in our trend analysis.
Previous studies on CO have shown that the vertical diffusion of CO
depends on the Inversion (or mixing) height. Unfortunately mixing height
data were only available atSalmn, Oregon, which is approximately 45 miles
from Portland and 60 ml 1 es from Eugene. There i s some question whether the
mixing height data for Salen are representative of the Portland and Eugene
mixing heights. Nevertheless we obtained dal1y morning and afternoon mixing
heights for the NCC station at Salem for the period 1970/1-1978/12.
1.6 Additional background
Several of the CO receptors were moved during the time span considered
in our analysis. The receptor at CAMS, which originally was constructed at
186 height, was extended downward to 144 in 1972/9. In 1978/3 the probe
was moved 20 ft. and extended to 12 ft. height. Beginning in 1976 the
Burnside traffic pattern was changed, parking was removed and one lane was
added to both east and westbound lanes. Construc ion disrupted traffic for
most of a one year period.
The receptor at Hollywood, originally established at the intersection
of 41st on Sandy Blvd. at 1 ft. height, was moved in 1975/10 to 12 ft. height
and 33 ft. from the intersection.
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In August 1975 the Eugene CO receptor was lowered 2 ft. and moved 18 ft.
closer to the road. At Alder and Lloyd no changes in the CO measurement
stations were made.
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2. Siminary Statistics and Graphical Descriptions of the Data
In this section we give suninary statistics and various graphical repre-
sentations of the CO concentrations, the traffic counts and the meteorological
data on wind speed, wind direction, temperature, relative htunidity, precipi-
tation and mixing height. These preliminary s muaries are a necessary first
step in any statistical analysis. They serve to (i) bring out the main
features of the data and (ii) provide the background.for the various statis-
tical models (diurnal models, time series intervention models, regression
models) In SectIons 3-5 where we relate the Co concentrations to traffic and
meteorological variables.
2.1 AnalysIs of CO-data
The stminary statistics and graphical representations of the CO data at
the 4 Portland stations and one Eugene station Include:
(a) Plots of monthly averages (Figures 2.la-e): Due to meteorological
factors the CO concentrations are seasonal, with high CO concentrations
occurring in the winter and low concentrations in the simmer. Looking at
the concentrations over time we can notice reductions in CO at all 5 stations.
These reductions will be quantified in Section 2.2. The monthly averages
are also calculated separately for weekdays (Monday through Friday) and week-
ends (Saturday, Sunday). The weekend averages are considerably lower than
the weekday averages (Figures 2.2a-e). This is due to the lower weekend
traffic voiwne.
(b) Composite plots of monthly 25th, 50th, 75th, and 95th percentiles
(Figures 2.3ae): In addition to plots of the monthly means in (a) it is
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also informative to study the various percentiles of the empirical frequency
distribution to see how the observations are distributed within each month.
The 50th percentile (or median) is a robust measure of the center of the
distribution; the distance between the 25th and 75th percentile is called
the lnterquartile range and provides a measure of the variation in the data.
The 95th percentile describes the upper tail of the distribution. Looking
at the time series plots of the 95th percentiles one can clearly notice a
reduction in the high CO concentrations at all 5 stations.
(c) Two way tables of monthly CO averages and of monthly 95th percentiles
(Tables 2.lae): Each row consists of monthly means (monthly 95th percentiles)
of a particular year; each column consists of means (95th percentiles) for a
particular month over different years. The row (column) means and standard
deviations are also given. Such representations are useful to discern seasonal
and overall trend in the data.
(d) Diurnal diagrams of hourly CO averages, separated according to
weekday/weekend and simmer (June-September)/winter (November-February)--.
Figures 2.4a, b and 2.5a, b: The weekday diurnal diagrams (Figures 2.4a, b)
are noticeably different from the weekend diurnal patterns (Figures 2.5a, b).
This is due to different traffic patterns. The weekday diurnal CO pattern is
characterized by two peaks, which correspond to the morning and afternoon rush
hour traffic. The weekend CO diagrams typically have only one peak occurring
in the afternoon.
The winter and sunmier CO patterns are similar in shape. However, the
winter CO patterns are almost uniformly higher than the ones in the summer.
This is readily explained by changes in the meteorology, as explained in
Section 2.4.
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Looking at the diurnal diagrams over time one can also assess the trend in
the CO concentrations, keeping the season and the type of day (weekday/weekend)
constant. Decreases at the 4 Portland and the one Eugene stations can be noticed.
(e) Two wey tables of monthly averages of hourly CO (Tables 2.2a-e):
Each row In such a table expresses the average diurnal CO pattern for a
particular month; each cohann Indicates the chronological variation of the
pollutant concentration for a particular hour. The entries can also be
classified according to the overall percentiles of the monthly hourly
averages. The 50th, 75th and 95th percentiles are given at the bottom of
the tables. To facilitate the trend analysis we can choose different colors
for the 4 ranges (below 50th, between 50th and 75th, between 75th and 95th,
above 95th percentile). In Tables 2.3a-e and 2.4a-e, similar monthly averages
are given separately for the weekdays and the weekends.
2.2 A preliminary CO trend analysis
We now give a preliminary trend analysis of the CO concentrations based
on the CO data alone. Such analysis does not account for possible changes in
traffic and the meteorological variables.
At CNIS the CO average for 1970 and 1971 (4.85 ig/m 3 ) was reduced to
3.55 pg/m 3 In 1978-79. This corresponds to a reduction of 26.8 percent, or
3.4 percent a year.
At Hollywood the reduction from 5.7 iig/m 3 in 1973 to 3.2 ug/m 3 in 1979
amounted to 43.9 percent, or 7.3 percent a year.
At Lloyd the reduction from 3.0 g/m 3 in 1976 to 2.5 ug/m 3 in 1979
amounted to 16.7 percent, or 5.6 percent a year.
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At Alder the reduction from 3.7 pg/rn 3 in 1976 to 2.9 pg/rn 3 in 1979
was 21.6 percent, or 7.2 percent a year.
At Eugene the CO average for 1971-72 (3.14 pg/rn 3 ) was reduced to
2.72 pg/rn 3 in 197879. This is a reduction of 13.3 percent, or 1.9 percent
a year. We notice that the reduction at Eugene, which is not affected by
the I/M program, is much smaller than the reductions at the Portland stations.
The CO receptor in Eugene however was moved in 1975/8. As indicated by
Figure 2.le this probe change led to an increase in the level of the CO
concentrations. If the probe change is taken into account as done in Section
4.4, the trend reduction will be larger than the 1.9 percent given above.
2.3 Analysis of traffic data
It was pointed out earlier that hourly traffic counts were only avail-
able for the Eugene station at 11th and Willaniette and furthermore only for
the period 1978/2-1979/4. These traffic counts, which were obtained from the
traffic engineer of the city of Eugene, are used in the diurnal models of
Section 3. For the Portland CO monitoring stations we could only obtain
hourly traffic counts for a few selected days during 1977 and 1979.
The hourly traffic count records, however, were not long enough to be
of use in a CO trend assessment. In order to incorporate traffic into the
CO trend analysis we used monthly averages of daily traffic counts at state
operated traffic counters. While especially in Portland, these counters
were not in close proximity to the CO receptors, as a first approximation
we assume that these traffic counts are proportional to the traffic at the
receptors in downtown Portland and Eugene. Monthly averages of traffic
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counts at the Franklin station at US 99 Pacific Highway West in Eugene, and
at I 80N at 21st Avenue in Portland are listed in Tables 2.5 and 2.6. Time
series plots are given in Figures 2.6 and 2.7. A seasonal pattern is apparent,
with traffic volumes higher In the suniner.
The traffic averages at Eugene and Portland both indicate a temporary
reduction in traffic volume during the oil and energy crisis in the fall and
winter of 1973. Apart from this temporary reduction the Portland traffic
Increased from an average of 98000 cars/day in 1970 to 111600 cars/day in
l979 . This Is an Increase of 13 percent, or 1 .44 percent a year. At the
Eugene station traffic Increased at a much slower pace; it grew from an
average of 24800 cars/day in 1970 to 26200 cars/day in 1979. This represents
an Increase of 5.7 percent, or .63 percent a year.
2.4 AnalysIs of meteorological data
Meteorological variables are important factors affecting CO concentra-
tions. In particular,
(a) wind speed and wind direction affect the transport and diffusion
of CO emissions (with low wind speed resulting In high CO levels),
(b) inversion or mixing heights affect the volume of air available for
dilution of CO emissions (with low mixing heights, or small volume, leading
to high CO levels);
(c) temperature, solar intensity and relative humidity affect the forma-
tion of free radicals which in turn can deplete CO concentrations (high levels
of these variables are associated with increased numbers of free radicals,
and thus low CO);
(d) meteorological variables (such as temperature) influence the eff i-
ciency factors of car engines (cold starts leading to higher CO levels).
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Thus, one should consider incorporating these variables as exogenous factors
in a trend analysis of CO.
In the following discussion we give various graphical representations
of wind speed, wind direction, relative humidity, temperature, and precipita-
tion for the Eugene and Portland airport locations (NCC data). In addition
we describe hourly wind speed and direction data recorded at the Hughes and
at the Federal Building in downtown Portland, and morning and afternoon mixing
height at the NCC station at Salem airport. Our analysis includes:
(a) plots of monthly averages;
(b) composite plots of 25th, 50th, 75th and 95th percentiles;
(c) two way tables of monthly averages;
(d) diurnal diagrams;
(e) two way tables of monthly averages of hourly observations;
(f) histograms of wind direction at selected hours during suniner and
winter periods of 1971;
(g) vector plots of resultant wind at selected hours during sumner
and winter periods of 1971 and 1976.
2.4.1 Wind speed
Monthly averages of wind speed at the Portland airport, the Eugene
airport and the Hughes/Federal building (Portland) are given in Figures 2.8a-c.
Comparing the wind speed data at the two Portland sites we find that the
Hughes/Federal Building wind speed averages are more than twice as large as
the wind speed averages at the airport. There is no good explanation for
this difference, except that it could be due to some variable intrinsic to
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the method of data collection such as the location of the monitor in relation
to the building. The wind speed at Eugene airport is slightly lower than
the wind speed at the Portland airport.
Looking at the plots over time we notice that. over the last 10 years
(197079) the wind, speed patterns at all three locations have stayed roughly
constant, and that they exhibit only weak seasonal ity. A period of excep-
tionally low wind speed can be noticed in Eugene at the end of 1976. This
is also reflected In the CO concentrations which are especially high during
the same period (Figure 2.le).
Apart fran the monthly averages of wind speed, we also constructed the
monthly 25th, 50th, 75th, and 95th percentiles (Figures 2.9a-c), and the
diurnal diagrams of wind speed for the suniner and the winter seasons (Figures
2.lOa-c). Wind speeds are usually highest during the afternoon hours. Also,
the speed the winter is usually higher than in the staaner, except during
the late afternoon period where the suniner wind speed is highest.
Two way tables of monthly means (Tables 2.7a-c) and two way tables of
hourly monthly.means (Tables 2.8a-c) are also given.
2.4.2 WInd direction
To represent the distribution of wind direction (WD) we divide the
direction 00_3600 (Qo is due north; clockwise) into 16 intervals of equal
length and calculate the relative frequencies of winds coming from these
Intervals. The histogram is then plotted on a circle (similar to a wind
rose). As illustrations we show such histograms for 1971. For these plots
we use hourly wind speed data from the downtown Portland (Hughes building)
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and Eugene (Oakway Mall) meteorological stations. rn order to assess
differences according to season and hour of the day, we have given these
histograms for suniner/winter, and morning (7-9 am)/afternoon (46 pm)
separately (Figure 2.11).
Wind direction and wind speed can also be represented jointly by
plotting the resultant wind vector (WSsinWD, WS cosWD) in two dimensional
space. The length of the vector represents the speed; the angle represents
the direction. Such vector plots are made for the Hughes station in Portland
and the Oakway Mall station in Eugene for 1971 and 1976, separated according
to season and hour of the day (Figures 2 12a, b).
These plots indicate (i) generally higher wind speeds during the after-
noon hours, (ii) large variability in the wind direction, and (iii) only
moderate shifts in wind direction due to the hour of the day and the season.
2.4.3 Mixing heights
Monthly averages of morning and afternoon mixing heights at Salem are
given in Tables 2.9a, b and are plotted in Figures 2.13a, b. Only data for
the period 1970-78 are available.
The afternoon mixing height data show a strong seasonal pattern with
high values occurring in the suniner and low ones during the winter. No
strong seasonal patterns are found in morning mixing height data. During
the winter (November-February) the morning mixing heights are usually slightly
higher than those in the afternoon. In all other months the afternoon values
are much higher, and this is due to the fact that the inversion layer usually
breaks in the afternoon.
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2.4.4 Relative humidity
Monthly averages (Figures 2.14a, b), composite percentile plots (Figures
2.15a, b),two way tables of monthly averages (Tables 2 .lOa, b), two way
tables of monthly averages of hourly readings (Tables 2.lla, b) and diurnal
diagrams (Figures 2 16a, b) of relative humidity at the Portland and the Eugene
airport are given. One can notice a strong seasonal pattern, with high rela-
ttve himtidity in winter and low values in the simmer. The diurnal diagrams
show that relative hL*nidity Is lowest during the afternoon; this is especially
true for the stainer months.
Relative humidity and mixing heights are inversely related. In fact,
In a previous study (Ledolter and Tlao (1979a)) we have used relative humidity
as a proxy for mixing height data which are difficult to obtain.
comparinα the Portland and Eugene monthly averages we find that, apart
from 1979, relative humidity at Eugene is slightly higher than that at Portland.
2.4.5 Preci ptt tiQn
Monthly frequencies of precipitation of any form (rain, snow, etc.) and
intensity (light, moderate, heavy), are calculated for both Portland and
Eugene (Tables 2.12a, b) and are plotted in Figures 2.17a, b. As expected,
the frequency of precipitation is largest during the winter season. As with
all other meteorological variables we notice only little change from one
year to the next. The only exception is a very dry winter in 1976/77.
The diurnal patterns of precipitation frequency are given in Figures
2.18a, b. Two way tables of monthly precipitation frequencies according to
the hour of day are given in Tables 2.13a, b. The precipitation frequencies
exhibit little variability during the day; the winter frequencies are uniformly
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higher than those in the sunnier.
2.4.6 Temperature
Monthly temperature averages at Portland and Eugene are given in
Tables 2.14a, b, The plots of monthly averages (Figures 2.19a, b), plots
of monthly percentiles (Figures 2.20a, b), and the two way tables of
monthly averages of hourly temperature (Tables 2.15a, b) illustrate the
expected seasonal and diurnal patterns in temperature. We also notice
little change in temperature from one year to the next.
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3. Diurnal Models of CO
In this and the next two sections, we discuss various models relating
CO to exogenous traffic and meteorological variables. The focus of this
section Is on modelling the diurnal behavior of CO which serves to Iden-
tify the main factors affecting CO, and thus motivates the trend model s
discussed In Sections 4 and 5.
As mentioned In the previous section, detailed traffic information,
such as hourly traffic counts, was available only for the Eugene station
at 11th and Wll1amette and only for the period 1978/2 to 1979/4. It is
this period which we now use to Identify the factors which affect the CO
concentrations.
3.1 Formulation of the models
In Figure 3.1 we plot the average hourly weekday and weekend CO con-
centrations for sumner 1978 (JuneAugust). In Figure 3.2 we plot the
corresponding hourly traffic averages for this period. We notice from these
graphs that CO Is essentially proportional to traffic counts, TR. implying
a relationship of the form
COt=a+kTRt+ t (3.1)
where t stands for hour, Is the error term and and k are two constants.
The CO concentrations, however, also depend on the meteorological variables.
In particular CO will vary with wind speed (US), mixing height (MH) and
relative hianidity (RH). This is illustrated in Figures 3.3a-c where we
plot CO as a function of US, MH and RH. To keep traffic and seasonal
effects constant, we consider the (3-6 pm) weekday period of suniner 1978.
Notice that CO decreases (diffuses) with increasing wind speed, with
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19
increasing mixing height (volume) and with decreasing relative humidity.
Relative humidity and mixing heights are in fact highly negatively correlated
(see Figure 3.4 which presents a plot of RH against MH for the summer 1978
afternoon period at Eugene airport). The scatterplots in Figures 3.3 and
3.4 were constructed by first grouping the dependent variable y according
to non-overlapping classes of the independent variable x, and then plotting
the averages of y against the averages of x. The classes of the independent
variable were chosen such that each group had the same number of observations.
Such a representation is very useful since the large variability in our data
would make it difficult to discern the relationship from a scatter plot of
the raw data.
Figure 3.4 indicates that relative humidity can be considered a
proxy for mixing height. As compared to mixing height, which is available
only twice a day, relative humidity is recorded every 3 hours. Further-.
more mixing height data are usually difficult to obtain. For these reasons
we use relative humidity as an explanatory variable in our diurnal models.
Diurnal diagrams for wind speed and relative humidity for summer 1978 are
given in Figures 3.5a, b. These hourly averages were obtained by a linear
interpolation of the averages which were available for every third hour.
Furthermore since there was no reason to distinguish between weekday and
weekend we calculated the combined average. Relative humidity decreases
during the daytime hours, and is lowest during the early afternoon (3 pm).
Wind speed follows an opposite pattern. Carbon monoxide (weekday/weekend),
traffic (weekday/weekend), wind speed and relative humidity for the four
seasons of the year are given in Figure 3.6. Here we consider spring
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(MarchMay 1978), simmer (JuneAugust 1978), fall (September-October 1978)
and winter (December-January 1979) separately. CO and the meteorological
variables vary from season to season. During the afternoon hours, relative
humidity is lowest In s umner; wind speed is highest in sumner afternoon.
Both meteorological variables show little variation in the winter. Traffic
counts are very similar for all seasons. The CO concentrations are high
during the fall and winter, and low during spring and summer.
FIgures 3.1-3.6 suggest the following models relating hourly CO to
traffic and the meteorological variables:
Ml: CO a+c
M2: COt2a+kTRt+ct
M3: CO = + kTRt/WS +
(3.2)
$4: CO, (a + kTRt)/WS +
$5: C0 = a+ kTRtRHt +
$6: cO (a + kTRt)RHt +
where a, k and *5 are constants. Model Ml is given here mainly for comparison
to evaluate the model improvement after including traffic and meteorological
variables. As mentioned earlier, model (12 expresses CO as a linear function
of the traffic TR. Models M3 and M4 take into account the diffusion effect of
the wind. In Model $4, the background CO level a is diffused by WS. Models
$5 and M6 express the effect of relative humidity on the CO concentrations.
3.2 Fitting results
Each of these models is fitted by least squares to weekday and weekend
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hourly averages for spring, summer, fall, and winter separately. For models M3
and M4 we had to resort to a nonlinear least squares procedure--see Draper and
Smith (1981). Nonlinear least squares routines are readily available in most
statistical computer packages. We used the routine ZXSSQ in the IMSL program
library; this program is based on the Marquardt-Levenberg algorithm for locating
the minimum of a sum of squares corresponding to a model which is nonlinear in
the parameters.
The results in Table 3.1 show the root mean square errors & for the
fitted models. The mean square error is given by = i i5 Z(CO -
where CO is the fitted value, p is the number of estimated parameters and
n = 24 is the number of data points corresponding to the hours of the day.
Table 3.1 shows that traffic is the most important variable in explaining CO,
leading to a considerable reduction in the variation. In addition, the inclusion
of wind speed (or relative humidity) leads in all cases except one (summer week-
days) to a further reduction in the variation. From the overall results of
Table 3.1 we can conclude that models M3 and M4 yield the best fit.
Detailed results on parameter estimates and their standard errors for
the suniner 1978 period are given in Table 3.2 (weekdays) and Table 3.3
(weekends). The best model is M4
co. = + kTRt)/WS + E:t. (3.3)
For weekdays this model leads to an 85.5 percent reduction in the standard
deviation (root mean square error) as compared to model Ml (or R 2 = .98).
For the weekend period the reduction in the root mean square error is 79.7
percent (or R 2 = .96). Note that the parameter estimates a, k and are
very similar for both weekday and weekend models.
In Figures 3.7a, b we compare the observed and the predicted hourly CO
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22
averages for the suniner 1978 weekday and weekend. The model CO = + kTRt)/WS
1 eads to an excel lent representation of the CO patterns. The model is capable
of tracking both weekday and weekend CO patterns; also the CO peak hours are
modelled extremely well.
Various other models have been tried, including ones which use discounted
traffic (Tiao, Box and Hanining (1975)), and both wind speed and relative humidity.
These models, however, did not lead to better fits.
Apart from learning which variables affect the CO concentrations, one can
use these models for a trend analysis provided detailed information on traffic
is available. If hourly traffic counts for the period prior to 1978/2 were
available, one could calculate hourly weekday/weekend traffic averages for
previous years, separated by season to adjust for seasonal differences. Then
models of the form
CO(i,s) = + k(j S)TR/( IS + (i,s) (3.4)
coul4 be fitted to averages of CO, traffic, wind speed and relative humidity
for season s of year i The parameters k( 15 ) express the CO emission factor
of season s in year i, after having accounted for possible changes in traffic
and meteorological variables. Such an approach was used by Tiao and Hilimer
(1978), Ledolter et al. (1978) and Ledolter and Tiao (1979b).
In the present study, since traffic data for Eugene and Portland are
available only at the monthly level prior to 1978, this approach cannot be
used.
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4. Time Series Intervention Models
In this section, we consider time series models relating monthly means
of CO at the Portland and Eugene sites to traffic and meteorological vari-
ables. The objective is to assess the trends in CO and the effect of the
I/M program.
4.1 Formulation of the trend models
The reduction in the CO concentrations which was reported in the prelim-
inary trend analysis of Section 2 can be attributed to several factors:
(1) Federal new car CO emission standards : Over the last 10 years the
federal CO emission standards became increasingly more stringent. For
example, they limit CO e iiissions to 34g/travelled mile for 1970/71 model
year cars; to 28g/mile for 1972-74 model year cars, and to 15g/mile for
197577 model years cars (as based on the current procedure). The effect of
these changes in the standards on the CO emissions in month t can be
approximately modelled as
kt ke 6 t (4.1)
where 8 is a parameter controlling the reduction in the CO emissions.
We would expect 6 to be of the order -.005; such a value would translate
into a monthly CO trend reduction of 100(1 - e 8 ) = .5 percent. O i an
annual basis this would correspond to a yearly reduction of 100(1 - e 128 )
= 5.8 percent. A graph of kt for 8 = -.005 and t = 1, ..., 120 (corresjionding
to the period 1970/11979/12 under study) is given in Figure 4.1. Since 8
is expected to be small, the term ke 6 t can also be approximated by a first
+
order expansion
+The linear expansion is presented as an alternative model for illustrative
purposes here and in equation (4.11) and Table 4.5. However, the exponential
form (4.1) is relied upon for the calculation of emission benefit.
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24
kt = k(1 + 8t). (4.2)
A plot of this function for 8 = -.005 is given in Figure 4.2. Over the
range of t (1, ..., 120), the functions (4.1) and (4.2) differ only slightly.
(2) The Portland I/M proaram : The Portland car inspection and maintenance
program is expected to have an additional impact on the CO emissions. Previous
studies by Becker and Rutherford (1979) and Rutherford and Waring (1980) show
that the effect of car maintenance on CO emissions lasts for approximately 9
to 12 months after maintenance, in spite of the fact that emissions from repaired
vehicles deteriorate after corrective action has been taken. Thus instead of
using the monthly Inspection nijnbers I/Mt (which are given in Table 1.2), we
use the discounted (total) munber of car inspections
I/Mg = Z&I/Mt_j = I/Mt + dI/Mt_i + d 2 1/Mt_ 2 + ... (4.3)
in our analysis. The discounting factor d has to be between 0 and 1. A
value d 0 describes the situation where the effect of the I/M program is
only imediate and loses its effect after the first month. A value d = 1
Indicates that the effect of car maintenance is permanent, without ever deteri-
orating again. A value of d between 0 and 1 describes the ininediate impact and
the deterioration after corrective maintenance. A value d = .7 was chosen in
our analysis. For this value the residual influence of inspections after 6
months nounts to 12 percent and the influence after 9 months is about 4 percents
Combining the effect 0 f the federal CO emission standards in (4.1) with
the effect due to the I/M program in (4.3), we can model the CO emissions as
kt = ke$t(l + eI/M ). (4.4)
The factor lOOeI/M measures the additional percent benefit due to the car
inspection and maintenance program. Figure 4.3 shows a graph of kt for
8 = -.005 and 0 -.001. To be able to work with coefficients of comparable
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25
size we have defined I/Mt as the monthly number of car inspections (in units
of 1000 cars). From Table 1.2 we find that the monthly average of the 1976-
1979 car inspections is T71i = 34. For d = .7, the average of the discounted
number of inspections is i7 =i7 f(i - d) 113. Thus for 0 -.001 the
average additional benefit due to I/M amounts to 11.3 percent.
The CO concentrations recorded at the CO receptors are functions of the
CO emissions , which are modelled in equation (4.4). In modelling the CO
concentrations however, one has to take account of additional factors such
as traffic changes, changes in the meteorological variables, seasonality in
the CO concentrations, and probe changes which affect the distance of the
receptors from the source traffic. We emphasize the distinction between the
reductions in CO concentrations (ambient CO levels) and the reductions in CO
concentrations after adjusting for changes in traffic, meteorological variables
and probe locations. The adjusted concentrations are then proportional to the
CO emissions.
(3) Traffic : It was discussed in Section 3 that CO concentrations are essen-
tially proportional to traffic:
CO ktTRt. (4.5)
The proportionality constant kt is the emission constant which is modelled
in (4.4). -
(4) Meteorological variables : Several functional forms relating CO concentra
tions to meteorological variables were considered in Section 3. These models
were of the form
C0 kTRtf(WSt , MHt, RHt). (4.6)
Since mixing height data were not available for 1979, the last year of our
study, we examined models which incorporate relative humidity and/or wind
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26
speed. Of these models we found that model s of the form
= ktTRtRHt = ke 8 t(l + OI/M )TRtRHt (4.7)
led to a good description of the data.
(5) Seasonality : We saw earlier In Figure 2.1 that CO concentrations exhibit
a strong seasonal pattern, with high concentrations in the winter and low
values in the sinner. This seasonal variation can be partially explained
to a large extent by changes in meteorological variables. To adjust for
any residual seasonal pattern not explained by relative humidity in model
(4.7),we Introduce 12 monthly coefficients k 5 Cs = 1, 2, ..., 12) and con-
sider a model of the form
= k 5 e 8 t(l + eI/M )TR RH . (4.8)
(6) Noise tenn: The right hand side of model (4.8) specifies the predictable
component of our, model. Since the data on CO, TR and RH are observed in the
form of time series, the noise or error component nt (which is the part not
explained by the model) is likely to be serially correlated. We characterize
the Serial correlation by a third order autoregressive model. This leads to
the model
= k 5 e 8 (l + eI/M )TR RH + nt, (4.9)
with It S i 1 t- + 2t-2 + 3 t-3 + Ct
where l 2 and are autoregressive parameters and the Ct S are independent
random variables with mean zero and constant variance cr 2 . For further discussion
of time series models, see Box and Jenkins (1970).
(7) Probe changes and other interventions : There have been several changes of
the location of the CO receptors at some of the sites considered, thus affecting
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27
the distance of the receptor from the traffic source. External interventions
such as abrupt changes in local traffic patterns due to construction also
occurred. To adjust for the effects of these changes we consider models of
the form
CO, = k 8 (l + eI/M )(l + c&jINDtj)TRtRHt nt (4.10)
where the variables INDt are indicator variables taking a value of 0 or 1,
and the a 1 1 s are constants.
At CAMS, for example, the probe was moved in 1978/3; thus INOti = 1 for
t 1978/3 and 0 otherwise. Also, Street construction disrupted traffic
during most of 1976. Thus the indicator variable INOt 2 was chosen such that
INDt 2 1 for all months in 1976 (1976/1 t 1976/12), and 0 otherwise.
The effects of these two changes can be seen from the monthly CO averages
at CAllS (Figure 2.la) which show corresponding reductions in CO. Note that
since these reductions occurred in even numbered years where most of the cars
were inspected (1976, 1978), there is a partial confounding of the effects
of the I/M program and those of the probe change and traffic disruption.
Including the factor (1 + iINDti + NDt 2 ) in the model will therefore
lessen the significance of the tIM program.
At Eugene, the probe was moved in 1975/8. Prior to this move the probe
was out of operation for a period of 6 months (1978/3-8). The plot of the CO
averages in Figure 2.le shows that the CO reduction at Eugene is rather small
if the probe change is ignored (1.9 percent, as indicated in Section 2).
However, by taking the effect of the probe change into account, one would
expect to find a larger trend reduction.
We note that despite the increase in traffic at both Portland and Eugene,
we have observed reductions in the Co concentrations at all S locations.
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After adjusting for the traffic increase, the reductions in the CO emissions
(expressed by the parameter 8 in models (4.9) and (4.10)) will be larger
than the corresponding reductions in the CO concentrations.
An alternative model
By making a first order linear expansion of the first term on the right
hand side of (4.10), we arrive at the alternative linear form
C0 = k 5 (l + 8t + OI/M + ctjINDtj)TRtRHt + t (4.11)
where nt * + + 3t-3 +
Since the parameters 8, 9 and a are all rather small nunbers the models in
(4.10) and (4.11) are quite similar.
4.2 Model estimation
The parameters in models (4.10) and (4.11) are fitted to monthly averages
of CO, TR and RH using a nonlinear least squares computer program (ZXSSQ of
the U4SL library). Fitting the model to Eugene where several months 0 f data
were completely missing Involved a two stage estimation approach. At the
first stage the model was fitted with independent errors (rib = ) and the
missing observations were replaced by their fitted values. At the second
stage thecamplete model with the autoregressive error terms was fitted to
the data.
The estimation results for the 5 stations and their interpretation follow.
The locations with the longest data records--CAMS in Portland, and the 11th
and Willamette station in Eugene, which serves as a control site--are discussed
first.
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4.3 Trend analysis for CAMS
The parameter estimates for models (4.9) and (4.10) are given at the
bottom of Table 4.1 (models C7, C8), and in more detail in Table 4.2 (C7)
and Table 4.3 (C8). For the model Cl the estimate for the trend parameter
is = .00409, which implies a trend reduction due to the more stringent
federal Co new car emission standards of 100(1 - e 12 B) = 4.8 percent 2
year . The estimate for 0 is = -.00106; this indicates that on the average
the I/M program has led to an additional annual percentage reduction of approx-
imately 12 percent (i.e., l00. i7i = 100 x (-.00106) x 113 = -12.). The
standard error in parentheses indicates that this I/Ft effect is statistically
significant at the 5 percent level.
Analysis of the t/M benefits
Ignoring the seasonal effects, a plot of the trend in the CO emissions
predicted by the estimated model is given in Figure 4.4. The area between
the curves e 8 t and e t(1 + I/M ) represents the additional benefit due to
the I/M program. The annual percentage benefits due to tIM are given in
Table 4.4. Also given in the same table are corresponding estimates of the
percentage mobile source emission benefit calculated by EPA using a Portland-
specific version of its MOBILE 2 model (James A. Rutherford, personal communi-
cation).
In model C8 we have introduced parameters to account for the probe
change in 1978/3 (INDtl) and for the traffic disruption in 1976 (INDt2).
It was discussed earlier that the introduction of these interventions, which
occurred precisely at times where the I/M program had its maximum impact
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30
(even ntmibered years), will lead to a partial confounding of the effects.
This Is reflected by the estimation results for model C8 in Tables 4.1, 4.3 and
4.4; the effect of the I/M program is approximately cut in half, leading to
an average benefit of 6.1 percent (as compared to 12 percent before). The
area between the curves eBt(l + + ct 2 INDt 2 ) and eBt(1 + eI/M )
x (1 + aiINO i + u 2 INO ) In Figure 4.5 expresses the additional benefit
due to I/N.
Summarizing the results of models Cl and C8, we can conclude that at
CPJIS there was evidence of additional benefits due to I/M. Our estimate at
this point of our analysis is that the average percentage benefit is somewhere
between 6 and 12 percent.
We also estimated the linearized model (4.11) in two versions corre-
sponding to Cl and C8. The main results given In Table 4.5 show that, as
expected, the linearized versions lead to essentially the same conclusions
concerning the trend in the Co emissions and the benefit due to the I/N
program.
Additional models fitted
Models CS and CS in Table 4.1 use only traffic as an explanatory vari-
able. Comparing the standard deviations of the errors, &, we find that the
failure to include meteorological variables (such as relative humidity) will
increase by approximately 17 percent (from .504 in C7 to .585 in CS). This
indicates that relative humidity is an important variable in our trend model.
Models C3 and 4 represent CO averages as functions of time only (thus
omitting both traffic and relative humidity). We notice that in these models
the estimates of8, which now represent-the reduction in the CO concentrations
(unad.justed for traffic and meteorological changes) and not in the emissions
as in the other models, are smaller. For example = -.0027 corresponds to
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31
an annual trend reduction of 3.2 percent. This compares well with the 3.4
percent CO reduction estimate of Section 2.
Models Cl and C2 are model s in which the CO averages are represented
by 12 seasonal means and errors which follow a third order autoregressive
model. Comparisons of their standard errors with those of Cl and C8 indicate
that time, traffic and relative humidity are important variables affecting
CO concentrations.
4.4 Trend analysis for Eugene
The model (4.10) is also fitted to the Eugene CO monthly averages. This
station serves as a control site, since the I/M program.does not extend beyond
Portland. rn estimating the model,
Cot k 5 eBt(l + eI/M )(1 + ctINDt)TRtRHt + t (4.12)
we use the Portland ElM inspection volumes. Since the I/M program is restricted
to the Portland area, we would expect that only a small fraction of the inspected
cars would ever be in Eugene. Thus its impact should be negligible and the
coefficient e should be zero. The indicator INDt , which takes on 1 for t > 1976/8
and 0 otherwise was introduced to model the effect of the probe change in 1976/8.
The main fitting results in Table 4.6 show that with or without the
adjustment for the probe change, there is no evidence of an effect due to
the I/M program. The parameter estimates of 0 in models E3 and E4 are
clearly insignificant.
Model E3 indicates that the probe change has had a positive effect on
the CO concentrations. After adjusting for the effect of the probe change,
we find that the estimate of 8 in model E3 is = - .00465. This corresponds
to an annual 5.4 percent (± 3.4) trend reduction in CO emissions. This
estimate is comparable to the trend reduction at CAMS apart from the effect
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32
of the TIM program.
The standard error of the trend estimate is quite large, and is
also correlated with & (correlation of -.84). Ignoring the probe change
would lead to a considerably lower trend reduction. The estimate = -.00201
In El corresponds to an annual reduction of 2.4 percent and, after adjusting
for the traffic increase, Is consistent with the reduction of CO concentra-
tions given In SectIon 2.
4.5 Trend analysis for Hollywood
The model CO k 5 e t(l + eI/M )(1 + c&INDt)Gt +
(1) 6 t 1 (trend model)
(ii) TRt (model incorporating traffic)
(111) TRtRHt (model incorporating traffic andrelative humidity)
was fitted to monthly CO averages at Hollywood (1973/1-1979/12). An indicator
variable(INDt 1 for t 1975/10 and 0 otherwise) was included to account
for the probe change in 1975/10. The results in Table 4.7 show that
(i) the annual trend reduction in the CO concentrations due to the
federal standards is approximately 5.0 percent (average B for models H1-H3
is .0043);
(Ii) after incorporating traffic and relative humidity we find that the
annual trend reduction in the CO nissions is approximately 7.5 percent
(average for models H4-H9 is = -.0065);
(iii) looking at the estimate of 9 we find no evidence that the TIM
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program has led to an additional improvement.
4.6 Trend analysis for Alder and Lloyd
CO records for Alder (1975/9.-1979/12) and Lloyd (1975/11-1979/12) are
relatively short, and only few observations are available for estimation
(52 for Alder, 50 for Lloyd). Thus our models have to be simplified.
Instead of fitting models with 12 seasonal (monthly) coefficients k 5 , we
fit models of the form
CO. = k 5 e (l + eI/M )TR RH +
with 4 seasonal (quarterly) constants; k 1 for DJF, k 2 for MAN, k 3 for JJA,
k 4 for SON.
The fitting results are given in Table 4.8. Due to the short data
record, we find large correlations among the parameter estimates, especially
among i and (negatively correlated). Looking at the estimate , we find
no evidence of a significant additional benefit due to the I/M program.
4.7 Suninary of the trend analysis of monthly CO averages
At the locations with the longest data records (CAMS and the control
site at Eugene) there is empirical evidence to indicate a real effect on
CO emissions which can be associated with the I/M program. The estimated
benefit of I/M lies between 6-12 percent.
At the other Portland stations we could not find a significant benefit.
However, the results from our analysis have to be interpreted with caution,
since at some of these stations we did not have long enough data records to
establish a good estimate of the pre I/M trend.
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5. Regression Trend Models for Hourly Co Concentrations During Peak Hours
The trend models In Section 4 were fitted to monthly averages of all
hourly readings. In this section we analyze CO concentrations during peak
hours, and consider the morning (6-9 am) and afternoon (3-6 pm) periods
separately. The objective Is to assess the trend In the CO concentrations
during these peak periods.
In this trend analysis we consider daily weekday morning (afternoon)
CO concentrations. Weekday (Monday-Friday) observations are chosen to keep
traffic constant. Furthermore to block out seasonality we consider the
staiiner (JuneSeptember) and winter (Nov nber-February) periods separately.
To calculate daily morning (afternoon) CO concentrations we average
the hourly observations over the 69 am (3-6 pm) periods. To calculate
morning (afternoon) wind speed, wind direction, temperature and relative
htsnidlty, we αveragethe 6 and 9 am (3 and 6 pm) observations. Precipita-
ti on is In the form of an indicator van abi e (0 if no rain during the 3
hour period; 1 If rain). Since mixing height at Salem is only recorded
twice a day (morning/afternoon), no averaging is necessary.
The model for the CO concentrations
cO a trends + g(met. variables) + (5.1)
where t stands for day, consists of two parts: a trend component which
models the reduction in CO through time and a component g(met. variables)
which models the relationship between CO concentrations and the meteorologica
variables (wind speed, wind direction, temperature, relative humidity, mixing
height, and precipitation).
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5.1 Meteorological variables
The scatter plots in Figures 5.la-f illustrate the relationship between
CO and the meteorological variables. To create these scatter plots we used
the same procedure as that in the construction of Figures 3.3 and 3.4.. The
scatter plots show:
(i) CO decreases with increasing wind speed WS; as in Ledolter and Tiao
(1979a), the relationship can be approximated by a model of the form
Co lws_l + zws_ 2 ,
(ii) CO changes with wind direction. One could represent this relation-
ship with indicator variables representing the quadrants of wind direction.
Instead, we model the relationship with a first order harmonics
CO Asin(WO_ ) 3 COSWD+8 4 siflWD
where A is the amplitude and p is the phase angle (B 3 = -A sin 4 , - A COS
This representation is more parsimonious (uses fewer parameters) and models
the relationship with a smooth function.
(iii) The relationships between Co and temperature, relative humidity
and mixing height are approximately linear; CO 5 Temp; CO 6 RH; CO 87 (4 1 1.
Since mixing height data was not available for the last year, 1979, of our
study, we dropped MH from our analysis and used relative humidity as a proxy
variable (Figure 5.lf indicates a strong inverse relationship between RH and
MH).
Combining (i)-(iii) we model the relationship between the CO concentra-
tions and the meteorological variables as
-------
36
g(met. variables) 8 1 WS 1 + 8 2 WS 2 + 83 cos WD + 84 sin WD
(5.2)
χ 8 5 Temp + 8 5 RH + 8 7 Precip.
5.2 Trend component
Our first objective is to derive yearly trend estimates for the CO
concentrations after allowing for the effects of the meteorological variables.
We introduce dummy variables to estimate the yearly effects, as compared to a
specified base period. To be able to compare CAMS and Eugene directly we
chose the base period to be 1971 (summer 1971 for sumer regressions;
winter 1971/72 for winter regressions). The yearly trend mode l takes the
form
p
trend = a 0 + Z a.jX. (5.3)
i=l
where = 1 for summer 1971 + i (winter 1971 + i/72 +i) and 0 otherwise.
The parameter a expresses the change in the CO concentrations from 1971 to
1971 + i. For the summer regressions at CAMS and Eugene we use p = 8 indi-
cator variables (p = 7 for winter). For the other stations the base periods
in the trend comparison are: Hollywood (summer 1973; winter 1973/74); Alder
(summer 1976; winter 1975/76); Lloyd (sumer 1976; winter 1975/76).
5.3 Regression trend model
Combining (5.2) and (5.3) our model for daily morning (afternoon) CO
concentrations can be written as
-------
37
CO = o + + + 2 WS + COS WD + S fl W0
(5.4)
+ 5 TemP + B 6 RHt + 7 Precip +
where t stands for day, and is the error term assumed independent across
days with zero mean and variance a 2 . The model was fitted by least squares
to morning (69 am)/afternoon (36 pm) and surrnner (JuneSeptember)/winter
(November-February) data separately. Meteorological data from the NCC
site at the Portland airport were used for the Portland locations, except that
at CAMS and Alder (downtown locations) wind speed and wind direction data
from the Hughes (Federal) Building were employed. For Eugene we used the
meteorological data recorded at the Eugene airport.
The regression results for the 5 stations are given in Table 5.la
(sumer) and Table 5.lb (winter). We also list the standard deviation of
the data obs and the standard deviation of the errors after filtering the
regression model (&model) The reduction in the standard deviations expresses
the explanatory power of the trend and meteorological variables. The results
show that on the average reduction in the standard deviations is approxi-
rnately 16 percent. This corresponds to R 2 = .30, which indicates that about
30 percent of the variation is explained by our regression model. Air pollu-
tant concentrations are highly variable; thus realistically higher reduction
could not be expected.
Our main interest is in the estimation of the parameters o 1 , since
they express the trend reductions in the CO concentrations. These trend
reduction estimates are adjusted for possible changes in the meteorological
variables. The trend estimates are plotted in Figure 5.2a (suniiier) and
-------
38
Figure 5.2b (winter). We make the following observations.
(i) For both seasons and both day periods the CO concentrations at
CAMS have clearly decreased.
(ii) The trend estimates at Eugene show considerably less evidence of
a reduction. During the afternoon the CO concentrations have actually
increased, as evidenced by positive a estimates for most of the years
considered. During the morning period only small reductions can be noticed.
(iii) The CO concentrations at Hollywood, Alder and Lloyd have steadily
decreased, especially during the suuuuer periods.
5.4 A model for the trend
The trend indicators a 1 are affected by changes in traffic volume, the
progressively more stringent federal CO emission standards, the Portland
I/M program, the various probe changes, and other interventions. A simple
model which expresses the trend as function of these variables is given by
trend = + cziTRt + a 2 TRtIt + a 3 TRtI/M + a 4 INDt. (5.5)
Combining this trend model with the meteorological component g(met. variables)
in (5.2) leads to the model
CO. = a 0 + ctiTRt + ct TRtI + a 3 TRtI/M + ct 4 INDt
+ 6 1 WSt + 8 2 ws; 2 + cos WDt + sin WD +
+ 6 RHt + 7 Precip + (5.6)
Daily morning (afternoon) traffic counts were not available for our analysis.
-------
39
Also monthly averages broken down by the hour of day were not available.
The only observations available were monthly average daily traffic counts.
From these averages we calculated traffic indicators for each season of
each year considered. As an example, for Portland sites, the suniner 1979
traffic indicator is given by (109.2 + 105.2 + 108.4 + 106.4)14 = 107.3
(see Table 2.5) and is assumed the same for each weekday morning (afternoon)
period in that season. Our analysis thus assumes that the actual morning!
afternoon averages are proportional to the overall daily traffic averages.
The variable models the linear time trend- in the CO concentrations.
It takes on a value of j for all days in season 1970 + I (It is 1 for days
in 1971, 2 for days in 1972, etc.).
The variable I/Mt represents the discounted total of monthly car inspec-
tions. As in Section 4 we choose d = .7 as the discount coefficient. I/M
stays constant for all observations during that month. For example for all
observations in June 1979 I/Mg is given by (20.57 + (.7)21.87 + (.7)220.71
+ ...)--see Table 1.2.
The indicators INDt are chosen to model the effect of probe changes
and other interventions. At CAMS an indicator is introduced to model the
traffic disruption in 1976; at Eugene the indicator adjusts for the probe
change in 1975/8. -
The model in (5.6) is fitted to CAMS and Eugene; the results are given
in Tables 5.2a, b.
5.5 Analysis of the fitting results for the trend model (5.6 )
From the estimates of and a 3 we calculate the average annual trend
-------
40
reductions and the additional percentage benefits due to the I/M program.
These results are given in Table 5.3a (CANS) and Table 5.3b (Eugene).
As an illustration of the calculation of Tables 5.3a, b, consider the
CAMS suniner am regression without the indicator for the traffic disruption
in 1976. From the estimate &2 = -.035, we calculate the average annual trend
reduction ct 2 .TR = -.035 x 101 = -3.53 where the TR = 101 is the sunnier traffic
average obtained from Table 2.5. Compared with the CAMS sumer am CO average
of 40(in units of 10 pg/rn 3 ), this corresponds to a 100 x (-3.53)/40 = -8.8
percent trend reduction per year. Similarly, the estimate = -.0006
is used to calculate the average additional I/M benefit. Specifically,
x TR x T7i -.0006 x 101 x 113 = -6.85. Compared with the CO average
of 40, this represents an estimated additional benefit of 100 x (-6.85)/40
= -17.1 percent.
The remaining entries in Tables 5.3a, b are obtained in a similar
manner. We have used (i) the CAMS traffic averages of 101 for sunmer and
91.9 for winter, (ii) the Eugene traffic averages of 24.0 for sunnier and
22.7 for winter, and (iii) the CO averages for CANS and Eugene given in
Table 5.4. Standard errors of the estimated annual trend reductions and
benefits due to I/M are given in the parentheses.
The results in Table 5.3a show that at CAMS, when averaged over the
two seasons and the two periods of the day, the additional benefit of I/M
is estimated at 15.1% without, and at 8.3% with indicator for the 1976
traffic disruption. These reductions are in good agreement with the results
in Section 4.3 corresponding to the models C7 and C8 in Tables 4.2 and 4.3 using
monthly CO averages (12% without and 6.1% with indicators for traffic disruption
-------
41
and a probe change). Table 5.3a also indicates that the tIM programs impact
is especially felt during the sun ner periods.
With regard to the annual trend reductions, the average reduction of
3.9% per year at Eugene in Table 5.3b is fairly close to the estimated 5.4%
per year obtained from modelling monthly CO averages as discussed in Section 4.4.
At CAMS, Table 5.3a shows that the estimated annual trend reductions during the
peak hours (8.l%9..5%) are larger than the reductions (4.8%-5%) from monthly
averages presented earlier in Section 4.3.
-------
References
Becker, J.P. and Rutherford, J.A. (1979), Analysis of Oregons Inspection
and Maintenance Program, paper presented at the 72nd Annual Meeting
of the Air Pollution Control Association.
Box, G.E.P. and Jenkins, G.M. (1970), Time Series Analysis Forecasting and
Control , Holden Day: San Francisco.
Draper, N.R. and Smith, H. (1981), Applied Regression Analysis , 2nd Ed.,
Wiley: New York.
Ledolter, J. and Tiao, G.C. (l979a), A statistical analysis of New Jersey CO
data, in Proceedings of APCA ASQC Conference on Quality Assurance in
Air Pollution Management , 282-293.
Ledolter, J. and Tiao, G.C. (1979b), Statistical models for ambient air
pollutants, with special reference to the Los Angeles Catalyst Study
(LACS) data, Environmental Science and Technology , 13, 1233.
Ledolter, J., Tiao, G.C., Hudak, G.B., Hsieh, J.T., and Graves, S.B. (1978),
Statistical analysis of the Los Angeles Catalyst Study data, Tech-
nical Report 539, Department of Statistics, University of Wisconsin.
Rutherford, J.A. and Waring, R.L. (1980), Update on EPAs study of the
Oregon Inspection/Maintenance Program, paper presented at the 73rd Annual
Meeting of the Air Pollution Control Association.
Tiao, G.C., Box, G.E.P. and Hamming, W.J. (1975), A statistical analysis
of the Los Angeles ambient carbon monoxide data 1955-1972, Journal
of Air Pollution Control Association , 25, 11301136.
Tiao, G.C. and Hilimer, S.C. (1978), Statistical models for ambient concen-
trations of carbon monoxide, lead and sulfate based on the LACS data,
Environmental Science and Technology , 12, 820.
-------
Table 1.1
Yearly number of car inspections, number of rejections and failure
rates--Portland I/M program (mandatory since July 1975)
number of number of rejection
inspections rejections rate
1974 23478 7909 33.7
1975 155475 46113 29.7
1976 589405 137419 23.3
1977 184524 41153 22.3
1978 577022 129302 22.4
1979 287518 59886 20.8
-------
Table 1.2
Monthly car inspectionsPortland I/M program
(mandatory since July 1975)
Month Year
1975
1976
1977
1978
1979
January 7671 54566 24367 45170 29775
February 8788 45586 18057 47930 27457
March 17174 55211 17920 57796 32348
April 16365 47744 13383 48985 20711
May 14911 49116 11449 46310 21869
June 16728 49466 12074 51704 20570
July 6535 50046 11630 40266 19975
August 7672 48960 10578 58723 21966
September 6650 58233 10290 56221 19420
October 8078 55929 13740 54478 23214
November 15611 42152 17868 39681 20908
December 29792 32396 23168 29758 29305
Total 155475 589405 184524 577022 287518
-------
*VC So
SC 10.0 99 zi. 1
59 73
74 6,
79 ol
61 7
59 53
So 45
49 54
50 45
0 52
Si 8 60
..0 4.7 l2, i0 .
4o Is..
51 lS. +
53 14.4
52 I4.3
43 Ii . ,
35 2.#
33 13 .o
40 1 .4
33 12.7
33 11.4
(monthly averages)
I 2 3 S 7 a 10 11 12 svC St
v( 137 123 10 93 7 #5 69 9 02 1.33 t S 147
SC 22.8 27.#. p5? 1#. tZ.S 1 ,3 .e 1 . 70.0 , .S O.q
I I 2 βVC St
Table 2.1(a)
Two way tables of monthly CO averages and
of monthly 95th percentiles-CAMS
1 2 3 5 7 a 10
70 63 57 47 97 30 32 22 28 1 9
7i 46 3 52 1+5 28 0 29 I 8 5
72 69 4 53 35 39 33 32 53
73 os ii 55 39 38 l 29 36 4 62
74 so s7 si 1,7 36 27 27 .28 31 53
7 5 +7 43 31 27 24 24 14 79 25 SO
76 e 32 2 24 1 i 16 27 2 04
77 99 49 39 30 3 23 19 30 1,7 82
73 ee 42 33 3 2.5 2 17 2 1 O
79 4t 50 32 30 23 1, 3 70 31 02
38
30
29
23
2
#ei
O .
.0
4.0
0.1
70
1/2
1.38
j76
97
60
2
o
2
17
1 1
181+
i71
3S.
!
Lΰ!
161
12
t26
92
itS
2
1. 3
i7
161
1R4
172
1.35
30.
72
1.38
J38
1 50
115
92
104
IC
92
t 3
ISO
I S
1 M
j33
30.1
-
73
173
iSO
138
2
92
100
81
11.5
138
161
181+
130
32.2
71,
127
121
115
115
42
81
51
92
q2
ISO
1.50
127
t12
23.6
75
127
92
30
θ
2
58
103
69
1 0
161
103
102
3o.7
16
115
92
9
e
sa
9
a
θ
115
115
1 9
88
21.7
77
126
lib
30
60
80
49
58
8
iS
138
176
1.24
9
26.2
73
126
92
tQ3
92
9
04
80
92
1 3
115
103
91
21.1
7,
1113
103
#0
e
63
SQ
44
57
80
1,
139
82
25.7
(95th percentiles)
-------
Table 2.1(b)
Two way tables of monthly CO averages and
of monthly 95th percentiles--Hollywood
1 2 3 S 7 Q I 1
70 . . ..
71 - .a . .4
72 .. e . 4. 4 S.. . S SsS S .
73 73 74 62 5 9 9 36 53 61 43 61 2
74 35 47 3 3* 32 22 31 35 42 &5 3 6
15 Sj SD 41 34 32 25 22 23 3 3 144 49
74 3$ 30 31 3 22 22 22 30 32 43 So
77 I 47 33 34 2* 23 20 30 33 50 SI 40
78 si 4 $ 30 20 27 25 31 3 42 5. 96
7i 93 3 36 3 20 23 I 25 33 3*
LYE S
.0
.5. .0
. eQ
57 L .7
92 12.7
38 10.2
35 12.0
38 12.1
39 10.8
32 8.4
LYE SI 49
SC 12. 5 12.8
42 35 30 24 2 5 32
.8 5.0 8., 0.3 8
3? 50 S3
.h 10.0 .8
Si -
8.0
(monthly averages)
1 2 3 4 5 4 7 8 10 11 12 LYE St
70 .4
71 .. .1 . ...
72 . . . . .
73 194 133 19
7 ,2 12 ? 10
7 13* 13 g its
74 103 80 9
77 138 ii 5 4
78 *26 I1 12
7 126 *03 42
S.. S .
5S 555 . . .5.
.. 5.. ee SS
I I . U 103 2 131
10 81 58 69 61
80 49 69 58 58
80 56 a 58 o9
60 58 6 44 49
69 6 58 58 e
46 4 5 57
a. S.. . 555
aS S .. S e
.. .. e
t38 lot tιl 173
115 173 16 1 101
80 126 126 115
80 103 126
8g 126 i3
92 92 126 126
10 3 106 103
.0
..i sQ
.S sQ
t 31.3
Ill 3o.
98 30.1
85 25.7
92 3 5i
9 4 27.9
C 26.5
*VC *3! I2 1O3 71 6) 61 7 94 125 L3 135
S E 30. - i o.c ; .14 1 .I I. 7 5.S . 2°. tO.)
(95th percentiles)
-------
Table 2.1(c)
Two way tables of monthly CO averages and
of monthly 95th percentiles-Alder
YR 2 3 5 6 1 9 *0 *1 12 AVE St
70 , 4 l 444 Il 1 1
71 . .. . . . . .
72 , , , 4 444 4 4 . l . 0
73 . 44* . 4 a .. 4 .. .0
7, , ,,, , ,.. . ... .. . .s . S .0
75 . . p.. .. .. e ,. , I .. 45 35 33 41 3
7* 28 2S 33 29 .30 3. 31 39 4 a 5i 37 9,i
77 b 35 3i 40 30 31 2* 37 32 35 3i 36 6.Q
75 36 30 30 5 3* 2 27 2 35 37 3 32 3.7
7 l 3* 31 27 20 22 2 25 2 28 30 2 5.1
AVE 39 30 32 32 : 30 2 32 36 3*
U 8.7 3,6 1.6 S.0 4.1 4.5 1.5 5.8 y.9 5.7 7.0 6.3
(monthly averages)
yR 1 2 3 S 7 8 10. *1 *2 &VC SE
70 .. . 4 S . 1. 4 .°
71 . . 4 . .4 . l . .0
12 l * Il 444 44 S. * a
73 le . ... . . 4 e . .14 . .3
44 4S 41 4 I I . .4. 4.4 Q
. i2a 2 *03 *15 10 *2.7
lτ a9 92 40 80 #o 80 ,2 403 flS 138 126 4
77 92 92 9 9 69 69 *0 6 o eu 3 4.3 15.6
75 2 a a 6 58 69 58 se 6 2 92 *03 75 15.0
1 103 49 *0 sa o 7, 7j 75 8, 74 11.7
q 9 75 83 74 τβ 9 9 7, * 9 9* 102
St C.5 3.U 9,6 ,S 9.1 i. 7.8 * ,O 43.0 ,Z ilτ
(95th percentiles)
-------
)VC 3 25 2 2 21 23 2M
SC $ , I. i 7.1 3.3 3.7 3.M
25 21 3M 36
M, 2 i.2 7.6 7.8
i 6 Q 110 128
S 3 ..1
Table 2.1(d)
Two way tables of monthly CO averages and
of monthly 95th percentiles--Lloyd
YR 1 2 3 S e 7 3 10 Il 12 £VC SC
70 ...
71 ..
72 ...
73 .4.
i. 4..
1 5 ...
76
77 1 5
21
.4S
.
S.
4
4
411
I
4
44
s
,.
4
.44
4
.
44
..4
4
S
4
.
.4
4
lS
l
.
Ie
...
.
s
..
.
.4
4
44
4
.
.4.
4..
4 .
4S
..4
..
.4.
.
4
e.S
.4
.
.s
s .
..
. .
..
33
17
0
1
I
21
22
26
27
23
3M
13
49
53
30
31
79
34
ZS
26
27
30
3a
7
36
3*
33
23
30
17
14
17
22
1
74
30
38
l
25
2
24
23
20
27
1,
22
27
25
30
25
.0
0
0
.0
.0
7.0
11.1
5.7
7.8
S.
(monthly averages)
YR 1 2 3 I S 6 7 $ 9 10 11 12 aYC S
4
.4
S..
S..
. 5
4
$0
3,
30
.
.
s
16
92
70 5..
71 ..1
72 ..
73 5..
74 4..
is
76 80
77 115
78 2
79 I:e
IVC 1C3
SC i3.2
..
SI.
..
I
44
53
30
53
58
64
.5
p..
S..
p..
S..
I..
58
Si
4
IA
52
... .
0 . p44
54 5
S.. aS
p.. 45
4S ..4
4 4,
33 58
IA
53 4.3
c S;
3.t 7. .
S..
...
..
0
,
..4
s
S
S
.0
.
.4
.
54.
.5
P.S
.0
.4
.
54.
Q
.*
..4
SU
e
.4
.0
55
55
103
128
121
17.5
30
103
l
1
32.
3c. 5
9
69
97
j03
124
3!
22
58
49
eQ
tZS
124
76
2 .9
59
5
8!
9!
10!
73
22.9
75 9
5.5 16.3
(95th percentiles)
-------
Table 2.1(e)
Two way tables of monthly CO averages and
of monthly 95th percentilesEugene
yR 1 3 5 7 9 11 12 * C
78 1 2 3 S 7 a 10 11 12 tYC SC
70
71
72
73
7
75
76-
77
73
7,
1 .S
43 1 63
i 2 115
81 *1 87
5 75 8
5 80 8
I i0 13o
l4 7 4t3
oq *5 aM
43 75 65
4VE 75 55 36 33 36 3 s i 61 78 83 87
S 23.6 32.5 21.0 76.0 2.S 1P. *c.s c ,.3 *3.5 1.7 ;o.
sQ
SO 16.5
iS 21.5
55 29.
3S 31.8
28 35.
67 27.i
68 19.7
40 Le.o
57 13.2
70
..
.
.
I
..
.
.
.
i.e
.
0
11
..
20
Ia
20
25
26
31
35
2,
26
6.1
72
2a
32
30
27
35
26
33
$1
5
13
2
34
t
73
32
II
e.
..
27
27
28
3
I
31
33
6.1
74
27
i.
e
...
...
e m.
a
20
3
3
3e
28
e.,
7 5
L
...
..
...
.
..
3
33
33
31
30
3.1
to
3$
39
7
30
21
21
21
23
25
38
S
57
32
1o.o
?
53
7
27
32
33
25
24
36
33
12
30
31
32
3.2
7$
39
3*
2
21
15
25
21
20
25
33
37
38
2$
i.e
7
34
28
3*
27
22
21
*9
21
2
30
3S
3
27
5.2
4VC
S
33
10.7
3
.8
2
1.
27
3.7
2
o.0
23
3.4
25
s.7
35
.7
30
a.3
37
5.9
36
.5
36
8.1
(monthly averages)
lee . .. elI Ill l Ill C
... C.. .. SO 39 35 Q Sb
63 7 5 se θ 43 52 5 75 97
6, 81 0 0 52 6 Sα II 6
58 0 0 0 0 50 35 40
30 C 3 0 0 0 0 0 75
o 70 S 40 40 40 O 45 55
*12 3S 60 56 47 41 48 (41 66
93 61 50 149 3 S sl 12 I S 53
81 62 b S 52 4S I 39 3 5*
(95th percentiles)
-------
I. *1 I tI 1e I?
S. * .3 47 134 I.C
.1 31 *3 14 III 61.
13 *4 12 .3 40 142
34 .4 35 13 75 1!
. 37 3? 44 43 *8
3. 31 36 43 68 34
a 30 44 33 64 63
6 33 23 3.. 41 II
*3 .7 *4 1* 76 47
31 sQ SI 1 .i .1 I II
6S 71 71 .1 143 644
4* ,? I I I I II 111 i.i
73 *3 3* .4 *3* 144
54 72 I .7 117 1*6
(1 I .2 72 *3 II?
3 . 34 17 1? 141 *
33 . 33 47 $1 37
36. 43 12 14 71 II
3. .0 46 46 44 44
.4 *7 44 34 II 47
34 *5 3* .4 31 *5
. .. .7 71 a. 111 1..
- , 5* *4 I I I 175 I )
. 4 4* 37 II I 144 134
$ 13 13 .4 13* 1.4
38 .8 .4 153 130 3.4
.8 iO 33 6.4 Ill 146
.4 .0 81 7* *4 1 3
.4 44 44 .4 5,) 84
3. $1 32 13 21 *8
6 4* 45 .4 .0 35
31 .0 3* 4. 14 .7
63 .4 *1 76 102 30
12 73 44 .6 .4
.3 .4 38 1.1 1 53 133
o .4 33 112 1.3 1.3
83 .2 .1 1 ,4 134 1.4
13 14 7* *14 1.3
53 sd 81 7* *41 111
44 .4 .4 * . .3 .2
34 47 6* 1* 43 44
34 IS 80 44 II 53
.4 .8 3 .5 SI .3
54 *0 4 $1 31 33
31 .4 II *7 .7 .3
73 .4 72 ..3 II I 617
6* .7 47 110 144 *3*
91 91 533 II ? *44 654
.3 3 *4 1* 44 114
64 .4 5 . 7* 14 1 133
3 44 88 10 44 .3
* .3 *3 66 .2 .4
.4 *7 .4 33 .4 54
3* 3? 11 .0 II 43
3* 2, 38 34 .3 *0
*3 *4 3* *4 46 .7
.35 43 33 .3 .4 .1
61 63 47 36 R * .4
*4 $5 74 84 131 3.4
77 73 74 44 113 *1*
19 .1 1! .3 113 133
53 I 31 12 6.4 24
34 *8 .3 14 43 33
33 47 46 46 40 .1
34 .4 33 .4 .4 33
3. 40 4* . 57 .6
*6 .43 5* 34 33 $
*3 .1 .4 *4 71 37
33 4$ 44 .4 17 .3
II 72 7* 34 *37 736
3* 44 73 8. $41 1.44
5,1 .1 *7 1* 74 .3
11 .8 *2 .4 47 .1
42 13 3 4? 44 44
3. 3* .3 54 3*.
3? 37 6* 37 51 .4
2. $ 34 44 32 .4
2. .4 2* 2 . 34 33
14 67 44 .0 44 .2
is 31 35 3* .4 sI
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5 . 67 .2 1* 3.
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11 #3 17 sI 333 I.e
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3* 64 .1 34 44 41
33 43 51 31 71 71
4. 36 .10 314 *3 83
34 *3 14 .4 4* 45
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37 .8 60 *8 50 .5
.6 67 3* 54 73 .2
. .3 44 53 71 .1
38 II II 7 5 74 111
II 33 34 16 90 42
66 *4 10 34 7* *3
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34 .3 4 *. 5$ SI
11 i* 37 .. II .0
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4. 7 d .2 33 4
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34 32 34 .3 3* 54
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31 .5 .4 i 77 4
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II II 20 31 22
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24 54 2* 33 34
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24 8* 33 24.71
3? 41 45 6.4 $0
s4 43 44 11 44
75 56 7? 34 35
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3* 4* 30 40 44
4* 44 3* 4* 4*
40 22 77 2* 2?
27 3. 31 34 34
34 IS 23 27 33
4* 24 31 31 30
6.3 44 47 44 43
70 5. 13 $7 5*3
7 77 71 74 71
30 *4 1* 14 34
72 14 36 43 10
SO $1 $S .4 37
77 73 .7 1 73
34 1! 4? SC I I
3 2. 23 34 33
44 74 31 3. 33
4. 77 5 24 2?
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.4 43 4* 42 34
13 5! 34 14 II
81 .4 .3 8* 51
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7* 4* SI *7 44
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71 34 *4 13 34
43 3; 31 40 3*
30 40 33 33 32
37 31 37 3* 34
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31 4 24 23 2$
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7; 43 *0 $3 $0
43 7? 43 $1 $3
8s 44 3I 47 43
$4 IC 43 44 6*
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41 47 4S 47 41
34 89 31 34 33
33 24 25 33 34
23 32 27 SI 23
22 41 20 23 *2
20 I ! 21 80 20
30 3* 74 44 21
a. 73 70 *4 54
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71 1* 4 3 .2
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3 43 40 .4 *.1
24 33 31 30 33
24 23 21 I 21
17 Il 22 2. 22
20 23 1* 77 13
IS II 11 13 18
73 2* 23 22 34
22 82 1* 21 a.
$7 .1 40 53 *1
77 73 1$ 34 34
53 47 43 31 36
17 12 63 41 40
61 31 31 2* 33
3* 31 77 27 25
27 2. 33 83 21
*1 84 Is 1$ 40
*4 53 *4 23 II
Is 1 13 Ii Ii
20 33 7 23 48
2$ 33 31 45 44
34 *0 *4 32
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84 4 . 4? .4 41
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3* 63 43 31 *4
37 II II 34 33
22 *1 t$ 34 2$
13 37 73 34 I ?
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30 .7 44 44 .3
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$1 35 11 II 10
5? 37 *3 .5 II
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53 41 23 33 28
43 27. 36 30 28
13 *6 14 1* 14
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64 .3 45 .4 *4
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20 20 42 28 22
II 12 32 7* 47
11 30 .4 41 44
16 *4 62 .4 33
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Table 2.2(a)
Two way tables of monthly averages of hourly CO--CAMS
1 .P .4 I 3 1 6. 1 *141*
I. 5 38 31 31 54 I. 31 6? , 13 72 17
7. 6* 3. .. .5 5* I I 42 74 7. 54 I I 3$
I. . 4* 4 . 14 1$ 34 22 I I 3 11 .7 14
3 . 14 . .. 65 *1 I I .5 12 Ii *4 *4 33
1. 1 23 £ IL 13 1* 12 4 .3 4 31 34 .5
1. * .7 .3 1. 15 3* 6. .1 3* *8 33 3. 34
7. I 17 17 *3 II 1* 13 84 4* . .4 43 .4
7. .3 1. I. *3 I. *4 1 2* 34 3. .34
7. 6. 3?. 44 5. 1$ I) 14 4 .. 3. .3 44 .4
1.6. 34 44 .1 . 1 . I. 1* 3* 36 7. .4 13 *7
2.15 34 4. . *7 1? 7* 7 61 7 1 . 411 54
l_1 4 44 3* *4 74 4. 54 34. 43 3. 25 7* 44
71 I 41 4. .2 .1 4. 17 47 4 l *3 .1 72
21 34 . . .2 .. .3 36 73 73 34 37 54
71 . .. 2. 1. I. 5* 24 .7 Is 34 11 43
TI 31 74 I. I) 13 5. .4 1* 1a .4 4? 31
713 . . I. 11 II I. I. *3 33 4 313I .56
1. 4 2? .. 1. 1* I. IS 4; 33 35 45 I I 11
ii 1 44 I. 13. 14 I I 14 Is 38 31 34 33 47
II . 4; .4 1? *5 1. 34 43 6 34 3* 44
71 33 4 . I a 1. 37 36 IS 7. 34 3* 5*
21*. 1 *24k.. .11 1 1 $ 5 .7 4 ! 7. .3 $$S
.6 51 33 37 34 1 41 *7 3. .2 .. 12 74 15
73 lg 4? t. .1 34 11 15 48 75 6. 21 7* 81
7. 1 .4 4. *4 17 36 70 , . i4 44 73
1. . . . 4 43 1! 3* II 32 64 7. *4 44 1?
3 . 3 .7 53 43 3. 14 57 34 44 _ .2 5. .4
47 4) 4. .3 7. .3 23 .1 1. *4 54 48
3 41 3. .1 *7 I. 11 2. 43 44 34 36 43
1. ; .3 . . 3 4. 5. 8.. 24221. 6.4 .8 50
7 1 27 . . I 3 I. I. 33 33 .3 14 2* 45
1. .3 .3 .3 Il 1. I . e .3 . 36 *3 .4
1 . . .. .. *2 I. 18 13 1. 54 *1 46
1. . .3 .3 ., . l *4 33 .5 S. 1. 54 .2
7. II $ *1 3 4 * 1. 31 .4 .4 72 7* .2
7. 1 51 1? 4. 4 1, 7* 77 .4
2. * 48 4 3. I. 3. $3 . 1 3 7 71
73 44 47 . .3 7. *4 15 34 43 *4 12
3. 47 38 .7 33 Is 1 4 43 14 54
Ii 4 33 45 . 4 * .2 3. .4 .. 14 13 43
113 3 .4 .4 69 I *4443.4. .344.7
74 32. .4 1. 13 81 12 54 4 5. *2 10 1 5
71 I .,. .4 66. 3. Ia 64 l 2! 3. 31 3. 43
1* 4 43 I. Is Il 87 17 3* 33 3 .3 44 52
S I Se 37 41 .3 * I 3* 34 4. 11 .7 54
73 1. *a 4. a. .8 4 .6 3) 71 *1 75 73 3*
73 11 47 31 a. 3 . 5. 17 33 71 11, 34 14 54
7 *3 53 41 33 4* .1 37 *7 1. 11 33 54
4 5 34 .3 3. 14 I a 13 35 4* 51 56 43 53
1.4 443 4. .3 45 *3 33 72 7* 70 71 .4
2. 5 .3 7. 3. 66 *7 22 .3 7. 2* .4 34
14 34 34 34 I I I. II $7 3. .4 34 3.
7* I ? 4. 1* 15 IS *5 4$ .1 41 43 *4
I. * *4 1. 13 1. 1. 11 *7 33 3. 31 33 34
2. 7 .3 *4 14 12 Is 3! 43 3* 3. 32 3* .4
74 a 17 1) 3 74 Ij I. 34 4$ 4. 31 46 .4
1. .4 1. I I 3. IS .3 .2 46 33 3 32
7. I. .3 .33 4. .2 3. .3 34 .3 71 *2 10 .7
14 II .7 36 .1 1$ I . 13 .7 .3 4. 1$ .4 $4
S. 4. 43 4 17 63 *1 5. 21 4. 31 $1 72
71 1 .4 1. 1 *2 34 12 .3 3. 1. .3 34 34
43 57 1* *5 1* , I I 1 5? 53 44 .3 53
14 4 44 1 1. 3 4 *4 33 4* 33 37 44
73 *4 17 3 3 4 17 41 3* 33 3.5
1.. s 14 4. S 2 .1 $ 1. 34 1: 41 2? 34
11 , 12 & * * 4 13 3. 31 3$ 33 31
431 35, 4 3 4
74 s 17 . 3 4 4 3 *2 .7 a. 34 33 44
1. 13 4 7 7 II 3g 3 4* 43 .54
2 ) 4. 32 44 I. 13 II *3 21 4 45 34 s O .7
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1 . I .34 33 1 1. 1) 17 .3 39 56 II $4
34 2.1.17 I I II II *8 *6 7. .4 33 37 3?
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7* 5 43 5 a * . 1 11 .3 31 .4 45 4?
1* 7 53 4. . s . I. .3 41 *3 44 67
11 .3 15 *3 4. 9 67 14 6* 21 31 37
12 1 Ia 16 13 1. II 33 33 . 24 33 31
2* 1. .4 3. .2 .4 I I II 4* 51 37 30 41 *3
2* 11 33 3. 4. 8 *1 5 .3 .4 3. .2 3. 14
14 13 .7 3i 2. .. 31 $3 76. 1.1 5. 73
(7 * 33 41 .4 41 Ii 37 .2 . 2* 32 3
33 4 6* 31 2 34 1 3$
47 37 4. 14 13 1$ 1) .4 3. 41 43 6*
33 43 21 .4 Ii I. *3 17 3. 32 3* aS
1 4 34 .. .3 13 I . 14 1! .4 4* .3 .7 44
71 1 11 II 3. 43 1 14 21 4. 55 .1 ii
fl 1 t II 1. t II IS as a. .1 4* 27
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ii 33 a* .2 3 1. l4. s 41 . 54
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1. I 1* .. 44 .6 . 14 43 *4 .8 84 5* 44
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1. i .8 5* 13 5. *4 *8 .3 .3 4. 2! 34 33
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11 53 33 *3 *2 .2 44 42 40 .4 40
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Table 2.2(b)
Two way tables of monthly averages of hourly CO--Hollywood
Si .. 13.1 7.. 4.. 14 3.1 4.1 .OT.1l1..l3. 4.t1?.2 14.3l?.1l 4. I . . . ?. 4. 435.? 14.oI I.213.7l 4.J
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34 *1 .3 .3 .4 .3 1 103 133 .3 44 II II 44 14
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Table 2.2(c)
Two way tables of monthly averages of hourly CO--Alder
U 1 2 3 S 7 9 10
11 12 13 14
13 16 17 11 19 23 21 22 73 AWl
si L, ..2 s.s .; a . . 4.4 .* 7.9 c . c. ; c.iiC.e 94 1.3 9.7I0.21 3.3a3.31I.aIa.3 a.a 5.4 5.3 7.s
.3T , SC?.. 731., 657. thC441lU , RhII.uP £48 LS3I#3
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-------
Table 2.2(d)
Two way tables of monthly averages of hourly CO--Lloyd
t . .. I 3 7 . 9 10 Ii 12 Ii 14 15 16 17 11 15 20 21 22 23 AVI GIV
13 11 52 14 7 6 1 S 30 .57 44 50 53 5 . στ cι 31 49 36 36 23 15 32 21.9
5 14 1 . 1 7 9 . .3 3 35 44 36 φ .3 4 54 117 109 74 4 37 30 27 20 1 34.4
I 10 3 3 .5 3 6 10 Z a a 34 6 1 .5 52 58 34 33 21 17 13 10 24 14.7
2 6 7 3 3 2 2 3 10 1, 13 20 21 V. 37 31 35 0 .7 32 3 24 27 16 15 l 12.7
7c 3 7 8 4 2 7 10 1 13 13 23 2. 30 2 1? 2 2 23 24 15 17 12 10 1512.3
to . 10 4 8 6 5 S i 17 2 17 V. o 32 31 35 . 7 S 3 E 3. 26 20 13 II 21 52.1
It. ; 7 a 1 7 c 14 1. 19 23 .SU 34 34 36 37 45 44 31 23 27 21 11 10 22 12.7
13 7 1 7 7 9 14 1 19 27 34 45 4 54 54 33 34 31 29 16 14 2.15.9
ic I 11. 1_ 9 , Ic 12 19 2. 22 32 33 C 40 .0 . 53 53 0 35 33 27 15 14 27 14.4
7 . . 13 1. 10 5 3 11 13 2i 25 32 . 51 . S 47 U 56 55 .3 34 33 30 15 16 V. 15.3
it. , 16 1 1 12 1.. 1 11 27 . 33 .3 .7 3 .8 45 51 O a? 50 47 42 33 21 19 3 10.4
7 L. 9 25 1 It. 1. IS 19 31 . 3 6 51 Sc .3 53 36 45 79 37 .3 55 33 37 34 43 18.3
11. 11 S I 14 1 52 17 IC 4. .4 51 50 61 .4 3 99 118 83 71 C l 51 33 31 50 ..S
7 14 27 25 21 3 1? 17 31 3 .1 .S 3 . c4 7e 01 87 .9 111 107 77 65 62 63 34 30 53 2 .2
ii 1 . 1.. 17 14 1. C 37 .. o 3 49 aS 31 1 71 6 o4 35 9 7 35 a 5 22.3
ii 22 1. 14 11 11 11 13 47 2. . 31 46 35 34 39 q 54 ol 43 35 36 34 29 23 31 13.3
17 3 1? iS 13 14 11 11 15 25 21 28 33 40 42 3 55 aC 44 34 31 30 23 13 29 13.9
11 7 Ic 1. 15 14 15 12. 45 g. 29 37 44 33 53 54 c3 71 52 50 45 40 28 2 38 1 .7
17 1.11 , c .131321g.. 412632353333 . . 4543363333261614 2512.5
7? 12 12 s 4 , 11 13 13 ;. 26 1 31 . 9 .0 2 .c .3 33 33 32 23 16 13 2. 13.1
71 7 15 14 1 11 10 12 15 20 2. .2 31 31 35 42 2 46 53 52 37 31 32 27 11 16 27 13.
77 £ 13 11 , a 6 14 21 2 26 32 4 52 53 50 52 .2 5 40 41 37 27 15 13 SC1 ..0
7! 5 it 1. 1 ; s 5 13 24 51 7 33 .35 3S 43 4$ .4 49 55 7 45 39 34 24 G 30 14.3
7? 1 21 17 1. 13 1. 14 U 33 37 34 .2 44 4 45 52 .3 71 *0 57 50 40 29 24 37 17.1
77 11 17 it 13 11 i 11 15 28 3 31 6 .1 3 33 51 6 73 74 53 .4 3; 45 37 26 38 19.2
1? 1 13 1. 4 5 5 4 21 2 31 41 50. 71 .4 74 l v bo .4 60 52 42 .1 22 14 3. 27.1
7 1 7 4 3 3 3 .16 21 20 28 31 4 3 49 55 85 .2 49 .3 34 29 15 13 24 21.3
7. 5 7 5 4 2 6 16 21 22 29 33 34 35 34 .3 51 82 31 32 25 26 15 13 2415.9
7: 3 11 13 5 5 7 1 Z4 .3 5 44 .3 43 41 41 37 5 9 54 44 61 23 18 3317.4
7. 4 3 4 3 4 1 5 11 1. 10 16 13 4 37 21 34 .2 30 31 27 21 19 11 7 17 13.3
7.. 5 4 3 4 . I 13 L 14 1. .3 V. 28 23 5 .2 25 21 23 17 7 4 1612.4
?. . 4 3 2 3 4 9 11 14 15 4 32 32 Is 34 33 4? 23 2 19 13 6 1712.4
7. 7 3 4 2 2 4 4 13 1) 1A 9 34 . 43 44 3 50 .7 31 3C 21 71 3 S 2217.5
a 2 3 2 2 1 4 1 1 1? 23 51 C 6 33 3 40 44 Zb 21 23 19 7 19 1..2
9 8 t. . 3 . 6 18 2 19 2.5 2 .37 45 34 9 50 53 36 39 33 33 24 16 24 13.2
7 . 1 14 11 5 6 8 1! 31 3. 51 3 Si 47 .2 0 44 54 44 50 41 36 32 21 19 33 16.3
? ii 15 1? 16 I I 13 17 13 3 a 7 .. Sc 53 39 .2 79 7 41 44 43 40 30 21 35 21.2
7: 1. 15 15 1 Si 10 1. 7 20 43 34 47 5c 57 81 sa 107 103 ? 65 33 53 33 23 .1 Z .7
7. 1 21 19 14 11 11 14 14 33 3 3 44 .4 45 36 63 71 44 56 *6 5 41 46 29 2 41 23.9
4 11 4 8 3 4 4 1! 3 24 33 34 .1 39 43 51 39 46 45 22 27 20 18 2. 13.6
1, 2. 6 , 0 1 13 26 43 25 4 31 4 41 31 2 6 7 34 31 26 21 13 11 2. 13.9
7, - . 13 13 4 5 7 9 12 1 18 23 23 32 3 32 .0 45 33 37 3 31 26 14 15 23 11.7
7. e S 3 s S 13 14 1. 18 2 4 53 1 32 34 39 41 26 22 24 21 12 9 2011.4
7 , a 13 . 7 0 a 7 9 15 1? 18 27 33 .0 43 31 35 40 21 28 26 23 15 9 22 13.3
7. 1 8 3 6 8 13 1/ 16 23 27 35 32 3.3 .. 37 17 26 2.1 21 13 10 6 19 11.4
6 5 4 3 6 6 16 2 21 27 33 61 41 39 a 4 50 36 32 29 23 14 10 2415.5
7. , 9 1 6 3 IC 2 .7 4 27 33 33 33 34 34 .13 40 26 31 26 23 12 II 22 11.7
7. I. 2 3 4 . e 9 23 2. 77 . . 31 3.8 . 58 36 39 51. 53 7 4; 4 40 20 14 27 16.4
7., I I 11 1. 4 4 & 17 .. 24. 5 28 32 49 .2 47 56 61 43 4C 30 23 11 11 25 16.6
1. 1. 13 1 . 7 5 3 16 27 23 Sc 44 52 52 34 67 69 50 32 33 29 17 11 30 20.1
.vt 13 11 9 7 7 11 21 2.. .5 31 36 44 .4 43 .9 59 83 5 43 35 32 20 15
51 o 7 5.7 4.2 ..4 6.1 4.1 4.8 7.1 .? 8.7 5.2 S.S11.517.313.414.71v.0 Q.413.717.411.i11.4 2.3 7.1
.3 1., 53 ? .. 75f , 531. t4C 4U LS, A1L .U 4
13 2 4 26 116 0
-------
Table 2.2(e)
Two way tables of monthly averages of hourly CO--Eugene
,. cc I 4 3 3 4 7 . 3 10 21 12 13 I I 14 14 1? 11 It 20 61 32 33 LVI II !
71 3 87 $4 t* t i 2* 13 .0 .1 1. 10 41 23 23 33 24 67 27 31 31 81 U 33 21 II 24 1.7
i i ,. 4 30 13 II 1! 1 Ia If 1 .3 .8 .2 U 24 IS II 16 16 24 I I IL 3.1
11 7 23 I. I. 2$ I a I . 1? 43 2 . 23 .1 21 .4 22 22 .3 . 2 .4 44 64 23 32 21 40 20 2.1
71 . 33 .2 13 ls 21 ... .4 37 3. 2. .4 .3 34 a 37 I 44 36 37 31 21 63 .6 23 1.4
7% 4 31 1. 1? 26 13 1. 322 31 3. 33 .5 34 .2 .a 24 .6 32 .3 31 33 31 83 30 31 26 3.7
11 I. 41 24 I I I I I. .m 3$ 44 3. U 32 37 35 33 33 32 41 3? .8 .2 .4 .3 33 33 3. 2.4
1211 2 17 IS S 2 . I I 43 3 1 Is 34 2. 34 4 o 33 a3 44 . . 42 37 34 Zf Z6 3315.1
ill. . 3132741.Iv.? U . .3303 13.3434424! 3333143 13$2323 331.2
1 . 3 I ? I Ii 3 2. 13 .4 4 .4 g7 33 4; 51 34 .2 53 3* 34 27 la 23 32 I I 2. .1
73 44 .4 14 27 Is 84 .1 36 3. 33 36 34 36 54 33 3? 47 .3 42 46 33 34 I I 4! 32 ..2
44 s .1 14 33 Li Ii .2 32 32 11 U 33 22 38. 32 33 3 3 3 . 15 12 14 31 21 34 7.6
.. . .3 .2 1. Il 14 .4 4$ 34 44 64 .5 4 Zi 31 5) 35 36 10 23 31 31 34 23 31 27 4.3
4. 3 34 3. 3.. a 3) .3 . . U 33 33 U 33 41 sI 3 ! 43 46 33 34 37 31 34 33 32 3) .7
7. s 32 23 I? 2$ 15 IT .1 23 4.4 iS £ 31 31 34 37 .4 3 34 44 34 48 26 23 32 36 S.;
7. 7 23 41 .5 63 .1 33 33 3 * 33 34 3) 3 . 34 .4 .3 83 31 32 33 33 36 33 52 33 3.1
1.. 3 ieb 34 4a8. .3 .23. 3.. 33314343.4333232.2434144414444 .17.3
7. . 1 35 U 33 4 13 U .2 .0 43 .1 12 14 IS II SI 5 14 62 37 44 34 44 l.a
7. 1. .4 34 . 2 .3 2. .i .0 32 32 43 34 82 43 .4 34 43 71 5) 74 74 71 .7 42 33 32 16.2
l i II 34 .i. 3 .8. 41 .4 34 4 4. 44 4 84 84 84 1* 72 78 .4 51 31 4 41 St 34 43 Ά4.4
7.1 . . 1 .2 .131.434 U 4. .341)155)03. sI 70 34 30 44 SI 41 35 24 4313.4
1 I .2 2. 81 1% 13 14 4) 33 I I 33 32 33 33 3! 41 51 37 .o 34 35 3* 34 33 24 32 23.7
1. .23316 .4. l 33823.333.43443234511171st 4 55 33 34 63 4I4.)
Sn .4 , . en a. on .414 .3
4. . .e *4.3 . . e44 .. .*443 .3
3 .7 .1 46 .8. 1. 11 11 33 3,. .1 24 44 32 I 33 34 3? 34 30 34 33 34 33 33 31 334
1. *2 *2 44 14 14 37 41 3 2 -27 44 41 4 30 34 33 14 13 47 37 21 31 14 41 31 3.1
4* 7 .4 .. 23 .4 5 .3 33 46 4 . .7 . a4 30 31 33 33 31 27 23 34 89 4! 2? 3.3
73 4 41 4 . 42 .1 .2 .2 33 c 31 3 33 3. 30 36 34 34 31 31 34 32 31 I I 3! 24 4.2
73 34 LI 3 , .3 33 .3 23 34 . 40 32 33 33 33 3$ 3* 33 3? 31 36 33 45 48 .4 34 5.4
s 3.* 2..4.. .l 4 433. .3 34 31 36 35 I I 1.441314433333433 ! *313.4
. 13 24 4 1, Ii 27 d l 11 a. 3! 43 34 41 34 44 47 51 .3 .6 46 35 34 30 43 33 11.3
43 1 27 IS 14 U 17 36 13 37 2. a. 34 43 .. 50 32 40 3. 34 1 2* 34. 1111.1
. I 4. 1 34 IV I L IF 44 23 3. . .8 41 il 31 2! 3) 31 37 37 33 23 31 31 3 25 4.3
Is e 4. .0
1. .4. n ... . 311 .3
7 4. .4. .4* .4 In .3
2. n .4
4. .4 4 . 5* 43! .3
7. 7 64 .5 2 .. I . 11 .2 63 .1 3. 33 34 aθ 34 .4 33 32 67 21 31 i3 34 34 31 3.1
7. , 13 14 l 23 I 13 14 24 3.1 33 30 21 41 .6 32 23 21 31 Il I I 17 l 1$ 2* II 3.1
f 3 1$ 13 *4 12 14 10 23 .3 .. .4 .2 32 .3 32 23 12 2 34 It 31 34 36 36 34 23 1.3
24 3 . a 4 .. 1, Ii 35 38. 33 4 43 55 33 31 32 33 34 43 3 40 4C 43 .3 42 37 33 t43
1. II . 4 .1 .4 14 1? 2! 31 3. U. 3 4 37 . 17 .4 .4 .4 4 13 3 4 3 31 53 34 3 3.4
7. 14 3$ 34 .8 £1 .1 4 54 2 *3 44 44 .1 .3 43 .5 .1 33 45 44 43 41 34 33 54 14
73 3 6 S 5 V 11 I .24 14 1 5. *9 44 44 It 33 40 ti $3 24 ti *4 13 34 434
14 . en 4. 0*. S* 4* *44 . fle S . 44 4 44 1 .0
13 ...... . . .4* Sn *4 ! .4
4. .*e S .. s 4$4 .3
73 ... en- . ,.. ,n 444 .4
33 - e4.4 em .. 441 .0
7: 7 S. 9 .4
4) 5 .4. . . . 434 .0
32 n 5. 54 17 1) .1 43 3 32 4* 34 .2 *3 35 2 4 33 4 ! 84 44 43 44 3421.4
15 I 33 48 17 24 14 84 14 30 3. 36 34 .8 4$ .3 34 .1 30 54 42 .4 34 44 34 34 33 11.4
I s 81 4 17 8 . I. II 84 a 3. 33 31 34 ja 33 31. 2 34 *4 38 41 44 48 3* 33 33 25.4
7. 83 1$ 14 14 U 24 2 ,. IS 27 3. .4 43 44 44 47 44 .. *4 34 41 31 33 13 3? 34 33 13.3
74 3 5! 3. 16 13 11 23 14 33 4 44 *3 61 4) 43 81 54 34 Il 33 51 43 46 7 l 34 31.4
7. H I? 16 Ii 4 14 21 31 1 33 .32 32 32 35 33 4 .1 3! 44 3? 3) 31 21 21 883.4
is 3 21 15 IS 1! 4 I 4 31 33 63 21 2 1 52 27 33 34 3* 44 2! 34 36 33 34 33 2* 3
7. 3$ II 14 I. II I . I I 31 3.. 3 1i 14 5? 57 31 51 48 44.44 31 32 32 3. . 33 32 30 334
7. s is £1 I. 11 14 2. 13 27 4. .7 4 45 44 26 S -26 7 20 23 28 26 84 .4 34 43
Is t ? 13 13 i 13 2735.3.23 45. 3.243$ 3. 7,3? 44271617302032 311.3
7$ 7 I I 1. 14 83 I I II I I 42 4 .* 30 81 24 41 2. 37 37 34 11 17 1$ 1? IS 20 31 6.4
8 *3 33 13 33 ii 3% 13 34 3. 14 33 24 1% 54 10 2 $ 52 51 23 21 31 24 24 2.2 23 7.4
74 4 43 1. *1 1. 1$ 14 2. 41 3. I I 3 3.2 32 s 2$ 47 2 21 32 32 32 34 24 .2 8$ e .Z
7. *4 33 .7 .3 23 Is 15 1 ! 31 4 . .5 5 .3 .j .4 .4 .2 41 50 45 41 41 .4 44 4? 34 21.5
14 11 4) 2 .1 1* 12 23 I? 31 3: .. ) . 54 3. .4 13 57 74 .2 .4 41 14 51 31 44 45 L3.3
? 1- 41 32 33 .1 3. It 44 .3 so 64 .4 74 74 1. 70 14 6 143 76 73 *3 *4 1 1 3* 37 33.3
77 I .1 . . 4 . .2 34 4* 11 42 34 54 33 34 54 .4 3j 4 14 .4 .3 .2 44 34 I .3
33 33 34 2 .4 1. 14 5. 17 31 33 34 3 41 44 43 43 31 ii 50 84 4) *7 3 37 23.4
SI I 11 I I 1. 13 11 *3 17 33 31 .2 33 33 32 54 os 41 .4 t 31 33 24 33 36 37 ..3
17 .4 .m 6.. II 11 I. .2 33 ;c 4 3. 37 .8 55 34 .e 5 .7 30 3. 77 34 37 33 33 1.6
ii 14 24 2. II 18. 14 31 i. .3 33 .3 32 31 41 32 3) 3? .2 20 1! 22 23 23 23 7.1
31 5 11 Is Ia ) I. 1) 11 22 2 52 37 54 33 34 13 37 34 .3 22 28 43 34 4 8) 1.4
: 1 .,. 13 3. . 33 1.. 11 .7 .3 .. 13 j4 54 57 33 34 14 3? 34 43 31 31 21 20 31 34 7.5
37 27 Is is $4 Ii 14 I I 35 3. 31 34 33 : 1 a) 36 33 3 , 37 43 33 3? .4 34 23 24 8.3
U . .3 Il 13 3 3 I 34 3. 33 36 .4 .4 .3 .3 .5 34 37 41 31 33 37 32 31 33 14.3
7? 1; 33 .4 1 i 13 13 14 32 5* - .1 45 46 II 31 52 33 54 II 54 SI II 51 34 .1 44 Ά1.3
I I 13 1 1) I I $ 7 1 4 14 4. 37 64 31 . .8 .1 .4 3$ sS .4 .4 31 33 30 24 50 15.3
74 1. *7 I . II 11 _1 j . 33 34 . 3 .7 53 37 43 30 .4 3S 27 31 It 31 11.4
7. I .. 5 2. 2. 13 1. I I U .4 .3 .5 .3 35 11 53 14 .4 .5 5. .4 36 31 32 27 39 Ά7.4
13 83 23 23 II 14 23 33 5. .4 .4 53 55 . 52 54 4 4 5 3 34 3. 23 4) 35 51 13.3
, S 11 17 1) 1* I I 1? . 43 41 33 34 33 36 33 *4 5. 34 46 38 18 21 21 21 34 is)
I. IS I. It I. . it II .. .2 21 23 .5 4 34 4. 32 35 43 47 24 37 2 33 23 7.
7. 13 843 2 ,. -. 7 23 4 41. 11 It 13 .2 44 23 .3 43 21 11 24 14 11 26 I I 1) *.6
1. . 14 $7 l 1$ 2. 2. I I 43 e *7 53 14 22 34 3.3 26 34 3) 2. 23 22 33 35 .2 2) 7.4
7. 3 IS L. 23 $ I I 22 34 3 30 33 .11 32 30 32 31 44 I I 5 24 42 17 21 1.3
a 13 I. s 7 7 1 2 . .48 .. .4 .2 3? .13 44 7 .1 30 24 .0 II 21 41 23 II 30
1. 23 Ia I . . 7 7 . I I .27 3. 27 44 32 33 33 33 3* 41 ii .7 3. 85 26 34 23 23 20.3
7. . 37 IS Ά3 1. 13 14 1 32 7 .3 .3 34 33 34 34 43 41 3! .6 I I 44 .4 23 33 22.3
.. I I 34 .3 17 $3 I. I I 1. 32 . .4 .8 42 44 .3 .4 .7 53 71 I I 10 42 .4 54 31 37 23.4
71 2. 44 .. I) IS I d 13 17 7 3. 33 .42 44 34 *4 .4 33 3 72 3 ! 52 42 43 37 31 34 26.2
7. $ 4 2. 27 14 14 23 Is 33 . 3m 37 .3 .3 .4 .. 30 .3 72 11 .3 4$ 31 32 .2 15 13.3
1. 27 * 5 2. 3 1 28 .4 3. 3! 34 33 34 35 35 .4 .4 II 3? 32 34 24 33 43 3111.4
1. , .1 2. 25 *2 II IS 24 36 3. S .54 21 24 2? 41 *7 45 54 34 j? 23 3* 31 41 31 II. ?
7. It I I) Ii. 12 l .1 4? 33 341 34 31 34 34 14 .2 .4 St 31 10 I I 36 34 47 14.1
7. 5 I I 21 28. 7 7 2 5 .3 .1 6 .1 41 33. 33 51 31 33 23 22 33 43 33 33 tt 24 4.3
7. , 13 II I . i I I . 33 37 .4 .2 53 33 31 34 3 4% 48. II 44 31 Ά7 31 .4
7. 1 12 . . 7 7 5 24 3 2. .4 .3 41 3. .7 7 9 43 21 It I i 2? 28 1$ Ά1 13 1.)
7. 1! II & 7 I ) 15 .3 . .7 .2 33 48 :2 33 .34 33 21 42 24 *5 23 ..3
7. 14 1. Ii S . 14 33 5. SI 212 31 38. .2 44 24 33 33 3$ 33 26 24 25 .2 24 4.6
7. 1 .1 II 13 4 Ii Ά8 33 *2 37 34 33 34 32 33 26 .1 51 *4 43 33 44 35 33 33 11.3
7. Ii :2 ii 13 24 . II 13 .4 2 38 34 3! 54 27 3 87 16 .4 8s $ 44 44 16 13 24 *4.4
7 . I . II 3 . 9 28 33 58 33 i s 34 .4 24 .4 33 .4 .4 34 13 3! .2 43 I$.,
I? 15 14 15 33 .4 3. 23 34 35 34 36 2? 3 .4 .5 36 3) 34 34 32 4
II 3 i .3 ?. 3.3 3.. 7. . ..I 7.6 ..II(. .I 3 5 4.3 !.4 4.424.311 .11a.3t1.41 .6 123332.21 131C.7 4.7
.38., 132.. 132 ., I. .%4C..lI1.13. 4.04 3 P 4I .8131.4
34 .. 34 34 II I ) 33*
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71 a
7 _I S
71 1
11 .
7, 9
71 2.
7l Ii
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14 $
7* .
7. 3
7 .
7. 7
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44 14
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73
73 2
33 3
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73 3
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11 .
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71 .
77 5
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77 3
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77 1.
I. I
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7. $
7. .
*4 1.
1. II
7. 2 *
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7. 1
1-. .
3. 4
1. I.
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7. 1.
way tables
C I 3 S
43 2) I I Is I ) SI
23 1* 13 23 33
34 .3 13 I l 1 43
33 1. $4 1.4 23 34.
.3 I . 13 I I 2* It 34
a . 3 I . I I 1) I S 4 !
*7 13 Ii I I I. II 17
5 I. 3. II 16 24 3 ,4
.4 I. I? II II L. 33
3* 14 1* 14 17 *3 .3
34 41 1. 11 1. 2? 34
4* 43 (5 14 a *7-33
33 3. .4 2 . U. 34 3
33 43 .3 41 42 .. 41
33 .2 27 *2 I? IS 33
3$ 2. 15 13 *3 I I 32
14 11 2 14 1* II .3
a It 23 I I 1. 1.
36 1. 13 14 11 Il 12
4* 23 1 5 *3 1. 12 37
33 .4 1. 1. I 37
34 .. .. 11 *3 II
35 3. 17 3.. II 41
45 33 14 *3 1* 1* Ii
35 .5 15 1. .4 .7.
34 *2 24 *2 II II 37
34 *3 11 23 .2
31 45 .3 .3 3. 24 4.
3* 2. 2. Ά2 1 5 Ά2 31
43 41 ... 17 17 14 3
.4 18 1 1) 3
.3 4*. 1. 14 * 3 I I .3
.3 II I I $3
3 . 3. 63 .4 1. II Is
54 . 23 .4 0 19 .7
.2 a. 3. 13 16 * 5 25
*1 3. 67 .3 II I. 3.
.3 i 44 I . U. Ii 3.
32 3* 3. it I . .3 4?
34 3? 37 .1 . .1
44 3 33 *7 I. 23 37
.37 31 I? I . I? Ii .7
-II I . 2? 20 1. II 43
34 lb 11 I? 17 I ? 35
113. 32 .1 36 .4 63
8. 3 It 4 3. *3 34
47 3. 2* *7 I 34
*3 33 31 3*3 31 .2 .3
a ..j 64 12 13 *3 4*
.3 3.. 4 .5 4* .3
13 34 1 24 I 32
4κ 33 Is 2 14 l 34
a I. 2* .2 I. IS 3*
14 64 13 23 2 13 63
.3 1) 23 *3 14 13 22
II I. 3. 2. 1 5 12 4S
4 11 1 s 24 I . 13 32
0 2. 32 1. 2$ . 3 3?
3? *3 Is 15 14 14 3
lb 1. 1. 1. *4 13 .4
.7 *4 I . Il 51 23 33
14 I l 3 $ II 34
4 *?8
14 - .1 - 4 - 7 : I
II -, 7 . - S 11
; 7 5 3 4 1 13
5 . -s 3 7 9
*3 1 a 3 a 3 14
16. . 3 . 5 7 *3
31 33 27 14 14 II 35
43 3.3 1$ 1 . IS 17 .v
3, j Is Is *4 1) .3
T 4 Is 1) 1) *8 47
1$ 1. I I 3 1* . II ill
ii 1. $ 5 S 1
*4 1? 3 73 .0
24 6 7 3 I S
7 S 3 3 3 1 14
1 a 1 3 3 3 I I
*1 Ά2. I. IC , 14 .1
.3 I s 1% 26 24 II 63
.4 . 3 I, II .3 I I
U 7 87 .2 1. .4 45
.3 3. 44 .* .1 at it
II 3. *4 . 3 41 .6 30
31 33 23 II *6 28 3*
. .1 11 12 1. 39
.7 .4 1$ *7 I . . *4
.1 a. 1. *3 12 I. .2
IT 13 i: . 4
1$ I. *1 13 12 *7
33 I . I . 23 *3 I. .2
16 .1 ., I. I. 23 31
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33 31 7? .3 44 *2 16
3* *. 2 .1 .
41 33 .2 13 II , .3
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23 17 13 *3 II 3 44
*3 3. *1 e 13 63
4 2. 1. 1* II I 31
11 I , . . . . * 12
IS 21 1* 1 II *1 1$
:1 . 2. 1 5 1,. $1 .4
33 .4 45 *1 1 43
.3 . II 47 3. 4 3*
3 $ ._ I . 1, 5 ,
. 1 .3 27 3?
3. *3 4. 2* 53 *3 42
a *3 Ia 13 13 II 43
.2 3. 1. 34 1 . II
$4 II Ii 2. 2* 21
1*. 3. 1 1 1* 13 *4
24 13 *3 Ά4 II. 1*
1? 1. I. 3 a 1.
II 26 *7 14 II 11 .4
.3 .3 I. Ά3 1. II 0
2: I. I. 21 I. 1
4* . 2. 21 23 25 34
4? IC,
53 .2
I 7,
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21 0
61 5 .
57 1.
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79 162
72 II I
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41 *2
32 .
51 94
71 4 .
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44, 3.
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3$ I . . .
7. 4.
31 1*
62 2
57 3 .
4 2 * 3
73 44
4* 4*
IS Ά71
1? 3,
4* .
6$ .2
,1 7.
42 *1
31 *4
*1 4.
31 8.
8) 51
.1 163
.4 4.
.2 4.
39 .4
.9 6.
7. 9.
52 73
57 1?
4! 6.
42
54 3.
2I 5*
7*
5* 54
0? 34
57 7.
54
20
33 5;
34 8.
33 -.
4$ 3.
41
.3 4.
.4 ..
3 7.
4 6-
11 1 5
.3 5.
43 .3
84 *4
32 31
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6 7.
3. 3.
6 1*
3 $2
33 3,
53 1-
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7 0.
3 4.
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28 . .
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$1 $4
77 .
.4 1:
4! 2*
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442 5
34 .3
LI .1
34 .1
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23 1.
3; . 3
*3 8.
.3 7
.7 .2
33
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:3
14 *5
Table 2.3(a)
7
of monthly weekday averages of hourly CO--CAMS
2 IS II I. *3 *3 IS IS I? IS
74 sI 9* 39 95 $ 1*.. 2.3 154 41
2$ 93 43 14 3* .5 93 134 *21 54
54 31 51 32 5? $2 44 *13 13* *4
34 81 3* 55 34 37 3 .7 .1 45
*1 .2 SI 1 . .1 *4 13 .4 11 31
13 43 *6 *4 .3 .. 74 4 S
24 43 S 34 j3 31 45 43 .4 41
43 .3 41 .2 34 6* 50 00 23
SI .4 .3 22 14 II *1 71 3*
52 1? *3 34 85 34 72 213 1.2 53
41 U 12 70 .4 93 .1 *31 *94 .4
61 .4 ,3 1233 1... 123 . II . 2.3 2.4 .7
41 7? .3 . .1 .4 I.? 173 311 .7
70 30 2* 77 .1 sa 4. 3.2 I II .7
.6 3.7 14 3) .1 *4 4 . * 143 *1
53 57 .1 3 ) .4 13 .. 22 13$ $2
33 33 3? 33 41 .3 13 54 .7 33
31 5. .4 .3 .1 54 $ 34 141 44
37 i v .2 46 5 .8 57 81 31
a 42 .7 44 33 10 1 *1 4. .4
.3 42 5* *2 .4 5* 35 Ά4.4 *4 13
72 84 55 a. 72 79 43 I I I ? 3 7.
*3 II 34 53 II 1 ..4 1.7 331 035 143
ii . 4 323 16.3 34 23 U.s *34 Ά45 93
*7 77 .7 71 .e .e 71.. 173 1,1 73
76 1* *5 45 92 24 II . *18 1*. 51
$5 7$ II ii SI 33 3 ,7 134 1 t4 73
73 *4 74 U 76 71 .4 Ά30 13* 54
32 3* 44 . .6 .4 57 72 .3 34
.4 51 11 .4 - . 7 18 13 74 47 4.
45 53 34 55 .3 39 33 79 73 31
.4 sI .5 $ 3 *3 53 .4 23 *7
5* .1 1 5 75 1. .4 .3 154 *11 50
73 .4 23 U *3 . 4 73 lIZ I $ 74
.4 .7 41 ,2 44 * . 2*3 311 21? *4 5
.3 .7 164 41 241 2.9 .5 24 112 9?
43 4 .* *5* 1.3 Ά54 III *1 6
72 73 .4 .. .1 4 14 1*8 I II 94
*1 .3 71 1? 71 .3 *4 5 I .) .7
45 *1 45 *4 *4 34 14 71 7* 44
35 52 24 5. 53 5 5 (I 79 22 55
ii 3 . 52 .4 3 45 77 .31 75 36
34 44 44 *4 .7 64 56 8* *7
SI 13 .3 *2 ,1 33 2 . 7 .3 s 33
41 *1 54 45 77 i .5 1.25 16$ 37
II *3 .5 13 1* 71 .9 *13 145 74
*3 .5 43 41 47 91 2.4 2*7 1*2 91
23 44 *44 l 154 *30 *44 242 1,7 13*
15 10 72 72 3 .a .. 110 *31 75
43 .1 14 14 1* 34 .. 134 137 82
.4 1* U 73 73 73 2 US 16$ 4*
$4 SI 44 6* 72 76 .1 133 5) 39
.1 31 31 36 52 53 63 7* .5 4)
40 45 4* 83 4 8* 32 .9 31 *6
3 4* SI 61 .3 4? 53 *7 38
.1 3* 56 Ii 5) 31 522 84 *4 22
3 3. 37 .1 43 41 4? 17 77 32
65 3. 53 4* 31 5) *7 342*8 1131
15 aS 13 7. 7? *4 IIj Ά3* 2*37 10*
15 33 *3 37 tI *3 I.. 14$ *81 7?
.40 13 .4 , Oil .7 170 2.3 1.4 .6
34*5 61 *1 #2 77 5 t O - .8
34 .4, *37 J , ,j 54 ii 5? - 44
41 43 4.4 *5 85 53 sI 11 .3 II
14 3) .4 .3 .1 Ii 64 15 41 I I
31 .4* 1* *6 84 17 e5 72 4* 23
.2 44 3? SI 4* 34 33 41 I I I?
3? .3 54 5 SI 14 os 109 71 25
34 34 3? 37 41 *4 4* *3 .9 44
74 15 52 74 *2 51 *4* 171 *3 ! 72
.43 4 5 12 .t .5 24 143 341 *56 43
14 34 .2 . 4 .5 44 sh II 42
5* 37 SI *7 55 57 44 1.33 *44 53
3 .2 41 33 4* 44 44 70 .7 30
:1 14 *3 . 41 *1 8* 54 43 35
1$ 34 .3 * .4 10 4 .0 .0 30
64 10 33 .9 .7 44 32 34 12 I I
32 32 ii 35 14 34 34 *1 11 24
.5 21 33 36 44 30 .. 31 27 15
4* 44 *5 37 .2 .5 42 34 *4 44
65 37 39 28 35 3* 34 33 *6 24
:3 .* a * *3 ii ., 12 .2
5 61 .2 .3 .3 *3 1* 11* *72 *9
7? 14 .7 7* .2 70 *13 Ά64 1.2 50
73 $1 $4 $4 71 11 .a II ? 11* 72
50 . . .2 73 66 72 1? 163 1.3 11
23 *4 33 51 51 31 34 aS .1 41
:s 31 .4 .. 34 50 31 1 .5 41
32 31 5-7 a. 54 17 .2 12 .9 .4
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II 1
7 13
33 .3
.:
.1 3. 3 .4 .4.
II 3$ SI 72 31 33
47
34
17
34
3 . 9.1
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Ά4
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3 IC
37. 1*
7.
44 64 43 54 .4
59 10 43 .1 70 3.
44
43
14
34
62
7. *4
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I
31 43
S
*7 33 3. 3. 5
.6 1 63 13 4 53
44
.3
34
13
$
24 I. 5 63 143 0
-------
Table 2.3(c)
Two way tables of monthly weekday averages of hourly CO--Alder
r. .. 3 6 7 9 he ii L. *3 14 15 16 1? 14 19 20 21 22 23 av8 9
7 ) 21 Ic 34 1 9 l 31 .0 . 41 *d 9 ICZ c4 .1 O s? a os 39 31 56 55 54 39 56 25.3
74 1 4 16 1 11 11 12 21 31 3 47 .9 49 44 54 42 33 7 55 51 45 37 40 3? 30 0 14.8
13 ii 3 2 l 13 13 13 1 40 3 4 43 o 4 ..3 46 .2 * SS 7. 43 o 39 34 41 11.
13 _ 23 1 . 14 12 1 13 ia .3 4.. .1 44 84 30 4 70 3 48 43 31 31 31 29 2 22.3
1. i 1 U e 5 e S 29 4.. 41 46 .7 47 42 46 30 .2 eS .0 31 26 23 23 25 32 17.3
7$ 15 1.. 9 S S 4 7 45 i.. (. 35 4% 41. 42 2 .4 43 43 31 SC 30 3* 30 26 24
17 1.. l a 6 7 1 36 .2 43 44 35 47 .3 34 73 15 47 34 30 36 24 24 3621.4.
a. 4 a is ii 02 : 1 34 .5 33 o 33 33 55 46 37 32 33 38 3? 31 32 16.5
14 IS 1 . 2 5 5 14 .33 * .3 , 43 .2 33 35 34 63 70 7 34 35 37 34 27 3 19.2
7. . .1 U 1 a 5 t 11 32 44 3 47 5? 33 2 42 57 73 72 38 36 35 41 3? 34 31 19.9
7. 7 t 1 3 g 4 I 26 4. 34 44 49 33 43 54 53 64 .6 40 37 3. 62 36 31 3617.0
/. 42 1, 1 12 7 1 1 7 31 43 44 81 .1 o6 01 7? 75 .2 47 41 44 9 41 37 43 21.4
I a. 3.. 2! .1 1 11 3 .2 32 .... 36 66 7* 7 72 76 71 , . 54 34 53 47 .0 36 5i3 23.0
7. 1 31 .7 21 16 13 1. 3 31 71 72 73 71 73 4 71 02 1 2 60 72 59 63 57 54 35 24.4
; Ii 31 2a. 22 i6 13 18 23 36 c . . 13 7o 54 iS 75 t .5 1 125 70 54 St 52 49 57 27.3
2. 1 3 .1 4 44. 46 4 7 6t .1 73 64 .7 43 . 1 5 1 6 75- 13 69 64 62 54 0 a.?
7? 1 13 2! 2. 1 1. .2 33 37 71 4 7j 72 11 76 31 107 116 7? 61 56 50 32 4 Ss 26.?
77 2 4 g . . 17 13 11 II 2 42 Sc- 44 49 4 .2 44 47 53 4 71 33 46 33 3 41 36 39 304
17 4 C I . 1. s .. & 17 .43 I a.? 51 47 50 35 to 69 5 49 37 36 33 29 20 33 20.6
ii 2; . 22 14 13 14 .2 e 3. 35 5* 53 32 53 56 69 .6 59 31 9 50 66 1 18.3
27 5 .6 U. *3 IC , c *7 37 41 55 .4 1 63 30 33 3* 59 42 34 32 30 52 28 4? 35 15.5
77 & i 1,. 10 c 11 1 . s. 40 0 50 6. 49 33 So .0 *1 33 33 34 33 36 30 35 16.2
17 7 U 1. 10 7 7 11 .2 . 9 44 54 4 4 39 3 57 c ii so 2. 26 47 23 25 32 14.0
1? . 13 14 12 1 1? 37 S . 31 62 10 Ot 70 .9 .3 10 83 44 33 37 34 34 ii 21.4
77 , 21 U. 15 1 .. 4 1i 13 47 4. 32 53 9 47 49 32 si 51 .0 40 C 36 34 30 29 36 15.8
Ii 1 3 .. 13 *3 1. 13 2 .4 03 61 56 53 43 0 49 52 55 72 35 45 44 42 33 35 1 1?..
11 11 a 2 19 14 1 . 15 .a. S 6. SI 53 .2 Sc 3* 36 53 ,o .0 4 35 38 40 3 44 43 14.1
i i U. 11 17 13 12 * IC A. .6 a . 36 44. 4 4 30 55 3? 4 69 43 36 31 32 27 23 3? 16.2
7.. 1 22 22 r, 11 16 2G 41 4 ,. 30 3. S SE .4 59 65 79 .0 9 .C 31 35 32 29 2 15.8
7. . U. 13 17 11 11 14 24 43 6 39 46 33 4 36 34 3 56 36 2! 24 27 23 23 29 12.6
Ic 3 21 1.. 10 13 12 13 17 37 4e oG 39 4% 34 .35 43 42 44 .6 42 38 29 31 26 28 33 12.9
7. o 21 ii 11 8 . 9 17 34 SC 34 4 3 .9 .9 69 71 4 3; 32 36 29 30 36 15.6
U. 3 19 1: 12 1C 14 33 4s 35 35 36 33 40 45 45 51 5? 30 26 27 31 31 31 30 13.7
1.. .. 1. Ii. I i 13 4 1? 31 3 , 0. 3 . 37 37 .2 39 44 36 36 31 3. 39 49 44 39 33 13.4.
7.. 7 2 14 1. 11 12. 1G 13 35 4.. .0 44 44 44 44 53 69 52 54 34 36 30 36 33 29 33 14.1
1: c 14 13 12 0 IC 13 37 4: 37 36 .1 46 43 i4 30 45 53 29 26 27 36 29 27 34 13.3
7.. *2 13 14. a. c 15 27 4 2 .3 43 35 .0 38 42 49 37 35 35 39 42 36 32 32 14.4
it. θ 4 16 3. 13 14 .o Sc It a.* 4. 65 51 sS 52 54 66 42 36 47 41 47 41 3? 44 19.9
1.. 11 44 2 &3 17 14 17 a 5. .4 44 .7 45 44 43 63 44 II 53 6 34 37 3 30 41 19.1
U. 2 22 1? 15 1 I I. 13 14 45 54 .5 .7 55 3* *0 .7 75 95 4? 50 43 36 41 33 30 4 24.3
7. 1 21 16 17 14 1. 12 41 39 . 42 .3! 9 .5 35 ,o3 35 89 .5 54 44 35 41 35 34 41
74 13 1.. 13 . a c 16 45 43 3? 4* 32 31 44 3 5 3? 53 .0 s? 37 41 20 25 23 32 16.4
I. 3 13 . 9 8 7 19 50 3 . . 4 50 .2 54 33 So 5 . 10 #5 35 32 31 29 26 21 36 20.3
7. . 14 11. i 3 3 4 14 4 4% 4.0 43 44 47 .7 31 47 85 .9 3 32 33 32 26 44 33 15.3
7, 3 9 4 5 13 29 3. 34 36 43 34 s3 41 44 54 54 22 15 16 21 19 14 25 1e.0
7. . 5 2 2 2 s 24 3. 31 33 40 2 45 49 46 53 52 22 22 23 2 23 13 43 16.3
7. 1 13 .. 7 S 3 13 32 33 38 33 5 . 34 oσ .0 08 62 32 48 25 25 2 22 19 32 21.2
7. 73 4 2 16 .9 .7. 33 .37 4 .3 43 40 5. sd .36 Z 27 27 29 4 34 17.0
7. , 14 1 1*,. 7 6 14 46 S.. .4 32 54 3 66 54 3? S ol 30 3C 30 29 2 23 34 19.2
1. 14 15 11 9 6 4 4 14 42 5* 48 64 43 44 44 43 50 3 71 44 38 37 40 34 23 35 ii. ?
1.13 1 11 ii. 6 2 9 13 37 4.. 41 43 .4 41 46 46 44 56.6 38 29 2 25 22 19 3116.6
1. 3. 17 13 U. 1c S S 17 .5? U 42 44 so 54 9 43 So oo 12 43 31 25 2 20 20 34 15.9
avi 71 14 14 10 9 70 17 40 3. 47 49 34 SC 32 5 4? *7 73 43 3; 36 38 34 31
ST ci&. o.2 . 3.1 4.3 4.3 4.7 5.3 2.11 ?. . 311.412.415.$11.SIC.91t.012.115.916.313.31i .310.1 9.1 9.4 9.1
41$ 5474, 751 ., 531s pt4C4477444, $AE1. U$ 44* L%5I 6
2g. 3 . 54 73. 12 4. 0
-------
Table 2.3(d)
Two way tables of monthly weekday averages of hourly CO-Lloyd
4v4 17 11 7 7 e 13 25 31 79 35 44) 7 .5 4* 33 44 49 31 47 1 37 72 17
11 L . t.! S.. 4. .3 4.4 ..3 5.1 6.911... .τiC.4iC.517.7l2.513.$1Z.2 0.724.11?.4144l 2 129 . 7.4
.31.. SC ? .. 1 )7., 4274 t4C . T1 .44. M**&PU 440 .135289
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37
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52
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43 115 111
77
77
82
44
48
39
50
32
31
27
19
19
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47 3 .9
7., 1.
12
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7
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27
31
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51
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5
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77
74
17
13
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4
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3
7
33
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15
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25
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24
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73
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7
5
7
10
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79
o3
47
51
30
50
59
13 32 45 71 132
0
-------
Table 2.3(e)
Two way tables of monthly weekday averages of hourly CO--Eugene
v i . 3 2 3 . 3 4 7 4 *0 23 Il 15 14 15 4 I ? 24 Il 30 it 19 19 ivt so,
31 3 *4 IS 13 I I 13 II .. 33 . .4 Z 34 33 34 4 5 31 34 32 21 31 23 23 30 17 31 4.7
1; e 23 Ii i 0 l . 16 1 33 2. .3 19 .2 .3 a io . 43 17 t O II I? 27 24 23 17 4.a
73 1 ii 95 IS 14 23 14 17 33 . .3 iS 24 43 34 34 .0 24 32 30 33 33 37 24 14 ii. 3.4
31 . . 3 21 I . Is 1? .1 7 2 1 .. 39 34 a 24 aI 49 .9 31 .5 (7 C 3* 7* 23 24 4.8
it II II I . IS I. 10 :2 .3 . 3* 30 30 9 43 4? 30 33 43 47 35 31 3? 21 .4 27 7.2
72 l . 34 43 I v IS I . 4 37 41 .2 42 34 43 34 S 37 43 5 .4 43 42 44 44 40 13 2* 9.1
71 11 1* 2? 19 11 14 is 33 00 4. 43 43 .4 84 41 51 .3 71 31 3.3 45 4* 34 34 (4 is 25.3
it 14 31 3. *4 I? 12 .4 21 08 3. 21 19 34 37 .7 43 .4 54 33 34 33 13 37 20 44 31 9.
1 1 24 15 1 23 I. 7? 35 43 34 It 30 34 3. 34 39 .3 *8 .5 U 32 39 37 23 ii 3! 21.2
.3 i. 1. is II It 31 42 3. 31 33 33 53 33 33 a 34 .4 4 2 40 33 ii 31 33 9.0
.3 1. 11 17 *7 .3 34 2? 4. 23 37 35 31 32 34 .2 30 3? 43 39 .3 35 34 4 ! 33 4.3
p. o to ii. 2. .t .. 34 .. 4 .7 4. 31 30 34 34 3 ! 30 Z 35 33 3! 34 .0 27 5.4
1. a 39 2. 33 .. 3. .0 41 37 33 35 37 2. 43 .0 40 so .5 34 33 31 3? 34 34 31 33 5.4
73 s LI 1. I? ti 1 5 89 (4 34 . 34 .3 15 35 .0 3 o . 3 .4 2* 21 33 Li 31 3? 4.4
1. 7 a 2. 37 so 33 .s 22 U 33 34 4 4- U 50 53 38 35 37 3! 3? 35 19 30 7.3
14 31 33 3 .4 m 32 43 39 36 44 47 53 to 31 34 .3 4- 43 41 31 30 iS 42 9.2
74 44 34 3* 4. . 3. 5* 41 9 43 4. 22 35 34 34 50 54 13 ii 37 54 32 44 4.3
14 1. 37 23 35 .3 3 L 44 70 2 . 4 4 .3 43 .1 5 ! s4 72 .4 70 75 04 03 37 .4 30 14.3
1. Il 34 4 . . .. .1 .7 b 3. .8 43 .1 43 42 51 .4 62 01 .3 5! 31 47 43 3! 3? .9 24.4
7.22. 35 . . 1 .1 i8 .3 34 4 .. 51 Ii SI 57 33 .3 .4 74 1! 1 .3 44 35 32 39 4323.7
1. 2 .4 .. 8. 16 t 19 *3 .2 3* .32 35 34 3 . .4 52 34 94 .7 3. 33 33 31 30 44 34 13.4
71 3 35 51 St .4 .4 .4 Z 1 53 44 .8 41 Oo 33 30 .3 14 77 .2 42 53 31 49 47 47 15.1
73 44 .3
73 444 .3
71 5 4 4. 19 I I *7 17 It 22 3.. 33 .9 33 3. 34 35 3? .4 31 II 31 33 31 34 33 30 0.4
12 8 4 3 .8 14 Ii ii 32 33 3. 29 .9 29 31 33 33 20 37 14 49 3* 29 33 34 33 36 5.7
73 7 .3 . . 6j . ii 34 2 16 33 33 IC I I 33 35 34 37 3! 39 29 30 23 23 29 ..3
7. .3 2.. .1 .. .. .1 .3 31 j ;9 Ii 33 34 .1 3o 3? 39 39 33 31 32 3! 3? 34 3! 5.4
73 54 3 .. 37 .4 so .3 44 42 43 37 35 31 34 31 34 3! 40 34 .3 l 47 .3 .1 37 7.4
13 I a4 SI 3. .3 .8 .1 .3 ot .. so .4 44 32 37 4* 43 52 23 42 32 33 14 SI 44 .3 11..
1. 1 .2 3. 17 24 I . 14 42 37 . 54 43 7 34 34 49 64 11 .. o7 49 34 33 (I .7 3325.3
7 Is 31 1. is IS 2 ; 14 Ii 32 3. * .3 43 .3 .3 o9 31 54 5 1 45 3 ! 34 33 31 .1 34 13.4
1. I 30 I. 13 Ii It ii 32 3! 5. 39 34 34 9 7 2! 33 39 37 27 35 33 31 33 34 29 6.3
S. 4 .3
7. Sen... ... n .441 .0
449 .3
3 $ 44 ! .3
14 4! ! .3
1& 1 a. a a 1# i. IS 33 31 3. 34 37 U 3* il oO 37 39 34 30 31 33 37 37 37 31 7.2
3. 24 13 *3 13 I I IS 14 43 4. .3 33 43 .2 33 43 33 33 17 14 *4 15 17 24 tI 4.3
1. 11 1 . 13 II 1. IC 1 . 36 3 39 24 2. 33 .3 so .3 3. 27 30 26 24 24 47 4 22 .4
7. *3 3. 37. I. 14 1) 13 .4 39 3 . 44 .9 3 ) 33 33 37 44 .4 53 3 03 43 04 3 34 35 13..
7 . II ..7 £5 43 14 17 I I .3 30 31 34 .4 41 ol . .5 4 4 31 .0 .1 44 3 44 44 3? 31 11.3
is I 35 2. 33 19 Ii I I 33 . .. .1 19 00 43 4 .3 44 47 53 55 .5 3 ii 31 30 37 33 24..
13 I 4 . 7 7 7 7 2 13 I . 23 73 Ia 24 13 33 43 33 Ά9 33 73 IS 23 Ά3 23 13 4.2
73 49! .0
15 1 .e .e 49 .3
35 .4.. .4
s5 $ ... . n ... .949 .4
7 S 5 49 a
7) 7 .. 44! .4
1 3 S. es .. 49! .0
33 9 4 3 1 Is Ia *4 34 33 7 44 5? 33 .4 44 .4 *3 ( 5 43 .7 *4 44 44 4? .3 *1 74.4
75 4 3* 17 II 13 12 17 33 44 27 40 44 44 1 .3 .3 37 57 .3 48 37 40 44 37 3) 13.4
7 5 11 33 14 14 II 13 I . 14 3 4. 54 34 44 .0 42 .4 4 1 5 35 53 43 4 41 14 30 21.0
13 4 IS *4 II 14 3 45 13 33 .4 43 44 $3 41 .4 .4 .7 .5 .3 31 36 33 35 23 14 17.3
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1 * 27 it IS I. 1. 14 I. 23 1 13 3? 3i 43 30 . .s .s . 33 33 22 34 21 .3 27 .i
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Table 2.4(a)
3;t .. ?3? . 65?. .4.344 114 .4. ..i. 4 441 8*11344
Two
way tables of monthly
weekend averages of hourly CO-CAMS
. ,
. 1 . 3 4 1
.
4 *2 II I . 13 *4
444 433
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74 *8 . I ? I I
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2 3? .3 Q 3. 17 34 34 57
3.3 46 4. .4 1 13 42 4 1*
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72 71 .1 .4 .? 3 *7 .4 .1
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II II II II 1* 74 *4 I 2*
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20 4 7 24 74 41 4* 32 30
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12 10 50 13 41 6. 6! 33 3
43 57 .4 .0 34 2* 31 3* 43
34 37
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1$ 3$ 31 53 3* 57 44 I 34 3? 37 34 34
1. 1 4% ( I 4 . 4
-------
Table 2.4(b)
Two way tables of monthly weekend averages of hourly CO--Hollywood
I . C I 5 4 4 7 9 t II 1. Ά3 Ά4 1% Ά4 Il II II 20 33 U 33 4.4 * 19
7a I
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V
-------
Table 2.4(c)
Two way tables of monthly weekend averages of hourly CO--Alder
,. C I 3 S 7 c 9 10 11 *2 13 14 15 16 17 Ii 19 20 21 22 23 &vt us
4. 33 3 1 14 I . S ; Ii I 15 0 23. 47 27 29 30 37 24 30 30 30 32 34 31
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73
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Table 2.4(d)
Two way tables of monthly weekend averages of hourly CO--Lloyd
I. 1 3 5 4 7 ., S 10 11 1 13 14 13 16 17 1$ 19 20 21 22 13 hot 4v
3 7 1. 15 22 3 37 7 49 47 44 32 35 22 16 12 10 9
IC 11 21 49 .0 3.3 75 2 .5 vZ 113 17.3 1 63 50 46 29 23
1 4 13 16 5 34 . ; 42 42 3 33 12 9 6 2 8 3
I S 1 14 14 .C .5 34 3 32 39 1 IC 5 7 S 4
1 8 1. 9 9 3 t 3 3 37 3 4 29 31 23 23 17 17
1 1 1 13 7? 21. 31 37 33 35 33 72 10 5 6 6 10
7 4 1 .i 12 16 44 .1 2 10 7 33 33 18 14 13 9 9 9
6 5 Ii 16 1 7 44 .3 43 44 42 46 21 17 13 11 13 10
13 12 I . 10 73 44 75 6 31 36 2 14 13 10 9
0 7 1.. 13 3 27 IC lU 35 2a 0 36 23 15 19 14 ii 11
12 13 1. 26 29 33 37 39 43 .6 52 ; 4 32 33 27 20 17
IC 11 1/ 19 19 .4 13 42 2 33 33 .2 31 25 29 33 27 25
10 1C 1. 41 34 40 3. 0 75 75 60 .2 71 55 2 44 37 33
16 15 21 32 3 53 40 77 .3 3 80 79 56 7 34 39 21 13
13 13 1. 23 28 39 45 53 30 33 4 52 32 24 25 20 32 23
13 11 1? 19 17 .6 3 25 33 26 0 43 29 28 23 20 21 15
11 16 13 20 10 33 2. a? 39 13 39 3? 24 23 16 16 16 14
1. 1 . 23 2? 3* 65 49 56 S 55 59 41 33 25 23 21 22
9 14 1 . 13 21 23 3? 36 34 33 1 3? 2 14 15 15 16 15
7 13 1. 16 21 29 34 32 39 2 3 39 23 23 23 23 11 13
13 13 1.. 17 27 9 40 44 62 44 50 44 25 15 23 19 17 17
9 13 1* 24 10 33 42 60 .3 6 37 41 11 14 14 10 9
4 9 1.. 17 23 27 33 e3 44 .9 39 .9 36 3C 74 23 20 18
14 17 23 24 7 33 .2 0 .5 7 9 41 33 31 21 19 20 13
9 11 1. .1 2. 1 SC 52 53 36 63 70 33 75 19 19 11 15
6 11 I 25 34 45 01 a3 13 3 42 .7 53 32 23 23 15 14
1 10 14 23 4 .2 23 .3 5* 44 13 11 8 10 3
4 5 1 11 26 313 3 40 42 .9 55 .5 33 22 15 13 13 12
7 5 ig 13 20 34 35 37 37 32 3? 2 32 84 33 30 29 22
3 3 6 9 13 12 21 23 a 34 37 19 15 7 7 6 3
4 4 13 Z 3 27 .6 27 49 31 30 1? 9 S 0 8 4
1 1 6 12 13 24 43 31 3 26 27 17 13 9 7 7 S
2 a 10 20 27 37 36 47 43 30 34 16 13 5 1 3 - 6
2 2 7 17 16 1 s 3 23 23 31 32 16 13 10 3 4
1 11443428364046462425152013 4
4 1 1 . 14 19 44 33 37 .34 31 39 45 33 21 19 14 16 15
3 13 17 a a S. aS 72 a9 63 55 3Z 35 34 32 23 26
10 0 7 10 19 2? 33 43 53 40 .3 56 .9 34 27 24 16 19
4 16 1/ 25 35 .41 .3 .0 06 03 94 .4 51 33 29 29 25 21
5 13 1 2 33 36 32 2 39 eO 50 53 33 33 1* 11 13 16
6 3 1.. 14 16 28 30 36 33 1 39 34 20 15 14 9 9 9
4 7 1. 15 17 19 21 21 29 32 34 30 21 22 Ά5 14 16 16
5 6 12 1 1? C 24 27 4 26 21 15 10 12 12 10 3
8 4 1.15 0 23 3 33 31 34 46 36 41 71 15 16 15 &
4 8 12 1* 23 2 .7 26 9 25 25 14 4 9 5 8 9
3 4 15 15 74 IC ..7 43 33 32 43 11 11 13 13 12 11
5 9 1. 16 20 40 27 16 32 53 37 33 19 16 12 9 5 6
5 7 1. 18 22 28 3 7 .5 49 44 S 31 21 23 22 15 5
S I 13 1 23 30 .4 τ 53 44 .0 25 26 13 12 Ά0
3 5 1? 7 33 37 45 56 37 57 54 36 23 19 17 6
9 11 22 33 113
-------
Table 2.4(e)
Two way tables of m nthly weekend averages of hourly CO-Eugene
14 04 3 1 3 e 3 4 7 , 4 13 15 l 13 24 I I 1$ I? I I I I 24 72 77 33 £uO 548
23 .. 1. 1. is 1 i* 14 1$ I I .0 .3 41 .1 H U .2 .4 .3 .1 71 24 35 73 i 30 .7
11 . If. 1. 1; o 7 7 . 14 11 13 33 23 I I Ii $3 34 15 13 Ii 11 13 IS 14 $7 -17 7.3
*1 8 34 I V. l o 2. 15 15 l I. 14 I I 11 15 24 27 I? 24 14 1* 22 71 2* 34 84 Is 3.7
a . It ii 11 .4 .2 .3 .2 3 - .1 43 .4 4 31 33 73 77 34 45 7 72 3.4
S I . .3 .7 3 19 14 Ii 71 .3 3. 4 .3 74 .3 14 4 34 .3 .4 .e SC 32 34 34 13 43 3.
71 I. 3$ i. i. Is ii .3 a . a t .3 4 , a e 47 .4 3. .34 35 3 $3 $6 4* .*
SI 11 33 13 15 15 13 11 14 32 1 33 6* 32 34 a 3 ii 7 14 37 24 a 27 IS I? H 7.3
8. 1 4 Il IS I I I a 24 25 1* I. 73 0 34 4) 34 .3 .1 31 31 34 34 71 34 31 II 4 4.3
7. 5 Ά4 1. 1. 11 1. 13 13 14 1 II IS .2 32 a. 74 .. 35 73 74 I I II II *8 11 1* 3.4
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11 . i. 3. 7. I I I I 17 IS .4- 0 .3 33 4 .3 .4 27 3% 35 71 78 .5 33 33 73 3 .3
31 73 1 33 II I I II Ii I. IS 62 34 .. 1 31 $8 74 IS Il 11 II 47 34 41 18 ..3
88 & .4 a. $ 1 I, 1.. 23 4 If I .3 . 30 .8 .3 .3 .4 41 34 4 (3 *3 II 30 .4 71 4.4
71 2 .C . I. II I I I 5 I. .. .1 33 . . .. 4 .4. 41 22 .1 IS 72 20 34 41 43 41 3.5
$1 33 I , ._ 13 I. 8: Ii 17 3. .2 .4 37 11 .1 34 .3 23 .2 42 74 23 56 24 .8 32 5.1
17 , 19 1* 23 (3 . . (5 I. 34 I. $4 11 II 34 I V 47 35 .4 23 1* IS 77 31 38 7.3
71 l .3 33 3. 8. I. II 24 $8 7. II 33 .3 . 33 37 31 13 33 34 15 55 .3 53 5 .4 4.3
71 1 4. 1. I I 3 4 1 S I. IS 15 .3 .3 0 2 .4 2* 33 31 43 71 24 5 I? 14 7.3
17 I. 11 *3 I. 4. 1 1 3 II I. 14 I ? ... 29 4 (I *3 43 .3 23 14 Ά7 2 Ά5 23 5.3
7. I It I. II 23 I. 11 I. 4 .. . 4& 31 . .3 .3 34 34 38 .1 47 43 33 75 73 21 7 13.?
1. 34 2. . .. 1* I. I i 13 I. *3 34 34 I . 37 l 35 13 34 . 3 23 72 74 .4 31 21.1
1 : .. .0
1. . IS Ii t II II a . 31 I. Ia i S I? ii 84 13 $1 1$ 5 18 73 34 34 7 Z *1 1.4
1. ) IS I I. I. . 1 (U 1. IS I ? It a I I (6 IS (7 17 22 IS 24 II 4 14 24 3.3
Ta . .3 I Is I. 15 14 i 7. .2 .3 43 .. .3 45 .5 24 .4 .2 47 23 47 23 7? 77
1. 1 .1 I. I. 4 Il I Ia .1 .2 .3 .3 .32 .3 .0 20 Il 13 18. 27 I v IS 17 3.4
1. a 13 I. t 7 1, 7 4 I. 13 IS 1$ 23 II 13 II 14 14 I I $5 1 Ii 14 16 I i 3.7
7 .2 I I I 7 7 14 34 ii 1) 4. 37 14 .1 40 13 19 22 23 20 42 73 Is 3.3
1.1. 24 .& .. II I. IC 23 IS I ae 34 34 .3 ii 3* I I I 44 34 2 34 5% 53 71 7.
1. II :3 3. I. $3 . S I, (3 41 .7 47 .1 .8 .9 34 .4 33 3 , 31 .7 41 35 34 34 41 125.2
I. I. .4 2j . 8 I. 14 I I 3, 3. .3 44 33 .3 .7 .0 .3 45 3* 37 33. 25 74 74 33 34 3.3
7. I 34 7. .. Is I I 27 4 II 4. .3 g. 41 .4 43 31 .2 II 34 37 3* II 11 .4 .3 a4 e.
1. .3 4. 14 $3 . $ * 12 I. .4 .1 .4 32 14 34 4.4 41 4 .4 47 74 I 73 13 21 s.4
7. 3 34 .8 I. I. I. 11 IS . .4 33 .3 .3 .3 .4 .4 23 II 47 2. a 27 27 .5 3 ..1
3 I 11 17 I_ 23 12 $4 I; .3 31 45 .4 .3 . .3 31 .5 .3 :1 24 73 74 73 71 5.3
1. ) IS I. 8. . Ii (7 U I? 11 IS I? 14 IS II 15 Ά4 14 Ά3 Ά4 iS I . 13 3.6
1. IS $1 I, I l I. 24. $4 25 2, II I. 18 31 IS 15 IV .1 .3 14 34 3 34 3 I? 17 3.7
I. I $3 . . 1 7 1 12. 34 II $4 13 14 IS 14 IS 14 $1 II $3 I I (4 14 14 IS Ά3 3.7
II 4 19 it I. 1e . 7 Ii (2 IS II *4 Il IS I? I? *8 II 17 I-. II 18 13 14 % 3.
I. . 31 1. I. Ii 13 I I 1. .1 .3 13 I? 23 24 I 5 1$ *3 I l 26 II II 32 17 1.7
7. 34 . 4; 3. 1 It 13 2. .3 a. .3 .1 . 21 . . 34 34 37 55 3! 44 .3 51 4. 12.1
13 1 33 17 Ά5 7 14 2 .. I V .3 a3 42 .1 .3 .1 50 .3 31 36 34 34 33 19 47 7.3
1. I. .. .1 7, 8. 11 I. I! II 1 I 33 33 33 34 42 57 .3 54 74 38 47 48 74 14.1
. .3 3. .2 17 I I 24 3* 13 4. .1 34 4.? 37 .7 t 47 1 30 .0 31 32 12 30 35
11 84 22.) ,.. 7. .4 4.. 434 .1 7.6 3.. s34 C.l 4.5 i. ..3 2.. 9..I1 .*IZ.Il3.1l1.*%l.1l2.31Z.3 1.2
.3.. 1.2.. Sl.. 4.,. t.4 . 4f 1634. nj4. 4 44 0 1)3 1 4
Ά7 31 4 5 54 ii.
-------
Table 2.5
Monthly average daily traffic counts (in 1000 cars/day),
Portland I 80N, Columbia River Highway at NE 21st Ave.
YR 1 2 3 4 5 6 7 3 9 10 1.1 12 AVE SE
70 80.5 36.7 90.2 89.9 93.9 100.8 97.1 100.4 96.7 93.0 89.0 90.0 92.3 5.6
71 82.3 89.8 91.9 94.4 92.9 98.9 96.3 99.1 97.8 95.2 90.0 91.6 93.3 4.4
72 84.8 87.4 93.2 93.5 92.8 97.1 95.2 96.7 94.8 94.2 95.6 91.5 93.1 3.5
73 85.2 96.1 98.9 98.8 97.1 101.2 95.9 100.7 96.2 94.0 90.3 87.1 95.1 4.9
74 79.3 79.4 87.1 91.9 93.4 99.2 96.0 99.4 93.8 94.8 92.8 92.9 91.7 6.3
75 88.9 89.1 95.3 95.8 96.1 101.9 98.0 98.9 94.2 94.3 92.5 91.7 94.8 3.7
76 88.9 92.1 96.1 98.8 96.9 104.4 100.2 103.7 98.2 99.5 98.4 99.5 98.1 4.1
77 89.3 98.3 102.6 103.9 101.2 107.1 105.4 106.7 101.8 102.2 96.7 101.9 101.4 4.7
78 93.0 101.1 105.6 104.8 103.8 111.5 108.4 110.5 105.3 105.6 97.4 101.8 104.1 5.0
79 86.1 101.7 107.7 105.8 103.7 109.2 105.2 108.4 106.4 104.1 101.5 102.0 103.5 5.8
85.8 92.2 96.9 97.3 97.2 103.1 99.8 102.5 98.5 97.7 94.4 95.0
4.1 6.7 6.4 5.3 4.1. 4. 4.6 4.4 4.3 4.5 3.9 5.4
Table 2.6
Monthly average daily traffic counts (in 1000 cars/day),
Eugene, US 99 Pacific Highway West (.02 miles NW of 11th Ave.)
YR 1 2 3 4 5 6 7 8 9 10 11 12 AVE SE
70 20.9 21.8 22.0 23.4 25.6 24.3 22.8 23.3 23.6 24.0 23.1 22.4 2 1 1.2
71 22.5 4.1 24.2 25.2 25.3 25.7 24.2 25.0 24.6 25.0 24.5 3.0 4.4 .9
72 21.2 24.1 24.4 23.8 23.8 24.1 22.4 23.0 22.5 22.6 24.8 21.3 22.3 1.1
73 22.4 24.2 23.7 23.1 25.1 24.1 0.8 22.8 23.1 22.7 21.8 19.8 23.0 1.6
74 19.4 19.1 20.2 22.4 22.3 22.7 21.7 23.2 22.9 22.3 22.3 21.3 21.9 1.4
75 21.1 22.1 22.6 22.2 23.9 22.2 21.1 22.8 23.0 23.4 22.6 20.7 22.4 1.0
76 22.0 23.0 23.0 24.0 25.7 29.1 22.0 25.6 24.3 24.4 23.0 21.3 24.0 1.7
77 22.9 4. 3.9 5.0 24.9 25.1 24.8 24.9 25.0 24.3 24.7 23.8 24.5 .6
78 23.9 24.4 24.9 25.5 25.8 27.2 24.3 25.3 4.9 26.0 24.4 22.3 25.0 1.1
79 23.3 24.6 24.4 26.0 25.4 25.2 24.3 24.4 25.0 24.7 23.7 21.6 24.4 1.1
AVE 22.0 23.2 23.3 4.2 4.9 25.1 .9 4.0 22.9 24.1 22.5 21.9
SE 1.3 1.7 1.3 1.1 .8 1.7 1.3 1.1 .9 1.0 1.0 1.1
-------
Table 2.7(a)
Two way tables of monthly wind speed averages
Portland-Airport
1 2 a a 7 q a II 12 C sc
70 $ 7 5 5 4 4 a I 7 1.2
71 1 a $ a 7 5 7 5 4 1.1
72 i 4 1 4 a 7 a a s 7 Ia 7 L.s
73 Ii 7 4 s a a 7 a S i .1 I 7 is
7 10 7 $ 7 7 $ 7 7 4 S * 1 7 1.2
7% $ I 7 7 7 7 7 1 7 $ 1 .
74 7 4 1 4 7 7 a e a 4 S 7 1.0
77 7 a $ s 7 7 i a i 11 7 1.4
7$ 1 7 i i 7 a s a 7 i.o
o 7 a y a a 7 10 $ 1.5
4 7 e a 7 7 a a a $ a
SC i.a t. .5 .7 . .4 5 .ι 1.0 1.1 1.7
Table 2.7(b)
Two way tables of monthly wind speed averages
EugeneAirport
7$ z z s κ 7 I 10 U 12 aV SC
70 I.. * 4 4 .0
0 7 7 7 7 7 I a 7
72 $ 1 4 $ $ $ 7 $ 1 7 $ $ .e
1 7 7 7 7 4 S $ 7 1 .a
a 7 7 a a a a a a S a a
7% ; i $ a a a a 7 7
7o 7 a S a e a a a a s , 3 θ
77 a 7 7 a a 7 a a .o
7$ a a a 7 7 4 7 4 4 a a .
7 a 4 a a S S a a S a
Vt 7 7 7 a a a 7 7 4
St 1.2 .7 i .0 A .8 .5 1 .7 .O ,7 1.2 l.a
-------
Table 2.7(c)
Two way tables of monthly wind speed averages
Portland Hughes/Federal Building
1 2 3 s o 7 8 9 10 11
12 vC
SE
24 23 22 23 2 26 26
25 2
23 2
10
..
.
..
.
.
.
...
29
29
.0
71
30
28
22
29.
20
25
26
27
20
19
27
25
3.6
12
28
2
9
23
214
30
28
2
22
16
23
23
5,14
73
23
t9
20
21
214
23
25
2
23
t
29
22
22
2.7
74
26
21
22
22
25
29
25
25
26
20
22
22
2
2.5
75
20
23
26
2
214
26
25
26
29
7
29
22
24
3.0
76
20
26
22
214
214.
25
2 4
26
22
18
19
15
22
3.3
77
16
21
21
25
2
28
25
28
23
23
29
33
25
14.3
78
19
2C
22
25
27
26
28
2
18
21
26
2
23
3.2
fl
31
26
25
23
2
31
29
22
25
2!
28
26
26
3i
SE S.C 3.1 .t j.3
1. 3.1 L.a
,..0 2.0 q.1 4.6
-------
Table 2.8(a)
Two way tables of monthly averages of hourly wind speed
PortlandAirport
7,77 7 3 S 3 4 a ii i IS 3 *1 ii it sa a S I SI 73 73 4 C 4 (7
fl I .* a a..* ... 4...... tee 4
77 S 3 S.. . . .. P . O P .6
77 7 *4. £ 3... 4 1.4
73 5 6 4 4 4
lea ... I 3... 1 1.
71 4 ... 7. 4 4 a i.t
77 , .4 S I. ..... P 7*..... 7... 4 za
7. , ...
7 4 4.. 3 . I a... 1.1
7, I . 3 7 ...
77 II * 3 ii ... .44 4 I *4. £4
77 Il ... a , .4.
* I 4.. P a S ..
7* 2 *44 P 3e. . 4 * 5*.
7* 3 446 7 .. ... t4 II 4 .44 4 1.1
7, .fl . 7 St. fle 4e so. * .. L.a
7*1.4. 3...... ...
1% 4 344*44* 3 *4 44 1 *4s 1 .6
71 7 flefl 7...... . i...
7, I 4...... .**n. I . . . .. . P a...... 1*.e j7
71 .44 4.4.... I ii a;.. , 1e
71 IS 4.4 O 3 S .e. .. a 1 1.3
7% II 7 7 P $ 4..
71 Ii .4. . . I 7 .. .* . . . ... , ,,
771 .4. an.... I .. i...... .4.. a
77 7 ... a S I IG I I...
77) 4 4*. a .4
77 4 ne - S a 7 44. 7 1.4
73 . 4 4 * 3*e.3 . . P I 74.4 4 1.*
77* S.. .. I ... 4 1.4
77 P ... 3 3 P I .. ... 7 3.2
7,. ... *.4 I I 744. 7
77 7 4 3 7 I 6* .. 6
nIl... I 4.n 4.* 3 (.4
7711 .4. ,..... . . .. 7 .a
77 Ii 44* II ..* * II 4.. 14 I II * I I .7
, , * I II I I I I I i 4 .4 II .. II 1.4
71 : 44 PS 45* P 44 ... i
7) 3 4.. I 7 a 3 ... 4
77 4 .4. 4.. 3 P ... .. $ 44. a .7
1) 4 .4. $..as.. P $ 4** & LI
7) 4 . 4. 3 ... a...... t...n. - en.... J .
7, .06 4 ** 5 fl 7 *44 4 fl ** I * v 1.1
7) 4 *7 I 3s.e ... 1 .e.... 4. 4.406 it.. a 1.6
73 4 7...... 34 I La
7111 54 344.4 ,4 3 e. . . . .. p s... . s
flit 44 P 7 a . 4 t 4444 4 .7
$ it $e F 0 4*. St I 44* $ 5 *44 I a sa
74 $ ..* 4 ... II 4 .4 I * S* I iI 444 - II I
7 3 .4* 4 4. ... . ... , .3
74) 454 7 7.4*... a... . .. * I 1.1
74 5 e 4. j.***.. l 4 *. .* I...S. . 6 7*e* P j,t
PS 4 5*4 *4 ... It P .. I 5$
p 4 . 3 4. 7 .4* 4 It I I .. 3 *54 S I a
75, 5...*.. 3. 4 P*j. F I.?
, . at. . ...... p...... ,... 3.4
ps 4 .. . .4 3 ... ... 3 .. . .. 6 .. .*. I. a
. 7 4- . . . _J 7 4 **__J * 7O 7 4 .8
7.1$ * 05 I I... 6 1.1
7*13 5.. 4 Se. .5 4 P. .*n. 7....,. *n. .e ...n. I , ...n. , 7
77 I ee Pn.fl. tt *w *e S5 I*...* 4**s . . . , 45.4 I ?
77 * .44 S 4 5.. 4.. I I I I .4
fl3*S 5*4 *54 .*056 7 .5. Z . D .. .ΰ.T 1...... a .. a..
7, 5 5*4 .44.44e 44... .. 4***t* 7*s .. .* S* I. . . . * fe.. 7 La
7, 4 fl 4flS e %4*S** 4. 4.6.44. ii . . . . .. 7 *.. 1i4
7744.4 1 $a. ... l* s*aa* I I S
77 7 .. $ . .* .. ISs . ... an. . .. an.... **fl * S s* P LI
74 77 & * 4.. $ I.. 4 4 5 . 4 I * I ... ** I4 *4 7 *S
73 4 .. %.a... $..*... $44.46. 5**** I I. . . .. an 7 2.4
7711 .5. 7.*. P 4 .. .*s* S4.. 4...... $ $4.. 7 1.4
nil S * ,e..... *4* t**...* j I a... 4
73 3 ate *44 4 P e* * 4 544 45* .44 .44 4 4*4 54* 7 54 54. 9 * * e $ *0 I .1
7* I Ste *.* 5.. 6 ... . .s 7*e 5. . In.... ..... 3...... 4554*44 7*4* .1
.. . .. .5. 5.. 54. 4 441 fl $ 1 44 .5* $1 4 4 544 4 ( 4
7*3 4*4 * 7 . . . . . I 75s. S I.
74.444 *4 4 * 4 4* I 7S44S. $ .. P 1.7
7.1* 7 .. 4
74 4 4 I I .... i
74, t &445 I I S s4 P .6
I .44 S 6 4
7$ 3 ... ... a * ... a 2.3
P414 *4 *... .. .. 7...... 4*.. 4 1.1
7$ II .. 6 ...
Pa If .44 S 4.. $ J
P 744. .4
71 ... , II I 44 5 1.1
I 6*44 I I4
.. 3 * .. 4 .3
. P
,, .5. 3 I I *S 7
77 ... I I II I S.. i
7 a 5.. , p.a
PT .4. .4. 4
ii I I I a *4 4
771* I 4 4**.44 II 95 4 .4
7 I I I 5 II ** 54 I 4 I II S.. ii .. II
PS ... 1$ I 4
at an. 7.. .... I 4...... 4 S i.l
7a ... , t ... 1.3
7S S .5 7... , 1.1
::: - - - :::; :: ; -
iS 7*44 * I 7*.. 7 $ 4
75 3 445 I P... 7 I.?
75 *4 1*.. 5.. ... 3.. _ . P . , _ jp* I ...._S . ___L_1tL
75 I 7 3 .. 3 1 ,4
III . ... a... a LI
7512 ... * p .. .
$fl .444 $ .*fl.5fl 3.4*s.14 .I*.444 7 ,tn., a I..a.... ......n 6.9 4 ,_tfl .fl
a, c. .* La .i .4 .5 .4 . .4 .1 -.4 Li . .j .& .a .a .3 . . .5 .4 . I., .4
341, 4114, 147 ., *45* . 4 tf S? IL( ). .*45 5 V4 6.4 4SSI 54
4 7 5 I I 3% 171 1
-------
Table 2.8(b)
Two way tables of monthly averages of hourly wind speed
EugeneAirport
. ..
. $ . :
12
. U IS .20 11 ii 33 a.e 44,
71
I ..
7
7 . .4 4 . .. I ...
.. .
.. - 4 ... . 7 .. . . . 4 .
I 1.0
71 3 ...
7 .. ...
7 .. ... 7
. a ..
.. $ 4 . . .
1
71 3 ... I
71 .... I
71 4 5 .
I. . . . 3..,...
73 7 !
71 a ...
71 ... 1
... .4. 4
S a.... I
.. S .. 7
7......
S * 6 1
-. .. 7 .. . .
7 . ...
Ά3 . .
3......
... I .
10 ..
7. I I ..
4..,... 1 $...7
. I . $
4 I e.. .
4 I ....... 7 ...
I .4.... 7...
... Ά1 I . . .. 7
.
I I .?
I.e
7 14
7 t
1 2.3
7,.,
I l.a
71 24 .7.
*4 t
I
I .
I ..
4 2.1
7471..
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7
4 .
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71 14 ...
4 ... .. 13 ...
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I 3 ... ..
4 ...
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72 I . . 4
4 ..
4.. ....
4......
4...
72 * ..
73 3 .4. 7 ... ...
74 ... 7
71 4
72 . . . .
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7 4 I :
7
S 74 7 7. I
4 .. .4. 7 7.. .. 4..
4 .. .*
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13 71*
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23 ..f ,..
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2.3
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1.3
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1
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73 4 ..
5 ... ... a
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4
72 4 ...
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73 2 . .. 7 ...
733... .. 4 ..... .
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74 4 .. 5 ... .7.
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4...... 3. .. ...
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73 j 7..
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7
7.4 7 47. 444 4 ...
2.2
73 71 4 ... ...
7 $ 7
4
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3 II .4.
14 1 p . 7
7 47 774 774 44 4
S !.. 1 .p. 4 ...
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743... . 4 5.4.47. . 444
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74 3 44.
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5 . 4.. 7 ...
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7 ...
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47.44*4 7 . 4 4 S 42.1
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74. 4 .4 3 ... ...
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... 50 ... ... 7 . . .. 4 .4 5 7.4
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74.5$ 4..
71 Ά
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74
P ...
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744.4. 4......
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754... $.. ..
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773.4. S....
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4 14 44 1 $2.3
I4 7.4 4444 I 7 52.4
1. ..... 44 $1.6
7 -... £ 7 I . 4 2.2
4 . . . . ... .3.4
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1 I .* I ... ... 5 . 7 2.6
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$ ... . - .___3_ . . j. . .__ ._ _ .
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a . 3.. ... ... 7 3 .4 4 2.5
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447.47. 4 3 ... 6 .3
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$ 54. . . .4 .2.1
4 I S 50 4 44 $ 1.7
7 .4. 7.. 7 6 ... 5 1.3
4 7 .. 41.7
7.44... 4... 3 7 5I.s
72.2
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I. $ I .. S 5.7
I $ 7 .. 7 .7
$ IS .. ... 4 7 ... .. 4.. $ .4
7. . .... 7 5... 6 .6
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7 . I I 4 ..* 1 2.7
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, ..2.3- .;. 3 ... S 3.3
.443 3.444.444 3.444.444 5.444.434 7.444.444 4.434.444 4.444.444 7.444.444 4.444
$1 eli .3 l.a .0 .0 1.4 .4 .4 5.4 .3 .4 1.2 .3 .0-2. ) .3 .3 1.3 .3 .3 2.3 .4 .2 1.) .4
Z$1. 5474. 711 .. II?. flICI$III$3. 4444444 4*4 *53415*
s___ 7 I II 13 Ά134
-------
Table 2.8(c)
Two way tables of monthly averages of hourly wind speed
Portland Hughes/Federal Building
C
i
i θ r
*
*t
12
*3 14
5
1 V i
11
34 I i
34 47
.1 34 34
3 .. *1
41
47
3 .
31 Z
.7
24
33
2*
2*
2*
33
35
47
2)
3*
22
2 ,
34
3.7
3.4
1*
44
. i .i
.. 33
;.
.
i
zs
* i7
.
23
.4
Il 4
11
I. . 32
.2 24
7 2
1?
7*
S
3
70 14
1
1
12
1
1*
14
7
71 i
it
71 1
*1
1?
1*
.s 37
17 14
*4 1*
4 ..
4 Is
17 7.
.o .7 U
II 1? 7.
71 .7 .2
.
2. 22
4 41
23
2.3
.7
31
.c
4.
4
22
4 1 21
42 44
.3
3*
71
37
33
33
47
34
u
.3
3*
33
63
32
23
37
27
33
44
ii
31
44
U
5
32
24
..1
1.3
71 a
71 1
17
32
I I *4
4 24
13 I.
71 31
l 2* 7*
.4 33 42
3. 5
.; 21
I a
41
34
.3
1)
3
.0 .2
43 41
U
2.4
2*
33
2*
34
27
I I
23
4 1
24
31
24
3*
U
23
2*
4.4
24
*.7
11 .
4
.. 24
.2 1
*4 42 21
2.e .7
33
41
2.2.
.4 33
j4
37
17
47
33
.4
7*
31
4 21
.2 31
.7 2* 42
47
37
44
34
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37
31
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2*
41
21
3*
71
t4
14 U
17 1?
Is IS 2*
.. .2
.7
42
.7
31 22
23
22
22
0
IC
74
2*
31
22
30
1$
1$
74
4
71 71
14
2. Ii
. I.
I 77 17
1: 1*
34
1%
44 63
71 2.
*4
3. 24
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.4 .3 34
41 .3
34
3*
31
.4 33
.7
23
6?
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37
27
34
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4. .
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31
IC
30 32
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24
4.
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44
2
3.0
1. £
37.
. . *7
.2 4.
I .3
i .7
47
3*
2$
8? 10
.7
33
.2
.2
71
24
7.3
7. 3
4
3
.
. ..
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2
22
u
ii 1?
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ie
14
4
IC
13
is
34
S.
.2
11 I.
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Is IV 24
4.. .4
.4
43
1
4* 21
. 3
34
3*
23
24
23
24
24
*4
7. 5
4C
I. *
1. I.
.6 11 33
.. 3
.4
33
43
43 33
.7
33
13
34
2.4
1.
7.?
*4
35
2* 14
.3?
II 7.
5 3
*4 ii 31
$ 7 7
4. .3
6*
.3
44
32
37
31
47
.4 2
3432
4
3.
31
3*
33
.4
3]
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34
3?
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33
24
34
3)
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34
1.2
74 5
. 3
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4* 47
.7 *3 44
3. .4
64
3*
.4
.S 81
3$
34
.4
I I
32
31
33
ii
13
3$
34
2.3
2. 4
43
3.. ..
.4 11
4 20 23
l .1
34
4
z.
a 4*
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2$
31
44
33
11
1 .
I
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.1 .3 .7
4. .2
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.3 27
44
31
4
32
34
44
70
*3
74
3.3
1. 21
73
73 74
7 Is
73 1* Ά1
*4 Ά1
43
4
42
.7 42
34
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41 . .
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74 42 33
.7 33
4.
4
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64
43
4
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33
32
41
41
31
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44
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74
Is 27
Il 13
73 Ά4 7?
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.7
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3s
.4 33
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26
71
24
17
1*
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2.4
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7.
27 14
I? Ά2
13 I ? 27
1. 42
*7
41
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23 .4
4)
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73
27
2* 14
1$ *3
13 II 4
1 22
43
13
23
.1 .1
2*
30
II
34
33
44
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33
37
34
33
13
2
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23 3
74
24 I.
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l .3
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32 IS
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. .2
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47
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$4
22 I.
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4
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43
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23
71
4*
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24
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1.3
13 24
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1) *5
14 II 1?
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4.
31
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44
34
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23
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4*
45.S
P. I
34
34
.4 4.
.7 4 .4
6. .4
33
30
31
33 33
20
35
14
21
2.7
1. .
10
. 14
.7. .1
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2 .7
22
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4 83
24
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11
II
33
II
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2 . 1
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32
4)
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23
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3.7
1.
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I. ..
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2* 2 .3
2. 44
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.5 23
5
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aS
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. 41.
.7. II I
61 27
33
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3
3κ
8*
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3
37
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33
44 *
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2 1 4?
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44
35 3
3*
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7
I 7
1*
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74 .2 43
.t 42
23
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25 34
33
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34
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34
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t 74
75 Ii IS
.4 1*
7*
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23 47
49
31
31
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27
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2
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14 33
3. 4
4
34
4*
33 33
33
.2
31
31
27
71
17
20
2.4
1. 1.
Ά7
17 I.
71 1.
2 4 I I
2 4
71
4
3 24
13
23
1
35
1*
41
22
34*
1. II
46
24 7.
.. .1
.2 24 .2
4.. 51
24
31
4$
21 4)
67
23
1*
77
22
31
24
22
IV
21
42
7.4
7. 7.
1*
Is I,*
IV 7.
IS II II
4. 22
20
.34
.2
23 22
21
7$
34
34
3.
1... 1
26
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33
4*
44
4, 42
3
32
tI
14
IS
1*
31
1*
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1.1
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1 . .
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.7 14 3
.._ 33
31
42
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4*
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22
3*
33
11
43
4. 33
.3 41
43 22 42
ZZ. 33
22
30
31
31 30
4)
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.30
24
2?
24
34
24
63
2$
34
23
21
28
.4
15 4
P1
74 .
. 33
.4 71 .1
4. 22
.4
.3
31
.3 3*
43
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24
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34
4 14
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1
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3*
84
4V
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35
43
1.1
73 7
43
61 14
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6. 23
11
1*
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41 23
42
81
13
34.
31
44
42
2*
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SI
34
d. 44
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41 42
15
33
.3
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.*
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3
32
33
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as
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.4e332 il
8 )17
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3731
14
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14
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3024
Is
17
77
17
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Is
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17 Is
7? 4 II
II 11
.7
11
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25
71
VI
11
14
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21
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23
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23
1.Z
72 1*
4
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4
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33
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33
1
3
4
30
13
Is 1
14
Ii I .
75 1.
14. I 24
7 IV
1*
32
44
4] .3
22
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44
21
21
23
31
24
42
4*
33
42
4.
3.5
I . .
44
2. 42
.2 2.
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2 10
31
2*
II
32 32
37
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24
22
2.4
7. 8
i
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.3 24
.7 .2 41
.3 .3
.s
23
4!
27 24
4
4*
22
31
23
42
24
II
44
1.
7*
II
7* Il
I . I ?
I ? Ά4 74
4. 43
7
4.
.1
30 35
31
34
.4
74 2
2.4
I? 14
.1 27
1* 67. .4
33 42
.3
21
5
5 2*
30
34
31
33
36
3*
37
34
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23
4.42
74 S
.3
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IV I.
II 7 II
I. II
IS
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IC
.. 27
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24
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is .
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4 Ά5
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1*
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33 41
34
83
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23
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IV 74
7? 11 7
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17
1*
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II
I )
342
11 I
73
II 2*
7? 1?
13 I I 2$
_ 7
.3
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43
.2 42
*
41
IV
14
II
It
14
24
1*
3.4
. 71
7*
73 71
17 Il
7$ I? 2$
22 .8
..
23
8?
.7 21
.4
21
II
23
I I
IS
74
13
2.7
I . I .
23
73 13
II l
II IS 74
1. *7
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Il
I?
II II
IV
12
I I
I I
I I
II
IS
4.8
Is I
2*
II Is
7. 74
.2 I! 24
7. Ά4
IS
21
I V
II 41
IV
7*
73
IS
74
IS
I I
3
77
a.?
5, .
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1. z
I I
1 71 74
I . .7
.
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44 35
2?
II
0*
4
10 1*
. 74
1, 627 3
a* 23
43
3*
.3
34 *7
.1
I I 140 *.7 .. 4... 4* o.. 2.4. !3 4.2 4.. 44 44 .4 1.2 3.3 3 74 .4 7. *42 7.4 4.5 34 44 4
.2.7.. 1335.. 1570 . 057w #t .42411*42. $*11 .P 45$ .12335*
I V 3. 4) 4 32
4$ 27 3) 42 34 43 22 U
-------
Table 2.9
Monthly mixing height averages at Salem
(a) morning
YR 1 2 3 4 5 6 7 8 . 10 1.1 1.2 AVE SE
7* 745 490 779 810 490 674 373 364 50 620 557 742 597 14 .0
71 952 646 773 637 702 821 471 494 506 542 709 994 679 155.1
72 979 854 819 864 544 446 271 269 361 334 71.3 71.6 597 45.6
73 758 460 964 590 548 731 440 469 513 55 989 757 640 169.2
74 724 674 ala 739 752 484 626 371 199 .93 549 520 536 1 8.3
75 903 771 930 771 630 736 484 674 80 3 71.5 620 695 172.2
76 633 764 831 732 560 567 481 551 42* 224 344 348 543 168.4
77 333 510 996 509 943 523 1 472 695 487 91.7 97 636 1.99.8
78 1 781 455 830 873 873 524 726 791. 361 467 5β3 653 172.2
79 U U** *z iu* u * **z* uu xz*t *u* u** ** *u 1U* .0
WE 71.8 a1 785 720 671 σSi 476 488 474 454 651 673
SE 187. 136. 154. 112. 1.49. 144. 1.02. 139. 180. 165. 183. 175.
(b) afternoon
YR 1 2 3 4 5 6 7 9 9 10 Li 12 AVE SE
70 445 σ76 1395 1742 1538 1512 1725 1.567 1433 835 370 363 1133 525.5
71. 314 535 921 1409 1399 1619 1471 1477 1311 99 456 454 102! 460.1.
.2 433 734 997 1507 1469 1359 1707 1.542 12e3 738 575 314 1055 460.?
73 504 708 1230 14 1494 1531 1699 1.300 1255 9 0 41.0 2i 1005 476.3
74 622 σ29 1110 1437 1703 1371 1.593 1.532 1419 985 za + 332 1110 447.8
75 363 72! 1151 1503 1551 1496 1.461 141)5 1162 777 495 26! 1030 400.4
76 314 7.3 1121 1.509 1445 1441 1593 1300 1208 826 433 300 1020 458.2
77 386 669 1429 1446 1697 1.379 1647 1.585 1200 738 536 307 1039 495.4
78 256 631 997 1324 1822 1.306 1.641 15Q9 1277 954 45 331 1073 5 3.3
79 TUS 4 U ** **i 4 K1 X*U !f **U T*U .0
AVE 41)5 1142 1478 1569 1.502 L .L15 1496 1293 48 0 330
SE 100. 192. 109. 1.34. 13!. 1.. 1.00. 9. 4. 9. 62.
-------
Table 2.10
Two way tables of monthly averages of relative humidity
(a) Portland
70 12 73
11 $5 7
72 74 74
73 73 74
74 73 7
75 I3 73
14 43 79
17 10 7$
7$ 15 $1
79 74 $4
$0 7$
4.4 3.7
77 e3
73 47 45
7 70 43
73 44 e2
7 72 4 .
SI 1 44
72 47 45
75 41 75
74 77 71
73 73 47
44 42 70
61
SI 6, 74
4.3 44 79
el SI 72
e 42 30
72 74
63 7 7,
3f3 77
70 12 79
75 I
33 73 72
33 77 70
19 13 70
$0 39 70
7 $ 72
$1 $3 73
32 3i 73
52 14 74
$0 13 79
8.0
3.;
1.,
1.1
3.4
7.,
1.0
.4
7.1
e . ..4 .
37 $4 $0 75 79
I9 $4 32 73 12
17 15 33 77 7,
it I $0 71 1
46 ,t IS $3 7$
14 77 71 75 70
$4 $5 73 73 77
I9 $7 79 79 73
30 82 7 73 .47
.. ..
4, 83
71 32 ,I
73 33 1 9
71 4$ 65 L 72 ,0
6, 3, 44 43 8 $3
47 61 70 77 87
,1 42 49 7 *3 54
70 64 ol
93 .4% .49 32
4 .. .0
*7 73 8.0
57 77 .S
$8 78 ,2
93 74 ,.3
3$ 73 10.2
93 74 4.9
57 77 8.1
79 77 4.i
33 67 17.7
So
.7 3.7 3.4 3.9
1 2 3 0 5 4 :7
a 10 11 12 LYC SL
3, S
43 40
44 35
42
4%
43
3
S
64
42
5;
42
:41
41
41
45
a
y3 70 47 43 40 45 66 77 II $3
.9 .0 3.7 2.0 3. .a 3.1 2 5 3.4
(b) Eugene
7 , 1 2 3 .4 5 4 1 I 10 1112
4YC SE
SO.
79 45 46
10 39 4.3
41 39 47
70
.71
72
73
7
75
74
77
7$
7,
$ 15 30 77
5( 5.2 3.6 351 7.8
30 79
7$
71 47
4.4 8.7
o0
1.3
49
S ,4
-------
Table 2.11(a)
Two way tables of monthly averages of hourly
relative humidity--Portland
2 1.1 I I 7 3 $ 4 . 7 2 * 4 I I Ii IS I II $4 I? Ii II 24 1 1 12 22 l C IC
,. ... ,, . . ..c ... . *7 2.2
74 2 c 44 c $1 4 7 4 $7 3 11 $ 4
,, ... $4 , 7.. 13 IG.
TI ,T $4 . c.. -cc . $ 7$ 4
T I 71 I 4 . 4 . cc. 4 , I.Z.4
74 a t . , s . I i c
7 cc 7* 77 7 a ccc
I I 7* $ I I 2 $
7 cc I 7t 74 S . 3 ncc ?il.4
7111 ne 84.ce. .c $4.cc ..c $$.cc.cc 7$ ceec $ S 42fl4 * 7$ $3.1
71 Ii c. I 7 71 I 1; ..4 ccc 7 ... 7; .83
7112 ccc $4ccccc 3 a I . za
I I I . $4..... .7 I I cal...... 13 I a . ;.cc II 2.2
TI 2 $2 a °c 4$ Ii c. ... I cc. ... $3 ... ,. ;$
1$ 3 c 77 $ 2 ec I 44 4 73 4.2 -
7$ a .e. p $ 8% cc $3 . cc. *3 .7 cc 4? 3.1
72 4 *n I 7 1 I e a. cc 2$ LId
72 4 .c S 44 .c. cc. c *4 dec 4.. $4 cc 7 4 11.1
7 2 c 7 4*... . .. 7 ccc cc 1cc l $3.3
7 4cc p ,, ... ccc $4 4 S .1 11.4
a.. tt $4 I a . .. ..
7$ a 4 c2 1 4 I I I c * e TI f.*
71 II ccc I it I 7 P *3 cc ace *5 .. 43 I d
77 Ii I *8 44. cc 4$ ce cc. 77 cc. ccc 7 7$ 71 cc .c S c. 1$ 7.1
7? I a.. $ ,4 pp .. cc. a κ7 ccc cc. ,, cc. 7 3
71 8 t 7 $ 2 c 7 a, cc. ,. S 1 4 ca. 74 0.,
73 3 cc $7 ... c I . I IS ..c... I cc $2 i 7 .. 7 ;3
2 ee 71 $3 *1 . c S S 4 4 73 cc 74 ,
17 * . 7 7 a S , ...... 41 cc. .4 z.3
cc 1 n. 1* cc ea cc 4.. $ 42 ..
7? 1 c. 4 7 a 21 .. ec 3 2 cc $1 ccc $1 j4 .
17 I 4 I $7 cc. cc. I a -3 2 4 5 ccc 84 . 4
77 a.. 44 $3 I I 52 ... 44 4 71 cc 70..
72 I I 4 $4 I 7 87 ee ... 32 7 ; c. 78 $1.1
It I I c. 2 7 4 ..n .c I I I dcc ccc 43 cc. $3 2.4
P7 02 cc 7 4 4 I, ace cc. 14 7 I 7 77 ccc 71 Z.
11 I c 11 I 75 c ccc 74 12 cc c.c cc. 7$ 44
7* 1 c. I I I I 1 4 I 4 7 71 ccc . ?c
733 4cc a $1 a 1 a $7 a
11 cc. ,* I. I 1 cc . 13 ccc .c. .4 3 42 .. 4$ I 3. 4
73 4 ccc 4$ ccc .. I .cc .cc 2 c .4 c 4. dcc 84 3.3
ii κ P 71 jiccecec 84c...c . flcfl.cc S . a.... .14. 1
3 c I I a.. C.. 73 . cc. c. $3 a.. ... a, 7.. ... 3 I $4 .4. $4 (3.?
11 S c. 7 4 4 ccc ccc U ... . . j ccc .cc *7 .. eec I 5 .4 c.c 41
73 2 ccc $ Ic. cc 44 a . $ 2* 1 75 .c. 4
73 4 cc 4$ 4, SI 4 4 I *3 cc. 14 7.4
71 eec 47 $7 4 cc 4$ cc. cc. 7 7 I .4 c. i 1.3
7, $2 cc .7 cc. cc. $4 c.. ccc 45 cc. cc. 7 71 I I 45 dcc I I 3.3
7 % cc . 7 1* 7 1... ... Icc .... I4cccccc 4 7 74 13 *41
7,2... $ 4l 7*ccecc I$.ec*c 7 1 7 4 c 7* 4.3
74 3 .e $4 $4 a I 4. 4* 4 74 c 12 $.
Il I c j, I $4 -. cc 15 ccc cc 44 ccc cc $4.dc. ccc 74 .. 12 c
7* $ ccc * I I I ccc cc. 3 $ a 73 cc. 44 3.4
7 1 4 eec 7 cc 7 e ccc ccc ... e II cc. cc. % 4 ....ce 45 ec 8$ 2.1
1e 7 ccc 7 7 c c 44 cc. 12 0 $4 S .4 cc .3 3..
7 4 cc$ t cc.... 7 5 73 4 44 cc. eec . 4 I 3.3
11 cc . 73 cc. ccc *4 .cea.c 4* a 21 cc. cc. 3 - i -.. Si 4.1
.2S 1 1 . 2cc $1 $4 c. eec. 4 c. 73 cc.... 3$ c..dcc.. 44 c,.cc..44...c -
71 I I ccc IS ccc c. $4 a .. cc. ccc $1 ccc s*
14 5 ccc IS 4 a a, cc. ccc p 1 I $8 ccc .. .7
1* I c $1 a .. I a t.cc.cc 7 $ 44 cc. 43 c.J
Psi ccc . I I 74...... 7;cc.ccc Ia.c.c I2 cc cc. 7lccc 7$ 5.4
11 3 eec c c 7$ 7 cc. .. I a a a 3 .41
14 4 4 7l ccc cc 4$ cI c.. 15 ccc . .. 4$ $ $1 ccc cc. S 14 ...
71 4 ccc I l 7 cc 43 cc. ccc 5$...... 5 cc 42 jc
7* 4 cq P 1* 7 4 cc. ce . 3 cc .1 cc.... S 8S 43 4.5
75 1 ccc 5 cc. cc. 7$ cc. ccc 7; ccc ccc 54 $ $ 82 .8 ii 43
7% I c 7 1 4 cc I cc 4 SI cc. .c. 34 ccc ccc cc .4 il.l
Is . ccc 7$ I a 3 SI cc. cc. 3 I 47 ccc *1 7.1
7% II c $4 $4 4 cc. cc. 74 c.. ccc θlc..ccc 7 I i ccc -SI P..
7 5 $5 ccc 44 .cc ccc .4 I 7 ...... 7$ cc. cc. 7j 8$ cc ccc $j ccc II 4.3
nia cc. 44...... a cc t in.... tccc..ec I. 44 fl a. ic?
74 4 ccc $4ccccc 7 47 ccc ccc II .cc cec t cc 7$ .cc ccc I 44 a.. I I 4.2
74 7 flc Mccc ccc as ...cn 47cc.... P lc. . . .. i 4$...... P 1 ncccc 7 ccc 4 3d
74 3 ccc IS ccc cc. $3 . -I I ccc c. 7 4$ ccc ccc 14 4 , ccc cc. 2 cc. 11 2.4
74 I cc. 17 cc ccc $4 .c.cce $4 cc. cc. .c $5 ccc cc. 34...... 44 ccc .cc 1$ ccc .7 I.7
15 3 ccc I cc 74 18 c.. cc a S 4$ cc. ccc 55 ccc ccc 5 . 4 .c 4%
7 52 14.c.ccc 7*cc4cccl 3*c.c..c 54c..cc. 44cc.... $ 3 ccc.cc 44 c.c 4311.4
74 7 ccc 1$ 74 71 ccc ccc 4 cc 1 -cc cc. $4 ccc ccc eq cc II j I.
74 * cc $4... cc. 47 ..c cc. I pp...... I c ccc ccc 4 71 c 7$ $2.2
74 I cc $3 ..cc.c 44 I I 17cc.... 43 cc ccc ecc....c 1$ ccc ?8
$5 c.c Ic cc ccc a ..c.c $ c.c ccc 7 4 $4 7$ ccc 45 ccc 74 7...
. a.. , e 2$ ... c . lca.ccc I 7 4.cc.cc P. II .8
78 12 c .. fc.cccc if ccc... 57c.cc.c .. $ 8 . 57.cc 44 2.2
tt I c I 8c.. .cc $1 i cc ccc $7 cc. ccc 14cc ccc 41cc.... 7 $3 cc I I Pcc
U I cc $8 ccc ccc cc ... cc. . cc. ccc 4 cc. .c I * 7 4 cc. $ 7.c
,7 3 cc. $ $4 ccc d .c I I ccc ccc 43 cccccc 74 ccc 14 .J
77 4 cc 4 $ $ I I ccc .cc 4$ cc P I ccc Clic .3
77 3 cc. 8 10 7 - 4$ cc. ccc 44...c c 4 $4 ccc 1$ 4.4
7 4 cc 77 ccc ccc 1 53 ccc c.c .t c.c ccc * 4 cc $4 Id
p 1 .c. c 7* cc.... 7 $7 cc. ccc 2 II sc)
7 c.e 7 5 cc. .... 7 cc. c . .3 ccc ccc cc .c ccc 24cc.... ccc... . 4 dcc 43 73c
72 cc. I 47 a 7 ccc c.c 4$ .4 cc. 34 ccc cc. 4 7$ ccc 1 77.2
ccq 4? 44 ccc__ .c__ *4 ccc ccc Ic.c..c. 4 cc 74 LId
... as ..c.. $7 cc. ccc I fcc. . . . 72 cc ccc ccc ccc $ $cc I? c$
7 42 c$ 4$ ccc cc. as 41 cc.... $4 1 7 3$ cc cc. 4$ ccc I I 1.
74 c. I $ 5 .ccc. I I I cc.... $0 . $ 4 a $7 cc. 44 2.7
1* 7 ccc I I cc.... 7 ccc ccc 28 ccc... $7ccc c c_7 . 7 7qcccc . 44 cc I c3
iT 3 ccc $c cccc.4t c.cc. I 74 cc. cc. 42 cc. cc. *2 a 7 i ccc 7 4.2
$ 4 ccc ..c c l ccc ... cI ccc ccc 1 4 4 ccccc 7 cccccc 34 cc. 77 2.1
lI _ i .c . 83 c.c ccc I? _4 $7 cc. ccc $ $c ccc... _44 77 ccc - t 74.?
75 .c.7 5 .c ..I3 .c...c a 42 cc. cc 4 ccc cc. ccc cc2 74 cc. 84 12c
75 7 ccc 77 ccc ccc p4 ccc ccc l4ccc... 4 53 ccc ccc 44 ccc . .. S ...ccc 4$ ccc 4 , 3c4
74 S ca 18 ccc ccc $3 ccc.. Ii ccc .cc 4 4$ ccc cc. 43cc.... ad cc ccc 7 7 1 c 3
ccc 44 4 43 71 cc. cc. 4$ ccc 44 0.3
74 $4 ccc 4 44 74cc.... 71cc.... 5 4 3.ccc.c ccc ccc 23 ccc 1? & .1
TI II cc 7 If ccc c.c P 45 dcc ccc c4 1.2
74 ii c. $4 c. cc. 7$ 4$ ccc cc. 7 as cc. cc. $ 5 cc. II .4
it . 1.. *4.t.tfl 3..cs.c4c If.*c..$.. 7 5. pc.fl aSc.I Ictfl $7...,ac. san.n. p .. qt
IT 7Cv c i ed .4 .4 c.3 .4 c i 4.4 c3 . 2. .4 .414.3 .4 .418.1 .3 .443.1 cI ci lc* .2
347. . SOT . l$7*c 4 ;?4 c(.ct .?fl.13. 4 1Ic.. 4.5 c 1 j4$ 4
43 1 * It $ 1 .4 171$
-------
Table 2.11(b)
Two way tables of monthly averages of hourly
relative humidity-Eugene
*4 U $ 3 3 4 1 6 t 4 U 4 3 4 44 ii (4 (7 I , 4 1* ii 23 33 _44( Sc
73 4 4.. 4 .4. .4. .4 ... ... 4 . . . It C.. e S I .4. .4 .7 .4. ... 5$ 4 4.4 .4 .. I I 3.4
11 3 e . ne eec flt a ... 3 ... C.. 17 C .. e . 7 en . .. 7 5 C.. . 4 $4 Ce see 4$ ... $4 3.3
fl 3 4.. I I p$e.... 7$ I .... I I 7.4
71 4 . . . 5 en .4 1 ... ... 9 .. C.. 77 ... ... C.. 3.4 . . C.. 7$ .4. e .. $4 .. 7$ I 4.4
q S ... l4 . 4..*. 4 . I tUse.... s4C.ee 37. . . . ..t Ile ?i Ii.4
71 5 fl .. . *.* 64...... I 3 $3 . na . 44 D .C.. e44. .
7 7 en $4 ... . 74 .n .n 7 34 .e. .. 4 3$ ... en en C.. 77 *4e S. 1 .4
yj .. . sIC..... tCneS I 43 .44 .7...... 34.4.... 3 7 li1 .
en 5 )..... 1. . .... 43...... D .44 4e ... .. *S 76ie4 14 .
, nq If ... ... .. .. C 43 ... ... 64 . . ... a a. i.e 4 1$ e. $3 I .J
73 II C.. ) 44 Ce. . .a en $ 44 .4 C.. 44 . .. .4- ... . , ... a.
7(44 en ....... II. .. . .n... 57C..... 53 . 4e4 II . .. $4...... 4 .. , a.L
73 $ en $4 . ..C .. I.. .. I $la.... ?*. ..n. 7...... I . I$ 4. $4 34
77 3 .4, *4 *4. .. ,i ... C.. $4 ... n Il en ... 74 C.. C . 73 I .4 .. $4 4.4
77 3 s . c *4 ... a. 4 .. .. 4$ ... . 4. 43 C.. e 73 .. .. 46 C.. 44 IS i. Ii 3.2
72 . 4* lIen... 54.4.4.. 5 77en. .e $4...n. 3e*. .n 7qne. I. ... ,a,.a
77 $ C.. 4...... p4 I o4Snn. 34...... %%.. .. 14Ce 7tIJ ΰ
13 $ C., 14 .. C.. 4 4, n 53 ... . 64 44. .4. 13 . 4 . ... 34 .. n. I IS 4. 76 .2
117 .S flC.e. . I 5. .e . 1% ..e . . . 4*C . 3 4 . . .n *4 . IIi . .4
77 5 e 4, 74 $3 ... ... ip ... .4. $ e.. ... C.. e 4 C.. 3 ii *4. 41 7.4
77 I $3...... 5.4...e 33...s.a 41 fl.e I I I ..
17 4 47 .. . . $4 en ... 49 .44 64 n. . 4. 7 47 34 4.6
77 44 i.e 7* en Ce 4 .4. *4. 14 ... ti *4- 5 £4 C44 C.. 4 e - C? 4.4
71 Il en 5* en .4. * ... ... .1 en ..e a ... 15 e. . .4 .4. 47 2.6
73 I 44e 45 C.. 44 7$ 5 n .. 5 .n en 7* Ca * . 4* 4.. *4* 74 C.. 4 n 57 C.?
71 2 en 44 C4 Ce4 $1 .. . C* 4 . .4. 73 . .. .4. 7 5 e ... 54 .4. . 4. 54 . $4 3..)
13 3 * 4e I 1 4 l9*44. 44C44.4 5..... . it...... ... . SI .4
73 I a .e. ,5 ... ... tS C.. * 74 a. ... 44 .. .... 4 a 73 a . . It 11.4
7) 3 en II 4 en e.. S so 5 .e C.. 34 .4- 33 .4. .n 44 $3 C.. 74 i C
73 * en 54 .. .e 44 . ,. 34 4 44 44 4 C4 34 .4. fl 31 .4. ... t C.. i4 344
73 en 4 .. C.. ... .4. 73 C.. n 31 .4. 34 .4. 4-C 3 7$ C.. 44 C
3 S 44$ 54 Ca *4. .4 C 4. *4. % *4. 44 44 a. S.. . . 4 * 4 S .. en 33 CC4 Cee 17 Ce4 £7 tot
774 .e 4 C* $3 4 5 . 5.4*44 $*C4 4.e *744.... 4 S IC*e 11II.
13 44 44* 44 * . 4. 44 34 C*i ee I I CC . C 4. C*i 444 5.4 *44 544 $5 *44 41 4- 51 1.4
73 (4 .4* 4 C e *44 *4 4. Ce. C 44 .4. 5 .. $4 5 .. C. . 54 .4. 4.. i 44 C.. 5$ 3.4
71 3 en ft e.. .. 74 54. em. 0 ... C,. 45 eee .4 53 e e a. 54 en ... S. e* C.. 34 .4. II 2.4
74 $ 44* 77 4 C e 7* ... C... t7 C.. CS. 77 .4- en 3 see see .4 ... i.e 4? .. . 54 7 4 C.. 73 5J
77 3 a. 44 s .4 . . . 4 .4 en IS en C.. 4 . .. y a. a. t* . C.. 43 C4 , *45 44 *44 iE 6.4
74 1 4.6 44 .4. 55e 54 ..e ... 4e ..e 64 C.. Ce .4 5.. .0* 41 .4. C.. 7* .. CC4 .6 C.. 6* CU
77 * en 44 a. *4. .5 ... ... 5* en Ce. 74 en . i 4 e . 4. 34 en ... 7 .4. 4.. II e. 7* 11.4
7* 5 en 5* * 4 C .4 4$ .4 *40 44 4 CC $4 *44 C 57 C4. Ce 54 Ca 4 *40 63 C.. 74 34 -
77 6 4-7 II .4 C.. 15 .4. 4. 41 *44 * . 44 44 444 1* .4. .44 4 Ce. n $4 .4. 4 $4 S 71 I I ..
7* 1 en %n *4. . 14*..... ee C CC. 4C .4. .. .4 en... .4 33 ... ... 77 .e .. *4 .
7* S Se. 53 ... C.. 54 .4 4e 94 e . 5 40 Ce. .4. 3$ e en 45 Ce. ... 33 4. .4 73 C . .4 .4 5
77 *e. 7* C .. C 4 54 CC *.C I I C.. 4 37 5.. . 34 ... 5*. 31 C .. n 34 54. 4 $4 *4 4 9 I SC
7* 54 en I l .4. 5. 74 .44 .4. Ic . * 77 C CC. 34 .4 Ce 47 . . .4. e 74 .. 77 4.4
1*11 Ce. 4 C e 47*4cC.. C 4 e 4 7* . eCe. 7* .4fle . *4 4.1
7* 2 4 4% .4. 44 fl .4 44 C .4 Se 44 Sq. .. 44 .4 C , 47 44 Cam 44 .4. .. 4$ in $3 I. 3
t% $ en ft en 0 45 .6. ea 41 4.. C.. 44 C C.e $ .4. 4.. 34 . C.. 4* 4.. *4. 44 .4. 4 4.4
73 3 .44 4% C.. .4 34 C4 4 .C 44 . 4 4 e 4 5*4 Ce $5 *S C4 $4 4* C.. 35 . *4. .5 .5 4$ i 4
17 1 . *3...... *4...... t4 . .*44 4e ie.4 77. * . 4444eee 3 C44C 4. 7$C e 44 $
73 4 ,n 7* .e. .. 47 ... C a 44e C.. 4? C4 C 4 $4 C44 5 54 Ce. C.. Ce $3 43..)
77 3 54 . 73 5.. .4. $4 ... Ce $4 4. 7 44 a. en 37 . ... a 54 5.. j l . I
7% 4 en 53 CC. .4. 43 C4- 54 54- C4. 57 Ca C e 37 C *44 31 C.. C 44 4 ea 13 .. SI . .
77 7 *44 4 .4C4C 4Cn 4. 4C.44 $4 .4.en 34 m 44.4. .C ?7*44 5 3*.6
77 I 4.. 4 en *C* 44 .. .4. It 5.. C 7$ C.. 144 35 . 4. *1 C.. C4 61 *4, . 44 4. 44 34.4
77 .44 54 .4. .4. 44 .4. C.. .4 Ce4 C44 54 .4 . 4 *4 i.e . 4 c 34 .4 . Ce. 34 C4 4 C.a 74 Ce 4 *4 1 .1
J7 .3 4S4 J. .$ .4.5 .* _,S. C 4 . C*4 ,. .14. C.. 4* . 4 . . . 4.13 . . . .e ._i3. e..
74 I I . .. 47 C .e .. it .4. C.. II . 4 . e.* C l se. C.. 73 . .4. I I ... a. 44 .4. C .. 74 ii 6.4
77 43 C4. 7* . . . ... 3$ ... .4. $4 C a4 e.. S. e. e 43 .4. C.. 54 5.. ..e 44 *e Cej 14 .4. II 3.5.
14 I Cec 44 ... C4. 44 a. S .. 43 4.4 C 4 4$ 4 ee. 7 IS .4. ... 44 .C Cn 4 C .. I D b.4
74 4 4., 5* C.. .4* 4 e . . .4. 44 .4. *44 77 en C.. 41 544 .4. $1 .4. .4. *4 i.4 ee0 55 C 71 5.3
74 3 . .e $4a.4.. 57...... 44e.s. 7* . . . ... .4 ...en 44C 4cC. 14 . . . e $4Ce .sp*4Τ 4
7* 4 .4. 44 . 4 . .4. $1 ... .4. 44 en ... 77 C.. 4*eC c a 3 I. 4 .n 74 C. e ii 7* 44.4
74 $ en $7 5.. ... 57 .e. C .. 4* 44 Ce 34 ..e 33 . 4. i.e 32 e C.. *4 4.4 n 74 CC. 45 J4
74 5 *44 $4 C4 C* 57 .4.4-. 77 C .4 .e $4 .4. ... 5 .4 . C.. 49 C44 C 54 .44 *44 71 ae 57 44.1
7% 7 en 75 en en . -a ... 74 ..4 .4. lι C.. ee 34 ie 34 .5. e4 * 4 4 COe 41 5.. *4
74 5 .0* 43 Ce . .. 5 4 . oe 14 C C .4 £7 en en $1 se .4. e4 C.. e 44 .4. 14 C.. t i .J
7 e 54 C.. a. 44 . . . $3 .4. CCa 44 .. C.. 33 . en C l C .. C . el C C *44 74 Cee 44 4.4
34 44 C e It C.. n 44 .. s.. ... Ce. 7 ee* 544 3* C44 Ce 33 *4. C44 7$ *44 5 .. 54 C CC 15 4..
9% 43 en 5% C .. 5.. *4 .. ... 47 ... 5., 4 C.. e e 7% * C.. 73 .4. se
7$ 53 en 47 * ..C.* C4 a. .4. 44 en .4. .7 .4* Se. $4 Ce. Cee 1$ C C. . 44 f e 0 C.. .4
1? 4 C.. *5 ... Ce. 44 . 4 .4 4 .4. .. I C C .. .e. 71 C .. e $4 Ce CC. 54 ie4 C.. 54 C4. 54
74 2 Sn 47 en en 41 *C 4.4 4* ... Ca C l Ces 4 7% .. 44 PU .4* .ea 7* C44 3.. 4 Ce4 5% 7.6
7 7 3 en 44 . .e C.. . .. 74 *44 *40 .3 *44 Ce. S 7 3 ..
73 4 en I 45 .n a 4. C.. C4 .1 i.e ..e 47 see en 4, C.. ee q ... P t .. 73 U.s
77 5 e.. .4 en a. $4 .e. ... II .4 *4. 74 44 C.. e 73 .44 *4. 4 e. 73 3.1
73 6 en $7 ee C.. 54 14 .. 4.e 47 . 4. *e4 $ 5e C.. C n $ 4 ..C 51 C C . 74 1
74 7 5.. $4 C see 54 ..4 .4 71 5.. C.. 34 C e 444 C s . ... 74 en C.. I 7* C 54 *4 41.4
77 4 en 7 C 4 5*4 $3 CC 0 43 4.4 C.. $4 e C4 .4 a. C.. 34 ... 5 CC C4. 74 44 i. .4
77 4 544 47 44 .. *6 e. . 49 .C44 a 31 e. ea $ 1$ 4. 43 3.4
J7J I - C. 4 *4 C .. C C tIC.. ... 47 Ce. a. $4 Ce C.. $ 31 *4 a. $4 Ii 4$..
,, It ..0 4 . CC* .e Ii CC. 75 C *4. 75 . . .. . 5.. a 4.4
74 3 *44 44 .44 34 ..* . I $ ee 4 ... ... 34 ... ... $ i7 *4 . 5, 4.i
77 4 54* *4 .4. ... 45 C.. C. 44 C i 4 5 Ce. ... 54 04 *4* 5, 1.5
U 2 * I .4. Ce I a. $9 en - 7* Ce. iS. 7% 0e. ec 14 C - 77 . ID 34
I 47* C4 C.. 7 44 *4e C.. 44 CC .ce 7 4 544 74
74 4 4. 4 *4 54 .4. SC. $4 54a C C. 7* C.. .44 *4 C.. C 4 4 7 *4 544 74 e 4
77 3 44454 .4 4C.Ca$4C..CC* I ... . a, C ... ...e - 53 e. 7$ I) 3
75 4 *4, $4 .4 e 57 ... ... 54 C.. 4 $4 Ce. 34 4. C., 4 $ 44 5.4 7* 1.5
7 5 7 a. II . . . .. $4 C.. CC 34 C.e I C. 13 CC 4. C.1
75 I 14. 5 57 C4e Sea 54 Ce. C*4 $4 *4. Ce 4* C4 Cea C 54 *4* C4. 7* Ce $4 4 5.4
77 ee 44 .4. 4 II *44 77 Cfl Ce0 43 * 5 4C 3 t CC 5 C 47 4. 54 44.1
7 I. se4 b 3 a eeC l. C4- $7 CC. 4 77 4.Ce t e. ... ... j *e. a. $4 .a 344
$14 $44 $4. 1 4Ie 74* 5e,..q-u,. ,.s v 14.444.44* 71. I*e 544 4eI44.74* 44.447. 14* 77.?4434*4 $J. .44
S I I C ? .459. , 4 4 3 $ .4 .! .4 j 4 .j .4 .444 .4 .4 .444.4 . 411.4 .4 .4 7.4 .4
3SI7 3474. 77.. 147* t 5C74?ts.43. 44*34*4 4 5 ftsll$4
49 I I 1* 77 *44
-------
Table 2.12
Monthly precipitation frequencies
(a) Portland
1. 2 3 4 5 6 7 3 10 ii . 12
10 40 4.6 41 53 9.6
7l 41 43 44 53 tO.0
72 39 43 49 53 10.3
72 38 44 47 54 9.7
74 35 42 44 54 10.1
75 41. 41 44 52 9.8
76 42 41 44 52
77 35 44 45 52 10.3
78 40 44 49 52 10.6
79 31 42 50 54 11.5
39 43 47 50 Z7 63 68 67 o3 54 45 41
3.1 1.5 24 2.0 2.0 .0 1.5 4 2.6 1.7 2.5 2.9
(b) Eugene
YR 1. 2 3 4 5 6 7 8 9 10 11 12 AVE SE
70 *** **z u *** *u xu **z * z zz u* uz i*z
71 40 41 43 47 54 57 66 67 58 49 44 39
12 38 43 48 46 56 61 6.8 68 53 50 47 35
73 38 43 44 49 56 61 67 63 61 52 44 44
74 39 43 49 51 56 64 67 68 66 54 49 46
75 4.6 43 44 46 56 61 68 65 44 53 46 43
76 42 41 44 4? 53 58 67 65 63 53 47
77 38 44 44 51 Si. 61 64 9 59 5: 44 43
78 43 44 48 48 53 32 67 .6.6 59 53 41 41
79 31 43 48 50 44 61 67 45 63 56 4 43
39 43 36 49 53 α 47 36 61 53 45 41
3.9 1.0 2. L.S 3.7 1. 1.1 1. .7 .1 2.4 3.4
AVE SE
48 57 aS . 3 47 60 53 47 40
5 ) 57 57 48 70 61 53 45 40
47 60 63 70 70 60 52 48 37
53 59 63 39 65 64 54 44 45
51 55 64 66 63 61 54 48 44
47 57 61 68 64 65 53 46 42.
50 56 60 66 64 63 54 47 . 39
53 53 63 66 70 60 53 44 42
50 55 65 67 67 60. 55 39 36
52 59 64 70 67 66 53 45 44
AVE
SE
*z* .0
50 9.4
52 10.4
52 9.1
54 9.5
53 9.1
52 9.6
53 94
52 9.1
51 10.3
AVE
SE
-------
Table 2.13(a)
Two way tables of monthly precipitation frequency
according to hour of day-Portland
- 14 ) 3 I 7 3 3 a . te I I 2 42 t4 44 . I. 2* 2* 7 . 3 78 C 4 (4
.. 4 34 32...... 3 74..4 34 .4
1? 4 e e I I I . an i I I 4% US lb .4
...n.-fl n.. .. 3 CflJI *1 7,. 4.3 TT
43 q ... ... U * . iJ a. . i 7 .* 7 .3
1? 1 44. 3 4 .. ... II ... I .3
ia. are. ..f-p.e. 7 ,ee . .-nt ..rne 1: . ..r... ..rr..e 1
7% s... , .
.. ,...a. e ....
.- . .T l.4V 7 W4 7b .-T... a.. -. ,-1 4 ...%4 4.6 ...1 ... oao 5...
3) 1) 0 I I A *44 4 4 , a .44 ii .. a. I 44 j** 3 .4
7 Is .44 ?7 4 I . I 2 3 33 a.. 74 .84
e. 2 . . . . 4 2. aS. I.. -4.3 * 74t
I I 4 34 a ... .
,i 3 . 2 1. an a. .3 .34 *44 34 .4
7T .4.. a.. ... 7. . .. ... 3: :. ..io .. .32r ... a.r-. .. ..
7% a.. 3 . e S 2* fl *44 1.3 fl .44 2 t I 7 .a 1* .1
7 % 3 .. 46 IA $3 I U* .4* 3 *44 .e 4 I . .e I I .84
I ,. r.r... .4a T i - ... 337f a. * .74 .t3 I
7$ I 4.e f I ... a.. j a.. n ..e i a
i-a. :... ,_.... 3...... ....... a... a .a
4...- I; . a .3 .φ 3.. 4 4 . .7_...___ j
71 43 .. 8 ae 4 74 a_. . I I IS e $7 .4
7i I .* 4 . *. C P. 33 7 7 I i -I a.. 7% .4
IT * .4* 37 . 4. 14 . 4 .44 .3 a. ... 3 73 a I%. r.
7% n 3. . . 7 a. ... 3 ii 34 a. II .8
Pp ... . a.. a.. 4 . - .. 3 3 31 . . 8 .a. 7. .4
S a.. .. -. 74 .. - ... is Z ..3..3 3,..3 a. ) ..7I 52
74 I 4 * 4 . $3 S.. i* .* a I 37 73 .4
77 4 ... a . .. 4 .. I $4 $3 _ . a.. ii .a
... ., . a. .. - ..r..r..rwrnr rrfl.-fl r.cra1r n .- .. - - I
7p p a.. p . 3 .. a.. 2 ..e a.. 3 ... 3 .4
8$ 3 5.. ... ... .. .. 3 a.. 3 .3
.. ... ag11 8.4 %. . 47k.. 4a 3 ... s .. r.....r r..y .
77 4) 444 4 44 I. . .. 3 ... I .4
PT II ee 7 a7 7 4 4 1? .4 It S* 4) 23 .1
fl- .tpane 74*44 44 W 7 * *P .. a.. 37 .e.e-.4q3 a.. fl %t a.. 4 fl 4qa..c 34ea.t 3t .. .,_
fl 4 Ua n .1 an a S I 8 .. 3 8 I A ae 7* .3
8) 7 ... 4 a.. is ..a ..e . ... I a. a. $1 .. a.. I a ..e Ii .8
r3r t. 3b.4%0I %4.n 4 432
8) a a. 3...... 8 P...... ?*...e 7 ea S Sann. .4
72 3 n .. 44 I IA ae *44 4 44 ae I 0 e 44 .4
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-------
Table 2.13(b)
Two way tables of monthly precipitation frequency
according to hour of day-Eugene
1* 3 I I I 4 4 4 7 p 4 u II I ? ii 4 ii ISi? is 4 72 2* 23 23 £4( *4
I I . l 14 Si 4.4 4 34 3 4 2 .4
, ... 1 14 7 is .. 3 I
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I, .e 1 2 I 7 I I 14 .4
Il 4 ... tS ii . 4. ... I I $ 43 II .3
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I I .e 4 I 7 3 . 444 3 .2
7% I 4.. 3 I .S * 4 .s 4. , 4 4 .4. 4 .7
..T ,. . 4. T47B7. 43 . 4 3;3 4 T ... Isi7433 4 ,
,t , ... I 12 .. . $3 $ 4 4 .2
P1 . .. 7? 7 7 3 3 3 24 31 3 .4
4 47 4P 33 .. * 44 1? 31 * 3* 37 54443 7 .4 . .
7 . 4.. . 1 I I 1$ I 14 .. 33
7 I 1 *. 74 .4. .4* 34 3 3* 4*4 31 .4
__ r Ti 4 74 - r... 4T4 - . - 4 Tr-- . .. .
is 4.. ... I 3 21 . ... 4 2* .4
I l I 4* 4 . I 1 I I 4 ... I I .1
r ___ ._Ct.t* .44 44 W4* 7 fl
7, 3 I . 3 ... I .4
3.* 3 .3
77 .3r1T4. .. .31 7 4 I7T. .4 7T . . b.. i . . S
l3 4*. l4 . . s 344 4.44 4 .2
7 I .4. 7 7 I I I I .. 3* .4
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4, 4.. 7_ 4 es...... 314.4. 5* 4 3 34 3 P3 . 3* .4
:i , .. I Pt . II .4 I I. I I .4
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-------
Table 2.14
Monthly t nperature averages
(a) Portland
YR 1 2 3 4 5 6 7 9 9 10 11 1 i VE E
70 40 46 47 48 57 65 68 σ7 60 53 47 40 53 9.6
.1 41 43 44 50 57 59 68 70 61 52 45 40 53 10.0
72 39 43 49 47 60 63 70 70 60 52 48 37 53 10.8
3 38 44 47 52 59 63 σ9 65 64 54 44 45 54 9.7
4 38 4 46 51 55 64 66 68 67 54 48 44 54 10.1
75 41 41 44 47 57 61 68 64 65 53 46 42 52 9.8
6 42 41 44 50 56 60 66 64 63 54 47 39 52 9.3
7 35 44 45 53 53 63 66 70 60 :3 44 42 52 10.3
78 40 44 49 50 55 65 67 67 60 55 39 36 52 10.6
79 31 4_ 50 52 59 64 70 67 66 58 45 44 54 11.5
4*JE 39 43 47 50 57 63 68 67 63 54 45 41
SE 3.1 1.5 2.0 2.0 2.0 1.5 2.2 2.6 1.7 2.5 2.9
(b) Eugene
YR 1 2 3 4 5 6 7 8 9 10 11 12 AVE SE
70 *** **z EU EU EU EU EU EU EU EU EU EU EU .0
71 40 41 43 47 54 57 66 67 58 49 44 39 50 9.4
72 38 43 48 46 56 61 68 68 58 50 47 35 52 10.4
73 38 43 44 49 56 61 67 63 61 52 44 52 9.1
74 39 43 49 51 56 64 67 68 66 54 49 46 54 °.5
7 5 46 *3 44 46 56 61 68 65 64 55 46 43 53 9.1
76 42 41 44 49 55 58 67 65 6 53 47 37 5 9.6
77 39 44 44 51 51 61 64 69 52 44 43 5 9.3
78 43 44 48 48 53 62 67 66 59 52 41 41 52 9.1
79 31 43 48 50 44 61 67 65 63 56 4 43 51 1 ).8
.WE 39 43 46 49 53 61 67 66 61 53 45 41
SE 3.9 .0 2.2 1.8 3.7 1.9 1.1 1.8 2.7 2.L 2.4 3.4
-------
Table 2.15(a)
Two way tables of monthly averages of hourly temperature readings-Portland
1414 S 1 4 1 4 5 4 7 $ I to is I I II to ii
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Table 2.15(b)
Two way tables of monthly averages of hourly temperature readings-Eugene
I I 3 3 4 3 $ 7 $ 3 14 14 Ii I I 1$ 11 $4 9? 3* 1$ 24 31 2* *3 404 44
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-------
Table 3.1
Root mean square errors & for the diurnal models
fitted to Eugene CO averages
Ml M2 M3 M4 M5 M6
WD .770 .287 .208 .199 .211 .185
Spring
WE .401 .360 .182 .185 .324 .268
WO 1.019 .219 .164 .148 .230 .257
S unrier
WE .433 .204 .095 .088 .115 .154
WD 1.430 .663 .485 .514 .454 .420
Fall
WE .617 .519 .300 .331 .418 .350
WD. 1.730 .697 .211 .234 .606 .574
Winter
WE .682 .400 .197 .218 .366 .366
-------
Table 3.2
Diurnal model-Eugene, suniner 1978 (weekdays)
&
A
a
a. + kTR
a + kTR/WS 5
(a + kTR)/US 6
+ kTRRH
(a + kTR)RH
2.43
(.21)
1 .05
(.08)
.83
(.08)
1 .63
(.15)
.76
(.09)
.0081
(.0012)
.55
(.11)
.49
(.08)
1 .93
(.09)
6.62
(1.58)
6.45
(1.30)
.041
(.002)
.047
(.002)
1 .019
.219
.164
.148
230
.257
Table 3.3
Diurnal model--Eugene, suimier 1978 (weekends)
A A
a k (S
A
a
a + kTR
a + kTR/WS
(a + kTR)/WS 5
a + kTR RH
(a + kTR)RH
1.76
(.09)
1.08
(.09)
.80
(.05)
1 .73
(.10)
.83
(.06)
0086
(.0008)
.79
(.06)
.53
(.05)
1 .51
(.17)
9.56
(1.36)
6.65
(.82)
034
(.002)
044
(.002)
.433
204
.095
.088
.115
.154
-------
Table 4.1
CAMS: estimation results for the model
C0. = k (1 + e11M )(1 + itNDti + 2 ThDt 2 )G +
= lt-l + 2 t-2 + 3 t-3 + Ct.
IND 1 t = 1 for t 1978/3 and 0 otherwise;
IND 2 t = I for 1976/1 t 1976/12 and 0 otherwise.
ModelG e &
Cl: 1 .629
C2: I .256 .189 .617
(.101) (.110)
C3: 1 -.00271 -.00103 .588
(.00116) (.00052)
C4: 1 .00287 - .000505 -.060 - .086 .591
(.00123) (.000775) (.092) (.080)
C5: TR
t
C6: TR
t
.00372 -.00116 .585
(.00115) (.00050)
- .00387 - .000655 - .055 - .086 .588
(.00126) (.000743) (.090) (.080)
C7: TR RH
t
C8: TR RH
t
-.00409 -.00106 .504
(.00105) (.00046)
-.00425 -.000540 -.060 -.084 .505
(.00115) (.000676) (.079) (.069)
-------
Table 4.2
CAMS: detailed estimation results for model C7
CO = ke 8 (l eI/M )TR RH + n + 2 t-2 + 3 1 t-3 +
k 5 xl0 4 8 8
1.06 (.07) -.00409 -.00106 .19 (.10)
.96 (.07) (.00105) (.00046) .25 (.10)
.84 (.06) .29 (.10)
.74 (.06) -4.8% -12%
.62 (.06) (±1.2) (±5.2)
.62 (.06)
.52 (.06)
.60 (.06)
.81 (.06)
.93 (.06)
1.04 (.07)
1.04 (.07)
Table 4.3
CAMS: detailed estimation results for model C8
C0. = k 5 8 (1 + κ1/M )(1 + aiI tl + 2 IN0t 2 )TRtRHt +
= l t-l χ 2 t-2 + 3 flt 3 +
kx10 4 8
1.07 (.07) -.00425 -.000540 -.060 .084 .15 (.10)
.96 (.07) (.00115) (.000677) (.079 (.069) .26 (10)
.84 (.06) .31 (.10)
.74 (.06) 5.0% 6.1%
.62 (.06) (±1.4) (±7.7)
.62 (.06)
.52 (.06)
.61 (.06)
.81 (.06)
.93 (.06)
1.05 (.07)
1.05 (.07)
-------
Table 4.4
Additional percentage benefit for fleet attributable to the I/M program;
omparison of EPA estimates and models C7 and C8
Year Additional benefit attributable to I/M
(1000) EPA Model C7 Model C8
1975
32.5
0.4
3.5
1.8
1976
145.8
8.3
15.5
7.9
1977
68.8
14.4
7.3
3.7
1978
143.7
16.8
15.2
7.8
1979
91.9
19.6
9.7
5.0
Table 4.5
CAMS
Co = k 5 (l +
nt =
B.
estimation results for model (4.11)
Bt + 0I/M + itN0ti + cL 2 INDt 2 )TRtRHt +
+ 2 n 2 + +
8
-.00313
(.00065)
-.000749
(.000374)
.505
-4.6%
(±1.0)
-10.4%
(±5.2)
-.00332
(.00070)
-.000361
(.000477)
-.026
(.053)
-.059
(.048)
.506
-5.0%
(±1.0)
-5.1%
(±6.7)
-------
Table 4.6
Eugene model fits
C0, = ke 8 (l + eI/M )(1 + cLINDt)TRtRHt + n , where
= l t-l + 2t2 + 3t3 +
INUt = 1 for t 75/9 and 0 otherwise.
Model 8 e &
El .00201 .631
(.00160)
E2 -.00320 .000843 .633
(.00255) (.001327)
E3 .00465 .206 .629
(.00290) (.198)
E4 -.00487 .000330 .194 .634
(.00334) (.001385) (.220)
-------
Table 4.7
Hollywood model fits
ke t(1 + I/M )(1 + ctINOt)Gt + where
+ $ 2 nt_ 2 + 3 t3 +
1 for t 75/10 and 0 otherwise.
cot =
nt =
INDt =
Model B
8
A
a.
Hi
1
.00422
(.00194)
.574
142
- .00446
(.00236)
.000149
(.000801)
.578
H3
-.00423
(.00278)
.000288
(.000849)
-.028
(.117)
.582
144
TR
t
.00642
(.00174)
.571
145
.00653
(.00209)
.000013
(.000710)
.575
146
.00604
(.00255)
.000037
(.000731)
- .028
(.111)
.579
147
TR RH
t
t
.00716
(.00181)
.577
H8
-.00709
(.00221)
-.000100
(.000760)
.582
149
-.00602
(.00258)
.000114
(.000810)
-.080
(.111)
.584
-------
Table 4,8
CO model fits at Alder and Lloyd
C0 = k 5 e (l + 9I/!4)TRtRHt +
= l t1 + 2 t- 2 + 3 T t-3 + Et.
e &
Alder: -.0136 .538
(.0051)
-.0122 .00055 .542
(.0046) (.00124)
Lloyd: -.0110 .633
(.0059)
.0110 -.00026 .641
(.0061) (.00178)
-------
Table 5.1(a)
Estimates in regression model (5.4)--suniuer
The CO observations are in 10 pg/rn 3 standard
1 2 errors
12 13 14 75 16 17 78 19 WS WS cos sin T RH PR obs model
F4.5 4.4 6.3 -5.0 -17.9 -13.4 -14.7 -20.0 -17.4 172 -132 -1.3 -5.6 -.67 .02 19.0
CAMS am (15.5) (3.1) (3.1) (3.1) (3.1) (4.1) (3.4) (3.2) (3.1) (18) (20) (1.1) (1.1) (1.7) (.10) (2.5) 24.6 18.9
29.8 -5.2 2.4 -21.2 -20.7 -44.7 -28.4 -42.0 -31.8 823 -1852 -8.6 -4.9 -.34 .83 39.5
pm (23.8) (5.4) (5.3) (5.3) (5.4) (7.8) (5.9) (5.6) (5.5) (94) (386) (2.0) (2.0) (.24) (.17) (5.3) 49.2 33.5
12.2 7.3 4.1 -5.0 24.3 -1.2 0.9 -1.9 -3.2 33 -25 0.2 0.5 .22 -.07 0.6
Eugene am (8.8) (1.6) (1.7) (1.9) (3.7) (1.6) (1.9) (1.6) (1.7) (9) (7) (0.6) (0.6) (.10) (.05) (1.7) 11.4 10.5
5.4 21.0 11.1 0.2 21.6 5.3 16.9 11.0 9.9 21 -10 0.7 0.4 .18 .09 1.5
pin (7.4) (1.6) (1.6) (1.8) (3.4) (1.5) (1.8) (1.6) (1.6) (9) (9) (0.6) (0.6) (.08) (.05) (1.9) 12.4 10.2
36.3 -2.7 -13.4 -16.9 -18.3 -11.5 -15.8 78 -62 0.9 1.0 -.45 .38 5.4
hollywood am (16.5) (3.2) (3.1) (3.1) (3.1) (3.2) (3.2) (14) (13) (1.1) (1.0) (.18) (.10) (2.5) 18.8 16.8
56.0 -17.6 -23.6 -29.3 -25.5 -28.0 -38.0 167 -133 1.5 0.1 -.20 .16 6.2
phi (15.6) (3.5) (3.3) (3.3) (3.4) (3.4) (3.4) (17) (17) (1.1) (1.2) (.16) (.10) (3.3) 23.4 18.5
-27.0 1.2 -2.8 -7.5 -3 1 1.2 1.1 .67 .28 12.0
Alder airi (15.3) (2.7) (2.6) (2.6) (18) (19) (1.1) (1.1) (.17) (.10) (2.4) 11.9 10.8
-38.6 -17.9 -26.2 -18.3 193 594 -0.8 -1.7 1.19 .69 -12.2
p ii (18.0) (3.5) (3.4) (3.4) (79) (365) (1.4) (1.4) (.18) (.13) (3.8) 17.4 14.3
-27.8 1.3 -8.3 -5.2 32 -24 -0.6 -0.2 .36 .31 -4.8
Lloyd aiii (10.1) (1.3) (1.3) (1.4) (8) (8) (0.7) (0.6) (.11) (.06) (1.5) 9.2 8.0
-21.0 -4.9 -14.5 -19.9 44 -31 0.5 0.0 .84 .26 0.7
pm (18.2) (2.8) (2.1) (2.9) (19) (18) (1.4) (1.4) (.18) (.12) (3.9) 18.9 17.3
Note: Wind speed and wind direction at CAllS and Alder from hhughes/Federal Buildings
-------
Table 5.1(b)
Estimates in regression mode) (5.4)--winter
The CO observations are in 10 pg/rn 3
standard
-1 -2 errors
72/3 73/4 74/5 75/6 76/7 77/6 78/9 WS WS COS St I RH PR abs model
22.3 -13.0 -11.7 -30.9 -31.) -28.8 -24.2 -32.4 156 -126 1.1 1.8 .79 .16 1.5
CAMS am (1.3) (3.5) (3.5) (3.7) (3.5) (4.1) (3.7) (3.7) (20) (21) (1.3) (1.4) (.13) (.08) (2.1) 27.2 22.3
18.) -5.5 -33.2 -44.3 -73.9 -58.3 -47.7 -55.9 193 -156 -0.1 -3.2 1.48 .84 15.8
pm (15.5) (7.6) (7.6) (7.6) (7,6) (6.7) (7.9) (7.7) (50) (66) (2.9) (2.9) (.27) (.17) 4.7) 58.5 47.9
21.1 0.5 -2.3 -5.8 -3.3 3.6 -9.5 -6.9 76 -59 -0.6 -2.1 -.21 -.15 -3.9
Eugene am (1.4) (2.4) (2.8) (2.8) (2.3) (2.4) (2.5) (3.1) (13) (12) (0.9) (0.9) (.10) (.09) (1.5) 16.8 14.3
1.2 10.1 2.9 -10.4 4.1 18.1 15.4 6.9 15) -118 -0.2 1.2 .28 .24 -9.1
pm (10.1) (3.6) (4.1) (4.2) (3.5) (3.6) (3.6) (4.6) (16) (15) (1.4) (1.4) (.14) (.11) (2.4) 26.8 21.6
Il. ) 1.3 -16.3 -9.4 -3.5 -15.6 107 -76 -5.2 -0.9 -.80 .51 -6.7
Uollywood am (11.7) (4.5) (4.6) (4.8) (4.5) (4.7) (25) (25) (1.9) (1.9) (.20) (.13) (3.2) 31.0 27.6
54.8 11.6 -19.5 -3.4 0.8 -23.4 161 -116 -10.0 -5.3 .19 .42 Li
pm (16.5) (6.6) (6.7) (7.1) (6.6) (6.9) (33) (33) (2.9) (2.7) (.29) (.17) (5.0) 45.5 41.3
-16.9 9.9 4.4 1.6 62 -63 -0.5 1.9 -.12 .62 -7.2
Alder am (8.9) (3.2) (2.8) (3.1) (22) (24) (1.5) (1.5) (.15) (.09) (2.4) 19.4 16.3
-25.3 23.2 -3.3 9.4 250 -277 -0.6 -6.2 -.01 1.09 -19.2
pm (16.6) (5.9) (5.2) (5.1) (55) (66) (2.8) (2.8) (.28) (.16) (4.6) 38.8 30.9
-1.6 8.4 2.1 -1.7 72 -51 -1.0 -0.3 -.31 .32 -2.5
Lloyd am (6.6) (2.3) (2.2) (2.3) (14) (14) (1.1) (1.1) (.11) (.07) (1.9) 16.8 12.8
48.1 16.3 4.7 -8.0 240 -188 -4.7 -2.5 -1.36 .68 16.3
pin (18.1) (5.9) (5.8) (6.2) (33) (32) (3.0) (2.8) (.30) (.17) (5.4) 44.1 34.8
Note: Wind speed and wind direction at CAMS and Alder from Hughes/Federal Buildings
-------
Table 5.2(a)
Estimates In regression model (5.6)--sun.ner
The CO observations are in 10 ig/m 3
Const TR It.TR J/MTR IND WS 1 WS 2 cos sin Temp RH Prec Obs Model
CAMS -97.7 1.74 -.036 -.0011 10.5 181 -141 -0.7 -5.4 -.55 .06 18.8 24.4 19.1
am (52.0) (.53) (.007) (.0003) (5.1) (18) (20) (1.1) (1.1) (.17) (.10) (2.5)
CAMS -170.8 2.14 -.055 -.0016 -3.5 676 -1035 -8.3 -3.4 -.33 .89 35.1 49.3 34.1
pm (92.5) (.95) (.012) (.0006) (9.4) (73) (219) (2.0) (1.9) (.24) (.17) (5.3)
CAMS -42.3 1.20 -.035 -.0006 181 -142 -0.7 -5.3 -.58 .05 18.3 24.4 19.1
am (44.8) (.47) (.007) (.0002) (18) (21) (1.1) (1.1) (.17) (.10) (2.5)
CAMS -187.7 2.35 -.054 -.0017 665 -1003 -8.3 -3.7 .35 .87 35.7 49.0 33.8
pin (80.4) (.83) (.011) (.0004) (73) (217) (2.0) (1.9) (.24) (.17) (5.2)
Eugene 80.7 -2.40 -.064 9.2 28 -21 0.3 0.5 .14 -.08 0.7 11.1 10.7
am (22.1) (.83) (.021) (3.9) (8) (1) (0.6) (0.6) (.10) (.05) (1.7)
Eugene 169.2 -6.38 -.055 19.2 11 3 0.2 0.4 .15 .11 1.4 12.1 11.4
pm (23.0) (.88) (.022) (4.0) (11) (12) (0.6) (0.7) (.08) (.05) (2.2)
IND: CAMS 1 if 1976 Eugene 1 Aug. 1975
0 otherwise 0 otherwise
-------
Table 5.2(b)
Estimates In regression model (5.6)--winter
IND: CAMS 1 if 1976
Eugene 1 Aug. 1975
Cons t
TR 1t TR
The CO observations are in 10
I/MTR IND
WS WS 2 cos
pg/rn 3
sin
Temp RH Prec Obs Model
CAMS
am
CAMS
pm
CAMS
am
CAMS
pm
Eugene
am
Eugene
pm
14.2
(32.5)
-101 .4
(73.4)
2.0
(32.3)
-146.6
(73.8)
20.1
(16.1)
-70.5
(23.5)
0.18
-.059
.0010
-12.6
(.37)
(.010)
(.0004)
(4.0)
1.70
-.142
.0013
-39.2
(.84)
(.022)
(.0010)
(8.9)
0.29
-.045
.0001
(.37)
(.009)
(.0003)
2.18
-.104
-.0015
(.85)
(.021,)
(.0007)
0.06
-.090
6.7
(.65)
(.026)
(2.8)
2.86
-.019
10.4
(.95)
(.039)
(4.3)
(20)
(22)
(1.4)
(1.4)
(.14)
(.08)
(2.1)
212
(54)
-170
(62)
2.5
(3.1)
-5.7
(3.1)
1.25
(.30)
.84
(.18)
15.4
(5.1)
157
(20)
131
(22)
2.1
(1.4)
1.0
(1.4)
.66
(.14)
.15
(.08)
3.5
(2.1)
182
(55)
-138
(63)
2.2
(3.2)
-5.6
(3.2)
.98
(.30)
.88
(.18)
14.8
(5.2)
92
(13)
-73
(12)
-0.5
(0.9)
-2.7
(0.9)
-0.23
(.09)
.19
(.10)
-3.5
(1.5)
165
(17)
-129
(16)
0.9
(1.4)
1.0
(LA)
-0.24
(.13)
.36
(.12)
-9.7
(2.4)
27.2 23.0
64.3 54.1
27.0 23.0
64.3 54.9
16.8 14.5
27.0 22.1
0 otherwise
0 otherwise
-------
Table 5.3(a)
Annual percentage trend reduction, and additional
average benefit due to tIM at CAMS
(standard errors given in parentheses)
without with
traffic disruption (1976) traffic disruption (1976)
trend
I/M
trend
I/M
suimier
am
-8.8
(1.8)
-17.1
(5.7)
-9.1
(1.8)
-31.4
(8.6)
sun ner
pm
-9.0
(1.8)
-32.1
(7.6)
-9.2
(2.0)
-30.2
(11.3)
winter
am
-6.9
(1.4)
1.7
(5.1)
-9.0
(1.5)
17.3
(6.9)
winter
pm
-7.8
(1.6)
-12.8
(6.0)
-10.7
(1.7)
11.1
(8.5)
average
-8.1
(1.6)
-15.1
(6.2)
-9.5
(1.8)
-8.3
(9.0)
Table 5.3(b)
Annual
(Standard
trend reduction
errors given in
at Eugene
parentheses)
trend
suniner
am
-5.4
(1.8)
suniner
pm
-3.6
(1.4)
winter
am
-5.8
(1.7)
winter
pm
-.7
(1.4)
average -3.9
(1.6)
-------
Table 5.4
CO suniner/winter and morning/afternoon 3
averages for CAMS and Eugene (in 10 .Ig/m )
suniner winter
morning afternoon morning afternoon
CAMS 40.0 60.5 60.0 122.0
Eugene 28.3 36.7 34.9 62.3
-------
Figure 1.1
Annual vehicle inspection and failure rates
cars inspected
(in 1000)
600
400
S
200.
TIME
74 75 76 77 78 79
failure rate
(percentage)
30
S
20
10
___________________________________________ TIME
I I I I
74 75 76 77 78 79
-------
Figure 1.2
Monthly car inspections-Portland I/M program
Cars Inspected
60000
40000
20000
C
70
72
74
program became
mandatory
76 78
------- An error occurred while trying to OCR this image.
-------
Figure 1.4
Map of Eugene indicating locations of CO receptor, traffic counter
and meteorological measur nent sites
/j \ .! ,1
- -- . - : . .
_______ / - - _
F - -
\ .. - - - --
- -
M i . I
-j _ . __ = -.--- /
_____ . I t
-. - --- - -. 1
-. . .- -
1 -: r -- -
. 7 I .
-u-; . j. . . . ____
\
__ -r: ,
_
- . -
- c
-
-.
-:: 1 ::
________ - - :
_____ = -- - _ - -
-. r .
____ rr zj -
___ ____ _______ /
= = . III It - p - -
I ____ __ ____
___ ;: -
- -
-- !r -
I
__ i
- .tY ZC -
- 14 2 = . . s- .
- - _ - -
-. -. --.. -
-
- - v - 5
- - - ?___.=
I;
_________ _ - L - - ,
= ::. : .: -. .- -
_. - - - 4
I
- -: -
S - - S S -
S. ,. -- - - - - \5 \ £ .
- ,.- -.. _;_j . - -
)
E:L1GE .
I
-------
l 19ure 2 . 1 ( α)
0
Plot of monthly CO averages--CAMS
O.OH
-------
Figure 2.1( h)
I0ft Ibv%3
ZO.c$1
Plot of monthly CO averages--Hollywood
j1;:,;ri
-------
FIgure 2.1(G)
1
65.01
32.0
Plot of monthly CO averages--Alder
0.04
1970I19fll972 l973Il9741l975 19Th ig 77 3 1 ;, 8 L 1 g 1 g
-------
Figure 2.1(d)
Plot of monthly CO averages--Lloyd
i97o l97i1i9721i973 l974tl975 i976tl977 1978 11979
3
1 O g/un
60.0 .L
30.0
OJL
-------
Figure 2.1(e)
32.0
Plot of monthly CO averages--Eugene
-------
Figure 2.2(a)
Plot of monthly CO averages--weekday/weekend comparison--CAMS
iOi to 1wi3
9D o
9 0 . a
weekend
0.0-t
weekday
_ ________ .
--- r1 /91_ti
-------
Elgure 2.2(b)
o,oi
Plot of monthly CO averages--weekday/weekend comparison--Hollywood
weekday
weekend
0.0 4
-------
Figure 2.2(c)
Plot of monthly CO averages--weekday/weekend comparison--Alder
weekday
0.0.1 -
1
weekend
I?? !;J;9iih,sjf
o,0 .
-------
IoSL& lw 3
o.O .4
lyure 2.2(d)
Plot of monthly averages--weekday/weekend comparison--Lloyd
weekday
a __. .
weekend
( O.O-
Do-
1 - , 177 1797k 7?
-------
10
5.0-4
Figure 2.2(e)
Plot of monthly Co averages--weekdaY/weekend comparison--Eugene
weekday
1\
. _.__ _____ __._
weekend
,I7c ; w r
-------
Figure 2.3(a)
Composite plots of monthly 25th, 50th, 75th, and 95th percentiles of CO--CAMS
A 25th
8 50th
C 75th
o 95th
$ I0.*
no. ,
$40.;
$20.4
too.,
no.,
σO.t
40.4
20.4
-------
Figure 2.3(b)
Composite plots of monthly 25th, 50th, 75th, and 95th percentIles of CO--Hollywood
a0. G
160.t
- cc
I 00 G
00.t
40.*
20.*
.- .f- I:
I ,,, I , _ J
C
* **
-------
Figure 2.3(c)
Composite plots of monthly 25th, 50th, 75th, and 95th percentIles of CO--Alder
IO, .tσIh3
100.1
70.1
40.4
2 .t
10.*
CC
C
C
C
-------
Figure 2.3(d)
Composite plots of monthly 25th, 50th, 75th, and 95th percentIles of CO--lloyd
CC
AC CCC b C C CC C C CC C CCC C CC C
CC 011 .L AA\ b0bi\
1 \A. /
AA * AAAAA AAAA AAAAAAAA I I6 0AAAAAAA2A
AA A
A l .A
Al. AAAAAAAAA
1;;;TI* 1;;±T:
I
*00.6
*60.4
*00.6
00.4
60.4
40. *
20.4
0.$
C
C
C C CC C
-------
Figure 2.3(e)
Composite plots of monthly 25th 50th 1 75th, and 95th percentiles of CO--Eugene
t . 6
cccc
AA
jl
CCC
C L
AA
A
44 44444 4444444
*l.-__ --i--__$ 1 -6 _______1t _________*1 - f --I--- -- ------1--f 1
ifia g1 13 iri ii u. l9 11 1971
£20. I
t0 . I
90.6
60.6
0.6
AAA
A
C
C
C
C
AAAAAAAAMA AA
A
A
A AA
A A A
-------
FIgure 2.4(a)
Diurnal diagrams of hourly CO averages for weekdays, 1970-1974
( Suniner, - - - - Winter)
V4N M. II U UU*1Ni$ tMI S fl
N
a
I.
I.
N
N
N
-
.DMI Nildil 1$ HIUINN,ea I*I .111
.u* NNaNI 6* s*, ....s, 6N&IN $64
I.
.4
I
-a
N
II
$
L.
I.
4 ,
... ...
WNl m *1 I Il**i$4 14$4* ma
a
6
0
1
a.
S
54 64 54 N a 114
-a
6aI $6t 44566 II INlI $*l$S 1N4 11$
N
. ,i . ..
S
a
I.
N
I
1
i.IUI* 4145 1 , 1* 141 1641 1 1 1 4U41 6114
a
N
1.
m
4 ,1 11*4 $61461 II 1114641141 161$ 6*96
I
1*6 NNNII 1* 141 (1411* 1 416$ 666*
a
N
* 1(141 1 lIl4lI 1$ 16 11 141 , 11 (41$ $I
N
N
I.
N
4
m
0
ID
0
-4
Ά
* 116*1*
I I (I 61 114 1 16a Nk6 1s45 1611
$141114
$ l (I 1611*411 11 lINt 14.41$ ($54
a
a
a.
N
a
- a
I .
IN
S
.1
La
1 J.
S
1970 1971 1972 1973 1974
-------
0.1*401 01001.1 LI l .l$001 $l 101011 14$
I
N-
4.. 4. ,4 4110111 II U4 101ll$l 101$ IHI
:
4
N-
0 1 10 ( 1 00441* (I 1011010111 011111101 IS IS
N-
I
iqm
I
4- 0 are .
Diurnal diagrams of hourly O averages for weekdays. 1975-1979 C Sunuier, - - - - Winter)
0,11 .44 11.011 I i . 101,flalll) 114 111 SIl l
N
N
1
N
0011001 504010 (S 10(101455) 4111 0*010 $110
I-
I
N-
t.S.* l.a ., (0 IINIOINIS 111*5 (10111 55*4
11
N-
1976
1011154 P4440 I I 5511514515 00*11 5 51)
N
N
N
I
I
N-
I
*550044 41*5 40 4111501115 (Sill $501
N
5-
0*11501 51 1411 (0 151 1551,01 tioss (loll. 5101
51(0411 51)41 ! (5 151(10115, (OilS Sill
N
N
114 114 . l i 114
5-
1918
511 .101 014*5 (5 ft i . 1 114 1 15 111454 5554
N
N
IN
I ..
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Diurnal diagrams of hourly CO averages for weekends, 1970-1974
( Suniner - - - - Winter)
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In Thousands
Figure 2.6
Pot of monthy average daiy traffic counts (In 1000 cars/day),
Portand I 80N, Co umbla River Highway at NE 2 st Ave.
-4 * 4 4*4.4.44S
I I tqia I j 73 I ,,i( I i ic I qi& I i9?1 mu 1 un
-------
I jure .I
Plot ol iwnithly avt rd je ddily traffic counts (in 1000 cdrs/day),
In Thousdflds lugenc, (iS 99 f dcIiic highway West (.02 iiifles NE of 11th Ave.)
-------
FIgure 2.8(a)
Monthly wind speed averages
Portland Airport
-------
Figure 2.8(b)
MontMy wind speed averages
Eugene Airport
I.
-------
FIgure 2.8(c)
Monthly wind speed averages
Portland Hughes/Federal Building
P1 PH
0.04-
-------
Figure 2.9
Composite plot of monthly 25th, 50th, 75th,
and 95th percentiles of wind speed
(a) Portland airport
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1973 [ 97f 197) I I
-------
Figure 2.9
Composite plot of monthly 25th, 50th, 15th,
95th percentiles of wind speed
11 P H (c) Portland Hughes/Federal Building
90.*
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Figure 2.10(a)
D1i rna1 diagrams of hourly wind speed averages--Portland Airport
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Diurnal diagrams of hourly wind speed averages--Eugene Airport
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Figure 2.10(c)
Diurnal diagrams of hourly wind speed averages--Portland Hughes/Federal Building
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Relative Frequency of wind direction
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(a) morning
_nn__ _ _
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I11,I197 I113 i iyl7j,η 17 79
-------
figure 2.14(a)
cr ccsat
l ao .
Plot of monthly averages of relative humidity--Portland
i..iW ..; 1*$$ 1 1 1j
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-------
F Igure 2.14(b)
Pc . 4t4
Plot of monthly averages of relative humidity--Eugene
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-------
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Composite percentile plots of monthly 25th, 50th, 75th, and 95th
percentiles of relative humidity
*
a
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/176 f 77 /, I /77 I
(b) Eugene
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Figure 2.16(a)
Diurnal diagrams of hourly averages of relative humidity--Portland
Ksua t s S.Ift reaRm. see.
1970
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Summer
Winter
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1975 1976 1977 1978
-------
FIgure 2.16(b)
Diurnal diagrams of hourly averages of relative humidity--Eugene
NIISII ..*IIS IVISIS SIPS
1972
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1974 1975
Summer
Ud IC P II
5(55
Winter
-------
Figure 2.17(a)
Plot of uiionthly frequencies of precipitation--Portland
I Ill i i
c - t
-------
Figure 2.11(b)
Plot of monthly frequencies of precipitation--Eugene
Per C . .. k
i20.04
30.0
-------
Figure 2.18(a)
Diurnal diagrams of hourly precipitation frequencies--Portland
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1978
Winter
-------
Figure 2 .18(b)
Diurnal diagrams of hourly precipitation frequencies--Eugene
a
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1971 1972
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Suniner
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N.
a
.... .. ...
N..
a
I .
NM NIf SN lS(SN$IIISIU 111151 III
.s.
- - -
______
NSSq.quIp SN .aletPISSUw SslS(
a.
a,
N
1
N
I ..
IN
1973
1974
1976
1977
Winter
-------
Figure 2.19(a)
/Su
Plot of monthly temperature averages--Portland
-------
Figure 2.19(b)
Plot of monthly temperature averages--Eugene
004 .
riwrwLi j3 q 19 7 6 I
r o. o-
-------
Figure 2.20
Composite percentile plots of monthly
25th, 50th, 75th, and 95th percentiles
(a) Portland
Ti 7I ( T I ?3 1?7Y I 7 I I 7 /977 I / 7
(b) Eugene
-m rr 7 - t T 1 I97 , ?7
I / 7
(A 25th, B 50th,
C 75th, 0 95th)
3.
0. 0.
it .a.
.0+
e3.0+
54.3.
. 0 .
l.0+
A A
A
... +
go. +
+0. i
50. i
,0. *
I
*
10. *
A
I /17? F
-------
Figure 3.1
Diurnal CO averages for Eugene, suniner 1978
weekday - - - weekend
.3
cD
\
/
\\
:3
our
Figure 3.2
Diurnal traffic averages for Eugene, suimner 1978 (in thousands of cars)
weekday - - - weekend
mabsf
of care
z. .
L5
_I. _
I
/
I .0 -
/
/
\
/
our
-------
Figure 3.3
Plots of (a) CO v WS, (b) CO vs NIH, Cc) CO vs RH
Eugene, sunvner 1978, weekday afternoon (3-6 pm)
CO (a) CO (b)
( g/m 3 ) ( ig/m 3 )
. .
so
I l .a
. .
I . 5
I . . I . .
..
.0
a
a Ia ss sa I . . l . a a .. I S a l .a I&S 5 S S 5 a is iS UIS hisS is iS
wS MM
(Knots) (Meters)
Co
3 (c)
( ig/m )
a.
h a
I
5-.
a a
l .a
. 0
I .- .
5 I S
RH
(Percent)
-------
Figure 3.4
Plot of RH vs MH, Eugene, sunrer 1978, afternoon (36 pm)
n .. I
$ $
l .. $ S
MN
1w..
S
- S
S
t u...
I
S . . ..
S . ..
S. 15. S . S.. los. 1 0
t4
-------
Figure 3.5(a)
Diurnal diagram of windspeed averages, Eugene suim er 1978
Knots
6
4
0
hour
Figure 3.5(b)
Diurnal diagram of relative humidity averages, Eugene sununer 1978
U
S.-
V
313
hour
a
-------
Figure 3.6
Qiurnal diagrams of CO traffic, wind speed, and relative humidity for four seasons at Eugene
weekday - - - - weekend
Spring Sunvuer Fall a
a , .i 1 . o o . . a
\
I I. I I L
a I . I. .. . 1
----- -s , . as
.. a
as ia
U
a.
SI
SI
p p ap a, a as
I I - s IS 5$
a..
I.
I.
SI
Winter
$ IS I . * aa
P is ii e5
Is 5* -. $S II
Co
TR
WS
RH
a.
-------
Figure 3.7
Observed vs predicted CX) hourly CO averages
4odel M4: CO, = + kTRt)/WS + Eugene summer 1978
CO ,
j.ig/m 3
hour
(a) weekday
hτ
(b) weekend
a
I
a
CO
gj 3 4
3,
a
I
C .,
-------
Figure 4.1
Plot of kt = keBt for B = .005; k = 1
Figure 4.2
Plotofk =k(1+Bt)forB_.005; k=1
kt
kt
hO
.5
.5
-------
Figure 4.3
Plot of k = ke 8 t(1 + eI/M ) for k = 1, 8 = -.005 and e = .001
3 1
t1 s
Figure 4.4
Plot of the estimated emission constants e8t(1 + eI/M ); model C7; CAMS
kt
11 73 75 77 79
tlii.
-------
Figure 4.5
Plot of the estimated emission constants
kt e8t(l + eI/M )(l + iINDt 1 + cQNDt 2 ); model Ca; CAMS
7 1 73 75 77 19
ttas
-------
Figure 5.1
Scatterplots of CO against meteorological variables
Portland suir ner 1977, afternoon
(a) CO vs WS (at Hughes)
1 -u..
S
- S
1
IOj.& °:
- a
4U.
S
U. U. U. U. 4Q. . u. 70.
(b) CO vs WD (at Hughes)
1.U.
- S
- a
4 ..,. -
-
- .
pa
-------
Figure 5.1
Scatterplot.s of CO against meteorological variables
Portland sulTuner 1977, afternoon
Cc) CO vs Temp (at NCC)
-
Co
-
-
.
au..
.
S
4.u σ.. 7. 7 j ThU .U. J %04.U
(d) CO vs RH (at NCC)
RIu. *
S
u. *
..v. S
-: -
-------
Figure 5.1
Scatterplots of CO against meteorological variables
Portland summer 1977, afternoon
Ce) CO vs MH (at Salem)
LOU.
*
70..
.
. .
p η
40U. 300. L0Q. L OU. U AI. 244 U. 3OO. 3u.i ,3 I0.
.(4 MFF(at S 1em)vs-RH (at NCC)
-
:ioo. +
P4 14
e
moo. +
S
:0. 10. 40. 0. 60. ?0. W. 90.
-------
Figure 5.2(a)
Plot of the trend indicator estimates for regression model (5.4): Sumer
t3 I$ . -r7 , 73 7 f 17 77
I I I I I I I I
a.-
0 I C.RHS
S
S
- 0 -
1 .
. S
S
S .
1 i i I
I
0 - I
S
S
. I
S
a
S (14 Lc )
.
o - --. I u
-------
Figure 5.2(b)
Plot of the trend indicator estimates for regression model (5.4): Winter
I,3 , 17ft, I I 1 ?
1 I
.
. . 0
.
S
S
S
0 i
S a
-a.-
S
0
.
S
. S __________________
0
-.2° -
a
0
- 0-
------- |