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 ------- 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 ------- 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. ------- 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: ------- 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. ------- 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 ------- 2 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. ------- 3 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, ------- 4 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 ------- 5 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 ------- 6 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. ------- 7 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. ------- 8 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 ------- 9 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. ------- 10 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. ------- 11 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 ------- 12 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). ------- 13 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 ------- 14 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) ------- 15 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. ------- 16 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 ------- 17 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. ------- 18 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 ------- 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 ------- 20 (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 ------- 21 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 ------- 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. ------- 23 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. ------- 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 ------- 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 ------- 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 ------- 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. ------- 28 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. ------- 29 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 ------- 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 ------- 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 ------- 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 ------- 33 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. ------- 34 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). ------- 35 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 3. 44 32 33 44 3 ! 41 30 12 s. . 74 .3 .1 34 .. I 133 4 77 14 .5 847 1.3 *1 34 4 1. *4 .4 *4 34 44 *4 3* 1.2 .5 10 II 3. 71 74 33 .0 4* .4 II 34 3. 40 10 26 .8 3* .3 36 33 3? 3* *0 33 .7 47 .2 74 *4 .3 35 44. .7 31 44 5 . 67 .2 1* 3. SC tI .. 4? .4 54 46 *3 74 *4 114 1* 11 17 La *14 II) 11 #3 17 sI 333 I.e .* .7 32 20 73 34 3* 64 .1 34 44 41 33 43 51 31 71 71 4. 36 .10 314 *3 83 34 *3 14 .4 4* 45 .4 46 .2 .4 77 .0 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 41 .4 6* 4* 101 134 34 .3 4 *. 5$ SI 11 i* 37 .. II .0 33 .1 34 *2 .3 3 4. 7 d .2 33 4 I I SI .. 30 .7 1. .4 24 .4 31 34 34 32 34 .3 3* 54 .. I I 53 .2 78 1? 31 .5 .4 i 77 4 .3 .4 .4 44 314 113 II II 20 31 22 .7 73 .5 .3 *6 7, 64 33 43 *0 1? 34 44 4* 43 *4 43 3* 31 *1 42 14 53 30 76 24 54 2* 33 34 41 *8 33 *7 1$ 24 8* 33 24.71 3? 41 45 6.4 $0 s4 43 44 11 44 75 56 7? 34 35 $7 54 $ 1 53 .1 .3 76 4* *3 3* .3 7; .4 54 .4 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 $S .4 37 77 73 .7 1 73 34 1! 4? SC I I 3 2. 23 34 33 44 74 31 3. 33 4. 77 5 24 2? 4$ 41 85 30 24 .4 43 4* 42 34 13 5! 34 14 II 81 .4 .3 8* 51 . 73 $1 .2 1? 7* 4* SI *7 44 .4 73 6* 65 .4 71 34 *4 13 34 43 3; 31 40 3* 30 40 33 33 32 37 31 37 3* 34 44 I I 1? II 24 31 4 24 23 2$ .7 II 4 42 43 7; 43 *0 $3 $0 43 7? 43 $1 $3 8s 44 3I 47 43 $4 IC 43 44 6* .5 37 36 II IS 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 .4 *4 14 *7 37 71 1* 4 3 .2 *4 4? *0 37 37 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 I I 41 *4 44 6* 73 44 3! 33 .0 .7 15 47 43 43 84 4 . 4? .4 41 *0 14 3$ 3$ 37 3* 63 43 31 *4 37 II II 34 33 22 *1 t$ 34 2$ 13 37 73 34 I ? .. 2 23 2 .7 30 .7 44 44 .3 I 51 18 *2 4 $1 35 11 II 10 5? 37 *3 .5 II . 1 73 *5 83 .2 .7 *3 34 .3 61 *5 43 42 43 *4 34 31 3* 37 4.3 53 41 23 33 28 43 27. 36 30 28 13 *6 14 1* 14 23 28 3* 75 33 64 .3 45 .4 *4 6.1 .1 43 8* 47 .5 38 6. 13 12 *3 SI *1 53 43 .4 44 2 4. .1 .4 .4 47 *2 II .0 3. 33 30 31 II 36 2! 50 33 .2 II II 80 21 IS I I 14 1$ IS * 1. 13 1$ 40 20 20 42 28 22 II 12 32 7* 47 11 30 .4 41 44 16 *4 62 .4 33 54 44 44 .4 45 *7 4. *1.4 ..* s. . 4.. 5. , ... 7.I8.3I8..l$.3*3.4 1 ,.2 14. 711.31 3. 173.733.33s.1 13.4I,. .I ..II3.1* 5.3 13.7 .57.. 87s. 73 !.. UI. 83 Sl!343 58.3.9 . 485 4*11744 Table 2.2(a) Two way tables of monthly averages of hourly CO--CAMS 1 .P .4 I 3 1 6. 1 *141* I. 5 38 31 31 54 I. 31 6? , 13 72 17 7. 6* 3. .. .5 5* I I 42 74 7. 54 I I 3$ 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 75 11 31 45 *3 14 Il *3 6 44 II *3 4. 33 5* 1$ 24 .3 Ii 11 11 34 *4 17 .4 40 11 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? Ds 49 ,13 8 1 1 716833.1*343* is 5 77 13 1 $ 4 5* 33 3. 7 7 34 46 5 13 . . 4 6 62 33 iS 4s 24 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 11 3 3. 5* s. 1. Ia .7 3 .. 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Ii .4 12 .. 54 5* * 32 .4 3. 14 8. 3. 4. 37 5. 44 4* 5* £3 3$ 13 3. 43 30 3* 64 4 44 43 41 46 3 4 $3 10 .0 as 32 24 3* 46 SI 81 so .4 30 48 .3 31 3* 2 4 2? 3 $4 17 55 .0 1 * SI 3* 34 3$ 44 $1 5 4 .3 a 40 SI .5 34 33 23 U 15 J O 57 Is -3 3, 3.5 32 23 *3 I, 23 43 3. 3, 31 32 26 23 1 l I . 13 43 4 13 4* 17 SI 4, .5 34 33 23 I. 27 .4 84 .4 5$ * 2 33 33 41 4$ 14 .2 .7 44 3 S. *3 I, 1 5 23 2I .4 3 3, 31 a., *4* 5. 33.4 1? .4.7 .7 23.6 3 14.2 30 11.1 52 Ii . ? 22 8.3 37 34.3 6* 13.4 4* 43.4 3* 32.3 73 31.3 54 34.4 .2 d4.3 *2 23.) 43 22.4 34 13.1 4; 15.4 25 II.) 3* 14.3 45 43.1 II is.4 74 43.3 .3 38.5 *4 30.1 54 33.3 $4 ES.4 14 54.2 33 13.. *4 16.2 33 *3.3 32 11.3 10 2.4 33 15.0 7* 6.4.3 S d 33. $4 34.8 * 31.4 15 44.4 34 $. II 13.7 40 11.4 25 *1.0 3. *3.2 41 56.0 .2 a..S .4 33.3 74 34.1 30 81.3 1* 42.3 $1 11.8 43 *5.4 34 *3.2 47 IC ,. 27 *6.5 24 1 ..3 31 1.3.2 33 45.. 3! 32.4 13 .4.1 64 21.2 43 21.3 36 I$.3 77 I..3 44 11.1 54 11.4 1$ 4.4 55 14.3 23 13.4 54 22.3 34 344 41 *4.0 *4 .44.4 32 1443 7* 14.3 75 *4.1 I! 4.3 I5 4.7 IS 7.4 27 *1.0 24 4.7 4 *4.4 *4 45.1 $1 35.2 *4 41.1 .5 *5,7 40 63.3 34 50.3 37 13.* 23 4.2 40 7.1 32 11.1 4? 73.4 54 14.3 10 4.4 57 4.,. 4* 4..1 .3 11.7 38 14.5 3! 13.4 21 14.6 7* 17,4 Il 5.! 2* *3.2 1 *3.3 *0 13.3 3* .3.4 *3 1*3. 4* 53.3 53 73.8 34 *1.4 30 10.3 43 a.! :2 .7 *5 1.? 20 S.. 31 *0.1 *2 *4.7 .3 14.7 13 48.4 11 53 33 *3 *2 .2 44 42 40 .4 40 z3 : . s. 77 *51 5, ------- 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 .3,.. 3; : .. 737.. 4.7. .s.4 .8 11443. 3 ...S 4. .1543.3 *. .. : t S . 7 . 4 tO 11 1. *3 14 I I *4 I? 1* 4 20 21 U LI 4.4 *4 , 1. I 31 3. 43 11 14 *4 3? 43 : 72 32 113 1.2 3.4 *10 I II *43 I.? .1 43 *4 73 3 .3 73 37.4 13 4 1! 4 33 3. a. 4 .3 Il 13 32 1 13 34 Ii t a i. 49 4 : 7) 34 14 113 III 1*0 .7 1. 4 I II .1 13 74 54 4 34 *1 .3 .3 .4 .3 1 103 133 .3 44 II II 44 14 74 2.3 43 &.3 13 . a. 11 1? 14 2 71 .. 83 . 33 83 .3 4$ 34 .1 .3 33 33 33 33 44 45 43 14.1 73 4 33 .4 *4 *3 1 18 33 ; 4* .1 3 * :1 73 11 .4 74 74 71 II 32 U II 3* 34 44 21.1 7. S II l II 11 11 I I 21 3* . .4 36 I I 8 *1 10 .3 71 3 ! :4 83 38 34 2* 33 4* 20.9 73 1 Ii is *3 *1 II 14 .4 .3 3. 4 44 SI 43 .5. .4 .3 54 43 40 41 34 33 U 14 34 14.4 1; . 7. . 38 .3 38 17 38 I? I I 3 . II .3 . ,i8 45 3. $4-li .4 a, 143 84 43 4 .3 41 44 71 7. 74 .9 44 34 *4.1.344 43I4 44 7 773 530.73421 43 l.3 84.4.3 73 I . 43 34 3 I. Is 37 37 71 .4 73 74 84 14 73 .2 43 133 43 *3 ? 3 74 83 14 43 IS II .7 34 18 *1 14 -IS 33 14 3. 34 .3 .1 .1 sI 131 131 133 II 71 II .0 .2 23 4* 33.7 la 14 33 .. 17 I. 1* Ii .2 *7 t. 54 44 74 4* 77 74 U. 134 *33 4* 48 47 9 34 43 83 32.7 7. * 1$ 18 *3 13 1. 1 41 .4 4. 43 .7 il 4! 31 44 .3 34 49 26 43 33 *4.3 1. 33 i 3 17 I I 13:7. Ti 34 $ 44 3. .0 34 .. .7 43 5. 33 *0 14 4* .3 61 17.3 i . a . . i i 4 1 13 8 22 2 3 33 .3 .4 II 4* 31 .4 .4 6! 44 63 34 43 14.4 14 1 . , 4 4 13 I.. 13 14 *4 Ii II 33 I I .4 .3 .4 44 3? .1 3. 33 .3 44 47 .3 .0 .3 .4 53 48 32 37 33 31 24 17 1 32 37 .3 .1 3. 64 33 .9 37 33 33 3. a 27 33 14.1 33 12.3 7. i. s 4. . 14 4 .3 1 1? 17 . *4 I l a II 14 . 11 *4 3. 14 73 . 33 4 ;7 z. 8 4. 14 .. . .s 3. :3 34 16 33 33 26 22 21 28 14 to 4 :3 3m .4 .7 .3 .1 .7 a 30 23 31 43 27 47 :7 . .4 I i 33 .1 .. 54 .4 33 57 31 41 30 34 .2 4.3 31 11.7 53 i3. 3. , 43 17 Is 1* U 45 33 74 5, 34 .3 3 4 .7 .3 18 *4 .3 34 33 33 33 37 31 42 17.1 14 U 14 II P. .4 4. .7 34 .4 .9 I I 2. 1 .4 33 14 31 33 .. 74 &; &3 32 33 13 31 34 .3 74 7! 43 117 44 *3 44 14 54 44 4. .. 71 74 44 *33 734 34 aS 73 *4 34 .4 .5 46.* $3 33.3 3. 1_ 8 a. 23. 14 13 11 31 77 7. 38 37 73 Ti 74 .3 .3 II 41 73 71 15 47 .1 44 54 .4.2 13 I 3 1 . I 4 I .. I .3 73 a: 44 54 *3 73 . 3 13 3 *34 41 43 34 II 43 37 33 3* 2..4 13 7. 39 47 I 14 I . *3 13 33 11 34 33 43 .4 .3 53 33 7 .3 l 78 1 74 7! .3 44 44 31 33 44 28 .3 .3 31 53 *3 43 43 41 .1 .1 .4 .4 II 34 34 50 42.. 41 *4.3 74 73 3 38 1 Ia 37 II . 14 14 14 11 * *3 23 34 43 49 5. 42 33 34 34 43 . .4 44 3* 34 .4 34 .3 14 43 47 Zo 32 13 3$ * ii .3 3 .1 .4 34 37 34 .4 33 44 34 1..a 31 12.3 3. 33 3 a . I ? 39 3. I 3. 14 25 .4 44 .4 41 14 23 47 2* 2 *4 14 33 7) 1 4 1 I 4 Il 3. 31 34 4 37 3) 42 44 13 43 24 23 31 21 24 *3 12 32 14.3 * 14 7 . 4 4 *4 34 3? .7 14 a 2 33 37 17 34 34 34 43 ii *2 33 19 14 23 12.1 75 *4 I I I I a S 11 47 87 . 34 .4 *3 .3 .1 .1 .4 54 54 34 35 34 34 32 41 38 13.4 2 1. U *7 33 9 1 113 44 77 44 .4 14 SI SI SI . $4 77 74 44 47 34 31 43 3 43 33.4 14 I I 38 1. 14 , I 14 21 40 1. 84 .4 .4 .4 .40 14 44 47 70 44 31 34 34 47 4 43.3 7 ,. I. 2* Ii II 3 a 4 45 .3 3. 4 .3 33 53 7 43 .4 44 *4 47 37 44 84 44 47 43 23.4 14 1 13 4 1 3 4 4 . ,1 .1 4* .1 .4 I I .3 34 71 79 74 32 47 34 31 23 24 31 3. . 74 24 . Il It 12 I. I ... I . 1 *3 3 . 4 . 14 4 . 14 4.. 4 , 33 4 .34 33 34 42 - 5 31 13 - 44 34 24 34 2? 33 39 44 33 34 35 34 44 II 37 47 43 44 33 ii 47 20 15.4 31 13.. 14 . 78 5 * 12 lj 4 13 4 4 4 4 31 ii 43 3. 6 4. 31 3 4* 37 .4 .3 43 54 33 45 I I 34 34 44 47 33 . 44 31 33 4 4 3! 34 4 4 31 . 23 47 13 14 34 14.4 22 10.4 7.. 4 II 7 3 . 3 14 32 2. .35 .33 12 :2 32 33 2$ 4* 14 12 .22 31 37 17 II 4? 11.3 l 7 13 . . 5 3 $ 14 34 2. .3 31 22 3. :4 31 3. .3 ) 11 4 .22 .23 .37 Is 1 22 11.3 is . 13 II I I 4 7 4 .3 14 4. 33 31 31 .3 .3 39 .3 -44 41 32 .34 .14 .31 23 24 30 12.2 1. 3 33 I? 14 39 I is 4 43 4: 37 14 34 .3 43 .1 . .3 43 43 34 I 40 33 4$ 34 33 11.7 1. 1. 14 2$ IS 14 14 3a 44 11 .5 .43 .3 ..3 .4 -44 Ii 34 11 14 64 4* .33 .30 45 43 15.3 2 Ii 4* 44 21 14 16 .b 44 3! 5 33 .3 33 44 - 5? .3 .2 I I I II I *9 *4 33 .0 - .4 37 34 23.9 Is 1. 37 31 43 .1 . I . .1 .4 .. .2 .3 72 74 .0 II ,2) 13.3 134 71 13 41 64 3$ 4? 40 23.3 U I 34 14 4. .3 _ .3 37 713 is . 14 *4 .7 7 13 .4 a. IC 2 U? a2 73 37 a. 37 30 .3 44.2 11 J1 4 33 3 , 1. .3 33 43 3. .4 .4 54 3! 33 33 *3 72 14 .7 31 50 33 312 44 .7 18.5 71 5 I. I ? 18 13 37 30 33 41 .4 33 .3 44 31 39 .5 .3 41 31 33 10 33 37 44 33 13.7 1? U .4 .4 1* II 14 si 44 3: 4 33 34 34 39 .4 44 44 41 43 4* 41 34 44 3 10.2 11 41 I d 24 II I . I ,. *4 33 34 33 31 33 II 34 32 .2 .5 .3 38 32 31 33 37 34 21 9.7 77 *7 1. 12 IC . 4 14 .0 23 48 1 24 33 31 3* 15 34 .34 31 21 26 24 23 II 23 4.3 I I 1 13 1. . 7 . a 34 .3 di .7 a? 31 .33 39 34 sQ 33 31 32 23 21 33 3! IS 20 4.4 71 a 31 *4 14 Ic 11 22 37 3. 14 33 41 .3 .3 4 .3 50 .4 29 33 24 10 *3 22 30 11.1 77 , 44 2.. *1 II . 4 .2 .7 . 14 13 33 .37 23 11 .3 42 13 43 44 45 31 32 24 33 14.4 73 . 27 1. 33 14 *4 I I Is s1 .s 34 33 5* 33 .4 47 51 13 73 *4 73 at 34 52 4? 34 17.3 1? 3* 14 3: .3 34 U 11 33 1 5 21 Ii 34 34 5* 37 44 44 74 84 Si .3 35 4* 31 224 41 *4 33 II. . . . 24 33 3. 13 3 4 12 7 4! *0 4 41 43 34 44 .3 32 34 47 34.8 I .. I 44 .3 17 14 1* I. 54 *4 8 *3 4 73 74 73 14 4 131 II? 7* 47 40 35 .. 3! 57 14.3 7 4 33 . II 1* a 34 2. 3. 37 32 .4 .1 .2 .7 .3 41 .1 74 43 41 37 6! 84 32 35.4 U $ 41 3. .1 Ii 14 17 2* 44 5 .3 47 4? I I 40 *3 14 73 83 44 41 17 38 SI 41 18.? 7. 4* 2 14 , I I I .3 3. 3. .3 4. 3. 55 33 48 .1 43 30 44 41 33 34 31 .4 33 33.2 1. i 1 II 11 4 . a *7 :7 1. 7 . 33 33 :1 53 3. 87 .2 33 2* 27 23 32 *4 34 11.5 7. . 13 13 13 4 . II * 14 3. 33 12 44 38 3* 34 23 4* 63 29 2: 33 24 23 .3 37 I.? 7: 7 *8 1$ 34 4 4 II 43 4 33 .1 3* 47 34 3* 34 34 2* 14 23 44 12 77 23 *3.4 7, 41 Il 1* 11 . *4 ae 44 3: 33 43 4. 43 .3 33 34 67 44 34 33 32 10 21 44 31 *2.5 7, 33 3: 1. *3 II 34 .4 34 4. 43 14 *3 43 .3 .3 .3 34 54 43 54 .4 14 44 .1 34 34.3 7. 1 . 1* s 4. 14 I. 18 3 5 41 41 .3 *3 85 .1 .4 .4 .. 3* al 34 II 4* 4 44 .4 43 14.3 1. *1 .4 3. 5.. .2 .4 41 83 35 43 34 55 44 3 34 3 48 74 .1 37 3 33 .3 52 71.8 7. . .3 .l 42 .1 I. 7 2; .3 4. 1. .3 .5 4. .3 34 .5 *4 44 70 52 *4 II 41 14.8 1. I .4 I: 14 I. U 14 3 I .1 34 .4 37 el 12 73 a. .4 32 3 .3 34 37 .3 13.3 7. 21 I. 13 12. 9 I, 43 5 4 33 .3. a 3. SI 38 :. 1* 43 44 54 13 *5 35 51 .3 .1.4 I. 3 71 13 Ii 11 1433 31.1 41 13.464.3 .1 .a 55 3*?43 41 1831 44 3433.3 7. 2$ I. 18 1* I. ; 2 ? .5 .3 .7 34 :. 33 14 .4 .9 .3 41 41 16 34 4! 47 14 10.4 1. 11 3 9 7 1 I. .2. :$ .. .3 .4 48 24 21 .1 1.. 34 * 32 21 77 11 Il 13 20 4.8 7. I. 1. 11 *4 12 .3 3. .7 13 22 34 4* 32 33 14 14 3* 21 I! 22 40 I I 23 ... 1. I 14 . , 4 a 6 11 43 U 4 14 33 14 .3 47 a 47 43 17 *4 II 19 14 73 14 7.3 lb . IS Ii I. 7 4 14 31 .7 :5 33 3* .2 42 .21 *2 31 28 3* 29 34 24 20 45 23.3 1. , 4? 2 2. *1 IS Ii 32 53 4. Is .1 31 . 57 44 43 .3 .3 .3 31 38 33 38 31 29 33 9.13 1. 1 1* 1. *1 3 1 3l 27 5. 54 43 3! 3 .3 ! .3 31 3 44 33 35 44 30 14 27 31 78.4 1. 5* 1. 55 . . . , 25 .3 6. 44 .3 .3 34 .9 33 4 73 4 .4 35 44 .3 36 23 39 21.2 78 4 I? * * z IC .3 47 * .. 54 33 34 73 .4 15 7! 51 si 9 3! 3* Ii *0 *9.1 ... 3 1. 14 14 Ii 53 .7 33 .1 .3 .4 3 32 33 54 c i 4? 32 44 44 43 33 3* 34 31 3. I. 4. 3 ------- 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 21 3 . 44 104 . 15 2. 20 I 11. 1? 25 34 1 44 7* 72 63 .0 4 52 54 39 6 48 44 46 41 45 15.3 15 1. 6 Ic 17 11 1C IC 1. 41 4? 40 43 47 42 4 6 6 56 70 45 41 36 3. 35 31 33 1. £3 11 27 1. 17 1 1.. 11 14 29 4. 27 47 37 3? 37 3o 7 55 73 51 43 34 56 31 29 33 1 .5 73 1 7 1 11 -13 11 13 1 34 .1 27 47 30 Sc 56 .0 71 7 79 41 43 57 35 34 32 43 19.σ Ic 1 23 1 14 10 7 7 9 4 43 36 37 40 6C 34 40 44 53 53 37 29 25 26 23 25 29 1.4 1 . 21 12 S 6 3 13 2 28 29 34 34 36 38 21 0 4? 29 Ze 27 31 27 24 2 12.4 1 15 14 9 5 11 .9 . 33 36 41 47 44 53 54 62 .6 5 34 32 31 28 25 33 17.1 is 22 1 . 11 7 3 4 IC 6 3. 24 30 37 33 44 34 .1 53 39 37 33 31 34 36 32 29 1..3 74 5 .1 Is 1 7 5 5 1 2S 3. ii 36 43 3. 9 S 44 52 55 32 31 33 35 32 d 30 18.0 7. 7. 1 25 25 17 la 12 1 o 5 7 .5 7 4 9 44 10 1 .5 31 32 3. 37 . 1 67 43 aS 44 56 6 51 61 c3 36 46 53 56 37 35 34 37 34 .4 39 43 34 37 34 34 14.5 31 13.1 . 73 2. IL. 12 7 11 13 31 41 3 ? 1 53 32 54 53 04 45 71 44 4C 4 6 41 36 39 17.7 , 31 2. 2. 14 II 13 fl 5 31 50 59 .3 47 .5 46 $3 74 76 54 56 55 49 44 43 47 19.4 I . 1. 11 .5 25 lc 13 I I .1 39 50 So .3 3. 1 Si L2 70 42 .7 62 54 57 53 30 4 16.4 7.. 11 52 . 2 15 1 13 .1 44 .3 57 62 74 oS .4 71 73 63 .4 42 46 46 45 .6 41 49 2 .3 ? 1 .1 33 31 6 £ 4 34 52 51 5? L4 04 44 73 1 1Q3 1 70 63 53 56 55 47 54 2.0 1l I 3 . 2 17 1, .0 22 44 5 , .1 $9 .9 .4 .3 .7 s5 40 55 c Se 51 51 50 3 51 a 32 42 1. 14 1 11 34 . 37 39 40 C 37 41 .5 53 59 .7 34 36 39 37 33 35 11.9 ii 3 21 I 13 13 . . 12 5 .a 43 47 45 4S 45 31 s9 .2 75 43 38 34 30 29 27 33 17.7 17 37 25 22 14 15 14 1 30 .4 i 7 s so 9 . 58 72 52 63 46 47 45 41 40 1 .1 77 2 2 1? IL. 1 11 IC IS 26 . 22 33 43 3 83 46 43 32 32 3 29 42 31 31 31 12.3 17 2 17 12 1 c it 16 i 3 . 22 .5 43 C 43 45 44 50 31 40 SC 34 3 43 33 32 13.1 77- 1 22 13 13 5 13 43 2 79 33 43 1 4 i 46 . 53 30 24 25 25 30 26 23 14.2 ii . 75 1 15 13 IC 16 31 . 2 5 5. 55 59 55 55 59 37 43 32 35 37 34 30 37 18.6 Ii 9 .2 lc IS l v 9 16 16 42 47 42 41 4C 41 43 47 44 31 36 Sc 33 33 32 31 32 12.3 71 I 29 24 1 55 11 13 17 33 5 . 44 44 45 32 41 41 2 47 39 46 42 0 39 36 34 36 12. 17 11 2. 73 3 14 11 14 20 15 . 44 43 52 SC 31 52 31 53 10 41 34 3 36 35 33 38 15.1 771. 23 1. 13 13 1 11 17 16 4 47 62 30 45 41 32 3. 1 s1 42 35 30 29 26 24 3315.7 1. 1 23 2. J 15 Ic 1. 17 a . 29 .7 47 .4 45 51 5 .4 oS 43 35 34 33 31 25 36 14.7 7 U fl 12 13 Ii 12 1. 32 3 36 36 33 32 34 36 36 46 32 34 25 26 25 29 27 29 13.0 7: 3 23 7v 22 14 13 12 15 79 3 34 30 34 34 34 33 7 2 34 37 34 30 33 32 30 30 i.1 . a 1 13 11 7 7 14 23 2. 3 9 39 33 34 43 43 54 c2 34 37 32 33 30 c ; 33 13.3 5 I . 14 9 . IC 14 7 .3. 33 33 33 34 33 39 .3 65 9 a 25 26 33 31 32 28 1 ..6 1. s 32 23 1. 11 1 6 1$ 75 2. 29 31 37 3. 36 34 0 1 49 30 32 39 41 45 3 3111.S 1 44 1. 1? 17 1C S 12 7 51 22 .55 38 ic 37 4 1 2 .4 13 23 29 43 31 30 29 14.4 7: . 77 1. 14 W 9 IC 13 23 s. 31 30 33 . $ 33 33 41 39 45 27 a 25 31 29 25 27 13.0 7. 42 13 13 9 o . 11 29 33 32 33 35 33 S 32 36 43 50 34 33 36 39 35 30 49 11.7 7_ 10 22 1 12 1. 12 23 45 37 51 9 49 44 4 43 43 54 6 50 45 39 43 39 36 35 14.8 7. 11 24 Z. 17 1. 16 .3 3 4. 39 .1 3 1 2 45 57 73 78 49 38 32 16 31 31 35 13.3 7. 1 26 2.. 1. I 11 17 13 12 3 26 37 44 44 47 34 1 76 77 49 36 31 33 79 7 36 17.8 7, 1 lό 5 21 .15 IS 13 21 .4 l .., 6 65 55 .0 57 66 oσ 3 43 39 4 44 42 2 14.6 17 1.. 1. IC c 7 is 4 3. 36 37 31 33 36 34 39 52 55 52 52 44 28 27 25 31 13.9 7. 3 IS 1.. 10 7 6 4 is 39 41 36 41 50 65 6 41 SI) 59 53 .51 30 29 2* 27 22 31 Io.3 7. 15 1. 17 6 5 S 1 26 31 23 35 3. 3. 35 2 ae 47 54 29 27 30 33 26 24 28 13.4 7 , 5 13 7 3 3 III 21 2 28 28 53 25 30 .32 7 43 .4 19 17 16 19 19 15 21 12.3 7. 0 14 c 5 3 .5 2 7 11 2. 26 3 .32 33 .35 41 39 43 44 21 i 22 25 26 18 27 13.1 1. 7 14 1.. i 2 5 8 11 28 ; 52 43 4 43 a 7 53 30 3 24 22 23 23 22 19 25 15.7 7, a 1 11 1.. 3 3 6 21 2 25 33 36 34 53 38 45 SO 46 29 23 23 28 27 23 75 13.3 1. , 1. 1.. 1 6 5 11 30 4. 56 .0 2 41 64 44 47 48 49 26 27 25 26 24 23 29 14.0 1 . 1.. 17 1.3 11 7 5 6 12 14 61 G 41 39 .C 41 41 45 53 cO 39 St 34 39 37 73 31 13.2 7. 11 12 13 12 2 g 9 Is 29 3 33 33 34 34 .3 41 47 50 57 . 33 27 23 26 21 15 24 13.2 1 , 2 1 I) 13 13 . 4 14 29 3.. 54 40 4. 47 43 61 50 37 ci 36 2? 73 22 19 15 30 15.3 ..L 25 1. 16 11 10. 11 52 4.. 25 4 45 43 64 46 49 5? .2 40 38 34 36 34 31 a ------- 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 . 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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.) 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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 . 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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* ------- Two 2. 7. I I. - i I. * 7. S 3. I 2. 1 - 3 . - I l 7. 2. 3I I f2 . 1 * a I I . 71 a 7 _I S 71 1 11 . 7, 9 71 2. 7l Ii it I . 14 $ 7* . 7. 3 7 . 7. 7 8. * 7: . I . I_I 44 14 is I *4 4 73 73 2 33 3 33 7 7: 13 . 3. 1.. 3. I I 1,4 14 7. I I. 7. 5 1. 1 4. S 1. 1. 7 . II 3. 1: I 1. 7, . 2 7 5 73 3 12 S a. , 1. 73 1* 33 I I 7) 14 IS I 1. a. 3. Is a 1s 7 Ie 3 5 7 I . 4. II S. *4 7. 2 11 . 7, 71 . 77 5 11 . 17 I 7l 77 3 I, IS II 77 1. I. I 1. 1. 3 7. $ 7. . *4 1. 1. II 7. 2 * I. 1 1. 1. 7. 1 1-. . 3. 4 1. I. 3. II 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 .1 *3 ,* I. Ii I I .2 33 31 7? .3 44 *2 16 3* *. 2 .1 . 41 33 .2 13 II , .3 .3 II I. 13 II II 33 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, .3 *9 ;1 I .1 .. 21 0 61 5 . 57 1. .1 79 162 72 II I g 2 1.? .- 4. 7* 7 S. SC 8. 41 *2 32 . 51 94 71 4 . I . . , . , 2.. .1 Ii. 44, 3. 34 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 *4 6 7. 3. 3. 6 1* 3 $2 33 3, 53 1- .0 4 7 0. 3 4. *1 .2 28 . . . 3. $1 $4 77 . .4 1: 4! 2* 1 t a 442 5 34 .3 LI .1 34 .1 .7 3. 23 1. 3; . 3 *3 8. .3 7 .7 .2 33 .7 3. :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 49 44 33 34 41 48 3* *1 .4 41 43 4 34 41 33 i i .3 36 3 I ) .4 33 .4 . . 39 .3 .8 39 .1 .2 3 33 54 4 .0 43 -1? . 31 *47 12 7 . 1 5 72 .3 11 17 .3 117 117 44 ,1 13 ii 1? 69 7* .2 III 74 57 .4 *7 $1 7) .7 1*3 *21 1.4 74 74 7. . .3 .1 47 Ά43 1 )1 II I *2 57 41 .3 11 53 43 3! 33 13 .1 .4 .2 1 33 34 * 14 .2 .3 *4 . . 1$ 3. *8 .4 *7 *1 *3 . 13 34 33 3. 32 33 33 . 7 .4 3* 36 .0 *1 31 42 23 II 7 .13 11 .4 *3 3) 3, 2% *4 26 .0 .1 .2 5 4* 85 5* 5* 33 .4 3* 33 5* 53 3* .4 47 *4 34 3* .1 .3 .. .4 53 13 .1 74 11 3 33 37 .1 52 $7 $4 I ? Ά1* 7* 34 31 .2 5. *3 57 .. Ά31 *14 75 .4 4* 33 32 37 31 43 23 a 13 22 .4 43 31 *3 * *1 111 Ά13 .3 41 11 .3 .22 33 . * 73 .3 I C 34 .3 .. .2 84 .2 .1 .2 33 .3 .4 .7 32 34 34 4 4? .0 43 .1 32 .1 44 32 33 3 31 47 II 1* 1. 44 .1 33 4* 4 34 1$ 20 4 .42 3. 33 30 .3 33 *2 43 4 .2 4. .3 *2 41 *7 .3 34 14 34 .3 St 33 33 50 .0 15 I l 33 .4 a. 33 11 .6 4 .1 *9 49 34 14 71 74 74 74 2 Ά1 5 I . ? *44 75 II 34 4l 05 82 55 13 10 31 35 26 28 41 31 21 14 32 37 64 44 5. -4, 54 *5 73 1 .2 71 7 01 5. 85 II 41 2. 2 2* 32 76 22 23 3 4* 65 4* 71 4* 73 73 *3 3. 35 54 SI 13 71 1. 13 23 24 2. 26 21 27 . ZI 43 4? .4 Si .3 1 75 .4 91 14 .1 77 71 40 8) 3 64 I V 3,: 33 2 1? 35 23 3. 36 S4 44 63 12 34 74 57 .4 36 *4 85 SI SO 3? 31 37 3* 21 11 34 31 3. 31 34. 7* 72 44 54 45 SC 43 *2 13 21 2* 24 II 22 27 34 12 II 21 34 41 I I 7 0? 73 eQ .3 40 3* *4 43 77 1* ii 44 34 13 11 IS IS 2 $ 32 33 22 29 32 52 3* . 7* 41 *6 .4 52 4* 3 31 37 34 3 33 14 *1 24 *3 2 2 .4 .5 11 .4 al .1 17 14 73 4S *. 1* 1 s8 .6 34 3$ 73 33 31 92 13 23 24 4; II *1 43 02 1. 1 12 44 31 *4 .9 3. 34 Ii 2 1$ 14 I I Ά4 I, Ά4 33 21 36 3 *1 42 51 49 31 22 23 .4 5* 12 *3 1* 13 12 4* 2* .22 5. 34 3* 23 :3 35 21 32 24 1* 1* 21 21 34 .5 .3 44 54 .1 40 .4 6 10 .4 .3 i5 *4 .3 it *9 *5 36 50 .3 .4 .4 .4 .1 31 I I 61 45 .31 .4 OS 34 33 34 30 33 51 43 .4 77 53 3* 72 72 7; *4 16 53 31 .4 .4 59 17 *9 0 69 *22 53 13 , 1* 30 .4 ,27 27 37 31 3* 31 32 33 44 43 31 23 17 14 12 34 40 71 .3 I I 21 70 .. 33 4 . *3 2. 11 3. 52 *0 37 43 34 55 34 30 31 31 30 3* 23 32 01 3. 64 *4 84 33 5* 11 53 .4 14 5. .3 37 43 34 37 *1 17 3) *3 6 4* 33 31 33 34 41 3* 25 2? 2* 15 2 25 31 40 .3 30 27 33 7* 76 I? .4 2 *7 .4 8. 34 .4 34 II 3 41 31 30 30 43 24 31 4* 3$ 44 *1 30 *1 II 5 11 23 33 3 a 21 25 21 5 51 .7 *7 45 .7 36 3* 33 *3 .3 3) 25 20 33 30 13 31 28 .3 44 21 I I I I 21 II *1 I II S 24 3* 31 21 31 21 47 1* 3.5 .1 .. .3 st .7 .3 65 .2 3? 4 53 10 31 3* 35 32 31 .3 37 34 :3 14 36 I I II I I It 23 21 :5 .3 .2 .1 .4 14 *1 1l 53 .4 1* 12 .1 4 .1 .7 33 .4 *4 43 .3 33 4 .3 37 3. 2* 23 3 21 31 17 10 I I .5 25 13 .9 53 .1 .9 *4 .3 1* 31 Ά2 3. 37 .1 *4 .5 *3 34 12 .2 33 I I :7 37 II 4 22 3? *4 *4 30 I I 14 33 Ά4 44 2. .2 34 21 41 .3 .5 35 3. 34 45 31 12 .1 4* 42 31 4.4 *4.1 74 .4.9 *1 34.4 33 33.. *1 43 31 *..2 35 2..9 33 13.3 33 134 al *4.3 53 35.. *4 .254 79 .4.1 is .1.s 0 334 14 I..S 13 3... 33 1..3 .3 31 5.4 3$ *4.3 5. 4.3 .5 33.. .3 1633 11 .... o 39.? *7 42.3 11 3..7 *3 23.4 3$ 11.3 34 11.7 33 13.9 33 1 ._I 1. 33.1 59 .3.3 9 64.1 7 4 .633 75 .1.3 .4 31.. .2 32.3 33 13.7 42 19.1 41 21.1 34 14.1 3* 34.5 34 27.3 54 .645 7. .2.3 .4.: 5* 31.3 .3 SI .1..S .1 23.3 40 I?. . 33 13.3 SI I..3 3* 17.. 3. 14.3 .3 3... *1 .4. . .2 n . . 13 33.3 .4 23.7 31 17.3 3. :634 34 *5.5 34 1631 *3 *336 33 27.2 37 3... *3 .24 87 .3. .. 3637 31 23.. 33 34.3 33 *5.) II Ά7.3 21 Ά3.4 23 Ά3.4 *6 11.4 3* I..7 3. 22.4 .4 Ά643 3. 45. . 15 34.. 11 18.. 13 .64* 63 *4.3 i S .1 ..3 23 1*4 3 U. 3 1641 13 .2.3 .4 2... 5. 41 31.1 73 32.. . .4 .2.3 .2 14.4 .5.3 77 24.. 11 3... 18 7 32 644 4l *4.1 .3 34.3 18 3 .7 .8 .3.4 .5 I.., 3 4..? 31 l..9 33 t..I 2 1639 12 1; 7.3 43 4.2 1. 1.4 .1 24.. 4! 42.1 .3 3.4 493 8* * 2. 14 I. *3 37 37 .5 34 *3 14 sa 3 *3 77 4* 5 ! 33 41 82 21 4 13 *635 7.1 s. 5.5 5.. 4.2 1..4*7.842. .Il.4 ?1.11*.0I .22C042.227.l*2.3464 137.44 1 .4*6.317.134414.1 .31., 10.. 7513, 411. 9t$C* I2 .64. $2t..5 3*4 IIIISI ------- Table 2.3(b) Two way tables of monthly weekday averages of hourly CO--F ollywood 414 33 I ? 1* I I Ii. 14 33 41 3. 43 4 14 3* *4 *7 74 71 33 71 .3 .1 3? 33 1* s... 4.7 7... 3.. 3.3 4..&3.j11.4...1c 14.4 11.314.440. .Ii. 04 0.* .36...,1 .131 . ). .. 133.2 1 1.41?.3I5.714.I .37.. 5 .. ?.. ills. 13T 01.44 . 14441 . 1140 4.4 41*94.4 ,. ... 3 7 7 * IS I I 73 15 lb 11 14 17 II II 21 21 22 21 M l all , 1 : . . t 1 II 113 111 13 .0 137 IIC 117 II I 1.5 132 ii s. * ? e. so i 41.2 13 7. .4 31 _ 15 3 ls 13 73 1 1 34 Id 57 734 4 7$ 3 1*3 7.7 113 .3 14 4 .7 .7 114 134 140 136 114 c i 44 *14 III 40 Is 43 73 .3 76 77 II 44 54 7$ .4 33.1 13 4 33 I. 3 .. . I, II 1 Ά4 11 It 3 . . 9 24 3.. . . .3 3 . 34 Si * 31 . 4 M 71 .3 .3 72 .5 3.3 71 72 .. .. .3 .2 34 54 *1 33 *4 43 34 33 3.2 47 l.4 *3 ? 7* 7 1 17 I I 13 II 13 14 I I 14 II Ii Ά3 34 3 4 64 64 3. 41 II 61 II 76 a4 43 34 s 71 73 .0 71 .4 37 3 71 44 9* *4 34 33 34 30 34 34 7 1 I I 37 14 45 73.9 37 23.4 I . 73 ii 14 I l 17 12 13 15.13.. 1. .4 7.1 77 43 73 3 6.. I 7.3 4 *3.1 133 .3 4. .7 74 .3 .4 l Ά43 Ά34 134 47 139 l,. 14 76 4.0 77 .4 703 13 34 11 44 . 4 27 73 42.1 44 .1.4 1. I. 54 ... . II I c Ά7 .4 111 5. 71 73 7. I I 79 13 61 13 ; 117 48 .7 73 77 44 44 54 24.7 74. 1 44. 14 73 13 Ά3 *3 34 ti S. 42 73 .4 4? .0 .7 11 141 135 171 71 .1 .4 64 67 54 37.3 7 7. 78 1. 13 I. I, 13 34 * 71 cl 77 c i 86 .2 .3 Ill 141 131 .4 34 14 71 34 44 .7 31.3 1.1 *3 137.13 1 13 .3.4 33 44*1.4.4.2 .1$74s4 3934 7? 44 43 19 3314.4 S. 37 7 . 3* 1? Is 14 37 . 77 42 3? 54 1 a? 54 11 74 78 34 13 Ά2 3 7 44 *1 41.7 7. 3 33 4* 1. %g 11 13 3.. 1.1 13 .7 37 54 52 37 33 *4 13 .1 *0 4. 44 41 63 31 3 20.4 1. * .3 1$ 17 3 I I I I 34 73 4 .3 46 11 .4 *1 34 73 41 41 .3 33 31 33 24 37 3! 11.9 1. 1% IS I. lb 1. *4 44 13 . 4 34 63 6* .4 63 3.. 46 39 .1 37 35 34 30 .7 3* 11.7 7. 4 Ά3 11 . 4 I 1* 3! 32 * 31 33 34 .0 62 61 61 .4 2$ 34 24 3* 22 14 41 13.7 7. 1 Is IS I. 1 I. .3 *6 67 3* 4* .3 17. 51 6* I I 32 .1 .41 31 32 34 71 33 Ά3.4 1. 1$ Ά3 7* 13 Ά4 IS 33 71 .. .7 64 34 36 72 *3 .1 II 33 35 34 43 21 43 34 14.4 14 , 4? I. 1. 3 II 13 37 136 73 14 64 I IC II 35 13 s 10 .. 33 64 34 47 44 32.5 7 3 34. .4 _ .4 57 144 13_ 4 *4 13 33 *4 .1 .9 4* 42 1.3 154 131 Ά75 4? .4 71 32.3 lb I I .3 I. 1* 3. 14 IS 1. 1.3 t3 .4 74 14 .3 44 113 I I I 1.3 14.1 I l 34 *0 .2 *3 70 43.3 7. 1. .4 .3 17 11 13 5 3? 44 11 4? 44 7? ;. 13 .4 14.7 141 1.3 #4 71 31 .4 37 4 .7 33.4 3 I 7 7) 77 7 Ά3 33 13 7. 57 4 73 .4 .4 I 1(3 Ά33 *11 47 33 53 43 37 33 14 34.7 43 . 43 1* 13 1 Ά3 .4 .7 7. 41 41 54 .4 .4 12 63 4! sI ii 73 14 71 43 40 34 37.6 1 5 17 I. , *3 1 *3 33 33 33 14 3* .1 .4 4 44 43 .3 61 Se 40 47 37 37 .3 23.? 1* . Ii 1. 13 1 I. 34 .3 s.I 61 .4 .3 64 53 *3 s . *1 13 63 43 64 51 24 77 34 Ά9.4 7 . 3 31 II I I *4. I I .7 .7 4.. 3 34 4 .3 46 50 *4 I I 50 3 5 34 3) 44 34 37 33 *3.3 73 e I I S 4 7C 51 3 . 31 37 3 37 5 . 32 34 34 43 33 77 21 34 lb $4 3$ 11.5 7) 7 I a . 4. 3 I 6 I I 3 1 14 3 I I .6 II . 33 34 63 41 7.3 4* 6* 34 .3 37 14 34 43 31 3 4 .3 64 41 *6 24 23 25 3? 23 I I 20 lb 74 38 17.0 35 *3.7 i s Ii 1 i 4 7 IS 10 .7 5 . 2! 4 43 44 50 31 37 .3 *3 33 37 31 6* 33 13 38 Ά7.4 7* 3. d 16 I. P *3 33 73 74 lb 73 11 .4 14 44 47 e4 #5 71 54 34 40 34 *4 24.4 1. II s It 13 4 73 31 41 .. Ii 3* 32 5* 4? 44 64 Ά40 II ? .7 71 S 75 44 33 5. 31.1 1.. 7 . Ii I I 4 . 3. 4 3.. 73 4. 43 4e 33 3. .1 ..2 7 ! Il *4 *3 3 ! 37 32 24 62 I lb I 1 3 a 4 43 . 4* .37 44 34 C .3 SI it 93 4.0 54 41 3? 34 27 21 .3 .7.3 74 a i S 7 3 4 I 53 2 2. 34 3! 36 33 44 54 U 14 *0 43 32 41 30 23 32 17.! 1? ii 1. 4 14 *4 54 4* 40 32 31 34 34 34 57 .1 . .1 33 33 77 44 33 15.3 74 IS I 13 7 I I 3 . 35 43 4* 37 44 .3 31 43 .7 3 3* 3* 34 23 3) 17.3 7. 3 4 7 4 4 4 4 40 41 I a 31 31 33 33 32 32 33 4* 34 4 21 23 34 *7 4 46 Ά3.7 7. . 10 11 43 3a 7$ 27 34 35 3? 37 2 30 3 3 42 22 23 4 *3 33 IS.! 1. 7 4 7 4 3 . 5 *7 3 .. .4 33 33 3 . 37 31 .i 43 37 77 i :3 27 IS 13 34 Ά3.4 1$ . 31 1. . 7 4 l. .3 *0 . 44 44 .3 4. 43 6* 64 33 II 36. 3* 34 33 34 34 33 44.3 7t 77 Is. 7* Ά3 3 53 54 42 5! .1 .1 .7 44 .7 44 37 34 33 30 21 2* 33 14.3 1. . 14. 33 I. I) , 1 36 54 41 51 41 64 4* .4 44 57 43 77. .3 76 13 34 44 41 44 14.4 7* I I .. .3 17 .7 40 71 73 (4 56 *3 7 .7 73 .4 134 137 .4 .4 64 57 47 43 4 34.0 1 16 34 J 4. 43 31 43 43 .4 7. 71 77 c i 3 44 93 7? III 11? 14 75 70 70 .3 33 4 .4.7 #7 I 38 3* a .3 13 14 37 1 4.0 44 14 61 .3 61 .4 115 16 ii 77 50 44 15 II 4? 33.2 1? 3 13 1. *3 I 1. 3 75 4 37 53 3 *8 54 35 70 44 .5 70 37 *2 14 50 .1 5* 46.7 17 3 *7 Is 1. I I 1 IS II 11 3 . .3 33 .1 *4 37 .4 43 41 31 34 32 30 31 21 24 33 13.4 11 . 2 *1 I. t I I 73 34. 54 .5 3? .4 41 .3 64 9 33 53 .4 .4 .3 * 34 3$ 3S Ά3. . 11 3 7 17 13 *4 I 14 43 4* * 35 .24 25 .. 3* 17 4 31 34 .4 33 33 3. 21 41 32 Ά2.8 77 14 I. I I 14. 4. 14 *4 35 7. 77 26 49 37 31 74 37 37 41 33 27 23 43 31 Ά4 44. 4.4. 7? 7 13 . a 4 s 4 *4 34 .7 .4 13 24 34 .2 24 37 37 33 34 21 20 20 II 13 33 10.7 Ii s I! Is *7 IC a II 17 61 I. 36 3* 4) *4 43 .5 4. 3* 4* 49 71 37 3$ 33 73 31 13.4 7? 24 3.. IS I I 3 *4 .0 si 31 3) 35 35 15 34 33 3 37 31 *1 .3 5 3s 30 43 34 74.3 77 14 34 32 .4 *4 14 43 44 4 4. 44 34 57 *5 .4 4! 59 44 II 74 $1 54 40 II *3 34 33.1 1? 11 41 3 I. 1. I I . 14. 3* 17 7*. 14 53 3* .6 43 43 74 4* 133 .4 72 St 51 SI II 33 75.3 77 3 I) IS 4 7 IC 4) 4 3 41 *4 73 11 13 1. 4 137 11.3 53 .6 50 44 35 35 51 44.1 1. $ 4 13 lb I I Is .3 .3 4 71 34 *3 .3 7* .7 *1.3 131 131 .4 72 *3 1! .4 64 53 36.0 14 73 .7 Il S I . .1 74 S. 34 57 St 84 47 73 , 14.1 14$ 74 4* 4$ 53 *0 44 5* 37.4 .4 3) .7 #.. Ά4. I) 17 .3 74. .4 3 32 52 St 33 3* is 71 71 .2 .1 #3 53 53 6* 50 14.1 1. . 17 13 5 5 4 .7 44 3 37 33 37 37 3! .3 .1 51 *3 .4 3* 34 3* 32 30 33 15.4 7. , 13 3 1 7 3 I I 4* . . .7 33 3. .4 34 . 4 53 *0 32 34 24 43 22 40 47 14.1 7. 5 14 1. *2 $ . Ά4 34 .4 .. 33 34 .9 .3 34 34 .3 64 .4 30 21 24 II 4* 21 30 13.1 7. 7 Ά4 I I *1 4 s. a I! 33 .3 14 3? .4 II 33 34 .3 3! 33 2 1 33 36 23 I I 27 *2.0 7. I 14 I. . 1. 4 II .7 .1 jl *4 47 Ii .7 41 44 13 33 34 24 45 75 23 16 33 15.1 71 3 34 4 3; *3 II *1 33 4 .s 4 3! 61 .4 .. 44 34 53 44 74 5* *7 II 50 44 .4 14.7 7. 1. 11 3. 3. Il II I? 4. . 7 53 44 61 .4 4* 3 SI 57 55 53 .4 63 44 40 47 .4 77.3 5. Il 44 3. 7. .3 14 73 .7 34 1. .4 5$ iJ .4 3 .4 70 . 44 *34 14 4. II 41 54 30 37 21.3 7. I . 3; .3 ._ I? I . 1 . II .4 5.. 67 50 53 5$ 3* II 73 77.4 134 44 7* 57 4$ 5$ .1 *2 76.7 7. 1 44 *4 Is lb I . II .2I 63 5. .5 .1 57 t. 54 44 57 34 40 .3 .4 .1 .4 ii 4$ 44 31.1 7. 4 I . l II . I . .. 4 .. Ά .3 .5 33 *3 56 I I 44 40 4.5 Si 33 52 .5 34 53 4* 43.2 a. .a 1* Ά4 Il I . I. 4: ; 1. . .3 . . .7 4 3 .. .3 .. J .. .o sI 36l4. 7. 6 .33 I) Ii 13 1. II .* . 1. .1 .*3 13 34 .7 3* 54. 3* 41 35 .3 34 34 3! .4 32 *3.4 7. 2 I I 7 4. *1 44 63 36 .4 , 3! 4i .4 .4 41 40 34 2. 22 II 37 7$ 14 33 Ά7 1. 6 II . 7. . It I . .4 .4 5. 31 34 5 1 *7 3s 3m 0 17 .3 41 1$ 47 I . 18 73 71.7 1. 7 11 a 1 1 . . .2 a1 *6 .3 3 33 5 *1 . *3 33 41 II Ii I I 7* 14 13 3 7.? 7. . lb . 4 4 * .7 37 3* 10 13 3. 4.. 34 33 3! 67 .3 41 SI 25 41 26 $ 37 .3..i 7. .4 .1 I. 6 I . Is 6.. 47 3 7.3 3s . 3. 37 34 67 .4 63 33 54 17 34 34 3.3 35 Ά7..? 7. 1 *3 II 1 7 13 33 .3 .: .1 3. 3 .4 .4. II 3$ SI 72 31 33 47 34 17 34 3 . 9.1 ,. II Ά4 *5 . 5 3 IC 37. 1* 7. 44 64 43 54 .4 59 10 43 .1 70 3. 44 43 14 34 62 7. *4 .1 IS Ii 14. 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.. 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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. 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I 4 8 S 7 S 7 I 5 14 31 II 14 13 43 31 .4 14 2. 48.3 Ii 3.4 Ii ..3 3$ 26 I. 2$ I. lb 53 I. 14 18 .4 13 .3 4. II 14 jI 3* I I £3 o 33 .4 47 33 .4 44 1 16 4* .1 .3 .4 3) 4.. 3 5 11.. 14 II I& 1. 41 4* I ) *1 II 11 .1 I. 14 11 13 I. 1 47 . 13 31 41 4? 43 47 37 33 3. 34 .0 24 4. *1.4 3! 13.3 II 1 47 3 . 5. 2. . .1 11 4. 61 6 .7 .4 .0 .0 4. .3 . .. .4 51 34 *4 48 .3 12 34 34 4. 4* .4 31 *1.2 .1 10. ) ii .3 ii . I I 3 77 4 ? 7 43 4 . .& *4 1. I 31 21 12 .7 .. 16 34 .4 4. . 3 1. *6 44 I4 14 *4 I. 13 It I. I I 4 4 7 14 II. I! 1$ 2. II 1* 3. 14 *4 7 5 I I .3 .7 3* 13 43 3? .1 .6 .3 31 4* 33 .3 2. 1 .14 36 .1 .1 34 4 2 33 I I II II I ) 1* 16 it 70.3 34 4.0 47 7.5 43 1.4 12 2.4 77 . SI 11 I. I ? II 44 8. .4. .1 .3 11 I . . I I 3m 13 Ii I? 1, IS 4. 3. 34 .6 1* 18 11 64 13 .2 3. 7 23 4. .1 I . .3 IS I ? *4 4. 1 * 13 12 1. *6 34 3. 3$ 44 3 37 10 .4 46 4 .4 13 . 3$ 33 .4 48 8* .4 41 3? 33 17 35 27 .2 13 sO 4) 3* 1? 2. 1.4 53 11.2 II 5.. 53 I I . ? .3 *4.3 S. 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II 54 $1 Sb 34 6? 73 5* .12 34 33 42 41 43 3 14 $4 4. 45 62 13 $o 31 33 4) 33 44 5* 45 53 54 32 5. .1 .4 4.3 3) 3* Ii *1 17 30 3 3 33 21 14 01 *4 13 4) 32 .3 34 4! 31 31 64 43 42 44 .3 24 57 .4 *7 44 80 14 43 3* .2 87 .4 11 71 71 74 7! .2 7. 71 10 64 SI 14 Si 58 14 It $4 SI 5$ 13 II Ii SI 53 U 4$ SI II 44 12 II 61 58 .8 5 41 42 4. 3! 44 34 30 44 33 30 30 31 33 *0 43 43 .34 24 .3 43 21 23 23 53 .8 _1 1$ 31 34 3* 31 33 35 35 .3 28 33 21 II It IS. *5 .15. 11 26 23. 36 22. 26 2! 2* 43. 13 35 23 4$ 37 34 34 42 31 SI 54 54 34 IS 44 65 4* $0 SI 74 .3 6 $4 41 3! .5 .4 74 54 7! 84 67 3) .8* 43 .3 41 4* 4 *4 41 41 44 44 .6 S . 16 SI 53 4! 41 44 .3 .4 .1 4. 44 .4 12 35 5$ $4 5* 31 31 44 33 2* 32 38 32 30 4. 13 33 4) 4: 23 33 31 32 17 IS 12 15 15 14 14.1$. 1!. I. 5* 13 Ia 11 42 46 *4 8 I? I ! I I 1* 37- 20. Ia 43.17 £3 16 25. 2) . 34 . .33 . 43 .33 - .31 .33 35 .2 35 . 41 .44 . .34 .44 .41 5 . 4 _i .10 . . 50 . .4 37 . .0 .. 43 6 . SI .44 .34 31 44 . 34 11 44 .5) 4 .3 3$ 36 31 33 31 31 33 .0 3 33 34 41 .1 *4 3! 5$ 57 * 48 36 37 32 33 11 3 I 7 3 6 13 8 10 43 .4 *4 34 22 1$ 21 41 14 27 Il Ά1 10 4 1 4 4 4 I ID 4 10 1 3 4 7 6 4* 1? 30 4*3 21 20 1! 23 .1 31 47 27 41 27 17 Ii 20 41 .4 62 54 $3 SC 1* 44 *4 33 ,s 37 34 *5 31 41 40 6? 4* 13 71 ft S6 -4 30 4 65 62 33 .4 41 4) 34 33 3) 37 36 4* 46 SI 85 .3 46 35 .4 4* 2 41 3, 3* 4* 23 1! 1! . *7 IS I? I I 40 4 *6 18 II 41 I, Ii LI II I I 3$ II 33 *7 3 1 1 - 1 7 I I .4 40 *3 *4 4 10 12 11 *4 *4 *4 4. 41 15 34 22 2) 41 46 13 30 44 31 43 44 34 33 .5 .3 .1 .4 7 12 44 30 12 .4 34 31 .2 .. 3. 5 25 .4 4. )1 37 *3 54 4 44 4. .2 42 .5 44 31 .4 45 40 40 35 4 $4 $3 33 66 43 4 27 .0 .4 4? 31 .3 37 38 40 2* .4 63 4. 34 .4 aa_ .4 37 3* 41 67 4* 37 32 44 4. 21 31 24 33 44 4* $ 23 21 27 1* .4 31 II. II *3 13 I l II *2 14 2 37 3; 36 33 41 31 41 4* 5.3 .4 .4 44 .3 35 .32 22 34 .4 5? 0? .4 13 3 3 *4 .2 .3 3. 51 .3 57 38 33 42 3* St SI $6 54 1. 47 3 .1 4* 14 $4 34 54 *1 4! 33 .5 $3 2 . 35 3? .3 4.3 26 43 44 31 23 33 3* 34 6 34 45 17 .3 .7 34 31 4 14 34 50 34 30 II 33 .8 44 72 73 43 43 I? 33 33 4. 41 33 2) .0 45 33 23 43 .5 54 17 1? 13 *6 17 27 48 32 .3 .1 27 2 34 *4 Is 35 36 .0 34 Ii 31 4* .1 6 .2 3! 3! 64 .4 32 *3 34 2 3? .3 Q 3. 17 34 34 57 3.3 46 4. .4 1 13 42 4 1* 17 38 34 32 34 34 3 .4 .6 72 71 .1 .4 .? 3 *7 .4 .1 .4 11 .4 33 12 33 23 47 4! .5 33 35 25 31 25 25 31 4* 44 21 1* I I *7 11 *7 4 27 .3 21 2 I a 1! 13 24 24 74 II II II II 1* 74 *4 I 2* 2$ 11 14 IS 14 41 I? 21 13 20 4 7 24 74 41 4* 32 30 $2 SO 8$ 43 34 53 44 5 .4 12 10 50 13 41 6. 6! 33 3 43 57 .4 .0 34 2* 31 3* 43 34 37 444 .4 34 4 II IS IS IS I 4: SI 11 11.1*4.1 4.7 7.2 6 5.1 3.6 1.5 3., 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 .3 31. 3. 1 1. . It 1. .. 33 *3 .3 *4 . 1*9 .4 Iii 330 17 94 74 *1 44 33 *1 fl.3 1; 56 61 23 2 I. I .7 34 . * .3 64 l3 Ά43 1.3 Ά .. 113 533 1C3 *3 74 .0 .0 12 35.3 7) 31 3. 1. Ά3 * I I 11 1 I 71 .. 7? 73 s .1 40 .5 72 32 3. Se 33 49 33.7 . 4 ..7 a. . . .s 31 34 46 47 . 3 .1 37 1. ;2 43 .. 51 34 59 4C 69 57 61 .3 43 34.4 1. s . .. 1. 1$ 17 .. 23 1. 64 37 56 *6 S4 59 51 33 33 43 41 37 43 43 43 63 15.4 73 . Z I . 5. 1 . 38 1 13 t *3 .9 7 ;3 . 35 3* S4 54 33 63 64 43 44 Z 26 36 Ά7.3 73 7 . Is II SI I Ii . 33 .. . 2 .1 .3 53 33 .4 64 3* 84 33 * 31 47 43 U 14.4 73 I 33 11 13 II II 5? .3 1? 3. ;5 6.3 3* U 53 .4 4.3 46 9 4 44 40 41 LI 34 Is.4 lΰ 3* t 16 1* .2 .1 3 4 3. tO .7 ,C $4 79 43 1. 71 19 13 52 44 44 2$ 3? 5) 23.6 U I. 35 ) 32 14 16 11 0 21 3; 22 .5 19 lΰ 1 74 4 U 5% 74 15 4, 53 2 34 41 31.3 1* II .4 . I. . I .4 36 3. 4 .4 77 .2 1% 44 $!J1 3.3 *4 94 49 40 3. 33 St .4.4 7; 1 .33 . .. 1 I. 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Pt4C&hT .LS. 8aL. s L I I I )I1Z )1..G 21 18.3 46 33.6 14 14.1 13 11.2 19 13.3 t3 12.1 16 6.4 20 14.3 15 o.9 19 11.2 26 12.9 27 10.S 39 25.7 4G 24.3 30 12.7 23 5.7 22 10.2 31 1 ..13 20 10.4 21 11.3 25 13.1 21 12.6 23 1 .3 27 11.3 25 18.3 32 .a.? 17 15.3 22 11.9 2 12.9 12 10.1 13 9.9 11 9.4 15 13.7 13 10.3 17 14.7 20 11.7 35 23.1 29 10.4 35 43.? 74 l ..3 16 12.6 lφ 7.3 12 7.7 19 11.4 13 . . 16 11.6 17 10.3 21 16.2 23 17.2 23 13.3 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? 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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.. . . .. 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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 .7 4 37 31 I i 2. 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 .. . .4 .3 34 41 .3 34 3* 31 .4 33 .7 23 6? , 7 7* 21 3) 37 .1 37 27 34 2.4 I.? 1. I 41 4. . *i 27 .. .4 21 31 IC 30 32 .s 3* 24 4. 11 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 . . .. . . 2 22 u ii 1? *7 ie 14 4 IC 13 is 34 S. .2 11 I. IS l 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] .1 34 3? 3? 33 24 34 3) 3) 34 1.2 74 5 . 3 *6 * 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* 3* 2$ 31 44 33 11 1 . I I . 1? IS .1 .3 .7 4. .2 .3 .4 *3 .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 I I *4 7. 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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 .1 41 42 2 .7 22 .2 33 4 83 24 42 11 II 33 II H .3 22 .4 I . .2 *4 I . 1*. I V 1* 1* 1? 20 2 . 1 .1 4 37. 32 4) 23 .2 3* 24 23 21 1* 22 3.7 1. 7* I. .. 1. 4 2* 2 .3 2. 44 41 .1 . .5 23 5 27 44 34 42 43 aS S. I .7 It 74 . 41. .7. II I 61 27 33 .4 .2 .4 34 3 3κ 8* 34 33 34 3 37 24 Is 4 33 44 * 34 2 1 4? 2. .. 2.3 25 44 35 3 3* 23 2* . 33 7 I 7 1* 1. 1* Ά5 17 74 .2 43 .t 42 23 23 25 34 33 .11 34 33 .22 34 43 24 4.1 S . I .6 IV 21 t 74 75 Ii IS .4 1* 7* II .4 23 47 49 31 31 34 27 33 23 34 .3 1* 5 2 3 7 .2 i 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 I . 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Ά4 IS 21 I V II 41 IV 7* 73 IS 74 IS I I 3 77 a.? 5, . *4 1. z I I 1 71 74 I . .7 . II *4. 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? 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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 ... ... .. .. 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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 . 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Cc 44 ece ccc 53 ccc ccc 77 11 ccc 44 eec cc. .5 cc. ccc .4 eec cc. . 7? 33 cc. 44 cc. cc. .3 ccc cc. ecceec44ce.c.c 7$ $ cc. 44 en eec .3 cc. cc. .4 . cc. .4 cc. c cc ?. 3 ccc .3 cc. ccc .3 cc. cc. .3 ccc cc. .4 cc. ccc * en c en cc. te l cc. ce 44 cc ccc .4 cc. 74 4 cc. 43 ccc .4 cc. cc. 43 ccc .n 33 eec cc. 7$ $ ccc 4$ en cc. .4 ccc cc. 4$ s.c cc. 57 e.. ccc 7$ en $4 cc. cc. $4 ccc eec 37 eec cc. 3 ccc cc. 74 7 cc. $7 en cc. 34 cc. en 3$ cc. cc. . ccc ccc 74 $ cc. $ 4 cc.nC 3$.cc..c 3$ cc. cc. c4encee 74 4 cc. 53 en $3 cc. en 33 cc. c.. 44 cc. cc. 7$ 34 cc. 44 eec en .. c.c cc 43 ccc cc. 34 cc. cc. 3* 14 7 . 3 44 Ά107. ------- 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 .. N- l4 (P51t lIOi LJ (S 10( 15015 51 (Ll$ ISI S N 50 N I : _ - - ------ I $5450101 55511, II 111(151115 I5lt1l0I$$ IS iS N N 5 I .ll0.. uI ! II 15015510115 101(11 IllS II 1 N- 0055500 101 141$ (S 150(15111, (505 $111 4 1 I *1 11444 5000.5 (5 15010501$) 55*11*114 S I lO N N I .. hI , 1lu flu $I? .M. 5014 1111544 50.010 (4 10(1115151 15*151405 II )) N N N I 501041 155101* (5 54111111* *1(550040 IllS I . ii m to CD CD C.) u 0 I I 0 - CD 1 0*11014 51)415 14 55(0110155 45*1(5(11 II I ) *510041 550055 (11 15(1155115 0550540 5015 S S I 5- 501 N o $050011 410115 (5 I 5(l.405 .l II I 0545 5455 N * 5 P -r -. .4 .4 . 51- 1977 I- -a 0 1< 0. 1979 ------- Figure 2.5(a) Diurnal diagrams of hourly CO averages for weekends, 1970-1974 ( Suniner - - - - Winter) s .,. . .. ..ψ .U II s l .IMI (M$ III Ma ..(MII M N4t U I NK( It ( M M (0 N . ii I I ISI NIM ( (I I(II IMl ( M l US N I. S. I. -a IIIIMS Mall (S IMIIINIII (S lIM (U I. I. SI -a SISISM Multi (0 INISIISSIS (MS ((U II U N I. N S Ma 1970 1971 1972 asl.lai NUItI (I IM(IlM$i SlIM I I N II N S.. II 1 N ((SlIM NINItS (I ISSlIlISSIS (I II IS IS N II N I 5 S. M S ShIMS NN S (S SMIIIhSIS (MIUSISI PIUS . 4 I .. I 1 14 IS I II 1973 ShIMS MM II (S wi ss.s, .t... 5Il N N lI I. S. N S. SI IS IS IS IS NI SS NI SIlaS Ma 1974 5*5. 51 NeSI IS SS.aMS%I SIll Ill N N S. S. I . N flt 5 tI II1UA U. m ID I D 0 -I I 5 .1. 1(1 S ( (5 IMIMISIS SIll 551.1 III ------- p-1 .r .b(D - . Diurnal CO averages for weekends, 1975-1979 ( St imer - - - jnter) .1 I 4 . . $l a I I I .1W I$ I *.i al M lt C WKI $I iI Ii .. Sill iliK NiSSI S U llPl SS up ..i. U s a t s, i w is .i is ..., a a a a : i. :: I :: 1: 1 i -.- .-.. . . ...,. - 7 5 a ss .u uu. bitIt. . 4. i. 41. tlJfl. 144 lId U, uI *4 %4!4 II UflI 41I!4 utw. U sd 1 4 I.i**Cl 55444(5 Cl UiI554 SI (ass IS I S Sli i( aSS ( 5 4455(5555 (Mat 5555 s..asg assist (5 IMISIIM$S (555 Il l S 55(5544 5544 5* (5 i 5flh( 51 5 5 LaS s 1551 5441111 114415 (5 WiliuSSl (051 sul i. :: 1. : i. : L : : I.. ....... .. a ..- . a I I I 1 II S £ II. 541 IIsd I I . A I t A 54J SI U t III IA IA 14. 1X IU A 544 U. UllA MS541 II A i j A IA U. U!t II U. 1 t U lsd 55 1sd usd 14 t s ic usd us w. * ._ 5550554 a.ssss, U s .a(.,si,i ..as,u..fl 555 5 5555544 .sasl , ( I 550( 1(5451 544 1 115111 5*15 44 5s44 lisa ., (S sI5stsl Ss saiip.ls s Sill 55,5441 IaIaa.S (5 15(1441 115 144115111 511 ISI INI 114415 (5 144(551551 ISa 5.415 I I I 51 : f . .... ........... . . 1 1. a S a 1iYi IIIIIsd l 5sd I4 U. 1sdUsdiA14sd1I 4 UIU1e. . II UiI uIAIA AItlIIi1IIU4 I4 14 . - .. ... .. ... 1a554s4 44555 (5 (5 a 51 1 5 554 5$5 api isasa SIPS 55(1501 55544(5 Ii 5 151( 151555 all 445(5 555) 5555044 5555(5(5 5515445555 455441( 1 IS IS 5115511 S.a., (S 1505511111 II I 55555 is s 1 ..: 1:: * i u u it sisd us ut is u- A I A Ut Ut lA usd wclAiV 1sd isd 1Usd-UUsdIA1A 14 I P1El9srU Ut slsd Ut iA is a a 1155801 15545( 1 (5 50115 15151 15450 (55105 151 1 l l544 lisa., (55 SaSla . .$l Iii. ( ISI S . 5511 la(Oal.4 51.5151 .usssi.s ii ( (sill 54 5. . .a. , Cs 11l44 s has l.a. ass - 4 - I. a 1 .:: I T. i .. a -tsd - -Ai. seWc UsdlsruIIsd1t A I I is, u s U. U Is-Ill It 1rn -4rIu usd-s*sd Ilsd-il. u . lPlrnusd sIsd U. S I l S U. 1975 -1 7 1078 _______________ ------- 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 .0+ :i L I 0+ .0+ 3.0 m * mm mmm mmm A A A * .0+ A * A ; -:;; I i971 t r!7a /97 1 I 7 1 I iii f I I 4 7 j /?7 t (b) Eugene airport la.0. 1e.$+ 4.0+ t:.0. C C 10.0+ CC CC 3.0+ 4.0+ : . . * a mm mm mmm * *A* A A *AA mm Ammm A *A* mA .0+ A am. * A * * mAmA A 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.* A C C :c ccc c ( C c C F------ , I L__t____i ___,_____-f-t f ----.-- - I9 I 4 1173 liii I17 . I?77 1978 ,?79 00 $ 70,t 60.I V .0. 40. . 0. I 20.1 10.1 cc c c C C c ccc C C ccc cc C C CC C cc CC C cc C AAA A AA AA 0 AA AA AM A A A A M A ------- Figure 2.10(a) D1i rna1 diagrams of hourly wind speed averages--Portland Airport 1 1 1 10W NSa i H 111 ,1110 I II a.. H a t1 HU Ut M urw. 1 1 1973 4 II . ! ILl 4 I A - NA tarp. -a. 1 1 10aM 011111.1 H aal10iI Iii , 1, 11 111 11111.1 ii 111S10 1 1 1115 0111*11 11111(1 ii 001 111*1 SIll a.. I . H I. I, I, I . I. . . . 1970 1971 OSUILT IS 01*111111 Sill 1972 lalisli *11111.1 ii 0 10 5 (101 Ii i I . I. I . I V I 1111111 $I l H raittliui SI .. N I I a I I I 1111*11 001*11 I I MUlli li I . a a I I I I 0 111*11 MellU as llalaIs N N N IS IS 1914 1975 1976 Suniner $1111 flAWI NA NA I V I A 1.1 1.1 1977 1978 Winter ------- Fiqure 2.10(b) Diurnal diagrams of hourly wind speed averages--Eugene Airport h.*t, w, sw. q n i I ,. -a Iiiiaii M.NIi e$ SUNM ifl I . /.... ..... a N S. S. a. I I I. a i. . p. fl.p fl.p Up .P 4 44 1971 IIiD Nqvti IWi liii N . 5 ...... -N I I I N I. . 5 1972 I,Uui( N1*U UI IUI(W $Il N. .5 ...... L N 1973 1974 1975 1976 1977 1978 Summer 1111111 11 11T U 5 15 1W nI ISI IUII Nitf UI 11.5111 lU l l 15 1 1115 55Il I I I 11 SITU Si 5 5 N I. I. .. ... 4. 45 Winter ------- Figure 2.10(c) Diurnal diagrams of hourly wind speed averages--Portland Hughes/Federal Building I ppiPid MU P PP ii(I-ISP(I IS IS 1971 IP(SSI( PIPPIP S sp g*SflNa pp j 1972 1973 fS W . II tI UI 515 1974 1975 ____- Summer 51 155 11 5 . 55 ( 1 U 5WIS-!IH I5( S II 55(5551 555 . 55 . 55JI55S IIMa5( I S IS I I 1976 1977 1978 Winter ------- Figure 2.11 Relative Frequency of wind direction - 1? I II S S. - It-C c LI ------- .lII. a.I Pe.,ia.. as. Ill alp 1111 assalNa Ill. . ,. .. all $ all. ll. . 45. 1 S (s4 a.. .4. a .7. C p a 7 C ... . . C.. 7 $ . S., a a I ) i i a all. .t a., u.s ii.. as.. C a . C 4 5 . 4 a . C a, I a. p a a- t - i I. *_ Scatter diagrams of wind vectors--1971 a flub S I PaISLaIP 4WN SIP lvii fiSlaIll. POr#14_ 44 cj. uiI ill, 4 ;i. 0.. u sa. aI .,. as.r I... a 1 a a. ,s. at at . 1 *. a I a a. Il.au S. 4.11 4 4 4 s a. aaa ,t , ,..Siei. a,, nit a.. sflS alealva I ,,. 155, ... . 47.55 5* I I I. .55.4. 11.1 all I ,. ., S. S.:. . S iw t ufr :. :i; 4 1 ; 1 5 . I i.. lu.q U.S . 1.5 is t&)s a +e r L ii fi S . S a. a 7 a S .55 It 55.5 5 1.1 71. 1 a a C a a a a . . .s. S:.a. Si. .. a .. .. as.. u. s u.s s. *1,1 all l.a Pill PelitlIs S .. DU II I SaiI fihibaisa isa 1 51. a .i, as.e vs ( Ia, 7 5 .4, j$..) I St as... N I .., -a. .55 I I U.S $5.5 ii .) S a . . 5 . h.( . a.. as. 7. $ a S .5 S . C.... S. 7 a a S ii s a - - .C .1 : .;;;:r ------- . I . a S $ . . .9 a. a. a a 4a . _ 5_ ,,s .i*.. ii I. .4 s i lift iii 41*1 UftSiISI *isi ii.. as . as., is I I .. u.e. as.. a 5 a 1 a a. 9 a 5 a a. a a a .n.. I a . 9 . a . a a a a. . a a a. .4.,, 9 a .454 . a . . I a 4 as at .1 4. . S 41 . 14. 1 .44.4 4.4 Figure 2.12(a) cont. Scatter diagrams of wind vectors--191t 44*4 III$ 1.IIi .asia$ 94.a 5* 14 u SIPS OS4suSSs 4 4 .S.S it Iii. 5$.. a$. ( I , 5.. . 4 a a a U.., a 9 a 9 a a .4 a IS $ age,. 95 a S It ( S S S 51 It S a a.. a 4. a at a .. 4. a ..II c l i i i P l 4 .4445 44.1 511$ 4 . $451 Miulat P434 II I . 11.*. t4 .P It I II . I*. . 15.1 r i Άj p.t4tp .I 1 ) a4 19 , 1 ______________________________________ a a a... .4* a. . 5 .1 11, . 5... 4 I..i U.S 5.1 3 14 cilIa 144 id44a WI 444$ .4 . 4449 041.1 1$ lI , :; . .4.1 Ii 4*9. 41.5. *1,1 a)... S.... S 4; a a a . a bet, 44 4 4 SI . iS .94.. a.. S 5. l .51.4 .4.9 44 e:I - I a .9 4.4 44.5 t I_ I il., a. S 9. a a 1 .4 I. a a . . l. . 5..,. 1 5 a a .4 it 4 a S Ia a. 5. a *.. .44.4 4.5 .4. II 44.1 u.s 41.1 I a a 5.9 a S $ a 40 S S .1. 4.1 11.1 $1.1 ------- .t. .ls . & I ,ia. j .p m 5p 15.5 5 .55555 Sal ll . l .. 41.5 5 $ 5 l Ji.i. i$. I a ,. t . SU. 5Ap.4gr Ii , . . J. St... s 1... a.. . 4 . 1 .a . a a. 4 l.a I. a a . s s . . .45.. Sa. . sg. 5 4.8 a. J a a a a. d a S I I scatter d1*graifl Of WInG VectOrs--I976 sass 454, ,. ass ss as., as.., ,. t Por+(o.. 44 ct 4.4. a S S S.. 5 $1. S. I I a .5 a. S S S S 5 55 5 55 S ll I 5 a 5$ 8 a . a N SI I. . $ I . .5 . 5 I. iai i as a ..a as .5 5 5 6 . .. I 1 S a a A) 5 I 6 a SI 5 I S S a a a S 5$ $ 8 1 I . . S 4 11$ S I I . a IS 554$ a a a. u.s. . 5.. .. a.... I £ - . . 5 5 a a . S a Si I . . a . a a $ 5 Sill, a aa5 5555155 1 5 s sI 5554 4 15 1 5 s..$s 5 .aSl l 5 SI . a .s .5 sil. Is.. 1$. tsJ I.I. .... .. . . ii. 5 5.S .55.8 8.5 .1 .$ $$$ IS.S 18. 5 -si u.s e. .s .s u.s si., as.$ 5 a a SI %/rJ 4(1558 LSI 51115565 6.5 5555 Ill flU LilISiass rile III, .1k.. S iii. u.s. u.s . 4.5. . a. ,. /4) . a a a .5 5.5 5 1.8 55., 48. 5 a.,. .51.15 a .. s. S S. $ 1 a S a I I. Ia.S .$l $ a., - .5 II $1.1 88.8 18.8 ------- a I a a.. 5 IS a a a I.. I . II a. I a. I a a a 1i4L$i . a. I S . a. a S I I Figure 2.12(b) cont. Scatter diaqrams of wir 4 . ... E LI.I $$I I.,. IS ,I . Is. . as .C 0 1 a . . S. 0sS S a.. a .$.. aaa . $ 5 S a a a. .. I . 1: a . N I a $ $5 I S I, I a s as S.. SS S $ I. a I I I a a 5 .e I S SP I . tIl S.4S $ t. S It H. lP I.II .$ aiPi I.I SI III . I l.) , lI.t I... . S4 50 t, 44 t 1 .. l. : : a . . I .5 .... .11.. u. S 13 . .4. . a.$ *.S $5.0 15.5 aatI .fl(. I I StII.t are flDS i . $eti aauas a , II I. d .. I. £ 5. 5 It ill. lI .P. £5.5 Is I . a .4. . L)pj4-tr 1i( f77 t. 5 II . . C. ,. a. a .. a a a I a. a a. a a a a 5.a S 5.... .___..._..__. tIll e 5 IS (SI ,ttia$ hi. II $ iS S U a9liI .S*a .tsi tua. Ih ass es iii. $.i. 5$.s Si... I Ia. . as P.,. 1 las.. . u. .a -. _.. .51. 5 Y-6 I .. . 5 . ,. d . lS P a 5 a a. I a. s. . _s.. a . ..a a... . . ------- ru ure Z.1 Plot of monthly mixing height averages at Salem (a) morning _nn__ _ _ (b) afternoon 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 0 ------- F Igure 2.14(b) Pc . 4t4 Plot of monthly averages of relative humidity--Eugene j ; ] ;fl 7q ;a iqn t Pfl! jcImiirrTin ,?7?$ 0 ------- Figure 2.15 Composite percentile plots of monthly 25th, 50th, 75th, and 95th percentiles of relative humidity * a (a) Portland /176 f 77 /, I /77 I (b) Eugene Pt? C&Ph* .08 S...; 58.0 + i v i i ,,i I I /177 /y7 (A 25th, 8 50th, C 75th, 0 95th) * a a A a s . 04.O4 S.. . ; n.0+ * * S. * * * 8 a * 8 * * * 8 * 8 * S. * 8 * * a * 8 * * * 8 * * * i 1O I 1971 /97.2 I /973 I i97 * * * a A * * * * * * * A * * A * * a * a A 8 a * * ------- Figure 2.16(a) Diurnal diagrams of hourly averages of relative humidity--Portland Ksua t s S.Ift reaRm. see. 1970 a U. U a W... . Summer Winter a I alIa,au m.s.ip, s.steai iii PM. 1971 P4tψ 5I?S ISIII ,N IPIS IP 1972 Mileusle eIMu,, p,e,im. sees \III p. 1973 -a 1 974 1975 1976 1977 1978 ------- FIgure 2.16(b) Diurnal diagrams of hourly averages of relative humidity--Eugene NIISII ..*IIS IVISIS SIPS 1972 .1 Id Pd lId 114 114 17.1 a.rp. 114 1 111,1, 1 1111,15 1 1 151515 SIll V &a II . I 1 1m 1973 MI SIIMISI SS 5Sl SWISS IS IS 1976 1977 1978 1971 NtISISS II 1 1SSSIS 555(15 SIPS 1515111 1 55IIS 155(11 III 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 I I I w p et,.ai.tsw ,.mIi. If, a. a a S. a p. a I. _..* _*v.___ __.S.._* . 1971 I I I n.IWNC p.us,s,alt.a IISk I fl a a a a a a S a I. Set a. pea aa p... a. 4 1972 I I I W PIUI I UIIW ISRIUS PI a S a a a a S. S. I. 1973 la(suI .Cp # .4tI,ItSSuS,N ,.tIImsψ If S. uiii iari . lassulact S. Pt(CIPl aIIsS PN,taaS IfS 0 1 I .. .. S. PIUIPIIMISS. P5551 5* 51 5 S a a a 1914 1975 1976 Summer IsIs Saact S. SR$P SISIIM P55 11541 S I 1 1 15456 5 54 PSICIPS$I 5IIN PMIti ISP, 5 S a a a ... : a . E a U Ia . . I; v c: sts gpcs S I PUIIPIIIIISS 1151 1541 lIPS a. * a i.. p.. -S 1970 I.. a. p... Ua l .4 5* St St - S 1977 1978 Winter ------- Figure 2 .18(b) Diurnal diagrams of hourly precipitation frequencies--Eugene a a N a I I 1971 1972 ,.ISgpc, 5p((5 55P tSSa SUSINI SIPS Suniner ,Itc,,,I.HSu SSSINI PSI. 1975 SUP*NII V PSft5 5I 5I5IU SSNIW SIPS USISUISNIS SN IIIISNSIS*SSII SPISSNI 557 1 a N a. a a a. a. .. . a. a N _.. .. fia I . ... ss iwsv u -w . SS I 1 SN S((IF 5IS7IIN INNUS 5575 N, a N I I .1 IN UN LI 4 ii . a. ii. a. a. a.. 1978 IUI SCV SN NSILI?SISPSIN S ISU IS IS S. a 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- ------- |