physical and chemical indicators of urban visual air quality judgments

Atmospheric Encrronmmt Vol 18, No. 4, pp. 861-870. 1984 000&6981/84 $3.00 + 0.00 Prmted m Great Britain. 0 1984 Pergamon Press Ltd.

PHYSICAL AND CHEMICAL INDICATORS OF URBAN VISUAL AIR QUALITY JUDGMENTS

PAULETTE MIDDLETON, THOMAS R. STEWART and DANIEL ELY

Environmental and Societal Impacts Group, National Center for Atmospheric Research, Boulder, CO 80307, U.S.A.

and

CHARLES W. LEWIS

Environmental Sciences Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, U.S.A.

(First received 4 May 1983 and receioedfor publication 1 September 1983)

Abstract-Key issues in the selection of physical/chemical measures of atmospheric properties as indicators ofjudgments of visual air quality are addressed. The relations between measures and judgments made over a variety of times, observation locations and atmospheric conditions are examined experimentally. Light scattering measured throughout the day at a site characterized by high aerosol concentrations was the single measure most strongly related to judgments of visual air quality. A combination of measures taken at a single site and/or other sites are somewhat better indicators of visual air quality than light scattering alone. Light extinction measured by a telephotometer is strongly related to midday visual air quality. The 4-h averaged fine particle sulfur and 12-h averaged fine particle S, sulfate, nitrate and ammonium are all strongly related to the corresponding mean visual air quality. Although these midday hourly and 4-h and 12-h average measures may be used to indicate trends in visual air quality, hourly measures taken throughout the day are required for monitoring diurnal patterns or worst case visual air quality. The application of the method and findings to understanding visual air quality in other urban areas is discussed.

INTRODUCTION

Visual air quality (VAQ), a major concern in pristine areas, is becoming a prominent issue in urban locations as well. This paper describes an important step towards understanding and managing VAQthe search for an indicator that can be used to establish a link between human judgments of VAQ and physical/chemical properties of the atmosphere. Such an indicator would be especially useful for the management of VAQ. Such a physical/chemical indicator would help, for example, to determine whether the trends in VAQ are the result of changes in visibility-reducing emissions such as diesel exhaust and woodburning.

In our research on urban VAQ, VAQ is defined as an aesthetic judgment which is based on those visually perceived elements of scenes that are affected by atmospheric conditions. In urban areas, such elements include the color of the air, the clarity with which distant objects can be seen, and the presence or absence of a distinct border between clear and discolored air. VAQ is not perceived, as are color and clarity, for example; it is judged on the basis of perceptual information (Middleton et al., 1983a). VAQ is an aesthetic judgment, as opposed to, say, a technical or moral judgment because it is a judgment of beauty or ugliness. The impact of physically measurable atmos-

pheric conditions on VAQ may vary from scene to scene and from observer to observer.

Because VAQ is an aesthetic judgment, any measure of VAQ should be based on or verified against human responses (Craik, 1983). As a result of several years of research in urban VAQ, Stewart et al. (1983) found that field observations of VAQ made by experienced observers provide a valid and reliable measure of VAQ.

Although human judgment is the fundamental measure of VAQ, it has several shortcomings as a tool for VAQ management. An historical record of VAQ based on human judgments is not available, and without such an historical record it is difficult to identify trends in VAQ and therefore to assess and monitor the effectiveness over time of different management strategies. In addition, measurement procedures involving human judgment are more cumber- some and expensive than most physical/chemical measurements. Finally, in order to address policy questions, human judgments must be linked to emissions and, as yet, established theory exists only for the relations between pollutant properties (e.g. concentrations and light extinction values) and emissions. These shortcomings can be overcome by identifying a physical measure or set of measures which is strongly related to VAQ judgments and which (a) has been monitored over time, or is strongly related to a measure with an

AE 18-4 N 861

862 PAULETTE MIDDLETON et al.

historical data base, (b) is relatively easy to measure and (c) can be directly related to emissions.

Although urban VAQ has been studied from both the human judgment (Mumpower et al., 1981) and the physical/chemical (Dzubay et al., 1982) perspectives, the 1982 Denver Winter Haze Study was the first attempt to carefully coordinate extensive physical/ chemical and judgmental measurements in order to investigate the effectiveness of physical/chemical measures as indicators of VAQ over a variety of conditions. Since it is a well established fact that aerosols are primarily responsible for light extinction by scattering and absorption of light in the visible wavelength range and since light extinction is thought to be a strong determinant of visual air quality, the study concentrated on obtaining particularly high quality and well coordinated measurements of aerosol concentrations, light extinction and visual air quality. During this study simultaneous physical/chemical and judgmental measurements were taken at several representative locations throughout the daylight hours, during 21 days of the winter season.

Five specific questions, central to the selection of a good indicator of VAQ, are addressed using the extensive data set from the 1982 Denver Winter Haze Study.

(1) What are the major temporal and spatial variations of VAQ?

(2) What is the best single indicator of VAQ? (3) Can a significantly better indicator of VAQ be

obtained by using more than one measure taken at the same site and/or a variety of sites?

(4) To what extent can total light extinction telephotometer measurements be used as indicators of VAQ?

(5) To what extent can 4-h and 12-h average aerosol properties be used as indicators of VAQ?

METHOD

Three sets of data are used in the analysis: (1) special physical/chemical measurements made by the U.S. Environmental Protection Agency (EPA); (2) routine monitoring data obtained from the State of Colorado and the National Weather Service and (3) human judgment data collected by the National Center for Atmospheric Research. Figure 1 summarizes the measurement locations for all of the three data bases. Table 1 summarizes the specific hourly physical/chemical measurements made at each site throughout the day as well as the daytime (8 : G%17 : 00) means and standard deviations of these measures for the study period. The EPA values are lower than the State of Colorado values for CO, NO, NO, and SO2 averaged over sites since the EPA measurements were taken at a location which is more removed from high emission areas than some of the State of Colorado’s sites.

EPA data

During the 1982 Denver Winter Haze Study, the Environmental Sciences Research Laboratory of the EPA conducted an extensive physical/chemical measurement program. The EPA data used in this analysis include midday hourly values of the total light extinction coefficient (b,, or

Fig. 1. Schematic diagram of the Denver metropolitan area. Pollution cloud is the area of high pollution concentrations. CBD is the Central Business District. A-E are the observation sites with arrows indicating directions of the views. Physical measurements were taken at Stapleton airport (A), the Colorado Department of Health monitoring sites (0) and the EPA site (Site A). The primary site is A; the remote site, B.

light extinction) from a telephotometer, hourly averaged light scattering extinction coefficient (b,,. or light scattering) from both a heated and unheated integratmg nephelometer, several hourly averaged gaseous pollutant concentrations, 4-h averaged fine particle properties (elemental carbon, volatile carbon, sulfur and light absorption extinction coefficient; b,,, or light absorption), and 12-h averaged fine particle properties (total mass, elemental and chemical concentrations, and b.J. The 12-h averages were from a sampler with a cutpoint of 2.5 pm aerodynamic diameter. The sampler for the 4-h averages had a larger, but not well defined, cutpoint. The cutpoint uncertainty has minimal importance since the four quantities obtained from the 4-h sampler were known to be predominantly associated with the size range below 2.5 pm. Fine particle nitrate was measured by the Denuder Difference method (Shaw et al., 1982) which is intended to avoid the positive and negative artifact problems associated with traditional nitrate measurement methods. The telephotometer measurements made use ofartificial black targets, using the detailed procedure developed by Dzubay (1982). The ‘heated nephelometer employed resistance heating of the inlet ducting to increase the temperature of the aerosol within the nephelometer to about 32°C. The interior temperature of the ‘unheated’ nephelometer was only a few “C above outside ambient temperatures. The sampling and analysis for all other measured quantities was performed in a manner similar to that described previously (Dzubay et al., 1982). The EPA data were collected at a ground level site located in an area which has typically high pollution concentrations especially for pollutants formed in the atmosphere (e.g. OS and fine particle sulfate and nitrate), yet is not directly influenced by large nearby sources on a regular basis.

Routine monitoring data

The routine monitoring data listed in Table 1 consist of pollutant concentrations, meteorological information and visual range at the airport. These measurements are ground based and taken at several locations throughout the city (see Fig. 1). Hourly averages of the pollutant concentrations, temperature and wind are reported by the Colorado State Department of Health. The National Weather Service reports

Physical and chemical indicators of urban visual air quality judgments 863

Table 1. Hourly physical measures used in the analysis

Data set Site

EPA Primary

ROUTINE MONITORING DATA

Measure

co 03 NO NO, SGZ

k$

Daylight statistics* Mean Std Dev. N

1.34 ppb 2.10 144 21.20 ppb 11.03 171 21.93 ppb 52.88 156 29.95 ppb 23.49 156

7.59 ppb 8.95 173

144 57 x x lo6 106m-’ mm ’ 197 80 173 103

STATE5

METS

VISIBS

A, C, CH, N A, C, CH, W C, W C, W C C C

A, CH, W, S S S

S

CO Average O3 Average NO Average NO2 Average SO* TSP HCII

Wind average Temp Humidity

Airport visual

range

3.87 ppm 2.55 189 20.26 ppb 10.05 189 66.10 ppb 63.63 189 40.22 ppb 25.64 189 18.97 ppb 21.70 189

320.74 me3 pg 253.25 189 2.34 ppm 1.09 189

6.59ms-’ 3.99 189 36.83 “F 13.80 180 48.48 “/,, 20.02 180

42.06 miles 23.06 180

* Daylight refers to 8:00-17: 00. t b,, refers to light scattering extinction measured by a heated nephelometer. $ b,,, refers to light extinction measured hourly by a telephotometer for only 10: Oo-14:OO. 6 Possible sites: Arvada = A. Camo = C, Carih = CH, National Jewish Hospital = N, Welby = W, Stapleton = S. [ HC refers to total hydrocarbons:

hourly values of the meteorological conditions and visual range at the airport.

Observer data

Just as the physical/chemical data collection procedure is designed to guarantee a representative sampling of the physical/chemical environment, the observer data collection program is designed to obtain a representative sampling of the visual environment. Previous experience over five field studies (Mumpower et a/., 1981; Stewart et al., 1983; Middleton er al., 1983b) has shown that this can be ac- complished by having observations made by several experienced observers, over the entire day at enough sites to include all of the major types of views seen by the general public.

The procedure used in the 1982 Winter Study observer program is described in detail elsewhere (Stewart et al., 1983). In summary, six observers made VAQ observations at five sites during the 21-day period. Each observer was sent to a number of sites, one of which was the EPA data collection site (the ‘primary’ site), in a random sequence at specified time periods (morning, noon, afternoon) during the study period. Typically an observer would visit three sites per time period and cover two time periods per day. One additional observer was stationed at the primary site during the entire study.

At an observation site, the observer was instructed to give his or her overall impression of the visual air quality and then to rate VAQ in each of several directions. The observer also rated the clarity with which 3 or 4 designated targets in each direction could be seen. These targets were generally build- ings or mountains located from a few tenths of a km to over 100 km from the observation site. Other judgments made for each directional view included the color of the air and the sharpness of a border between disclored and clear air. Clarity, color and border have been found to be important perceptual elements of VAQ judgments (Mumpower et al., 1981). VAQ, clarity and border were rated using a seven-point scale with 7 corresponding to best VAQ. Color was judged against 13 Munsel color chips.

ANALYSIS

The observation procedure described above pro- duced a complex data set consisting of 3118 sets of directional observations over 21 different views made during 725 visits to five sites by seven observers during

three observation periods over the 21 days of the study. Before the observational data were analyzed, they were simplified by (a) scaling the color chips, (b) computing the average target clarity and (c) averaging over the observers. In addition, two sites, the primary site and a

remote site, were selected for more detailed analysis than the other three sites. Finally, samples of obser-

vations to be used for each analysis were defined. These preliminary steps in the analysis are described below.

During the field observations described above ob-

servers selected, from a chart containing 13 standard Munsel color chips, the chip most closely matching the air color. If an observer felt that no single chip matched the color of the air, he or she was instructed to select a primary chip that best matched the air color and an alternative chip that indicated the difference between the primary chip and the air color.

A color scale was developed by computing the mean VAQ judgment associated with each color chip over all observations. This color scale was checked against scales generated using different data, different methods and different people to insure that the bias was not introduced by the scale being data-, method- or individual- specific. In cases where two chips were

864 PAULEITE MIDDLETON et al.

chosen (about 21% of the directional observations), the VAQ judgments generally fell between the scale values for the two chips. Therefore, scale values for the two chips were combined into one value by computing a weighted average of the two. Twice as much weight was given to the primary chip because it represented the best color match. The color scale resulting from this procedure is used in all analyses reported below.

Average target clarity

Since different targets were used for each view, observers’ judgments of the clarity with which target objects could be seen were not strictly comparable from view to view. Two methods for converting the clarity judgments of specific targets into an overall clarity measure that was not site-specific were investigated. The first method was to simply average the clarity judgments for each view over the targets for that view. This approach was justified by the generally high (median = 0.85) correlation coefficients between clarity ratings of different targets in the same view. The second method was to average ‘corrected’ target clarity judgments within views. This correction involved using multiple regression analysis to derive procedures which would statistically equalize the different targets with respect to distance from the observer, size, color and contrast with the background. Both procedures gave essentially identical results, so the simpler procedure, averaging over targets within a view, was used to derive the clarity scale which is used in subsequent analyses.

It should be noted that the clarity judgments are strongly associated with the common visual range measure. Visual range is a standard measure of the greatest distance over which an observer is able to distinguish a large black object against the horizon sky (Middleton, 1954). An indication of the strength of the relation between clarity judgments and visual range is given by the correlation between the mean clarity judgment at each hour and visual range at the airport. For the 1982 Denver Winter Haze Study data this correlation coefficient is 0.69.

Averaging over observers

A number of observations were replicated (two or more observers made an observation at the same time and same place) to ensure that the reliability and validity of the VAQ measure could be tested. Since intercorrelations among observers’ judgments were uniformly high (Stewart et al., 1983) averaging judgments across observers is justified and improves the reliability of the judgments. When two or more observers made a site visit within 1 h of one another (the median difference between observations was 8 min), the mean of their judgments was used in subsequent analyses. This reduced the number of sets of directional judgments to 1354, of which 24% are based on a single observer, 337, represent the mean judgments of two observers, and 437” represent the mean judgments of three or more observers.

Site selection

The major results reported in this paper are based on observations from the primary site, where observations were made at least once during every observation period and where the special physical measurements were taken. Other sites were used as well to investigate the spatial variability of VAQ, but the number of observations at these sites was not sufficient for a detailed analysis. One remote site (site B in Fig. 1) was selected for special attention in further analyses because it was visited more often than the others, was quite distant from the primary site (20 km), was located beyond the area of high gas and aerosol concentrations, and potentially provided a clear view of downtown Denver and the mountains west of Denver. If results from the primary site generalize to this remote site, then the results should generalize to other sites not so far removed from the primary site.

Sample definition

Sample sizes used in the analyses vary depending upon the availability of VAQ judgments and physical/ chemical measurements suitable for a particular analysis. In general, when data for one variable were missing, the entire set of observations for that hour was deleted. This resulted in analyses based on from 44 to 121 hourly observations or 11 to 21 daily averages. Whenever the sample size was reduced because of missing data, distributions of variables and correlations were checked to assure that bias was not introduced. The major cause of missing data was the limited number of observers. Observers could not be at every site during every hour of the study. The observers schedule was randomized so that visits to each site represent a random sample of the population of possible hours. Therefore, systematic bias due to missing data was not expected and not found in any case.

RESULTS

In the following sections, the characteristics of the judgments of VAQ are described and the relations between VAQ and the physical/chemical measures are examined.

(1) VAQ characteristics

Indicators of VAQ should be sensitive to spatial and temporal variations of VAQ. Since the current Clean Air Act establishes a precedent for regulating worst case conditions for health related pollutants on an hourly basis at particular sites rather than on a daily average urban area average basis, it is likely that any future regulation of VAQ will also be based on hourly averaged information at specific sites. Therefore, it is essential to establish the spatial and temporal patterns of VAQ and the relationships between indicators and VAQ on an hour by hour and site by site basis.

The spatial variation in VAQ is examined (1) by


comparing VAQ at the primary site with VAQ at the remote site (sites A and B in Fig. l), and (2) by comparing VAQ at each site with a regional average VAQ. In addition, the temporal means for the views at each site are compared.

Since the primary site and the remote site are 20 km apart and provide quite different perspectives on the pollution cloud, the correlation between VAQ judgments at these sites will indicate the degree of differences to be expected among sites. Each site included two views toward the center of the pollution cloud. The means of the two views were computed for both sites and correlated over the 45 observation times when observers were at both sites. This correlation coefficient was 0.76 indicating that VAQ judgments at individual sites will be strongly correlated.

The mean VAQ over all sites and all views except those views looking directly away from the pollution cloud correlated 0.92 with VAQ at the primary site (averaged over two views) and 0.94 with VAQ at the remote site over the 45 h when observers were at both sites. The correlation coefficients between the VAQ at the other 3 sites and average VAQ were all 0.95 or above. These high correlation coefficients indicate that VAQ averaged over sites, as expected, represents well the site by site VAQ that went into the average even when the sites are far apart and afford quite different views. In other words, the correlation coefficients among VAQ judgments at different sites are high enough that the judgments can be meaningfully aggre- gated into a regional average VAQ. Therefore, a regional average VAQ can be used in the evaluation of indicators. The ability to define a regional average VAQ may not generalize to other cities, however, so the use of multiple observation sites is strongly recommended until the effects of site differences are understood.

Inspection of the means for VAQ for each view over the study period gives an indication of the ‘typically’ worst case view. The best VAQ (mean = 5.6 on a l-7 scale) occurs when the observer is southeast of the metro area looking northeast. The worst VAQ (mean = 3.6) occurs when the observer is north looking into the cloud in the direction of the primary site (site A in Fig. 1). As a result it is expected that measures taken at the primary site will be good indicators of worst case VAQ.

The temporal variations in VAQ are examined (1) by comparing diurnal patterns of VAQ and (2) by comparing the relations between worst vase VAQ and VAQ judged at other hours. As shown in Fig. 2, VAQ has a diurnal pattern that varies from day to day. The worst case of VAQ, however, occurs most frequently in the morning. This might be associated with other early morning phenomena: trapping of pollution near the ground because of shallow mixing heights, high emissions from mobile sources and from woodburning, slightly higher relative humidity, and strong sun angle effects.

The correlation coefficient between worst hour

VA0

J.--n 13 14 c

15

TIME OF DAY

Fig. 2. Visual air quality (VAQ), rated on a l-7 (bad-good) scale, averaged over sites and views for each hour for selected representative days and the frequency of worst case VAQ vs time of day

for the 21-day study Period.

VAQ and daily average VAQ is 0.96 for the 21 day period. The correlation coefficient of worst hour VAQ with MO, 1200 and 1490 VAQ are 0.84,0.86 and 0.84, respectively. These results suggest that measurements taken only during selected hours or measurements which are averages over the entire day are probably good indicators of trends in daily worst case VAQ. They would not necessarily be good indicators of actual worst case VAQ on a day by day basis.

(2) Best single indicator

The most complete indicator of VAQ is one which can monitor all important aspects of VAQ: hourly, worst case and daily average VAQ. Such an indicator must be measured throughout the day and have an averaging period of 1 h or less. Therefore, relations between daylight hourly physical/chemical data and VAQ are first examined. Then, in order to get an understanding of how site and view differences affect a measure’s performance, results for the primary and remote sites are examined in detail.

The square of the correlation coefficients (r’) are used in the following comparative analysis. These values are adopted since they can be directly compared to the adjusted squared multiple regression coefficients (R’) presented in subsequent analyses.

The correlation coefficient is a measure of the strength of a linear relation between two variables. VAQ judgments would not, in general be expected to be linearly related to physical properties of the atmosphere. Research in psychophysics over the last century indicates that equal intervals on psychological variables (such as VAQ) often correspond to equal ratios


on physical variables (Guilford, 1954). This suggests a logarithmic relation.

In general the logarithms of the physical/chemical measures used in this study (a)are more linearly related to VAQ, (b) are more symmetrically distributed and (c) are more highly correlated with VAQ, than are the raw measures. The exceptions to this are measures of O3 and 12-h average data. For these measures, the logarithmic transformation did not significantly affect the relation with VAQ. For all other measures, logarithms have been used throughout the analysis.

As shown in Table 2, the single measure that is most highly correlated with VAQ regardless of direction or location of observations as well as for the VAQ averaged over sites and views, is b,,. It should be noted again that two instruments were used to measure b,,: the heated and the unheated nephelometer. Since the correlation coefficient between the two instruments was greater than 0.99 and since the correlations of each with the judgmental variables differed by 0.02”/0 at most, all of the analyses used only the heated nephelometer b,, values.

Next, but much less strongly correlated with VAQ, are NOp and CO. This result for NO2 is not surprising since it is known to be the most important gaseous contributor to atmospheric light absorption. CO, however, does not interact with light in the visible range. Therefore, it is significantly related to VAQ possibly because the atmospheric conditions that lead

Table 2. Squared correlations among EPA measures and VAQ*

CO 0, NO NO, r2 r2 rz rz $

Primary site North 0.216 0.193 0.216 0.613 Southwest 0.408 0.259 0.215 0.656 South 0.310 0.213 0.581 Southwest 0.293 0.093 0.224 0.669 West 0.278 0.095 0.183 0.632 Av. over views 0.346 0.142 0.234 0.706

Remote site Northeast 0.173 0.203 South 0.211 West 0.127 0.143 0.394 Northwest 0.227 0.118 0.246 0.499 Av. over views 0.187 0.236 0.486

Av. over views and all sites 0.282 0.125 0.259 0.656

*All of the correlation coefficients (r) are negative except for the O,-VAQ values. Observations are given by views, an average over all views at the primary site, an average over west and northwest views at the remote site and an average over all sites and all views excluding views away from the pollution cloud. Physical/chemical measures are taken at the primary site. b refers to light scattering extinction measured by a nephelo&ter. Natural logs of all physical measures except for 0, are used in the correlations. Sample size is 46. Only values significant at the 0.01 level are included in the table. There were no significant correlations for SO,.

to CO buildup may also lead to accumulation of other visible pollutants.

VAQ averaged over sites and views is, as expected, most strongly related to b,,. This is because, as reported in the previous section, VAQ for different views and sites are highly correlated.

Even though this data set includes the key hourly gas and aerosol measurements associated with air quality, it is not exhaustive. Other hourly measures such as light absorption or fine particle mass concentrations, not included in this study might also be strongly correlated with VAQ judgments and should be included in future studies of VAQ.

The reasons that b,, is a good indicator of VAQ regardless of direction or location of observation can be understood, in part, by comparing in more detail the results at two widely separated sites: the primary and the remote sites. The correlations among bSP, VAQ and the perceptual elements of VAQ (color, border and clarity) are given in Table 3.

From these comparisons, it can be concluded that b,,is a good indicator of VAQ at both the primary and remote sites for different but not unrelated reasons. The b,, measure correlates highly with the major perceptual elements of VAQ at both sites: clarity at the primary site and border at the remote site. Furthermore, clarity at the primary site is highly correlated with border at the remote site (r = - 0.72). These interrelationships mean that the conditions that obscure target objects viewed from within the cloud are often the same conditions that produce a sharp border seen from outside the cloud. The conditions that degrade VAQ at the primary site also degrade VAQ at the remote site, but in a perceptually different way.

It is not surprising that bSP is the best indicator of VAQ of all of the measures considered in this analysis.

Table 3. Correlations among b , VAQ and elements of VAQ (color, borde;Pand clarity)*

VAQ b (primary site) (prima& site) r r= r r 2

Primary site Clarity 0.97 0.94 -0.80 0.64 Color -0.12 0.52 0.67 0.45 Border -0.53 0.28 0.63 0.40 VAQ 1.00 1.00 - 0.84 0.71

VAQ b (remote site) (primas& site)

Remote site Clarity 0.60 0.36 - 0.48 0.23 Color - 0.69 0.48 0.71 0.59 Border -0.71 0.50 0.72 0.52 VAQ 1.00 1.00 -0.70 0.49

*Observations are averaged over all views at the primary site and over the west and northwest views at the remote site. bsP is measured at the primary site and refers to light scattering extinction measured by nephelometer. Natural logs of bsp are used in the correlations. Sample size is 45.


Table 4. Regressions predicting visual air quality judgments*

Data base

EPA STATE MET VISIBt EPA + STATE EPA + STATE

+MET EPA + STATE

+ MET + VISIB

Total No. of Primary Remote All sites

predictor site site average variables Adjusted RZ Adjusted R2 Adjusted R2

6 0.73 0.55 0.67 7 0.59 0.43 0.57 3 0.59 0.47 0.60 I 0.41 0.31 0.41

13 0.74 0.69 0.77

16 0.74 0.69 0.77

17 0.82 0.75 0.86

* R* are adjusted to account for additional predictor variables so that results can be compared directly to Table 3 results. The adjustment formula is the one used in the Stafisrical Packagefor the Social Sciences (Nie et al., 1975). Sample size is 44. All independent variables are forced into the equations. Observations averaged over views for the primary and remote sites and averaged over views and sites for the all-sites average. Data included in each set are noted in Table 1,

t Since the data base VISIB is composed of only one measure, r’s, not adjusted R’s, are calculated.

Visual air quality is a phenomenon determined in part by the interaction of sunlight and aerosols and bSP is the only quantity that explicitly measures this interaction.

(3) Groups of measures as indicators

Even though b,, at one location was found to be an overall good indicator of VAQ, the question arises whether a much better indicator of VAQ can be obtained by considering combinations of measures taken at the same site and/or additional site as predictors. This question is investigated by comparing multiple regressions of various combinations of the physical/chemical data sets with VAQ judgments. As in the previous analysis, observations at the primary and remote sites and the average over all of the sites are included to illustrate site differences and to compare these differences to the overall average. The regression results are given in Table 4.

Comparison of Tables 2 and 4 shows that adding variables to the regressions strengthens the relations in all cases provided b,, measured at the primary site is included in the data set. In particular, the ability to predict judgments at the remote site is substantially improved by including measurements from other locations.

The weak but significant correlation of airport visual range with VAQ deserves special note. As a single measure, airport visibility is second only to b,, as an indicator of VAQ. This can be explained, in part, by the close relationship between clarity judgments and visual range and the importance of clarity as an element of VAQ. Because airport visibility has some strength as an indicator of VAQ and because it does have an historical record, airport visibility has some potential use as a qualitative indicator of past VAQ.

It can be concluded that bSP measured at the primary site is an adequate indicator of VAQ at the primary

site. The addition of physical/chemical variables from the primary site or from other sites does not significantly strengthen the relation between measures and VAQ at the primary site. The correlation between the physical/chemical measures and VAQ at the remote site as well as VAQ averaged over all of the sites, however, is improved by adding more variables from other sites.

(4) Telephotometer measures

The relation between VAQ and (a) light extinction (b,,J measured by a telephotometer and (b) light scattering b,, measured by a nephelometer were compared for midday hours. The telephotometer was operated hourly only between 10:OOand 14:00, when it was expected to be least sensitive to sun angle variations. The results are given in Table 5. Both measures are good indicators of VAQ with b,, being slightly better than b,,,.

Comparison of the squared correlations in Table 5 with the results given in Tables 2 and 4 show that the

Table 5. Squared correlation coefficients between b,,, and bSp and the VAQ judgment9

Primary Av. over site all sites

b e?., b sn

0.71 0.71 0.77 0.76

* Values of r* are reported to facilitate comparison with results in other tables. Both be,, (measured by the telephotometer) and b,, (measured by the nephelometer) were measured at the primary site from 10: 00 to 14 : 00, hourly for b,,, and hourly average for tsp. Natural logs ofboth measures were used in the correlattons. Sample size was 78. The correlation coefficient between bcxt and bSr is 0.95.


relations for both b,, and b,,, with midday VAQ are somewhat stronger than the relations for the various measures with VAQ taken throughout the day. This is to be expected because the effects of sun angle are weaker during the midday hours. As a result, aerosol properties can account for a greater proportion of variation in VAQ.

The use of either b,,, or b,, as an indicator of midday VAQ is justified given these results. Since changes in worst case VAQ are strongly related to changes in midday VAQ, strong indicators of midday VAQ could be useful indicators of trends in worst case VAQ. If the telephotometer is to be considered as an alternative to the nephelometer as a monitor of diurnal patterns and worst case VAQ, comparison of the two instruments over the entire daylight period is required.

(5) 4-h and 12-h average measures

The use of 4-h and 12-h average fine particle properties as indicators of the mean VAQ for the corresponding periods is examined. Correlations between these average measures and mean VAQ are given in Tables 6 and 7.

As shown in Table 6, fine particle S is the 4-h aerosol composition measure most strongly related to VAQ. Carbon (elemental and volatile) is also significantly, but less strongly, related to VAQ. Both light scattering (b,,) and light absorption (b,,) are significantly correlated with VAQ. The b,, measure, however, has a much higher correlation with VAQ than any b,,. Light extinction (b,,,) averaged over the 10: Oo-14:OO for comparison to the 4-h measures, is also strongly related to VAQ. This result is not surprising since b,,, is

composed of hap and bSp. The 12-h comparisons given in Table 7 show that

fine particle S, sulfate, nitrate and ammonium are all

strongly related to VAQ. These constituents are also major contributors to the aerosol mass concentration. Volatile and elemental C together account for a greater fraction of the total fine mass, but their correlations with VAQ although still substantial are not as large. It seems likely that the correlations are reduced, in part, because of the larger measurement errors associated with the C components compared with those of the other chemical species.

Overall, the importance of sulfate, nitrate, ammonia, volatile and elemental C, with respect to VAQ closely mirrors the influence of these same species on visibility reduction found by Gorblicki et al. (1981) in their 1978 study of the Denver winter haze. It is important to emphasize that visibility reduction in their studies was defined as light extinction. In our study the complete link from the fundamental VAQ measure (human judgment of VAQ) to light extinction and aerosol concentrations is established.

The various 4-h and 12-h average aerosol measures which are highly correlated with the corresponding mean VAQs are potentially useful as indicators of average VAQ. In addition, these measures could be of some value as indicators of trends in worst case VAQ since changes in daily average VAQ are strongly related to changes in worst case VAQ.

CONCLUSIONS AND RECOMMENDATIONS

This study reveals several important aspects of VAQ for Denver, Colorado, and provides the basis for observations regarding VAQ in general. Examination of VAQ characteristics results in two important con- siderations which influence the selection of VAQ indicators. The most direct indicator of VAQ is one

Table 6. Correlations between 4-h average EPA data and VAQ’

EPA measures Mean values r r2 Significance

level Sample

size

Entire day ElemC Vol c S b

aP b

SP 10:0&14:00

Elem C Vol c S

b aP b

SP b ext

2103 ngme3 -0.59 0.34 0.0001 44 6288 ngm-J - 0.63 0.40 0.0001 44

682 ngm-3 - 0.84 0.71 0.0001 44 22x 106m-’ -0.58 0.34 0.0001 44 43 x IO6 m-r - 0.83 0.69 0.0001 44

1808 ngmm3 -0.76 0.58 0.0010 15 5785 ngmm3 -0.73 0.53 0.0022 15

793 ngm-’ -0.91 0.83 0.0001 15

20x106m~’ - 0.70 0.49 0.0040 48 x 106m~’ - 0.94 0.88 0.0001 1: 70x 10bm~’ - 0.94 0.88 0.0001 15

l All data measurements were made a& the primary site. For the entire day correlations there are three time periods (6:0+10:00, 10:0&14:00 and 14:OCk18:00). VAQ is averaged over sites and views for 8 : 00-10 : 00, 10 : 00-14 : 00 and 14 : 00-17 : 00. Data includes elemental carbon (ElemC), volatile carbon (Vol C), sulfur (S) light absorption coefficient (b,,) and 4-h averages of hourly data for light scattering extinction (b,,) from the heated nephelometer and total light extinction from the telephotometer (b,,,). Natural logarithms of all measures were used in the analysis.

Physical and chemical indicators of urban visual air quality judgments

Table 7. Correlations between 12-h average EPA data and VAQ*

869

EPA Measures Mean values

Total Mass Al Si P s Cl K Ca Fe cu Z&t Br Pb SO, NO, NH., ElemC VolC b CaA

Significance Sample r r2 level size

- 16544 ngm-’ -0.72

389 ngmMJ 0.19 260 ngmm3 - 0.05

37 ngm3 0.36 773 ng m3 - 0.88 48 ng m3 - 0.23 57 ngm3 -0.51 43 ngm3 -0.16 84 ng m3 -0.19 8 ngm” -0.14

42 ng m3 0.01 82 ng m3 -0.36

291 ngm3 - 0.41 2288 ng m3 - 0.84 2517 ngm’ - 0.87 662 ng m3 - 0.80

2160 ngm” - 0.49 6459 ng m3 - 0.48 33x106m-’ - 0.46 1597 ppb - 0.49

20 ppb 0.70 33 ppb -0.38 26 ppb - 0.45

8 ppb 0.06 49 x lo6 m-l - 0.77

0.52 0.04 0.003 0.13 0.77 0.05 0.26 0.03 0.03 0.02 0.0001 0.13 0.17 0.71 0.76 0.64 0.24 0.08 14 0.23 0.08 14 0.21 0.026 18 0.24 0.027 16 0.50 0.001 18 0.14 0.064 17 0.20 0.036 17 0.004 0.400 18 0.60 0.001 18

0.001 18 0.214 18 0.417 18 0.072 18 0.001 18 0.182 18 0.016 18 0.266 18 0.230 18 0.288 18 0.487 18 0.073 18 0.046 18 0.001 18 0.001 18 0.001 18

l All data measurements were made at the primary site. VAQ is the daylight (8:00 through 17 : 00) average of VAQ averaged over sites and views. Only fine particle analyses are used. The data includes total mass, elemental analysis (Al through Pb), sulfate (SO,), nitrate (NO,), ammonium (NH,), elemental carbon (ElemC), volatile carbon (VolC), light absorption extinction (b,,) and 12-h (6:00-18~00) averages of the hourly data for CO, O,, NO, NO,, SO, and light scattering extinction (b,) from the heated nephelometer.

which can monitor hourly, worst case and daily average VAQ and, therefore, should be measured throughout the day with an averaging period of 1 h or less and can be located at one site. Trends in VAQ can be monitored by measurements taken at selected hours or averaged over several hours.

The single physical/chemical measure which was found to best satisfy these major criteria for a direct indicator of VAQ is b,,. This measure is a good indicator of VAQ, regardless of direction or location of observation, because it is the measure most strongly related to the main perceptual elements of VAQ from within the pollution cloud (clarity) and outside of the cloud (border).

Combinations of physical/chemical measures are somewhat better indicators of VAQ than b,, alone, provided b,, is one of the measures in the combinations. The use of additional measures is especially important for indicating VAQ at the remote site.

Measures which were taken hourly but only during selected time periods or which were averaged over the entire day or portions of the day were also investigated. It was found that b,,, measured hourly by the telephotometer and b,, measured by the nephelometer are equally good indicators of VAQ during midday hours when the telephotometer measures are least sensitive to sun angle. Fine particle 4-h averaged S and 12-h averaged S, sulfate, nitrate and ammonium are all

strongly associated with the corresponding mean VAQs. Since changes in midday and daily average VAQ are strongly related to changes in worst case VAQ, these measures could all be used as indicators of trends in worst case VAQ.

Several implications for future urban VAQ studies can be drawn from these results for Denver. The effectiveness of b,, as an indicator of VAQ in other areas should be tested. Since Denver has a higher b,, to b,, ratio than any other urban area for which such measurements are available, it is suspected the b,, may be an even better indicator of VAQ in other urban areas.

Selection of measurement site or sites depends on the spatial variability in VAQ. For Denver, the correlations between VAQ and indicators measured within the high pollution area are much higher for VAQ near the measurement site than for VAQ at a distant site outside of the high pollution area. Since the site by site variation in VAQ could be even more important in other cities, it is important to investigate site differences elsewhere. The optimum experiment for any city would include simultaneous measurements at two or more sites ofall the key physical/chemical quantities and judgments. With such a data base questions regarding indicator site location and number could be more precisely addressed.

The use of temporally averaged measures (e.g. 4-h


and 12-h averaged fine particle measurements) and selected hourly measures (e.g. midday b,,, from a telephotometer) as indicators of trends in worst case VAQ in other areas depends on the temporal variations in VAQ in the areas. A close correspondence between changes in midday and daily average VAQ and changes in worst case VAQ is required.

can be monitored by daytime hourly averaged b,, at a site characterized by high pollution concentrations. Although it is expected that b,, would be a good single indicator in other cities as well, the relationship

The most encouraging aspect of this study is the discovery that one measure at one site could be used as an indicator of VAQ over a much larger region. Both the diurnal natterns in VAQ as well as worst case VAQ

review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.

REFERENCES

Craik K. H. (1983) A role theoretic analysis of scenic quality judgments. In Managing Air Quality and Scenic Resources at National Parks and Wilderness Areas (edited by Rowe R. D. and Chestnut L. G.), pp. 117-126. Westview Press, Boulder, Colorado.

Technol. 16, 514524. Dzubay T. G. (1982) Telephotometric measurements of light

extinction coefficients; instructions for measurements using

Dzubay T. G., Stevens R. K. and Lewis C. W. (1982) Visibility and aerosol composition in Houston, Texas. Enoir. Sri.

black targets. Environmental Research Laboratory operat- ing procedure, U.S. EPA, Research Triangle Park, NC.

between b,, and VAQ needs to be tested using simul- Groblicki P. J., Wolff G. T. and Countess R. J. (1981)

taneous measurements of both the physical/chemi- Visibility-reducing species in the Denver “Brown Cloud”-

cal indicators and the human judgments in several I. Relationships between extinction and chemical com-

distinct urban areas. position. Atmospheric Enuironmenr 15, 247332484.

Guilford J. P. (1954) Psychometric Methods. McGraw-Hill,

Acknowledgement.s-The National Center for Atmospheric Research is operated by the University Corporation for Atmospheric Research and is sponsored by the National Science Foundation. The authors wish to acknowledge the support of both Region VIII and the Environmental Sciences Research Laboratory of the U.S. Environmental Protection Agency during the research described in this article. The Colorado Department of Health (S. Arnold) and the National Weather Service are acknowledged for providing the monitoring data used in this study. Most of the EPA data was the result of sampling and analyses performed by W. Courtney, B. Ellenson, K. Kronmiller, G. Russwurm, M. Beaman, M. Mason, C. Owen, C. Pressley and S. Tysinger of Northrop Services, Inc. T. G. Dzubay designed the telephotometer measurement configuration and computed the resulting extinction coefficients. R. A. Anthes, A. C. Delany, R. L. Dennis, M. Krenz and S. L. Rhodes are thanked for their comments on an earlier version of this paper. Although the research described in this article has been funded in part by

New York. Middleton, W. E. K. (1954) Vision Through the Atmosphere.

Oxford University Press, Oxford. Middleton P., Stewart T. R. and Dennis R. L. (1936)

Implications of NCAR’s urban visual air quality assessment method for pristine areas. In Managing Air Quality and Scenic Resources at National Parks and Wlderness Areas (edited by Rowe R. D. and Chestnut L. G), pp. 6474. Westview Press, Boulder, Colorado.

Middleton P., Stewart T. R. and Dennis R. L. (1983b) Modeling human judgments of urban visual air quality. Atmospheric Environment 17, 1015-1021.

Mumpower J., Middleton P., Dennis R. L., Stewart T. R. and Viers V. (1981) Visual air quality assessment: Denver Case Study. Armospheric Emironment 15, 243332441.

Nie N. H., Hull C. H., Jenkins J. G., Steinbrenner K. and Bent D. H. (1975) Statistical Package for the Social Sciences (Second Ed.), p. 358. McGraw-Hill, New York.

Shaw R. W., Stevens R. K., Bowermaster J., Tesch J. W. and Tew E. (1982) Measurements of atmospheric nitrate and nitric acid; the denuder difference experiment. Atmospheric Environment 16, 8455853.

the U.S. Environmental Protection Agency through inter- Stewart T. R., Middleton P. and Ely D. (1983) Urban visual agency agreement number AD-49-F-O-167-0 to the National air quality judgments: reliability and validity. .!. of Science Foundation, it has not been subjected to Agency Environmental Psychology 3, 129-145.

physical and chemical indicators of urban visual air quality judgments

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