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“Local background” levels of carbon monoxide in an urban area
Truns~~n &X-A. Vol. 30, No. 6, pp. 399-413,1996 Copyright 8 1996 ElsevierScienceLtd Printed in Great Britain. All rights reserved 0965-8564/96 Sl5.00 + 0.00
Pergamon
PII: so%5_8564(%)oooo7-9
“LOCAL BACKGROUND” LEVELS OF CARBON MONOXIDE AN URBAN AREA TIMOTHY
LARSON*, LARS MOSEHOLM,
IN
DAVID SLATER and CYRA CAIN
University of Washington, Environmental Engineering and Science Program, Department of Civil and Environmental Engineering, Box 352700 Seattle, WA 98195, U.S.A. (Received
1 I August
1994; in
revised form
25 January
1996)
Abstract-The objective of this study was to obtain a better understanding of carbon monoxide (CO) concentrations immediately upwind of urban roadways, the “local background” values, and how these concentrations depend upon the surrounding traffic and the general meteorology. Measurements were made at seven sites in Seattle, WA during the winter of 1993. Local background CO concentrations were characterized by an absence of short term fluctuations, a steady buildup during the 3 p.m. to 11 p.m. period, and a lack of spatial gradients in the 8-h average values. Distinctly different log-normal distributions of the 8-h averages were observed for “trafficked” sites versus “urban park” sites, with mean values of I .6 and 1.O ppm respectively. A simple regression model was developed to predict the local background CO that includes distance from roadway. average daily traffic of nearby roadways, and the frequency of occurrence of low wind speeds (R’ = 0.74; F = 170). Copyright 0 1996 Elsevier Science Ltd I. INTRODUCTION
Carbon monoxide (CO) concentrations in ambient air have decreased dramatically in the last 15 years as a result of reduced emissions from motor vehicles. Nevertheless, when a new roadway project is proposed its environmental impacts need to be accurately assessed. These incremental impacts are assessed by adding the estimated incremental changes to the area’s “local background” concentrations. This “background” concentration is not the lowest background level upwind of the urban area but, rather, a local area reference concentration that is not directly attributable to the emissions from any one source, roadway, or intersection. A number of approaches have been reported in the literature to estimate these “local background” levels. Cooper (1987) has published a good general discussion on this subject. One approach is to directly measure the CO levels at an appropriate location near the site in question (Ott & Eliassen, 1973; Ott, 1977). However, no consensus exists as to the acceptable distance that a background monitor should be placed away from the roadway or the acceptable magnitude of traffic on the roadway nearest to the background site (Clagett et al., 1981; Green et al., 1979; Chang et al., 1980). In addition, there are no criteria to confirm whether a given location is indeed a local background site. Finally, there is no specific guidance on the number of samples that are needed per site or the meteorological conditions that are most appropriate for sampling. Another approach to estimate the background CO concentrations is to use a meteorological dispersion model (Matzoros & Van Vliet, 1992; Petsios et al., 1993; Liu & Jeng, 1993; Ireson, 1993). The uncertainties in these models include the spatial distribution of emissions, the effects of complex terrain (hills, valleys, and surrounding buildings), the effect of very low wind speeds that cannot be measured with standard wind sensors, and the magnitude of vertical mixing under stable conditions. However, the most common practice is to use one of several default values suggested by regulatory agencies in the absence of any of the above information (US EPA, 1992; Schewe et al., 1990). This paper discusses the relationships between measured background CO concentrations and site location, surrounding traffic, and meteorology; and attempts to identify those traffic and location characteristics that best predict the “local background” CO concentrations. *Author for correspondence. 399
400
Timothy Larson et ul. 2. METHODS
2.1. Experimental
Our approach was to select background sampling locations, collect and measure CO concentrations, and summarize the results in a way that is useful to those responsible for assessing the environmental impacts of proposed roadway projects. We chose to sample during the most meteorologically stable period of the winter to accurately reflect “worst case” meteorological conditions. We also chose to sample from 3 p.m. to 11 p.m. because this period includes not only high traffic flows but also stable conditions after sunset. In addition, we established a meteorological monitoring site within the study area to measure wind speeds typical of urban intersections rather than open sites. Finally, the information on CO and meteorology was combined with traffic information into a statistical mode1 describing background CO concentrations. Sampling locations were chosen on the basis of combined distance/traffic criteria that were based on the field work of Ott and co-workers (Ott & Eliassen, 1973; Ott, 1977) and of Perardi et al. (1984) and the theoretical results of Matzoros and Van Vliet (1992). We chose “trafficked” sites that were: (a) at least 30 m from any roadway; (b) 30-100 m from roads with at least 8000-10,000 vehicles per day; and (c) at least 200 m from major freeways. With regard to criterion (b), at one of our sites we monitored 50 m away from a road with an average of 46,000 vehicles per day. Although criterion (c) does not specify the magnitude of traffic on a “major freeway,” the one major freeway through North Seattle, Interstate 5, has a volume of 250,000 vehicles per day. Conservatively, we suggest being at least 200 m from any freeway with more than 100,000 vehicles per day. In addition, “less trafficked” sites were located within urban parks a distance of 40&700 m from similarly trafficked roads. All sites were a minimum of 70 m from major intersections. The selected sites are listed in Table 1, and their locations are indicated in Fig. 1. A bag sampler was utilized at all sites except those having continuous monitors operated by the Washington State Department of Ecology (WDOE). Air samples were collected in the field at a constant flow rate using the bag sampler. These collected samples were analyzed in the lab with a Lear-Siegler model ML9830 non-dispersive infrared photometer. Zero and span gases were used for daily calibration of the instrument, along with a third, intermediate-value gas used as a precision check. Instrument accuracy was 4.6% and instrument precision was 0.4%. A wind sensor (Weatherpak-100 TM Automatic Weather Station, Coastal Climate Company, Seattle, Wash.) was placed 3 m above the roof of Wilcox Hall on the UW main campus. The Weatherpak was programmed to store 15-min averages, on the hour, of air temperature, wind speed, wind direction, and standard deviation of the wind direction. To assess the adequacy of our network to estimate the site group mean values over time and space, the spatial and temporal correlations characteristic of CO background concentrations had to be taken into account. To achieve a given accuracy when estimating the regional mean, Gilbert (1987) developed an expression for the relationship between the required number, n, of samples taken at each site, as well as the required number of sites, nsr for a pre-specified level of accuracy: where
n = (l/n,) (2f.~/d)~[l + 2r,] [l + r&n, - l)]
(1)
&= the residual variance, i.e. the variance that is not associated with between-day and between-site variability (= [0.325 ppm12, as estimated by ANOVA) d = the uncertainty of the sample mean (absolute error) in ppm units at the 95% confidence level r, = the auto-regression parameter assuming an AR- 1 process (= 0.56 as an average for our data). r, = the average spatial correlation coefficient between sites (= 0.8 as an average for our data). Our best estimates of (r?, r, and rc are [0.325 ppm]‘, 0.56, and 0.8 (as an average over all sites) respectively, based upon the results of a pilot study in the region as well as the final results of this study. Given these values, equation (1) becomes
401
Carbon monoxide in an urban area Table I. Local traffic at sampling sites Site
Site code
Distance to roads (m)
Roadway traffic*
Background sites’ Isolated park
Discovery Park Richmond Beach Pk.
DIS
625 650
5000
7000
RBH
320 460
1500: 10,000~
MAC
730
14,000
Roosevelt
ROS
30 75
19,oOQ 10,000
Green Lake
GRL
Urban park
Magnuson Park TraJicked
Maple Leaf
Univ. of Wash.
MLF
uw
210
10,000 250,000
90 160 180
12,000 9000
loo
50
8ooo
230
46,000 15.000
2 30
25.000
2 300
30,000 250,000
Street
Zanadurj Northgate Mall8
ZA
NC
8000
“1991 average weekday values from the City of Seattle Traffic Engineering Division. ‘See text for further description of the site categories. :I992 average weekday values from King County Transportation Planning Section. $Air monitoring sites operated by Washington State Department of Ecology for regulatory purposes.
n =
(0.9/&) (l/n,) [l + 0.8(n, - l)]
(la)
To estimate the CO 8-h mean with an absolute error of d = 0.2 ppm, one would need to sample for 20 days at three sites, or 19 days at six sites. More sites give only a slightly better representation of the region mean because of the high spatial correlation between sites. With four sites, 30 days of sampling corresponds to an estimated uncertainty of 0.16 ppm. If we had wanted to reduce this uncertainty in the sample mean by a factor or two, we would have had to sample for an additional 90 days under these winter conditions, clearly an impractical task with diminishing returns. 2.2. Theoretical The conceptual models that were considered as a part of this study included a box model and a line source model. However, both of these conceptual models required the use of site specific turbulence parameters which were impossible to deduce during the most stagnant meteorological periods. These were also the periods of highest CO. Therefore, a “stagnation” model was formulated. The concept behind this model is that background CO concentrations increase only during periods of very calm winds, when existing atmospheric turbulence theory does not apply. In our analysis, turbulence theory cannot be used to describe vertical wind speed and temperature profiles when the 15 m average wind speed is less than or equal to 0.2 m/s. A simple index of this phenomena is nwindrthe number of hours when wind speed is less than or equal to 0.2 m/s during 3 p.m.
402
Timothy
Fig.
I. Location
of background
Larson
sampling
et al.
sites (*) and curbside
to 11 p.m. In the absence of any physical theory, butions are log-normal, we hypothesize that
sites (squares).
and given that the concentration
In(C) is proportional
to nwind.
(2)
To estimate background CO levels (weekdays only), an ANOVA framework that is based on equation (2) as well as our site classification scheme discussed models we tested apply to weekdays only and are given below.
ln( Cji = .s,+ a,[ln( V,,,fl - Ptl + In(c);
= S; + c+,,,d
aZlnwind
- P.21+ e
-
distri-
/+I + E
was used later. The
(3) (4)
403
Carbon monoxide in an urban area
where In(C), = the expected log-transformed value of the daily mean 8-h average CO concentration (in ppm) for site class i, [i = l-3 for Isolated Park, Urban Park and Trafficked sites, respectively; see text for details] = the effect of site group si = covariate constants ai I/l& = the highest average weekday traffic volume within 200 m of the site (vehicles per day, see Table 1) = the mean value of V,raffor all sites (vehicles per day) /Jl = the mean value of nwindfor all sites !J? E = the random error (N[O,(a)*])
3. RESULTS
3.1. Distinguishing characteristics of background sites One important phenomenon that distinguishes background sites from curbside (street) sites is short-term fluctuations in the CO concentration due to local source impacts. This phenomenon was examined at several of our background sites. One-minute average CO concentrations were recorded over a 4 to 8-h period on different days at the UW, GRL and ROS sites as well as at the ZA street site using our continuous Lear-Siegler monitor. To characterize the fluctuations in these short-term concentrations, we computed the geometric standard deviation (s,) of CO concentration for each non-overlapping, 5-min period within the overall sampling period. This resulted in 12 values each hour. The results are summarized in Table 2. As shown, sg was larger at the street sites than at the background sites, reflecting the fact that short term fluctuations at the street sites were more pronounced. In a separate experiment, the continuous CO analyzer also recorded I-hr averages at the UW site for 36 days during the study period. We compared the time of occurrence of the maximum hourly CO concentration during the 3 p.m. to 11 p.m. period on a given day at the UW site with the corresponding values at the ZA and NG street sites. At the street sites, the maximum hourly value usually occurred early in the 8-h period, coincident with peak traffic. At the UW background site, the maximum hourly value usually occurred later in the 8-h sampling period. Pair-wise Kolmogorov-Smirnov (K-S) twosample tests revealed that the distribution of peak times at the background site was significantly different from the distributions at both street sites @<0.03), whereas both street sites had similar distributions. In a third, separate experiment, a network of air samplers was employed near the UW site to directly observe the spatial gradients in 8-h average (3 p.m. to 11 p.m.) CO concentrations near a major roadway (46,000 vehicles per day; see Table 1). Near the UW site was a strong spatial gradient in CO concentration within 10 m of the roadway edge. Table 2. Observed short-term fluctuations at background and street sites Number of measurements (n)
Site Tra#icked
GRL ROS
uw Street
sites 2+
ROS ZA
background
Geometric standard deviation
sites
480 480 330
1.045 (0.004)*
480 240
1.243 1.353
1.044(0.005) 1.109 (0.008) (0.010) (0.016)
*Mean of 5 measurements; parentheses indicate standard error of the mean, see text for details. ‘Temporary site near ROS background site but located 2m from nearby roadway (19,000 vehicles per day; see Table 1 and Fig. 1 for site descriptions).
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Timothy Larson et al.
This can be contrasted to the noticeable lack of a spatial gradient at the site itself, which was only about 50 m from this roadway. In contrast to this localized gradient, there was a larger scale, spatial gradient near the major interstate highway (Interstate 5) that passes through the city. None of our background sites were located within 200 m of this major freeway. The two sites closest to the freeway, ROS and GRL, were sufficiently far away that they were not within the gradient shown here. Given the above results, we can characterize a background site as a location that does not show large fluctuations in I-min average CO concentrations, that is devoid of obvious spatial gradients in the 8-h average concentrations over this same time period, and that experiences peak hourly values relatively late in the 8-h period from 3 p.m. to I1 p.m. In contrast, street sites have the opposite features. We would also expect that background sites would also be spatially correlated with each other, but this is not necessarily a unique feature. The spatial correlations will be discussed later. 3.2. Eight hour average concentrations and site groupings Eight-hour average concentrations were simultaneously measured at all sites during the daily period from 3 p.m. to 11 p.m. on a regular basis from 25 January to 11 March 1993. Figure 2 summarizes the CO levels by sampling site. Shown are the median (50% fractile), upper and lower quartiles (25% and 75% fractiles), and minimum and maximum values for each site. The lowest measured 8-h concentration was 0.4 ppm observed at the DIS site; the highest was 4.2 ppm observed at the UW site. The overall average was 1.32 ppm CO for all background sites. The background sites can be grouped into three concentration levels (I,II,III), as summarized in Table 3. For comparison, we have included the corresponding 8-h average CO concentrations at the ZA and NG street sites operated by the WDOE. This grouping of background sites (I,II,III) was tested with a one-way ANOVA analysis on the log-transformed CO concentration values. The F- ratio for testing the hypothesis that the site groups’ means were significantly different was 22.8 (degrees of freedom (d.f.) = 6; 244). Figure 3 shows the estimated 95% confidence limits of the site mean values. The mean CO levels at the two street sites (group IV) were significantly higher than those of the group III sites (pcO.0001) 4.5
T
4 3.5 1
Maximum
f I$
3
{
2.5 --
-
75%
f
Median
-
25%
E g 0 :: ”
2--
Minimum
1.5 --
+
1 --
t 0.5 -0
1 I I
I
f
DIS
RBH
I
I
MLF
GRL
I I
ROS
i
UW
MAG
Fig. 2. 8-h average CO concentrations at seven “background” sites in North Seattle during January - March 1993.
405
Carbon monoxide in an urban area Table 3. Grouping of sites Sites*
Group IlO
1
@IS)
Group name
8-h mean CO concentration+
Isolated Park
0.70 (0.03)
II
(RBH, MAC)
Urban Park
1.04 (0.07)
III
(MLF, GRL, ROS, UW)
Trafficked
1.62 (0.11)
IV
(ZA, NG)
Street
2.73 (0.16)
*See Table I and Fig. I for site descriptions. ‘3 p.m. to I I p.m. PST; standard errors of the mean are in parentheses.
This grouping of sites, although it is based solely on CO concentrations, is nevertheless consistent with local traffic patterns near each site. Group I contained the site most isolated not only from local traffic but also from the rest of the city, whereas group III contained sites located relatively near trafficked roadways. Group II sites were relatively isolated from local traffic but are nonetheless nearer to more densely trafficked areas than the Group I site. All three groups of sites could be distinguished from the Group IV sites which are typical EPA microscale sites located a few meters from roadways and/or intersections. We will subsequently refer to these site groups as follows: Group I = “Isolated Park,” Group II = “Urban Park,” Group III = “Trafficked,” and Group IV = “Street” sites. The CO 8-h average cumulative density functions for all four groups of sites are shown in Fig. 4. Pair-wise, K-S two-sample tests were performed to determine whether the distributions were the same. The tests revealed that all frequency distributions were highly significantly different from each other (p
I i
DIS
RBH
MLF
QRL
ROS
VW
MAO
Fig. 3. 8-h means (and 95% confidence limits) for CO at seven “background” sites in North Seattle during January-March 1993.
406
Timothy Larson et al. 100
60
1
6o
i i 3
-
Street Sites
v
Trafficked Sites
-
Urban Park Sites
V
Isolated Park Site
40
20
0 0
1
2
3
4
5
6
CO @pm) Fig. 4. Cumulative distribution function of 8-h average CO concentration for each site group. Distributions are shown for the daily mean of all samples within a site group.
during the study period. From the number of samples in a given site group, we could compute the cumulative percentage corresponding to a given day’s CO value. For example, the second highest value out of 30 values for our Trafficked background sites was 3.11 ppm. This corresponds to the 95th percentile (100 [1 -0.5{(1/30) + (2/30)}] = 95). The log-normal distributions depicted by the solid lines in Fig. 5 can be mathematically described by the following relationship:
c = nzg(Sg)~ C = mB = % = z =
(5)
site group mean 8 h average CO concentration at a given percentile (ppm) geometric mean of a given site group (ppm) geometric standard deviation of a given site group number of standard deviations in the cumulative normal distribution corresponding to the given percentile (e.g.) z = 0 for 50th percentile; z = 1.96 for 95th percentile).
Table 4 lists the estimated parameter values for the log-normal distribution corresponding to each site group. The values for the two Street sites are listed individually. CO concentrations as a function of percentile and site group can be predicted with equation (5) and the parameter values in Table 4. For example, the 95th percentile value of CO at the Trafficked background sites is estimated as C = ( 1.69)(l.33)‘.g6 = 2.96 ppm. This predicted value is lower than the observed 95th percentile value of 3.11 because equation (5) does not perfectly describe the observed values (see Fig. 5). Despite the variability from day to day in CO concentrations, the three site groups have similar concentrations on any given day. Scatter plots showing the relationships between 8-h average site group means are shown in Fig. 6, together with the spatial correlation
407
Carbon monoxide in an urban area 99.9
99 95
p
se
If r 3
6o 50
z
3
20
5
1
0.1 0.3
0.6
1.1
1.7
25
3.5
4.75
6.4
CO(Ppm)
Fig. 5. Log-normal probability plot of daily group mean 8-h average CO concentrations windspeeds (< 1.4 m/s).
for day with low
coefficient for each pair of site groups. The Isolated Park site had the poorest correlation with any other site group (R2 was between 0.10 and 0.30). Best correlated among the background sites were the Urban Park sites and the Trafficked sites (R2 = 0.72). Street sites were also correlated with Trafficked sites (R2 = 0.64). 3.3. Background vs curbside concentrations The estimated ratios of 8-h average background concentrations to street concentrations are given in Table 5. On average, the CO concentrations at the Trafficked background sites were about one-half the corresponding values observed at the Street sites. The ratios reported in Table 5 are the mean values over all sampling days. The ratios associated with high CO levels at the Street sites were also examined. During our 46 day study period, the ratio of the daily mean CO level at all Trafficked sites relative to the concentration at the Zanadu site was 0.49 (SE = 0.08) for the 2 days with the highest 8-h average CO levels at the Zanadu site. The corresponding value for the highest days at the Northgate site was 0.44 (SE = 0.28). Table 4. Estimated parameters of the two-parameter log-normal distributions of the daily group mean of 8-h average CO concentrations (wind speed* cl.4 m/s) Parameter
m,
s,
Trafficked Urban Park Isolated Park
1.69 1.08 0.74
1.33 1.37 1.18
Street sites Zanadu (ZA) Northgate (NG)
2.84 2.62
1.68 1.35
Background sites
*8-h average wind speed measured at UW site.
408
Timothy Larson et al.
R2
=
.
0.47
n
l
n
n
.
Ft* = 0.64
,Q
n
&‘im 0.4
0.6
0.6
1
+
n
1
1.5
2
n
0
Urban Park Sites
lsolaled Park Site
.
n BmJ?*
n
0.5
m
+m
4
2
Trafficked
Sites
CO hwm)
Fig. 6. Scatter plots of daily mean 8-h average CO concentrations by site
group.
3.4. Model predictions vs measurements
The performance of each model is summarized in Table 6. Note that the R2 value refers to a prediction of in(C) rather than C. The model constants for equation (3) are shown in Table 7. Figure 7 shows a plot of the predicted versus observed values of the 8-h average CO concentration (3 p.m. - 11 p.m. on weekdays) at each site during the study period. The predictions were made with equation (3). The model residuals were lognormally distributed, as shown in Fig. 8, and therefore, increased with increasing CO concentration on a linear scale, as shown in Fig. 7. The models described above were modified to include the previous day’s CO concentration. This was done because of the temporal correlations we had observed in the data. However, the previous day’s concentration was not a significant predictor of the CO concentration and therefore this term was not included in the final models presented here. 4. DISCUSSION AND CONCLUSIONS
Consider the various components of the 8-h average CO concentration in an urban area. The measured concentration consists of (1) a local source contribution, (2) a local background contribution, (3) a “regional” background contribution, and (4) a global background contribution. We have already discussed the relative magnitude of the local source contribution relative to “background”, i.e. the total of the latter three components. Table 5. Ratio of background to curbside concentrations* Background site group lsolated Park Urban Park Trafficked
Ratio relative to ZA site 0.20 (o.ol)+* 0.30 (0.02) 0.48 (0.02)
Ratio relative to NC site 0.25 (0.02) 0.39 (0.02) 0.59 (0.03)
*8-h average background values from bag sampler; street site values from continuous monitors. **Value in parentheses is standard error of the mean.
Carbon monoxide in an urban area
409
Table 6. Model predictions vs observed values (weekdays only) Statistics*
Model
Equation (3) Equation (4)
R?
2
F-value
0.75 0.74
(0.226)? (0.230)’
135 170
*R is the correlation coefficient; ur residual variance; F-value is based upon the ANOVA model.
Table 7. Constants for ANOVA Model constants
Value a.344 0.0069 0.4218 0.0480 9.9794 0.1298 2.8297
Sl S? s1 a, CL1 a: CL>
A reasonable estimate of the magnitude mean value at the Isolated Park site. observed level at Trafficked background tions of high wind and low traffic. From MC)i
1
=
of the “regional” background contribution is the This site is, on average, about one-half of the sites (see Table 3). This is also true under condiequation (3) with nwlnd= 0 and Vtrar= 0, we have
s,- aIo(Llraf) -
(6)
a?(Pwind)
2
3
Predicted Values @pm) Fig. 7. Predicted vs observed 8-h average CO concentrations for all sites and weekdays (model is given by equation (3) in text).
Timothy Larson et al.
~____-------_---________--------______________..
99.9
I
99
I
I
/
I
I
I
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_____--
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0
0.2
0.4
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Model Residuals
Fig. 8. Log-normal probability plot of model residuals (model predictions are shown in Fig. 3).
For the Trafficked sites,
i = 3, In(C) = (0.4218 - 0.048(9.9794) - 0.1298(2.8297)} = -0.42, and therefore C = exp(-O.42) = 0.65 ppm. Park sites, i = 1 and 2, respectively, with the result that C = 0.30 and 0.43 ppm, respectively. The predicted value from the Isolated Park site is again about one-half of the value for the Trafficked sites. The predicted value of 0.30 ppm at the Isolated Park site can also be compared with the lowest value measured in this study, 0.4 ppm, and with the previously reported values of the northern hemispheric background CO level of 0.13 ppm (Parrish et al., 1991; Seiler et af,, 1976) in the winter. The estimated value at the Isolated Park site under conditions of high wind and no local traffic is about twice that of the northern hemispheric background level in the winter. The mean value at the Isolated Park site during this study was about 5 times the northern hemispheric background level. For the Isolated
Park and Urban
Carbon monoxide in an urban area
411
The siting criteria developed in this study can be used to site a background monitor in an urban area. These criteria are somewhat different than those first proposed by Ott (1977). He concluded that all background sites should be located in urban parks a minimum of 100 m from lightly traveled roadways. We have found that the urban park sampler should also be placed from 400 to 700 m away from roadways with traffic volumes as low as a 8000-10,000 vehicles per day. In addition, distance from the road is more important than variations in traffic volume on that road. Although highly trafficked background sites can be located relatively close to major roadways (30-200 m), once a site has been established, it should not experience the large fluctuations in short term (1-min average) CO concentrations that are found at nearroadway, EPA microscale sites. The average geometric standard deviation of the 1-min CO concentrations about the mean of non-overlapping 5-min periods is a simple and useful criterion for assessing the magnitude of these fluctuations and, therefore, the magnitude of local source contributions. The values shown in Table 2 should be applicable to any location in an urban area. We would further expect that the peak hour within the afternoon 8-hr sampling period will occur later in the period than it does at sites next to the road. Again, this simply reflects the relative importance of mobile source emission patterns and the relative unimportance of atmospheric transport phenomena at street sites in comparison to local background sites. Our site classification was based upon observed concentrations measured simultaneously at a number of background locations. What was most striking about these observations was the high degree of spatial correlation within a site group, even though sites within a given group were spaced relatively far apart. This result does not imply that there was one single background level over the entire north end of Seattle. The fact that the Isolated Park sites were located on both the east and the west sides of the study area demonstrated that this was not the case. This result also does not imply that the general background level over the north end of Seattle was slightly higher in the center of the study area (near the freeway) and lower at the edges (near the water). The fact that the UW site had some of the highest background values but was also near the eastern edge of the study area counters this concept. The only explanation consistent with our results is that each site must be classified according to its proximity to, and to a lesser degree the magnitude of, nearby traffic. This traffic “density” index places the Isolated Park sites on the edges of our study area because, in general, traffic was more densely spaced in the center of our study area than on the edges. Ott and Eliassen (1973) recognized the importance of local traffic effects in his pioneering work in this area. Our site classification criteria are consistent with this notion. Our review of previous research in this area uncovered no previous reports of the frequency distribution parameters at background sites. The log-normal distribution model, coupled with our site classification, provides a very good description of our measured background CO concentrations. Note that our measurements were made in the middle of winter during the most meteorologically stable period of the entire year. To use the lognormal model, one must understand that the percentiles chosen were for the winter period, in which the highest CO concentrations occur. Therefore, the worst case day in 20 winter days (95th percentile) may actually have represented the worst case day of that year (99.7th percentile). The winter period that we sampled appeared to be similar to the previous two winters in both the frequency of occurrence of stable days and the wind speed distribution at nearby monitoring sites. In this study, we chose our sampling sites to avoid contributions from local sources, specifically individual mobile sources on nearby roadways. The concentrations we observed were therefore consistently lower than simultaneous measurements taken at curbside sites located for regulatory purposes within a few meters of the roadway edge. These latter sites are heavily influenced by local sources. Our best estimates are that the local source contributions at the Zanadu and Northgate sites are between 40-50% of the total observed 8-h average CO concentration (see Table 5). The remaining CO comes from “background.” This observation implies that a 20% emission reduction on a
412
Timothy Larson et al.
localized scale, for example as a result of improved traffic flow at a given intersection, would result in only a 10% reduction in the CO concentration at that intersection. Further reductions would only be achieved if the emissions from all sources in the area were reduced. These results are consistent with the earlier findings of Perardi et al. (1984) who compared street measurements in San Jose, CA. with simultaneous measurements at nearby sites located several blocks away from major roads. He found that 75% of the observed CO was due to background sources. However, his observations were made in the late 1970s and early 1980s when CO concentrations were generally higher than they are now in most urban areas. Nevertheless, the fact that we observed a significant contribution from background should not be surprising in light of these earlier observations. Background CO levels for given sites and characteristic meteorology can be estimated by using our measurements directly, by using the log-normal parameterization of our measurements, or by using our ANOVA model that includes meteorological co-variates. We recommend the latter approach in order to include “worst case” meteorology into the background estimate and therefore to generalize our results to other locations within the urban area independent of meteorological variability from site to site. For example, one can specify nwind= 8 for use in “worst case” EIS project analyses. This corresponds to 8 out of a possible 8 h with stagnant conditions. For Trafficked sites, we can write equation (3) with n,,,ind= 8 as: In(C), = s3 + a,[ln( V,& - p,] + (r,[8 - pz]. Using the values listed in Table 7 for So, (Y,,p,, o2 and p2, we obtain the following simplified expression for Trafficked sites: background CO (ppm) = 1 .85( V,raf)0.048
(3a)
where VrraC= the highest average weekday traffic volume within 200 m of the receptor site (vehicles per day). For Urban Park sites, with nwind= 8, we ignore the small effect of traffic and use equation (4): In(C), = s2 + 02[8 - pd. Using the values listed in Table 7, we obtain the following simplified expression for “urban park” sites: background CO (ppm) = exp[0.0069 + 0.6711 = 2.0 ppm
(4a)
Equally important, the model also gives us error bounds on these estimates. The model (Equation 3) standard error is 0.226 ppm, and so the estimated background CO value, C, for Trafficked sites would be greater than exp[ln 0 - (z,&(O.226)] and less than exp[ln C3 - (z,_,,,)(O.226)]. For Urban Park sites, the model (equation 4) standard error is 0.230 ppm, and so the estimated background value would be greater than exp[ln CZ - (~,_~,~)(0.230)]and less than exp[ln C, - (z,_,,,)(O.230)]. If one chooses the 95% confidence limits, for example, then from the standard normal distribution, z,_,,,, = 1.95. The uncertainty quantified here reflects the variability not accounted for in the model estimate of background CO. The fact that background concentrations are an important part of any project level analysis requires that they be accurately assessed. If one wishes to directly measure the background level, we have provided objective criteria for siting a background monitor. In addition, we have provided objective criteria for determining whether or not it is properly sited. Specifically, we can characterize a background site as a location that does not show large fluctuations in I-min average CO concentrations, that is devoid of obvious spatial gradients in 8-h average concentrations, and that experiences peak hourly values relatively late in the period from 3 p.m. to 11 p.m. Second, we have provided a site classification framework and corresponding log-normal distribution parameters that allow direct use of our observations. Finally, we have proposed a model that describes the influence of meteorology on the background levels in an urban area and that can be used to assess the relative importance of local versus more regional background levels and therefore help to generalize our observations.
Carbon monoxide in an urban area
413
4. I. Study limitations Our measurements were made in an urban area that is elevated with respect to the surrounding water (see Fig. 1). In contrast, some urban areas are located at the bottom of a valley with surrounding, elevated terrain. Under stable nighttime conditions, the wind flow patterns in our study area may be very different from those in the valley. Valley drainage winds would be expected to “pool” over the urban area, possibly creating very different background CO concentrations and different spatial patterns of the background concentrations. It is unclear whether the siting criteria we used in this study would apply in the valley case. This should be examined experimentally. The site locations in this study were chosen to avoid significant impacts from stationary sources, most notably residential wood heating devices. The USEPA emission factors for conventional wood stoves and fireplaces reports a ratio of CO to fine particle emissions of between 6.1 to 1 and 6.2 to 1. Based upon measurements of PM,,, at the Lake Forest Park monitor operated by the Puget Sound Air Pollution Control Agency, maximum fine particle wood smoke levels in the North Seattle area do not exceed 100 E.cg/m’ on a 24 h basis. This implies a maximum CO concentration from wood burning of about 600 pglrn’ or 0.5 ppm on a 24 h basis. However, the wood burning peak impacts occur between 7 p.m. and 7 a.m., whereas the peak evening traffic hours are between 3 p.m. and 11 p.m. In addition, the maximum wood smoke impacts occur at the bottom of creek valleys which occupy a relatively small fraction of the urban area. Therefore this 0.5 ppm level is an upper bound estimate of the background CO contribution from wood burning in these valleys. However, this estimate is qualified by the fact that we did not make measurements of CO in these wood smoke impacted areas. project was funded by the Washington State Department of Transportation, Project T9233. Task 37.
Acknowledement-This
Research
REFERENCES Chang T. Y., Norbeck J. M. and Weinstock B. (1980) Ambient temperature effect of urban CO air quality. Atmos.
Environ.
14, 603.
Claggett M., Shrock J. and Nell K. E. (1981) Carbon monoxide near an urban intersection. Atmos. Environ. 15, 1633-1642.
Cooper C. D. (1987) Indirect source impact analysis-carbon monoxide modeling JAPCA 37, 1308-I 3 13. Gilbert R. 0. (1987) Statistical Methods for Environmental Pollution Monitoring. pp. 40-42. Van Nostrand Reinhold, New York. Green N. J., Bullin J. A. and Polasek J. C. (1979) Dispersion of carbon monoxide from roadways at low wind speeds. JAPCA 29, 1057-1061. Ireson R. (1993) The project-system dilemma in conformity analysis-integrating microscale and areawide emission and dispersion modeling Paper No. 93-TP-53.08. Proceedings of the A WMA Annual Meeting, Denver, CO, June. Liu J. J. and Jeng F. T. (1993) The microscale air quality models of near road area in urban [sic] for exposure assessment. Paper No. 93-MP-I .Ol. Proceedings of the A WMA Annual Meefing, Denver, CO, June. Matzoros A. and Van Vliet D. (1992) A model of air pollution from road traffic. based on the characteristics of interrupted flow and junction control: Part II-Model results. Transpn. Rex-A 26, 33 l-3.55. Ott W.R. and Eliassen R. (1973) A survey technique for determining the representativeness of urban air monitoring stations with respect to carbon monoxide. JAPCA 23, 685-690. Ott W.R. (1977) Development of criteria for siting air monitoring stations. JAPCA 27, 543-547. Parrish D. D., Trainer M., Buhr M. P., Watkins B. A. and Fehsenfeld F. C. (1991) Carbon monoxide concentrations and their relation to concentrations of total reactive oxidized nitrogen at two rural U.S. sites. J. Geophys. Res. %, 9309-9320. Perardi T. E., DeMandel R. E., Duker D. C. and Siu W. (1984) Carbon monoxide: hotspots, coldspots and implications for attainment planning. Paper No. 84-118.4 Proceedings of fhe APCA Annual Meeting, San Francisco, CA, June. Petsios S., Nudelman H. and Soden, J. (1993) The CAL3QHC (version 2) mobile source air quality dispersion model: critical input parameters. Paper No. 93-TP-53.05 Proceedings of the A WMA Annual Meefing, Denver. CO, June. Schewe G. J., DiGirolamo V. and Adams R. (1990) Guideline for Modeling Carbon Monoxide from Roadway Intersections, U.S. EPA, Office of Air Quality Planning and Standards, RTP, NC (report prepared by PEI Associates EPA Contract No. 68-024394, Work Assignment No. 45, PN-3759-45). October. Seiler W.. Geihl H. and Ellis H. (1976) A method for monitoring of background CO and first results of continuous monitoring of CO concentrations on Mauna Loa Observatory. In Special Environmental Reporr No. IO pp. 31-39. World Meteorological Organization, Geneva. U.S. EPA Office of Air Quality Planning and Standards (1992) Guideline,for Modeling Carbon Monoxide from Roadway Intersections Report EPA_454/R-92-005. November.
Pergamon
PII: so%5_8564(%)oooo7-9
“LOCAL BACKGROUND” LEVELS OF CARBON MONOXIDE AN URBAN AREA TIMOTHY
LARSON*, LARS MOSEHOLM,
IN
DAVID SLATER and CYRA CAIN
University of Washington, Environmental Engineering and Science Program, Department of Civil and Environmental Engineering, Box 352700 Seattle, WA 98195, U.S.A. (Received
1 I August
1994; in
revised form
25 January
1996)
Abstract-The objective of this study was to obtain a better understanding of carbon monoxide (CO) concentrations immediately upwind of urban roadways, the “local background” values, and how these concentrations depend upon the surrounding traffic and the general meteorology. Measurements were made at seven sites in Seattle, WA during the winter of 1993. Local background CO concentrations were characterized by an absence of short term fluctuations, a steady buildup during the 3 p.m. to 11 p.m. period, and a lack of spatial gradients in the 8-h average values. Distinctly different log-normal distributions of the 8-h averages were observed for “trafficked” sites versus “urban park” sites, with mean values of I .6 and 1.O ppm respectively. A simple regression model was developed to predict the local background CO that includes distance from roadway. average daily traffic of nearby roadways, and the frequency of occurrence of low wind speeds (R’ = 0.74; F = 170). Copyright 0 1996 Elsevier Science Ltd I. INTRODUCTION
Carbon monoxide (CO) concentrations in ambient air have decreased dramatically in the last 15 years as a result of reduced emissions from motor vehicles. Nevertheless, when a new roadway project is proposed its environmental impacts need to be accurately assessed. These incremental impacts are assessed by adding the estimated incremental changes to the area’s “local background” concentrations. This “background” concentration is not the lowest background level upwind of the urban area but, rather, a local area reference concentration that is not directly attributable to the emissions from any one source, roadway, or intersection. A number of approaches have been reported in the literature to estimate these “local background” levels. Cooper (1987) has published a good general discussion on this subject. One approach is to directly measure the CO levels at an appropriate location near the site in question (Ott & Eliassen, 1973; Ott, 1977). However, no consensus exists as to the acceptable distance that a background monitor should be placed away from the roadway or the acceptable magnitude of traffic on the roadway nearest to the background site (Clagett et al., 1981; Green et al., 1979; Chang et al., 1980). In addition, there are no criteria to confirm whether a given location is indeed a local background site. Finally, there is no specific guidance on the number of samples that are needed per site or the meteorological conditions that are most appropriate for sampling. Another approach to estimate the background CO concentrations is to use a meteorological dispersion model (Matzoros & Van Vliet, 1992; Petsios et al., 1993; Liu & Jeng, 1993; Ireson, 1993). The uncertainties in these models include the spatial distribution of emissions, the effects of complex terrain (hills, valleys, and surrounding buildings), the effect of very low wind speeds that cannot be measured with standard wind sensors, and the magnitude of vertical mixing under stable conditions. However, the most common practice is to use one of several default values suggested by regulatory agencies in the absence of any of the above information (US EPA, 1992; Schewe et al., 1990). This paper discusses the relationships between measured background CO concentrations and site location, surrounding traffic, and meteorology; and attempts to identify those traffic and location characteristics that best predict the “local background” CO concentrations. *Author for correspondence. 399
400
Timothy Larson et ul. 2. METHODS
2.1. Experimental
Our approach was to select background sampling locations, collect and measure CO concentrations, and summarize the results in a way that is useful to those responsible for assessing the environmental impacts of proposed roadway projects. We chose to sample during the most meteorologically stable period of the winter to accurately reflect “worst case” meteorological conditions. We also chose to sample from 3 p.m. to 11 p.m. because this period includes not only high traffic flows but also stable conditions after sunset. In addition, we established a meteorological monitoring site within the study area to measure wind speeds typical of urban intersections rather than open sites. Finally, the information on CO and meteorology was combined with traffic information into a statistical mode1 describing background CO concentrations. Sampling locations were chosen on the basis of combined distance/traffic criteria that were based on the field work of Ott and co-workers (Ott & Eliassen, 1973; Ott, 1977) and of Perardi et al. (1984) and the theoretical results of Matzoros and Van Vliet (1992). We chose “trafficked” sites that were: (a) at least 30 m from any roadway; (b) 30-100 m from roads with at least 8000-10,000 vehicles per day; and (c) at least 200 m from major freeways. With regard to criterion (b), at one of our sites we monitored 50 m away from a road with an average of 46,000 vehicles per day. Although criterion (c) does not specify the magnitude of traffic on a “major freeway,” the one major freeway through North Seattle, Interstate 5, has a volume of 250,000 vehicles per day. Conservatively, we suggest being at least 200 m from any freeway with more than 100,000 vehicles per day. In addition, “less trafficked” sites were located within urban parks a distance of 40&700 m from similarly trafficked roads. All sites were a minimum of 70 m from major intersections. The selected sites are listed in Table 1, and their locations are indicated in Fig. 1. A bag sampler was utilized at all sites except those having continuous monitors operated by the Washington State Department of Ecology (WDOE). Air samples were collected in the field at a constant flow rate using the bag sampler. These collected samples were analyzed in the lab with a Lear-Siegler model ML9830 non-dispersive infrared photometer. Zero and span gases were used for daily calibration of the instrument, along with a third, intermediate-value gas used as a precision check. Instrument accuracy was 4.6% and instrument precision was 0.4%. A wind sensor (Weatherpak-100 TM Automatic Weather Station, Coastal Climate Company, Seattle, Wash.) was placed 3 m above the roof of Wilcox Hall on the UW main campus. The Weatherpak was programmed to store 15-min averages, on the hour, of air temperature, wind speed, wind direction, and standard deviation of the wind direction. To assess the adequacy of our network to estimate the site group mean values over time and space, the spatial and temporal correlations characteristic of CO background concentrations had to be taken into account. To achieve a given accuracy when estimating the regional mean, Gilbert (1987) developed an expression for the relationship between the required number, n, of samples taken at each site, as well as the required number of sites, nsr for a pre-specified level of accuracy: where
n = (l/n,) (2f.~/d)~[l + 2r,] [l + r&n, - l)]
(1)
&= the residual variance, i.e. the variance that is not associated with between-day and between-site variability (= [0.325 ppm12, as estimated by ANOVA) d = the uncertainty of the sample mean (absolute error) in ppm units at the 95% confidence level r, = the auto-regression parameter assuming an AR- 1 process (= 0.56 as an average for our data). r, = the average spatial correlation coefficient between sites (= 0.8 as an average for our data). Our best estimates of (r?, r, and rc are [0.325 ppm]‘, 0.56, and 0.8 (as an average over all sites) respectively, based upon the results of a pilot study in the region as well as the final results of this study. Given these values, equation (1) becomes
401
Carbon monoxide in an urban area Table I. Local traffic at sampling sites Site
Site code
Distance to roads (m)
Roadway traffic*
Background sites’ Isolated park
Discovery Park Richmond Beach Pk.
DIS
625 650
5000
7000
RBH
320 460
1500: 10,000~
MAC
730
14,000
Roosevelt
ROS
30 75
19,oOQ 10,000
Green Lake
GRL
Urban park
Magnuson Park TraJicked
Maple Leaf
Univ. of Wash.
MLF
uw
210
10,000 250,000
90 160 180
12,000 9000
loo
50
8ooo
230
46,000 15.000
2 30
25.000
2 300
30,000 250,000
Street
Zanadurj Northgate Mall8
ZA
NC
8000
“1991 average weekday values from the City of Seattle Traffic Engineering Division. ‘See text for further description of the site categories. :I992 average weekday values from King County Transportation Planning Section. $Air monitoring sites operated by Washington State Department of Ecology for regulatory purposes.
n =
(0.9/&) (l/n,) [l + 0.8(n, - l)]
(la)
To estimate the CO 8-h mean with an absolute error of d = 0.2 ppm, one would need to sample for 20 days at three sites, or 19 days at six sites. More sites give only a slightly better representation of the region mean because of the high spatial correlation between sites. With four sites, 30 days of sampling corresponds to an estimated uncertainty of 0.16 ppm. If we had wanted to reduce this uncertainty in the sample mean by a factor or two, we would have had to sample for an additional 90 days under these winter conditions, clearly an impractical task with diminishing returns. 2.2. Theoretical The conceptual models that were considered as a part of this study included a box model and a line source model. However, both of these conceptual models required the use of site specific turbulence parameters which were impossible to deduce during the most stagnant meteorological periods. These were also the periods of highest CO. Therefore, a “stagnation” model was formulated. The concept behind this model is that background CO concentrations increase only during periods of very calm winds, when existing atmospheric turbulence theory does not apply. In our analysis, turbulence theory cannot be used to describe vertical wind speed and temperature profiles when the 15 m average wind speed is less than or equal to 0.2 m/s. A simple index of this phenomena is nwindrthe number of hours when wind speed is less than or equal to 0.2 m/s during 3 p.m.
402
Timothy
Fig.
I. Location
of background
Larson
sampling
et al.
sites (*) and curbside
to 11 p.m. In the absence of any physical theory, butions are log-normal, we hypothesize that
sites (squares).
and given that the concentration
In(C) is proportional
to nwind.
(2)
To estimate background CO levels (weekdays only), an ANOVA framework that is based on equation (2) as well as our site classification scheme discussed models we tested apply to weekdays only and are given below.
ln( Cji = .s,+ a,[ln( V,,,fl - Ptl + In(c);
= S; + c+,,,d
aZlnwind
- P.21+ e
-
distri-
/+I + E
was used later. The
(3) (4)
403
Carbon monoxide in an urban area
where In(C), = the expected log-transformed value of the daily mean 8-h average CO concentration (in ppm) for site class i, [i = l-3 for Isolated Park, Urban Park and Trafficked sites, respectively; see text for details] = the effect of site group si = covariate constants ai I/l& = the highest average weekday traffic volume within 200 m of the site (vehicles per day, see Table 1) = the mean value of V,raffor all sites (vehicles per day) /Jl = the mean value of nwindfor all sites !J? E = the random error (N[O,(a)*])
3. RESULTS
3.1. Distinguishing characteristics of background sites One important phenomenon that distinguishes background sites from curbside (street) sites is short-term fluctuations in the CO concentration due to local source impacts. This phenomenon was examined at several of our background sites. One-minute average CO concentrations were recorded over a 4 to 8-h period on different days at the UW, GRL and ROS sites as well as at the ZA street site using our continuous Lear-Siegler monitor. To characterize the fluctuations in these short-term concentrations, we computed the geometric standard deviation (s,) of CO concentration for each non-overlapping, 5-min period within the overall sampling period. This resulted in 12 values each hour. The results are summarized in Table 2. As shown, sg was larger at the street sites than at the background sites, reflecting the fact that short term fluctuations at the street sites were more pronounced. In a separate experiment, the continuous CO analyzer also recorded I-hr averages at the UW site for 36 days during the study period. We compared the time of occurrence of the maximum hourly CO concentration during the 3 p.m. to 11 p.m. period on a given day at the UW site with the corresponding values at the ZA and NG street sites. At the street sites, the maximum hourly value usually occurred early in the 8-h period, coincident with peak traffic. At the UW background site, the maximum hourly value usually occurred later in the 8-h sampling period. Pair-wise Kolmogorov-Smirnov (K-S) twosample tests revealed that the distribution of peak times at the background site was significantly different from the distributions at both street sites @<0.03), whereas both street sites had similar distributions. In a third, separate experiment, a network of air samplers was employed near the UW site to directly observe the spatial gradients in 8-h average (3 p.m. to 11 p.m.) CO concentrations near a major roadway (46,000 vehicles per day; see Table 1). Near the UW site was a strong spatial gradient in CO concentration within 10 m of the roadway edge. Table 2. Observed short-term fluctuations at background and street sites Number of measurements (n)
Site Tra#icked
GRL ROS
uw Street
sites 2+
ROS ZA
background
Geometric standard deviation
sites
480 480 330
1.045 (0.004)*
480 240
1.243 1.353
1.044(0.005) 1.109 (0.008) (0.010) (0.016)
*Mean of 5 measurements; parentheses indicate standard error of the mean, see text for details. ‘Temporary site near ROS background site but located 2m from nearby roadway (19,000 vehicles per day; see Table 1 and Fig. 1 for site descriptions).
404
Timothy Larson et al.
This can be contrasted to the noticeable lack of a spatial gradient at the site itself, which was only about 50 m from this roadway. In contrast to this localized gradient, there was a larger scale, spatial gradient near the major interstate highway (Interstate 5) that passes through the city. None of our background sites were located within 200 m of this major freeway. The two sites closest to the freeway, ROS and GRL, were sufficiently far away that they were not within the gradient shown here. Given the above results, we can characterize a background site as a location that does not show large fluctuations in I-min average CO concentrations, that is devoid of obvious spatial gradients in the 8-h average concentrations over this same time period, and that experiences peak hourly values relatively late in the 8-h period from 3 p.m. to I1 p.m. In contrast, street sites have the opposite features. We would also expect that background sites would also be spatially correlated with each other, but this is not necessarily a unique feature. The spatial correlations will be discussed later. 3.2. Eight hour average concentrations and site groupings Eight-hour average concentrations were simultaneously measured at all sites during the daily period from 3 p.m. to 11 p.m. on a regular basis from 25 January to 11 March 1993. Figure 2 summarizes the CO levels by sampling site. Shown are the median (50% fractile), upper and lower quartiles (25% and 75% fractiles), and minimum and maximum values for each site. The lowest measured 8-h concentration was 0.4 ppm observed at the DIS site; the highest was 4.2 ppm observed at the UW site. The overall average was 1.32 ppm CO for all background sites. The background sites can be grouped into three concentration levels (I,II,III), as summarized in Table 3. For comparison, we have included the corresponding 8-h average CO concentrations at the ZA and NG street sites operated by the WDOE. This grouping of background sites (I,II,III) was tested with a one-way ANOVA analysis on the log-transformed CO concentration values. The F- ratio for testing the hypothesis that the site groups’ means were significantly different was 22.8 (degrees of freedom (d.f.) = 6; 244). Figure 3 shows the estimated 95% confidence limits of the site mean values. The mean CO levels at the two street sites (group IV) were significantly higher than those of the group III sites (pcO.0001) 4.5
T
4 3.5 1
Maximum
f I$
3
{
2.5 --
-
75%
f
Median
-
25%
E g 0 :: ”
2--
Minimum
1.5 --
+
1 --
t 0.5 -0
1 I I
I
f
DIS
RBH
I
I
MLF
GRL
I I
ROS
i
UW
MAG
Fig. 2. 8-h average CO concentrations at seven “background” sites in North Seattle during January - March 1993.
405
Carbon monoxide in an urban area Table 3. Grouping of sites Sites*
Group IlO
1
@IS)
Group name
8-h mean CO concentration+
Isolated Park
0.70 (0.03)
II
(RBH, MAC)
Urban Park
1.04 (0.07)
III
(MLF, GRL, ROS, UW)
Trafficked
1.62 (0.11)
IV
(ZA, NG)
Street
2.73 (0.16)
*See Table I and Fig. I for site descriptions. ‘3 p.m. to I I p.m. PST; standard errors of the mean are in parentheses.
This grouping of sites, although it is based solely on CO concentrations, is nevertheless consistent with local traffic patterns near each site. Group I contained the site most isolated not only from local traffic but also from the rest of the city, whereas group III contained sites located relatively near trafficked roadways. Group II sites were relatively isolated from local traffic but are nonetheless nearer to more densely trafficked areas than the Group I site. All three groups of sites could be distinguished from the Group IV sites which are typical EPA microscale sites located a few meters from roadways and/or intersections. We will subsequently refer to these site groups as follows: Group I = “Isolated Park,” Group II = “Urban Park,” Group III = “Trafficked,” and Group IV = “Street” sites. The CO 8-h average cumulative density functions for all four groups of sites are shown in Fig. 4. Pair-wise, K-S two-sample tests were performed to determine whether the distributions were the same. The tests revealed that all frequency distributions were highly significantly different from each other (p
I i
DIS
RBH
MLF
QRL
ROS
VW
MAO
Fig. 3. 8-h means (and 95% confidence limits) for CO at seven “background” sites in North Seattle during January-March 1993.
406
Timothy Larson et al. 100
60
1
6o
i i 3
-
Street Sites
v
Trafficked Sites
-
Urban Park Sites
V
Isolated Park Site
40
20
0 0
1
2
3
4
5
6
CO @pm) Fig. 4. Cumulative distribution function of 8-h average CO concentration for each site group. Distributions are shown for the daily mean of all samples within a site group.
during the study period. From the number of samples in a given site group, we could compute the cumulative percentage corresponding to a given day’s CO value. For example, the second highest value out of 30 values for our Trafficked background sites was 3.11 ppm. This corresponds to the 95th percentile (100 [1 -0.5{(1/30) + (2/30)}] = 95). The log-normal distributions depicted by the solid lines in Fig. 5 can be mathematically described by the following relationship:
c = nzg(Sg)~ C = mB = % = z =
(5)
site group mean 8 h average CO concentration at a given percentile (ppm) geometric mean of a given site group (ppm) geometric standard deviation of a given site group number of standard deviations in the cumulative normal distribution corresponding to the given percentile (e.g.) z = 0 for 50th percentile; z = 1.96 for 95th percentile).
Table 4 lists the estimated parameter values for the log-normal distribution corresponding to each site group. The values for the two Street sites are listed individually. CO concentrations as a function of percentile and site group can be predicted with equation (5) and the parameter values in Table 4. For example, the 95th percentile value of CO at the Trafficked background sites is estimated as C = ( 1.69)(l.33)‘.g6 = 2.96 ppm. This predicted value is lower than the observed 95th percentile value of 3.11 because equation (5) does not perfectly describe the observed values (see Fig. 5). Despite the variability from day to day in CO concentrations, the three site groups have similar concentrations on any given day. Scatter plots showing the relationships between 8-h average site group means are shown in Fig. 6, together with the spatial correlation
407
Carbon monoxide in an urban area 99.9
99 95
p
se
If r 3
6o 50
z
3
20
5
1
0.1 0.3
0.6
1.1
1.7
25
3.5
4.75
6.4
CO(Ppm)
Fig. 5. Log-normal probability plot of daily group mean 8-h average CO concentrations windspeeds (< 1.4 m/s).
for day with low
coefficient for each pair of site groups. The Isolated Park site had the poorest correlation with any other site group (R2 was between 0.10 and 0.30). Best correlated among the background sites were the Urban Park sites and the Trafficked sites (R2 = 0.72). Street sites were also correlated with Trafficked sites (R2 = 0.64). 3.3. Background vs curbside concentrations The estimated ratios of 8-h average background concentrations to street concentrations are given in Table 5. On average, the CO concentrations at the Trafficked background sites were about one-half the corresponding values observed at the Street sites. The ratios reported in Table 5 are the mean values over all sampling days. The ratios associated with high CO levels at the Street sites were also examined. During our 46 day study period, the ratio of the daily mean CO level at all Trafficked sites relative to the concentration at the Zanadu site was 0.49 (SE = 0.08) for the 2 days with the highest 8-h average CO levels at the Zanadu site. The corresponding value for the highest days at the Northgate site was 0.44 (SE = 0.28). Table 4. Estimated parameters of the two-parameter log-normal distributions of the daily group mean of 8-h average CO concentrations (wind speed* cl.4 m/s) Parameter
m,
s,
Trafficked Urban Park Isolated Park
1.69 1.08 0.74
1.33 1.37 1.18
Street sites Zanadu (ZA) Northgate (NG)
2.84 2.62
1.68 1.35
Background sites
*8-h average wind speed measured at UW site.
408
Timothy Larson et al.
R2
=
.
0.47
n
l
n
n
.
Ft* = 0.64
,Q
n
&‘im 0.4
0.6
0.6
1
+
n
1
1.5
2
n
0
Urban Park Sites
lsolaled Park Site
.
n BmJ?*
n
0.5
m
+m
4
2
Trafficked
Sites
CO hwm)
Fig. 6. Scatter plots of daily mean 8-h average CO concentrations by site
group.
3.4. Model predictions vs measurements
The performance of each model is summarized in Table 6. Note that the R2 value refers to a prediction of in(C) rather than C. The model constants for equation (3) are shown in Table 7. Figure 7 shows a plot of the predicted versus observed values of the 8-h average CO concentration (3 p.m. - 11 p.m. on weekdays) at each site during the study period. The predictions were made with equation (3). The model residuals were lognormally distributed, as shown in Fig. 8, and therefore, increased with increasing CO concentration on a linear scale, as shown in Fig. 7. The models described above were modified to include the previous day’s CO concentration. This was done because of the temporal correlations we had observed in the data. However, the previous day’s concentration was not a significant predictor of the CO concentration and therefore this term was not included in the final models presented here. 4. DISCUSSION AND CONCLUSIONS
Consider the various components of the 8-h average CO concentration in an urban area. The measured concentration consists of (1) a local source contribution, (2) a local background contribution, (3) a “regional” background contribution, and (4) a global background contribution. We have already discussed the relative magnitude of the local source contribution relative to “background”, i.e. the total of the latter three components. Table 5. Ratio of background to curbside concentrations* Background site group lsolated Park Urban Park Trafficked
Ratio relative to ZA site 0.20 (o.ol)+* 0.30 (0.02) 0.48 (0.02)
Ratio relative to NC site 0.25 (0.02) 0.39 (0.02) 0.59 (0.03)
*8-h average background values from bag sampler; street site values from continuous monitors. **Value in parentheses is standard error of the mean.
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Table 6. Model predictions vs observed values (weekdays only) Statistics*
Model
Equation (3) Equation (4)
R?
2
F-value
0.75 0.74
(0.226)? (0.230)’
135 170
*R is the correlation coefficient; ur residual variance; F-value is based upon the ANOVA model.
Table 7. Constants for ANOVA Model constants
Value a.344 0.0069 0.4218 0.0480 9.9794 0.1298 2.8297
Sl S? s1 a, CL1 a: CL>
A reasonable estimate of the magnitude mean value at the Isolated Park site. observed level at Trafficked background tions of high wind and low traffic. From MC)i
1
=
of the “regional” background contribution is the This site is, on average, about one-half of the sites (see Table 3). This is also true under condiequation (3) with nwlnd= 0 and Vtrar= 0, we have
s,- aIo(Llraf) -
(6)
a?(Pwind)
2
3
Predicted Values @pm) Fig. 7. Predicted vs observed 8-h average CO concentrations for all sites and weekdays (model is given by equation (3) in text).
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Fig. 8. Log-normal probability plot of model residuals (model predictions are shown in Fig. 3).
For the Trafficked sites,
i = 3, In(C) = (0.4218 - 0.048(9.9794) - 0.1298(2.8297)} = -0.42, and therefore C = exp(-O.42) = 0.65 ppm. Park sites, i = 1 and 2, respectively, with the result that C = 0.30 and 0.43 ppm, respectively. The predicted value from the Isolated Park site is again about one-half of the value for the Trafficked sites. The predicted value of 0.30 ppm at the Isolated Park site can also be compared with the lowest value measured in this study, 0.4 ppm, and with the previously reported values of the northern hemispheric background CO level of 0.13 ppm (Parrish et al., 1991; Seiler et af,, 1976) in the winter. The estimated value at the Isolated Park site under conditions of high wind and no local traffic is about twice that of the northern hemispheric background level in the winter. The mean value at the Isolated Park site during this study was about 5 times the northern hemispheric background level. For the Isolated
Park and Urban
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The siting criteria developed in this study can be used to site a background monitor in an urban area. These criteria are somewhat different than those first proposed by Ott (1977). He concluded that all background sites should be located in urban parks a minimum of 100 m from lightly traveled roadways. We have found that the urban park sampler should also be placed from 400 to 700 m away from roadways with traffic volumes as low as a 8000-10,000 vehicles per day. In addition, distance from the road is more important than variations in traffic volume on that road. Although highly trafficked background sites can be located relatively close to major roadways (30-200 m), once a site has been established, it should not experience the large fluctuations in short term (1-min average) CO concentrations that are found at nearroadway, EPA microscale sites. The average geometric standard deviation of the 1-min CO concentrations about the mean of non-overlapping 5-min periods is a simple and useful criterion for assessing the magnitude of these fluctuations and, therefore, the magnitude of local source contributions. The values shown in Table 2 should be applicable to any location in an urban area. We would further expect that the peak hour within the afternoon 8-hr sampling period will occur later in the period than it does at sites next to the road. Again, this simply reflects the relative importance of mobile source emission patterns and the relative unimportance of atmospheric transport phenomena at street sites in comparison to local background sites. Our site classification was based upon observed concentrations measured simultaneously at a number of background locations. What was most striking about these observations was the high degree of spatial correlation within a site group, even though sites within a given group were spaced relatively far apart. This result does not imply that there was one single background level over the entire north end of Seattle. The fact that the Isolated Park sites were located on both the east and the west sides of the study area demonstrated that this was not the case. This result also does not imply that the general background level over the north end of Seattle was slightly higher in the center of the study area (near the freeway) and lower at the edges (near the water). The fact that the UW site had some of the highest background values but was also near the eastern edge of the study area counters this concept. The only explanation consistent with our results is that each site must be classified according to its proximity to, and to a lesser degree the magnitude of, nearby traffic. This traffic “density” index places the Isolated Park sites on the edges of our study area because, in general, traffic was more densely spaced in the center of our study area than on the edges. Ott and Eliassen (1973) recognized the importance of local traffic effects in his pioneering work in this area. Our site classification criteria are consistent with this notion. Our review of previous research in this area uncovered no previous reports of the frequency distribution parameters at background sites. The log-normal distribution model, coupled with our site classification, provides a very good description of our measured background CO concentrations. Note that our measurements were made in the middle of winter during the most meteorologically stable period of the entire year. To use the lognormal model, one must understand that the percentiles chosen were for the winter period, in which the highest CO concentrations occur. Therefore, the worst case day in 20 winter days (95th percentile) may actually have represented the worst case day of that year (99.7th percentile). The winter period that we sampled appeared to be similar to the previous two winters in both the frequency of occurrence of stable days and the wind speed distribution at nearby monitoring sites. In this study, we chose our sampling sites to avoid contributions from local sources, specifically individual mobile sources on nearby roadways. The concentrations we observed were therefore consistently lower than simultaneous measurements taken at curbside sites located for regulatory purposes within a few meters of the roadway edge. These latter sites are heavily influenced by local sources. Our best estimates are that the local source contributions at the Zanadu and Northgate sites are between 40-50% of the total observed 8-h average CO concentration (see Table 5). The remaining CO comes from “background.” This observation implies that a 20% emission reduction on a
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Timothy Larson et al.
localized scale, for example as a result of improved traffic flow at a given intersection, would result in only a 10% reduction in the CO concentration at that intersection. Further reductions would only be achieved if the emissions from all sources in the area were reduced. These results are consistent with the earlier findings of Perardi et al. (1984) who compared street measurements in San Jose, CA. with simultaneous measurements at nearby sites located several blocks away from major roads. He found that 75% of the observed CO was due to background sources. However, his observations were made in the late 1970s and early 1980s when CO concentrations were generally higher than they are now in most urban areas. Nevertheless, the fact that we observed a significant contribution from background should not be surprising in light of these earlier observations. Background CO levels for given sites and characteristic meteorology can be estimated by using our measurements directly, by using the log-normal parameterization of our measurements, or by using our ANOVA model that includes meteorological co-variates. We recommend the latter approach in order to include “worst case” meteorology into the background estimate and therefore to generalize our results to other locations within the urban area independent of meteorological variability from site to site. For example, one can specify nwind= 8 for use in “worst case” EIS project analyses. This corresponds to 8 out of a possible 8 h with stagnant conditions. For Trafficked sites, we can write equation (3) with n,,,ind= 8 as: In(C), = s3 + a,[ln( V,& - p,] + (r,[8 - pz]. Using the values listed in Table 7 for So, (Y,,p,, o2 and p2, we obtain the following simplified expression for Trafficked sites: background CO (ppm) = 1 .85( V,raf)0.048
(3a)
where VrraC= the highest average weekday traffic volume within 200 m of the receptor site (vehicles per day). For Urban Park sites, with nwind= 8, we ignore the small effect of traffic and use equation (4): In(C), = s2 + 02[8 - pd. Using the values listed in Table 7, we obtain the following simplified expression for “urban park” sites: background CO (ppm) = exp[0.0069 + 0.6711 = 2.0 ppm
(4a)
Equally important, the model also gives us error bounds on these estimates. The model (Equation 3) standard error is 0.226 ppm, and so the estimated background CO value, C, for Trafficked sites would be greater than exp[ln 0 - (z,&(O.226)] and less than exp[ln C3 - (z,_,,,)(O.226)]. For Urban Park sites, the model (equation 4) standard error is 0.230 ppm, and so the estimated background value would be greater than exp[ln CZ - (~,_~,~)(0.230)]and less than exp[ln C, - (z,_,,,)(O.230)]. If one chooses the 95% confidence limits, for example, then from the standard normal distribution, z,_,,,, = 1.95. The uncertainty quantified here reflects the variability not accounted for in the model estimate of background CO. The fact that background concentrations are an important part of any project level analysis requires that they be accurately assessed. If one wishes to directly measure the background level, we have provided objective criteria for siting a background monitor. In addition, we have provided objective criteria for determining whether or not it is properly sited. Specifically, we can characterize a background site as a location that does not show large fluctuations in I-min average CO concentrations, that is devoid of obvious spatial gradients in 8-h average concentrations, and that experiences peak hourly values relatively late in the period from 3 p.m. to 11 p.m. Second, we have provided a site classification framework and corresponding log-normal distribution parameters that allow direct use of our observations. Finally, we have proposed a model that describes the influence of meteorology on the background levels in an urban area and that can be used to assess the relative importance of local versus more regional background levels and therefore help to generalize our observations.
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4. I. Study limitations Our measurements were made in an urban area that is elevated with respect to the surrounding water (see Fig. 1). In contrast, some urban areas are located at the bottom of a valley with surrounding, elevated terrain. Under stable nighttime conditions, the wind flow patterns in our study area may be very different from those in the valley. Valley drainage winds would be expected to “pool” over the urban area, possibly creating very different background CO concentrations and different spatial patterns of the background concentrations. It is unclear whether the siting criteria we used in this study would apply in the valley case. This should be examined experimentally. The site locations in this study were chosen to avoid significant impacts from stationary sources, most notably residential wood heating devices. The USEPA emission factors for conventional wood stoves and fireplaces reports a ratio of CO to fine particle emissions of between 6.1 to 1 and 6.2 to 1. Based upon measurements of PM,,, at the Lake Forest Park monitor operated by the Puget Sound Air Pollution Control Agency, maximum fine particle wood smoke levels in the North Seattle area do not exceed 100 E.cg/m’ on a 24 h basis. This implies a maximum CO concentration from wood burning of about 600 pglrn’ or 0.5 ppm on a 24 h basis. However, the wood burning peak impacts occur between 7 p.m. and 7 a.m., whereas the peak evening traffic hours are between 3 p.m. and 11 p.m. In addition, the maximum wood smoke impacts occur at the bottom of creek valleys which occupy a relatively small fraction of the urban area. Therefore this 0.5 ppm level is an upper bound estimate of the background CO contribution from wood burning in these valleys. However, this estimate is qualified by the fact that we did not make measurements of CO in these wood smoke impacted areas. project was funded by the Washington State Department of Transportation, Project T9233. Task 37.
Acknowledement-This
Research
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