Temporal and Spatial Variation in Monitoring of Ambient Urban Air

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Chapter 17

Temporal and Spatial Variation in Monitoring of Ambient Urban Air Pollutants Margaret L. Phillips, Nurtan A . Esmen, Daping Wang, and Thomas A . Hall Department of Occupational and Environmental Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104

Air pollution data from monitoring stations are widely used as a surrogate for human exposure. Automated continuous monitors provide greater temporal resolution than cumulative samplers, but cost and practical constraints generally limit their use to small numbers of sites in any urban area. Spatial and temporal variation of air pollution and the representativeness of urban monitoring stations have been investigated on distance scales ranging from street segments to citywide. Review of the research suggests that spatial variability may be scale-invariant and may sometimes modulate characteristic temporal patterns.

Governmental agencies in many countries have created networks of fixed point monitoring stations to record airborne concentrations of major environmental air contaminants, such as carbon monoxide (CO), sulfur dioxide (S0 ), ozone (0 ), nitric oxide (NO), nitrogen dioxide (N0 ), mixed oxides of nitrogen (NO ), lead (Pb), and particulate matter. The pollutant concentrations measured at monitoring stations located in urban areas have been used as surrogate measures of human exposure. For example, monitoring sites which are set up primarily for the purpose of determining compliance with air quality 2

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© 2002 American Chemical Society Lipnick et al.; Chemicals in the Environment ACS Symposium Series; American Chemical Society: Washington, DC, 2002.


307 standards are often selected to be "representative" of population exposure on an urban or local scale. Another important use of these surrogate measures has been in epidemiological studies in which conclusions about health effects of air pollutants have been based on apparent relationships between concentration time series and health effect incidence time series (/). Because health effects such as death or disease episodes are manifested on the level of the individual member of the population, and personal exposure is usually dominated by indoor sources (2), the relationship between exposure and effect should ideally be modeled using measurements of personal exposure to airborne contaminants. However, measurement of personal exposure is a practical possibility only in research studies involving limited numbers of individuals for limited durations. Fixed-point monitoring data are also used in the development and validation of mathematical models for predicting air pollution concentrations. Air pollutant concentrations vary in time and space under the influence of many factors, which are summarized in Table I. Discussion of these factors may be found in basic texts on air pollution (5). Potential determinants of ambient exposure (such as meteorological factors, traffic volume, and industrial emissions) and suspected health effects also show spatial and temporal variation.

Table I. Factors influencing air pollutant levels Generation of primary pollutants Process Examples of pollutants emitted combustion NO, N 0 , CO, soot, S 0 vaporization hydrocarbons mechanical comminution coarse particles, e.g. dusts, sea spray Generation of secondary pollutants Process Examples of pollutants generated tropospheric chemical reactions 0 , O H radical, N 0 , aldehydes, (mainly photodissociations and peroxyacetyl nitrate (PAN), H S 0 oxidations) condensation and coagulation ultrafine and fine particles ( P M ) Transport diffusion wind flow turbulence mixing height Removal processes chemical reactions wet deposition dry deposition 2

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The existence of variations makes possible the use of powerful statistical methods for identifying apparent relationships - possibly causal - among air pollution data, determinants of exposure and health effects. On the other hand, variation presents a challenge in the design of monitoring strategies; the number and location of monitoring sites and the frequency and duration of sampling events must be selected carefully to yield data that will be representative of pollutant concentrations on the distance and time scales of interest.

Temporal and Spatial Monitoring Strategies Air pollution sampling methods may be classified in three categories based on the temporal patterns of the sampling process: automated continuous sampling, cumulative sampling, and grab sampling. Automated monitoring devices sample the air on a continuous or repetitive short-term batch basis, analyze the pollutant level in situ, and record the results sequentially, thus providing detailed information on temporal variation. The U.S. E P A reference methods for determining compliance with National Ambient A i r Quality Standards (NAAQS) include automated methods for C O , 0 , and N 0 (4). Automated methods are also widely used for S 0 and particulate matter (5). Typically particulate matter is sampled using sizeselective sampling methods to collect the particulate fraction having aerodynamic diameter less than 10 μηι (PM ) or less than 2.5 μηι ( P M ) . Volatile organic compounds (VOCs) and peroxyacetyl nitrate (PAN) have been measured with hourly resolution using automated gas chromatographs, and airborne formaldehyde concentrations have been determined with half-hour resolution by automated derivatization with fluorescence detection (6). 3

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For many automated monitoring devices the sensitivity to temporal variation comes at the price of portability: they are used principally in fixedpoint monitoring applications because they are expensive to purchase, large, heavy, and require a temperature-controlled housing. However, a number of researchers have overcome these constraints to some extent by using mobile air monitoring laboratories to sample spatial as well as temporal variation (7, 8, 9). Relatively inexpensive portable datalogging devices with electrochemical sensors have been used to measure spatial and temporal variation in C O levels (10). Though low-cost datalogging electrochemical monitors are also available commercially for other pollutants, including N O and N 0 , these devices may not be sensitive enough to quantify concentrations below 100-500 parts per billion (ppb), limiting their usefulness for ambient urban air monitoring. Portable fine particulate counters have been applied to personal exposure monitoring studies (77), and could be used to study spatial-temporal variation. 2



309 Cumulative sampling is the collection of air contaminants over a defined sampling period, which may be on the order of hours or days, for subsequent laboratory analysis. In the sampling process, the contaminants may be separated from the air and captured on a suitable filter or sorbent medium, or an entire volume of air may be collected in an evacuated container. Sampling may be active, air being drawn through the collection medium by a calibrated pump, or passive, relying on diffusion to transport the contaminant into and through the collection medium. Quantification of the mass of contaminant collected by cumulative sampling yields a measure of the ambient concentration at the sampling point, time-averaged over the sampling period. Therefore, cumulative sampling methods are insensitive to variation on time scales shorter than the sampling period. The minimum sampling period sufficient to allow quantification of ambient contaminant concentrations in all samples collected during a sampling campaign depends upon the anticipated lowest average ambient concentration, the limit of detection of the analytical method, and the rate of transport (active or passive) of the contaminant onto the collection medium. For example, passive sampling tubes for measurement of N 0 have been used with sampling durations ranging from three days (7) to two weeks (12, 13) to ensure sufficient sensitivity and precision in the measurement of ambient N 0 levels. Cumulative samplers can be used to greatest advantage in determination of spatial variations in pollutant concentration; their low cost and small size relative to automated air monitoring devices makes it practical to deploy samplers at multiple locations simultaneously throughout a study area. Cumulative sampling is also the only feasible method currently available to make quantitative measurements of personal exposure to most air contaminants at typical ambient concentrations in the general urban environment. Grab sampling is the collection of short-term samples (on the order of minutes) to provide a "snapshot" of air contaminant concentrations at a particular time and place. Grab sampling may be used to assess the influence of short-term events, such as a contaminant release episode or an unusual meteorological condition, on contaminant concentrations. 2

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Siting Criteria for Monitoring Stations Air monitoring data generated by public authorities are a readily accessible and potentially valuable resource for researchers. However, an understanding of the criteria for siting monitoring stations is necessary for appropriate interpretation of these data. Two alternative approaches to siting are (1) to locate monitoring sites in a regular grid pattern or (2) to select monitoring sites



310 that are believed to be representative of important features of the concentration profile of an urban area, e.g. "maximum", "background", "residential", etc. (14) Air quality standards developed by the U.S. EPA under the Clean A i r Act of 1970 mandated the creation of air monitoring networks, the State and Local Air Monitoring Stations (SLAMS). The design objectives for S L A M S networks with respect to urban air quality are to determine (1) "highest concentrations expected to occur in the area covered by the network"; (2) "representative concentrations in areas of high population density"; (3) "the impact on ambient pollution levels of significant sources or source categories"; (4) "general background concentration levels." (15). The concentration of an air pollutant monitored at a site that was selected to meet one of these four objectives is considered to be "representative" on a spatial scale over which concentrations are reasonably uniform. EPA defines four spatial scales relevant to urban air monitoring: microscale refers to areas with dimensions up to about 100 meters, e.g. about the size of a city block; middle scale refers to areas with dimensions of 100 to 500 meters, e.g. several city blocks; neighborhood scale refers to areas on the scale of 0.5 to 4.0 kilometers with fairly uniform land use; and urban scale refers to the city as a whole, with dimensions of about 4 to 50 kilometers. (15). The scale represented by a given monitoring site depends upon the site objective and the characteristic spatial variation of the target contaminant. For example, a monitoring site selected to detect the highest concentration of C O might be located in an urban canyon, i.e. a street in which the height of buildings on both sides is similar to the width of the street. Concentrations measured at the midpoint between intersections (16) may be considered representative on the microscale, and possibly on the middle scale as well, if traffic intensity and land use is uniform over the length of several blocks.

Assessments of Spatial Variation in Urban Air Quality

Relative importance of spatial and temporal variation Spicer et al. (6) collected 16 consecutive 3-hour cumulative samples of VOCs, trace elements, and particle-bound organics on three different occasions over a period of four weeks at each of six sampling locations in different sections of Columbus, Ohio. The sampling sites included a downtown commercial location and several residential areas with varying proximity to major roads, highways, and business areas. Analysis of variance performed on the sampling results indicated that variability between sampling locations was


311 generally small relative to temporal variability for hydrocarbons. Low spatial and temporal variability and large spatial-temporal interaction were found for halogenated hydrocarbons and potassium, reflecting sporadic releases from local sources at some sampling sites. In the Small Area Variations in A i r Quality and Health (SAVIAH) study (13), 40 to 80 simultaneous sampling sites, intended to reflect regional background, urban background, and street level concentrations of N 0 , were set up in each of four European cities (Amsterdam, Netherlands; Huddersfield, U . K . ; Poznan, Poland; Prague, Czech Republic). Two-week cumulative passive sampling was conducted in each city in four separate seasonal surveys. In each city, both spatial and seasonal variations were statistically significant, but spatial variation accounted for most of the overall variability among samples. The spatial coefficient of variation ranged from 22% in Amsterdam to 42% in Prague. Site concentrations tended to be highly correlated across seasonal surveys. However, the site-survey interaction was also statistically significant, indicating that seasonal variations were more pronounced in some sites. The difference between the Columbus study and the S A V I A H study in the relative importance of spatial variation probably reflects the different time scales used in the two studies and the more complex role of N 0 in photochemical cycles.


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Urban scale variation Kuttler and Strassburger (8) took continuous measurements of N O , N 0 , and 0 in a mobile laboratory during several 50 to 60 km long round-trip traverses of the city of Essen, Germany, spanning green areas, residential areas, secondary streets, main roads, and highways. Traverses were made between the daily rush hours, when traffic emissions were expected to be relatively stable. N O and N 0 concentrations varied by as much as 20-fold between land use areas. 0 concentration and the N 0 / N O ratio showed the opposite trend, reflecting the typical suppression of 0 by N O in high traffic areas. Studies of N 0 concentrations in Lancaster, U.K. (12) and Seattle-Bellevue, Washington (9) found a similar relationship between N 0 concentration and proximity to major traffic routes. Despite significant differences between sampling sites in the Seattle-Bellevue study, in which four-month average concentrations ranged from 13 ppb to 26 ppb between sites, successive 3-week passive sampling measurements at the highest concentration site were highly correlated over time with measurements at all other sites. 2

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McCurdy et al. (17) took 24-hour samples of acid aerosols at roughly 2-3 day intervals for three months at four locations in the Pittsburgh, Pennsylvania metropolitan area. Sulfate and ammonium ion concentrations were found to be similar at all sites, and their temporal patterns were also very similar. Strong


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acid, measured as H , was also temporally correlated across sites, but the concentration at an "upwind" suburban site was significantly higher (by a factor of 2) than at the city center, suggesting the importance of regional transport of acid aerosols combined with partial neutralization by local sources of ammonia. To determine urban and regional scale representativeness of time-series data from urban monitoring stations, the temporal correlation between monitors was evaluated over seven adjacent states in the heavily industrialized northcentral region of the United States (18). After statistical removal of seasonal effects and longer-term trends, temporal correlations between monitors within 100 miles of each other were generally higher for P M , 0 , and N 0 than for S 0 and CO. Correlations decreased more markedly with distance for P M and N 0 than for the other pollutants, dropping from about 0.7 (zero distance intercept) to about 0.5 at 30 miles separation. Correlation between monitors for some pollutants was also influenced by land use (residential vs. industrial vs. commercial), location (urban vs. suburban), and/or monitoring objective (population exposure vs. maximum concentration). 10

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Prediction of spatial variation in air quality on the urban scale is an important feature of mathematical models that use emissions inventories or estimates from stationary and mobile sources within an urban area in simulations of multiple source dispersion processes. McNair et al (19) used the Carnegie/California Institute of Technology (CIT) photochemical airshed model to calculate the volume-averaged concentrations of N 0 , CO, 0 , and other pollutants in 5 km by 5 km grid cells covering the Los Angeles basin. The calculated spatial distribution pattern for 0 was consistent with measured data from 37 monitoring stations, though the model was biased downward or upward depending upon the motor vehicle emissions estimates used as an input to the model. The magnitude of bias in the model was comparable to the magnitude of observed inhomogeneity (about 10-20%) between monitoring sites on a spatial scale similar to the model grid cell spacing, i.e. 5 km. Georgopoulos et al (20) evaluated the ability of the Urban Airshed Model (UAM-IV) to reproduce spatial and temporal exposure patterns throughout the state of New Jersey and neighboring areas during two ozone episodes. Normalized bias and error was comparable to that found by McNair et al (19). However, comparison of population-weighted exposure estimates derived from interpolation of monitoring data with population-weighted exposure estimates derived from detailed or interpolated U A M calculations suggested that spatial variation in population-weighted exposure depends more on population distribution than on detailed concentration patterns (20). 2

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313 Neighborhood and middle scale variation Research results indicate that neighborhoods or land use areas with high average concentrations of pollutants also tend to have higher spatial variability within the neighborhood (8, 12). This scaling of variability with average value is characteristic of lognormal concentration distributions. The coefficient of variation of annual mean N 0 concentrations within neighborhoods in the Lancaster study (12) was about 30-40%; this was comparable to the variation between neighborhoods. Variation in pollutant concentrations within neighborhoods, like variation between neighborhoods, may be associated with proximity to traffic. As part of the S A V I A H study, sampling for P M , P M , particle-bound polycyclic aromatic hydrocarbons (PAHs), soot, and VOCs was conducted outside homes on high-traffic main streets and low-traffic side streets in central Amsterdam (21). At each residence, the cumulative samplers were placed on a balcony at one floor above street level, facing the street. The mean concentrations of PAHs, soot, and VOCs were about twice as high on high-traffic streets compared to low traffic streets, and particulate concentrations were about 1520% higher. Contaminant concentrations measured simultaneously inside the homes were also significantly higher on high-traffic streets, potentially affecting personal exposure. Chan and Hwang (7) assessed the spatial representativeness of a fixed point hourly monitoring station in Taipei, Taiwan, by comparing the fixed-point station results to N 0 measurements taken using 3-day passive samplers at 22 sites within the same radius. Representativeness was evaluated in terms of a statistic which quantified the relative absence of bias between fixed-point and satellite site measurements. The fixed-point monitoring station was found overall to be highly representative within a 500 meter radius. Representativeness decreased at larger distances. The monitoring station was also found to be more representative of surrounding high-traffic locations than low-traffic locations. To assess the representativeness of time-series data from the fixed-point monitoring station, a mobile monitoring laboratory was used to measure hourly concentrations of PM10, C O , N O , N 0 2 , N O x , S02, total hydrocarbons (THC) and non-methane hydrocarbons (NMHC) for three days each at six locations within a radius of 750 meters from the station. Monitoring results from the fixed-point and mobile stations were moderately correlated for all pollutants, except T H C and N M H C , which were only weakly correlated, apparently due to the influence of local sources.


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314 Microscale variation A number of researchers have measured or modeled spatial variation on the scale of a city block. Variability within urban canyons has been of particular interest. Laxen and Noordally (22) used 1-week cumulative sampling to investigate variation of N 0 in three orthogonal directions in urban canyons. N 0 concentrations were found to be highest at the center line of the road, decreasing to near local background level within 10-15 meters transversely from the center. Concentrations measured longitudinally at curbside tended to increase in the direction of traffic flow upstream of traffic lights, a result that is consistent with higher local emissions due to increased vehicle density, idling, and acceleration after traffic stops. N 0 concentrations decreased with height to near background level at the top of the canyon. In a study of horizontal and vertical microscale variations in P M and total suspended particulates in Taipei (23), particulate concentrations measured at open windows of a high-rise building decreased with height between the second and seventh floors but showed no consistent change between the seventh and fourteenth floors. At street level, no consistent patterns in spatial variation were found between the roadside, sidewalk, and covered walkway along a high-traffic main road, nor between main streets, side streets, and alleys. The absence of typical dispersion patterns could be due to emissions from sources such as motorcycles which were not confined to streets. In symmetrical urban canyons, cross-canyon wind flow at roof level creates a vortex such that the wind direction at street level is opposite that at roof level. Consider, for example, a street canyon running north-south, with roughly equal building heights on both sides. If the wind at roof level is from the west, air from roof level will sweep down the front of buildings on the east side of the street, across the street, and up the front of buildings on the west side of the street. Pollution concentrations would tend to be higher on the west side and lower on the east side of the street. Studies of the influence of roof top wind direction on continuous monitoring data in central London found as much as a threefold difference in simultaneous C O concentrations at mid-block monitors on opposite sides of a street (24), and about a two-fold variation in concentration (normalized to remove the effect of wind speed) at a single C O monitor located near an intersection of two canyons (25). 2


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T e m p o r a l Variations Seasonal and diurnal patterns in urban air pollution concentrations have been widely reported. Average concentrations of the primary combustion


315 products N O and CO tend to be higher in the winter than in the summer (26-28), while average concentrations of the secondary photochemical product 0 show the opposite seasonal behavior (26-29). Diurnal patterns of urban NO, N 0 , and CO concentrations typically show daytime peaks corresponding to rush hours (8, 25-27, 30). In contrast, hourly ambient concentrations of benzene and toluene measured in Columbus, Ohio in the summers showed a broad peak during night-time hours, probably the effect of nocturnal inversions (6). To provide a more detailed overview of temporal variation patterns in fixed-point monitoring, we will present analyses of several time series of urban air pollution concentration measurements. Hourly concentration data on N O , N 0 , and 0 , recorded at a S L A M S in central Oklahoma City between October 1998 and June 1999, were obtained from the Oklahoma Department of Environmental Quality. The station, which was designed to be representative of population exposure on the neighborhood scale, was located on a sprawling campus not immediately adjacent to high-traffic streets. The probe was at the unusually high elevation of 15.5 meters above street level. Daily average concentrations of NO and 0 were calculated from the hourly data. Daily average N O concentrations were lognormally distributed with a geometric mean of 5 ppb and a geometric standard deviation of 2.75. The distribution of daily average 0 concentrations was less skewed, with a geometric mean of 24 ppb and a geometric standard deviation of 1.65. Daily average N 0 concentrations had a geometric mean of 11 ppb and a geometric standard deviation of 1.65. As shown in Figure 1, the winter season was marked by frequent high daily concentrations of NO, with large day-to-day variation above a very low baseline. 0 daily concentrations showed a complementary seasonal dependence and a higher baseline compared to NO. This result is not surprising, because N O concentrations tend to decrease with horizontal or vertical distance from roadways due to dispersion and oxidation, whereas 0 tends to build up due to photochemical conversion. The distributions of hourly concentrations were more strongly right-skewed than the daily averages, with geometric standard deviations of 2.88 for N O , 2.43 for 0 , and 2.18 for N 0 . To investigate diurnal and weekly periodicity, the autocovariance functions of the hourly concentration time series were calculated as: 3


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V(T) = Z c i C i _ - c 2 i T

where the Cj are the sequential hourly concentrations, c is the concentration averaged over the entire time series, and τ is the lag between data points. The autocovariance functions, plotted in Figure 2, showed a strong 24-hour



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317 periodicity in the 0 time series and 12- and 24-hour periodicity in the N 0 time series. On the other hand, the periodic component in the N O autocovariance function was obscured by random associations on time scales longer than 24 hours. Similarly, spectral analysis of an autoregressive model of the time series, performed using S-Plus 2000 statistical software, showed peaks for N 0 and 0 corresponding to 6, 8, 12, and 24 hour components, but yielded no discernible periodic components for NO. The diurnal relationships between N O , N 0 , and 0 are illustrated in Figures 3 and 4 for weekdays and weekends, respectively. To obtain these curves, the hourly concentrations were first normalized by dividing by the maximum hourly concentration for each day. Normalization removed the effect of day-to-day fluctuations in the peak concentration. The normalized hourly concentrations for each hour of the day were then averaged over the entire monitoring period, consisting of 176 weekdays and 72 Saturdays and Sundays. The average normalized N 0 diurnal curve showed strong morning and evening rush hour peaks on weekdays, while on weekends the morning peak was attenuated. Similar weekday and weekend diurnal patterns were seen in C O and PM data from other S L A M S in Oklahoma City and Tulsa. The average normalized N O diurnal curves showed only a morning peak. Late afternoon or evening rush hour N O peaks, which occurred at inconsistent times and in only 30% of the days monitored, were almost completely smoothed away in the averaging process. The afternoon peaks occurred mostly in the late autumn and winter when 0 formation was lower. A similar seasonal effect has been reported in urban green areas (8). This suggests that N O emissions from afternoon rush hour traffic were extinguished by the high 0 concentrations around the monitoring station. 3

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Implications Monitoring studies indicate that time-averaged concentrations of some air pollutants often vary by factors of two or more between monitoring points on the same street segment, the same neighborhood, or the same city. This apparent scale-invariance may suggest that methods for analysis of fractals could be fruitfully applied to the problem of spatial variability, as they have already been applied to temporal variability of pollution (28). The existence in some cases (6, 7, 13) of significant interaction between spatial and temporal variability renders the use of fixed-point data as surrogates for human exposure in time series analysis highly problematic. The uncertainties inherent in using an Eulerian average (i.e. from a stationary sampler) to estimate a Lagrangian average (i.e. for a mobile person) are



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Figure 3. Weekday diurnal variation of NO, N0 , and 0 in Oklahoma City. 2

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Figure 4. Weekend diurnal variation of NO, N0 , and 0 in Oklahoma City. 2

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compounded under conditions of temporal variability at the monitoring locale, because the time average over the period spent by the person in the concentration milieu spatially "represented" by the sampler is not necessarily representative of the overall time average recorded by the sampler (31). Empirical studies using simultaneous automated personal and fixed-point monitoring can help elucidate the relationship between Eulerian and Lagrangian exposure patterns.

Acknowledgements We gratefully acknowledge the assistance of Leon Ashford and co-workers at the Air Quality Division, Oklahoma Department of Environmental Quality, in providing air monitoring data and supporting information. This research was funded by the U.S. EPA National Center for Environmental Research and Quality Assurance under Agreement No. R82-6786-010.

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320 12. Hewitt, C. N. Atmos. Environ. 1991, 25B, 429-434. 13. Lebret, Ε.; Briggs, D.; van Reeuwijk, H . ; Fischer, P.; Smallbone, K . ; Harssema, H . ; Kriz, B.; Gorynski, P.; Elliott, P. Atmos. Environ. 2000, 34, 177-185. 14. Larssen, S. In Urban Air Pollution - European Aspects; Fenger, J.; Hertel, O.; Palmgren, F., Eds.; Environmental Pollution 1; Kluwer Academic Publishers: Dordrecht, Netherlands, 1998; pp. 297-316. 15. Code of Federal Regulations; July 1, 1999; Title 40, Part 58, Appendix D. 16. Code of Federal Regulations; July 1, 1999; Title 40, Part 58, Appendix E. 17. McCurdy, T.; Zelenka, M. P.; Lawrence, P. M.; Houston, R. M.; Burton, R. Atmos. Environ. 1999, 33, 5133-5145. 18. Ito, K.; Thurston, G. D.; Nadas, Α.; Lippmann, M. J. Expos. Anal. Environ. Epidemiol. 2001, 11, 21-32. 19. McNair, L . Α.; Harley, R. Α.; Russell, A . G. Atmos. Environ. 1996, 24, 4291-4301. 20. Georgopoulos, P. G.; Purushothaman, V . ; Chiou, R. J. Expos. Anal. Environ. Epidemiol. 1997, 7, 191 -215. 21. Fischer, P. H . ; Hoek, G.; van Reeuwijk, H . ; Briggs, D. J.; Lebret, E.; van Wijnen, J. H . ; Kingham, S.; Elliott, P. E. Atmos. Environ. 2000, 34, 37133722. 22. Laxen, D. P. H.; Noordally, E. Atmos. Environ. 1987, 21, 1899-1903. 23. Chen, M.-L.; Mao, I.-F. Sci. Total Environ. 1998, 209, 225-231. 24. Croxford, B.; Penn, A . Atmos. Environ. 1998, 32, 1049-1057. 25. Scaperdas, Α.; Colvile, R. N. Atmos. Environ. 1999, 33, 661-674. 26. Mayer, H. Atmos. Environ. 1999, 33, 4029-4037. 27. Bogo, H . ; Negri, R. M.; San Roman, E. Atmos. Environ. 1999, 33, 25872598. 28. Lanfredi, M; Macchiato, M.; Ragosta, M.; Serio, C. Fractals, 1998, 6, 151158. 29. Varshney, C. K.; Aggarwal, M. Atmos. Environ. 1992, 26B, 291-294. 30. Hao, J.; He, D.; Wu, Y . ; Fu, L.; He, K. Atmos. Environ. 2000, 34, 453-465. 31. Esmen, Ν. Α.; Hall, Τ. Α.; Appl. Occup. Environ. Hyg. 2000, 15, 114-119.


Temporal and Spatial Variation in Monitoring of Ambient Urban Air

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