Traffic and meteorological impacts on near-road air quality: summary of methods and trends from the Raleigh Near-Road Study

A growing number of epidemiological studies conducted worldwide suggest an increase in the occurrence of adverse health effects in populations living, working, or going to school near major roadways. A study was designed to assess traffic emissions impacts on air quality and particle toxicity near a heavily traveled highway. In an attempt to describe the complex mixture of pollutants and atmospheric transport mechanisms affecting pollutant dispersion in this near-highway environment, several real-time and time-integrated sampling devices measured air quality concentrations at multiple distances and heights from the road. Pollutants analyzed included U.S. Environmental Protection Agency (EPA)-regulated gases, particulate matter (coarse, fine, and ultrafine), and air toxics. Pollutant measurements were synchronized with real-time traffic and meteorological monitoring devices to provide continuous and integrated assessments of the variation of near-road air pollutant concentrations and particle toxicity with changing traffic and environmental conditions, as well as distance from the road. Measurement results demonstrated the temporal and spatial impact of traffic emissions on near-road air quality. The distribution of mobile source emitted gas and particulate pollutants under all wind and traffic conditions indicated a higher proportion of elevated concentrations near the road, suggesting elevated exposures for populations spending significant amounts of time in this microenvironment. Diurnal variations in pollutant concentrations also demonstrated the impact of traffic activity and meteorology on near-road air quality. Time-resolved measurements of multiple pollutants demonstrated that traffic emissions produced a complex mixture of criteria and air toxic pollutants in this microenvironment. These results provide a foundation for future assessments of these data to identify the relationship of traffic activity and meteorology on air quality concentrations and population exposures.     

Traffic and Meteorological Impacts on Near-Road Air Quality: Summary of Methods and Trends from the Raleigh Near-Road Study

Abstract

A growing number of epidemiological studies conducted worldwide suggest an increase in the occurrence of adverse health effects in populations living, working, or going to school near major roadways. A study was designed to assess traffic emissions impacts on air quality and particle toxicity near a heavily traveled highway. In an attempt to describe the complex mixture of pollutants and atmospheric transport mechanisms affecting pollutant dispersion in this near-highway environment, several real-time and time-integrated sampling devices measured air quality concentrations at multiple distances and heights from the road. Pollutants analyzed included U.S. Environmental Protection Agency (EPA)-regulated gases, particulate matter (coarse, fine, and ultrafine), and air toxics. Pollutant measurements were synchronized with real-time traffic and meteorological monitoring devices to provide continuous and integrated assessments of the variation of near-road air pollutant concentrations and particle toxicity with changing traffic and environmental conditions, as well as distance from the road. Measurement results demonstrated the temporal and spatial impact of traffic emissions on near-road air quality. The distribution of mobile source emitted gas and particulate pollutants under all wind and traffic conditions indicated a higher proportion of elevated concentrations near the road, suggesting elevated exposures for populations spending significant amounts of time in this microenvironment. Diurnal variations in pollutant concentrations also demonstrated the impact of traffic activity and meteorology on near-road air quality. Time-resolved measurements of multiple pollutants demonstrated that traffic emissions produced a complex mixture of criteria and air toxic pollutants in this microenvironment. These results provide a foundation for future assessments of these data to identify the relationship of traffic activity and meteorology on air quality concentrations and population exposures.     

Transport of atmospheric fine particulate matter: part 2--findings from recent field programs on the intraurban variability in fine particulate matter

Air quality field data, collected as part of the fine particulate matter Supersites Program and other field measurements programs, have been used to assess the degree of intraurban variability for various physical and chemical properties of ambient fine particulate matter. Spatial patterns vary from nearly homogeneous to quite heterogeneous, depending on the city, parameter of interest, and the approach or method used to define spatial variability. Secondary formation, which is often regional in nature, drives fine particulate matter mass and the relevant chemical components toward high intraurban spatial homogeneity. Those particulate matter components that are dominated by primary emissions within the urban area, such as black carbon and several trace elements, tend to exhibit greater spatial heterogeneity. A variety of study designs and data analysis approaches have been used to characterize intraurban variability. High temporal correlation does not imply spatial homogeneity. For example, there can be high temporal correlation but with spatial heterogeneity manifested as smooth spatial gradients, often emanating from areas of high emissions such as the urban core or industrial zones.     

Spatial and temporal variability of ambient air toxics data

This paper summarizes information on the spatial and temporal variability of selected air toxics pollutants collected on a national basis primarily for a period encompassing 1990-2003. Spatial information on pollutant concentrations is characterized in terms of within-city and between-city variability. Temporal information is summarized as diurnal and seasonal variability and in multiyear trends. The information on variability is presented in the framework of a larger need for systematic documentation of information on air toxics pollutants to assess progress in air pollution control programs.     

Transport of Atmospheric Fine Particulate Matter: Part 2—Findings from Recent Field Programs on the Intraurban Variability in Fine Particulate Matter

Abstract

Air quality field data, collected as part of the fine particulate matter Supersites Program and other field measurements programs, have been used to assess the degree of intraurban variability for various physical and chemical properties of ambient fine particulate matter. Spatial patterns vary from nearly homogeneous to quite heterogeneous, depending on the city, parameter of interest, and the approach or method used to define spatial variability. Secondary formation, which is often regional in nature, drives fine particulate matter mass and the relevant chemical components toward high intraurban spatial homogeneity. Those particulate matter components that are dominated by primary emissions within the urban area, such as black carbon and several trace elements, tend to exhibit greater spatial heterogeneity. A variety of study designs and data analysis approaches have been used to characterize intraurban variability. High temporal correlation does not imply spatial homogeneity. For example, there can be high temporal correlation but with spatial heterogeneity manifested as smooth spatial gradients, often emanating from areas of high emissions such as the urban core or industrial zones.     

Furnace Sorbent Injection

Abstract

This paper summarizes information on the spatial and temporal variability of selected air toxics pollutants collected on a national basis primarily for a period encompassing 1990–2003. Spatial information on pollutant concentrations is characterized in terms of within-city and between-city variability. Temporal information is summarized as diurnal and seasonal variability and in multiyear trends. The information on variability is presented in the framework of a larger need for systematic documentation of information on air toxics pollutants to assess progress in air pollution control programs.     

Effects of instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, Georgia

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO2, CO, NOx, and O3) and fine particulate matter ([PM2.5] PM2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7-40% of the temporal variation in the daily pollutant measures and was largest for the PM2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO2, CO, NOx, and EC). Population-weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O3 to 70% of the temporal variation for SO2 and EC. Wind     

Temporal variability of selected air toxics in the United States

Ambient measurements of hazardous air pollutants (HAPs, air toxics) collected in the United States from 1990 to 2005 were analyzed for diurnal, seasonal, and/or annual variability and trends. Visual and statistical analyses were used to identify and quantify temporal variations in air toxics at national and regional levels. Sufficient data were available to analyze diurnal variability for 14 air toxics, seasonal variability for 24 air toxics, and annual trends for 26 air toxics. Four diurnal variation patterns were identified and labeled invariant, nighttime peak, morning peak, and daytime peak. Three distinct seasonal patterns were identified and labeled invariant, cool, and warm. Multiple air toxics showed consistent decreasing trends over three trend periods, 1990–2005, 1995–2005, and 2000–2005. Trends appeared to be relatively consistent within chemically similar pollutant groups. Hydrocarbons such as benzene, 1,3-butadiene, styrene, xylene, and toluene decreased by approximately 5% or more per year at more than half of all monitoring sites. Concentrations of carbonyl compounds such as formaldehyde, acetaldehyde, and propionaldehyde were equally likely to have increased or decreased at monitoring sites. Chlorinated volatile organic compounds (VOCs) such as tetrachloroethylene, dichloromethane, and methyl chloroform decreased at more than half of all monitoring sites, but decreases among these species were much more variable than among the hydrocarbons. Lead particles decreased in concentration at most monitoring sites, but trends in other metals were not consistent over time.     

Characterization of spatially homogeneous regions based on temporal patterns of fine particulate matter in the continental United States

Statistical analyses of time-series or spatial data have been widely used to investigate the behavior of ambient air pollutants. Because air pollution data are generally collected in a wide area of interest over a relatively long period, such analyses should take into account both spatial and temporal characteristics. The objective of this study is 2-fold: (1) to identify an efficient way to characterize the spatial variations of fine particulate matter (PM2.5) concentrations based solely upon their temporal patterns, and (2) to analyze the temporal and seasonal patterns of PM2.5 concentrations in spatially homogenous regions. This study used 24-hr average PM2.5 concentrations measured every third day during a period between 2001 and 2005 at 522 monitoring sites in the continental United States. A k-means clustering algorithm using the correlation distance was used to investigate the similarity in patterns between temporal profiles observed at the monitoring sites. A k-means clustering analysis produced six clusters of sites with distinct temporal patterns that were able to identify and characterize spatially homogeneous regions of the United States. The study also presents a rotated principal component analysis (RPCA) that has been used for characterizing spatial patterns of air pollution and discusses the difference between the clustering algorithm and RPCA.     

Assessing the impact of differential measurement error on estimates of fine particle mortality

In air pollution epidemiology, error in measurements of correlated pollutants has been advanced as a reason to distrust regressions that find statistically significant weak associations. Much of the related debate in the literature and elsewhere has been qualitative. To promote quantitative evaluation of such errors, this paper develops an air pollution time-series model based on correlations among unit-normal variables. Assuming there are no other sources of bias present, the model shows the expected amount of relative bias in the regression coefficients of a bivariate regression of coarse and fine particulate matter measurements on daily mortality. The model only requires information on instrumental error and spatial variability, along with the observed regression coefficients and information on the true fine-course correlation. Analytical results show that if one pollutant is truly more harmful than the other, then it must be measured more precisely than the other in order not to bias the ratio of the fine and course regression coefficients. Utilizing published data, a case study of the Harvard Six-Cities study illustrates use of the model and emphasizes the need for data on spatial variability across the study area. Current epidemiology time-series regressions can use this model to address the general concern of correlated pollutants with differing measurement errors.     

Sources of variability in ambient air toxics monitoring data

Under the Clean Air Act Amendments, the United States Environmental Protection Agency is required to regulate emissions of 188 hazardous air pollutants. The EPA, Office of Air Quality Planning and Standards is currently conducting a National-scale Air Toxics Assessment with a goal to identify air toxics which are of greatest concern, in terms of contribution to population inhalation risk. The results will be used to set priorities for the collection of additional air toxics emissions and monitoring data. Expanded ambient air toxics monitoring will take the form of a national air toxics monitoring network. With all monitoring data, however, comes uncertainty in the form of environmental variability (spatial and temporal) and monitoring error (sample collection and laboratory analysis). With this in mind, existing data from the Urban Air Toxics Monitoring Program (UATMP) were analyzed to obtain a general understanding of these sources of variability and then provide recommendations for managing the data uncertainties of a national network. The results indicate that environmental variability, in particular temporal, comprises most of the overall variability observed in the UATMP data. However, at lower ambient levels (on the order of 0.1–0.5 ppbv or lower) environmental variability tends to dissipate and monitoring error takes over, most notably analytical error. Overall, the results suggest that common techniques in ambient air toxics monitoring for carbonyls and volatile organic compounds may satisfy many of the primary objectives of a national air toxics monitoring network.     

Binational school-based monitoring of traffic-related air pollutants in El Paso, Texas (USA) and Ciudad Juárez, Chihuahua (México)

Paired indoor and outdoor concentrations of fine and coarse particulate matter (PM), PM2.5 reflectance [black carbon(BC)], and nitrogen dioxide (NO₂) were determined for sixteen weeks in 2008 at four elementary schools (two in high and two in low traffic density zones) in a U.S.-Mexico border community to aid a binational health effects study. Strong spatial heterogeneity was observed for all outdoor pollutant concentrations. Concentrations of all pollutants, except coarse PM, were higher in high traffic zones than in the respective low traffic zones. Black carbon and NO₂ appear to be better traffic indicators than fine PM. Indoor air pollution was found to be well associated with outdoor air pollution, although differences existed due to uncontrollable factors involving student activities and building/ventilation configurations. Results of this study indicate substantial spatial variability of pollutants in the region, suggesting that children’s exposures to these pollutants vary based on the location of their school.     

Characterization of Spatially Homogeneous Regions Based on Temporal Patterns of Fine Particulate Matter in the Continental United States

Abstract

Statistical analyses of time-series or spatial data have been widely used to investigate the behavior of ambient air pollutants. Because air pollution data are generally collected in a wide area of interest over a relatively long period, such analyses should take into account both spatial and temporal characteristics. The objective of this study is 2-fold: (1) to identify an efficient way to characterize the spatial variations of fine particulate matter (PM_2.5) concentrations based solely upon their temporal patterns, and (2) to analyze the temporal and seasonal patterns of PM_2.5 concentrations in spatially homogenous regions. This study used 24-hr average PM_2.5 concentrations measured every third day during a period between 2001 and 2005 at 522 monitoring sites in the continental United States. A k-means clustering algorithm using the correlation distance was used to investigate the similarity in patterns between temporal profiles observed at the monitoring sites. A k-means clustering analysis produced six clusters of sites with distinct temporal patterns that were able to identify and characterize spatially homogeneous regions of the United States. The study also presents a rotated principal component analysis (RPCA) that has been used for characterizing spatial patterns of air pollution and discusses the difference between the clustering algorithm and RPCA.     

Effects of Instrument Precision and Spatial Variability on the Assessment of the Temporal Variation of Ambient Air Pollution in Atlanta,Georgia

Abstract

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO₂, CO, NO_x, and O₃) and fine particulate matter ([PM_2.5] PM_2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7–40% of the temporal variation in the daily pollutant measures and was largest for the PM_2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO₂, CO, NO_x, and EC). Population–weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O₃ to 70% of the temporal variation for SO₂ and EC. Wind rose plots, corrected for diurnal and seasonal pattern effects, are used to demonstrate the impacts of local sources on monitoring station data. The results presented are being used to quantify the impacts of instrument precision and spatial variability on the assessment of health effects of ambient air pollution in Atlanta and are relevant to the interpretation of results from time series health studies that use data from fixed monitors.     

Background concentrations of 18 air toxics for North America

The U.S. Clean Air Act identifies 188 hazardous air pollutants (HAPs), or "air toxics," associated with adverse human health effects. Of these air toxics, 18 were targeted as the most important in a 10-City Pilot Study conducted in 2001 and 2002 as part of the National Air Toxics Trend Sites Program. In the present analysis, measurements available from monitoring networks in North America were used to estimate boundary layer background concentrations and trends of these 18 HAPs. The background concentrations reported in this study are as much as 85% lower than those reported in recent studies of HAP concentrations. Background concentrations of some volatile organic compounds were analyzed for trends at the 95% confidence level; only carbon tetrachloride (CCI4) and tetrachloroethylene decreased significantly in recent years. Remote background concentrations were compared with the one-in-a-million (i.e., 10(6)) cancer benchmarks to determine the possible causes of health risk in rural and remote areas; benzene, chloroform, formaldehyde, and chromium (Cr) fine particulate were higher than cancer benchmark values. In addition, remote background concentrations were found to contribute between 5% and 99% of median urban concentrations.     

Temporal and spatial statistical analysis of ambient air quality of Assam (India)

ABSTRACT

Present paper represents the spatio-temporal variation of air quality and performances of geostatistical tools for the identification of pollutants zone in various districts of Assam (India). Geographic Information System (GIS) and geostatistical analysis were utilized to estimate the spatio-temporal variations (2015–2017) of gaseous and particulate air pollutants. Data of 23 fixed monitoring stations were collected from the Central Pollution Control Board (CPCB). It was observed that SO₂ and NO_x concentrations are the major pollutants to the deterioration of air quality in Assam State. Exploratory data analysis was considered for the determination of spatial and temporal patterns of air pollutants. Air Quality index (AQI) was calculated based on the air pollutants and particulate matter. Radial Basis Function (RBF) interpolation techniques were used to analyze the spatial and temporal variation of air quality in Assam. Cross-validation is applied to evaluate the accuracy of interpolation methods in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash–Sutcliffe Equation (NSE) and Accuracy Factor (ACFT). In 2015, the high value of AQI portrayed in the central and northeast of the state. In 2016, the central and entire east of the study area was recorded the highest value of AQI. In 2017, it was observed that mostly the central part of the state recorded the high value of AQI. The spatio-temporal variation trend of air pollutants provides sound scientific basis for its management and control. This information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes.     

Evaluation of the use of diffusive air samplers for determining temporal and spatial variation of volatile organic compounds in the ambient air of urban communities

The Houston-Galveston metropolitan area has a relatively high density of point and mobile sources of air toxics, and determining and understanding the relationship between emissions and ambient air concentrations of air toxics is important for evaluating potential impacts on public health and formulating effective regulatory policies to control this impact, both in this region and elsewhere. However, conventional ambient air monitoring approaches are limited with regard to expense, siting limitations, and representative sampling necessary for adequate exposure assessment. The overall goal of this multiphase study is to evaluate the use of simple passive air samplers to determine temporal and spatial variability of the ambient air concentrations of selected volatile organic compounds (VOCs) in urban areas. Phase 1 of this study, reported here, was a field evaluation of 3M organic vapor monitors (OVMs) involving limited comparisons with commonly used active sampling methods, an assessment of sampler precision, a determination of optimal sampling duration, and an investigation of the utility of a simple modification of the commercial sampler. The results indicated that a sampling duration of 72 hr exhibited generally low bias relative to automated continuous gas chromatography measurements, good overall precision, and an acceptable number of measurements above detection limits. The modified sampler showed good correlation with the commercial sampler, with higher sampling rates, although lower than expected.     

Solar Radiation Measurements Augment Air Pollution Studies

For several years an ongoing project at the U. S. Water Conservation Laboratory has dealt with the measurement and analysis of solar radiation. Generally, this study has been directed toward fulfilling the needs of agriculture. As a byproduct, however, much has also been learned about the particulate matter content of the air, since the work involved has all been carried out within the metropolitan Phoenix, Ariz. area. In this paper, the results of several years of research on the atmospheric transmission of solar radiation in this area are presented. These results are considered in light of concurrent meteorological conditions, and inferences are drawn with respect to spatial and temporal variations in airborne particulates. It is demonstrated that solar radiation measurements can greatly enhance our knowledge of air pollution dynamics.