An optimal spatial sampling design for intra-urban population exposure assessment

This article offers an optimal spatial sampling design that captures maximum variance with the minimum sample size. The proposed sampling design addresses the weaknesses of the sampling design that Kanaroglou, P.S., M. Jerrett, J. Morrison, B. Beckerman, M.A. Arain, N.L. Gilbert, and J.R. Brook (2005. Establishing an air pollution monitoring network for intra-urban population exposure assessment: a location-allocation approach. Atmospheric Environment 39(13), 2399–409) used for identifying 100 sites for capturing population exposure to NO₂ in Toronto, Canada. Their sampling design suffers from a number of weaknesses and fails to capture the spatial variability in NO₂ effectively. The demand surface they used is spatially autocorrelated and weighted by the population size, which leads to the selection of redundant sites. The location-allocation model (LAM) available with the commercial software packages, which they used to identify their sample sites, is not designed to solve spatial sampling problems using spatially autocorrelated data. A computer application (written in C++) that utilizes spatial search algorithm was developed to implement the proposed sampling design. The proposed design has already been tested and implemented in three different urban environments – namely Cleveland, OH; Delhi, India; and Iowa City, IA – to identify optimal sample sites for monitoring airborne particulates.     

A holistic approach for optimal design of air quality monitoring network expansion in an urban area

This paper presents an objective methodology for determining the optimum number of ambient air quality stations in a monitoring network. The methodology integrates the multiple-criteria method with the spatial correlation technique. The pollutant concentration and population exposure data are used in this methodology in different ways. In the first stage, the Fuzzy Analytic Hierarchy Process (FAHP) with triangular fuzzy numbers (TFNs) is used to identify the most desirable monitoring locations. The network configuration is then determined on the basis of the concept of sphere of influences (SOIs). The SOIs are dictated by a predetermined cutoff value (r_c) in the spatial correlation coefficients (r) between the pollutant concentrations at the monitoring stations identified from first step and the corresponding concentrations at neighboring locations in the region. Finally, the optimal station locations are ranked by using combined utility scores gained from the first and second steps. The expansion of air quality monitoring network of Riyadh city in Saudi Arabia is used as a case study to demonstrate the proposed methodology.     

A spatial multicriteria model for determining air pollution at sample locations

Atmospheric pollution in urban centers has been one of the main causes of human illness related to the respiratory and circulatory system. Efficient monitoring of air quality is a source of information for environmental management and public health. This study investigates the spatial patterns of atmospheric pollution using a spatial multicriteria model that helps target locations for air pollution monitoring sites. The main objective was to identify high-priority areas for measuring human exposures to air pollutants as they relate to emission sources. The method proved to be viable and flexible in its application to various areas.

Implications:?Spatial multicriteria models provide a tool for air pollution management in urban areas. Analytic hierarchy process (AHP) modeling can help with the process of prioritizing monitoring site locations and minimizing costs.     

Site Redundancy in Urban Ozone Monitoring

This article describes two statistical methods that enable air pollution control agencies to assess the effectiveness of the spatial distribution of current stationary ozone monitoring networks by providing measures of site redundancy. These methods analyze site redundancy by determining the degree to which ozone measurements at one site can be successfully predicted from data collected at other monitoring sites. The first method, the similarity (SIM) measure, calculates redundancy based on the percentage of common operational days during which two monitoring stations report similar daily maximum ozone concentrations. The second method, a modeling technique, relates site redundancy in ozone measurement to an R-squared statistic from an autoregressive model. The model uses meteorological components recorded at a central location and ozone concentrations reported by the network’s other monitoring stations. Both techniques can assist in effective allocation of limited monitoring resources and offer a statistical approach to ambient air monitoring network design. The techniques are applied to data collected at six ozone monitoring stations in Harris County, Texas, during an eight-year period in the 1980s. The methods identified two sites in the six-site network that exhibit a high degree of redundancy.     

Sequencing diurnal air flow patterns for ozone exposure assessment around Houston,Texas

Understanding the human health impacts of ground level ozone requires detailed knowledge of its spatial–temporal distribution beyond that provided by surface monitoring networks. Here, a novel methodology based on unsupervised multivariate statistical techniques has been developed and used to identify the transport and dispersion patterns of tropospheric ozone. The hierarchical clustering method is used to visualize air flow patterns at two time scales relevant for ozone buildup. Sequentially executed statistical methods consider hourly 1-h surface wind field measurements. First, clustering is performed at the hourly time scale to identify 1-h surface flow patterns. Then, sequencing is performed at the daily time scale to identify groups of days sharing similar diurnal cycles for the surface flow. Selection of appropriate numbers of air flow patterns allows inference of regional transport and dispersion patterns for understanding population exposure to ozone. The methods are applied to the Houston, Galveston, and Beaumont-Port Arthur, TX study domain. Representative hourly wind field patterns are determined for the entire 2004 ozone season. Then, sequencing is performed for the 32 days in exceedance of the NAAQS for 8-h ozone. Four diurnal flow patterns capturing different ozone exceedance scenarios are isolated; each scenario is associated with a distinct spatial distribution for atmospheric pollutants.     

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.     

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.     

Spatial heterogeneity of PM10 and O3 in São Paulo, Brazil, and implications for human health studies

Developing exposure estimates is a challenging aspect of investigating the health effects of air pollution. Pollutant levels recorded at centrally located ambient air quality monitors in a community are commonly used as proxies for population exposures. However, if ample intraurban spatial variation in pollutants exists, city-wide averages of concentrations may introduce exposure misclassification. We assessed spatial heterogeneity of particulate matter with an aerodynamic diameter < or = 10 microm (PM10) and ozone (O3) and evaluated implications for epidemiological studies in S?o Paulo, Brazil, using daily (24-hr) and daytime (12-hr) averages and 1-hr daily maximums of pollutant levels recorded at the regulatory monitoring network. Monitor locations were also analyzed with respect to a socioeconomic status index developed by the municipal government. Hourly PM10 and O3 data for the Sāo Paulo Municipality and Metropolitan Region (1999-2006) were used to evaluate heterogeneity by comparing distance between monitors with pollutants' correlations and coefficients of divergence (CODs). Both pollutants showed high correlations across monitoring sites (median = 0.8 for daily averages). CODs across sites averaged 0.20. Distance was a good predictor of CODs for PM10 (p < 0.01) but not O3, whereas distance was a good predictor of correlations for O3 (p < 0.01) but not PM10. High COD values and low temporal correlation indicate a spatially heterogeneous distribution of PM10. Ozone levels were highly correlated (r > or = 0.75), but high CODs suggest that averaging over O3 levels may obscure important spatial variations. Of municipal districts in the highest of five socioeconomic groups, 40% have > or = 1 monitor, whereas districts in the lowest two groups, representing half the population, have no monitors. Results suggest that there is a potential for exposure misclassification based on the available monitoring network and that spatial heterogeneity depends on pollutant metric (e.g., daily average vs. daily 1-hr maximum). A denser monitoring network or alternative exposure methods may be needed for epidemiological research. Findings demonstrate the importance of considering spatial heterogeneity and differential exposure misclassification by subpopulation.     

Assessment of traffic-related air pollution in two street canyons in Paris: implications for exposure studies

The small-scale spatial variability of air pollution observed in urban areas has created concern about the representativeness of measurements used in exposure studies. It is suspected that limit values for traffic-related pollutants may be exceeded near busy streets, although respected at urban background sites. In order to assess spatial concentration gradients and identify weather conditions that might induce air pollution episodes in urban areas, different sampling and modelling techniques were studied.Two intensive monitoring campaigns were carried out in typical street canyons in Paris during winter and summer. Steep cross-road and vertical concentration gradients were observed within the canyons, in addition to large differences between roadside and background levels. Low winds and winds parallel to the street axis were identified as the worst dispersion conditions. The correlation between the measured compounds gave an insight into their sources and fate. An empirical relationship between CO and benzene was established. Two relatively simple mathematical models and an algorithm describing vertical pollutant dispersion were used. The combination of monitoring and modelling techniques proposed in this study can be seen as a reliable and cost-effective method for assessing air quality in urban micro-environments. These findings may have important implications in designing monitoring studies to support investigation on the health effects of traffic-related air pollution.     

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.     

Examining intra-urban variation in fine particle mass constituents using GIS and constrained factor analysis

Recent studies have used land use regression (LUR) techniques to explain spatial variability in exposures to PM_2.5 and traffic-related pollutants. Factor analysis has been used to determine source contributions to measured concentrations. Few studies have combined these methods, however, to construct and explain latent source effects. In this study, we derive latent source factors using confirmatory factor analysis constrained to non-negative loadings, and develop LUR models to predict the influence of outdoor sources on latent source factors using GIS-based measures of traffic and other local sources, central site monitoring data, and meteorology. We collected 3–4 day samples of nitrogen dioxide (NO₂) and PM_2.5 outside of 44 homes in summer and winter, from 2003 to 2005 in and around Boston, Massachusetts. Reflectance analysis, X-ray fluorescence spectroscopy (XRF), and high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) were performed on particle filters to estimate elemental carbon (EC), trace element, and water-soluble metals concentrations. Within our constrained factor analysis, a five-factor model was optimal, balancing statistical robustness and physical interpretability. This model produced loadings indicating long-range transport, brake wear/traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust. LUR models largely corroborated factor interpretations through covariate significance. For example, ‘long-range transport’ was predicted by central site PM_2.5 and season; ‘brake wear/traffic exhaust’ and ‘resuspended road dust’ by traffic and residential density; ‘diesel exhaust’ by percent diesel traffic on nearest major road; and ‘fuel oil combustion’ by population density. Results suggest that outdoor residential PM_2.5 source contributions can be partially predicted using GIS-based terms, and that LUR techniques can support factor interpretation for source apportionment. Together, LUR and factor analysis facilitate source identification, assessment of spatial and temporal variability, and more refined source exposure assignment for evaluation of source contributions to health outcomes in epidemiological studies.     

Tradescantia micronucleus test indicates genotoxic potential of traffic emissions in European cities

Urban atmospheres contain complex mixtures of air pollutants including mutagenic and carcinogenic substances such as benzene, diesel soot, heavy metals and polycyclic aromatic hydrocarbons. In the frame of a European network for the assessment of air quality by the use of bioindicator plants, the Tradescantia micronucleus (Trad-MCN) test was applied to examine the genotoxicity of urban air pollution. Cuttings of Tradescantia clone #4430 were exposed to ambient air at 65 monitoring sites in 10 conurbations employing a standardised methodology. The tests revealed an elevated genotoxic potential mainly at those urban sites which were exposed to severe car traffic emissions. This bioassay proved to be a suitable tool to detect local 'hot spots' of mutagenic air pollution in urban areas. For its use in routine monitoring programmes, however, further standardisation of cultivation and exposure techniques is recommended in order to reduce the variability of results due to varying environmental conditions.     

Redundant Measurements of Urban Air Monitoring Networks in Air Quality Reporting

Abstract

This paper describes a statistical method to assess site redundancy of urban air monitoring networks in reporting daily Pollutant Standards Index (PSI), average concentrations, and the number of exceedances. Such a statistical method has identified significant redundancy in monitoring sites for one-year measurements of two air monitoring networks in Taiwan. There are five redundant sites out of 15 monitoring sites in the Taipei area and eight redundant sites out of 18 monitoring sites in the Kaohsiung area. By using the statistical method presented in this paper to downsize the monitoring networks, we can determine not only the number of redundant sites but also the priority of site removals. The derived sub-networks can maintain consistency in reporting air quality without significant changes in the spatial variations of air measurements for an existing air monitoring network.     

Air quality mapping using GIS and economic evaluation of health impact for Mumbai City,India

Mumbai, a highly populated city in India, has been selected for air quality mapping and assessment of health impact using monitored air quality data. Air quality monitoring networks in Mumbai are operated by National Environment Engineering Research Institute (NEERI), Maharashtra Pollution Control Board (MPCB), and Brihanmumbai Municipal Corporation (BMC). A monitoring station represents air quality at a particular location, while we need spatial variation for air quality management. Here, air quality monitored data of NEERI and BMC were spatially interpolated using various inbuilt interpolation techniques of ArcGIS. Inverse distance weighting (IDW), Kriging (spherical and Gaussian), and spline techniques have been applied for spatial interpolation for this study. The interpolated results of air pollutants sulfur dioxide (SO₂), nitrogen dioxide (NO₂) and suspended particulate matter (SPM) were compared with air quality data of MPCB in the same region. Comparison of results showed good agreement for predicted values using IDW and Kriging with observed data. Subsequently, health impact assessment of a ward was carried out based on total population of the ward and air quality monitored data within the ward. Finally, health cost within a ward was estimated on the basis of exposed population. This study helps to estimate the valuation of health damage due to air pollution.

Implications: Operating more air quality monitoring stations for measurement of air quality is highly resource intensive in terms of time and cost. The appropriate spatial interpolation techniques can be used to estimate concentration where air quality monitoring stations are not available. Further, health impact assessment for the population of the city and estimation of economic cost of health damage due to ambient air quality can help to make rational control strategies for environmental management. The total health cost for Mumbai city for the year 2012, with a population of 12.4 million, was estimated as USD8000 million.     

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.     

Product Guide/1988

Investigation, mitigation, and clean-up of hazardous materials at Superfund sites normally requires on-site workers to perform hazardous and sometimes potentially dangerous functions. Such functions include site surveys and the reconnaissance for airborne and buried toxic environmental contaminants. Airborne contaminants of concern usually emanate from spilled materials and require monitoring the air at the perimeter and throughout the clean-up site to ascertain the extent of contamination. Buried contaminants of major concern are often the result of leaking underground drums containing toxic wastes and require "reconnaissance excavations" to determine their location. Workers conducting on-site air monitoring risk dermal, ocular and inhalation exposure to hazardous chemicals, while those performing excavations also risk the potential exposure to fire, explosion, and other physical injury. EPA's current efforts to protect its workers and mitigate these risks include the use of robotic devices. Using robots offers the ultimate in personnel protection by removing the worker from the site of potential exposure, especially during site investigations, when there is almost always a certain encounter with unknown chemical wastes having unknown toxicity.

This paper describes the demonstration of a commercially-available robotic plat form modified and equipped for air monitoring and the ongoing research for the development of a ground penetrating radar (GPR) system to detect buried chemical waste drums. These robotic devices can ultimately be routinely deployed in the field for the purpose of conducting inherently safe reconnaissance activities during Superfund / SARA remedial operations.     

Spatial variation of airborne pollutant concentrations in Brisbane,Australia and its potential impact on population exposure assessment

Spread of air pollution sources and non-uniform mixing conditions in urban or regional air sheds often result in spatial variation of pollutant concentrations over different parts of the air sheds. A comprehensive understanding of this variation of concentrations is imperative for informed planning, monitoring and assessment in a range of critical areas including assessment of monitoring network efficiency or assessment of population exposure variation as a function of the location in the city. The aims of this work were to study the citywide variability of pollutants as measured by “urban background” type monitoring stations and to interpret the results in relation to the applicability of the data to population exposure assessments and the network efficiency. A comparison between ambient concentrations of NO_x, ozone and PM₁₀ was made for three stations in the Brisbane air shed network. The best correlated between the three stations were ozone concentrations followed by NO_x concentration, with the worst correlations observed for PM₁₀. With a few exceptions correlations of all pollutants between the stations were statistically significant. Marginally better were the correlations for the lower concentrations of pollutants that represent urban background, over the correlations for higher concentrations, representing peak values. Implications of these findings on application of the monitoring data to air-quality management, as well as the need for further investigations has been discussed.     

National review of ambient air toxics observations

Ambient air observations of hazardous air pollutant (HAPs), also known as air toxics, derived from routine monitoring networks operated by states, local agencies, and tribes (SLTs), are analyzed to characterize national concentrations and risk across the nation for a representative subset of the 187 designated HAPs. Observations from the National Air Toxics Trend Sites (NATTS) network of 27 stations located in most major urban areas of the contiguous United States have provided a consistent record of HAPs that have been identified as posing the greatest risk since 2003 and have also captured similar concentration patterns of nearly 300 sites operated by SLTs. Relatively high concentration volatile organic compounds (VOCs) such as benzene, formaldehyde, and toluene exhibit the highest annual average concentration levels, typically ranging from 1 to 5 µg/m³. Halogenated (except for methylene chloride) and semivolatile organic compounds (SVOCs) and metals exhibit concentrations typically 2–3 orders of magnitude lower. Formaldehyde is the highest national risk driver based on estimated cancer risk and, nationally, has not exhibited significant changes in concentration, likely associated with the large pool of natural isoprene and formaldehyde emissions. Benzene, toluene, ethylbenzene, and 1,3-butadiene are ubiquitous VOC HAPs with large mobile source contributions that continue to exhibit declining concentrations over the last decade. Common chlorinated organic compounds such as ethylene dichloride and methylene chloride exhibit increasing concentrations. The variety of physical and chemical attributes and measurement technologies across 187 HAPs result in a broad range of method detection limits (MDLs) and cancer risk thresholds that challenge confidence in risk results for low concentration HAPs with MDLs near or greater than risk thresholds. From a national monitoring network perspective, the ability of the HAPs observational database to characterize the multiple pollutant and spatial scale patterns influencing exposure is severely limited and positioned to benefit by leveraging a variety of emerging measurement technologies.

Implications:?Ambient air toxics observation networks have limited ability to characterize the broad suite of hazardous air pollutants (HAPs) that affect exposures across multiple spatial scales. While our networks are best suited to capture major urban-scale signals of ubiquitous volatile organic compound HAPs, incorporation of sensing technologies that address regional and local-scale exposures should be pursued to address major gaps in spatial resolution. Caution should be exercised in interpreting HAPs observations based on data proximity to minimum detection limit and risk thresholds.     

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.