Shaibal Mukerjee

An environmental scoping study in the Lower Rio Grande Valley of Texas — III. Residential microenvironmental monitoring for air, house dust, and soil

A principal aspect of the 1993 Lower Rio Grande Valley Environmental Scoping Study was the analysis and interpretation of residential air, household dust, and soil pollutant concentration data for exposure assessments. Measurements included respirable particulate matter (PM2.5), volatile organic compounds (VOCs), pesticides, and polycyclic aromatic hydrocarbons (PAHs) in indoor and outdoor air. Household dust, road dust, and yard soil were analyzed for elements, pesticides, and PAHs. Nine residences were monitored for three weeks in the spring of 1993. Additional monitoring was conducted at six of the nine residences for ten days the following summer. Generally good agreement was found between outdoor residential air and same-species measurements collected concurrently at a non-residential central site in Brownsville, TX (Ellenson et al. 1997) for fine particulate matter, elements, and VOCs indicating the dominance of regional influences. PM2.5 mass and element concentrations in residential indoor and outdoor air were generally higher in the summer than in the spring. Indoor air concentrations of many species were higher than outdoor air concentrations and were attributed to household activities, ventilation of residences, and track-in of dislodged soils. Evidence of agricultural activities was noted in the occurrence of crop-related pesticides (e.g., malathion and chlorpyrifos) in indoor and outdoor air. Concentrations of common household pesticides (e.g., chlordane, chlorpyrifos, diazinon, heptachlor, and propoxur) were generally higher indoors than outdoors and were also present in house dust. Seasonal comparisons of pesticides and PAHs were also presented using matched residences in spring and summer; VOCs also may have indicated seasonal effects. VOCs (notably propane and butane isomers) and PAHs were higher indoors, presumably due to cooking-related activities.

Spatial analysis and land use regression of VOCs and NO2 from school-based urban air monitoring in Detroit/Dearborn, USA

Passive ambient air sampling for nitrogen dioxide (NO(2)) and volatile organic compounds (VOCs) was conducted at 25 school and two compliance sites in Detroit and Dearborn, Michigan, USA during the summer of 2005. Geographic Information System (GIS) data were calculated at each of 116 schools. The 25 selected schools were monitored to assess and model intra-urban gradients of air pollutants to evaluate impact of traffic and urban emissions on pollutant levels. Schools were chosen to be statistically representative of urban land use variables such as distance to major roadways, traffic intensity around the schools, distance to nearest point sources, population density, and distance to nearest border crossing. Two approaches were used to investigate spatial variability. First, Kruskal-Wallis analyses and pairwise comparisons on data from the schools examined coarse spatial differences based on city section and distance from heavily trafficked roads. Secondly, spatial variation on a finer scale and as a response to multiple factors was evaluated through land use regression (LUR) models via multiple linear regression. For weeklong exposures, VOCs did not exhibit spatial variability by city section or distance from major roads; NO(2) was significantly elevated in a section dominated by traffic and industrial influence versus a residential section. Somewhat in contrast to coarse spatial analyses, LUR results revealed spatial gradients in NO(2) and selected VOCs across the area. The process used to select spatially representative sites for air sampling and the results of coarse and fine spatial variability of air pollutants provide insights that may guide future air quality studies in assessing intra-urban gradients.

GIS-Modeled Indicators of Traffic-Related Air Pollutants and Adverse Pulmonary Health Among Children in El Paso, Texas

Investigators examined 5,654 children enrolled in the El Paso, Texas, public school district by questionnaire in 2001. Exposure measurements were first collected in the late fall of 1999. School-level and residence-level exposures to traffic-related air pollutants were estimated using a land use regression model. For 1,529 children with spirometry, overall geographic information system (GIS)-modeled residential levels of traffic-related ambient air pollution (calibrated to a 10-ppb increment in nitrogen dioxide levels) were associated with a 2.4% decrement in forced vital capacity (95% confidence interval (CI): -4.0, -0.7) after adjustment for demographic, anthropomorphic, and socioeconomic factors and spirometer/technician effects. After adjustment for these potential covariates, overall GIS-modeled residential levels of traffic-related ambient air pollution (calibrated to a 10-ppb increment in nitrogen dioxide levels) were associated with pulmonary function levels below 85% of those predicted for both forced vital capacity (odds ratio (OR) = 3.10, 95% CI: 1.65, 5.78) and forced expiratory volume in 1 second (OR = 2.35, 95% CI: 1.38, 4.01). For children attending schools at elevations above 1,170 m, a 10-ppb increment in modeled nitrogen dioxide levels was associated with current asthma (OR = 1.56, 95% CI: 1.08, 2.50) after adjustment for demographic, socioeconomic, and parental factors and random school effects. These results are consistent with previous studies in Europe and California that found adverse health outcomes in children associated with modeled traffic-related air pollutants.

STATISTICAL APPROACHES IN WIND SECTOR ANALYSES FOR ASSESSING LOCAL SOURCE IMPACTS

Abstract Nonparametric statistical methods were used as part of a wind sector analysis assessment for the purpose of investigating local source impacts. Linear-angular rank correlations were employed to test for the presence of association between pollutant tracer concentrations and wind direction for a local area dominated by a single emission source (a biomedical waste combustor). Testing for the presence of these associations in ambient data from a local area likely to include multiple pollutant sources (including a resource recovery facility) was accomplished using the nonparametric Kruskal-Wallis test. Modified pollutant wind rose plots were used to qualitatively investigate the nature of the associations detected using these statistical tests, focusing on the sources of interest known to emit the pollutant tracers-of-opportunity. The statistical methods presented provide a quantitative basis for the assessment of source impacts based on observed associations between wind direction and pollutant concentrations.

Association of Roadway Proximity with Fasting Plasma Glucose and Metabolic Risk Factors for Cardiovascular Disease in a Cross-Sectional Study of Cardiac Catheterization Patients

Background The relationship between traffic-related air pollution (TRAP) and risk factors for cardiovascular disease needs to be better understood in order to address the adverse impact of air pollution on human health. Objective We examined associations between roadway proximity and traffic exposure zones, as markers of TRAP exposure, and metabolic biomarkers for cardiovascular disease risk in a cohort of patients undergoing cardiac catheterization. Methods We performed a cross-sectional study of 2,124 individuals residing in North Carolina (USA). Roadway proximity was assessed via distance to primary and secondary roadways, and we used residence in traffic exposure zones (TEZs) as a proxy for TRAP. Two categories of metabolic outcomes were studied: measures associated with glucose control, and measures associated with lipid metabolism. Statistical models were adjusted for race, sex, smoking, body mass index, and socioeconomic status (SES). Results An interquartile-range (990 m) decrease in distance to roadways was associated with higher fasting plasma glucose (β = 2.17 mg/dL; 95% CI: –0.24, 4.59), and the association appeared to be limited to women (β = 5.16 mg/dL; 95% CI: 1.48, 8.84 compared with β = 0.14 mg/dL; 95% CI: –3.04, 3.33 in men). Residence in TEZ 5 (high-speed traffic) and TEZ 6 (stop-and-go traffic), the two traffic zones assumed to have the highest levels of TRAP, was positively associated with high-density lipoprotein cholesterol (HDL-C; β = 8.36; 95% CI: –0.15, 16.9 and β = 5.98; 95% CI: –3.96, 15.9, for TEZ 5 and 6, respectively). Conclusion Proxy measures of TRAP exposure were associated with intermediate metabolic traits associated with cardiovascular disease, including fasting plasma glucose and possibly HDL-C. Citation Ward-Caviness CK, Kraus WE, Blach C, Haynes CS, Dowdy E, Miranda ML, Devlin RB, Diaz-Sanchez D, Cascio WE, Mukerjee S, Stallings C, Smith LA, Gregory SG, Shah SH, Hauser ER, Neas LM. 2015. Association of roadway proximity with fasting plasma glucose and metabolic risk factors for cardiovascular disease in a cross-sectional study of cardiac catheterization patients. Environ Health Perspect 123:1007–1014; http://dx.doi.org/10.1289/ehp.1306980

Techniques to assess cross-border air pollution and application to a US-Mexico border region

A year-long assessment of cross-border air pollution was conducted in the eastmost section of the US-Mexico border region, known as the Lower Rio Grande Valley, in South Texas. Measurements were conducted on the US side and included fine particle mass (PM2.5) and elemental composition, volatile organic compounds (VOCs) and meteorology. Wind sector analyses of chemical tracers and diagnostic ratios, in addition to principal component analysis (PCA), were initially applied to assess cross-border and overall air shed influences. Linear-angular correlation statistics [Biometrika, 63, (1976), 403-405] and nonparametric multiple comparisons between wind sectors were computed with the particle element data using principal component scores from PCA to determine the direction of source classes. Findings suggest crustal particles and salts carried or stirred by sea breeze winds from a southerly and southeasterly direction from the Gulf of Mexico heavily influenced the elemental composition of the particulate samples. Pair-wise comparisons of wind directions for the principal component scores suggest possible oil combustion influences from utilities or boilers coming from the south and possible coal combustion influences from the north and northwest. The techniques discussed can provide a methodology to assess future ambient levels and cross-border influences in the Valley as conditions change.

An environmental scoping study in the lower Rio Grande Valley of Texas : I. Comparative assessment of air sampling methods

Abstract The atmospheric monitoring component of the 1993 Lower Rio Grande Valley Environmental Scoping Study measured a wide range of pollutant species from different sampling and analysis methods. Extensive QA/QC activities were also conducted on the sampling and analysis techniques. This enabled a unique comparison of these methods to provide insights into air sampling for larger, long-term exposure monitoring studies. Pollutants monitored were particulate mass and elements, acidic gases, volatile organic compounds, pesticides, and polycyclic aromatic hydrocarbons. This included collocated monitoring devices which monitored same-species pollutants. Sample collection efficiencies of certain atmospheric pollutants are discussed. Finally, data from two sites located in the Lower Rio Grande Valley are also presented and compared.

A concept of risk apportionment of air emission sources for risk reduction considerations

Abstract Receptor and dispersion modeling techniques are extended to develop a health risk apportionment concept in which inhalation exposure to emission sources of ambient element pollutants are estimated. A preliminary demonstration of the concept is performed using ambient and emission inventory data from an industrial air shed located in a residential area. It is shown that risks from identified emission sources can be quantified and that a total, additive risk can be estimated for the sources in the air shed. Potential risk reduction measures can then be considered on the main risk sources without arbitrarily reducing risk for all existing sources in the air shed. Dispersion modeling is utilized from emission inventory data so that risk estimates for the primary sources can be modeled and compared with both ambient and receptor model risk estimates.

Evaluation of Land Use Regression Models for Nitrogen Dioxide and Benzene in Four US Cities

Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO2) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. LURs were successfully developed to estimate pollutant concentrations throughout the study areas. Comparisons of development and predictive capabilities of LURs from these four cities are presented to address this issue of uniform application of LURs across study areas. Traffic and other urban variables were important predictors in the LURs although city-specific influences (such as border crossings) were also important. In addition, transferability of variables or LURs from one city to another may be problematic due to intercity differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult.

Seasonal effects in land use regression models for nitrogen dioxide, coarse particulate matter, and gaseous ammonia in Cleveland, Ohio

Abstract Passive ambient air sampling for nitrogen dioxide (NO 2 ), coarse particulate matter (PMc), and gaseous ammonia (NH 3 ) was conducted at 22 monitoring sites, a compliance site, and a background site in the Cleveland, Ohio, USA area during summer 2009 and winter 2010. This air monitoring network was established to assess intra–urban gradients of air pollutants and evaluate the impact of traffic and urban emissions on air quality. Method evaluations of passive monitors, which were weeklong in duration for NO 2 and PMc and two–weeklong for NH 3 , demonstrated the ability of the NO 2 and NH 3 monitors to adequately measure air pollution concentrations, while the precision of the PMc sampler showed the need for improvement. Seasonal differences were obvious from visual inspection for NO 2 (higher in winter) and NH 3 (higher in summer) but were less apparent for PMc levels. Land use regression models (LURs) revealed spatial gradients for NO 2 and PMc from traffic and industrial sources. A strong summer/winter seasonal influence was detected in the LURs, with season being the only significant predictor of NH 3 . Explicit use of summer and winter seasons in the LURs revealed both a seasonal effect, per se , and also seasonal interaction with other predictor variables.