Estimating long-term average particulate air pollution concentrations: application of traffic indicators and geographic information systems

Land use regression modelling estimating nitrogen oxides exposure in industrial south Durban, South Africa

BACKGROUND

The South Durban (SD) area of Durban, South Africa, has a history of air pollution issues due to the juxtaposition of low-income communities with industrial areas. This study used measurements of oxides of nitrogen (NO_x) to develop a land use regression (LUR) model to explain the spatial variation of air pollution concentrations in this area.

METHODS

Ambient NO_x was measured over two two-week sampling periods at 32 sites using Ogawa badges. Following the ESCAPE approach, an annual adjusted average was calculated for these results and regressed against pre-selected geographic predictor variables in a multivariate regression model. The LUR model was then applied to predict the NO_x exposure of a sample of pregnant women living in South Durban.

RESULTS

Measured NO_x levels ranged from 22.3-50.9μg/m³ with a median of 36μg/m³. The model developed accounts for 73% of the variance in ambient NO_x measurements using three input variables (length of minor roads within a 1000m radius, length of major roads within a 300m radius, and area of open space within a 1000m radius). Model cross validation yielded a R² of 0.59. Subsequent participant exposure estimates indicated exposure to ambient NO_x ranged from 19.9-53.2μg/m³, with a mean of 39μg/m³.

DISCUSSION AND CONCLUSION

This is the first study to develop a land use regression model that predicts ambient concentrations of NO_x in a South African context. The findings of this study indicate that the participants in the South Durban are exposed to high levels of NO_x that can be attributed mainly to traffic.

A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States

Fine ambient particulate matter has been widely associated with multiple health effects. Mitigation hinges on understanding which sources are contributing to its toxicity. Black Carbon (BC), an indicator of particles generated from traffic sources, has been associated with a number of health effects however due to its high spatial variability, its concentration is difficult to estimate. We previously fit a model estimating BC concentrations in the greater Boston area; however this model was built using limited monitoring data and could not capture the complex spatio-temporal patterns of ambient BC. In order to improve our predictive ability, we obtained more data for a total of 24,301 measurements from 368 monitors over a 12 year period in Massachusetts, Rhode Island and New Hampshire. We also used Nu-Support Vector Regression (nu-SVR) - a machine learning technique which incorporates nonlinear terms and higher order interactions, with appropriate regularization of parameter estimates. We then used a generalized additive model to refit the residuals from the nu-SVR and added the residual predictions to our earlier estimates. Both spatial and temporal predictors were included in the model which allowed us to capture the change in spatial patterns of BC over time. The 10 fold cross validated (CV) R² of the model was good in both cold (10-fold CV R² = 0.87) and warm seasons (CV R² = 0.79). We have successfully built a model that can be used to estimate short and long-term exposures to BC and will be useful for studies looking at various health outcomes in MA, RI and Southern NH.

Development of PM2.5 and NO2 models in a LUR framework incorporating satellite remote sensing and air quality model data in Pearl River Delta region, China

High resolution pollution maps are critical to understand the exposure and health effect of local residents to air pollution. Currently, none of the single technologies used to measure or estimate concentrations of pollutants can provide sufficient resolved exposure data. Land use regression (LUR) models were developed to combine ground-based measurements, satellite remote sensing (SRS) and air quality model (AQM), together with geographic and local source related spatial inputs, to generate high resolution pollution maps for both PM_2.5 and NO₂ in Pearl River Delta (PRD), China. Four sets of LUR models (LUR without SRS or AQM, with SRS only, with AQM only, and with both SRS and AQM), all including local traffic emissions and land use variables, were compared to evaluate the contribution of SRS and AQM data to the performance of LUR models in PRD region. For NO₂, the annual model with SRS estimate performed best, explaining 60.5% of the spatial variation. For PM_2.5, the annual model with traditional predictor variables without SRS or AQM estimates showed the best performance, explaining 88.4% of the spatial variation. Pollution surfaces at 200 m*200 m resolution were generated according to the best performed models.

Residential Proximity to Roadways and Ischemic Placental Disease in a Cape Cod Family Health Study

Exposure to air pollution may adversely impact placental function through a variety of mechanisms; however, epidemiologic studies have found mixed results. We examined the association between traffic exposure and placental-related obstetric conditions in a retrospective cohort study on Cape Cod, MA, USA. We assessed exposure to traffic using proximity metrics (distance of residence to major roadways and length of major roadways within a buffer around the residence). The outcomes included self-reported ischemic placental disease (the presence of at least one of the following conditions: preeclampsia, placental abruption, small-for-gestational-age), stillbirth, and vaginal bleeding. We used log-binomial regression models to estimate risk ratios (RR) and 95% confidence intervals (CI), adjusting for potential confounders. We found no substantial association between traffic exposure and ischemic placental disease, small-for-gestational-age, preeclampsia, or vaginal bleeding. We found some evidence of an increased risk of stillbirth and placental abruption among women living the closest to major roadways (RRs comparing living <100 m vs. ≥200 m = 1.75 (95% CI: 0.82-3.76) and 1.71 (95% CI: 0.56-5.23), respectively). This study provides some support for the hypothesis that air pollution exposure adversely affects the risk of placental abruption and stillbirth; however, the results were imprecise due to the small number of cases, and may be impacted by non-differential exposure misclassification and selection bias.

Left ventricular function in relation to chronic residential air pollution in a general population

Background

In view of the increasing heart failure epidemic and awareness of the adverse impact of environmental pollution on human health, we investigated the association of left ventricular structure and function with air pollutants in a general population.

Methods

In 671 randomly recruited Flemish (51.7% women; mean age, 50.4 years) we echocardiographically assessed left ventricular systolic strain and strain rate and the early and late peak velocities of transmitral blood flow and mitral annular movement (2005−2009). Using subject-level data, left ventricular function was cross-sectionally correlated with residential long-term exposure to air pollutants, including black carbon, PM2.5, PM10 (particulate matter) and nitrogen dioxide (NO2), while accounting for clustering by residential address and confounders.

Results

Annual exposures to black carbon, PM2.5, PM10 and NO2 averaged 1.19, 13.0, 17.7, and 16.8 µg/m³. Systolic left ventricular function was worse (p ≤ 0.027) with higher black carbon, PM2.5, PM10 and NO2 with association sizes per interquartile interval increment ranging from −0.339 to −0.458% for longitudinal strain and from −0.033 to −0.049 s⁻¹ for longitudinal strain rate. Mitral E and a′ peak velocities were lower (p ≤ 0.021) with higher black carbon, PM2.5 and PM10 with association sizes ranging from −1.727 to −1.947 cm/s and from −0.175 to −0.235 cm/s, respectively. In the geographic analysis, the systolic longitudinal strain sided with gradients in air pollution. The path analysis identified systemic inflammation as a possible mediator of associations with black carbon.

Conclusions

Long-term low-level air pollution is associated with subclinical impairment of left ventricular performance and might be a risk factor for heart failure.

Individual exposure to nitrogen dioxide and adverse pregnancy outcomes in Kaunas study

Several epidemiological studies have found some evidence suggesting that exposure to air pollutants during pregnancy increases the risk of adverse birth outcomes. In this cohort study, we assessed individual maternal exposure to nitrogen dioxide (NO₂) during pregnancy and examined the association between the exposure and pregnancy outcomes, such as low birth weight (LBW), term low birth weight (TLBW), small for gestational age (SGA) and preterm birth (PB). 3292 women living in Kaunas city, Lithuania, data and their singleton newborns were included in the study. Exposure to NO₂ was assigned to each individual subject during pregnancy based on residential address using an AIRVIRO dispersion model and geographic information system (GIS). The results of the logistic regression analysis showed that LBW risk increased statistically significantly with increasing exposure to NO₂. Increased maternal exposure to NO₂ tended to increase the risk for TLBW, SGA and PB.

Modeling the intraurban variation in nitrogen dioxide in urban areas in Kathmandu Valley, Nepal

BACKGROUND

With growing urbanization, traffic has become one of the main sources of air pollution in Nepal. Understanding the impact of air pollution on health requires estimation of exposure. Land use regression (LUR) modeling is widely used to investigate intraurban variation in air pollution for Western cities, but LUR models are relatively scarce in developing countries. In this study, we developed LUR models to characterize intraurban variation of nitrogen dioxide (NO₂) in urban areas of Kathmandu Valley, Nepal, one of the fastest urbanizing areas in South Asia.

METHODS

Over the study area, 135 monitoring sites were selected using stratified random sampling based on building density and road density along with purposeful sampling. In 2014, four sampling campaigns were performed, one per season, for two weeks each. NO₂ was measured using duplicate Palmes tubes at 135 sites, with additional information on nitric oxide (NO), NO₂, and nitrogen oxide (NOx) concentrations derived from Ogawa badges at 28 sites. Geographical variables (e.g., road network, land use, built area) were used as predictor variables in LUR modeling, considering buffers 25-400m around each monitoring site.

RESULTS

Annual average NO₂ by site ranged from 5.7 to 120ppb for the study area, with higher concentrations in the Village Development Committees (VDCs) of Kathmandu and Lalitpur than in Kirtipur, Thimi, and Bhaktapur, and with variability present within each VDC. In the final LUR model, length of major road, built area, and industrial area were positively associated with NO₂ concentration while normalized difference vegetation index (NDVI) was negatively associated with NO₂ concentration (R²=0.51). Cross-validation of the results confirmed the reliability of the model.

CONCLUSIONS

The combination of passive NO₂ sampling and LUR modeling techniques allowed for characterization of nitrogen dioxide patterns in a developing country setting, demonstrating spatial variability and high pollution levels.

Robustness of Land-Use Regression Models Developed from Mobile Air Pollutant Measurements

Land-use regression (LUR) models are useful for resolving fine scale spatial variations in average air pollutant concentrations across urban areas. With the rise of mobile air pollution campaigns, characterized by short-term monitoring and large spatial extents, it is important to investigate the effects of sampling protocols on the resulting LUR. In this study a mobile lab was used to repeatedly visit a large number of locations (∼1800), defined by road segments, to derive average concentrations across the city of Montreal, Canada. We hypothesize that the robustness of the LUR from these data depends upon how many independent, random times each location is visited (N_vis) and the number of locations (N_loc) used in model development and that these parameters can be optimized. By performing multiple LURs on random sets of locations, we assessed the robustness of the LUR through consistency in adjusted R² (i.e., coefficient of variation, CV) and in regression coefficients among different models. As N_loc increased, R²_adj became less variable; for N_loc = 100 vs N_loc = 300 the CV in R²_adj for ultrafine particles decreased from 0.088 to 0.029 and from 0.115 to 0.076 for NO_2. The CV in the R²_adj also decreased as N_vis increased from 6 to 16; from 0.090 to 0.014 for UFP. As N_loc and N_vis increase, the variability in the coefficient sizes across the different model realizations were also seen to decrease.

Sex-Specific Associations between Particulate Matter Exposure and Gene Expression in Independent Discovery and Validation Cohorts of Middle-Aged Men and Women

BACKGROUND

Particulate matter (PM) exposure leads to premature death, mainly due to respiratory and cardiovascular diseases.

OBJECTIVES

Identification of transcriptomic biomarkers of air pollution exposure and effect in a healthy adult population.

METHODS

Microarray analyses were performed in 98 healthy volunteers (48 men, 50 women). The expression of eight sex-specific candidate biomarker genes (significantly associated with PM₁₀ in the discovery cohort and with a reported link to air pollution-related disease) was measured with qPCR in an independent validation cohort (75 men, 94 women). Pathway analysis was performed using Gene Set Enrichment Analysis. Average daily PM_2.5 and PM₁₀ exposures over 2-years were estimated for each participant's residential address using spatiotemporal interpolation in combination with a dispersion model.

RESULTS

Average long-term PM₁₀ was 25.9 (± 5.4) and 23.7 (± 2.3) μg/m³ in the discovery and validation cohorts, respectively. In discovery analysis, associations between PM₁₀ and the expression of individual genes differed by sex. In the validation cohort, long-term PM₁₀ was associated with the expression of DNAJB5 and EAPP in men and ARHGAP4 (p = 0.053) in women. AKAP6 and LIMK1 were significantly associated with PM₁₀ in women, although associations differed in direction between the discovery and validation cohorts. Expression of the eight candidate genes in the discovery cohort differentiated between validation cohort participants with high versus low PM₁₀ exposure (area under the receiver operating curve = 0.92; 95% CI: 0.85, 1.00; p = 0.0002 in men, 0.86; 95% CI: 0.76, 0.96; p = 0.004 in women).

CONCLUSIONS

Expression of the sex-specific candidate genes identified in the discovery population predicted PM₁₀ exposure in an independent cohort of adults from the same area. Confirmation in other populations may further support this as a new approach for exposure assessment, and may contribute to the discovery of molecular mechanisms for PM-induced health effects.

COVARIATE-ADAPTIVE CLUSTERING OF EXPOSURES FOR AIR POLLUTION EPIDEMIOLOGY COHORTS

Cohort studies in air pollution epidemiology aim to establish associations between health outcomes and air pollution exposures. Statistical analysis of such associations is complicated by the multivariate nature of the pollutant exposure data as well as the spatial misalignment that arises from the fact that exposure data are collected at regulatory monitoring network locations distinct from cohort locations. We present a novel clustering approach for addressing this challenge. Specifically, we present a method that uses geographic covariate information to cluster multi-pollutant observations and predict cluster membership at cohort locations. Our predictive k-means procedure identifies centers using a mixture model and is followed by multi-class spatial prediction. In simulations, we demonstrate that predictive k-means can reduce misclassification error by over 50% compared to ordinary k-means, with minimal loss in cluster representativeness. The improved prediction accuracy results in large gains of 30% or more in power for detecting effect modification by cluster in a simulated health analysis. In an analysis of the NIEHS Sister Study cohort using predictive k-means, we find that the association between systolic blood pressure (SBP) and long-term fine particulate matter (PM2.5) exposure varies significantly between different clusters of PM2.5 component profiles. Our cluster-based analysis shows that for subjects assigned to a cluster located in the Midwestern U.S., a 10 μg/m3 difference in exposure is associated with 4.37 mmHg (95% CI, 2.38, 6.35) higher SBP.

Infants' indoor and outdoor residential exposure to benzene and respiratory health in a Spanish cohort

Benzene exposure represents a potential risk for children's health. Apart from being a known carcinogen for humans (group 1 according to IARC), there is scientific evidence suggesting a relationship between benzene exposure and respiratory problems in children. But results are still inconclusive and inconsistent. This study aims to assess the determinants of exposure to indoor and outdoor residential benzene levels and its relationship with respiratory health in infants. Participants were 1-year-old infants (N = 352) from the INMA cohort from Valencia (Spain). Residential benzene exposure levels were measured inside and outside dwellings by means of passive samplers in a 15-day campaign. Persistent cough, low respiratory tract infections and wheezing during the first year of life, and covariates (dwelling traits, lifestyle factors and sociodemographic data) were obtained from parental questionnaires. Multiple Tobit regression and logistic regression models were performed to assess factors associated to residential exposure levels and health associations, respectively. Indoor levels were higher than outdoor ones (1.46 and 0.77 μg/m³, respectively; p < 0.01). A considerable percentage of dwellings, 42% and 21% indoors and outdoors respectively, surpassed the WHO guideline of 1.7 μg/m³ derived from a lifetime risk of leukemia above 1/100 000. Monitoring season, maternal country of birth and parental tobacco consumption were associated with residential benzene exposure (indoor and outdoors). Additionally, indoor levels were associated with mother's age and type of heating, and outdoor levels were linked with zone of residence and distance from industrial areas. After adjustment for confounding factors, no significant associations were found between residential benzene exposure levels and respiratory health in infants. Hence, our study did not support the hypothesis for the benzene exposure effect on respiratory health in children. Even so, it highlights a public health concern related to the personal exposure levels, since a considerable number of children surpassed the abovementioned WHO guideline for benzene exposure.

Spatial variation in diesel-related elemental and organic PM2.5 components during workweek hours across a downtown core

Capturing intra-urban variation in diesel-related pollution exposures remains a challenge, given its complex chemical mix, and relatively few well-characterized ambient-air tracers for the multiple diesel sources in densely-populated urban areas. To capture fine-scale spatial resolution (50×50m grid cells) in diesel-related pollution, we used geographic information systems (GIS) to systematically allocate 36 sampling sites across downtown Pittsburgh, PA, USA (2.8km²), cross-stratifying to disentangle source impacts (i.e., truck density, bus route frequency, total traffic density). For buses, outbound and inbound trips per week were summed by route and a kernel density was calculated across sites. Programmable monitors collected fine particulate matter (PM_2.5) samples specific to workweek hours (Monday-Friday, 7 am-7 pm), summer and winter 2013. Integrated filters were analyzed for black carbon (BC), elemental carbon (EC), organic carbon (OC), elemental constituents, and diesel-related organic compounds [i.e., polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes]. To our knowledge, no studies have collected this suite of pollutants with such high sampling density, with the ability to capture spatial patterns during specific hours of interest. We hypothesized that we would find substantial spatial variation for each pollutant and significant associations with key sources (e.g. diesel and gasoline vehicles), with higher concentrations near the center of this small downtown core. Using a forward stepwise approach, we developed seasonal land use regression (LUR) models for PM_2.5, BC, total EC, OC, PAHs, hopanes, steranes, aluminum (Al), calcium (Ca), and iron (Fe). Within this small domain, greater concentration differences were observed in most pollutants across sites, on average, than between seasons. Higher PM_2.5 and BC concentrations were found in the downtown core compared to the boundaries. PAHs, hopanes, and steranes displayed different spatial patterning across the study area by constituent. Most LUR models suggested a strong influence of bus-related emissions on pollution gradients. Buses were more dominant predictors compared to truck and vehicular traffic for several pollutants. Overall, we found substantial variation in diesel-related concentrations in a very small downtown area, which varied across elemental and organic components.

Spatial variation in nitrogen dioxide concentrations and cardiopulmonary hospital admissions

BACKGROUND

Air pollution episodes are associated with increased cardiopulmonary hospital admissions. Cohort studies showed associations of spatial variation in traffic-related air pollution with respiratory and cardiovascular mortality. Much less is known in particular about associations with cardiovascular morbidity. We explored the relation between spatial variation in nitrogen dioxide (NO₂) concentrations and cardiopulmonary hospital admissions.

METHODS

This ecological study was based on hospital admissions data (2001-2004) from the National Medical Registration and general population data for the West of the Netherlands (population 4.04 million). At the 4-digit postcode area level (n=683) associations between modeled annual average outdoor NO₂ concentrations and hospital admissions for respiratory and cardiovascular causes were evaluated by linear regression with the log of the postcode-specific percentage of subjects that have been admitted at least once during the study period as the dependent variable. All analyses were adjusted for differences in composition of the population of the postcode areas (age, sex, income).

RESULTS

At the postcode level, positive associations were found between outdoor NO₂ concentrations and hospital admission rates for asthma, chronic obstructive pulmonary disease (COPD), all cardiovascular causes, ischemic heart disease and stroke (e.g. adjusted relative risk (95% confidence interval) for the second to fourth quartile relative to the first quartile of exposure were 1.87 (1.46-2.40), 2.34 (1.83-3.01) and 2.81 (2.16-3.65) for asthma; 1.44 (1.19-1.74), 1.50 (1.24-1.82) and 1.60 (1.31-1.96) for COPD). Associations remained after additional (indirect) adjustment for smoking (COPD admission rate) and degree of urbanization.

CONCLUSIONS

Our study suggests an increased risk of hospitalization for respiratory and cardiovascular causes in areas with higher levels of NO₂. Our findings add to the currently limited evidence of a long-term effect of air pollution on hospitalization. The ecological design of our study is a limitation and more studies with individual data are needed to confirm our findings.

Maximizing the spatial representativeness of NO2 monitoring data using a combination of local wind-based sectoral division and seasonal and diurnal correction factors

This article describes a new methodology for increasing the spatial representativeness of individual monitoring sites. Air pollution levels at a given point are influenced by emission sources in the immediate vicinity. Since emission sources are rarely uniformly distributed around a site, concentration levels will inevitably be most affected by the sources in the prevailing upwind direction. The methodology provides a means of capturing this effect and providing additional information regarding source/pollution relationships. The methodology allows for the division of the air quality data from a given monitoring site into a number of sectors or wedges based on wind direction and estimation of annual mean values for each sector, thus optimising the information that can be obtained from a single monitoring station. The method corrects for short-term data, diurnal and seasonal variations in concentrations (which can produce uneven weighting of data within each sector) and uneven frequency of wind directions. Significant improvements in correlations between the air quality data and the spatial air quality indicators were obtained after application of the correction factors. This suggests the application of these techniques would be of significant benefit in land-use regression modelling studies. Furthermore, the method was found to be very useful for estimating long-term mean values and wind direction sector values using only short-term monitoring data. The methods presented in this article can result in cost savings through minimising the number of monitoring sites required for air quality studies while also capturing a greater degree of variability in spatial characteristics. In this way, more reliable, but also more expensive monitoring techniques can be used in preference to a higher number of low-cost but less reliable techniques. The methods described in this article have applications in local air quality management, source receptor analysis, land-use regression mapping and modelling and population exposure studies.

A novel principal component analysis for spatially misaligned multivariate air pollution data

We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.

Association between Ambient Air Pollution and Asthma Prevalence in Different Population Groups Residing in Eastern Texas, USA

Air pollution has been an on-going research focus due to its detrimental impact on human health. However, its specific effects on asthma prevalence in different age groups, genders and races are not well understood. Thus, the present study was designed to examine the association between selected air pollutants and asthma prevalence in different population groups during 2010 in the eastern part of Texas, USA.The pollutants considered were particulate matter (PM2.5 with an aerodynamic diameter less than 2.5 micrometers) and surface ozone. The population groups were categorized based on age, gender, and race. County-wise asthma hospital discharge data for different age, gender, and racial groups were obtained from Texas Asthma Control Program, Office of Surveillance, Evaluation and Research, Texas Department of State Health Services. The annual means of the air pollutants were obtained from the United States Environmental Protection Agency (U.S. EPA)'s air quality system data mart program. Pearson correlation analyzes were conducted to examine the relationship between the annual mean concentrations of pollutants and asthma discharge rates (ADR) for different age groups, genders, and races. The results reveal that there is no significant association or relationship between ADR and exposure of air pollutants (PM2.5, and O₃). The study results showed a positive correlation between PM2.5 and ADR and a negative correlation between ADR and ozone in most of the cases. These correlations were not statistically significant, and can be better explained by considering the local weather conditions. The research findings facilitate identification of hotspots for controlling the most affected populations from further environmental exposure to air pollution, and for preventing or reducing the health impacts.

PM2.5 Population Exposure in New Delhi Using a Probabilistic Simulation Framework

This paper presents a Geographical Information System (GIS) based probabilistic simulation framework to estimate PM2.5 population exposure in New Delhi, India. The framework integrates PM2.5 output from spatiotemporal LUR models and trip distribution data using a Gravity model based on zonal data for population, employment and enrollment in educational institutions. Time-activity patterns were derived from a survey of randomly sampled individuals (n = 1012) and in-vehicle exposure was estimated using microenvironmental monitoring data based on field measurements. We simulated population exposure for three different scenarios to capture stay-at-home populations (Scenario 1), working population exposed to near-road concentrations during commutes (Scenario 2), and the working population exposed to on-road concentrations during commutes (Scenario 3). Simulated annual average levels of PM2.5 exposure across the entire city were very high, and particularly severe in the winter months: ∼200 μg m(-3) in November, roughly four times higher compared to the lower levels in the monsoon season. Mean annual exposures ranged from 109 μg m(-3) (IQR: 97-120 μg m(-3)) for Scenario 1, to 121 μg m(-3) (IQR: 110-131 μg m(-3)), and 125 μg m(-3) (IQR: 114-136 μ gm(-3)) for Scenarios 2 and 3 respectively. Ignoring the effects of mobility causes the average annual PM2.5 population exposure to be underestimated by only 11%.

Applications of GPS-tracked personal and fixed-location PM(2.5) continuous exposure monitoring

Continued development of personal air pollution monitors is rapidly improving government and research capabilities for data collection. In this study, we tested the feasibility of using GPS-enabled personal exposure monitors to collect personal exposure readings and short-term daily PM2.5 measures at 15 fixed locations throughout a community. The goals were to determine the accuracy of fixed-location monitoring for approximating individual exposures compared to a centralized outdoor air pollution monitor, and to test the utility of two different personal monitors, the RTI MicroPEM V3.2 and TSI SidePak AM510. For personal samples, 24-hr mean PM2.5 concentrations were 6.93 μg/m³ (stderr = 0.15) and 8.47 μg/m³ (stderr = 0.10) for the MicroPEM and SidePak, respectively. Based on time-activity patterns from participant journals, exposures were highest while participants were outdoors (MicroPEM = 7.61 µg/m³, stderr = 1.08, SidePak = 11.85 µg/m³, stderr = 0.83) or in restaurants (MicroPEM = 7.48 µg/m³, stderr = 0.39, SidePak = 24.93 µg/m³, stderr = 0.82), and lowest when participants were exercising indoors (MicroPEM = 4.78 µg/m³, stderr = 0.23, SidePak = 5.63 µg/m³, stderr = 0.08). Mean PM(2.5) at the 15 fixed locations, as measured by the SidePak, ranged from 4.71 µg/m³ (stderr = 0.23) to 12.38 µg/m³ (stderr = 0.45). By comparison, mean 24-h PM(2.5) measured at the centralized outdoor monitor ranged from 2.7 to 6.7 µg/m³ during the study period. The range of average PM(2.5) exposure levels estimated for each participant using the interpolated fixed-location data was 2.83 to 19.26 µg/m³ (mean = 8.3, stderr = 1.4). These estimated levels were compared with average exposure from personal samples. The fixed-location monitoring strategy was useful in identifying high air pollution microclimates throughout the county. For 7 of 10 subjects, the fixed-location monitoring strategy more closely approximated individuals' 24-hr breathing zone exposures than did the centralized outdoor monitor. Highlights are: Individual PM(2.5) exposure levels vary extensively by activity, location and time of day; fixed-location sampling more closely approximated individual exposures than a centralized outdoor monitor; and small, personal exposure monitors provide added utility for individuals, researchers, and public health professionals seeking to more accurately identify air pollution microclimates.

IMPLICATIONS

Personal air pollution monitoring technology is advancing rapidly. Currently, personal monitors are primarily used in research settings, but could they also support government networks of centralized outdoor monitors? In this study, we found differences in performance and practicality for two personal monitors in different monitoring scenarios. We also found that personal monitors used to collect outdoor area samples were effective at finding pollution microclimates, and more closely approximated actual individual exposure than a central monitor. Though more research is needed, there is strong potential that personal exposure monitors can improve existing monitoring networks.

Socioeconomic Disparities and Air Pollution Exposure: a Global Review

The existing reviews and meta-analyses addressing unequal exposure of environmental hazards on certain populations have focused on several environmental pollutants or on the siting of hazardous facilities. This review updates and contributes to the environmental inequality literature by focusing on ambient criteria air pollutants (including NOx), by evaluating studies related to inequality by socioeconomic status (as opposed to race/ethnicity) and by providing a more global perspective. Overall, most North American studies have shown that areas where low-socioeconomic-status (SES) communities dwell experience higher concentrations of criteria air pollutants, while European research has been mixed. Research from Asia, Africa, and other parts of the world has shown a general trend similar to that of North America, but research in these parts of the world is limited.

Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures

Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts.

Timing and Duration of Traffic-related Air Pollution Exposure and the Risk for Childhood Wheeze and Asthma

RATIONALE

The timing and duration of traffic-related air pollution (TRAP) exposure may be important for childhood wheezing and asthma development.

OBJECTIVES

We examined the relationship between TRAP exposure and longitudinal wheezing phenotypes and asthma at age 7 years.

METHODS

Children completed clinical examinations annually from age 1 year through age 4 years and age 7 years. Parental-reported wheezing was assessed at each age, and longitudinal wheezing phenotypes (early-transient, late-onset, persistent) and asthma were defined at age 7 years. Participants' time-weighted exposure to TRAP, from birth through age 7 years, was estimated using a land-use regression model. The relationship between TRAP exposure and wheezing phenotypes and asthma was examined.

MEASUREMENTS AND MAIN RESULTS

High TRAP exposure at birth was significantly associated with both transient and persistent wheezing phenotypes (adjusted odds ratio [aOR] = 1.64; 95% confidence interval [CI], 1.04-2.57 and aOR = 2.31; 95% CI, 1.28-4.15, respectively); exposure from birth to age 1 year and age 1 to 2 years was also associated with persistent wheeze. Only children with high average TRAP exposure from birth through age 7 years were at significantly increased risk for asthma (aOR = 1.71; 95% CI, 1.01-2.88).

CONCLUSIONS

Early-life exposure to TRAP is associated with increased risk for persistent wheezing, but only long-term exposure to high levels of TRAP throughout childhood was associated with asthma development.

Assessing traffic and industrial contributions to ambient nitrogen dioxide and volatile organic compounds in a low pollution urban environment

Land use regression (LUR) modeling is an effective method for estimating fine-scale distributions of ambient air pollutants. The objectives of this study are to advance the methodology for use in urban environments with relatively low levels of industrial activity and provide exposure assessments for research on health effects of air pollution. Intraurban distributions of nitrogen dioxide (NO2) and the volatile organic compounds (VOCs) benzene, toluene and m- and p-xylene were characterized based on spatial monitoring and LUR modeling in Ottawa, Ontario, Canada. Passive samplers were deployed at 50 locations throughout Ottawa for two consecutive weeks in October 2008 and May 2009. Land use variables representing point, area and line sources were tested as predictors of pooled pollutant distributions. LUR models explained 96% of the spatial variability in NO2 and 75-79% of the variability in the VOC species. Proximity to highways, green space, industrial and residential land uses were significant in the final models. More notably, proximity to industrial point sources and road network intersections were significant predictors for all pollutants. The strong contribution of industrial point sources to VOC distributions in Ottawa suggests that facility emission data should be considered whenever possible. The study also suggests that proximity to road network intersections may be an effective proxy in areas where reliable traffic data are not available.

Nocturnal dry cough in the first 7 years of life is associated with asthma at school age

BACKGROUND

Childhood wheeze is an important, well-known risk factor for asthma, yet little is known about the contribution of nocturnal dry cough. We investigated the association of nocturnal dry cough at ages 1-7 years with doctor-diagnosed asthma at 8 years of age, both in the presence and absence of wheeze.

METHODS

Data of 3,252 children from the PIAMA birth cohort were studied. Parents reported the presence of nocturnal dry cough, wheeze, and doctor-diagnosed asthma in the past 12 months yearly, from birth up to the age of 8 years.

RESULTS

Nocturnal dry cough without wheeze was significantly associated with doctor-diagnosed asthma at age 8, except for age 1 (range of Relative Risks (RR) at ages 2-7: 1.8 (age 5) - 7.1 (age 7), all P-values <0.048). As expected, wheeze without nocturnal dry cough was strongly associated with doctor-diagnosed asthma at age 8 (range of RR: 2.0 (age 1) - 22.2 (age 7), all P-values <0.003). Of interest, nocturnal dry cough with wheeze showed the strongest association with doctor-diagnosed asthma at age 8 (range of RR: 3.7 (age 1) - 26.0 (age 7), all P-values <0.001). The relative excess risk of asthma at age 8 due to interaction of nocturnal dry cough with wheeze at age 1 year was 1.8 (0.1-3.6, P < 0.01).

CONCLUSION

Nocturnal dry cough and wheeze in early childhood are both independently associated with asthma at school age. The presence of both nocturnal dry cough and wheeze at age 1 almost doubles the risk of asthma at age 8 compared to wheeze alone.

Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, Particle Size) Using Mobile Monitoring

Land Use Regression (LUR) models typically use fixed-site monitoring; here, we employ mobile monitoring as a cost-effective alternative for LUR development. We use bicycle-based, mobile measurements (∼85 h) during rush-hour in Minneapolis, MN to build LUR models for particulate concentrations (particle number [PN], black carbon [BC], fine particulate matter [PM2.5], particle size). We developed and examined 1224 separate LUR models by varying pollutant, time-of-day, and method of spatial and temporal smoothing of the time-series data. Our base-case LUR models had modest goodness-of-fit (adjusted R(2): ∼0.5 [PN], ∼0.4 [PM2.5], 0.35 [BC], ∼0.25 [particle size]), low bias (<4%) and absolute bias (2-18%), and included predictor variables that captured proximity to and density of emission sources. The spatial density of our measurements resulted in a large model-building data set (n = 1101 concentration estimates); ∼25% of buffer variables were selected at spatial scales of <100m, suggesting that on-road particle concentrations change on small spatial scales. LUR model-R(2) improved as sampling runs were completed, with diminishing benefits after ∼40 h of data collection. Spatial autocorrelation of model residuals indicated that models performed poorly where spatiotemporal resolution of emission sources (i.e., traffic congestion) was poor. Our findings suggest that LUR modeling from mobile measurements is possible, but that more work could usefully inform best practices.

Establishing integrated rural-urban cohorts to assess air pollution-related health effects in pregnant women, children and adults in Southern India: an overview of objectives, design and methods in the Tamil Nadu Air Pollution and Health Effects (TAPHE) study

INTRODUCTION

In rapidly developing countries such as India, the ubiquity of air pollution sources in urban and rural communities often results in ambient and household exposures significantly in excess of health-based air quality guidelines. Few efforts, however, have been directed at establishing quantitative exposure-response relationships in such settings. We describe study protocols for The Tamil Nadu Air Pollution and Health Effects (TAPHE) study, which aims to examine the association between fine particulate matter (PM2.5) exposures and select maternal, child and adult health outcomes in integrated rural-urban cohorts.

METHODS AND ANALYSES

The TAPHE study is organised into five component studies with participants drawn from a pregnant mother-child cohort and an adult cohort (n=1200 participants in each cohort). Exposures are assessed through serial measurements of 24-48 h PM2.5 area concentrations in household microenvironments together with ambient measurements and time-activity recalls, allowing exposure reconstructions. Generalised additive models will be developed to examine the association between PM2.5 exposures, maternal (birth weight), child (acute respiratory infections) and adult (chronic respiratory symptoms and lung function) health outcomes while adjusting for multiple covariates. In addition, exposure models are being developed to predict PM2.5 exposures in relation to household and community level variables as well as to explore inter-relationships between household concentrations of PM2.5 and air toxics. Finally, a bio-repository of peripheral and cord blood samples is being created to explore the role of gene-environment interactions in follow-up studies.

ETHICS AND DISSEMINATION

The study protocols have been approved by the Institutional Ethics Committee of Sri Ramachandra University, the host institution for the investigators in this study. Study results will be widely disseminated through peer-reviewed publications and scientific presentations. In addition, policy-relevant recommendations are also being planned to inform ongoing national air quality action plans concerning ambient and household air pollution.

Performance comparison of LUR and OK in PM2.5 concentration mapping: a multidimensional perspective

Methods of Land Use Regression (LUR) modeling and Ordinary Kriging (OK) interpolation have been widely used to offset the shortcomings of PM_2.5 data observed at sparse monitoring sites. However, traditional point-based performance evaluation strategy for these methods remains stagnant, which could cause unreasonable mapping results. To address this challenge, this study employs ‘information entropy’, an area-based statistic, along with traditional point-based statistics (e.g. error rate, RMSE) to evaluate the performance of LUR model and OK interpolation in mapping PM_2.5 concentrations in Houston from a multidimensional perspective. The point-based validation reveals significant differences between LUR and OK at different test sites despite the similar end-result accuracy (e.g. error rate 6.13% vs. 7.01%). Meanwhile, the area-based validation demonstrates that the PM_2.5 concentrations simulated by the LUR model exhibits more detailed variations than those interpolated by the OK method (i.e. information entropy, 7.79 vs. 3.63). Results suggest that LUR modeling could better refine the spatial distribution scenario of PM_2.5 concentrations compared to OK interpolation. The significance of this study primarily lies in promoting the integration of point- and area-based statistics for model performance evaluation in air pollution mapping.

Environmental equity research: review with focus on outdoor air pollution research methods and analytic tools

The objective of this study was to review environmental equity research on outdoor air pollution and, specifically, methods and tools used in research, published in English, with the aim of recommending the best methods and analytic tools. English language publications from 2000 to 2012 were identified in Google Scholar, Ovid MEDLINE, and PubMed. Research methodologies and results were reviewed and potential deficiencies and knowledge gaps identified. The publications show that exposure to outdoor air pollution differs by social factors, but findings are inconsistent in Canada. In terms of study designs, most were small and ecological and therefore prone to the ecological fallacy. Newer tools such as geographic information systems, modeling, and biomarkers offer improved precision in exposure measurement. Higher-quality research using large, individual-based samples and more precise analytic tools are needed to provide better evidence for policy-making to reduce environmental inequities.

Reduced-Rank Spatio-Temporal Modeling of Air Pollution Concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution

There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members. In general, spatio-temporal models are limited in their efficacy for large data sets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of oxides of nitrogen (NO _x )-a pollutant of primary interest in MESA Air-in the Los Angeles metropolitan area via cross-validated R². Our findings suggest that use of reduced-rank models can improve computational efficiency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings.

Airway oxidative stress and inflammation markers in exhaled breath from children are linked with exposure to black carbon

BACKGROUND

The current study aimed at assessing the associations between black carbon (BC) exposure and markers for airway inflammation and oxidative stress in primary school children in a Western European urban area.

METHODS

In 130 children aged 6-12 years old, the fraction of exhaled nitric oxide (FeNO), exhaled breath condensate (EBC) pH, 8-isoprostane and interleukin (IL)-1β were measured in two seasons. BC concentrations on the sampling day (2-h average, 8:00-10:00 AM) and on the day before (24-h average) were assessed using measurements at a central monitoring site. Land use regression (LUR) models were applied to estimate weekly average BC exposure integrated for the time spent at home and at school, and seasonal average BC exposure at the home address. Associations between exposure and biomarkers were tested using linear mixed effect regression models. Next to single exposure models, models combining different BC exposure metrics were used.

RESULTS

In single exposure models, an interquartile range (IQR) increase in 2-h BC (3.10 μg/m(3)) was linked with a 5.9% (95% CI: 0.1 to 12.0%) increase in 8-isoprostane. FeNO increased by 16.7% (95% CI: 2.2 to 33.2%) per IQR increase in 24-h average BC (4.50 μg/m(3)) and by 12.1% (95% CI: 2.5 to 22.8%) per IQR increase in weekly BC (1.73 μg/m(3)). IL-1β was associated with weekly and seasonal (IQR=1.70 μg/m(3)) BC with respective changes of 38.4% (95% CI: 9.0 to 75.4%) and 61.8% (95% CI: 3.5 to 153.9%) per IQR increase in BC. An IQR increase in weekly BC was linked with a lowering in EBC pH of 0.05 (95% CI: -0.10 to -0.01). All associations were observed independent of sex, age, allergy status, parental education level and meteorological conditions on the sampling day. Most of the associations remained when different BC exposure metrics were combined in multiple exposure models, after additional correction for sampling period or after exclusion of children with airway allergies. In additional analyses, FeNO was linked with 24-h PM10 levels, but the effect size was smaller than for BC. 8-Isoprostane was not linked with either 2-h or 24-h concentrations of PM2.5 or PM10.

CONCLUSION

BC exposure on the morning of sampling was associated with airway oxidative stress while 24-h and weekly exposures were linked with airway inflammation.

Performance of multi-city land use regression models for nitrogen dioxide and fine particles

BACKGROUND

Land use regression (LUR) models have been developed mostly to explain intraurban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area.

OBJECTIVES

We aimed to develop European and regional LUR models and to examine their transferability to areas not used for model development.

METHODS

We evaluated LUR models for nitrogen dioxide (NO2) and particulate matter (PM; PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE (European Study of Cohorts for Air Pollution Effects) study areas across 14 European countries for PM and NO2. Models were evaluated with cross-validation (CV) and hold-out validation (HV). We investigated the transferability of the models by successively excluding each study area from model building.

RESULTS

The European model explained 56% of the concentration variability across all sites for NO2, 86% for PM2.5, and 70% for PM2.5 absorbance. The HV R2s were only slightly lower than the model R2 (NO2, 54%; PM2.5, 80%; PM2.5 absorbance, 70%). The European NO2, PM2.5, and PM2.5 absorbance models explained a median of 59%, 48%, and 70% of within-area variability in individual areas. The transferred models predicted a modest-to-large fraction of variability in areas that were excluded from model building (median R2: NO2, 59%; PM2.5, 42%; PM2.5 absorbance, 67%).

CONCLUSIONS

Using a large data set from 23 European study areas, we were able to develop LUR models for NO2 and PM metrics that predicted measurements made at independent sites and areas reasonably well. This finding is useful for assessing exposure in health studies conducted in areas where no measurements were conducted.

Modeling horizontal and vertical variation in intraurban exposure to PM2.5 concentrations and compositions

Land use regression (LUR) models are increasingly used to evaluate intraurban variability in population exposure to fine particulate matter (PM2.5). However, most of these models lack information on PM2.5 elemental compositions and vertically distributed samples. The purpose of this study was to evaluate intraurban exposure to PM2.5 concentrations and compositions for populations in an Asian city using LUR models, with special emphasis on examining the effects of having measurements on different building stories. PM2.5 samples were collected at 20 sampling sites below the third story (low-level sites). Additional vertically stratified sampling sites were set up on the fourth to sixth (mid-level sites, n=5) and seventh to ninth (high-level sites, n=5) stories. LUR models were built for PM2.5, copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), sulfur (S), silicon (Si), and zinc (Zn). The explained concentration variance (R(2)) of the PM2.5 model was 65%. R(2) values were >69% in the Cu, Fe, Mn, Ni, Si, and Zn models and <44% in the K and S models. Sampling height from ground level was a significant predictor in the PM2.5 and Si models. This finding stresses the importance of collecting vertically stratified information on PM2.5 mass concentrations to reduce potential exposure misclassification in future health studies. In addition to traffic variables, some models identified gravel-plant, industrial, and port variables with large buffer zones as important predictors, indicating that PM from these sources had significant effects at distant places.

Perinatal risk factors for wheezing phenotypes in the first 8 years of life

BACKGROUND

A novel data-driven approach was used to identify wheezing phenotypes in pre-schoolchildren aged 0-8 years, in the Prevention and Incidence of Asthma and Mite Allergy (PIAMA) birth cohort. Five phenotypes were identified: never/infrequent wheeze, transient early wheeze, intermediate onset wheeze, persistent wheeze and late onset wheeze. It is unknown which perinatal risk factors drive development of these phenotypes.

OBJECTIVE

The objective of the study was to assess associations of perinatal factors with wheezing phenotypes and to identify possible targets for prevention.

METHODS

In the PIAMA study (n = 3963), perinatal factors were collected at 3 months, and wheezing was assessed annually until the age of 8 years. Associations between perinatal risk factors and the five wheezing phenotypes were assessed using weighted multinomial logistic regression models. Odds ratios were adjusted for confounding variables and calculated with 'never/infrequent wheeze' as reference category.

RESULTS

Complete data were available for 2728 children. Risk factors for transient early wheeze (n = 455) were male gender, maternal and paternal allergy, low maternal age, high maternal body mass index, short pregnancy duration, smoking during pregnancy, presence of older siblings and day-care attendance. Risk factors for persistent wheeze (n = 83) were male gender, maternal and paternal allergy, and not receiving breastfeeding for at least 12 weeks. Intermediate onset wheeze (n = 98) was associated with a lower birth weight and late onset wheeze (n = 45) with maternal allergy.

CONCLUSION AND CLINICAL RELEVANCE

We identified different risk factors for specific childhood wheezing phenotypes. Some of these are modifiable, such as maternal age and body mass index, smoking, day-care attendance and breastfeeding, and may be important targets for prevention programmes.

Spatio-temporal semiparametric models for NO₂ and PM₁₀ concentration levels in Athens, Greece

BACKGROUND AND AIMS

Studies of air pollution effects on health are often based on ecological measurements. Our aim was to develop spatio-temporal models that estimate daily levels of NO2 and PM10 at every point in space, within the greater Athens area.

METHODS

We applied a semiparametric approach using spatial and temporal covariates and a bivariate smooth thin plate function. We evaluated the predictions of our models against the exposure estimates that are typically used in health studies. For model validation we used a temporal and a spatial approach.

RESULTS

The adjusted-R(2) of the developed exposure models was 0.53 and 0.75 for PM10 and NO2 respectively; the spatial terms in our models explained 41.5% and 64.5% and the temporal explained 52.85% and 32.0% of the variability in PM10 and NO2, respectively. There was no temporal or spatial left over autocorrelation in the residuals. We performed a leave-one-out cross validation and the adjusted-R(2) were 0.41 for PM10 and 0.71 for NO2. The developed model showed good validity when comparing predicted and observed measures for the 2010 data. Our models performed better compared to the "ecological" estimates and estimates based on the "nearest monitoring site".

CONCLUSIONS

Our spatio-temporal model makes valid predictions, it introduces substantial geographical variability, it reduces the bias when compared with the "ecological" estimates and the estimates based on the "nearest monitoring site" and it can be used for a more personalized exposure assessment in health studies.

Determinants of the Spatial Distributions of Elemental Carbon and Particulate Matter in Eight Southern Californian Communities

Emerging evidence indicates that near-roadway pollution (NRP) in ambient air has adverse health effects. However, specific components of the NRP mixture responsible for these effects have not been established. A major limitation for health studies is the lack of exposure models that estimate NRP components observed in epidemiological studies over fine spatial scale of tens to hundreds of meters. In this study, exposure models were developed for fine-scale variation in biologically relevant elemental carbon (EC). Measurements of particulate matter (PM) and EC less than 2.5 μm in aerodynamic diameter (EC_2.5) and of PM and EC of nanoscale size less than 0.2 μm were made at up to 29 locations in each of eight Southern California Children's Health Study communities. Regression-based prediction models were developed using a guided forward selection process to identify traffic variables and other pollutant sources, community physical characteristics and land use as predictors of PM and EC variation in each community. A combined eight-community model including only CALINE4 near-roadway dispersion-estimated vehicular emissions accounting for distance, distance-weighted traffic volume, and meteorology, explained 51% of the EC_0.2 variability. Community-specific models identified additional predictors in some communities; however, in most communities the correlation between predicted concentrations from the eight-community model and observed concentrations stratified by community were similar to those for the community-specific models. EC_2.5 could be predicted as well as EC_0.2. EC_2.5 estimated from CALINE4 and population density explained 53% of the within-community variation. Exposure prediction was further improved after accounting for between-community heterogeneity of CALINE4 effects associated with average distance to Pacific Ocean shoreline (to 61% for EC_0.2) and for regional NO_x pollution (to 57% for EC_2.5). PM fine spatial scale variation was poorly predicted in both size fractions. In conclusion, models of exposure that include traffic measures such as CALINE4 can provide useful estimates for EC_0.2 and EC_2.5 on a spatial scale appropriate for health studies of NRP in selected Southern California communities.

GSTP1 and TNF Gene variants and associations between air pollution and incident childhood asthma: the traffic, asthma and genetics (TAG) study

BACKGROUND

Genetics may partially explain observed heterogeneity in associations between traffic-related air pollution and incident asthma.

OBJECTIVE

Our aim was to investigate the impact of gene variants associated with oxidative stress and inflammation on associations between air pollution and incident childhood asthma.

METHODS

Traffic-related air pollution, asthma, wheeze, gene variant, and potential confounder data were pooled across six birth cohorts. Parents reported physician-diagnosed asthma and wheeze from birth to 7-8 years of age (confirmed by pediatric allergist in two cohorts). Individual estimates of annual average air pollution [nitrogen dioxide (NO2), particulate matter ≤ 2.5 μm (PM2.5), PM2.5 absorbance, ozone] were assigned to each child's birth address using land use regression, atmospheric modeling, and ambient monitoring data. Effect modification by variants in GSTP1 (rs1138272/Ala114Val and rs1695/IIe105Val) and TNF (rs1800629/G-308A) was investigated.

RESULTS

Data on asthma, wheeze, potential confounders, at least one SNP of interest, and NO2 were available for 5,115 children. GSTP1 rs1138272 and TNF rs1800629 SNPs were associated with asthma and wheeze, respectively. In relation to air pollution exposure, children with one or more GSTP1 rs1138272 minor allele were at increased risk of current asthma [odds ratio (OR) = 2.59; 95% CI: 1.43, 4.68 per 10 μg/m3 NO2] and ever asthma (OR = 1.64; 95% CI: 1.06, 2.53) compared with homozygous major allele carriers (OR = 0.95; 95% CI: 0.68, 1.32 for current and OR = 1.20; 95% CI: 0.98, 1.48 for ever asthma; Bonferroni-corrected interaction p = 0.04 and 0.01, respectively). Similarly, for GSTP1 rs1695, associations between NO2 and current and ever asthma had ORs of 1.43 (95% CI: 1.03, 1.98) and 1.36 (95% CI: 1.08, 1.70), respectively, for minor allele carriers compared with ORs of 0.82 (95% CI: 0.52, 1.32) and 1.12 (95% CI: 0.84, 1.49) for homozygous major allele carriers (Bonferroni-corrected interaction p-values 0.48 and 0.09). There were no clear differences by TNF genotype.

CONCLUSIONS

Children carrying GSTP1 rs1138272 or rs1695 minor alleles may constitute a susceptible population at increased risk of asthma associated with air pollution.

Land use regression models as a tool for short, medium and long term exposure to traffic related air pollution

BACKGROUND AND AIMS

In the HEAPS (Health Effects of Air Pollution in Antwerp Schools) study the importance of traffic-related air pollution on the school and home location on children's health was assessed. 130 children (aged 6 to 12) from two schools participated in a biomonitoring study measuring oxidative stress, inflammation and cardiovascular markers.

METHODS

Personal exposure of schoolchildren to black carbon (BC) and nitrogen dioxide (NO2) was assessed using both measured and modeled concentrations. Air quality measurements were done in two seasons at approximately 50 locations, including the schools. The land use regression technique was applied to model concentrations at the children's home address and at the schools.

RESULTS

In this paper the results of the exposure analysis are given. Concentrations measured at school 2h before the medical examination were used for assessing health effects of short term exposure. Over two seasons, this short term BC exposure ranged from 514 ng/m(3) to 6285 ng/m(3), and for NO2 from 11 μg/m(3) to 36 μg/m(3). An integrated exposure was determined until 10 days before the child's examination, taking into account exposures at home and at school and the time spent in each of these microenvironments. Land use regression estimates were therefore recalculated into daily concentrations by using the temporal trend observed at a fixed monitor of the official air quality network. Concentrations at the children's homes were modeled to estimate long term exposure (from 1457 ng/m(3) to 3874 ng/m(3) for BC; and from 19 μg/m(3) to 51 μg/m(3) for NO2).

CONCLUSIONS

The land use regression technique proved to be a fast and accurate means for estimating long term and daily BC and NO2 exposure for children living in the Antwerp area. The spatial and temporal resolution was tailored to the needs of the epidemiologists involved in this study.

Land use regression models for estimating individual NOx and NO₂ exposures in a metropolis with a high density of traffic roads and population

This study is conducted to characterize the intra-urban distribution of NOx and NO2; develop land use regression (LUR) models to assess outdoor NOx and NO2 concentrations, using the ESCAPE modeling approach with locally specific land use data; and compare NOx and NO2 exposures for children in the Taipei Metropolis by the LUR models, the nearest monitoring station, and kriging methods based on data collected at the measurement sites. NOx and NO2 were measured for 2 weeks during 3 seasons at 40 sampling sites by Ogawa passive badges to represent their concentrations at urban backgrounds and streets from October 2009 to September 2010. Land use data and traffic-related information in different buffer zones were combined with measured concentrations to derive LUR models using supervised forward stepwise multiple regressions. The annual average concentrations of NOx and NO2 in Taipei were 72.4 ± 22.5 and 48.9 ± 12.2 μg/m(3), respectively, which were at the high end of all 36 European areas in the ESCAPE project. Spatial contrasts in Taipei were lower than those of the European areas in the ESCAPE project. The NOx LUR model included 6 land use variables, which were lengths of major roads within 25 m, 25-50 m, and 50-500 m, urban green areas within 300 m and 300-5,000 m, and semi-natural and forested areas within 500 m, with R(2)=0.81. The NO2 LUR model included 4 land use variables, which were lengths of major roads within 25 m, urban green areas within 100 m, semi-natural and forested areas within 500 m, and low-density residential area within 500 m, with R(2)=0.74. The LUR models gave a wider variation in estimating NOx and NO2 exposures than either the ordinary kriging method or the nearest measurement site did for the children of Taiwan Birth Cohort Study (TBCS) in Taipei.

Monitoring street-level spatial-temporal variations of carbon monoxide in urban settings using a wireless sensor network (WSN) framework

Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings. The system consists of two major components. The first component is the deployment of wireless sensors. We deployed 44 sensor nodes, 40 transmitter nodes and four gateway nodes in this study. Each sensor node includes a signal processing module, a CO sensor and a wireless communication module. In order to capture realistic human exposure to traffic pollutants, all sensors were deployed at a height of 1.5 m on lampposts and traffic signs. The study area covers a total length of 1.5 km of Keelung Road in Taipei City. The other component is a map-based monitoring platform for sensor data visualization and manipulation in time and space. Using intensive real-time street-level monitoring framework, we compared the spatial-temporal patterns of air pollution in different time periods. Our results capture four CO concentration peaks throughout the day at the location, which was located along an arterial and nearby traffic sign. The hourly average could reach 5.3 ppm from 5:00 pm to 7:00 pm due to the traffic congestion. The proposed WSN-based framework captures detailed ground information and potential risk of human exposure to traffic-related air pollution. It also provides street-level insights into real-time monitoring for further early warning of air pollution and urban environmental management.

A longitudinal analysis of associations between traffic-related air pollution with asthma, allergies and sensitization in the GINIplus and LISAplus birth cohorts

Background. There is a need to study whether the adverse effects of traffic-related air pollution (TRAP) on childhood asthma and allergic diseases documented during early-life persist into later childhood. This longitudinal study examined whether TRAP is associated with the prevalence of asthma, allergic rhinitis and aeroallergen sensitization in two German cohorts followed from birth to 10 years. Materials. Questionnaire-derived annual reports of doctor diagnosed asthma and allergic rhinitis, as well as eye and nose symptoms, were collected from 6,604 children. Aeroallergen sensitization was assessed for 3,655 children who provided blood samples. Associations between these health outcomes and nitrogen dioxide (NO2), particles with aerodynamic diameters less than 2.5 µg/m(3) (PM2.5) mass, PM2.5 absorbance and ozone, individually estimated for each child at the birth, six and 10 year home addresses, were assessed using generalized estimation equations including adjustments for relevant covariates. Odds ratios [95% confidence intervals] per increase in interquartile range of pollutant are presented for the total population and per geographical area (GINI/LISA South, GINI/LISA North and LISA East, Germany). Results. The risk estimates for the total population were generally null across outcomes and pollutants. The area-specific results were heterogeneous. In GINI/LISA North, all associations were null. In LISA East, associations with ozone were elevated for all outcomes, and those for allergic rhinitis and eyes and nose symptom prevalence reached statistical significance (1.30 [1.02, 1.64] and 1.35 [1.16, 1.59], respectively). For GINI/LISA South, two associations with aeroallergen sensitization were significant (0.84 [0.73, 0.97] for NO2 and 0.87 [0.78, 0.97] for PM2.5 absorbance), as well as the association between allergic rhinitis and PM2.5 absorbance (0.83 [0.72, 0.96]). Conclusions. This study did not find consistent evidence that TRAP increases the prevalence of childhood asthma, allergic rhinitis or aeroallergen sensitization in later childhood using data from birth cohort participants followed for 10 years in three locations in Germany. Results were heterogeneous across the three areas investigated.

Associations between arrhythmia episodes and temporally and spatially resolved black carbon and particulate matter in elderly patients

OBJECTIVES

Ambient air pollution has been associated with sudden deaths, some of which are likely due to ventricular arrhythmias. Defibrillator discharge studies have examined the association of air pollution with arrhythmias in sensitive populations. No studies have assessed this association using residence-specific estimates of air pollution exposure.

METHODS

In the Normative Aging Study, we investigated the association between temporally resolved and spatially resolved black carbon (BC) and PM2.5 and arrhythmia episodes (bigeminy, trigeminy or couplets episodes) measured as ventricular ectopy (VE) by 4 min ECG monitoring in repeated measures of 701 subjects, during the years 2000-2010. We used a binomial distribution (having or not a VE episode) in a mixed effect model with a random intercept for subject, controlling for seasonality, temperature, day of the week, medication use, smoking, having diabetes, body mass index and age. We also examined whether these associations were modified by genotype or phenotype.

RESULTS

We found significant increases in VE with both pollutants and lags; for the estimated concentration averaged over the 3 days prior to the health assessment, we found increases in the odds of having VE with an OR of 1.52 (95% CI 1.19 to 1.94) for an IQR (0.30 μg/m(3)) increase in BC and an OR of 1.39 (95% CI 1.12 to 1.71) for an IQR (5.63 μg/m(3)) increase in PM2.5. We also found higher effects in subjects with the glutathione S-transferase theta-1 and glutathione S-transferase mu-1 variants and in obese (p<0.05).

CONCLUSIONS

Increased levels of short-term traffic-related pollutants may increase the risk of ventricular arrhythmia in elderly subjects.

Mapping and modeling airborne urban phenanthrene distribution using vegetation biomonitoring

To capture the spatial distribution of phenanthrene in an urban setting we used vegetation biomonitoring with Jeffrey pine trees (Pinus jeffreyi). The major challenge in characterizing spatial variation in polycyclic aromatic hydrocarbon (PAH) concentrations within a metropolitan area has been sampling at a fine enough resolution to observe the underlying spatial pattern. However, field and chamber studies show that the primary pathway through which PAHs enter plants is from air into leaves, making vegetation biomonitoring a feasible way to examine the spatial distribution of these compounds. Previous research has shown that phenanthrene has adverse health effects and that it is one of the most abundant PAHs in urban air. We collected 99 pine needle samples from 91 locations in Fresno in the morning on a winter day, and analyzed them for PAHs in the inner needle. All 99 pine needle samples had detectable levels of phenanthrene, with mean concentration of 41.0 ng g-1, median 36.9 ng g-1, and standard deviation of 28.5 ng g-1 fresh weight. The ratio of the 90th:10th percentile concentrations by location was 3.3. The phenanthrene distribution had a statistically significant Moran's I of 0.035, indicating a high degree of spatial clustering. We implemented land use regression to fit a model to our data. Our model was able to explain a moderate amount of the variability in the data (R 2 = 0.56), likely reflecting the major sources of phenanthrene in Fresno. The spatial distribution of modeled airborne phenanthrene shows the influences of highways, railroads, and industrial and commercial zones.

ESTIMATING DAILY NITROGEN DIOXIDE LEVEL: EXPLORING TRAFFIC EFFECTS

Data used to assess acute health effects from air pollution typically have good temporal but poor spatial resolution or the opposite. A modified longitudinal model was developed that sought to improve resolution in both domains by bringing together data from three sources to estimate daily levels of nitrogen dioxide (NO₂) at a geographic location. Monthly NO₂ measurements at 316 sites were made available by the Study of Traffic, Air quality and Respiratory health (STAR). Four US Environmental Protection Agency monitoring stations have hourly measurements of NO₂. Finally, the Connecticut Department of Transportation provides data on traffic density on major roadways, a primary contributor to NO₂ pollution. Inclusion of a traffic variable improved performance of the model, and it provides a method for estimating exposure at points that do not have direct measurements of the outcome. This approach can be used to estimate daily variation in levels of NO₂ over a region.

Traffic, asthma and genetics: combining international birth cohort data to examine genetics as a mediator of traffic-related air pollution's impact on childhood asthma

Associations between traffic-related air pollution and incident childhood asthma can be strengthened by analysis of gene-environment interactions, but studies have typically been limited by lack of study power. We combined data from six birth cohorts on: asthma, eczema and allergic rhinitis to 7/8 years, and candidate genes. Individual-level assessment of traffic-related air pollution exposure was estimated using land use regression or dispersion modeling. A total of 11,760 children were included in the Traffic, Asthma and Genetics (TAG) Study; 6.3 % reported physician-diagnosed asthma at school-age, 16.0 % had asthma at anytime during childhood, 14.1 % had allergic rhinitis at school-age, 10.0 % had eczema at school-age and 33.1 % were sensitized to any allergen. For GSTP1 rs1138272, the prevalence of heterozygosity was 16 % (range amongst individual cohorts, 11-17 %) and homozygosity for the minor allele was 1 % (0-2 %). For GSTP1 rs1695, the prevalence of heterozygosity was 45 % (40-48 %) and homozygosity for the minor allele, 12 % (10-12 %). For TNF rs1800629, the prevalence of heterozygosity was 29 % (25-32 %) and homozygosity for the minor allele, 3 % (1-3 %). TAG comprises a rich database, the largest of its kind, for investigating the effect of genotype on the association between air pollution and childhood allergic disease.

Land use patterns and SO2 and NO2 pollution in Ulaanbaatar, Mongolia

We proposed to study spatial distribution and source contribution of SO2 and NO2 pollution in Ulaanbaatar, Mongolia. We collected 2-week ambient SO2 and NO2 concentration samples at 38 sites, which were classified by major sources of air pollution such as ger areas and/or major roads, in three seasons as warm (September, 2011), cold (November-December, 2011), and moderate (March, 2012) in Ulaanbaatar. The SO2 and NO2 concentrations were collected by Ogawa ambient air passive samplers and analyzed by ion chromatography and spectrophotometry methods, respectively. Stepwise regression models were used to estimate the contribution of emission proxies, such as the distance to major roads, ger areas, power plants, and city center, to the ambient concentrations of SO2 and NO2. We found that the SO2 and NO2 concentrations were significantly higher in the cold season than in the warm and moderate seasons at all 38 ambient sampling sites. The SO2 concentrations in 20 ger sites (46.60 ppb in the cold season and 17.82 ppb in the moderate season) were significantly higher than in 18 non-ger sites (23.35 ppb in the cold season and 12.53 ppb in the moderate season). The NO2 concentrations at 19 traffic/road sites (12.85 ppb in the warm season and 20.48 ppb in the moderate season) were significantly higher than those at 19 urban sites (7.60 ppb and 14.39 ppb in the moderate season). Multiple regression models show that SO2 concentrations decreased by 23% in the cold and 17% in the moderate seasons at 0.70 km from the ger areas, an average of all sampling sites, and by 29% in the moderate season at 4.83 km from the city center, an average of all sampling sites. Multiple regression models show that the NO2 concentrations at 4.83 km from the city center decreased by 38% in the warm and 29% in the moderate seasons. Our models also report that NO2 concentrations at 0.16 km from the main roads decreased by 15% and 9% in the warm and the moderate seasons, respectively, and by 16% in the cold season decreased at the location 0.70 km from the ger area. The NO2 concentration at the location 4.83 km from the city center was decreased by 18% and at the location 4.79 km from the power plants by 21%. Our study concludes that SO2 and NO2 concentrations are very high in Ulaanbaatar, especially in the winter, and can be explained by several land use variables, including the distance to the ger areas, the city center, the main roads, and the power plants.

Inflammatory cytokine response to ambient particles varies due to field collection procedures

In vitro assays of biological activity induced by particulate matter (PM) are a tool for investigating mechanisms of PM health effects. They have potential application to exposure assessment in chronic disease epidemiology. However, there has been little reporting of the impact of real-world PM collection techniques on assay results. Therefore, we examined the effect of sampling duration and postsampling delays in freezing on PM-induced biological activity. Duplicate samples of respirable ambient Los Angeles PM were collected on polyurethane foam filters during 17 days and during three contemporaneous consecutive shorter periods. After collection, one duplicate was stored at ambient temperature for 24 hours before freezing; the other was frozen immediately. Cytokine response (IL-1β, IL-6, IL-8, and TNF-α) to PM aqueous extract was assessed in THP-1 cells, a model for evaluating monocyte/macrophage lineage cell responses. There was consistent 3- to 4-fold variation in PM-induced cytokine levels across the three collection intervals. Compared with levels induced by PM pooled across the three periods, continuously collected PM-induced levels were reduced by 25% (IL-6) to 39% (IL-8). The pattern of cytokine gene expression response was similar. Cytokine level variation by time to freezing was not statistically significant. PM-induced inflammatory response varied substantially over a weekly time scale. We conclude that long PM sampling interval induced less activity than the average of equivalent shorter consecutive sampling intervals. Time to freezing was less important. Implications for development of metrics of long-term spatial variation in biological exposure metrics for study of chronic disease merit further investigation.

Childhood allergic rhinitis, traffic-related air pollution, and variability in the GSTP1, TNF, TLR2, and TLR4 genes: results from the TAG Study

BACKGROUND

Associations between traffic-related air pollution (TRAP) and allergic rhinitis remain inconsistent, possibly because of unexplored gene-environment interactions.

OBJECTIVE

In a pooled analysis of 6 birth cohorts (Ntotal = 15,299), we examined whether TRAP and genetic polymorphisms related to inflammation and oxidative stress predict allergic rhinitis and sensitization.

METHODS

Allergic rhinitis was defined with a doctor diagnosis or reported symptoms at age 7 or 8 years. Associations between nitrogen dioxide, particulate matter 2.5 (PM2.5) mass, PM2.5 absorbance, and ozone, estimated for each child at the year of birth, and single nucleotide polymorphisms within the GSTP1, TNF, TLR2, or TLR4 genes with allergic rhinitis and aeroallergen sensitization were examined with logistic regression. Models were stratified by genotype and interaction terms tested for gene-environment associations.

RESULTS

Point estimates for associations between nitrogen dioxide, PM2.5 mass, and PM2.5 absorbance with allergic rhinitis were elevated, but only that for PM2.5 mass was statistically significant (1.37 [1.01, 1.86] per 5 μg/m(3)). This result was not robust to single-cohort exclusions. Carriers of at least 1 minor rs1800629 (TNF) or rs1927911 (TLR4) allele were consistently at an increased risk of developing allergic rhinitis (1.19 [1.00, 1.41] and 1.24 [1.01, 1.53], respectively), regardless of TRAP exposure. No evidence of gene-environment interactions was observed.

CONCLUSION

The generally null effect of TRAP on allergic rhinitis and aeroallergen sensitization was not modified by the studied variants in the GSTP1, TNF, TLR2, or TLR4 genes. Children carrying a minor rs1800629 (TNF) or rs1927911 (TLR4) allele may be at a higher risk of allergic rhinitis.

Development of a distributed air pollutant dry deposition modeling framework

A distributed air pollutant dry deposition modeling system was developed with a geographic information system (GIS) to enhance the functionality of i-Tree Eco (i-Tree, 2011). With the developed system, temperature, leaf area index (LAI) and air pollutant concentration in a spatially distributed form can be estimated, and based on these and other input variables, dry deposition of carbon monoxide (CO), nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), and particulate matter less than 10 microns (PM10) to trees can be spatially quantified. Employing nationally available road network, traffic volume, air pollutant emission/measurement and meteorological data, the developed system provides a framework for the U.S. city managers to identify spatial patterns of urban forest and locate potential areas for future urban forest planting and protection to improve air quality. To exhibit the usability of the framework, a case study was performed for July and August of 2005 in Baltimore, MD.