Air pollution exposure assessment for epidemiologic studies of pregnant women and children: lessons learned from the Centers for Children's Environmental Health and Disease Prevention Research

Model development and validation of personal exposure to PM2.5 among urban elders

Indirect measurements through a combination of microenvironment concentrations and personal activity diaries provide a potentially useful alternative for PM_2.5 exposure estimates. This study was to optimize a personal exposure model based on spatiotemporal model predictions for PM_2.5 exposure in a sub-cohort study. Personal, home indoor, home outdoor, and ambient monitoring data of PM_2.5 were conducted for an elderly population in the Taipei city of Taiwan. The proposed microenvironment exposure (ME) models incorporate PM_2.5 measurements and individual time-activity information with a generalized estimating equation (GEE) analysis. We evaluated model performance with daily personal PM_2.5 exposure based on the coefficient of determination, accuracy, and mean bias error. Ambient and home outdoor measures as exposure surrogates are likely to under- and overestimate personal exposure to PM_2.5 in our study population, respectively. Measured and predicted indoor exposures were highly correlated with personal PM_2.5 exposure. The awareness of peculiar smells is an important factor that significantly increases personal PM_2.5 exposure by 46-70%. The model incorporating home indoor PM_2.5 can achieve the highest agreement (R² = 0.790) with personal exposure and the lowest measurement error. The ME model with the GEE analysis combining home outdoor PM_2.5 determined by LUR model with a machine learning technique can improve the prediction (R² = 0.592) of personal PM_2.5 exposure, compared with the prediction of the traditional LUR model (R² = 0.385).

A new method for interpolation of missing air quality data at monitor stations

Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (STPI-BSHADE) interpolation method was introduced to address this issue. The method was based on the spatial statistic trinity theory, where the statistical error is determined by the population properties, the condition of the sample, and the method of estimation. In our study, the spatial association of the variables was quantified by the covariance and the ratio of air quality data between stations, resulting in linear unbiased estimates of the missing data. STPI-BSHADE was compared with two widely used statistical methods, inverse distance weighting (IDW) and Kriging. Theoretically, IDW and Kriging are short of the capacity of using the heterogeneous characteristics of the population and remedying the sample bias. Empirically, the accuracy of the STPI-BSHADE method was assessed using hourly particulate matter 2.5 data, collected from May 13 to December 31, 2014, in the Beijing-Tianjin-Hebei areas, where air quality presents spatial heterogeneity. The experimental results also demonstrated that STPI-BSHADE significantly outperformed the traditional methods.

Land-Use Regression Modeling to Estimate NO2 and VOC Concentrations in Pohang City, South Korea

Land-use regression (LUR) has emerged as a promising technique for air pollution modeling to obtain the spatial distribution of air pollutants for epidemiological studies. LUR uses traffic, geographic, and monitoring data to develop regression models and then predict the concentration of air pollutants in the same area. To identify the spatial distribution of nitrogen dioxide (NO2), benzene, toluene, and m-p-xylene, we developed LUR models in Pohang City, one of the largest industrialized areas in Korea. Passive samplings were conducted during two 2-week integrated sampling periods in September 2010 and March 2011, at 50 sampling locations. For LUR model development, predictor variables were calculated based on land use, road lengths, point sources, satellite remote sensing, and population density. The averaged mean concentrations of NO2, benzene, toluene, and m-p-xylene were 28.4 µg/m3, 2.40 µg/m3, 15.36 µg/m3, and 0.21 µg/m3, respectively. In terms of model-based R2 values, the model for NO2 included four independent variables, showing R2 = 0.65. While the benzene and m-p-xylene models showed the same R2 values (0.43), toluene showed a lower R2 value (0.35). We estimated long-term concentrations of NO2 and VOCs at 167,057 addresses in Pohang. Our study could hold particular promise in an epidemiological setting having significant health effects associated with small area variations and encourage the extended study using LUR modeling in Asia.

Spatial-Temporal Distribution Variation of Ground-Level Ozone in China's Pearl River Delta Metropolitan Region

Long-term exposure to ozone pollution will cause severe threats to residents' physical and mental health. Ground-level ozone is the most severe air pollutant in China's Pearl River Delta Metropolitan Region (PRD). It is of great significance to accurately reveal the spatial-temporal distribution characteristics of ozone pollution exposure patterns. We used the daily maximum 8-h ozone concentration data from PRD's 55 air quality monitoring stations in 2015 as input data. We used six models of STK and ordinary kriging (OK) for the simulation of ozone concentration. Then we chose a better ozone pollution prediction model to reveal the ozone exposure characteristics of the PRD in 2015. The results show that the Bilonick model (BM) model had the highest simulation precision for ozone in the six models for spatial-temporal kriging (STK) interpolation, and the STK model's simulation prediction results are significantly better than the OK model. The annual average ozone concentrations in the PRD during 2015 showed a high spatial variation in the north and east and low in the south and west. Ozone concentrations were relatively high in summer and autumn and low in winter and spring. The center of gravity of ozone concentrations tended to migrate to the north and west before moving to the south and then finally migrating to the east. The ozone's spatial autocorrelation was significant and showed a significant positive correlation, mainly showing high-high clustering and low-low clustering. The type of clustering undergoes temporal migration and conversion over the four seasons, with spatial autocorrelation during winter the most significant.

Exposure assessment of indoor particulate matter during pregnancy: a narrative review of the literature

OBJECTIVE

The aim of this review was to summarize the evidence of the exposure assessment approaches of indoor particulate matter (PM) during pregnancy and to recommend future focus areas.

CONTENT

Exposure to indoor PM during pregnancy is associated with adverse birth outcomes. However, many questions remain about the consistency of the findings and the magnitude of this effect. This may be due to the exposure assessment methods used and the challenges of characterizing exposure during pregnancy. Exposure is unlikely to remain constant over the nine-month period. Pregnant females' mobility and activities vary - for example, employment status may be random among females, but among those employed, activities are likely to be greater in the early pregnancy than closer to the delivery of the child.

SUMMARY

Forty three studies that used one of the five categories of indoor PM exposure assessment (self-reported, personal air monitoring, household air monitoring, exposure models and integrated approaches) were assessed. Our results indicate that each of these exposure assessment approaches has unique characteristics, strengths, and weaknesses. While questionnaires and interviews are based on self-report and recall, they were a major component in the reviewed exposure assessment studies. These studies predominantly used large sample sizes. Precision and detail were observed in studies that used integrated approaches (i. e. questionnaires, measurements and exposure models).

OUTLOOK

Given the limitations presented by these studies, exposure misclassification remains possible because of personal, within and between household variability, seasonal changes, and spatiotemporal variability during pregnancy. Therefore, using integrated approaches (i. e. questionnaire, measurements and exposure models) may provide better estimates of PM levels across trimesters. This may provide precision for exposure estimates in the exposure-response relationship.

Space-time hot spots of critically ill small for gestational age newborns and industrial air pollutants in major metropolitan areas of Canada

We assessed the association of spatiotemporal hot spots of critically ill small for gestational age (ciSGA) newborns and industrial air emissions. Using neonatal admission data from the Canadian Neonatal Network between 2006 and 2010 (n = 32,836 infants), we aggregated maternal residential postal codes from nineteen census metropolitan areas (CMA) into space-time cubes and applied emerging hot spot analyses. Using National Pollutant Release Inventory data (n = 161 chemicals) and Environment Canada weather station data (n = 19 sites), we estimated monthly wind-dispersion of air emissions and calculated hot spots. We associated the patterns using logistic regression, with covariates for low socioeconomic status, NO₂ pollution, and number of infants. A total of 5465 infants were identified as ciSGA and the larger CMAs had more and larger hot spots (i.e. accumulation of events in space and time). Seventy-eight industrial chemical hot spots were associated with ciSGA hot spots. The highest number of positive associations were for 28 different pollutants, which differed by CMA. Twenty-one were known or suspected developmental toxicants, such as particulate matter, carbon monoxide, heavy metals, and volatile organic compounds. Associations with hot spots of industrial chemical emissions were geographically specific and may help explain the space-time trends of ciSGA.

Predictors of urban household variability of indoor PM2.5 in low socio-economic communities

In epidemiological studies, levels of PM2.5 need to be estimated over time and space. Because of logistical constraints, very few studies have been conducted to assess the variability within and across homes and the predictors of this variability. This study evaluated within- and between-home variability of indoor PM2.5 and identified predictors for PM2.5 in homes of mothers participating in the urban Mother and Child in the Environment birth cohort study in Durban, South Africa. Thirty homes were selected from 300 homes that were previously sampled for PM2.5. Two measurements of PM2.5 levels were conducted in each home within a 1 week interval in both warm and cold seasons (four samplings per home) using Airmetrics MiniVol samplers. A linear mixed-effect model was used to evaluate within- and between-home variability and to identify fixed effects (predictors) that result in reduced variability. The PM2.5 levels in the 30 homes ranged from 2 to 303 μg m-3. The within-home variability accounted for 94% of the total variability in the log-transformed PM2.5 levels for the 30 homes. The fixed effects extracted from the repeated samplings in the present study were used to improve a previously developed multivariable linear regression model for 300 homes, and thereby increased the R2 from 0.50 to 0.54. Inclusion of fixed-effects in multivariable linear regression models resulted in a reasonably robust model that can be used to predict PM2.5 levels in unmeasured homes of the cohort.

Effects of childhood exposure to PM2.5 in a Memphis pediatric asthma cohort

The effects of childhood exposure to ambient air pollution and their influences on healthcare utilization and respiratory outcomes in Memphis pediatric asthma cohort are still unknown. This study seeks to (1) investigate individual-level associations between asthma and exposure measures in high asthma rate and low asthma rate areas and (2) determine factors that influence asthma at first year of a child's life, first 2 years, first 5 years, and during their childhood. Datasets include physician-diagnosed asthma patients, on-road and individual PM_2.5 emissions, and high-resolution spatiotemporal PM_2.5 estimates. Spatial analytical and logistic regression models were used to analyze the effects of childhood exposure on outcomes. Increased risk was associated with African American (AA) (odds ratio (OR) = 3.09, 95% confidence interval (CI) 2.80-3.41), aged < 5 years old (OR = 1.31, 95% 1.17-1.47), public insurance (OR = 2.80, 95% CI 2.60-3.01), a 2.5-km radius from on-road emission sources (OR = 3.06, 95% CI 2.84-3.30), and a 400-m radius from individual PM_2.5 sources (OR = 1.33, 95% CI 1.25-1.41) among the cohort with residence in high asthma rate areas compared to low asthma rates areas. A significant interaction was observed between race and insurance with the odds of AA being approximately five times (OR = 4.68, 95% CI 2.23-9.85), public insurance being about three times (OR = 2.65, 95% CI 1.68-4.17), and children in their first 5 years of life have more hospital visits than other age groups. Findings from this study can guide efforts to minimize emissions, manage risk, and design interventions to reduce disease burden.

Estimated health benefits of exhaust free transport in the city of Malmö, Southern Sweden

Air pollution is responsible for one in eight premature deaths worldwide, and thereby a major threat to human health. Health impact assessments of hypothetic changes in air pollution concentrations can be used as a mean of assessing the health impacts of policy, plans and projects, and support decision-makers in choices to prevent disease. The aim of this study was to estimate health impacts attributable to a hypothetical decrease in air pollution concentrations in the city of Malmö in Southern Sweden corresponding to a policy on-road transportations without tail-pipe emissions in the municipality. We used air pollution data modelled for each of the 326,092 inhabitants in Malmö by a Gaussian dispersion model combined with an emission database with >40,000 sources. The dispersion model calculates Nitrogen Oxides (NO_x) (later transformed into Nitrogen Dioxide (NO₂)) and particulate matter with an aerodynamic diameter < 2.5 μg/m³ (PM_2.5) with high spatial and temporal resolution (85 m and 1 h, respectively). The average individual reduction was 5.1 (ranging from 0.6 to 11.8) μg/m³ in NO_2, which would prevent 55 (2% of all deaths) to 93 (4%) deaths annually, depending on dose-response function used. Furthermore, we estimate that the NO₂ reduction would result in 21 (6%) fewer cases of incident asthma in children, 95 (10%) fewer children with bronchitis every year, 30 (1%) fewer hospital admissions for respiratory disease, 87(4%) fewer dementia cases, and 11(11%) fewer cases of preeclampsia every year. The average reduction in PM_2.5 of 0.6 (ranging from 0.1 till 1.7) μg/m³ would mean that 2729 (0.3%) work days would not be lost due to sick-days and that there would be 16,472 fewer restricted activity days (0.3%) that year had all on-road transportations been without tail-pipe emissions. Even though the estimates are sensitive to the dose-response functions used and to exposure misclassification errors, even the most conservative estimate of the number of prevented deaths is 7 times larger than the annual traffic fatalities in Malmö, indicating a substantial possibility to reduce the health burden attributed to tail-pipe emissions in the study area.

Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model

Excessive exposure to ambient (outdoor) air pollution may greatly increase the incidences of respiratory and cardiovascular diseases. Accurate reports of the spatial-temporal distribution characteristics of daily PM2.5 exposure can effectively prevent and reduce the harm caused to humans. Based on the daily average concentration data of PM2.5 in Beijing in May 2014 and the spatio-temporal kriging (STK) theory, we selected the optimal STK fitting model and compared the spatial-temporal prediction accuracy of PM2.5 using the STK method and ordinary kriging (OK) method. We also reveal the spatial-temporal distribution characteristics of the daily PM2.5 exposure in Beijing. The results show the following: (1) The fitting error of the Bilonick model (BM) model which is the smallest (0.00648), and the fitting effect of the prediction model of STK is the best for daily PM2.5 exposure. (2) The cross-examination results show that the STK model (RMSE = 8.90) has significantly lower fitting errors than the OK model (RMSE = 10.70), so its simulation prediction accuracy is higher. (3) According to the interpolation of the STK model, the daily exposure of PM2.5 in Beijing in May 2014 has good continuity in both time and space. The overall air quality is good, and overall the spatial distribution is low in the north and high in the south, with the highest concentration in the southwestern region. (4) There is a certain degree of spatial heterogeneity in the cumulative duration at the good, moderate, and polluted grades of China National Standard. The areas with the longest cumulative duration at the good, moderate and polluted grades are in the north, southeast, and southwest of the study area, respectively.

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.

Comparing residence-based to actual path-based methods for defining adolescents' environmental exposures using granular spatial data

This paper uses data from a population-based case control study of daily activities and assault injury to examine residence-based versus actual path-based approaches to measuring environmental exposures that pose risks for violence among adolescents. Defining environmental exposures based on participant home address resulted in significant misclassification compared to gold standard daily travel path measures. Dividing participant daily travel paths into origin-destination segments, we explore a method for defining spatial counterfactuals by comparing actual trip path exposures to shortest potential trip path exposures. Spatial methods explored herein can be utilized in future research to more accurately quantify environmental exposures and associations with health outcomes.

Exposure estimation errors to nitrogen oxides on a population scale due to daytime activity away from home

Accurate estimation of exposure to air pollution is necessary for assessing the impact of air pollution on the public health. Most environmental epidemiology studies assign the home address exposure to the study subjects. Here, we quantify the exposure estimation error at the population scale due to assigning it solely at the residence place. A cohort of most schoolchildren in Israel (~950,000), age 6-18, and a representative cohort of Israeli adults (~380,000), age 24-65, were used. For each subject the home and the work or school addresses were geocoded. Together, these two microenvironments account for the locations at which people are present during most of the weekdays. For each subject, we estimated ambient nitrogen oxide concentrations at the home and work or school addresses using two air quality models: a stationary land use regression model and a dynamic dispersion-like model. On average, accounting for the subjects' work or school address as well as for the daily pollutant variation reduced the estimation error of exposure to ambient NO_x/NO₂ by 5-10ppb, since daytime concentrations at work/school and at home can differ significantly. These results were consistent regardless which air quality model as used and even for subjects that work or study close to their home. Yet, due to their usually short commute, assigning schoolchildren exposure solely at their residential place seems to be a reasonable estimation. In contrast, since adults commute for longer distances, assigning exposure of adults only at the residential place has a lower correlation with the daily weighted exposure, resulting in larger exposure estimation errors. We show that exposure misclassification can result from not accounting for the subjects' time-location trajectories through the spatiotemporally varying pollutant concentrations field.

Temporal aspects of air pollutant measures in epidemiologic analysis: a simulation study

Numerous observational studies have assessed the association between ambient air pollution and chronic disease incidence, but there is no uniform approach to create an exposure metric that captures the variability in air pollution through time and determines the most relevant exposure window. The purpose of the present study was to assess ways of modeling exposure to air pollution in relation to incident hypertension. We simulated data on incident hypertension to assess the performance of six air pollution exposure metrics, using characteristics from the Black Women’s Health Study. Each metric made different assumptions about how to incorporate time trends in pollutant data, and the most relevant window of exposure. We use observed values for particulate matter ≤2.5 microns (PM_2.5) for this cohort to create the six exposure metrics and fit Cox proportional hazards models to the simulated data using the six metrics. The optimal exposure metric depends on the underlying association between PM_2.5 and disease, which is unknown. Metrics that incorporate exposure information from multiple years tend to be more robust and suffer from less bias. This study provides insight into factors that influence the metric used to quantifying exposure to PM_2.5 and suggests the need for careful sensitivity analyses.

Air Pollution and the Risk of Cardiac Defects: A Population-Based Case-Control Study

Previous epidemiologic studies have assessed the role of the exposure to ambient air pollution in the development of cardiac birth defects, but they have provided somewhat inconsistent results. To assess the associations between exposure to ambient air pollutants and the risk of cardiac defects, a population-based case-control study was conducted using 1087 cases of cardiac defects and a random sample of 10,870 controls from 1,533,748 Taiwanese newborns in 2001 to 2007.Logistic regression was performed to calculate odds ratios for 10 ppb increases in O3 and 10 μg/m increases in PM10. In addition, we compared the risk of cardiac defects in 4 categories-high exposure (>75th percentile); medium exposure (75th to 50th percentile); low exposure (<50th-25th percentile); reference (<25th percentile) based on the distribution of each pollutant. The risks of ventricular septal defects (VSD), atrial septal defects (ASD), and patent ductus arteriosus (PDA) were associated with 10 ppb increases in O3 exposure during the first 3 gestational months among term and preterm babies. In comparison between high PM10 exposure and reference category, there were statistically significant elevations in the effect estimates of ASD for all and terms births. In addition, there was a negative or weak association between SO2, NO2, CO, and cardiac defects.The study proved that exposure to outdoor air O3 and PM10 during the first trimester of gestation may increase the risk of VSD, ASD, and PDA.

Back-extrapolating a land use regression model for estimating past exposures to traffic-related air pollution

Land use regression (LUR) models rely on air pollutant measurements for their development, and are therefore limited to recent periods where such measurements are available. Here we propose an approach to overcome this gap and calculate LUR models several decades before measurements were available. We first developed a LUR model for NOx using annual averages of NOx at all available air quality monitoring sites in Israel between 1991 and 2011 with time as one of the independent variables. We then reconstructed historical spatial data (e.g., road network) from historical topographic maps to apply the model's prediction to each year from 1961 to 2011. The model's predictions were then validated against independent estimates about the national annual NOx emissions from on-road vehicles in a top-down approach. The model's cross validated R2 was 0.74, and the correlation between the model's annual averages and the national annual NOx emissions between 1965 and 2011 was 0.75. Information about the road network and population are persistent predictors in many LUR models. The use of available historical data about these predictors to resolve the spatial variability of air pollutants together with complementary national estimates on the change in pollution levels over time enable historical reconstruction of exposures.

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.

The microenvironmental modelling approach to assess children's exposure to air pollution - A review

Exposures to a wide spectrum of air pollutants were associated to several effects on children's health. Exposure assessment can be used to establish where and how air pollutants' exposures occur. However, a realistic estimation of children's exposures to air pollution is usually a great ethics challenge, especially for young children, because they cannot intentionally be exposed to contaminants and according to Helsinki declaration, they are not old enough to make a decision on their participation. Additionally, using adult surrogates introduces bias, since time-space-activity patterns are different from those of children. From all the different available approaches for exposure assessment, the microenvironmental (ME) modelling (indirect approach, where personal exposures are estimated or predicted from microenvironment measurements combined with time-activity data) seemed to be the best to assess children's exposure to air pollution as it takes into account the varying levels of pollution to which an individual is exposed during the course of the day, it is faster and less expensive. Thus, this review aimed to explore the use of the ME modelling approach methodology to assess children's exposure to air pollution. To meet this goal, a total of 152 articles, published since 2002, were identified and titles and abstracts were scanned for relevance. After exclusions, 26 articles were fully reviewed and main characteristics were detailed, namely: (i) study design and outcomes, including location, study population, calendar time, pollutants analysed and purpose; and (ii) data collection, including time-activity patterns (methods of collection, record time and key elements) and pollution measurements (microenvironments, methods of collection and duration and time resolution). The reviewed studies were from different parts of the world, confirming the worldwide application, and mostly cross-sectional. Longitudinal studies were also found enhancing the applicability of this approach. The application of this methodology on children is different from that on adults because of data collection, namely the methods used for collecting time-activity patterns must be different and the time-activity patterns are itself different, which leads to select different microenvironments to the data collection of pollutants' concentrations. The most used methods to gather information on time-activity patterns were questionnaires and diaries, and the main microenvironments considered were home and school (indoors and outdoors). Although the ME modelling approach in studies to assess children's exposure to air pollution is highly encouraged, a validation process is needed, due to the uncertainties associated with the application of this approach.

Ambient Air Pollution and Hypertensive Disorders of Pregnancy: A Systematic Review and Meta-analysis

Hypertensive disorders of pregnancy (HDP, including gestational hypertension, preeclampsia, and eclampsia) have a substantial public health impact. Maternal exposure to high levels of air pollution may trigger HDP, but this association remains unclear. The objective of our report is to assess and quantify the association between maternal exposures to criteria air pollutants (ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, and particulate matter ≤ 10, 2.5 μm) on HDP risk. PubMed, EMBASE, MEDLINE, Current Contents, Global Health, and Cochrane were searched (last search: September, 2013). After a detailed screening of 270 studies, 10 studies were extracted. We conducted meta-analyses if a pollutant in a specific exposure window was reported by at least four studies. Using fixed- and random-effects models, odds ratios (ORs) and 95% CIs were calculated for each pollutant with specific increment of concentration. Increases in risks of HDP (OR per 10 ppb = 1.16; 95% CI, 1.03-1.30) and preeclampsia (OR per 10 ppb = 1.10; 95% CI, 1.03-1.17) were observed to be associated with exposure to NO2 during the entire pregnancy, and significant associations between HDP and exposure to CO (OR per 1 ppm = 1.79; 95% CI, 1.31-2.45) and O3 (OR per 10 ppb = 1.09; 95% CI, 1.05-1.13) during the first trimester were also observed. Our review suggests an association between ambient air pollution and HDP risk. Although the ORs were relatively low, the population-attributable fractions were not negligible given the ubiquitous nature of air pollution.

A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.

New models for large prospective studies: is there a risk of throwing out the baby with the bathwater?

Manolio et al. (Am J Epidemiol. 2012;175:859-866) proposed that large cohort studies adopt novel models using "temporary assessment centers" to enroll up to a million participants to answer research questions about rare diseases and "harmonize" clinical endpoints collected from administrative records. Extreme selection bias, we are told, will not harm internal validity, and "process expertise to maximize efficiency of high-throughput operations is as important as scientific rigor" (p. 861). In this article, we describe serious deficiencies in this model as applied to the United States. Key points include: 1) the need for more, not less, specification of disease endpoints; 2) the limited utility of data collected from existing administrative and clinical databases; and 3) the value of university-based centers in providing scientific expertise and achieving high recruitment and retention rates through community and healthcare provider engagement. Careful definition of sampling frames and high response rates are crucial to avoid bias and ensure inclusion of important subpopulations, especially the medically underserved. Prospective hypotheses are essential to refine study design, determine sample size, develop pertinent data collection protocols, and achieve alliances with participants and communities. It is premature to reject the strengths of large national cohort studies in favor of a new model for which evidence of efficiency is insufficient.

Respiratory health effects of air pollution: update on biomass smoke and traffic pollution

Mounting evidence suggests that air pollution contributes to the large global burden of respiratory and allergic diseases, including asthma, chronic obstructive pulmonary disease, pneumonia, and possibly tuberculosis. Although associations between air pollution and respiratory disease are complex, recent epidemiologic studies have led to an increased recognition of the emerging importance of traffic-related air pollution in both developed and less-developed countries, as well as the continued importance of emissions from domestic fires burning biomass fuels, primarily in the less-developed world. Emissions from these sources lead to personal exposures to complex mixtures of air pollutants that change rapidly in space and time because of varying emission rates, distances from source, ventilation rates, and other factors. Although the high degree of variability in personal exposure to pollutants from these sources remains a challenge, newer methods for measuring and modeling these exposures are beginning to unravel complex associations with asthma and other respiratory tract diseases. These studies indicate that air pollution from these sources is a major preventable cause of increased incidence and exacerbation of respiratory disease. Physicians can help to reduce the risk of adverse respiratory effects of exposure to biomass and traffic air pollutants by promoting awareness and supporting individual and community-level interventions.

Nutrition can modulate the toxicity of environmental pollutants: implications in risk assessment and human health

BACKGROUND

The paradigm of human risk assessment includes many variables that must be viewed collectively in order to improve human health and prevent chronic disease. The pathology of chronic diseases is complex, however, and may be influenced by exposure to environmental pollutants, a sedentary lifestyle, and poor dietary habits. Much of the emerging evidence suggests that nutrition can modulate the toxicity of environmental pollutants, which may alter human risks associated with toxicant exposures.

OBJECTIVES

In this commentary, we discuss the basis for recommending that nutrition be considered a critical variable in disease outcomes associated with exposure to environmental pollutants, thus establishing the importance of incorporating nutrition within the context of cumulative risk assessment.

DISCUSSION

A convincing body of research indicates that nutrition is a modulator of vulnerability to environmental insults; thus, it is timely to consider nutrition as a vital component of human risk assessment. Nutrition may serve as either an agonist or an antagonist (e.g., high-fat foods or foods rich in antioxidants, respectively) of the health impacts associated with exposure to environmental pollutants. Dietary practices and food choices may help explain the large variability observed in human risk assessment.

CONCLUSION

We recommend that nutrition and dietary practices be incorporated into future environmental research and the development of risk assessment paradigms. Healthful nutrition interventions might be a powerful approach to reduce disease risks associated with many environmental toxic insults and should be considered a variable within the context of cumulative risk assessment and, where appropriate, a potential tool for subsequent risk reduction.

Predicting regional space-time variation of PM2.5 with land-use regression model and MODIS data

PURPOSE

Existing land-use regression (LUR) models use land use/cover, population, and traffic information to predict long-term intra-urban variation of air pollution. These models are limited to explaining spatial variation of air pollutants, and few of them are capable of addressing temporal variability. This article proposes a space-time LUR model at a regional scale by incorporating aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS).

METHODS

A multivariate regression model was established to predict the distribution of particle matters less than 2.5 μm in aerodynamic diameter (PM(2.5)) in Florida, USA. Monthly PM(2.5) averages at 34 monitoring sites in the year 2005 were used as the dependent variable, while independent variables include land-use patterns, population, traffic, and topographic characteristics. In addition, a monthly AOD variable derived from the MODIS data was integrated into the regression as a space-time predictor. Cross-validation procedures were conducted to validate this AOD-enhanced LUR model.

RESULTS

The final regression model yields a coefficient of determination (R (2)) of 0.63, which is comparable to other studies that employ aerodynamic/meteorological models. The cross validation indicated a good agreement between the observed and predicted PM(2.5) with a mean residual of 0.02 μg/m(3). The distance to heavy-traffic roads is negatively associated with the concentrations of PM(2.5), while agricultural land use is positively correlated. PM(2.5) tends to concentrate in high-latitude areas of Florida and during summer/fall seasons. The monthly AOD has a significant contribution to explaining the variation of PM(2.5) and remarkably enhances the model performance.

CONCLUSIONS

This research is the first attempt to improve current LUR models by integrating remote sensing technologies. The integrative model approach offers an effective means to estimate air pollution over time and space, and could be an alternative to the classic meteorological approach. The model results would provide adequate measurements for epidemiological studies, particularly for chronic health effects in large populations.

Exposure to moderate air pollution during late pregnancy and cord blood cytokine secretion in healthy neonates

Background/Objectives

Ambient air pollution can alter cytokine concentrations as shown in vitro and following short-term exposure to high air pollution levels in vivo. Exposure to pollution during late pregnancy has been shown to affect fetal lymphocytic immunophenotypes. However, effects of prenatal exposure to moderate levels of air pollutants on cytokine regulation in cord blood of healthy infants are unknown.

Methods

In a birth cohort of 265 healthy term-born neonates, we assessed maternal exposure to particles with an aerodynamic diameter of 10 µm or less (PM₁₀), as well as to indoor air pollution during the last trimester, specifically the last 21, 14, 7, 3 and 1 days of pregnancy. As a proxy for traffic-related air pollution, we determined the distance of mothers' homes to major roads. We measured cytokine and chemokine levels (MCP-1, IL-6, IL-10, IL-1ß, TNF-α and GM-CSF) in cord blood serum using LUMINEX technology. Their association with pollution levels was assessed using regression analysis, adjusted for possible confounders.

Results

Mean (95%-CI) PM₁₀ exposure for the last 7 days of pregnancy was 18.3 (10.3–38.4 µg/m³). PM₁₀ exposure during the last 3 days of pregnancy was significantly associated with reduced IL-10 and during the last 3 months of pregnancy with increased IL-1ß levels in cord blood after adjustment for relevant confounders. Maternal smoking was associated with reduced IL-6 levels. For the other cytokines no association was found.

Conclusions

Our results suggest that even naturally occurring prenatal exposure to moderate amounts of indoor and outdoor air pollution may lead to changes in cord blood cytokine levels in a population based cohort.

Ambient air pollution and risk of congenital anomalies: a systematic review and meta-analysis

OBJECTIVE

We systematically reviewed epidemiologic studies on ambient air pollution and congenital anomalies and conducted meta-analyses for a number of air pollutant-anomaly combinations.

DATA SOURCES AND EXTRACTION

From bibliographic searches we extracted 10 original epidemiologic studies that examined the association between congenital anomaly risk and concentrations of air pollutants. Meta-analyses were conducted if at least four studies published risk estimates for the same pollutant and anomaly group. Summary risk estimates were calculated for a) risk at high versus low exposure level in each study and b) risk per unit increase in continuous pollutant concentration.

DATA SYNTHESIS

Each individual study reported statistically significantly increased risks for some combinations of air pollutants and congenital anomalies, among many combinations tested. In meta-analyses, nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) exposures were related to increases in risk of coarctation of the aorta [odds ratio (OR) per 10 ppb NO₂ = 1.17; 95% confidence interval (CI), 1.00-1.36; OR per 1 ppb SO₂ = 1.07; 95% CI, 1.01-1.13] and tetralogy of Fallot (OR per 10 ppb NO₂ = 1.20; 95% CI, 1.02-1.42; OR per 1 ppb SO₂ = 1.03; 95% CI, 1.01-1.05), and PM₁₀ (particulate matter ≤ 10 µm) exposure was related to an increased risk of atrial septal defects (OR per 10 μg/m³ = 1.14; 95% CI, 1.01-1.28). Meta-analyses found no statistically significant increase in risk of other cardiac anomalies and oral clefts.

CONCLUSIONS

We found some evidence for an effect of ambient air pollutants on congenital cardiac anomaly risk. Improvements in the areas of exposure assessment, outcome harmonization, assessment of other congenital anomalies, and mechanistic knowledge are needed to advance this field.

A growing role for gender analysis in air pollution epidemiology

Epidemiologic studies of air pollution effects on respiratory health report significant modification by sex, although results are not uniform. Importantly, it remains unclear whether modifications are attributable to socially derived gendered exposures, to sex-linked physiological differences, or to some interplay thereof. Gender analysis, which aims to disaggregate social from biological differences between males and females, may help to elucidate these possible sources of effect modification. Studies of children suggest stronger effects among boys in early life and among girls in later childhood. The qualitative review describes possible sources of difference in air pollution response between women and men, which may vary by life stage, coexposures, hormonal status, or other factors. The sources of observed effect modifications remain unclear, although gender analytic approaches may help to disentangle gender and sex differences in pollution response. A framework for incorporating gender analysis into environmental epidemiology is offered, along with several potentially useful methods from gender analysis.

Does exposure to air pollution in urban parks have socioeconomic, racial or ethnic gradients?

Little is known about the levels of air pollution at public parks where regular exercise takes place or in park-adjacent neighborhoods where people have easy access to parks. In this study we investigated the ambient concentrations of criteria pollutants nitrogen dioxide (NO(2)), fine particulate (PM(2.5)) and ozone (O(3)) at public parks and in park-adjacent neighborhoods for metropolitan Los Angeles. Socioeconomic and racial-ethnic inequalities in exposure to the three criteria pollutants were also investigated using multiple linear regression models. In addition, differences in inhalation doses from breathing the three +criteria pollutants were investigated for the top and bottom quartile racial composition in the parks and neighborhoods. Our research showed that although public parks had on average the lowest pollutant concentrations of NO(2) and PM(2.5), they had relatively high O(3) concentrations. Park-adjacent neighborhoods, by contrast, had the highest NO(2) and PM(2.5) concentrations, but the lowest O(3) concentrations. Higher exposures to NO(2) and PM(2.5) were systematically identified for the lower socioeconomic position or higher minority population neighborhoods. For children and adolescents aged 6-15 engaging in high and moderate intensity activities in and around public parks, those from the top quartile of primarily Hispanic neighborhoods had much higher (63%) inhaled doses of NO(2) compared to the bottom quartile counterpart. PM(2.5) showed a similar but less pronounced pattern of inhalation doses. Evidence of socioeconomic and racial-ethnic gradients was found in air pollution exposure and inhalation doses in and around the urban parks in Los Angeles. This suggests that patterns of exposure inequality found in other environmental justice research are present in exposures in and around urban parks.

Association between maternal exposure to ambient air pollution and congenital heart disease: A register-based spatiotemporal analysis

Recent studies have linked maternal exposure to air pollution with a range of adverse pregnancy outcomes. However, the available evidence linking this exposure to congenital anomalies is still limited and controversial. The present case-control study tested the hypothesis that maternal exposure to ambient black smoke and sulfur dioxide is a risk factor for the occurrence of congenital heart disease. The authors used registry-based data on congenital heart disease for the population of the northeast of England in 1985-1996. A 2-stage spatiotemporal model was developed to predict weekly black smoke and sulfur dioxide levels at each maternal place of residence. Controls were frequency-matched to cases by year of birth (control-to-case ratio of 4:1). Two sets of analyses were performed, using predicted mean values of exposure and 1,000 simulated scenarios of exposure. The analyses were adjusted for birth year, socioeconomic status, infant sex, season of conception, and degree of urbanity. The authors found a weak association between maternal exposure to black smoke and congenital malformations of cardiac chambers and connections only when using exposure as a continuous variable. When the authors used quartiles of exposure, odds ratios did not show a dose-response relation for consecutive quartiles. For sulfur dioxide, the results were not indicative of any association.

The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates

Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79-0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ~16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63-0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0-2 h spent at least 10 km away, 0.66 for ≥ 10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates.

Vulnerability-based spatial sampling stratification for the National Children's Study, Worcester County, Massachusetts: capturing health-relevant environmental and sociodemographic variability

BACKGROUND

The National Children's Study is the most ambitious study ever attempted in the United States to assess how environmental factors impact child health and development. It aims to follow 100,000 children from gestation until 21 years of age. Success requires breaking new interdisciplinary ground, starting with how to select the sample of > 1,000 children in each of 105 study sites; no standardized protocol exists for stratification of the target population by factoring in the diverse environments it inhabits. Worcester County, Massachusetts, like other sites, stratifies according to local conditions and local knowledge, subject to probability sampling rules.

OBJECTIVES

We answer the following questions: How do we divide Worcester County into viable strata that represent its health-relevant environmental and sociodemographic heterogeneity, subject to sampling rules? What potential does our approach have to inform stratification at other sites?

RESULTS

We developed a multivariable, vulnerability-based method for spatial sampling consisting of two descriptive indices: a hazards/stressors exposure index (comprising three proxy variables), and an adaptive capacity/sociodemographic character index (five variables). Multivariable, health-relevant stratification at the start of the study may improve detection power for environment-child health associations down the line. Eighteen strata capture countywide heterogeneity in the indices and have optimal relative homogeneity within each. They achieve comparable expected birth counts and conform to local concepts of space.

CONCLUSION

The approach offers moderate to high potential to inform other sites, limited by intersite differences in data availability, geodemographics, and technical capacity. Energetic community engagement from the start promotes local stratification coherence, plus vital researcher-community trust and co-ownership for sustainability.

Multi-perspective analysis and spatiotemporal mapping of air pollution monitoring data

Space-time data analysis and assimilation techniques in atmospheric sciences typically consider input from monitoring measurements. The input is often processed in a manner that acknowledges characteristics of the measurements (e.g., underlying patterns, fluctuation features) under conditions of uncertainty; it also leads to the derivation of secondary information that serves study-oriented goals, and provides input to space-time prediction techniques. We present a novel approach that blends a rigorous space-time prediction model (Bayesian maximum entropy, BME) with a cognitively informed visualization of high-dimensional data (spatialization). The combined BME and spatialization approach (BME-S) is used to study monthly averaged NO2 and mean annual SO4 measurements in California over the 15-year period 1988-2002. Using the original scattered measurements of these two pollutants BME generates spatiotemporal predictions on a regular grid across the state. Subsequently, the prediction network undergoes the spatialization transformation into a lower-dimensional geometric representation, aimed at revealing patterns and relationships that exist within the input data. The proposed BME-S provides a powerful spatiotemporal framework to study a variety of air pollution data sources.

Prenatal exposure to traffic-related air pollution and ultrasound measures of fetal growth in the INMA Sabadell cohort

BACKGROUND

Few studies have used longitudinal ultrasound measurements to assess the effect of traffic-related air pollution on fetal growth.

OBJECTIVE

We examined the relationship between exposure to nitrogen dioxide (NO2) and aromatic hydrocarbons [benzene, toluene, ethylbenzene, m/p-xylene, and o-xylene (BTEX)] on fetal growth assessed by 1,692 ultrasound measurements among 562 pregnant women from the Sabadell cohort of the Spanish INMA (Environment and Childhood) study.

METHODS

We used temporally adjusted land-use regression models to estimate exposures to NO2 and BTEX. We fitted mixed-effects models to estimate longitudinal growth curves for femur length (FL), head circumference (HC), abdominal circumference (AC), biparietal diameter (BPD), and estimated fetal weight (EFW). Unconditional and conditional SD scores were calculated at 12, 20, and 32 weeks of gestation. Sensitivity analyses were performed considering time-activity patterns during pregnancy.

RESULTS

Exposure to BTEX from early pregnancy was negatively associated with growth in BPD during weeks 20-32. None of the other fetal growth parameters were associated with exposure to air pollution during pregnancy. When considering only women who spent < 2 hr/day in nonresidential outdoor locations, effect estimates were stronger and statistically significant for the association between NO2 and growth in HC during weeks 12-20 and growth in AC, BPD, and EFW during weeks 20-32.

CONCLUSIONS

Our results lend some support to an effect of exposure to traffic-related air pollutants from early pregnancy on fetal growth during mid-pregnancy..

A growing role for gender analysis in air pollution epidemiology

OBJECTIVE

Epidemiologic studies of air pollution effects on respiratory health report significant modification by sex, although results are not uniform. Importantly, it remains unclear whether modifications are attributable to socially derived gendered exposures, to sex-linked physiological differences, or to some interplay thereof. Gender analysis, which aims to disaggregate social from biological differences between males and females, may help to elucidate these possible sources of effect modification.

DATA SOURCES AND DATA EXTRACTION

A PubMed literature search was performed in July 2009, using the terms "respiratory" and any of "sex" or "gender" or "men and women" or "boys and girls" and either "PM2.5" (particulate matter <or= 2.5 microm in aerodynamic diameter) or "NO2" (nitrogen dioxide). I reviewed the identified studies, and others cited therein, to summarize current evidence of effect modification, with attention to authors' interpretation of observed differences. Owing to broad differences in exposure mixes, outcomes, and analytic techniques, with few studies examining any given combination thereof, meta-analysis was not deemed appropriate at this time.

DATA SYNTHESIS

More studies of adults report stronger effects among women, particularly for older persons or where using residential exposure assessment. Studies of children suggest stronger effects among boys in early life and among girls in later childhood.

CONCLUSIONS

The qualitative review describes possible sources of difference in air pollution response between women and men, which may vary by life stage, coexposures, hormonal status, or other factors. The sources of observed effect modifications remain unclear, although gender analytic approaches may help to disentangle gender and sex differences in pollution response. A framework for incorporating gender analysis into environmental epidemiology is offered, along with several potentially useful methods from gender analysis.

Analyses of school commuting data for exposure modeling purposes

Human exposure models often make the simplifying assumption that school children attend school in the same census tract where they live. This paper analyzes that assumption and provides information on the temporal and spatial distributions associated with school commuting. The data were obtained using Oak Ridge National Laboratory's LandScan USA population distribution model applied to Philadelphia, PA. It is a high-resolution model used to allocate individual school-aged children to both a home and school location, and to devise a minimum-time home-to-school commuting path (called a trace) between the two locations. LandScan relies heavily on Geographic Information System (GIS) data. With respect to school children attending school in their home census tract, the vast majority does not in Philadelphia. Our analyses found that: (1) about 32% of the students walk across two or more census tracts going to school and 40% of them walk across four or more census blocks; and (2) 60% drive across four or more census tracts going to school and 50% drive across 10 or more census blocks. We also find that: (3) using a 5-min commuting time interval - as opposed to the modeled "trace" - results in misclassifying the "actual" path taken in 90% of the census blocks, 70% of the block groups, and 50% of the tracts; (4) a 1-min time interval is needed to reasonably resolve time spent in the various census unit designations; and (5) approximately 50% of both the homes and schools of Philadelphia school children are located within 160 m of highly traveled roads, and 64% of the schools are located within 200 m. These findings are very important when modeling school children's exposures, especially, when ascertaining the impacts of near-roadway concentrations on their total daily body burden. As many school children also travel along these streets and roadways to get to school, a majority of children in Philadelphia are in mobile source-dominated locations most of the day. We hypothesize that exposures of school children in Philadelphia to benzene and particulate matter will be much higher than if home and school locations and commuting paths at a 1-min time resolution are not explicitly modeled in an exposure assessment. Undertaking such an assessment will be the topic of a future paper.

Maternal exposure to particulate matter increases postnatal ozone-induced airway hyperreactivity in juvenile mice

RATIONALE

Epidemiologic studies implicate air pollutant exposure during pregnancy as a risk factor for wheezing in offspring. Ozone exposure is linked to exacerbations of wheezing in children.

OBJECTIVES

To determine if maternal pulmonary exposure to traffic-related particles during pregnancy augments ozone-induced airway hyperresponsiveness in offspring.

METHODS

C57BL6 time-mated mice were given NIST SRM#1648 (particulate matter [PM]) 0.48 mg, saline vehicle, or no treatment by tracheal insufflation twice weekly for 3 weeks. PM exposure augmented maternal lung inflammation and placental TNF-alpha, Keratinocyte-derived cytokine (KC), and IL-6 (measured at gestation Day 18). After parturition, dams and litters were exposed to air or ozone 1 ppm 3 h/d, every other day, thrice weekly for 4 weeks. Respiratory system resistance in pups was measured at baseline and after administration of nebulized methacholine.

MEASUREMENTS AND MAIN RESULTS

Ozone increased airway hyperresponsiveness, but the increase was greatest in pups born to PM-treated dams. Whole-lung TNF-alpha, IL-1beta, KC, IL-6, and MCP-1 were increased in ozone-treated pups, with the greatest increase in pups born to dams given PM. Airway epithelial mucous metaplasia estimated by periodic acid-Schiff Alcian blue staining was increased in ozone-exposed pups born to PM-treated dams. Alveolar development, determined by morphometry, and airway smooth muscle bulk, estimated using alpha-actin histochemistry, were unaffected by prenatal or postnatal treatment.

CONCLUSIONS

Maternal pulmonary exposure to PM during pregnancy augments placental cytokine expression and postnatal ozone-induced pulmonary inflammatory cytokine responses and ozone-induced airway hyperresponsiveness without altering airway structure.

Asthma mitigation strategies: professional, charitable, and community coalitions

BACKGROUND

Asthma symptoms, severity, and mortality are known to be affected by personal, family, and neighborhood social factors. Many groups have become involved in asthma research, education, and activism in the past 20 years. This study explores the approaches to asthma taken by community-based organizations compared with those taken by other organizations that have a focus on asthma.

METHODS

Priorities in asthma research and intervention were assessed through interviews with representatives of urban community-based participatory research (CBPR) coalitions; interviews with staff from charities focused on asthma, allergy, or lung diseases; interviews with physicians and scientists studying and treating asthma; participation in community forums; and participant observation of urban asthma coalitions. Interviews and data analysis were conducted in 2008.

RESULTS

There are marked differences in priorities and approaches to asthma among experts in the field, organizations and coalitions at the national and local levels, and other stakeholders in asthma research and activism. CBPR coalitions are more likely than asthma-focused organizations to explore environmental and community-level structural factors that exacerbate asthma or complicate its management, while disease-focused organizations, especially physician specialty groups, place more emphasis on individual-level factors. CBPR coalitions have been particularly strong in producing the data needed to demonstrate that individual communities are affected by pollution hot spots or that local neighborhoods lack geographic access to affordable medical care, and in providing this data to improve local policy-making.

CONCLUSIONS

Because of its focus on structural rather than individual factors, CBPR has helped to broaden the debate on asthma beyond clinical care and education into social and environmental justice.

Association between GIS-based exposure to urban air pollution during pregnancy and birth weight in the INMA Sabadell Cohort

BACKGROUND

There is growing evidence that traffic-related air pollution reduces birth weight. Improving exposure assessment is a key issue to advance in this research area.

OBJECTIVE

We investigated the effect of prenatal exposure to traffic-related air pollution via geographic information system (GIS) models on birth weight in 570 newborns from the INMA (Environment and Childhood) Sabadell cohort.

METHODS

We estimated pregnancy and trimester-specific exposures to nitrogen dioxide and aromatic hydrocarbons [benzene, toluene, ethylbenzene, m/p-xylene, and o-xylene (BTEX)] by using temporally adjusted land-use regression (LUR) models. We built models for NO(2) and BTEX using four and three 1-week measurement campaigns, respectively, at 57 locations. We assessed the relationship between prenatal air pollution exposure and birth weight with linear regression models. We performed sensitivity analyses considering time spent at home and time spent in nonresidential outdoor environments during pregnancy.

RESULTS

In the overall cohort, neither NO(2) nor BTEX exposure was significantly associated with birth weight in any of the exposure periods. When considering only women who spent < 2 hr/day in nonresidential outdoor environments, the estimated reductions in birth weight associated with an interquartile range increase in BTEX exposure levels were 77 g [95% confidence interval (CI), 7-146 g] and 102 g (95% CI, 28-176 g) for exposures during the whole pregnancy and the second trimester, respectively. The effects of NO(2) exposure were less clear in this subset.

CONCLUSIONS

The association of BTEX with reduced birth weight underscores the negative role of vehicle exhaust pollutants in reproductive health. Time-activity patterns during pregnancy complement GIS-based models in exposure assessment.

A cohort study of traffic-related air pollution and mortality in Toronto, Ontario, Canada

BACKGROUND

Chronic exposure to traffic-related air pollution (TRAP) may contribute to premature mortality, but few studies to date have addressed this topic.

OBJECTIVES

In this study we assessed the association between TRAP and mortality in Toronto, Ontario, Canada.

METHODS

We collected nitrogen dioxide samples over two seasons using duplicate two-sided Ogawa passive diffusion samplers at 143 locations across Toronto. We calibrated land use regressions to predict NO2 exposure on a fine scale within Toronto. We used interpolations to predict levels of particulate matter with aerodynamic diameter < or = 2.5 microm (PM(2.5)) and ozone levels. We assigned predicted pollution exposures to 2,360 subjects from a respiratory clinic, and abstracted health data on these subjects from medical billings, lung function tests, and diagnoses by pulmonologists. We tracked mortality between 1992 and 2002. We used standard and multilevel Cox proportional hazard models to test associations between air pollution and mortality.

RESULTS

After controlling for age, sex, lung function, obesity, smoking, and neighborhood deprivation, we observed a 17% increase in all-cause mortality and a 40% increase in circulatory mortality from an exposure contrast across the interquartile range of 4 ppb NO2. We observed no significant associations with other pollutants.

CONCLUSIONS

Exposure to TRAP was significantly associated with increased all-cause and circulatory mortality in this cohort. A high prevalence of cardiopulmonary disease in the cohort probably limits inference of the findings to populations with a substantial proportion of susceptible individuals.

Application of land use regression to regulatory air quality data in Japan

A land use regression (LUR) model has been used successfully for predicting traffic-related pollutants, although its application has been limited to Europe and North America. Therefore, we modeled traffic-related pollutants by LUR then examined whether LUR models could be constructed using a regulatory monitoring network in Shizuoka, Japan. We used the annual-mean nitrogen dioxide (NO2) and suspended particulate matter (SPM) concentrations between April 2000 and March 2006 in the study area. SPM accounts for particulate matter with an aerodynamic diameter less than 8 microm (PM(8)). Geographic variables that are considered to predict traffic-related pollutants were classified into four groups: road type, traffic intensity, land use, and physical component. Using geographical variables, we then constructed a model to predict the monitored levels of NO2 and SPM. The mean concentrations of NO2 and SPM were 35.75 microg/m(3) (standard deviation of 11.28) and 28.67 microg/m(3) (standard deviation of 4.73), respectively. The final regression model for the NO2 concentration included five independent variables. R(2) for the NO2 model was 0.54. On the other hand, the regression model for the SPM concentration included only one independent variable. R(2) for the SPM model was quite low (R(2) = 0.11). The present study showed that even if we used regulatory monitoring air quality data, we could estimate NO2 moderately well. This result could encourage the wide use of LUR models in Asian countries.

A lifestage approach to assessing children's exposure

Understanding and characterizing risks to children has been the focus of considerable research efforts at the U.S. Environmental Protection Agency (EPA). Potential health risks resulting from environmental exposures before conception and during pre- and postnatal development are often difficult to recognize and assess because of a potential time lag between the relevant periods of exposure during development and associated outcomes that may be expressed at later lifestages. Recognizing this challenge, a lifestage approach for assessing exposure and risk is presented in the recent EPA report titled A Framework for Assessing Health Risks of Environmental Exposures to Children (U.S. EPA, 2006). This EPA report emphasizes the need to account for the potential exposures to environmental agents during all stages of development, and consideration of the relevant adverse health outcomes that may occur as a result of such exposures. It identifies lifestage-specific issues associated with exposure characterization for regulatory risk assessment, summarizes the lifestage-specific approach to exposure characterization presented in the Framework, and discusses emerging research needs for exposure characterization in the larger public-health context. This lifestage approach for characterizing children's exposures to environmental contaminants ensures a more complete evaluation of the potential for vulnerability and exposure of sensitive populations throughout the life cycle.

Diesel exhaust increases EGFR and phosphorylated C-terminal Tyr 1173 in the bronchial epithelium

Background

Epidemiological studies have demonstrated adverse health effects of environmental pollution. Diesel exhaust (DE) is a major contributor to particulate matter pollution. DE exposure has been shown to induce a pronounced inflammatory response in the airways, together with an enhanced epithelial expression of cytokines such as IL-8, Gro-α, IL-13 and activation of redox sensitive transcription factors (NFκB, AP-1), and MAP kinases (p38, JNK). The aim of the present investigation was to elucidate the involvement of the epidermal growth factor receptor (EGFR) signalling pathway in the epithelial response to DE in-vivo.

Results

Immunohistochemical staining was used to quantify the expression of the EGFR, phosphorylated Tyrosine residues, MEK and ERK in the bronchial epithelium of archived biopsies from 15 healthy subjects following exposure to DE (PM₁₀, 300 μg/m³) and air. DE induced a significant increases in the expression of EGFR (p = 0.004) and phosphorylated C-terminal Tyr 1173 (p = 0.02). Other investigated EGFR tyrosine residues, Src related tyrosine (Tyr 416), MEK and ERK pathway were not changed significantly by DE.

Conclusion

Exposure to DE (PM₁₀, 300 μg/m³) caused enhanced EGFR expression and phosphorylation of the tyrosine residue (Tyr 1173) which is in accordance with the previously demonstrated activation of the JNK, AP-1, p38 MAPK and NFkB pathways and associated downstream signalling and cytokine production. No effects were seen on the MEK and ERK pathway suggesting that at the investigated time point (6 hours post exposure) there was no proliferative/differentiation signalling in the bronchial epithelium. The present findings suggest a key role for EGFR in the bronchial response to diesel exhaust.

Estimation of outdoor NO(x), NO(2), and BTEX exposure in a cohort of pregnant women using land use regression modeling

Land use regression (LUR) has been successfully used to assess the intraurban variability of air pollution. In the INMA (Environment and Childhood) Study, ambient nitrogen oxides (NO(x) and NO(2)) and aromatic hydrocarbons (BTEX) were measured at 57 sampling sites in Sabadell (northeast Spain). Multiple regression models were developed to predict residential outdoor concentrations in a cohortof pregnantwomen (n = 657), using geographic data as predictor variables. The models accounted for 68 and 69% of the variance in NO(x) and NO(2) levels, respectively, with four predictor variables (altitude, land coverage, and two road length indicators). These percentages of explained variability could be further improved by replacing the two road length indicators with an ordinal indicator (road type). To our knowledge, this is the first study using LUR to assess the intraurban variability of BTEX in Europe, with a model including altitude and source-proximity variables that explained 74% of the variance in BTEX levels. These models will be used to study the association between prenatal exposure to air pollution and adverse pregnancy outcomes and early childhhod effects in the cohort.

A review of land-use regression models for characterizing intraurban air pollution exposure

Epidemiologic studies of air pollution require accurate exposure assessments at unmonitored locations in order to minimize exposure misclassification. One approach gaining considerable interest is the land-use regression (LUR) model. Generally, the LUR model has been utilized to characterize air pollution exposure and health effects for individuals residing within urban areas. The objective of this article is to briefly summarize the history and application of LUR models to date outlining similarities and differences of the variables included in the model, model development, and model validation. There were 6 studies available for a total of 12 LUR models. Our findings indicated that among these studies, the four primary classes of variables used were road type, traffic count, elevation, and land cover. Of these four, traffic count was generally the most important. The model R2 explaining the variability in the exposure estimates for these LUR models ranged from .54 to .81. The number of air sampling sites generating the exposure estimates, however, was not correlated with the model R2 suggesting that the locations of the sampling sites may be of greater importance than the total number of sites. The primary conclusion of this study is that LUR models are an important tool for integrating traffic and geographic information to characterize variability in exposures.

Concentrations of vehicle-related air pollutants in an urban parking garage

There is growing evidence that traffic-related air pollution poses a public health threat, yet the dynamics of human exposure are not well understood. The urban parking garage is a microenvironment that is of concern but has not been characterized. Using time-resolved measurement methods, we evaluated air toxics levels within an urban parking garage and assessed the influence of vehicle activity and type on their levels. Carbon monoxide (CO) and particle-bound polycyclic aromatic hydrocarbons (pPAH) were measured with direct-reading instruments. Volatile organic compounds (VOCs) were measured in 30 min intervals using a sorbent tube loaded sequential sampler. Vehicle volume and type were evaluated by video recording. Sampling was conducted from June 24 to July 17, 2002. We observed garage traffic median volumes of 71 counts/h on weekdays and 6 counts/h on weekends. The 12-fold reduction in traffic volume from weekday to weekend corresponded with a decrease in median air pollution that varied from a minimum 2- (CO) to a maximum 7 (pPAH)-fold. The actual 30-min median weekday and weekend values were: CO--2.6/1.2 ppm; pPAH--19/2.6 ng/m(3); 1,3-butadiene-0.5/0.2 microg/m(3), MTBE-7.4/0.4 microg/m(3); and benzene-2.7/0.3 microg/m(3). The influence of traffic was quantified using longitudinal models. The pollutant coefficients provide an indication of the average air pollution vehicle source contribution and ranged from 0.31 (CO) to 1.08 (pPAH) percent increase/vehicle count. For some pollutants, a slightly higher (0.5-0.6%) coefficient was observed for light-trucks relative to cars. This study has public health relevance in providing a unique assessment of air pollution levels and source contribution for the urban parking garage.

A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA

Land use regression (LUR) models have been used successfully for predicting local variation in traffic pollution, but few studies have explored this method for deriving fine particle exposure surfaces. The primary purpose of this method is to develop a LUR model for predicting fine particle or PM(2.5) mass over the five county metropolitan statistical area (MSA) of Los Angeles. PM(2.5) includes all particles with diameter less than or equal to 2.5 microns. In the Los Angeles MSA, 23 monitors of PM(2.5) were available in the year 2000. This study uses GIS to integrate data regarding land use, transportation and physical geography to derive a PM(2.5) dataset covering Los Angeles. Multiple linear regression was used to create the model for predicting the PM(2.5) surface. Our parsimonious model explained 69% of the variance in PM(2.5) with three predictors: (1) traffic density within 300 m, (2) industrial land area within 5000 m, and (3) government land area within 5000 m of the monitoring site. These results suggest the LUR method can refine exposure models for epidemiologic studies in a North American context.

Methodologic and logistic issues in conducting longitudinal birth cohort studies: lessons learned from the Centers for Children's Environmental Health and Disease Prevention Research

In anticipation of the National Children's Study, lessons can be learned from the smaller birth cohort studies conducted by five Centers for Children's Environmental Health and Disease Prevention Research funded by the National Institute of Environmental Health Sciences and the U.S. Environmental Protection Agency. The populations studied are diverse in ethnicity and social class and reside in urban and rural environments. Although almost all of the centers chose to enroll participants through medical care facilities, they had to develop independent staffs and structures because of the overburdened medical care system. Some of the lessons learned by the centers include the importance of continuous funding, building community partnerships to conduct culturally appropriate research, hiring bilingual and bicultural staff from the community, prioritizing research goals, developing biorepositories to ensure future utility of samples, instituting quality control procedures for all aspects of specimen and data collection, maintaining frequent contact with study participants, ensuring ethical conduct of the research in a changing medical-legal climate, and communicating results in a timely and appropriate manner to participants and the wider community. All centers underestimated the necessary start-up time, staff, and costs in conducting these birth cohort studies. Despite the logistical complexity and added expenses, all centers emphasize the importance of studying the impact of environmental exposures on those children most at risk, those living in minority and low-income communities. These centers present barriers encountered, solutions found, and considerations for future research, with the hope that the lessons learned can help inform the planning and conduct of the National Children's Study.