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

Assessing the spatial distribution of nitrogen dioxide in London, Ontario

Land use regression (LUR) models have been widely used to characterize the spatial distribution of urban air pollution and estimate exposure in epidemiologic studies. However, spatial patterns of air pollution vary greatly between cities due to local source type and distribution. London, Ontario, Canada, is a medium-sized city with relatively few and isolated industrial point sources, which allowed the study to focus on the contribution of different transportation sectors to urban air pollution. This study used LUR models to estimate the spatial distribution of nitrogen dioxide (NO2) and to identify local sources influencing NO2 concentrations in London, ON. Passive air sampling was conducted at 50 locations throughout London over a 2-week period in May-June 2010. NO2 concentrations at the monitored locations ranged from 2.8 to 8.9 ppb, with a median of 5.2 ppb. Industrial land use, dwelling density, distance to highway, traffic density, and length of railways were significant predictors of NO2 concentrations in the final LUR model, which explained 78% of NO2 variability in London. Traffic and dwelling density explained most of the variation in NO2 concentrations, which is consistent with LUR models developed in other Canadian cities. We also observed the importance of local characteristics. Specifically, 17% of the variation was explained by distance to highways, which included the impacts of heavily traveled corridors transecting the southern periphery of the city. Two large railway yards and railway lines throughout central areas of the city explained 9% of NO2 variability. These results confirm the importance of traditional LUR variables and highlight the importance of including a broader array of local sources in LUR modeling. Finally, future analyses will use the model developed in this study to investigate the association between ambient air pollution and cardiovascular disease outcomes, including plaque burden, cholesterol, and hypertension.

Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

[Health risk assessment of traffic-related air pollution near busy roads]

BACKGROUND

Although ambient urban air pollution has well-established health effects, epidemiology faces many difficulties in estimating the risks due to exposure to traffic pollutants near busy roads. This review aims to summarize how exposure to traffic-related air pollution near busy roads is assessed in epidemiological studies and main findings regarding health effects.

METHOD

After presenting the specificity of emissions due to traffic road, this review identifies the key methods and main results found in epidemiologic studies seeking to measure the influence of exposure to nearby traffic on health published over the past decade.

RESULTS

The characterization and measurement of population exposure to traffic pollution faces many difficulties. Thus, epidemiological studies have used two broad categories of surrogates to assess exposure: direct measures of traffic itself such as distance of the residence to the nearest road and traffic volume and modeled concentrations of pollutant surrogates. Studies that implemented these methods showed that people living near heavy traffic road or exposed to near-road air pollution tend to report more health outcomes.

DISCUSSION

Traffic-related air pollution near busy roads is the subject of increasing attention, and tends to be better characterized. However, its health impacts remain difficult to grasp, especially because of the vast diversity of approaches used in epidemiological studies. Greater consistency in the protocols would be desirable to provide better understanding of the health issue of traffic in urban areas and thus to better implement policies to protect those most at risk.

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.

Ambient air pollution and allergic diseases in children

The prevalence of allergic diseases has increased worldwide, a phenomenon that can be largely attributed to environmental effects. Among environmental factors, air pollution due to traffic is thought to be a major threat to childhood health. Residing near busy roadways is associated with increased asthma hospitalization, decreased lung function, and increased prevalence and severity of wheezing and allergic rhinitis. Recently, prospective cohort studies using more accurate measurements of individual exposure to air pollution have been conducted and have provided definitive evidence of the impact of air pollution on allergic diseases. Particulate matter and ground-level ozone are the most frequent air pollutants that cause harmful effects, and the mechanisms underlying these effects may be related to oxidative stress. The reactive oxidative species produced in response to air pollutants can overwhelm the redox system and damage the cell wall, lipids, proteins, and DNA, leading to airway inflammation and hyper-reactivity. Pollutants may also cause harmful effects via epigenetic mechanisms, which control the expression of genes without changing the DNA sequence itself. These mechanisms are likely to be a target for the prevention of allergies. Further studies are necessary to identify children at risk and understand how these mechanisms regulate gene-environment interactions. This review provides an update of the current understanding on the impact of air pollution on allergic diseases in children and facilitates the integration of issues regarding air pollution and allergies into pediatric practices, with the goal of improving pediatric health.

Principal component analysis optimization of a PM2.5 land use regression model with small monitoring network

The use of land-use regression (LUR) techniques for modeling small-scale variations of intraurban air pollution has been increasing in the last decade. The most appealing feature of LUR techniques is the economical monitoring requirements. In this study, principal component analysis (PCA) was employed to optimize an LUR model for PM2.5. The PM2.5 monitoring network consisted of 13 sites, which constrained the regression model to a maximum of one independent variable. An optimized surrogate of vehicle emissions was produced by PCA and employed as the predictor variable in the model. The vehicle emissions surrogate consisted of a linear combination of several traffic variables (e.g., vehicle miles traveled, speed, traffic demand, road length, and time) obtained from a road network used for traffic modeling. The vehicle-emissions surrogate produced by the PCA had a predictive capacity greater (R2=.458) than the traffic variable, Traffic Demand summarized for a 1 km buffer, with best predictive capacity (R2=.341). The PCA-based method employed in this study was effective at increasing the fit of an ordinary LUR model by optimizing the utilization of a PM2.5 dataset from small-n monitoring network. In general, the method used can contribute to LUR techniques in two major ways: 1) by improving the predictive power of the input variable, by substituting a principal component for a single variable and 2) by creating an orthogonal set of predictor variables, and thus fulfilling the no colinearity assumption of the linear regression methods. The proposed PCA method, should be universally applicable to LUR methods and will expand their economical attractiveness.

Association of long-term exposure to community noise and traffic-related air pollution with coronary heart disease mortality

In metropolitan areas, road traffic is a major contributor to ambient air pollution and the dominant source of community noise. The authors investigated the independent and joint influences of community noise and traffic-related air pollution on risk of coronary heart disease (CHD) mortality in a population-based cohort study with a 5-year exposure period (January 1994-December 1998) and a 4-year follow-up period (January 1999-December 2002). Individuals who were 45-85 years of age and resided in metropolitan Vancouver, Canada, during the exposure period and did not have known CHD at baseline were included (n = 445,868). Individual exposures to community noise and traffic-related air pollutants, including black carbon, particulate matter less than or equal to 2.5 μm in aerodynamic diameter, nitrogen dioxide, and nitric oxide, were estimated at each person's residence using a noise prediction model and land-use regression models, respectively. CHD deaths were identified from the provincial death registration database. After adjustment for potential confounders, including traffic-related air pollutants or noise, elevations in noise and black carbon equal to the interquartile ranges were associated with 6% (95% confidence interval: 1, 11) and 4% (95% confidence interval: 1, 8) increases, respectively, in CHD mortality. Subjects in the highest noise decile had a 22% (95% confidence interval: 4, 43) increase in CHD mortality compared with persons in the lowest decile. These findings suggest that there are independent effects of traffic-related noise and air pollution on CHD mortality.

A review of spatial methods in epidemiology, 2000-2010

Understanding the impact of place on health is a key element of epidemiologic investigation, and numerous tools are being employed for analysis of spatial health-related data. This review documents the huge growth in spatial epidemiology, summarizes the tools that have been employed, and provides in-depth discussion of several methods. Relevant research articles for 2000-2010 from seven epidemiology journals were included if the study utilized a spatial analysis method in primary analysis (n = 207). Results summarized frequency of spatial methods and substantive focus; graphs explored trends over time. The most common spatial methods were distance calculations, spatial aggregation, clustering, spatial smoothing and interpolation, and spatial regression. Proximity measures were predominant and were applied primarily to air quality and climate science and resource access studies. The review concludes by noting emerging areas that are likely to be important to future spatial analysis in public health.

Association of long-term air pollution with ventricular conduction and repolarization abnormalities

BACKGROUND

Short-term exposure to air pollution may affect ventricular repolarization, but there is limited information on how long-term exposures might affect the surface ventricular electrocardiographic (ECG) abnormalities associated with cardiovascular events. We carried out a study to determine whether long-term air pollution exposure is associated with abnormalities of ventricular repolarization and conduction in adults without known cardiovascular disease.

METHODS

A total of 4783 participants free of clinical cardiovascular disease in the Multi-Ethnic Study of Atherosclerosis underwent 12-lead ECG examinations, cardiac-computed tomography, and calcium scoring, as well as estimation of air pollution exposure using a finely resolved spatiotemporal model to determine long-term average individual exposure to fine particulate matter (PM(2.5)) and proximity to major roadways. We assessed ventricular electrical abnormalities including presence of QT prolongation (Rautaharju QTrr criteria) and intraventricular conduction delay (QRS duration >120 milliseconds). We used logistic regression to determine the adjusted relationship between air pollution exposures and ECG abnormalities.

RESULTS

A 10-μg/m³ increase in estimated residential PM(2.5) was associated with an increased odds of prevalent QT prolongation (adjusted odds ratio [OR] = 1.6 [95% confidence interval (CI) = 1.2-2.2]) and intraventricular conduction delay (1.7 [1.0-2.6]), independent of coronary-artery calcium score. Living near major roadways was not associated with ventricular electrical abnormalities. No evidence of effect modification by traditional risk factors or study site was observed.

CONCLUSIONS

This study demonstrates an association between long-term exposure to air pollution and ventricular repolarization and conduction abnormalities in adults without clinical cardiovascular disease, independent of subclinical coronary arterial calcification.

Spatial analysis of binary health indicators with local smoothing techniques The Viadana study

INTRODUCTION

When pollution data from a monitoring network is not available, mapping the spatial distribution of disease can be useful to identify populations at risk and to suggest a potential role for suspected emission sources. We aimed at obtaining a continuous spatial representation of the prevalence of symptoms that are potentially associated with the exposure to the pollutants emitted from the wood factories in the children who live in the district of Viadana (Northern Italy).

METHODS

In 2006, all the parents of the children aged 3-14 years residing in the Viadana district (n = 3854), filled in a questionnaire on respiratory symptoms, irritation symptoms of the eyes and skin, use of health services. The children's residential addresses were also collected and geocoded. Generalized additive models and local weighted regression (LOWESS) were used to estimate the distribution of the symptoms, to test for spatial trends of the symptoms' prevalence and to control for potential confounders. Permutation tests were used to identify the areas of significantly increased risk ("hot spots").

RESULTS

The prevalence of respiratory symptoms, eye symptoms and the use of health services showed a statistically significant spatial variation (p < 0.05), but skin symptoms did not. Symptoms' prevalence was lower in the northern part of the district, where no wood factories were present, and it was higher in the southern part, where the two big chipboard industries were located. Hot spots were identified fairly near to one of the two chipboard industries in the district.

CONCLUSIONS

The north-to-south trend in the prevalence of respiratory and eye symptoms, but not of skin symptoms, as well as the location of hot spots, are consistent with the potential exposure to air pollutants both emitted by the wood factories and related to traffic. In these "high risk areas" monitoring of pollution and preventive actions are clearly needed.

A land use regression model incorporating data on industrial point source pollution

Advancing the understanding of the spatial aspects of air pollution in the city regional environment is an area where improved methods can be of great benefit to exposure assessment and policy support. We created land use regression (LUR) models for SO2, NO2 and PM10 for Tianjin, China. Traffic volumes, road networks, land use data, population density, meteorological conditions, physical conditions and satellite-derived greenness, brightness and wetness were used for predicting SO2, NO2 and PM10 concentrations. We incorporated data on industrial point sources to improve LUR model performance. In order to consider the impact of different sources, we calculated the PSIndex, LSIndex and area of different land use types (agricultural land, industrial land, commercial land, residential land, green space and water area) within different buffer radii (1 to 20 km). This method makes up for the lack of consideration of source impact based on the LUR model. Remote sensing-derived variables were significantly correlated with gaseous pollutant concentrations such as SO2 and NO2. R2 values of the multiple linear regression equations for SO2, NO2 and PM10 were 0.78, 0.89 and 0.84, respectively, and the RMSE values were 0.32, 0.18 and 0.21, respectively. Model predictions at validation monitoring sites went well with predictions generally within 15% of measured values. Compared to the relationship between dependent variables and simple variables (such as traffic variables or meteorological condition variables), the relationship between dependent variables and integrated variables was more consistent with a linear relationship. Such integration has a discernable influence on both the overall model prediction and health effects assessment on the spatial distribution of air pollution in the city region.

Traffic density and stationary sources of air pollution associated with wheeze, asthma, and immunoglobulin E from birth to age 5 years among New York City children

Exposures to ambient air traffic-related pollutants and their sources have been associated with respiratory and asthma morbidity in children. However, longitudinal investigation of the effects of traffic-related exposures during early childhood is limited. We examined associations of residential proximity and density of traffic and stationary sources of air pollution with wheeze, asthma, and immunoglobulin (Ig) E among New York City children between birth and age 5 years. Subjects included 593 Dominican and African American participants from the Columbia Center for Children's Environmental Health cohort. Prenatally, through age 5 years, residential and respiratory health data were collected every 3-6 months. At ages 2, 3, and 5 years, serum IgE was measured. Spatial data on the proximity and density of roadways and built environment were collected for a 250 m buffer around subjects' homes. Associations of wheeze, asthma, total IgE, and allergen-specific IgE with prenatal, earlier childhood, and concurrent exposures to air pollution sources were analyzed using generalized estimating equations or logistic regression. In repeated measures analyses, concurrent residential density of four-way intersections was associated significantly with wheeze (odds ratio: 1.26; 95% confidence interval [CI]: 1.01, 1.57). Age 1 exposures also were associated with wheeze at subsequent ages. Concurrent proximity to highway was associated more strongly with total IgE (ratio of the geometric mean levels: 1.25; 95% CI: 1.09, 1.42) than were prenatal or earlier childhood exposures. Positive associations also were observed between percent commercial building area and asthma, wheeze, and IgE and between proximity to stationary sources of air pollution and asthma. Longitudinal investigation suggests that among Dominican and African American children living in Northern Manhattan and South Bronx during ages 0-5 years, residence in neighborhoods with high density of traffic and industrial facilities may contribute to chronic respiratory morbidity, and concurrent, prenatal, and earlier childhood exposures may be important. These findings may have broad implications for other urban populations that commonly have high asthma prevalence and exposure to a high density of traffic and stationary air pollution sources.

Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

BACKGROUND: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. METHODS: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R(2) and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. RESULTS: UK models consistently performed as well as or better than the analogous LUR models. The best CV R(2) values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R(2) values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R(2) values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. CONCLUSION: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.

Survival analysis with error-prone time-varying covariates: a risk set calibration approach

Occupational, environmental, and nutritional epidemiologists are often interested in estimating the prospective effect of time-varying exposure variables such as cumulative exposure or cumulative updated average exposure, in relation to chronic disease endpoints such as cancer incidence and mortality. From exposure validation studies, it is apparent that many of the variables of interest are measured with moderate to substantial error. Although the ordinary regression calibration (ORC) approach is approximately valid and efficient for measurement error correction of relative risk estimates from the Cox model with time-independent point exposures when the disease is rare, it is not adaptable for use with time-varying exposures. By recalibrating the measurement error model within each risk set, a risk set regression calibration (RRC) method is proposed for this setting. An algorithm for a bias-corrected point estimate of the relative risk using an RRC approach is presented, followed by the derivation of an estimate of its variance, resulting in a sandwich estimator. Emphasis is on methods applicable to the main study/external validation study design, which arises in important applications. Simulation studies under several assumptions about the error model were carried out, which demonstrated the validity and efficiency of the method in finite samples. The method was applied to a study of diet and cancer from Harvard's Health Professionals Follow-up Study (HPFS).

Ambient carbon monoxide and fine particulate matter in relation to preeclampsia and preterm delivery in western Washington State

BACKGROUND

Preterm delivery and preeclampsia are common adverse pregnancy outcomes that have been inconsistently associated with ambient air pollutant exposures.

OBJECTIVES

We aimed to prospectively examine relations between exposures to ambient carbon monoxide (CO) and fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM2.5)] and risks of preeclampsia and preterm delivery.

METHODS

We used data from 3,509 western Washington women who delivered infants between 1996 and 2006. We predicted ambient CO and PM2.5 exposures using regression models based on regional air pollutant monitoring data. Models contained predictor terms for year, month, weather, and land use characteristics. We evaluated several exposure windows, including prepregnancy, early pregnancy, the first two trimesters, the last month, and the last 3 months of pregnancy. Outcomes were identified using abstracted maternal medical record data. Covariate information was obtained from maternal interviews.

RESULTS

Predicted periconceptional CO exposure was significantly associated with preeclampsia after adjustment for maternal characteristics and season of conception [adjusted odds ratio (OR) per 0.1 ppm=1.07; 95% confidence interval (CI), 1.02-1.13]. However, further adjustment for year of conception essentially nullified the association (adjusted OR=0.98; 95% CI, 0.91-1.06). Associations between PM2.5 and preeclampsia were nonsignificant and weaker than associations estimated for CO, and neither air pollutant was strongly associated with preterm delivery. Patterns were similar across all exposure windows.

CONCLUSIONS

Because both CO concentrations and preeclampsia incidence declined during the study period, secular changes in another preeclampsia risk factor may explain the association observed here. We saw little evidence of other associations with preeclampsia or preterm delivery in this setting.

STEMS-Air: a simple GIS-based air pollution dispersion model for city-wide exposure assessment

Current methods of air pollution modelling do not readily meet the needs of air pollution mapping for short-term (i.e. daily) exposure studies. The main limiting factor is that for those few models that couple with a GIS there are insufficient tools for directly mapping air pollution both at high spatial resolution and over large areas (e.g. city wide). A simple GIS-based air pollution model (STEMS-Air) has been developed for PM(10) to meet these needs with the option to choose different exposure averaging periods (e.g. daily and annual). STEMS-Air uses the grid-based FOCALSUM function in ArcGIS in conjunction with a fine grid of emission sources and basic information on meteorology to implement a simple Gaussian plume model of air pollution dispersion. STEMS-Air was developed and validated in London, UK, using data on concentrations of PM(10) from routinely available monitoring data. Results from the validation study show that STEMS-Air performs well in predicting both daily (at four sites) and annual (at 30 sites) concentrations of PM(10). For daily modelling, STEMS-Air achieved r(2) values in the range 0.19-0.43 (p<0.001) based solely on traffic-related emissions and r(2) values in the range 0.41-0.63 (p<0.001) when adding information on 'background' levels of PM(10). For annual modelling of PM(10), the model returned r(2) in the range 0.67-0.77 (P<0.001) when compared with monitored concentrations. The model can thus be used for rapid production of daily or annual city-wide air pollution maps either as a screening process in urban air quality planning and management, or as the basis for health risk assessment and epidemiological studies.

How to select the best tree planting locations to enhance air pollution removal in the MillionTreesNYC initiative

Highest priority zones for tree planting within New York City were selected by using a planting priority index developed combining three main indicators: pollution concentration, population density and low canopy cover. This new tree population was projected through time to estimate potential air quality and carbon benefits. Those trees will likely remove more than 10,000 tons of air pollutants and a maximum of 1500 tons of carbon over the next 100 years given a 4% annual mortality rate. Cumulative carbon storage will be reduced through time as carbon loss through tree mortality outweighs carbon accumulation through tree growth. Model projections are strongly affected by mortality rate whose uncertainties limit estimations accuracy. Increasing mortality rate from 4 to 8% per year produce a significant decrease in the total pollution removal over a 100 year period from 11 000 tons to 3000 tons.

Exposure assessment in cohort studies of childhood asthma

BACKGROUND

The environment is suspected to play an important role in the development of childhood asthma. Cohort studies are a powerful observational design for studying exposure-response relationships, but their power depends in part upon the accuracy of the exposure assessment.

OBJECTIVE

The purpose of this paper is to summarize and discuss issues that make accurate exposure assessment a challenge and to suggest strategies for improving exposure assessment in longitudinal cohort studies of childhood asthma and allergies.

DATA SYNTHESIS

Exposures of interest need to be prioritized, because a single study cannot measure all potentially relevant exposures. Hypotheses need to be based on proposed mechanisms, critical time windows for effects, prior knowledge of physical, physiologic, and immunologic development, as well as genetic pathways potentially influenced by the exposures. Modifiable exposures are most important from the public health perspective. Given the interest in evaluating gene-environment interactions, large cohort sizes are required, and planning for data pooling across independent studies is critical. Collection of additional samples, possibly through subject participation, will permit secondary analyses. Models combining air quality, environmental, and dose data provide exposure estimates across large cohorts but can still be improved.

CONCLUSIONS

Exposure is best characterized through a combination of information sources. Improving exposure assessment is critical for reducing measurement error and increasing power, which increase confidence in characterization of children at risk, leading to improved health outcomes.

Long-term exposure to traffic-related air pollution and the risk of coronary heart disease hospitalization and mortality

BACKGROUND

Epidemiologic studies have demonstrated that exposure to road traffic is associated with adverse cardiovascular outcomes.

OBJECTIVES

We aimed to identify specific traffic-related air pollutants that are associated with the risk of coronary heart disease (CHD) morbidity and mortality to support evidence-based environmental policy making.

METHODS

This population-based cohort study included a 5-year exposure period and a 4-year follow-up period. All residents 45-85 years of age who resided in Metropolitan Vancouver during the exposure period and without known CHD at baseline were included in this study (n=452,735). Individual exposures to traffic-related air pollutants including black carbon, fine particles [aerodynamic diameter ≤ 2.5 µm (PM(2.5))], nitrogen dioxide (NO(2)), and nitric oxide were estimated at residences of the subjects using land-use regression models and integrating changes in residences during the exposure period. CHD hospitalizations and deaths during the follow-up period were identified from provincial hospitalization and death registration records.

RESULTS

An interquartile range elevation in the average concentration of black carbon (0.94 × 10(-5)/m filter absorbance, equivalent to approximately 0.8 µg/m(3) elemental carbon) was associated with a 3% increase in CHD hospitalization (95% confidence interval, 1-5%) and a 6% increase in CHD mortality (3-9%) after adjusting for age, sex, preexisting comorbidity, neighborhood socioeconomic status, and copollutants (PM(2.5) and NO(2)). There were clear linear exposure-response relationships between black carbon and coronary events.

CONCLUSIONS

Long-term exposure to traffic-related fine particulate air pollution, indicated by black carbon, may partly explain the observed associations between exposure to road traffic and adverse cardiovascular outcomes.

Medium-term exposure to traffic-related air pollution and markers of inflammation and endothelial function

BACKGROUND

Exposure to traffic-related air pollution (TRAP) contributes to increased cardiovascular risk. Land-use regression models can improve exposure assessment for TRAP.

OBJECTIVES

We examined the association between medium-term concentrations of black carbon (BC) estimated by land-use regression and levels of soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecule-1 (sVCAM-1), both markers of inflammatory and endothelial response.

METHODS

We studied 642 elderly men participating in the Veterans Administration (VA) Normative Aging Study with repeated measurements of sICAM-1 and sVCAM-1 during 1999-2008. Daily estimates of BC exposure at each geocoded participant address were derived using a validated spatiotemporal model and averaged to form 4-, 8-, and 12-week exposures. We used linear mixed models to estimate associations, controlling for confounders. We examined effect modification by statin use, obesity, and diabetes.

RESULTS

We found statistically significant positive associations between BC and sICAM-1 for averages of 4, 8, and 12 weeks. An interquartile-range increase in 8-week BC exposure (0.30 μg/m3) was associated with a 1.58% increase in sICAM-1 (95% confidence interval, 0.18-3.00%). Overall associations between sVCAM-1 and BC exposures were suggestive but not statistically significant. We found a significant interaction with diabetes-where diabetics were more susceptible to the effect of BC-for both sICAM-1 and sVCAM-1. We also observed an interaction with statin use, which was statistically significant for sVCAM-1 and suggestive for sICAM-1. We found no evidence of an interaction with obesity.

CONCLUSION

Our results suggest that medium-term exposure to TRAP may induce an increased inflammatory/endothelial response, especially among diabetics and those not using statins.

Efficient measurement error correction with spatially misaligned data

Association studies in environmental statistics often involve exposure and outcome data that are misaligned in space. A common strategy is to employ a spatial model such as universal kriging to predict exposures at locations with outcome data and then estimate a regression parameter of interest using the predicted exposures. This results in measurement error because the predicted exposures do not correspond exactly to the true values. We characterize the measurement error by decomposing it into Berkson-like and classical-like components. One correction approach is the parametric bootstrap, which is effective but computationally intensive since it requires solving a nonlinear optimization problem for the exposure model parameters in each bootstrap sample. We propose a less computationally intensive alternative termed the "parameter bootstrap" that only requires solving one nonlinear optimization problem, and we also compare bootstrap methods to other recently proposed methods. We illustrate our methodology in simulations and with publicly available data from the Environmental Protection Agency.

Traffic-related air pollution, preterm birth and term birth weight in the PIAMA birth cohort study

BACKGROUND

Maternal exposure to air pollution has been associated with adverse pregnancy outcomes. Few studies took into account the spatial and temporal variation of air pollution levels.

OBJECTIVES

To evaluate the impact of maternal exposure to traffic-related air pollution during pregnancy on preterm birth and term birth weight using a spatio-temporal exposure model.

METHODS

We estimated maternal residential exposure to nitrogen dioxide (NO(2)), particulate matter (PM(2.5)) and soot during pregnancy (entire pregnancy, 1st trimester, and last month) for 3853 singleton births within the Dutch PIAMA prospective birth cohort study by means of temporally adjusted land-use regression models. Associations between air pollution concentrations and preterm birth and term birth weight were analyzed by means of logistic and linear regression models with and without adjustment for maternal physical, lifestyle, and socio-demographic characteristics.

RESULTS

We found positive, statistically non-significant associations between exposure to soot during entire pregnancy and during the last month of pregnancy and preterm birth [adj. OR (95% CI) per interquartile range increase in exposure 1.08 (0.88-1.34) and 1.09 (0.93-1.27), respectively]. There was no indication of an adverse effect of air pollution exposure on term birth weight.

CONCLUSIONS

In this study, maternal exposure to traffic-related air pollution during pregnancy was not associated with term birth weight. There was a tendency towards an increased risk of preterm birth with increasing air pollution exposure, but statistical power was low.

Changes in residential proximity to road traffic and the risk of death from coronary heart disease

BACKGROUND

Residential proximity to road traffic is associated with increased coronary heart disease (CHD) morbidity and mortality. It is unknown, however, whether changes in residential proximity to traffic could alter the risk of CHD mortality.

METHODS

We used a population-based cohort study with a 5-year exposure period and a 4-year follow-up period to explore the association between changes in residential proximity to road traffic and the risk of CHD mortality. The cohort comprised all residents aged 45-85 years who resided in metropolitan Vancouver during the exposure period and without known CHD at baseline (n = 450,283). Residential proximity to traffic was estimated using a geographic information system. CHD deaths during the follow-up period were identified using provincial death registration database. The data were analyzed using logistic regression.

RESULTS

Compared with the subjects consistently living away from road traffic (>150 m from a highway or >50 m from a major road) during the 9-year study period, those consistently living close to traffic (<or=150 m from a highway or <or=50 m from a major road) had the greatest risk of CHD mortality (relative risk [RR] = 1.29 [95% confidence interval = 1.18-1.41]). By comparison, those who moved closer to traffic during the exposure period had less increased risk than those who were consistently exposed (1.20 [1.00-1.43]), and those who moved away from traffic had even less increase in the risk (1.14 [0.95-1.37]). All analyses were adjusted for baseline age, sex, pre-existing comorbidities (diabetes, chronic obstructive pulmonary disease, hypertensive heart disease), and neighborhood socioeconomic status.

CONCLUSIONS

Living close to major roadways was associated with increased risk of coronary mortality, whereas moving away from major roadways was associated with decreased risk.

Modelling air pollution for epidemiologic research--Part I: A novel approach combining land use regression and air dispersion

A common limitation of epidemiological studies on health effects of air pollution is the quality of exposure data available for study participants. Exposure data derived from urban monitoring networks is usually not adequately representative of the spatial variation of pollutants, while personal monitoring campaigns are often not feasible, due to time and cost restrictions. Therefore, many studies now rely on empirical modelling techniques, such as land use regression (LUR), to estimate pollution exposure. However, LUR still requires a quantity of specifically measured data to develop a model, which is usually derived from a dedicated monitoring campaign. A dedicated air dispersion modelling exercise is also possible but is similarly resource and data intensive. This study adopted a novel approach to LUR, which utilised existing data from an air dispersion model rather than monitored data. There are several advantages to such an approach such as a larger number of sites to develop the LUR model compared to monitored data. Furthermore, through this approach the LUR model can be adapted to predict temporal variation as well as spatial variation. The aim of this study was to develop two LUR models for an epidemiologic study based in Greater Manchester by using modelled NO(2) and PM(10) concentrations as dependent variables, and traffic intensity, emissions, land use and physical geography as potential predictor variables. The LUR models were validated through a set aside "validation" dataset and data from monitoring stations. The final models for PM(10) and NO(2) comprised nine and eight predictor variables respectively and had determination coefficients (R²) of 0.71 (PM(10): Adj. R²=0.70, F=54.89, p<0.001, NO(2): Adj. R²=0.70, F=62.04, p<0.001). Validation of the models using the validation data and measured data showed that the R² decreases compared to the final models, except for NO(2) validation in the measured data (validation data: PM(10): R²=0.33, NO(2): R²=0.62; measured data: PM(10): R²=0.56, NO(2): R²=0.86). The validation further showed low mean prediction errors and root mean squared errors for both models.

Influence of indoor factors in dwellings on the development of childhood asthma

Asthma has become the most common, childhood chronic disease in the industrialized world, and it is also increasing in developing regions. There are huge differences in the prevalence of childhood asthma across countries and continents, and there is no doubt that the prevalence of asthma was strongly increasing during the past decades worldwide. Asthma, as a complex disease, has a broad spectrum of potential determinants ranging from genetics to life style and environmental factors. Environmental factors are likely to be important in explaining the regional differences and the overall increasing trend towards asthma's prevalence. Among the environmental conditions, indoor factors are of particular interest because people spend more than 80% of their time indoors globally. Increasing prices for oil, gas and other sources of primary energy will further lead to better insulation of homes, and ultimately to reduced energy costs. This will decrease air exchange rates and will lower the dilution of indoor air mass with ambient air. Indoor air quality and potential health effects will therefore be an area for future research and for gaining a better understanding of asthma epidemics. This strategic review will summarize the current knowledge of the effects of a broad spectrum of indoor factors on the development of asthma in childhood in Western countries based on epidemiological studies. In conclusion, several epidemiological studies point out, that indoor factors might cause asthma in childhood. Stronger and more consistent findings are seen when exposure to these indoor factors is assessed by surrogates for the source of the actual toxicants. Measurement-based exposure assessments for several indoor factors are less common than using surrogates of the exposure. These studies, however, mainly showed heterogeneous results. The most consistent finding for an induction of asthma in childhood is related to exposure to environmental tobacco smoke, to living in homes close to busy roads, and in damp homes where are visible moulds at home. The causing agents of the increased risk of living in damp homes remained uncertain and needs clarification. Exposure to pet-derived allergens and house dust mites are very commonly investigated and thought to be related to asthma onset. The epidemiological evidence is not sufficient to recommend avoidance measures against pet and dust mites as preventive activities against allergies. More research is also needed to clarify the potential risk for exposure to volatile and semi-volatile organic compounds due to renovation activities, phthalates and chlorine chemicals due to cleaning.

Traffic-related air pollution and incident type 2 diabetes: results from the SALIA cohort study

BACKGROUND

Cross-sectional and ecological studies indicate that air pollution may be a risk factor for type 2 diabetes, but prospective data are lacking.

OBJECTIVE

We examined the association between traffic-related air pollution and incident type 2 diabetes.

DESIGN

Between 1985 and 1994, cross-sectional surveys were performed in the highly industrialized Ruhr district (West Germany); a follow-up investigation was conducted in 2006 using data from the Study on the Influence of Air Pollution on Lung, Inflammation and Aging (SALIA) cohort.

PARTICIPANTS

1,775 nondiabetic women who were 54-55 years old at baseline participated in both baseline and follow-up investigations and had complete information available.

MATERIALS AND METHODS

Using questionnaires, we assessed 16-year incidence (1990-2006) of type 2 diabetes and information about covariates. Complement factor C3c as marker for subclinical inflammation was measured at baseline. Individual exposure to traffic-related particulate matter (PM) and nitrogen dioxide was determined at different spatial scales.

RESULTS

Between 1990 and 2006, 87 (10.5%) new cases of diabetes were reported among the SALIA cohort members. The hazards for diabetes were increased by 15-42% per interquartile range of PM or traffic-related exposure. The associations persisted when different spatial scales were used to assess exposure and remained robust after adjusting for age, body mass index, socioeconomic status, and exposure to several non-traffic-related sources of air pollution. C3c was associated with PM pollution at baseline and was a strong independent predictor of incident diabetes. Exploratory analyses indicated that women with high C3c blood levels were more susceptible for PM-related excess risk of diabetes than were women with low C3c levels.

CONCLUSIONS

Traffic-related air pollution is associated with incident type 2 diabetes among elderly women. Subclinical inflammation may be a mechanism linking air pollution with type 2 diabetes.

RELEVANCE TO CLINICAL PRACTICE

Our study identifies traffic-related air pollution as a novel and potentially modifiable risk factor of type 2 diabetes.

Effects of traffic-related outdoor air pollution on respiratory illness and mortality in children, taking into account indoor air pollution, in Indonesia

OBJECTIVE

To evaluate the effects of outdoor air pollution, taking into account indoor air pollution, in Indonesia.

METHODS

The subjects were 15,242 children from 2002 to 2003 Indonesia Demographic and Health Survey. The odds ratios and their confidence intervals for adverse health effects were estimated.

RESULTS

Proximity increased the prevalence of acute respiratory infection both in urban and rural areas after adjusting for indoor air pollution. In urban areas, the prevalence of acute upper respiratory infection increased by 1.012 (95% confidence intervals: 1.005 to 1.019) per 2 km proximity to a major road. Adjusted odds ratios tended to be higher in the high indoor air pollution group.

CONCLUSION

Exposure to traffic-related outdoor air pollution would increase adverse health effects after adjusting for indoor air pollution. Furthermore, indoor air pollution could exacerbate the effects of outdoor air pollution.

Indoor and outdoor concentrations and determinants of NO2 in a cohort of 1-year-old children in Valencia, Spain

Nitrogen dioxide (NO2) is produced from the exhausts of vehicles and gas appliances and is known to pose certain health risks. In this study, we characterize the exposure to this substance during the first year of life, which is an important period of development. To this end, we used passive samplers to measure indoor and outdoor NO2 levels for 2 weeks in the homes of 352 children. To compensate for the fact that NO2 levels were measured only once in each home, a correction factor was calculated to assign each child an outdoor NO2 exposure value for the first year of life. The outdoor NO2 concentrations were 26.1 microg/m(3) while those measured indoors averaged 18.0 microg/m(3). A multivariate linear regression analysis showed that the main determinants of outdoor NO2 levels were the degree of urbanization and the frequency of vehicle traffic at the location of the residence while for indoor NO2 levels the principal determinants were the type of cooking range and water heater present in the home, the season of the year, and both the country of origin and educational level of the mother.

PRACTICAL IMPLICATIONS

Exposure to NO2 has been related to respiratory and other health problems among children. Precise identification of the main sources of both indoor and outdoor NO2 should shed light on appropriate intervention periods and methods. Our results indicate that while population density and traffic-related variables are the main determinants of outdoor NO2 levels, the use of gas appliances have the greatest impact on indoor levels. Strategies should thus be developed to reduce such exposure, especially with regard to reducing emissions from vehicle traffic.

Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association

In 2004, the first American Heart Association scientific statement on "Air Pollution and Cardiovascular Disease" concluded that exposure to particulate matter (PM) air pollution contributes to cardiovascular morbidity and mortality. In the interim, numerous studies have expanded our understanding of this association and further elucidated the physiological and molecular mechanisms involved. The main objective of this updated American Heart Association scientific statement is to provide a comprehensive review of the new evidence linking PM exposure with cardiovascular disease, with a specific focus on highlighting the clinical implications for researchers and healthcare providers. The writing group also sought to provide expert consensus opinions on many aspects of the current state of science and updated suggestions for areas of future research. On the basis of the findings of this review, several new conclusions were reached, including the following: Exposure to PM <2.5 microm in diameter (PM(2.5)) over a few hours to weeks can trigger cardiovascular disease-related mortality and nonfatal events; longer-term exposure (eg, a few years) increases the risk for cardiovascular mortality to an even greater extent than exposures over a few days and reduces life expectancy within more highly exposed segments of the population by several months to a few years; reductions in PM levels are associated with decreases in cardiovascular mortality within a time frame as short as a few years; and many credible pathological mechanisms have been elucidated that lend biological plausibility to these findings. It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM(2.5) exposure and cardiovascular morbidity and mortality. This body of evidence has grown and been strengthened substantially since the first American Heart Association scientific statement was published. Finally, PM(2.5) exposure is deemed a modifiable factor that contributes to cardiovascular morbidity and mortality.

Protecting human health from air pollution: shifting from a single-pollutant to a multipollutant approach

To date, the assessment of public health consequences of air pollution has largely focused on a single-pollutant approach aimed at estimating the increased risk of adverse health outcomes associated with the exposure to a single air pollutant, adjusted for the exposure to other air pollutants. However, air masses always contain many pollutants in differing amounts, depending on the types of emission sources and atmospheric conditions. Because humans are simultaneously exposed to a complex mixture of air pollutants, many organizations have encouraged moving towards "a multipollutant approach to air quality." Although there is general agreement that multipollutant approaches are desirable, the challenges of implementing them are vast.

Relation of whole blood carboxyhemoglobin concentration to ambient carbon monoxide exposure estimated using regression

Exposure to carbon monoxide (CO) and other ambient air pollutants is associated with adverse pregnancy outcomes. While there are several methods of estimating CO exposure, few have been evaluated against exposure biomarkers. The authors examined the relation between estimated CO exposure and blood carboxyhemoglobin concentration in 708 pregnant western Washington State women (1996-2004). Carboxyhemoglobin was measured in whole blood drawn around 13 weeks' gestation. CO exposure during the month of blood draw was estimated using a regression model containing predictor terms for year, month, street and population densities, and distance to the nearest major road. Year and month were the strongest predictors. Carboxyhemoglobin level was correlated with estimated CO exposure (rho = 0.22, 95% confidence interval (CI): 0.15, 0.29). After adjustment for covariates, each 10% increase in estimated exposure was associated with a 1.12% increase in median carboxyhemoglobin level (95% CI: 0.54, 1.69). This association remained after exclusion of 286 women who reported smoking or being exposed to secondhand smoke (rho = 0.24). In this subgroup, the median carboxyhemoglobin concentration increased 1.29% (95% CI: 0.67, 1.91) for each 10% increase in CO exposure. Monthly estimated CO exposure was moderately correlated with an exposure biomarker. These results support the validity of this regression model for estimating ambient CO exposures in this population and geographic setting.

Characterizing urban traffic exposures using transportation planning tools: an illustrated methodology for health researchers

Exposure to elevated levels of vehicular traffic has been associated with adverse cardiovascular and respiratory health effects in a range of populations, including children, the elderly, and individuals with pre-existing heart conditions, diabetes, obesity, and genetic susceptibilities. As these relationships become clearer, public health officials will need to have access to methods to identify areas of concern in terms of elevated traffic levels and susceptible populations. This paper briefly reviews current approaches for characterizing traffic exposure and then presents a detailed method that can be employed by public health officials and other researchers in performing screening assessments to define areas of potential concern within a particular locale and, with appropriate caveats, in epidemiologic studies examining traffic-related health impacts at the intra-urban scale. The method is based on two exposure parameters extensively used in numerous epidemiologic studies of traffic and health-proximity to high traffic roadways and overall traffic density. The method is demonstrated with publically available information on susceptible populations, traffic volumes, and Traffic Analysis Zones, a transportation planning tool long used by Metropolitan Planning Agencies and planners across the USA but presented here as a new application which can be used to spatially assess possible traffic-related impacts on susceptible populations. Recommendations are provided for the appropriate use of this methodology, along with its limitations.

Effect of early life exposure to air pollution on development of childhood asthma

BACKGROUND

There is increasing recognition of the importance of early environmental exposures in the development of childhood asthma. Outdoor air pollution is a recognized asthma trigger, but it is unclear whether exposure influences incident disease. We investigated the effect of exposure to ambient air pollution in utero and during the first year of life on risk of subsequent asthma diagnosis in a population-based nested case-control study.

METHODS

We assessed all children born in southwestern British Columbia in 1999 and 2000 (n = 37,401) for incidence of asthma diagnosis up to 34 years of age using outpatient and hospitalization records. Asthma cases were age- and sex-matched to five randomly chosen controls from the eligible cohort. We estimated each individual's exposure to ambient air pollution for the gestational period and first year of life using high-resolution pollution surfaces derived from regulatory monitoring data as well as land use regression models adjusted for temporal variation. We used logistic regression analyses to estimate effects of carbon monoxide, nitric oxide, nitrogen dioxide, particulate matter <or= 10 microm and <or= 2.5 microm in aerodynamic diameter (PM10 and PM2.5), ozone, sulfur dioxide, black carbon, woodsmoke, and proximity to roads and point sources on asthma diagnosis.

RESULTS

A total of 3,482 children (9%) were classified as asthma cases. We observed a statistically significantly increased risk of asthma diagnosis with increased early life exposure to CO, NO, NO2, PM10, SO2, and black carbon and proximity to point sources. Traffic-related pollutants were associated with the highest risks: adjusted odds ratio = 1.08 (95% confidence interval, 1.041.12) for a 10-microg/m3 increase of NO, 1.12 (1.071.17) for a 10-microg/m3 increase in NO2, and 1.10 (1.061.13) for a 100-microg/m3 increase in CO. These data support the hypothesis that early childhood exposure to air pollutants plays a role in development of asthma.

Improving spatial concentration estimates for nitrogen oxides using a hybrid meteorological dispersion/land use regression model in Los Angeles, CA and Seattle, WA

Predictions from a simple line source dispersion model, Caline3, were included as a covariate in a land use regression (LUR) model for NO(X)/NO(2) in Los Angeles, CA and Seattle, WA. The Caline3 model prediction assumed a unit emission factor for all roadway segments (1.0g/vehicle-mile). The NO(X) and/or NO(2) measurements for LA and Seattle were obtained from a comprehensive measurement campaign that is part of the Multi-Ethnic Study of Atherosclerosis Air Pollution Study (MESA Air). The measurement campaigns in both cities were approximately 2weeks in duration employing approximately 145 measurement sites in Greater LA and 26 sites in Seattle. The best "standard" LUR model (obtained without the inclusion of the Caline3 predictions) in LA had R(2) values of 0.53 for NO(X) and 0.74 for NO(2). The leave-one-out cross-validated R(2) values for NO(X) and NO(2) were 0.45 and 0.71, respectively. The equivalent "standard" NO(2) model for Seattle had an R(2) of 0.72 and a leave-one-out cross-validated R(2) of 0.63. When the Caline3 variable was included in the LA hybrid model, the R(2) values were 0.71 and 0.79 for NO(X) and NO(2), respectively. The corresponding cross-validated R(2) values were 0.66 and 0.77, for NOX and NO2, respectively. In Seattle, the inclusion of the Caline3 variable resulted in a NO(2) model with an R(2) of 0.81 and a corresponding cross-validated R(2) of 0.67. In LA, hybrid model performance was not affected by excluding roadways with annual average daily traffic volumes (AADT)<100,000. When the Caline3 predictions for heavy-duty trucks and lighter-duty vehicles were modelled as separate terms, the estimated fleet average NO(X) emission factors were 8.9 (SE=0.7) and 0.16 (SE=0.12) grams NO(X)/vehicle mile for heavy-duty and lighter-duty vehicles, respectively. These values are consistent with fleet average emission factors computed for LA with EMFAC 2007.

A land use regression for predicting NO2 and PM10 concentrations in different seasons in Tianjin region, China

Land use regression (LUR) model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression (MLR) equations were established based on the most significant variables for NO2 in heating season (R2 = 0.74), and non-heating season (R2 = 0.61) in the whole study area; and PM10 in heating season (R2 = 0.72), and non-heating season (R2 = 0.49). Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations (resolution is 10 km). Intercepts of MLR equations were 0.050 (NO2, heating season), 0.035 (NO2, non-heating season), 0.068 (PM10, heating season), and 0.092 (PM10, non-heating season) in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 (NO2, heating season), 0.043 (NO2, non-heating season), 0.087 (PM10, heating season), and 0.096 (PM10, non-heating season). These intercept values might imply an area's background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. R2 values increased 0.09 (heating season) and 0.18 (non-heating season) for NO2, and 0.08 (heating season) and 0.04 (non-heating season) for PM10. In terms of R2, LUR model performed more effectively in heating season than non-heating season in the study area and gave a better result for NO2 compared with PM10.

A GIS-based method for modelling air pollution exposures across Europe

A GIS-based moving window approach was developed as a means for generating high resolution air pollution maps over large geographic areas. The approach is demonstrated by modelling annual mean NO(2) pollution for the EU-15 (excluding Sweden) at the 1 km level on the basis of emissions and meteorological data. Models were developed using monitoring data from 714 background NO(2) sites for 2001 and validated by comparing predicted with observed NO(2) concentrations for a reserved set of 228 background sites. First the emission map (NO(x)) was derived by disaggregating national emissions estimates, categorised by source, to a 1 km grid, using proxies including population and road density, traffic statistics and land cover. A set of annuli was then constructed, of varying radii, and these passed over the emissions grid to derive a calibration between measured annual average concentrations at each monitoring site and distance-weighted emissions in the surrounding area, using a focalsum function. The resulting model was then used to predict concentrations at the reserved set of validation sites, and measures of performance (R(2), RMSE and fractional bias) obtained. Validation gave R(2)=0.61, RMSE=6.59 and FB=-0.01, and indicated performance equivalent to universal kriging and better than ordinary kriging and land use regression.

Estimation of personal NO2 exposure in a cohort of pregnant women

There is a growing concern about the possible adverse effects of exposure to air pollution on health during pregnancy. Therefore, a priority of the INMA (environment and childhood) study was to estimate personal exposure to traffic-related air pollution. In the cohort from Valencia (n=855), ambient levels of NO(2) were measured at 93 sampling sites spread over the study area during four different sampling periods of 7 days each. Multiple regression models were used to map ambient NO(2) over the area. Geographical data and predictions from kriging obtained by the "let one out" procedure were used as predictors. Individual exposure was assigned as 1) the estimated ambient NO(2) level at the home address and 2) the average of estimated ambient NO(2) levels at home and work addresses, weighted by the time spent in each environment. Estimations were temporally customized using the NO(2) levels registered daily by the regional Air Pollution Monitoring Network. The entire pregnancy and each trimester were taken as exposure windows. The model for the mean levels of NO(2) during the sampling periods explained 81% of the variation in NO(2) levels. Relative percent differences between the two models of personal exposure assignment were less than 9% for more than 90% of the participants; however the rest of them showed marked differences. Personal exposure estimates were slightly higher in the second model. In both cases, exposure during the whole pregnancy was strongly correlated with exposure in the second trimester. Considering periods shorter than the entire pregnancy will provide us the opportunity to identify specific windows of susceptibility.

Spatial modeling of PM10 and NO2 in the continental United States, 1985-2000

BACKGROUND

Epidemiologic studies of air pollution have demonstrated a link between long-term air pollution exposures and mortality. However, many have been limited to city-specific average pollution measures or spatial or land-use regression exposure models in small geographic areas.

OBJECTIVES

Our objective was to develop nationwide models of annual exposure to particulate matter < 10 microm in diameter (PM(10)) and nitrogen dioxide during 1985-2000.

METHODS

We used generalized additive models (GAMs) to predict annual levels of the pollutants using smooth spatial surfaces of available monitoring data and geographic information system-derived covariates. Model performance was determined using a cross-validation (CV) procedure with 10% of the data. We also compared the results of these models with a commonly used spatial interpolation, inverse distance weighting.

RESULTS

For PM(10), distance to road, elevation, proportion of low-intensity residential, high-intensity residential, and industrial, commercial, or transportation land use within 1 km were all statistically significant predictors of measured PM(10) (model R(2) = 0.49, CV R(2) = 0.55). Distance to road, population density, elevation, land use, and distance to and emissions of the nearest nitrogen oxides-emitting power plant were all statistically significant predictors of measured NO(2) (model R(2) = 0.88, CV R(2) = 0.90). The GAMs performed better overall than the inverse distance models, with higher CV R(2) and higher precision.

CONCLUSIONS

These models provide reasonably accurate and unbiased estimates of annual exposures for PM(10) and NO(2). This approach provides the spatial and temporal variability necessary to describe exposure in studies assessing the health effects of chronic air pollution.

Predicting Intra-Urban Variation in Air Pollution Concentrations with Complex Spatio-Temporal Dependencies

We describe a methodology for assigning individual estimates of long-term average air pollution concentrations that accounts for a complex spatio-temporal correlation structure and can accommodate spatio-temporally misaligned observations. This methodology has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the U.S. EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. Our hierarchical model decomposes the space-time field into a "mean" that includes dependence on covariates and spatially varying seasonal and long-term trends and a "residual" that accounts for spatially correlated deviations from the mean model. The model accommodates complex spatio-temporal patterns by characterizing the temporal trend at each location as a linear combination of empirically derived temporal basis functions, and embedding the spatial fields of coefficients for the basis functions in separate linear regression models with spatially correlated residuals (universal kriging). This approach allows us to implement a scalable single-stage estimation procedure that easily accommodates a significant number of missing observations at some monitoring locations. We apply the model to predict long-term average concentrations of oxides of nitrogen (NO_x) from 2005-2007 in the Los Angeles area, based on data from 18 EPA Air Quality System regulatory monitors. The cross-validated R² is 0.67. The MESA Air study is also collecting additional concentration data as part of a supplementary monitoring campaign. We describe the sampling plan and demonstrate in a simulation study that the additional data will contribute to improved predictions of long-term average concentrations.

Validity of a traffic air pollutant dispersion model to assess exposure to fine particles

INTRODUCTION

Fine particles (PM(2.5)) are an important component of air pollution. Epidemiological studies have shown health effects due to ambient air particles, particularly allergies in children. Since the main difficulty is to determine exposure to such pollution, traffic air pollutant (TAP) dispersions models have been developed to improve the estimation of individual exposure levels. One such model, the ExTra index, has been validated for nitrogen oxide concentrations but not for other pollutants. The purpose of this study was to assess the validity of the ExTra index to assess PM(2.5) exposure.

METHODS

We compared PM(2.5) concentrations calculated by the ExTra index to reference measures (passive samplers situated under the covered part of the playground), in 15 schools in Bordeaux, in 2000. First, we collected the input data required by the ExTra index: background and local pollution depending on traffic, meteorology and topography. Second, the ExTra index was calculated for each school. Statistical analysis consisted of a graphic description; then, we calculated an intraclass correlation coefficient.

RESULTS

Concentrations calculated with the ExTra index and the reference method were similar. The ExTra index underestimated exposure by 2.2 microg m(-3) on average compared to the reference method. The intraclass correlation coefficient was 0.85 and its 95% confidence interval was [0.62; 0.95].

CONCLUSIONS

The results suggest that the ExTra index provides an assessment of PM(2.5) exposure similar to that of the reference method. Although caution is required in interpreting these results owing to the small number of sites, the ExTra index could be a useful epidemiological tool for reconstructing individual exposure, an important challenge in epidemiology.

Spatial analysis and land use regression of VOCs and NO(2) from school-based urban air monitoring in Detroit/Dearborn, USA

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

A distance-decay variable selection strategy for land use regression modeling of ambient air pollution exposures

Land use regression (LUR) has emerged as an effective and economical means of estimating air pollution exposures for epidemiological studies. To date, no systematic method has been developed for optimizing the variable selection process. Traditionally, a limited number of buffer distances assumed having the highest correlations with measured pollutant concentrations are used in the manual stepwise selection process or a model transferred from another urban area. In this paper we propose a novel and systematic way of modeling long-term average air pollutant concentrations through "A Distance Decay REgression Selection Strategy" (ADDRESS). The selection process includes multiple steps and, at each step, a full spectrum of correlation coefficients and buffer distance decay curves are used to select a spatial covariate of the highest correlation (compared to other variables) at its optimized buffer distance. At the first step, the series of distance decay curves is constructed using the measured concentrations against the chosen spatial covariates. A variable with the highest correlation to pollutant levels at its optimized buffer distance is chosen as the first predictor of the LUR model from all the distance decay curves. Starting from the second step, the prediction residuals are used to construct new series of distance decay curves and the variable of the highest correlation at its optimized buffer distance is chosen to be added to the model. This process continues until a variable being added does not contribute significantly (p>0.10) to the model performance. The distance decay curve yields a visualization of change and trend of correlation between the spatial covariates and air pollution concentrations or their prediction residuals, providing a transparent and efficient means of selecting optimized buffer distances. Empirical comparisons suggested that the ADDRESS method produced better results than a manual stepwise selection process of limited buffer distances. The method also enables researchers to understand the likely scale of variables that influence pollution levels, which has potentially important ramifications for planning and epidemiological studies.

Estimating regional spatial and temporal variability of PM(2.5) concentrations using satellite data, meteorology, and land use information

BACKGROUND

Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters <or= 2.5 microm (PM(2.5)) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM(2.5) ground networks to cover a much larger area.

OBJECTIVES

In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM(2.5) concentrations.

METHODS

We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM(2.5) concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain.

RESULTS

The AOD model has a higher predicting power judged by adjusted R(2) (0.79) than does the non-AOD model (0.48). The predicted PM(2.5) concentrations by the AOD model are, on average, 0.8-0.9 microg/m(3) higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM(2.5), meteorologic parameters are major contributors to the better performance of the AOD model.

CONCLUSIONS

GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM(2.5) concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM(2.5) spatial patterns related to AOD availability.

A review of the urban development and transport impacts on public health with particular reference to Australia: trans-disciplinary research teams and some research gaps

Urbanization and transport have a direct effect on public health. A transdisciplinary approach is proposed and illustrated to tackle the general problem of these environmental stressors and public health. Processes driving urban development and environmental stressors are identified. Urbanization, transport and public health literature is reviewed and environmental stressors are classified into their impacts and which group is affected, the geographical scale and potential inventions. Climate change and health impacts are identified as a research theme. From an Australian perspective, further areas for research are identified.

BME estimation of residential exposure to ambient PM10 and ozone at multiple time scales

BACKGROUND

Long-term human exposure to ambient pollutants can be an important contributing or etiologic factor of many chronic diseases. Spatiotemporal estimation (mapping) of long-term exposure at residential areas based on field observations recorded in the U.S. Environmental Protection Agency's Air Quality System often suffer from missing data issues due to the scarce monitoring network across space and the inconsistent recording periods at different monitors.

OBJECTIVE

We developed and compared two upscaling methods: UM1 (data aggregation followed by exposure estimation) and UM2 (exposure estimation followed by data aggregation) for the long-term PM10 (particulate matter with aerodynamic diameter < or = 10 microm) and ozone exposure estimations and applied them in multiple time scales to estimate PM and ozone exposures for the residential areas of the Health Effects of Air Pollution on Lupus (HEAPL) study.

METHOD

We used Bayesian maximum entropy (BME) analysis for the two upscaling methods. We performed spatiotemporal cross-validations at multiple time scales by UM1 and UM2 to assess the estimation accuracy across space and time.

RESULTS

Compared with the kriging method, the integration of soft information by the BME method can effectively increase the estimation accuracy for both pollutants. The spatiotemporal distributions of estimation errors from UM1 and UM2 were similar. The cross-validation results indicated that UM2 is generally better than UM1 in exposure estimations at multiple time scales in terms of predictive accuracy and lack of bias. For yearly PM10 estimations, both approaches have comparable performance, but the implementation of UM1 is associated with much lower computation burden.

CONCLUSION

BME-based upscaling methods UM1 and UM2 can assimilate core and site-specific knowledge bases of different formats for long-term exposure estimation. This study shows that UM1 can perform reasonably well when the aggregation process does not alter the spatiotemporal structure of the original data set; otherwise, UM2 is preferable.

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.

Predicting chronic fine and coarse particulate exposures using spatiotemporal models for the Northeastern and Midwestern United States

BACKGROUND

Chronic epidemiologic studies of particulate matter (PM) are limited by the lack of monitoring data, relying instead on citywide ambient concentrations to estimate exposures. This method ignores within-city spatial gradients and restricts studies to areas with nearby monitoring data. This lack of data is particularly restrictive for fine particles (PM with aerodynamic diameter < 2.5 microm; PM(2.5)) and coarse particles (PM with aerodynamic diameter 2.5-10 microm; PM(10-2.5)), for which monitoring is limited before 1999. To address these limitations, we developed spatiotemporal models to predict monthly outdoor PM(2.5) and PM(10-2.5) concentrations for the northeastern and midwestern United States.

METHODS

For PM(2.5), we developed models for two periods: 1988-1998 and 1999-2002. Both models included smooth spatial and regression terms of geographic information system-based and meteorologic predictors. To compensate for sparse monitoring data, the pre-1999 model also included predicted PM(10) (PM with aerodynamic diameter < 10 microm) and extinction coefficients (km(-1)). PM(10-2.5) levels were estimated as the difference in monthly predicted PM(10) and PM(2.5), with predicted PM(10) from our previously developed PM(10) model.

RESULTS

Predictive performance for PM(2.5) was strong (cross-validation R2 = 0.77 and 0.69 for post-1999 and pre-1999 PM(2.5) models, respectively) with high precision (2.2 and 2.7 microg/m3, respectively). Models performed well irrespective of population density and season. Predictive performance for PM(10-2.5) was weaker (cross-validation R2 = 0.39) with lower precision (5.5 microg/m3). PM(10-2.5) levels exhibited greater local spatial variability than PM(10) or PM(2.5), suggesting that PM(2.5) measurements at ambient monitoring sites are more representative for surrounding populations than for PM(10) and especially PM(10-2.5).

CONCLUSIONS

We provide semiempirical models to predict spatially and temporally resolved long-term average outdoor concentrations of PM(2.5) and PM(10-2.5) for estimating exposures of populations living in the northeastern and midwestern United States.