Nitrogen dioxide prediction in Southern California using land use regression modeling: potential for environmental health analyses

Combining a road pollution dispersion model with GIS to determine carbon monoxide concentration in Tennessee

The purpose of this paper is to develop an air pollution model that is independent from pollution monitoring sites and highly accurate through space and time. Total carbon monoxide concentration is computed with the use of traffic flow data, vehicle speed and dimensions, emission rates, wind speed, and temperature. The data are interpolated using a geographic information system universal kriging technique, and the end results produce state level air pollution maps with high local accuracy. The model is validated against Environment Protection Agency (EPA) pollution data. Overall, the model has 71 % agreement with EPA, overestimating values of carbon monoxide for less than 1 ppm. The model has three advantages over already assessed air pollution models. First, it is completely independent of any air pollution monitoring stations; thus, possible temporary or permanent unreliability or lack of the data is avoided. Second, being based on a 5,710 traffic count network, the problem of remote places coverage is avoided. Third, it is based on a straightforward equation, where minimal preprocessing of traffic and climatic data is required.

Development of a distributed air pollutant dry deposition modeling framework

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

Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states

Satellite-derived aerosol optical depth (AOD) measurements have the potential to provide spatiotemporally resolved predictions of both long and short-term exposures, but previous studies have generally shown moderate predictive power and lacked detailed high spatio- temporal resolution predictions across large domains. We aimed at extending our previous work by validating our model in another region with different geographical and metrological characteristics, and incorporating fine scale land use regression and nonrandom missingness to better predict PM(2.5) concentrations for days with or without satellite AOD measures. We start by calibrating AOD data for 2000-2008 across the Mid-Atlantic. We used mixed models regressing PM(2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We used inverse probability weighting to account for nonrandom missingness of AOD, nested regions within days to capture spatial variation in the daily calibration, and introduced a penalization method that reduces the dimensionality of the large number of spatial and temporal predictors without selecting different predictors in different locations. We then take advantage of the association between grid-cell specific AOD values and PM(2.5) monitoring data, together with associations between AOD values in neighboring grid cells to develop grid cell predictions when AOD is missing. Finally to get local predictions (at the resolution of 50 m), we regressed the residuals from the predictions for each monitor from these previous steps against the local land use variables specific for each monitor. "Out-of-sample" 10-fold cross-validation was used to quantify the accuracy of our predictions at each step. For all days without AOD values, model performance was excellent (mean "out-of-sample" R(2) = 0.81, year-to-year variation 0.79-0.84). Upon removal of outliers in the PM(2.5) monitoring data, the results of the cross validation procedure was even better (overall mean "out of sample"R(2) of 0.85). Further, cross validation results revealed no bias in the predicted concentrations (Slope of observed vs predicted = 0.97-1.01). Our model allows one to reliably assess short-term and long-term human exposures in order to investigate both the acute and effects of ambient particles, respectively.

Inequalities in cumulative environmental burdens among three urbanized counties in California

Low-income communities and communities of color often suffer from multiple environmental hazards that pose risks to their health. Here we extended a cumulative environmental hazard inequality index (CEHII) - developed to assess inequalities in air pollution hazards - to compare the inequality among three urban counties in California: Alameda, San Diego, and Los Angeles. We included a metric for heat stress to the analysis because exposure to excessively hot weather is increasingly recognized as a threat to human health and well-being. We determined if inequalities from heat stress differed between the three regions and if this added factor modified the metric for inequality from cumulative exposure to air pollution. This analysis indicated that of the three air pollutants considered, diesel particulate matter had the greatest inequality, followed by nitrogen dioxide (NO(2)) and fine particulate matter (PM(2.5)). As measured by our index, the inequalities from cumulative exposure to air pollution were greater than those of single pollutants. Inequalities were significantly different among single air pollutant hazards within each region and between regions; however, inequalities from the cumulative burdens did not differ significantly between any two regions. Modeled absolute and relative heat stress inequalities were small except for relative heat stress in San Diego which had the second highest inequality. Our analysis, techniques, and results provide useful insights for policy makers to assess inequalities between regions and address factors that contribute to overall environmental inequality within each region.

Real-time prediction of size-resolved ultrafine particulate matter on freeways

Ultrafine particulate matter (UFP; diameter <0.1 μm) concentrations are relatively high on the freeway, and time spent on freeways can contribute a significant fraction of total daily UFP exposure. We model real-time size-resolved UFP concentrations in summer time on-freeway air. Particle concentrations (32 bins, 5.5 to 600 nm) were measured on Minnesota freeways during summer 2006 and 2007 ( Johnson, J. P.; Kittelson, D. B.; Watts, W. F. Environ. Sci. Technol. 2009 , 43 , 5358 - 5364 ). Here, we develop and apply two-way stratified multilinear regressions, using an approach analogous to mobile-monitoring land-use regression but using real-time meteorological and traffic data. Our models offer the strongest predictions in the 10-100 nm size range (adj-R(2): 0.79-0.89, average adj-R(2): 0.85) and acceptable but weaker predictions in the 130-200 nm range (adj-R(2): 0.41-0.62, average adj-R(2): 0.52). The aggregate model for total particle counts performs well (adj-R(2) = 0.77). Bootstrap resampling (n = 1000) indicates that the proposed models are robust to minor perturbations in input data. The proposed models are based on readily available real-time information (traffic and meteorological parameters) and can thus be exploited to offer spatiotemporally resolved prediction of UFPs on freeways within similar geographic and meteorological environments. The approach developed here provides an important step toward modeling population exposure to UFP.

Residential proximity to major roads and placenta/birth weight ratio

Exposure to air pollution has been demonstrated to increase the risk of preterm birth and low birth weight. We examined whether proximity to major roads (as a marker of exposure to air pollution) is associated with increased placenta/birth weight ratio (as a biomarker of the placental transport function). Data on parental characteristics and birth outcomes were extracted from the database maintained by a major hospital in Shizuoka Prefecture, Japan. We restricted the analysis to mothers who delivered liveborn single births from 1997 to 2008 (n = 14,189). Using geocoded residential information, each birth was classified according to proximity to major roads. We examined the association between proximity to major roads and the placenta/birth weight ratio, using multiple linear regression. Proximity to major roads was associated with higher placenta/birth weight ratio. After adjusting for potential confounders, living within 200 m of a major road increased the ratio by 0.48% (95% CI = 0.15 to 0. 80). In addition, proximity to major roads was associated with lower placenta weight and birth weight. These observed associations were stronger among participants living closer to major roads. Exposure to traffic-related air pollution is associated with higher placenta/birth weight ratio. Impaired placental oxygen and nutrient transport function might be a mechanism for explaining the observed association between air pollution and low birth weight as well as preterm birth.

Noise, air pollutants and traffic: continuous measurement and correlation at a high-traffic location in New York City

BACKGROUND

Epidemiological studies have linked both noise and air pollution to common adverse health outcomes such as increased blood pressure and myocardial infarction. In urban settings, noise and air pollution share important sources, notably traffic, and several recent studies have shown spatial correlations between noise and air pollution. The temporal association between these exposures, however, has yet to be thoroughly investigated despite the importance of time series studies in air pollution epidemiology and the potential that correlations between these exposures could at least partly confound statistical associations identified in these studies.

METHODS

An aethelometer, for continuous elemental carbon measurement, was co-located with a continuous noise monitor near a major urban highway in New York City for six days in August 2009. Hourly elemental carbon measurements and hourly data on overall noise levels and low, medium and high frequency noise levels were collected. Hourly average concentrations of fine particles and nitrogen oxides, wind speed and direction and car, truck and bus traffic were obtained from nearby regulatory monitors. Overall temporal patterns, as well as day-night and weekday-weekend patterns, were characterized and compared for all variables.

RESULTS

Noise levels were correlated with car, truck, and bus traffic and with air pollutants. We observed strong day-night and weekday-weekend variation in noise and air pollutants and correlations between pollutants varied by noise frequency. Medium and high frequency noise were generally more strongly correlated with traffic and traffic-related pollutants than low frequency noise and the correlation with medium and high frequency noise was generally stronger at night. Correlations with nighttime high frequency noise were particularly high for car traffic (Spearman rho=0.84), nitric oxide (0.73) and nitrogen dioxide (0.83). Wind speed and direction mediated relationships between pollutants and noise.

CONCLUSIONS

Noise levels are temporally correlated with traffic and combustion pollutants and correlations are modified by the time of day, noise frequency and wind. Our results underscore the potential importance of assessing temporal variation in co-exposures to noise and air pollution in studies of the health effects of these urban pollutants.

Advances and current themes in occupational health and environmental public health surveillance

The essential purpose of public health surveillance is to monitor important health outcomes and risk factors and provide actionable information to practitioners, policy makers, researchers, and the public to prevent or ameliorate exposure, disease, and death. Although separate 1970s-era acts of Congress made possible the creation of modern occupational health and environmental public health surveillance, these acts also led to fragmented responsibilities and unconnected data across federal agencies. Having a well-defined purpose for systematically collecting relevant data is key, and state and local programs play a crucial role in conducting meaningful surveillance and connecting it with evidence-based outreach and interventions. Congress has directed monies to environmental public health surveillance and capacity has improved, yet no analagous funding has occurred to address the fragmentation found within occupational health surveillance. This article provides a review of the advances and important themes within occupational health and environmental public health surveillance over the past decade.

Urban land-use and respiratory symptoms in infants

BACKGROUND

Children's respiratory health has been linked to many factors, including air pollution. The impacts of urban land-use on health are not fully understood, although these relationships are of key importance given the growing populations living in urban environments.

OBJECTIVES

We investigated whether the degree of urban land-use near a family's residence is associated with severity of respiratory symptoms like wheeze among infants.

METHODS

Wheeze occurrence was recorded for the first year of life for 680 infants in Connecticut for 1996-1998 from a cohort at risk for asthma development. Land-use categories were obtained from the National Land Cover Database. The fraction of urban land-use near each subject's home was related to severity of wheeze symptoms using ordered logistic regression, adjusting for individual-level data including smoking in the household, race, gender, and socio-economic status. Nitrogen dioxide (NO(2)) exposure was estimated using integrated traffic exposure modeling. Different levels of urban land-use intensity were included in separate models to explore intensity-response relationships. A buffer distance was selected based on the log-likelihood value of models with buffers of 100-2000 m by 10 m increments.

RESULTS

A 10% increase in urban land-use within the selected 1540 m buffer of each infant's residence was associated with 1.09-fold increased risk of wheeze severity (95% confidence interval, 1.02-1.16). Results were robust to alternate buffer sizes. When NO(2), representing traffic pollution, was added to the model, results for urban land-use were no longer statistically significant, but had similar central estimates. Higher urban intensity showed higher risk of prevalence and severity of wheeze symptoms.

CONCLUSIONS

Urban land-use was associated with severity of wheeze symptoms in infants. Findings indicate that health effect estimates for urbanicity incorporate some effects of traffic-related emissions, but also involve other factors. These may include differences in housing characteristics or baseline healthcare status.

A comparison of different approaches to estimate small-scale spatial variation in outdoor NO₂ concentrations

BACKGROUND

In epidemiological studies, small-scale spatial variation in air quality is estimated using land-use regression (LUR) and dispersion models. An important issue of exposure modeling is the predictive performance of the model at unmeasured locations.

OBJECTIVE

In this study, we aimed to evaluate the performance of two LUR models (large area and city specific) and a dispersion model in estimating small-scale variations in nitrogen dioxide (NO₂) concentrations.

METHODS

Two LUR models were developed based on independent NO₂ monitoring campaigns performed in Amsterdam and in a larger area including Amsterdam, the Netherlands, in 2006 and 2007, respectively. The measurement data of the other campaign were used to evaluate each model. Predictions from both LUR models and the calculation of air pollution from road traffic (CAR) dispersion model were compared against NO₂ measurements obtained from Amsterdam.

RESULTS AND CONCLUSION

The large-area and the city-specific LUR models provided good predictions of NO₂ concentrations [percentage of explained variation (R²) = 87% and 72%, respectively]. The models explained less variability of the concentrations in the other sampling campaign, probably related to differences in site selection, and illustrated the need to select sampling sites representative of the locations to which the model will be applied. More complete traffic information contributed more to a better model fit than did detailed land-use data. Dispersion-model estimates for NO₂ concentrations were within the range of both LUR estimates.

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.

Residential proximity to heavy traffic and birth weight in Shizuoka, Japan

An association between exposure to traffic-related air pollution and reduced birth weight has been suggested. However, previous studies have failed to adjust for maternal size, which is an indicator of individual genetic growth potential. Therefore, we evaluated the association of air pollution with birth weight, term low birth weight (term-LBW), and small for gestational age (SGA), with adjustment for maternal size. Individual data were extracted from a database that is maintained by a maternal and perinatal care center in Shizuoka, Japan. We identified liveborn singleton births (n=14,204). Using geocoded residential information, each birth was assigned a number of traffic-based exposure indicators: distance to a major road; distance-weighted traffic density; and estimated concentration of nitrogen dioxide by land use regression. The multivariate adjusted odds ratios and their 95% confidence intervals (CIs) for the associations between exposure indicators and outcomes were then estimated using logistic regression models. Overall, exposure indicators of air pollution showed no clear pattern of association. Although there are many limitations, we did not find clear associations between birth-weight-related outcomes and the three markers of traffic-related air pollution.

Validation of a spatiotemporal land use regression model incorporating fixed site monitors

Land use regression (LUR) has been widely adopted as a method of describing spatial variation in air pollutants; however, traditional LUR methods are not suitable for characterizing short-term or time-variable exposures. Our aim was to develop and validate a spatiotemporal LUR model for use in epidemiological studies examining health effects attributable to time-variable air pollution exposures. A network of 42 NO(2) passive samplers was deployed for 12 two week periods over three years. A mixed effects model was tested using a combination of spatial predictors, and readings from fixed site continuous monitors, in order to predict NO(2) values for any two week period over three years in the defined study area. The final model, including terms based on traffic density at 50 and 150 m, population density within 500 m, commercial land use area within 750 m, and NO(2) concentrations at a central fixed site monitor, explained over 80% of the overall variation in NO(2) concentrations. We suggest that such a model can be used to study the association between variable air pollutant exposures and health effects in epidemiological studies.

Nitrogen dioxide concentrations in neighborhoods adjacent to a commercial airport: a land use regression modeling study

BACKGROUND

There is growing concern in communities surrounding airports regarding the contribution of various emission sources (such as aircraft and ground support equipment) to nearby ambient concentrations. We used extensive monitoring of nitrogen dioxide (NO2) in neighborhoods surrounding T.F. Green Airport in Warwick, RI, and land-use regression (LUR) modeling techniques to determine the impact of proximity to the airport and local traffic on these concentrations.

METHODS

Palmes diffusion tube samplers were deployed along the airport's fence line and within surrounding neighborhoods for one to two weeks. In total, 644 measurements were collected over three sampling campaigns (October 2007, March 2008 and June 2008) and each sampling location was geocoded. GIS-based variables were created as proxies for local traffic and airport activity. A forward stepwise regression methodology was employed to create general linear models (GLMs) of NO2 variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored.

RESULTS

Higher concentrations of NO2 were seen near the airport terminal, entrance roads to the terminal, and near major roads, with qualitatively consistent spatial patterns between seasons. In our final multivariate model (R2 = 0.32), the local influences of highways and arterial/collector roads were statistically significant, as were local traffic density and distance to the airport terminal (all p < 0.001). Local meteorology did not significantly affect associations with principal GIS variables, and the regression model structure was robust to various model-building approaches.

CONCLUSION

Our study has shown that there are clear local variations in NO2 in the neighborhoods that surround an urban airport, which are spatially consistent across seasons. LUR modeling demonstrated a strong influence of local traffic, except the smallest roads that predominate in residential areas, as well as proximity to the airport terminal.

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.

Childhood incident asthma and traffic-related air pollution at home and school

BACKGROUND

Traffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. However, there has been little study of effects of traffic exposure at school on new-onset asthma.

OBJECTIVES

We evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools.

METHODS

Parent-reported physician diagnosis of new-onset asthma (n = 120) was identified during 3 years of follow-up of a cohort of 2,497 kindergarten and first-grade children who were asthma- and wheezing-free at study entry into the Southern California Children's Health Study. We assessed traffic-related pollution exposure based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO(2)), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. Hazard ratios (HRs) for new-onset asthma were scaled to the range of ambient central site pollutants and to the residential interquartile range for each traffic exposure metric.

RESULTS

Asthma risk increased with modeled traffic-related pollution exposure from roadways near homes [HR 1.51; 95% confidence interval (CI), 1.25-1.82] and near schools (HR 1.45; 95% CI, 1.06-1.98). Ambient NO(2) measured at a central site in each community was also associated with increased risk (HR 2.18; 95% CI, 1.18-4.01). In models with both NO(2) and modeled traffic exposures, there were independent associations of asthma with traffic-related pollution at school and home, whereas the estimate for NO(2) was attenuated (HR 1.37; 95% CI, 0.69-2.71).

CONCLUSIONS

Traffic-related pollution exposure at school and homes may both contribute to the 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 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.

Emergency hospital admissions for cardiovascular diseases and ambient levels of carbon monoxide: results for 126 United States urban counties, 1999-2005

BACKGROUND

Evidence on risk of cardiovascular disease (CVD) hospitalization associated with short-term exposure to outdoor carbon monoxide (CO), an air pollutant primarily generated by traffic, is inconsistent across studies. Uncertainties remain on the degree to which associations are attributable to other traffic pollutants and whether effects persist at low levels.

METHODS AND RESULTS

We conducted a multisite time-series study to estimate risk of CVD hospitalization associated with short-term CO exposure in 126 US urban counties during 1999-2005 for >9.3 million Medicare enrollees aged > or =65 years. We considered models with adjustment by other traffic-related pollutants: NO2, fine particulate matter (with aerodynamic diameter < or =2.5 microm), and elemental carbon. We found a positive and statistically significant association between same-day CO and increased risk of hospitalization for multiple CVD outcomes (ischemic heart disease, heart rhythm disturbances, heart failure, cerebrovascular disease, total CVD). The association remained positive and statistically significant but was attenuated with copollutant adjustment, especially NO2. A 1-ppm increase in same-day daily 1-hour maximum CO was associated with a 0.96% (95% posterior interval, 0.79%, 1.12%) increase in risk of CVD admissions. With same-day NO(2) adjustment, this estimate was 0.55% (0.36%, 0.74%). The risk persisted at low CO levels <1 ppm.

CONCLUSIONS

We found evidence of an association between short-term exposure to ambient CO and risk of CVD hospitalizations, even at levels well below current US health-based regulatory standards. This evidence indicates that exposure to current CO levels may still pose a public health threat, particularly for persons with CVD.

Predicting traffic-related air pollution in Los Angeles using a distance decay regression selection strategy

Land use regression (LUR) has emerged as an effective means of estimating exposure to air pollution in epidemiological studies. We created the first LUR models of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOX) for the complex megalopolis of Los Angeles (LA), California. Two-hundred and one sampling sites (the largest sampling design to date for LUR estimation) for two seasons were selected using a location-allocation algorithm that maximized the potential variability in measured pollutant concentrations and represented populations in the health study. Traffic volumes, truck routes and road networks, land use data, satellite-derived vegetation greenness and soil brightness, and truck route slope gradients were used for predicting NOX concentrations. A novel model selection strategy known as "ADDRESS" (A Distance Decay REgression Selection Strategy) was used to select optimized buffer distances for potential predictor variables and maximize model performance. Final regression models explained 81%, 86% and 85% of the variance in measured NO, NO2 and NOX concentrations, respectively. Cross-validation analyses suggested a prediction accuracy of 87-91%. Remote sensing-derived variables were significantly correlated with NOX concentrations, suggesting these data are useful surrogates for modeling traffic-related pollution when certain land use data are unavailable. Our study also demonstrated that reactive pollutants such as NO and NO2 could have high spatial extents of influence (e.g., > 5000 m from expressway) and high background concentrations in certain geographic areas. This paper represents the first attempt to model traffic-related air pollutants at a fine scale within such a complex and large urban region.

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.

Mobile monitoring of particle light absorption coefficient in an urban area as a basis for land use regression

Land use regression (LUR) is used to map spatial variability in air pollutant concentrations for risk assessment epidemiology, and air quality management. Conventional LUR requires long-term measurements at multiple locations, so application to particulate matter has been limited. Here we use mobile monitoring to characterize spatial variability in black carbon concentrations for LUR modeling. A particle soot absorption photometer in a moving vehicle was used to measure the absorption coefficient (sigma(ap)) during summertime periods of peak afternoon traffic at 39 locations. LUR was used to model the mean and 25th, 50th, 75th, and 90th percentile values of the distribution of 10 s measurements at each location. Model performance (measured by R2) was higher for the 25th and 50th percentiles (0.72 and 0.68, respectively) than for the mean, 75th and 90th percentiles (0.51, 0.55, and 0.54, respectively). Performance was similar to that reported for conventional LUR models of NO2 and NO in this region (116 sites) and better than that for mean sigma(ap) from fixed-location samplers (25 sites). Models of the mean, 75th, and 90th percentiles favored predictors describing truck, rather than total, traffic. This approach is applicable to other urban areas to facilitate the development of LUR models for particulate matter.

A participant-based approach to indoor/outdoor air monitoring in community health studies

Community health studies of traffic-related air pollution have been hampered by the cost and participant burden associated with collecting household-level exposure data. The current study utilized a participant-based approach to collect indoor and outdoor air monitoring data from 92 asthmatic and nonasthmatic children (9-13 years old) enrolled in two concurrent health studies in Detroit, Michigan (Mechanistic Indicators of Childhood Asthma and Detroit Children's Health Study) conducted by the US Environmental Protection Agency (EPA). Passive samplers were shipped to participating households and deployed by parents of study participants to collect indoor and outdoor measurements of nitrogen dioxide (NO(2)), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs) including naphthalene. Half of the households deployed VOC and NO(2) samplers for 7 days; the other half deployed PAH and NO(2) samplers for 2 days and additional PAH samplers for 1 day. Approximately 88% of the households that received air sampling kits completed the air monitoring. Compliance was significantly higher among participants asked to deploy all samplers for 7 days (85%) compared with participants asked to deploy some samplers for 2 days and others for 1 day (56%). The 7-day homes were also more likely to provide usable data (96%) compared with the 1- and 2-day homes (73%). Compliance and providing usable data did not vary between parents of asthmatic versus nonasthmatic study participants and were not reduced among households deploying duplicate samplers. These results suggest that participant-based sampling may be a feasible and cost-effective alternative to traditional exposure assessment approaches that can be applied in future epidemiological and community-based health studies.

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.

Assessing the distribution of volatile organic compounds using land use regression in Sarnia, "Chemical Valley", Ontario, Canada

BACKGROUND

Land use regression (LUR) modelling is proposed as a promising approach to meet some of the challenges of assessing the intra-urban spatial variability of ambient air pollutants in urban and industrial settings. However, most of the LUR models to date have focused on nitrogen oxides and particulate matter. This study aimed at developing LUR models to predict BTEX (benzene, toluene, ethylbenzene, m/p-xylene and o-xylene) concentrations in Sarnia, 'Chemical Valley', Ontario, and model the intra-urban variability of BTEX compounds in the city for a community health study.

METHOD

Using Organic Vapour Monitors, pollutants were monitored at 39 locations across the city of Sarnia for 2 weeks in October 2005. LUR models were developed to generate predictor variables that best estimate BTEX concentrations.

RESULTS

Industrial area, dwelling counts, and highways adequately explained most of the variability of BTEX concentrations (R2: 0.78 - 0.81). Correlations between measured BTEX compounds were high (> 0.75). Although most of the predictor variables (e.g. land use) were similar in all the models, their individual contributions to the models were different.

CONCLUSION

Yielding potentially different health effects than nitrogen oxides and particulate matter, modelling other air pollutants is essential for a better understanding of the link between air pollution and health. The LUR models developed in these analyses will be used for estimating outdoor exposure to BTEX for a larger community health study aimed at examining the determinants of health in Sarnia.

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.

Exposure information in environmental health research: current opportunities and future directions for particulate matter, ozone, and toxic air pollutants

Understanding and quantifying outdoor and indoor sources of human exposure are essential but often not adequately addressed in health effect studies for air pollution. Air pollution epidemiology, risk assessment, health tracking, and accountability assessments are examples of health effect studies that require but often lack adequate exposure information. Recent advances in exposure modeling along with better information on time-activity and exposure factor data provide us with unique opportunities to improve the assignment of exposures for both future and ongoing studies linking air pollution to health impacts. In September 2006, scientists from the US Environmental Protection Agency and the Centers for Disease Control and Prevention along with scientists from the academic community and state health departments convened a symposium on air pollution exposure and health to identify, evaluate, and improve current approaches for linking air pollution exposures to disease. This manuscript presents the key issues, challenges, and recommendations identified by the exposure working group, who used case studies of particulate matter, ozone, and toxic air-pollutant exposure to evaluate health effects for air pollution. One of the overarching lessons of this workshop is that obtaining better exposure information for these different health effect studies requires both goal setting for what is needed and mapping out the transition pathway from current capabilities for meeting these goals. Meeting our long-term goals requires definition of incremental steps that provide useful information for the interim and move us toward our long-term goals. Another overarching theme among the three different pollutants and the different health study approaches is the need for integration among alternate exposure-assessment approaches. For example, different groups may advocate exposure indicators, biomonitoring, mapping methods (GIS), modeling, environmental media monitoring, and/or personal exposure modeling. However, emerging research reveals that the greatest progress comes from integration among two or more of these efforts.

Intercity transferability of land use regression models for estimating ambient concentrations of nitrogen dioxide

Land use regression (LUR) is a method for predicting the spatial distribution of traffic-related air pollution. To facilitate risk and exposure assessment, and the design of future monitoring networks and sampling campaigns, we sought to determine the extent to which LUR can be used to predict spatial patterns in air pollution in the absence of dedicated measurements. We evaluate the transferability of one LUR model to two other geographically comparable areas with similar climates and pollution types. The source model, developed in 2003 to estimate ambient nitrogen dioxide (NO(2)) concentrations in Vancouver (BC, Canada) was applied to Victoria (BC, Canada) and Seattle (WA, USA). Model estimates were compared with measurements made with Ogawa passive samplers in both cities. As part of this study, 42 locations were sampled in Victoria for a 2-week period in June 2006. Data obtained for Seattle were collected for a different project at 26 locations in March 2005. We used simple linear regression to evaluate the fit of the source model under three scenarios: (1) using the same variables and coefficients as the source model; (2) using the same variables as the source model, but calculating new coefficients for local calibration; and (3) developing site-specific equations with new variables and coefficients. In Scenario 1, we found that the source model had a better fit in Victoria (R(2)=0.51) than in Seattle (R(2)=0.33). Scenario 2 produced improved R(2)-values in both cities (Victoria=0.58, Seattle=0.65), with further improvement achieved under Scenario 3 (Victoria=0.61, Seattle=0.72). Although it is possible to transfer LUR models between geographically similar cities, success may depend on the between-city consistency of the input data. Modest field sampling campaigns for location-specific model calibration can help to produce transfer models that are equally as predictive as their sources.

Field comparison of passive air samplers with reference monitors for ambient volatile organic compounds and nitrogen dioxide under week-long integrals

This study evaluates performance of nitrogen dioxide (NO2) and volatile organic compound (VOC) passive samplers with corresponding reference monitors at two sites in the Detroit, Michigan area during the summer of 2005. Ogawa passive NO2 samplers and custom-made, re-useable Perkin-Elmer (PE) tubes with Carbopack X sorbent for VOCs were deployed under week-long sampling periods for six weeks. Precise results (5% relative standard deviation, RSD) were found for NO2 measurements from collocated Ogawa samplers. Reproducibility was also good for duplicate PE tubes for benzene, toluene, ethylbenzene, and xylene isomers (BTEX species, all < or = 6% RSD). As seen in previous studies, comparison of Ogawa NO2 samplers with reference chemiluminescence measurements suggested good agreement. Generally good agreement was also found between the PE tubes and reference methods for BTEX species.

A land-use regression model for estimating microenvironmental diesel exposure given multiple addresses from birth through childhood

The Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS) is a prospective birth cohort whose purpose is to determine if exposure to high levels of diesel exhaust particles (DEP) during early childhood increases the risk for developing allergic diseases. In order to estimate exposure to DEP, a land-use regression (LUR) model was developed using geographic data as independent variables and sampled levels of a marker of DEP as the dependent variable. A continuous wind direction variable was also created. The LUR model predicted 74% of the variability in sampled values with four variables: wind direction, length of bus routes within 300 m of the sample site, a measure of truck intensity within 300 m of the sampling site, and elevation. The LUR model was subsequently applied to all locations where the child had spent more than eight hours per week from through age three. A time-weighted average (TWA) microenvironmental exposure estimate was derived for four time periods: 0-6 months, 7-12 months, 13-24 months, 25-36 months. By age two, one third of the children were spending significant time at locations other than home and by 36 months, 39% of the children had changed their residential addresses. The mean cumulative DEP exposure estimate increased from age 6 to 36 months from 70 to 414 microg/m3-days. Findings indicate that using birth addresses to estimate a child's exposure may result in exposure misclassification for some children who spend a significant amount of time at a location with high exposure to DEP.

Environmental public health tracking of childhood asthma using California health interview survey, traffic, and outdoor air pollution data

BACKGROUND

Despite extensive evidence that air pollution affects childhood asthma, state-level and national-level tracking of asthma outcomes in relation to air pollution is limited.

OBJECTIVES

Our goals were to evaluate the feasibility of linking the 2001 California Health Interview Survey (CHIS), air monitoring, and traffic data; estimate associations between traffic density (TD) or outdoor air pollutant concentrations and childhood asthma morbidity; and evaluate the usefulness of such databases, linkages, and analyses to Environmental Public Health Tracking (EPHT).

METHODS

We estimated TD within 500 feet of residential cross-streets of respondents and annual average pollutant concentrations based on monitoring station measurements. We used logistic regression to examine associations with reported asthma symptoms and emergency department (ED) visits/hospitalizations.

RESULTS

Assignment of TD and air pollution exposures for cross-streets was successful for 82% of children with asthma in Los Angeles and San Diego, California, Counties. Children with asthma living in high ozone areas and areas with high concentrations of particulate matter < 10 microm in aerodynamic diameter experienced symptoms more frequently, and those living close to heavy traffic reported more ED visits/hospitalizations. The advantages of the CHIS for asthma EPHT include a large and representative sample, biennial data collection, and ascertainment of important socio-demographic and residential address information. Disadvantages are its cross-sectional design, reliance on parental reports of diagnoses and symptoms, and lack of information on some potential confounders.

CONCLUSIONS

Despite limitations, the CHIS provides a useful framework for examining air pollution and childhood asthma morbidity in support of EPHT, especially because later surveys address some noted gaps. We plan to employ CHIS 2003 and 2005 data and novel exposure assessment methods to re-examine the questions raised here.

A sub-neighborhood scale land use regression model for predicting NO(2)

This study set out to develop a land use regression model at sub-neighborhood scale (0.01-1 km) for Portland, Oregon using passive measurements of NO(2) at 77 locations. Variables used to develop the model included road and railroad density, traffic volume, and land use with buffers of 50 to 750 m surrounding each measurement site. An initial regression model was able to predict 66% of the variation in NO(2). Including wind direction in the regression model increased predictive power by 15%. Iterative random exclusion of 11 sites during model calibration resulted in a 3% variation in predictive power. The regression model was applied to the Portland metropolitan area using 10 m gridded land use layers. This study further validates land use regression for use in North America, and identifies important considerations for their use, such as inclusion of railways, open spaces and meteorological patterns.

Comparison of regression models with land-use and emissions data to predict the spatial distribution of traffic-related air pollution in Rome

Spatial modeling of traffic-related air pollution typically involves either regression modeling of land-use and traffic data or dispersion modeling of emissions data, but little is known to what extent land-use regression models might be improved by incorporating emissions data. The aim of this study was to develop a land-use regression model to predict nitrogen dioxide (NO2) concentrations and compare its performance with a model including emissions data. The association between each land-use variable and NO2 concentrations at 68 locations in Rome in 1995 and 1996 was assessed by univariate linear regression and a multiple linear regression model that was constructed based on the importance of each variable. Traffic emissions (particulate matter, carbon monoxide, nitrogen oxides, and benzene) were estimated for 164 areas of the city based on vehicle type, traffic counts and driving patterns. Mean NO2 concentration across the 68 sites was 46.8 microg/m3 (SD 9.8 microg/m3; inter-quartile range 11.5 microg/m3; min 24 microg/m3; max 73 microg/m3). The most important predicting variables were the circular traffic zones (main ring road, green strip, inner ring road, traffic-limited zone), distance from busy streets, size of the census block, the inverse population density, and altitude. A multiple regression model including these variables resulted in an R2 of 0.686. The best-fitting model adding an emission term of benzene resulted in an R2 of 0.690, but was not significantly different from the model without emissions (P=0.147). In conclusion, these results suggest that a land-use regression model explains the traffic-related air pollution levels with reasonable accuracy and that emissions data do not significantly improve the model.

An empirical model for estimating census unit population exposure in areas lacking air quality monitoring

This study presents the methods and results of part of the HAPiNZ (Health and Air Pollution in New Zealand) study. A part of this project was to produce accurate measures of pollution exposure for the entire population of New Zealand living in urban areas. Suitable data are limited in most parts of New Zealand with some areas having no monitoring at all. As a result, this project has developed an empirical model to estimate annual exposure values for the whole country down to the census area unit level. This uses surrogate emission indicators and meteorological variables. Data sources used include census data on domestic heating, industrial emissions estimates, vehicle kilometres travelled and meteorological measurements. These were used to calculate annual exposure estimates and were then compared to monitored data for the areas where monitoring data were available. Results show a good association between the model estimates and the monitored data, enabling advanced health effects assessments for the country's entire urban population.

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.

Cardiovascular disease and air pollutants: evaluating and improving epidemiological data implicating traffic exposure

Evidence suggests that traffic-related pollutants play a role in the observed associations between air pollution and adverse cardiovascular health effects. The contribution of traffic to individual exposures is difficult to quantify in traditional epidemiological studies, however, and researchers have employed various approaches in attempt to isolate its effects. Many investigators have employed ambient measurements such as nitrogen oxides, carbon monoxide, or black carbon as surrogates for traffic in studying associations with health outcomes. Source-apportionment techniques also have been used in a few studies to identify associations with the mixture of pollutants from specific origins, including traffic. In other studies, estimates of traffic near a person's home have predicted cardiovascular endpoints, and local traffic levels have modified the effect of regional air pollution. More recently, studies have linked changes in cardiovascular health to time spent in traffic. In this article, we review the epidemiological evidence regarding the impact of traffic-related pollution on cardiovascular diseases and examine the different techniques used to examine this important research question. We conclude with a discussion of the future directions being used in ongoing epidemiological studies to identify the cardiovascular health impacts of traffic.

Error and bias in determining exposure potential of children at school locations using proximity-based GIS techniques

BACKGROUND

The widespread availability of powerful tools in commercial geographic information system (GIS) software has made address geocoding a widely employed technique in spatial epidemiologic studies.

OBJECTIVE

The objective of this study was to determine the effect of the positional error in geocoding on the analysis of exposure to traffic-related air pollution of children at school locations.

METHODS

For a case study of Orange County, Florida, we determined the positional error of geocoding of school locations through comparisons with a parcel database and digital orthophotography. We used four different geocoding techniques for comparison to establish the repeatability of geocoding, and an analysis of proximity to major roads to determine bias and error in environmental exposure assessment.

RESULTS

RESULTS INDICATE THAT THE POSITIONAL ERROR IN GEOCODING OF SCHOOLS IS VERY SUBSTANTIAL: We found that the 95% root mean square error was 196 m using street centerlines, 306 m using TIGER roads, and 210 and 235 m for two commercial geocoding firms. We found bias and error in proximity analysis to major roads to be unacceptably large at distances of < 500 m. Bias and error are introduced by lack of positional accuracy and lack of repeatability of geocoding of school locations.

CONCLUSIONS

These results suggest that typical geocoding is insufficient for fine-scale analysis of school locations and more accurate alternatives need to be considered.

A spatial model of urban winter woodsmoke concentrations

In many urban areas, residential wood burning is a significant wintertime source of PM2.5. In this study, we used a combination of fixed and mobile monitoring along with a novel spatial buffering procedure to estimate the spatial patterns of woodsmoke. Two-week average PM2.5 and levoglucosan (a marker for wood smoke) concentrations were concurrently measured at upto seven sites in the study region. In addition, pre-selected routes spanning the major population areas in and around Vancouver, B.C. were traversed during 19 cold, clear winter evenings from November, 2004 to March, 2005 by a vehicle equipped with GPS receiver and a nephelometer. Fifteen-second-average values of light scattering coefficient (bsp) were adjusted for variations between evenings and then combined into a single, highly resolved map of nighttime winter bsp levels. A relatively simple but robust (R(2) = 0.64) land use regression model was developed using selected spatial covariates to predict these temporally adjusted bsp values. The bsp values predicted by this model were also correlated with the measured average levoglucosan concentrations at our fixed site locations (R(2) = 0.66). This model, the first application of land use regression for woodsmoke, enabled the identification and prediction of previously unrecognized high woodsmoke regions within an urban airshed.

Influence of geocoding quality on environmental exposure assessment of children living near high traffic roads

Background

The widespread availability of powerful geocoding tools in commercial GIS software and the interest in spatial analysis at the individual level have made address geocoding a widely employed technique in epidemiological studies. This study determined the effect of the positional error in street geocoding on the analysis of traffic-related air pollution on children.

Methods

For a case-study of a large sample of school children in Orange County, Florida (n = 104,865) the positional error of street geocoding was determined through comparison with a parcel database. The effect of this error was evaluated by analyzing the proximity of street and parcel geocoded locations to road segments with high traffic volume and determining the accuracy of the classification using the results of street geocoding. Of the original sample of 163,886 addresses 36% were not used in the final analysis because they could not be reliably geocoded using either street or parcel geocoding. The estimates of positional error can therefore be considered conservative underestimates.

Results

Street geocoding was found to have a median error of 41 meters, a 90^th percentile of 100 meters, a 95^th percentile of 137 meters and a 99^th percentile of 273 meters. These positional errors were found to be non-random in nature and introduced substantial bias and error in the estimates of potential exposure to traffic-related air pollution. Street geocoding was found to consistently over-estimate the number of potentially exposed children at small distances up to 250 meters. False positives and negatives were also found to be very common at these small distances.

Conclusion

Results of the case-study presented here strongly suggest that typical street geocoding is insufficient for fine-scale analysis and more accurate alternatives need to be considered.

Asthma and air quality

PURPOSE OF REVIEW

There is evidence for an association between asthma and air pollutants, including ozone, NO2 and particulate matter. Since these pollutants are ubiquitous in the urban atmosphere and typically correlated with each other it has been difficult to ascertain the specific sources of air pollution responsible for the observed effects. Similarly, uncertainty in determining a causal agent, or multiple agents, has complicated efforts to identify the mechanisms involved in pollution-mediated asthma events and whether air pollution may cause asthma as well as exacerbate preexisting cases.

RECENT FINDINGS

Numerous studies have examined specific sources of air pollution and their relationship to asthma. This review summarizes recent work conducted, specifically, on traffic pollution and presents results that elucidate several plausible biological mechanisms for the observed effects. Of note are studies linking susceptibility to several genetic polymorphisms. Together, these studies suggest that remaining uncertainties in the asthma-air pollution association may be addressed through enhanced assessment of both exposures and outcomes.

SUMMARY

Air-pollution research is evolving rapidly; in the near future, clinicians and public health agencies may be able to use this new information to provide recommendations for asthmatics that go beyond only paying attention to the air-pollution forecast.

A comparison of proximity and land use regression traffic exposure models and wheezing in infants

BACKGROUND

We previously reported an association between infant wheezing and residence < 100 m from stop-and-go bus and truck traffic. The use of a proximity model, however, may lead to exposure misclassification.

OBJECTIVE

Results obtained from a land use regression (LUR) model of exposure to truck and bus traffic are compared with those obtained with a proximity model. The estimates derived from the LUR model were then related to infant wheezing.

METHODS

We derived a marker of diesel combustion--elemental carbon attributable to traffic sources (ECAT)--from ambient monitoring results of particulate matter with aerodynamic diameter < 2.5 microm. We developed a multiple regression model with ECAT as the outcome variable. Variables included in the model were locations of major roads, bus routes, truck traffic count, and elevation. Model parameter estimates were applied to estimate individual ECAT levels at infants' homes.

RESULTS

The levels of estimated ECAT at the monitoring stations ranged from 0.20 to 1.02 microg/m(3). A LUR model of exposure with a coefficient of determination (R(2)) of 0.75 was applied to infants' homes. The mean (+/- SD) ambient exposure of ECAT for infants previously categorized as unexposed, exposed to stop-and-go traffic, or exposed to moving traffic was 0.32 +/- 0.06, 0.42 +/- 0.14, and 0.49 +/- 0.14 microg/m(3), respectively. Levels of ECAT from 0.30 to 0.90 mug/m(3) were significantly associated with infant wheezing.

CONCLUSIONS

The LUR model resulted in a range of ECAT individually derived for all infants' homes that may reduce the exposure misclassification that can arise from a proximity model.

The air quality impacts of road closures associated with the 2004 Democratic National Convention in Boston

BACKGROUND

The Democratic National Convention (DNC) in Boston, Massachusetts in 2004 provided an opportunity to evaluate the impacts of a localized and short-term but potentially significant change in traffic patterns on air quality, and to determine the optimal monitoring approach to address events of this nature. It was anticipated that the road closures associated with the DNC would both influence the overall air pollution level and the distribution of concentrations across the city, through shifts in traffic patterns.

METHODS

To capture these effects, we placed passive nitrogen dioxide badges at 40 sites around metropolitan Boston before, during, and after the DNC, with the goal of capturing the array of hypothesized impacts. In addition, we continuously measured elemental carbon at three sites, and gathered continuous air pollution data from US EPA fixed-site monitors and traffic count data from the Massachusetts Highway Department.

RESULTS

There were significant reductions in traffic volume on the highway with closures north of Boston, with relatively little change along other highways, indicating a more isolated traffic reduction rather than an across-the-board decrease. For our nitrogen dioxide samples, while there was a relatively small change in mean concentrations, there was significant heterogeneity across sites, which corresponded with our a priori classifications of road segments. The median ratio of nitrogen dioxide concentrations during the DNC relative to non-DNC sampling periods was 0.58 at sites with hypothesized traffic reductions, versus 0.88 for sites with no changes hypothesized and 1.15 for sites with hypothesized traffic increases. Continuous monitors measured slightly lower concentrations of elemental carbon and nitrogen dioxide during road closure periods at monitors proximate to closed highway segments, but not for PM2.5 or further from major highways.

CONCLUSION

We conclude that there was a small but measurable influence of DNC-related road closures on air quality patterns in the Boston area, and that a low-cost monitoring study combining passive badges for spatial heterogeneity and continuous monitors for temporal heterogeneity can provide useful insight for community air quality assessments.

Traffic, susceptibility, and childhood asthma

Results from studies of traffic and childhood asthma have been inconsistent, but there has been little systematic evaluation of susceptible subgroups. In this study, we examined the relationship of local traffic-related exposure and asthma and wheeze in southern California school children (5-7 years of age). Lifetime history of doctor-diagnosed asthma and prevalent asthma and wheeze were evaluated by questionnaire. Parental history of asthma and child's history of allergic symptoms, sex, and early-life exposure (residence at the same home since 2 years of age) were examined as susceptibility factors. Residential exposure was assessed by proximity to a major road and by modeling exposure to local traffic-related pollutants. Residence within 75 m of a major road was associated with an increased risk of lifetime asthma [odds ratio (OR)=1.29; 95% confidence interval (CI), 1.01-1.86], prevalent asthma (OR=1.50; 95% CI, 1.16-1.95), and wheeze (OR=1.40; 95% CI, 1.09-1.78). Susceptibility increased in long-term residents with no parental history of asthma for lifetime asthma (OR=1.85; 95% CI, 1.11-3.09), prevalent asthma (OR=2.46; 95% CI, 0.48-4.09), and recent wheeze (OR=2.74; 95% CI, 1.71-4.39). The higher risk of asthma near a major road decreased to background rates at 150-200 m from the road. In children with a parental history of asthma and in children moving to the residence after 2 years of age, there was no increased risk associated with exposure. Effect of residential proximity to roadways was also larger in girls. A similar pattern of effects was observed with traffic-modeled exposure. These results indicate that residence near a major road is associated with asthma. The reason for larger effects in those with no parental history of asthma merits further investigation.