Jason G. Su

Childhood obesity and proximity to urban parks and recreational resources: A longitudinal cohort study

The objective of the research was to assess how proximity to parks and recreational resources affects the development of childhood obesity through a longitudinal study. Data were collected on 3173 children aged 9-10 from 12 communities in Southern California in 1993 and 1996. Children were followed for eight years to collect longitudinal information, including objectively measured body mass index (BMI). Multilevel growth curve models were used to assess associations between attained BMI growth at age 18 and numerous environmental variables, including park space and recreational program access. For park acres within a 500 m distance of childrens homes, there were significant inverse associations with attained BMI at age 18. Effect sizes were larger for boys than for girls. Recreation programs within a 10 km buffer of childrens homes were significantly and inversely associated with achieved levels in BMI at age 18, with effect sizes for boys also larger than those for girls. We conclude that children with better access to parks and recreational resources are less likely to experience significant increases in attained BMI.

Green spaces and cognitive development in primary schoolchildren

Significance Green spaces have a range of health benefits, but little is known in relation to cognitive development in children. This study, based on comprehensive characterization of outdoor surrounding greenness (at home, school, and during commuting) and repeated computerized cognitive tests in schoolchildren, found an improvement in cognitive development associated with surrounding greenness, particularly with greenness at schools. This association was partly mediated by reductions in air pollution. Our findings provide policymakers with evidence for feasible and achievable targeted interventions such as improving green spaces at schools to attain improvements in mental capital at population level. Exposure to green space has been associated with better physical and mental health. Although this exposure could also influence cognitive development in children, available epidemiological evidence on such an impact is scarce. This study aimed to assess the association between exposure to green space and measures of cognitive development in primary schoolchildren. This study was based on 2,593 schoolchildren in the second to fourth grades (7–10 y) of 36 primary schools in Barcelona, Spain (2012–2013). Cognitive development was assessed as 12-mo change in developmental trajectory of working memory, superior working memory, and inattentiveness by using four repeated (every 3 mo) computerized cognitive tests for each outcome. We assessed exposure to green space by characterizing outdoor surrounding greenness at home and school and during commuting by using high-resolution (5 m × 5 m) satellite data on greenness (normalized difference vegetation index). Multilevel modeling was used to estimate the associations between green spaces and cognitive development. We observed an enhanced 12-mo progress in working memory and superior working memory and a greater 12-mo reduction in inattentiveness associated with greenness within and surrounding school boundaries and with total surrounding greenness index (including greenness surrounding home, commuting route, and school). Adding a traffic-related air pollutant (elemental carbon) to models explained 20–65% of our estimated associations between school greenness and 12-mo cognitive development. Our study showed a beneficial association between exposure to green space and cognitive development among schoolchildren that was partly mediated by reduction in exposure to air pollution.

Green space, health inequality and pregnancy

Green spaces have been suggested to improve physical and mental health and well-being by increasing physical activity, reducing air pollution, noise, and ambient temperature, increasing social contacts and relieving psychophysiological stress. Although these mechanisms also suggest potential beneficial effects of green spaces on pregnancy outcomes, to our knowledge there is no available epidemiological evidence on this impact. We investigated the effects of surrounding greenness and proximity to major green spaces on birth weight and gestational age at delivery and described the effect of socioeconomic position (SEP) on these relationships. This study was based on a cohort of births (N=8246) that occurred in a major university hospital in Barcelona, Spain, during 2001-2005. We determined surrounding greenness from satellite retrievals as the average of Normalized Difference Vegetation Index (NDVI) in a buffer of 100 m around each maternal place of residence. To address proximity to major green spaces, a binary variable was used to indicate whether maternal residential address is situated within a buffer of 500 m from boundaries of a major green space. For each indicator of green exposure, linear regression models were constructed to estimate change in outcomes adjusted for relevant covariates including individual and area level SEP. None of the indicators of green exposure was associated with birth weight and gestational age. After assessing effect modification based on the level of maternal education, we detected an increase in birth weight (grams) among the lowest education level group (N=164) who had higher surrounding NDVI (Regression coefficient (95% confidence interval (CI) of 436.3 (43.1, 829.5)) or lived close to a major green space (Regression coefficient (95% CI)) of 189.8 (23.9, 355.7)). Our findings suggest a beneficial effect of exposure to green spaces on birth weight only in the lowest SEP group.

Influence of Vegetation, Slope, and Lidar Sampling Angle on DEM Accuracy

Detailed GIS studies across spatially complex rangeland landscapes, including the Aspen Parkland of western Canada, require accurate digital elevation models (DEM). Following the interpolation of last return lidar (light detection and ranging) data into a DEM, a series of 256 reference plots, stratified by vegetation type, slope and lidar sensor sampling angle, were surveyed using a total laser station, differential GPS and 27 interconnected benchmarks to assess variation in DEM accuracy. Interpolation using Inverse Distance Weighting IDW resulted in lower mean error than other methods. Across the study area, overall signed error and RMSE were � 0.02 m and 0.59 m, respectively. Signed errors indicated elevations were over-estimated in forest but under-estimated within meadow habitats. Increasing slope gradient increased vertical absolute errors and RMSE. In contrast, lidar sampling angle had little impact on measured error. These results have implications for the development and use of high-resolution DEM models derived from lidar data.

A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM2.5 in the Contiguous United States

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

Traffic-Related Air Toxics and Term Low Birth Weight in Los Angeles County, California

Background: Numerous studies have linked criteria air pollutants with adverse birth outcomes, but there is less information on the importance of specific emission sources, such as traffic, and air toxics. Objectives: We used three exposure data sources to examine odds of term low birth weight (LBW) in Los Angeles, California, women when exposed to high levels of traffic-related air pollutants during pregnancy. Methods: We identified term births during 1 June 2004 to 30 March 2006 to women residing within 5 miles of a South Coast Air Quality Management District (SCAQMD) Multiple Air Toxics Exposure Study (MATES III) monitoring station. Pregnancy period average exposures were estimated for air toxics, including polycyclic aromatic hydrocarbons (PAHs), source-specific particulate matter < 2.5 μm in aerodynamic diameter (PM2.5) based on a chemical mass balance model, criteria air pollutants from government monitoring data, and land use regression (LUR) model estimates of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx). Associations between these metrics and odds of term LBW (< 2,500 g) were examined using logistic regression. Results: Odds of term LBW increased approximately 5% per interquartile range increase in entire pregnancy exposures to several correlated traffic pollutants: LUR measures of NO, NO2, and NOx, elemental carbon, and PM2.5 from diesel and gasoline combustion and paved road dust (geological PM2.5). Conclusions: These analyses provide additional evidence of the potential impact of traffic-related air pollution on fetal growth. Particles from traffic sources should be a focus of future studies.

Long-Term Ozone Exposure and Mortality in a Large Prospective Study

RATIONALE Tropospheric ozone (O3) is potentially associated with cardiovascular disease risk and premature death. Results from long-term epidemiological studies on O3 are scarce and inconclusive. OBJECTIVES In this study, we examined associations between chronic ambient O3 exposure and all-cause and cause-specific mortality in a large cohort of U.S. adults. METHODS Cancer Prevention Study II participants were enrolled in 1982. A total of 669,046 participants were analyzed, among whom 237,201 deaths occurred through 2004. We obtained estimates of O3 concentrations at the participants residence from a hierarchical Bayesian space-time model. Estimates of fine particulate matter (particulate matter with an aerodynamic diameter of up to 2.5 μm [PM2.5]) and NO2 concentrations were obtained from land use regression. Cox proportional hazards regression models were used to examine mortality associations adjusted for individual- and ecological-level covariates. MEASUREMENTS AND MAIN RESULTS In single-pollutant models, we observed significant positive associations between O3, PM2.5, and NO2 concentrations and all-cause and cause-specific mortality. In two-pollutant models adjusted for PM2.5, significant positive associations remained between O3 and all-cause (hazard ratio [HR] per 10 ppb, 1.02; 95% confidence interval [CI], 1.01-1.04), circulatory (HR, 1.03; 95% CI, 1.01-1.05), and respiratory mortality (HR, 1.12; 95% CI, 1.08-1.16) that were unchanged with further adjustment for NO2. We also observed positive mortality associations with both PM2.5 (both near source and regional) and NO2 in multipollutant models. CONCLUSIONS Findings derived from this large-scale prospective study suggest that long-term ambient O3 contributes to risk of respiratory and circulatory mortality. Substantial health and environmental benefits may be achieved by implementing further measures aimed at controlling O3 concentrations.

An index for assessing demographic inequalities in cumulative environmental hazards with application to Los Angeles, California.

Researchers in environmental justice contend that low-income communities and communities of color face greater impacts from environmental hazards. This is also of concern for policy makers. In this context, our paper has two principal objectives. First, we propose a method for creating an index capable of summarizing racial-ethnic and socioeconomic inequalities from the impact of cumulative environmental hazards. Second, we apply the index to Los Angeles County to illustrate the potential applications and complexities of its implementation. Individual environmental inequality indices are calculated based on unequal shares of environmental hazards for racial-ethnic groups and socioeconomic positions. The illustrated hazards include ambient concentrations of particulate matter, nitrogen dioxide, and estimates of cancer risk associated with modeled estimates for diesel particulate matter. The cumulative environmental hazard inequality index (CEHII) then combines individual environmental hazards, using either a multiplicative or an additive model. Significant but modest inequalities exist for both individual and cumulative environmental hazards in Los Angeles. The highest level of inequality among racial-ethnic and socioeconomic groups occurs when a multiplicative model is used to estimate cumulative hazard. The CEHII provides a generalized framework that incorporates environmental hazards and socioeconomic characteristics to assess inequalities in cumulative environmental risks.

Traffic-related air toxics and preterm birth: a population-based case-control study in Los Angeles county, California

BackgroundNumerous studies have associated air pollutant exposures with adverse birth outcomes, but there is still relatively little information to attribute effects to specific emission sources or air toxics. We used three exposure data sources to examine risks of preterm birth in Los Angeles women when exposed to high levels of traffic-related air pollutants - including specific toxics - during pregnancy.MethodsWe identified births during 6/1/04-3/31/06 to women residing within five miles of a Southern California Air Quality Management District (SCAQMD) Multiple Air Toxics Exposure Study (MATES III) monitoring station. We identified preterm cases and, using a risk set approach, matched cases to controls based on gestational age at birth. Pregnancy period exposure averages were estimated for a number of air toxics including polycyclic aromatic hydrocarbons (PAHs), source-specific PM2.5 (fine particulates with aerodynamic diameter less than 2.5 μm) based on a Chemical Mass Balance model, criteria air pollutants based on government monitoring data, and land use regression (LUR) estimates of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx). Associations between these metrics and odds of preterm birth were estimated using conditional logistic regression.ResultsOdds of preterm birth increased 6-21% per inter-quartile range increase in entire pregnancy exposures to organic carbon (OC), elemental carbon (EC), benzene, and diesel, biomass burning and ammonium nitrate PM2.5, and 30% per inter-quartile increase in PAHs; these pollutants were positively correlated and clustered together in a factor analysis. Associations with LUR exposure metrics were weaker (3-4% per inter-quartile range increase).ConclusionsThese latest analyses provide additional evidence of traffic-related air pollutions impact on preterm birth for women living in Southern California and indicate PAHs as a pollutant of concern that should be a focus of future studies. Other PAH sources besides traffic were also associated with higher odds of preterm birth, as was ammonium nitrate PM2.5, the latter suggesting potential importance of secondary pollutants. Future studies should focus on accurate modeling of both local and regional spatial and temporal distributions, and incorporation of source information.

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 (NO2) 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 (R2=0.51) than in Seattle (R2=0.33). Scenario 2 produced improved R2-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.