Local Variability in the Impacts of Residential Particulate Matter and Pest Exposure on Children's Wheezing Severity: A Geographically Weighted Regression Analysis of Environmental Health Justice.

Two assumptions have underpinned environmental justice over the past several decades: 1) uneven environmental exposures yield correspondingly unequal health impacts and 2) these effects are stable across space. To test these assumptions, relationships for residential pest and PM2.5 exposures with children's wheezing severity are examined using global (ordinary least squares) and local (geographically weighted regression [GWR]) models using cross-sectional observational survey data from El Paso (Texas) children. In the global model, having pests and higher levels of PM2.5 were weakly associated with greater wheezing severity. The local model reveals two types of asthmogenic socio-environments where environmental exposures more powerfully predict greater wheezing severity. The first is a lower-income context where children are disproportionately exposed to pests and PM2.5 and the second is a higher-income socio-environment where children are exposed to lower levels of PM2.5, yet PM2.5is counterintuitively associated with more severe wheezing. Findings demonstrate that GWR is a powerful tool for understanding relationships between environmental conditions, social characteristics and health inequalities.

Modeling particle number concentrations along Interstate 10 in El Paso, Texas.

Annual average daily particle number concentrations around a highway were estimated with an atmospheric dispersion model and a land use regression model. The dispersion model was used to estimate particle concentrations along Interstate 10 at 98 locations within El Paso, Texas. This model employed annual averaged wind speed and annual average daily traffic counts as inputs. A land use regression model with vehicle kilometers traveled as the predictor variable was used to estimate local background concentrations away from the highway to adjust the near-highway concentration estimates. Estimated particle number concentrations ranged between 9.8 × 103 particles/cc and 1.3 × 105 particles/cc, and averaged 2.5 × 104 particles/cc (SE 421.0). Estimates were compared against values measured at seven sites located along I10 throughout the region. The average fractional error was 6% and ranged between -1% and -13% across sites. The largest bias of -13% was observed at a semi-rural site where traffic was lowest. The average bias amongst urban sites was 5%. The accuracy of the estimates depended primarily on the emission factor and the adjustment to local background conditions. An emission factor of 1.63 × 1014 particles/veh-km was based on a value proposed in the literature and adjusted with local measurements. The integration of the two modeling techniques ensured that the particle number concentrations estimates captured the impact of traffic along both the highway and arterial roadways. The performance and economical aspects of the two modeling techniques used in this study shows that producing particle concentration surfaces along major roadways would be feasible in urban regions where traffic and meteorological data are readily available.

Ultrafine particle levels at an international port of entry between the US and Mexico: exposure implications for users, workers, and neighbors.

Exposure to diesel-emitted particles has been linked to increased cancer risk and cardiopulmonary diseases. Because of their size (<100 nm), exposure to ultrafine particles (UFPs) emitted from heavy-duty diesel vehicles (HDDV) might result in greater health risks than those associated with larger particles. Seasonal UFP levels at the International Bridge of the Americas, which connects the US and Mexico and has high HDDV traffic demands, were characterized. Hourly average UFP concentrations ranged between 1.7 × 10(3)/cc and 2.9 × 10(5)/cc with a mean of 3.5 × 10(4)/cc. Wind speeds <2 m s(-1) and temperatures <15 °C were associated with particle number concentrations above normal conditions. The presence of HDDV had the strongest impact on local UFP levels. Varying particle size distributions were associated with south- and northbound HDDV traffic. Peak exposure occurred on weekday afternoons. Although in winter, high exposure episodes were also observed in the morning. Particle number concentrations were estimated to reach background levels at 400 m away from traffic. The populations exposed to UFP above background levels include law enforcement officers, street vendors, private commuters, and commercial vehicle drivers as well as neighbors on both sides of the border, including a church and several schools.

The effect of ventilation, age, and asthmatic condition on ultrafine particle deposition in children.

Ultrafine particles (UFPs) contribute to health risks associated with air pollution, especially respiratory disease in children. Nonetheless, experimental data on UFP deposition in asthmatic children has been minimal. In this study, the effect of ventilation, developing respiratory physiology, and asthmatic condition on the deposition efficiency of ultrafine particles in children was explored. Deposited fractions of UFP (10-200 nm) were determined in 9 asthmatic children, 8 nonasthmatic children, and 5 nonasthmatic adults. Deposition efficiencies in adults served as reference of fully developed respiratory physiologies. A validated deposition model was employed as an auxiliary tool to assess the independent effect of varying ventilation on deposition. Asthmatic conditions were confirmed via pre-and post-bronchodilator spirometry. Subjects were exposed to a hygroscopic aerosol with number geometric mean diameter of 27-31 nm, geometric standard deviation of 1.8-2.0, and concentration of 1.2 × 10(6) particles cm(-3). Exposure was through a silicone mouthpiece. Total deposited fraction (TDF) and normalized deposition rate were 50% and 32% higher in children than in adults. Accounting for tidal volume and age variation, TDF was 21% higher in asthmatic than in non-asthmatic children. The higher health risks of air pollution exposure observed in children and asthmatics might be augmented by their susceptibility to higher dosages of UFP.

Evaluation of land use regression models for NO2 in El Paso, Texas, USA.

Developing suitable exposure estimates for air pollution health studies is problematic due to spatial and temporal variation in concentrations and often limited monitoring data. Though land use regression models (LURs) are often used for this purpose, their applicability to later periods of time, larger geographic areas, and seasonal variation is largely untested. We evaluate a series of mixed model LURs to describe the spatial-temporal gradients of NO(2) across El Paso County, Texas based on measurements collected during cool and warm seasons in 2006-2007 (2006-7). We also evaluated performance of a general additive model (GAM) developed for central El Paso in 1999 to assess spatial gradients across the County in 2006-7. Five LURs were developed iteratively from the study data and their predictions were averaged to provide robust nitrogen dioxide (NO(2)) concentration gradients across the county. Despite differences in sampling time frame, model covariates and model estimation methods, predicted NO(2) concentration gradients were similar in the current study as compared to the 1999 study. Through a comprehensive LUR modeling campaign, it was shown that the nature of the most influential predictive variables remained the same for El Paso between 1999 and 2006-7. The similar LUR results obtained here demonstrate that, at least for El Paso, LURs developed from prior years may still be applicable to assess exposure conditions in subsequent years and in different seasons when seasonal variation is taken into consideration.

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

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