No abstract

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.

Particulate matter less than 10 μm (PM10) has been shown to be associated with aggravation of asthma and respiratory and cardiopulmonary morbidity. There is also great interest in the potential health effects of PM 2.5. Particulate matter (PM) varies in composition both spatially and temporally depending on the source, location and seasonal condition. El Paso County which lies in the Paso del Norte airshed is a unique location to study ambient air pollution due to three major points: the geological land formation, the relatively large population and the various sources of PM. In this study, dichotomous filters were collected from various sites in El Paso County every seven days for a period of one year. The sampling sites were both distant and near border crossings, which are near heavily populated areas with high traffic volume. Fine (PM2.5) and Coarse (PM10-2.5) PM filter samples were extracted using dichloromethane and were assessed for biologic activity and polycyclic aromatic (PAH) content. Three sets of marker genes human BEAS2B bronchial epithelial cells were utilized to assess the effects of airborne PAHs on biologic activities associated with specific biological pathways associated with airway diseases. These pathways included in inflammatory cytokine production (IL-6, IL-8), oxidative stress (HMOX-1, NQO-1, ALDH3A1, AKR1C1), and aryl hydrocarbon receptor (AhR)-dependent signaling (CYP1A1). Results demonstrated interesting temporal and spatial patterns of gene induction for all pathways, particularly those associated with

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.

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 × 106 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.