Application of an Improved Gas-constrained Source Apportionment Method Using Data Fused Fields: a Case Study in North Carolina, USA.

A number of studies have found differing associations of disease outcomes with PM2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO2, NOx, O3, and SO2), total PM2.5 mass, and speciated PM2.5 (including crustal elements) over North Carolina for 2002-2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

Exposure to Mobile Source Air Pollution in Early-life and Childhood Asthma Incidence: The Kaiser Air Pollution and Pediatric Asthma Study.

BACKGROUND: Early-life exposure to traffic-related air pollution exacerbates childhood asthma, but it is unclear what role it plays in asthma development. METHODS: The association between exposure to primary mobile source pollutants during pregnancy and during infancy and asthma incidence by ages 2 through 6 was examined in the Kaiser Air Pollution and Pediatric Asthma Study, a racially diverse birth cohort of 24,608 children born between 2000 and 2010 and insured by Kaiser Permanente Georgia. We estimated concentrations of mobile source fine particulate matter (PM2.5, µg/m), nitrogen oxides (NOX, ppb), and carbon monoxide (CO, ppm) at the maternal and child residence using a Research LINE source dispersion model for near-surface releases. Asthma was defined using diagnoses and medication dispensings from medical records. We used binomial generalized linear regression to model the impact of exposure continuously and by quintiles on asthma risk. RESULTS: Controlling for covariates and modeling log-transformed exposure, a 2.7-fold increase in first year of life PM2.5 was associated with an absolute 4.1% (95% confidence interval, 1.6%, 6.6%) increase in risk of asthma by age 5. Quintile analysis showed an increase in risk from the first to second quintile, but similar risk across quintiles 2-5. Risk differences increased with follow-up age. Results were similar for NOX and CO and for exposure during pregnancy and the first year of life owing to high correlation. CONCLUSIONS: Results provide limited evidence for an association of early-life mobile source air pollution with childhood asthma incidence with a steeper concentration-response relationship observed at lower levels of exposure.

MnO2/g-C3N4 nanocomposite with highly enhanced supercapacitor performance.

A novel sandwich-like MnO2/g-C3N4 nanocomposite (NC) based on the integration of high-density MnO2 nanorods (NRs) onto the surfaces of two-dimensional (2D) g-C3N4 sheets has been successfully fabricated through a facile soft chemical route at low temperature. The MnO2/g-C3N4 NC electrode enhanced the supercapacitor (SC) performance, benchmarked against both the bare MnO2 NRs electrode and the MnO2/graphene oxide (GO) NC electrode, exhibiting high specific capacitance of 211 F/g at a current density of 1 A/g, with good rate capacity and cycling stability. The sandwich-like hybrid structure, the unique 2D structure of the g-C3N4 sheets and the presence of nitrogen in the g-C3N4 all contributed to the promising SC performance of the MnO2/g-C3N4 NC. This work demonstrated the advantages of the g-C3N4 sheets over the commonly-used GO sheets in the design of novel hybrid composite for enhanced capacitance performance of MnO2-based electrochemical SCs, and the results could be extended to other electrode materials for SCs.

Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution.

Investigations of ambient air pollution health effects rely on complete and accurate spatiotemporal air pollutant estimates. Three methods are developed for fusing ambient monitor measurements and 12 km resolution chemical transport model (CMAQ) simulations to estimate daily air pollutant concentrations across Georgia. Temporal variance is determined by observations in one method, with the annual mean CMAQ field providing spatial structure. A second method involves scaling daily CMAQ simulated fields using mean observations to reduce bias. Finally, a weighted average of these results based on prediction of temporal variance provides optimized daily estimates for each 12 × 12 km grid. These methods were applied to daily metrics of 12 pollutants (CO, NO2, NOx, O3, SO2, PM10, PM2.5, and five PM2.5 components) over the state of Georgia for a seven-year period (2002-2008). Cross-validation demonstrates a wide range in optimized model performance across pollutants, with SO2 predicted most poorly due to limitations in coal combustion plume monitoring and modeling. For the other pollutants studied, 54-88% of the spatiotemporal variance (Pearson R(2) from cross-validation) was captured, with ozone and PM2.5 predicted best. The optimized fusion approach developed provides daily spatial field estimates of air pollutant concentrations and uncertainties that are consistent with observations, emissions, and meteorology.

Spatial PM2.5 mobile source impacts using a calibrated indicator method.

Motor vehicles are major sources of fine particulate matter (PM2.5), and the PM2.5 from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM2.5, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NOx) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NOx estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM2.5, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects. Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM2.5 mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM2.5 source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.

Associations of mobile source air pollution during the first year of life with childhood pneumonia, bronchiolitis, and otitis media.

BACKGROUND: Exposure to pollution from motor vehicles in early life may increase susceptibility to common pediatric infections. METHODS: We estimated associations between residential exposure to primary fine particulate matter (PM2.5), nitrogen oxides (NOx), and carbon monoxide (CO) from traffic during the first year of life and incident pneumonia, bronchiolitis, and otitis media events by age two years in 22,441 children from the Kaiser Air Pollution and Pediatric Asthma Study, a retrospective birth cohort of children born during 2000-2010 and insured by Kaiser Permanente Georgia. Time to first clinical diagnosis of each outcome was defined using medical records. Exposure to traffic pollutants was based on observation-calibrated estimates from A Research LINE-source dispersion model for near surface releases (RLINE) and child residential histories. Associations were modeled using Cox proportional hazards models, with exposure as a continuous linear variable, a natural-log transformed continuous variable, and categorized by quintiles. RESULTS: During follow-up 2,181 children were diagnosed with pneumonia, 5,533 with bronchiolitis, and 14,373 with otitis media. We observed positive associations between early-life traffic exposures and all three outcomes; confidence intervals were widest for pneumonia as it was the least common outcome. For example, adjusted hazard ratios for a 1-unit increase in NOx on the natural log scale (a 2.7-fold increase) were 1.19 (95% CI 1.12, 1.27) for bronchiolitis, 1.17 (1.12, 1.22) for otitis media, and 1.08 (0.97, 1.20) for pneumonia. CONCLUSIONS: Our results provide evidence for modest, positive associations between exposure to traffic emissions and common pediatric infections during early childhood.

Measurement error in mobile source air pollution exposure estimates due to residential mobility during pregnancy.

Prenatal air pollution exposure is frequently estimated using maternal residential location at the time of delivery as a proxy for residence during pregnancy. We describe residential mobility during pregnancy among 19,951 children from the Kaiser Air Pollution and Pediatric Asthma Study, quantify measurement error in spatially resolved estimates of prenatal exposure to mobile source fine particulate matter (PM2.5) due to ignoring this mobility, and simulate the impact of this error on estimates of epidemiologic associations. Two exposure estimates were compared, one calculated using complete residential histories during pregnancy (weighted average based on time spent at each address) and the second calculated using only residence at birth. Estimates were computed using annual averages of primary PM2.5 from traffic emissions modeled using a Research LINE-source dispersion model for near-surface releases (RLINE) at 250 m resolution. In this cohort, 18.6% of children were born to mothers who moved at least once during pregnancy. Mobile source PM2.5 exposure estimates calculated using complete residential histories during pregnancy and only residence at birth were highly correlated (rS>0.9). Simulations indicated that ignoring residential mobility resulted in modest bias of epidemiologic associations toward the null, but varied by maternal characteristics and prenatal exposure windows of interest (ranging from -2% to -10% bias).