| 1. |
- Dutcher, Dabrina D., et al.
(författare)
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Emissions from soy biodiesel blends: A single particle perspective
- 2011
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Ingår i: Atmospheric Environment. - : Elsevier BV. - 1352-2310. ; 45:20, s. 3406-3413
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Tidskriftsartikel (refereegranskat)abstract
- Biodiesel has recently reemerged as a common fuel. However, emissions from biodiesel combustion have been studied in much less detail than those from traditional petroleum-based diesel. In this experiment, emissions from the combustion of different fuel blends (BOO, B02, B20, B99, where the number after B indicates the percentage, by volume, of biodiesel in the fuel) in a VW TDi engine were analyzed by aerosol time-of-flight mass spectrometers (ATOFMS) for single-particle composition and vacuum aerodynamic size. The ATOFMS results show that the PAH molecular weight distribution is not significantly affected by the fuel composition, and that sulfates are reduced by increased biodiesel content. Octanedioic acid (a carbonyl species) is increased with increased biodiesel concentration. Clustering results from the single-particle spectra show that the particles fall in five main types by chemical composition. The aerodynamic size distribution of these individual clusters was also determined. These results also show that methods used to identify diesel particle emissions for source apportionment are not applicable when significant concentrations of biodiesel are used in fuels. (C) 2011 Elsevier Ltd. All rights reserved.
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| 2. |
- Mercer, Laina D., et al.
(författare)
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Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)
- 2011
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Ingår i: Atmospheric Environment. - : Elsevier BV. - 1352-2310. ; 45:26, s. 4412-4420
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Tidskriftsartikel (refereegranskat)abstract
- Background: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land-use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. Methods: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R-2 and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. Results: UK models consistently performed as well as or better than the analogous LUR models. The best CV R-2 values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R-2 values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R-2 values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. Conclusion: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK. (C) 2011 Elsevier Ltd. All rights reserved.
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| 3. |
- Mills, Gina, 1959, et al.
(författare)
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New stomatal flux-based critical levels for ozone effects on vegetation
- 2011
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Ingår i: Atmospheric Environment. - : Elsevier BV. - 1873-2844 .- 1352-2310. ; 45:28, s. 5064-5068
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Tidskriftsartikel (refereegranskat)abstract
- The critical levels for ozone effects on vegetation have been reviewed and revised by the LRTAP Convention. Eight new or revised critical levels based on the accumulated stomatal flux of ozone (POD gamma, the Phytotoxic Ozone Dose above a threshold flux of Y nmol m(-2) PLA s(-1), where PLA is the projected leaf area) have been agreed. For each receptor, data were combined from experiments conducted under naturally fluctuating environmental conditions in 2-4 countries, resulting in linear dose response relationships with response variables specific to each receptor (r(2) = 0.49-0.87, p
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| 4. |
- Sampson, Paul D., et al.
(författare)
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Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data
- 2011
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Ingår i: Atmospheric Environment. - : Elsevier BV. - 1352-2310. ; 45:36, s. 6593-6606
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Tidskriftsartikel (refereegranskat)abstract
- Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation. (C) 2011 Elsevier Ltd. All rights reserved.
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