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Modeling Daily Ambient Air Pollution...

Jiang, Xiangyu.

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Modeling Daily Ambient Air Pollution Using Community Multiscale Air Quality (CMAQ) System.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling Daily Ambient Air Pollution Using Community Multiscale Air Quality (CMAQ) System./
作者:	Jiang, Xiangyu.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	121 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-12, Section: B.
Contained By:	Dissertations Abstracts International81-12B.
標題:	Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27995439
ISBN:	9798617049116

Modeling Daily Ambient Air Pollution Using Community Multiscale Air Quality (CMAQ) System.
Jiang, Xiangyu.

Modeling Daily Ambient Air Pollution Using Community Multiscale Air Quality (CMAQ) System. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 121 p.

Source: Dissertations Abstracts International, Volume: 81-12, Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2020.

This item must not be sold to any third party vendors.

Fine particulate matter (PM2.5) is a complex mixture of particles originating from anthropogenic aerosols and natural emission sources that can cause serious adverse health effects. Most health studies have estimated human exposures to PM2.5 using ground observations despite their limited spatial and temporal coverage. Satellite aerosol optical depth (AOD) has been increasingly used as a proxy variable to sparse ground observations, but the availability of satellite AOD is restricted by physical conditions. More importantly, neither ground PM2.5 observations nor satellite AOD-based model predictions can distinguish PM2.5 emanating from different emission sources. The Community Multiscale Air Quality (CMAQ) model has the potential to fill these gaps by providing spatially wall-to-wall, temporally continuous, and deterministic estimations of source-specific PM2.5 concentrations. However, CMAQ model outputs may be subject to systematic biases and uncertainties arising from error-prone inputs and imperfect parameter settings, such as horizontal grid resolution and domain size. This dissertation aims to determine the optimal parameter settings of CMAQ models for PM2.5 predictions and to improve the accuracy of CMAQ-modeled source-specific PM2.5 predictions for health impact assessments. The objectives were achieved through the following three studies: (1) investigation of the effect of the CMAQ grid resolution on PM2.5-related health impact assessments; (2) assessment of the influence of the CMAQ domain size on regional PM2.5 predictions; (3) calibration of CMAQ-based source-specific PM2.5 predictions for uncertainty-aware health impact assessments. More specifically, the first study was designed to investigate the uncertainty of CMAQ prediction accuracy associated with two horizontal grid resolutions of 4 km and 12 km and to assess their impacts on human health studies. The findings showed that CMAQ model simulation at 12 km resolution with further calibration and/or downscaling is a viable option for capturing small-scale within-city variations of PM2.5 concentrations. The second study presented an approach for CMAQ model uncertainty assessment with respect to domain size and reported the spatio-temporal variations of CMAQ model performance over two study domains: a relatively small domain and a large domain. The results suggested that the overall model performance was better for CMAQ simulations with a large domain relative to the smaller domain. In the third study, I proposed a two-stage calibration approach as a means of adjusting biases in CMAQ-based source-specific PM2.5 predictions and demonstrated its application to wildland fire-specific PM2.5 estimations over the eastern United States in 2014. Based on the findings, I concluded that the proposed calibration strategy could provide reliable wildland fire-specific PM2.5 predictions and health burden estimates to support policy development for reducing fire-related risks.

ISBN: 9798617049116Subjects--Topical Terms:

524010
Geography.
Subjects--Index Terms:

Daily ambient air pollution

Modeling Daily Ambient Air Pollution Using Community Multiscale Air Quality (CMAQ) System.
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520 $a Fine particulate matter (PM2.5) is a complex mixture of particles originating from anthropogenic aerosols and natural emission sources that can cause serious adverse health effects. Most health studies have estimated human exposures to PM2.5 using ground observations despite their limited spatial and temporal coverage. Satellite aerosol optical depth (AOD) has been increasingly used as a proxy variable to sparse ground observations, but the availability of satellite AOD is restricted by physical conditions. More importantly, neither ground PM2.5 observations nor satellite AOD-based model predictions can distinguish PM2.5 emanating from different emission sources. The Community Multiscale Air Quality (CMAQ) model has the potential to fill these gaps by providing spatially wall-to-wall, temporally continuous, and deterministic estimations of source-specific PM2.5 concentrations. However, CMAQ model outputs may be subject to systematic biases and uncertainties arising from error-prone inputs and imperfect parameter settings, such as horizontal grid resolution and domain size. This dissertation aims to determine the optimal parameter settings of CMAQ models for PM2.5 predictions and to improve the accuracy of CMAQ-modeled source-specific PM2.5 predictions for health impact assessments. The objectives were achieved through the following three studies: (1) investigation of the effect of the CMAQ grid resolution on PM2.5-related health impact assessments; (2) assessment of the influence of the CMAQ domain size on regional PM2.5 predictions; (3) calibration of CMAQ-based source-specific PM2.5 predictions for uncertainty-aware health impact assessments. More specifically, the first study was designed to investigate the uncertainty of CMAQ prediction accuracy associated with two horizontal grid resolutions of 4 km and 12 km and to assess their impacts on human health studies. The findings showed that CMAQ model simulation at 12 km resolution with further calibration and/or downscaling is a viable option for capturing small-scale within-city variations of PM2.5 concentrations. The second study presented an approach for CMAQ model uncertainty assessment with respect to domain size and reported the spatio-temporal variations of CMAQ model performance over two study domains: a relatively small domain and a large domain. The results suggested that the overall model performance was better for CMAQ simulations with a large domain relative to the smaller domain. In the third study, I proposed a two-stage calibration approach as a means of adjusting biases in CMAQ-based source-specific PM2.5 predictions and demonstrated its application to wildland fire-specific PM2.5 estimations over the eastern United States in 2014. Based on the findings, I concluded that the proposed calibration strategy could provide reliable wildland fire-specific PM2.5 predictions and health burden estimates to support policy development for reducing fire-related risks.
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