Development of PM<sub>2.5</sub> source impact spatial fields using a hybrid source apportionment air quality model

An integral part of air quality management is knowledge of the impact of pollutant sources on ambient concentrations of particulate matter (PM). There is also a growing desire to directly use source impact estimates in health studies; however, source impacts cannot be directly measured. Several limi...

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Main Authors: C. E. Ivey, H. A. Holmes, Y. T. Hu, J. A. Mulholland, A. G. Russell
Format: Article
Language:English
Published: Copernicus Publications 2015-07-01
Series:Geoscientific Model Development
Online Access:http://www.geosci-model-dev.net/8/2153/2015/gmd-8-2153-2015.pdf
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spelling doaj-4009000adc0e471baede2f45a65dc12f2020-11-24T23:17:52ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032015-07-01872153216510.5194/gmd-8-2153-2015Development of PM<sub>2.5</sub> source impact spatial fields using a hybrid source apportionment air quality modelC. E. Ivey0H. A. Holmes1Y. T. Hu2J. A. Mulholland3A. G. Russell4Georgia Institute of Technology, Atlanta, Georgia, USAUniversity of Nevada Reno, Reno, Nevada, USAGeorgia Institute of Technology, Atlanta, Georgia, USAGeorgia Institute of Technology, Atlanta, Georgia, USAGeorgia Institute of Technology, Atlanta, Georgia, USAAn integral part of air quality management is knowledge of the impact of pollutant sources on ambient concentrations of particulate matter (PM). There is also a growing desire to directly use source impact estimates in health studies; however, source impacts cannot be directly measured. Several limitations are inherent in most source apportionment methods motivating the development of a novel hybrid approach that is used to estimate source impacts by combining the capabilities of receptor models (RMs) and chemical transport models (CTMs). The hybrid CTM–RM method calculates adjustment factors to refine the CTM-estimated impact of sources at monitoring sites using pollutant species observations and the results of CTM sensitivity analyses, though it does not directly generate spatial source impact fields. The CTM used here is the Community Multiscale Air Quality (CMAQ) model, and the RM approach is based on the chemical mass balance (CMB) model. This work presents a method that utilizes kriging to spatially interpolate source-specific impact adjustment factors to generate revised CTM source impact fields from the CTM–RM method results, and is applied for January 2004 over the continental United States. The kriging step is evaluated using data withholding and by comparing results to data from alternative networks. Data withholding also provides an estimate of method uncertainty. Directly applied (hybrid, HYB) and spatially interpolated (spatial hybrid, SH) hybrid adjustment factors at withheld observation sites had a correlation coefficient of 0.89, a linear regression slope of 0.83 ± 0.02, and an intercept of 0.14 ± 0.02. Refined source contributions reflect current knowledge of PM emissions (e.g., significant differences in biomass burning impact fields). Concentrations of 19 species and total PM<sub>2.5</sub> mass were reconstructed for withheld observation sites using HYB and SH adjustment factors. The mean concentrations of total PM<sub>2.5</sub> at withheld observation sites were 11.7 (± 8.3), 16.3 (± 11), 8.59 (± 4.7), and 9.2 (± 5.7) μg m<sup>−3</sup> for the observations, CTM, HYB, and SH predictions, respectively. Correlations improved for concentrations of major ions, including nitrate (CMAQ–DDM (decoupled direct method): 0.404, SH: 0.449), ammonium (CMAQ–DDM: 0.454, SH: 0.492), and sulfate (CMAQ–DDM: 0.706, SH: 0.730). Errors in simulated concentrations of metals were reduced considerably: 295 % (CMAQ–DDM) to 139 % (SH) for vanadium; and 1340 % (CMAQ–DDM) to 326 % (SH) for manganese. Errors in simulated concentrations of some metals are expected to remain given the uncertainties in source profiles. Species concentrations were reconstructed using SH results, and the error relative to observed concentrations was greatly reduced as compared to CTM-simulated concentrations. Results demonstrate that the hybrid method along with a spatial extension can be used for large-scale, spatially resolved source apportionment studies where observational data are spatially and temporally limited.http://www.geosci-model-dev.net/8/2153/2015/gmd-8-2153-2015.pdf
collection DOAJ
language English
format Article
sources DOAJ
author C. E. Ivey
H. A. Holmes
Y. T. Hu
J. A. Mulholland
A. G. Russell
spellingShingle C. E. Ivey
H. A. Holmes
Y. T. Hu
J. A. Mulholland
A. G. Russell
Development of PM<sub>2.5</sub> source impact spatial fields using a hybrid source apportionment air quality model
Geoscientific Model Development
author_facet C. E. Ivey
H. A. Holmes
Y. T. Hu
J. A. Mulholland
A. G. Russell
author_sort C. E. Ivey
title Development of PM<sub>2.5</sub> source impact spatial fields using a hybrid source apportionment air quality model
title_short Development of PM<sub>2.5</sub> source impact spatial fields using a hybrid source apportionment air quality model
title_full Development of PM<sub>2.5</sub> source impact spatial fields using a hybrid source apportionment air quality model
title_fullStr Development of PM<sub>2.5</sub> source impact spatial fields using a hybrid source apportionment air quality model
title_full_unstemmed Development of PM<sub>2.5</sub> source impact spatial fields using a hybrid source apportionment air quality model
title_sort development of pm<sub>2.5</sub> source impact spatial fields using a hybrid source apportionment air quality model
publisher Copernicus Publications
series Geoscientific Model Development
issn 1991-959X
1991-9603
publishDate 2015-07-01
description An integral part of air quality management is knowledge of the impact of pollutant sources on ambient concentrations of particulate matter (PM). There is also a growing desire to directly use source impact estimates in health studies; however, source impacts cannot be directly measured. Several limitations are inherent in most source apportionment methods motivating the development of a novel hybrid approach that is used to estimate source impacts by combining the capabilities of receptor models (RMs) and chemical transport models (CTMs). The hybrid CTM–RM method calculates adjustment factors to refine the CTM-estimated impact of sources at monitoring sites using pollutant species observations and the results of CTM sensitivity analyses, though it does not directly generate spatial source impact fields. The CTM used here is the Community Multiscale Air Quality (CMAQ) model, and the RM approach is based on the chemical mass balance (CMB) model. This work presents a method that utilizes kriging to spatially interpolate source-specific impact adjustment factors to generate revised CTM source impact fields from the CTM–RM method results, and is applied for January 2004 over the continental United States. The kriging step is evaluated using data withholding and by comparing results to data from alternative networks. Data withholding also provides an estimate of method uncertainty. Directly applied (hybrid, HYB) and spatially interpolated (spatial hybrid, SH) hybrid adjustment factors at withheld observation sites had a correlation coefficient of 0.89, a linear regression slope of 0.83 ± 0.02, and an intercept of 0.14 ± 0.02. Refined source contributions reflect current knowledge of PM emissions (e.g., significant differences in biomass burning impact fields). Concentrations of 19 species and total PM<sub>2.5</sub> mass were reconstructed for withheld observation sites using HYB and SH adjustment factors. The mean concentrations of total PM<sub>2.5</sub> at withheld observation sites were 11.7 (± 8.3), 16.3 (± 11), 8.59 (± 4.7), and 9.2 (± 5.7) μg m<sup>−3</sup> for the observations, CTM, HYB, and SH predictions, respectively. Correlations improved for concentrations of major ions, including nitrate (CMAQ–DDM (decoupled direct method): 0.404, SH: 0.449), ammonium (CMAQ–DDM: 0.454, SH: 0.492), and sulfate (CMAQ–DDM: 0.706, SH: 0.730). Errors in simulated concentrations of metals were reduced considerably: 295 % (CMAQ–DDM) to 139 % (SH) for vanadium; and 1340 % (CMAQ–DDM) to 326 % (SH) for manganese. Errors in simulated concentrations of some metals are expected to remain given the uncertainties in source profiles. Species concentrations were reconstructed using SH results, and the error relative to observed concentrations was greatly reduced as compared to CTM-simulated concentrations. Results demonstrate that the hybrid method along with a spatial extension can be used for large-scale, spatially resolved source apportionment studies where observational data are spatially and temporally limited.
url http://www.geosci-model-dev.net/8/2153/2015/gmd-8-2153-2015.pdf
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