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Title: Differential Probability Functions for Investigating Long-term Changes in Local and Regional Air Pollution Sources

Abstract

Conditional probability functions are commonly used for source identification purposes in air pollution studies. CBPF (conditional bivariate probability function) categorizes the probability of high concentrations being observed at a location by wind direction/speed and investigate the directionality of local sources. PSCF (potential source contribution function), a trajectory-ensemble method, identifies the source regions most likely to be associated with high measured concentrations. However, these techniques do not allow the direct identification of areas where changes in emissions have occurred. This study presents an extension of conditional probability methods in which the differences between conditional probability values for temporally different sets of data can be used to explore changes in emissions from source locations. The differential CBPF and differential PSCF were tested using a long-term series of air quality data (12 years; 2005/2016) collected in Rochester, NY. The probability functions were computed for each of 4 periods that represent known changes in emissions. Correlation analyses were also performed on the results to find pollutants undergoing similar changes in local and regional sources. The differential probability functions permitted the identification of major changes in local and regional emission location. In Rochester, changes in local air pollution were related to the shutdown of amore » large coal power plant (SO 2) and to the abatement measures applied to road and off-road traffic (primary pollutants). The concurrent effects of these changes in local emissions were also linked to reduced concentrations of nucleation mode particles. Changes in regional source areas were related to the decreases in secondary inorganic aerosol and organic carbon. As a result, the differential probabilities for sulfate, nitrate, and organic aerosol were consistent with differences in the available National Emission Inventory annual emission values. Changes in the source areas of black carbon and PM2.5 mass concentrations were highly correlated.« less

Authors:
 [1];  [1]; ORCiD logo [2];  [1];  [3]
  1. Univ. of Rochester Medical Center, Rochester, NY (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Univ. of Rochester Medical Center, Rochester, NY (United States); Clarkson Univ., Potsdam, NY (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1504018
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Aerosol and Air Quality Research
Additional Journal Information:
Journal Volume: 19; Journal Issue: 4; Journal ID: ISSN 1680-8584
Publisher:
Chinese Association for Aerosol Research in Taiwan
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Differential probability functions; Long-term trends; Air pollution

Citation Formats

Masiol, Mauro, Squizzato, Stefania, Cheng, Meng -Dawn, Rich, David Q., and Hopke, Philip K. Differential Probability Functions for Investigating Long-term Changes in Local and Regional Air Pollution Sources. United States: N. p., 2019. Web. doi:10.4209/aaqr.2018.09.0327.
Masiol, Mauro, Squizzato, Stefania, Cheng, Meng -Dawn, Rich, David Q., & Hopke, Philip K. Differential Probability Functions for Investigating Long-term Changes in Local and Regional Air Pollution Sources. United States. doi:10.4209/aaqr.2018.09.0327.
Masiol, Mauro, Squizzato, Stefania, Cheng, Meng -Dawn, Rich, David Q., and Hopke, Philip K. Tue . "Differential Probability Functions for Investigating Long-term Changes in Local and Regional Air Pollution Sources". United States. doi:10.4209/aaqr.2018.09.0327. https://www.osti.gov/servlets/purl/1504018.
@article{osti_1504018,
title = {Differential Probability Functions for Investigating Long-term Changes in Local and Regional Air Pollution Sources},
author = {Masiol, Mauro and Squizzato, Stefania and Cheng, Meng -Dawn and Rich, David Q. and Hopke, Philip K.},
abstractNote = {Conditional probability functions are commonly used for source identification purposes in air pollution studies. CBPF (conditional bivariate probability function) categorizes the probability of high concentrations being observed at a location by wind direction/speed and investigate the directionality of local sources. PSCF (potential source contribution function), a trajectory-ensemble method, identifies the source regions most likely to be associated with high measured concentrations. However, these techniques do not allow the direct identification of areas where changes in emissions have occurred. This study presents an extension of conditional probability methods in which the differences between conditional probability values for temporally different sets of data can be used to explore changes in emissions from source locations. The differential CBPF and differential PSCF were tested using a long-term series of air quality data (12 years; 2005/2016) collected in Rochester, NY. The probability functions were computed for each of 4 periods that represent known changes in emissions. Correlation analyses were also performed on the results to find pollutants undergoing similar changes in local and regional sources. The differential probability functions permitted the identification of major changes in local and regional emission location. In Rochester, changes in local air pollution were related to the shutdown of a large coal power plant (SO2) and to the abatement measures applied to road and off-road traffic (primary pollutants). The concurrent effects of these changes in local emissions were also linked to reduced concentrations of nucleation mode particles. Changes in regional source areas were related to the decreases in secondary inorganic aerosol and organic carbon. As a result, the differential probabilities for sulfate, nitrate, and organic aerosol were consistent with differences in the available National Emission Inventory annual emission values. Changes in the source areas of black carbon and PM2.5 mass concentrations were highly correlated.},
doi = {10.4209/aaqr.2018.09.0327},
journal = {Aerosol and Air Quality Research},
number = 4,
volume = 19,
place = {United States},
year = {2019},
month = {1}
}

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Works referencing / citing this record:

Changes in triggering of ST-elevation myocardial infarction by particulate air pollution in Monroe County, New York over time: a case-crossover study
journal, September 2019