Mauro Masiol 1, Stefania Squizzato1, Meng-Dawn Cheng2, David Q. Rich1,3,4, Philip K. Hopke 1,5


Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY 14642, USA
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY 14642, USA
Department of Medicine, University of Rochester Medical Center, Rochester, NY 14642, USA
Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY 13699, USA



Received: September 28, 2018
Revised: October 26, 2018
Accepted: October 29, 2018
Download Citation: ||https://doi.org/10.4209/aaqr.2018.09.0327  


Cite this article:
Masiol, M., Squizzato, S., Cheng, M.D., Rich, D.Q. and Hopke, P.K. (2019). Differential Probability Functions for Investigating Long-term Changes in Local and Regional Air Pollution Sources. Aerosol Air Qual. Res. 19: 724-736. https://doi.org/10.4209/aaqr.2018.09.0327


HIGHLIGHTS

  • Emission changes over time are visualized using conditional probability differences.
  • Local source changes are identified with differential conditional bivariate probabilities.
  • Regional changes are shown by differential potential source contribution function.
  • These methods have been applied to PM components measured in Rochester, NY.

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 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. 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.


Keywords: Differential probability functions; Long-term trends; Air pollution.

 



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