Abstract
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.
Highlights
Long-range transported pollutants are evaluated with trajectory ensemble methods based on back-trajectories, including clustering (Harris and Kahl, 1990; Brankov et al, 1998; Cape et al, 2000; Abdalmogith and Harrison, 2005; Squizzato et al, 2012), potential source contribution function (PSCF) (Ashbaugh et al, 1985; Malm et al, 1986; Polissar et al, 2001; Poirot et al, 2001; Penkey et al, 2006), concentration field analysis (CFA) (Seibert et al, 1994), residence time weighted concentration (RTWC) (Stohl et al, 1996; Zhou et al, 2004), quantitative transport bias analysis (QTBA) (Keeler, 1987), simplified QTBA (SQTBA) (Zhou et al, 2004), and concentration weighted trajectory (CWT) (Hsu et al, 2003; Zhou et al, 2004)
The two periods (τ1 and τ2) to be compared in the differential probability functions should be chosen based on known changes in local and regional emissions
While the selection of relatively short periods may result in a high temporal resolution, a short time interval decreases the number of data values and lowers the reliability of ΔCBPF and ΔPSCF values
Summary
Air pollution is decreasing in many developed countries (Colette et al, 2011; Guerreiro et al, 2014; Ahmed et al, 2015; Masiol et al, 2017a), including the United States (Parrish et al, 2011; Pouliot et al, 2015; Duncan et al, 2016; Nopmongcol et al, 2016; Emami et al, 2018; Masiol et al, 2018; Squizzato et al, 2018). Long-range transported pollutants are evaluated with trajectory ensemble methods based on back-trajectories, including clustering (Harris and Kahl, 1990; Brankov et al, 1998; Cape et al, 2000; Abdalmogith and Harrison, 2005; Squizzato et al, 2012), potential source contribution function (PSCF) (Ashbaugh et al, 1985; Malm et al, 1986; Polissar et al, 2001; Poirot et al, 2001; Penkey et al, 2006), concentration field analysis (CFA) (Seibert et al, 1994), residence time weighted concentration (RTWC) (Stohl et al, 1996; Zhou et al, 2004), quantitative transport bias analysis (QTBA) (Keeler, 1987), simplified QTBA (SQTBA) (Zhou et al, 2004), and concentration weighted trajectory (CWT) (Hsu et al, 2003; Zhou et al, 2004) Such methods extensively reviewed and tested elsewhere (Lupu and Maenhaut, 2002; Hsu et al, 2003; Zhou et al, 2004; Penkey et al, 2006; Kabashnikov et al, 2011; Fleming et al, 2012; Brereton and Johnson, 2012; Squizzato and Masiol, 2015; Hopke, 2016). These techniques do not identify areas where emission changes have occurred
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