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.
| Original language | English |
|---|---|
| Pages (from-to) | 724-736 |
| Number of pages | 13 |
| Journal | Aerosol and Air Quality Research |
| Volume | 19 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 2019 |
Funding
This work was supported by the New York State Energy Research and Development Authority (NYSERDA) under agreements #59800 and 59802. Air quality data used in this study are available from the EPA Air Data: Air Quality Data Collected at Outdoor Monitors across the U.S. (https://www.epa.gov/outdoor-air-quality-data). Weather data are available from the NOAA National Climatic Data Center (https://www.ncdc.noaa.gov/data-access). NCEP Reanalysis data are provided by the NOAA/OAR/ESRL
Keywords
- Air pollution
- Differential probability functions
- Long-term trends