The highest mass concentrations of airborne fine particulate matter (PM2.5) and PM-bound sulfate of North America are recorded over the Eastern US, which also exhibits high concentrations of ammonium and elemental carbon. However, the emission scenario has significantly changed during the last decades (e.g., US standards for vehicle emissions, the implementation of the highway diesel fuel sulfur program, use of heating oil containing less than 15 ppm sulfur). This study aims to detect the trends of the most impacting PM2.5 sources across the state of New York in the last decade (2005-2015). PM2.5 chemical speciation data were retrieved from the US-EPA Chemical Speciation Network (CSN, https://aqs.epa.gov/api). PM2.5 samples were collected in 8 sites scattered over the State and characterized by different emission scenarios: 6 urban (Albany, Buffalo, Rochester, New York - Bronx, New York-Division St, New York - Queens) and 2 rural (Pinnacle and Whiteface) background sites. Samples were analyzed for PM2.5 mass, water-soluble inorganic ions (NH4 +, K+, Na+, NO3 -, SO4 2-), elemental (EC) and organic (OC) carbon and elements with atomic number ≥1. The final dataset was split in pre-IMPROVE and post-IMPROVE OC/EC analysis. EPA PMF 5.0 was applied to identify and apportion the most impacting sources on PM2.5 over the sites. Subsequently, the Theil-Sen method coupled with the non-parametric Mann- Kendall approach has been used to analyze the inter-annual trends of extracted PM sources. For example, 9 PM2.5 sources were extracted for Queens all over the period: ammonium sulfate (accounting for 34% of PM2.5mass), ammonium nitrate (17%), gasoline (15%), diesel (10%), biomass burning (6%), aged sea salt (5%), road dust (4%), residual oil (3%) and fresh sea salt (2%). Among the main PM2.5 contributors, a significant decrease was observed for ammonium sulfate (-7% y-1) and ammonium nitrate (-6% y- 1) whereas the emissions associated with gasoline vehicles increased (+6% y-1).

Changes in PM2.5 Sources across New York State during 2005-2015

SQUIZZATO S;MASIOL M;
2017

Abstract

The highest mass concentrations of airborne fine particulate matter (PM2.5) and PM-bound sulfate of North America are recorded over the Eastern US, which also exhibits high concentrations of ammonium and elemental carbon. However, the emission scenario has significantly changed during the last decades (e.g., US standards for vehicle emissions, the implementation of the highway diesel fuel sulfur program, use of heating oil containing less than 15 ppm sulfur). This study aims to detect the trends of the most impacting PM2.5 sources across the state of New York in the last decade (2005-2015). PM2.5 chemical speciation data were retrieved from the US-EPA Chemical Speciation Network (CSN, https://aqs.epa.gov/api). PM2.5 samples were collected in 8 sites scattered over the State and characterized by different emission scenarios: 6 urban (Albany, Buffalo, Rochester, New York - Bronx, New York-Division St, New York - Queens) and 2 rural (Pinnacle and Whiteface) background sites. Samples were analyzed for PM2.5 mass, water-soluble inorganic ions (NH4 +, K+, Na+, NO3 -, SO4 2-), elemental (EC) and organic (OC) carbon and elements with atomic number ≥1. The final dataset was split in pre-IMPROVE and post-IMPROVE OC/EC analysis. EPA PMF 5.0 was applied to identify and apportion the most impacting sources on PM2.5 over the sites. Subsequently, the Theil-Sen method coupled with the non-parametric Mann- Kendall approach has been used to analyze the inter-annual trends of extracted PM sources. For example, 9 PM2.5 sources were extracted for Queens all over the period: ammonium sulfate (accounting for 34% of PM2.5mass), ammonium nitrate (17%), gasoline (15%), diesel (10%), biomass burning (6%), aged sea salt (5%), road dust (4%), residual oil (3%) and fresh sea salt (2%). Among the main PM2.5 contributors, a significant decrease was observed for ammonium sulfate (-7% y-1) and ammonium nitrate (-6% y- 1) whereas the emissions associated with gasoline vehicles increased (+6% y-1).
2017 AAAR Annual Conference Abstracts
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3724890
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