Distinguishing Air Pollution Due to Stagnation, Local Emissions, and Long-Range Transport Using a Generalized Additive Model to Analyze Hourly Monitoring Data

Benjamin de Foy (7543262)
Md. Golam Saroar (11313323)
Abdus Salam (1918387)
James J. Schauer (415196)

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Publication date

January 1753

DOI

10.1021/acsearthspacechem.1c00206.s001

Publisher

American Chemical Society (ACS)

Abstract

Long-term surface monitoring of hourly <2.5 μm diameter particulate matter (PM2.5) concentrations in megacities provides an essential record to estimate adverse health effects experienced by many people. In addition, hidden from immediate view, the time series of measurements contains a wealth of information on the types of sources and the impact of meteorology. A generalized additive model (GAM) was developed to analyze multiple years of hourly data in order to distinguish the sources leading to adverse air quality and the response of the concentrations to boundary layer mixing, wind transport, and precipitation. The model estimates the impact of long-range transport by using clusters of back-trajectories and a trajectory cluster contribut...

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Distinguishing Air Pollution Due to Stagnation, Local Emissions, and Long-Range Transport Using a Generalized Additive Model to Analyze Hourly Monitoring Data

Abstract

Extracted data

Distinguishing Air Pollution Due to Stagnation, Local Emissions, and Long-Range Transport Using a Generalized Additive Model to Analyze Hourly Monitoring Data

Abstract

Extracted data

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