Year-end Sale % OFF 
Abstract
In New York State (NYS), episodic high fine particulate matter (PM2.5) concentrations associated with aerosols originated from the Midwest, Mid-Atlantic, and Pacific Northwest states have been reported. In this study, machine learning techniques, including multiple linear regression (MLR) and artificial neural network (ANN), were used to estimate surface PM2.5 mass concentrations at air quality monitoring sites in NYS during the summers of 2016–2019. Various predictors were considered, including meteorological, aerosol, and geographic predictors. Vertical predictors, designed as the indicators of vertical mixing and aloft aerosols, were also applied. Overall, the ANN models performed better than the MLR models, and the application of vertical predictors generally improved the accuracy of PM2.5 estimation of the ANN models. The leave-one-out cross-validation results showed significant cross-site variations and were able to present the different predictor-PM2.5 correlations at the sites with different PM2.5 characteristics. In addition, a joint analysis of regression coefficients from the MLR model and variable importance from the ANN model provided insights into the contributions of selected predictors to PM2.5 concentrations. The improvements in model performance due to aloft aerosols were relatively minor, probably due to the limited cases of aloft aerosols in current datasets.
Highlights
Fine particulate matter (PM2.5 ) is one of the criteria of air pollutants because of its detrimental impacts on human health and the environment [1,2]
The United States (US) Environmental Protection Agency (EPA) collects real-time air quality measurements collects real-time quality from over surface monitoring sites nationwide maintained by state air or local airmeasurements quality agencies
Under weak advection with no change in aloft aerosols, RPM2.5 could be higher than RAOD, since surface PM2.5 concentration is mainly determined by local emissions
Summary
Fine particulate matter (PM2.5 ) (particulate matter with aerodynamic diameter less than 2.5 μm) is one of the criteria of air pollutants because of its detrimental impacts on human health and the environment [1,2]. In Dutkiewicz et al (2004) [43], 1-year observations of the concentrations of PM2.5 sulfate showed that more than 40% of the high sulfate concentrations were associated with westerly flows and around 20–30% were associated with southwesterly flows, reflecting the influences of transported pollutants from the Midwest and Mid-Atlantic states, respectively. The contribution of such transported pollutants was more significant at rural sites, as up to 60%. Since transported aerosols make significant contributions to the high PM2.5 concentrations in NYS, understandings of the relationships between the vertical mixing of these aloft aerosols and surface PM2.5 concentrations are critical for air quality forecast, monitoring and management. To understand the influences of predictors on the PM2.5 concentration in NYS, the results at monitoring sites with different ambient conditions were discussed
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
