Presentation Title

What's in the air? Using Mathematical Models to Predict Boston Air Quality

Faculty Mentor

Wiewei Pan

Start Date

18-11-2017 9:45 AM

End Date

18-11-2017 10:00 AM

Location

9-285

Session

Physical Sciences 2

Type of Presentation

Oral Talk

Subject Area

physical_mathematical_sciences

Abstract

Exposure to pollutants such as NO2, SO2, and PM2.5 are a significant concern, especially for those living in large cities. However, most major cities have five or fewer active air quality sensors. Various studies have shown that geostatistical models using traffic count, elevation, and land cover as variables can predict pollutant levels with high accuracy. However, collecting training data containing sufficient geospatial variation often involves large scale deployment of sensors over the area of interest. In this study, we trained geospatial and spatio-temporal models for three EPA criteria pollutants - NO2, SO2, and PM2.5 - using data collected from 398 counties across the US and applied the models to produce intra-urban pollution concentration levels for a 107.495 square mile region covering the Greater Boston area. The performance of our geospatial model (Land Use Regression) and spatio-temporal model (Gaussian Process) were found to be comparable of similar models in literature. Our study addresses also the public health challenge of effectively and meaningfully communicating scientific findings in environmental science to the general public. Specifically, we designed an interactive web interface for visualizing our Boston air pollution predictions. This interface serves as a proof-of-concept for an accessible, educational, and scientific tool.

This document is currently not available here.

Share

COinS
 
Nov 18th, 9:45 AM Nov 18th, 10:00 AM

What's in the air? Using Mathematical Models to Predict Boston Air Quality

9-285

Exposure to pollutants such as NO2, SO2, and PM2.5 are a significant concern, especially for those living in large cities. However, most major cities have five or fewer active air quality sensors. Various studies have shown that geostatistical models using traffic count, elevation, and land cover as variables can predict pollutant levels with high accuracy. However, collecting training data containing sufficient geospatial variation often involves large scale deployment of sensors over the area of interest. In this study, we trained geospatial and spatio-temporal models for three EPA criteria pollutants - NO2, SO2, and PM2.5 - using data collected from 398 counties across the US and applied the models to produce intra-urban pollution concentration levels for a 107.495 square mile region covering the Greater Boston area. The performance of our geospatial model (Land Use Regression) and spatio-temporal model (Gaussian Process) were found to be comparable of similar models in literature. Our study addresses also the public health challenge of effectively and meaningfully communicating scientific findings in environmental science to the general public. Specifically, we designed an interactive web interface for visualizing our Boston air pollution predictions. This interface serves as a proof-of-concept for an accessible, educational, and scientific tool.