Issue 6, 2022

Application of machine learning and statistical modeling to identify sources of air pollutant levels in Kitchener, Ontario, Canada

Abstract

Machine learning is used across many disciplines to identify complex relations between outcomes and numerous potential predictors. In the case of air quality research in heavily populated urban centers, such techniques were used to correlate the impacts of Traffic-Related Air Pollutants (TRAP) on vulnerable members of communities, future pollutant levels, and potential solutions that mitigate adverse effects of poor air quality. However, machine learning tools have not been used to assess the variables that influence measured pollutant levels in a suburban environment. The objective of this study is to apply a novel combination of Random Forest (RF) modeling, a machine learning algorithm, and statistical significance analysis to assess the impacts of anthropogenic and meteorological variables on observed pollutant levels in two separate datasets collected during and after the COVID-19 lockdowns in Kitchener, Ontario, Canada. The results highlight that TRAP levels studied here are linked to meteorology and traffic count/type, with relatively higher sensitivity to the former. Upon taking statistical significance into account when assessing relative importance of variables affecting pollutant levels, our study found that traffic variables had a more discernible influence than many meteorological variables. Additional studies with a larger dataset and spread throughout the year are needed to expand upon these initial findings. The proposed approach outlines a “blueprint” method of quantifying the importance of traffic in mid-size cities experiencing fast population growth and development.

Graphical abstract: Application of machine learning and statistical modeling to identify sources of air pollutant levels in Kitchener, Ontario, Canada

Supplementary files

Article information

Article type
Paper
Submitted
11 Jul 2022
Accepted
05 Okt 2022
First published
06 Okt 2022
This article is Open Access
Creative Commons BY-NC license

Environ. Sci.: Atmos., 2022,2, 1389-1399

Application of machine learning and statistical modeling to identify sources of air pollutant levels in Kitchener, Ontario, Canada

W. Mohammed, A. Adamescu, L. Neil, N. Shantz, T. Townend, M. Lysy and H. A. Al-Abadleh, Environ. Sci.: Atmos., 2022, 2, 1389 DOI: 10.1039/D2EA00084A

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements