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Indirect Positive Effects of the COVID-19 Pandemic on Air Pollution in Canadian CMAs/CAs

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posted on 2024-06-19, 00:44 authored by Nelson Damba

This paper investigated how the restrictions during the COVID-19 pandemic have affected air pollution within Canadian Census Metropolitan Areas/Census Agglomerations (CMAs/CAs) and provinces/territories in 2020 compared to 2019 (pre-pandemic). It explored the connection based on emission levels of two air pollutants (NO and PM ) obtained from the National Air Pollution 2 2.5 Surveillance Program (NAPS) website, pandemic restrictions in Canadian cities obtained from COVID19 Government Measures Dataset, and socio-demographic factors from the 2016 Census which are the pollutants causes. This secondary data compiled in Excel database was used within multiple analyses which included, descriptives, pairwise t-test, correlation, stepwise regression done in SPSS, and using GIS methods to map the final results. The results showed a reduction in NO emi2sions in many CMAs/CAs and provinces, but increased PM emiss2.5s in 31 CMAs/CAs and 8 provinces. Reductions in NO 2nd PM wer2.5ost prominent in March and April of 2020 when the most stringent COVID-19 restrictions were implemented. The overall results showed a small but significant connection between the air pollutants and the few socio-demographic variables chosen through the stepwise technique (i.e. commuting time, public transit, and work location) contributed to the connection. These results corroborate findings from previous studies on air pollution and mobility restrictions and indicate that strategies such as remote work, and conversion to environmentally friendly modes of transportation (hybrid vehicles) can be leveraged to reducing NO and PM emissions in the future. These strategies 2 2.5 could especially be beneficial to the provinces and CMAs/CAs with high PM emissi2.5. Lack of data limited how thorough the analysis of the topic could be conducted.

History

Language

English

Degree

  • Master of Spatial Analysis

Program

  • Spatial Analysis

Granting Institution

Ryerson University

LAC Thesis Type

  • MRP

Thesis Advisor

Tor Oiamo

Year

2022

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    Spatial Analysis (Theses)

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