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High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city.

Clark, SN; Kulka, R; Buteau, S; Lavigne, E; Zhang, JJY; Riel-Roberge, C; Smargiassi, A; Weichenthal, S; Van Ryswyk, K (2024) High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city. Environ Pollut, 356. p. 124353. ISSN 1873-6424 https://doi.org/10.1016/j.envpol.2024.124353
SGUL Authors: Clark, Sierra Nicole

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Abstract

The development of high-resolution spatial and spatiotemporal models of air pollutants is essential for exposure science and epidemiological applications. While fixed-site sampling has conventionally provided input data for statistical predictive models, the evolving mobile monitoring method offers improved spatial resolution, ideal for measuring pollutants with high spatial variability such as ultrafine particles (UFP). The Quebec Air Pollution Exposure and Epidemiology (QAPEE) study measured and modelled the spatial and spatiotemporal distributions of understudied pollutants, such as UFPs, black carbon (BC), and brown carbon (BrC), along with fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in Quebec City, Canada. We conducted a combined fixed-site (NO2 and O3) and mobile monitoring (PM2.5, BC, BrC, and UFPs) campaign over 10-months. Mobile monitoring routes were monitored on a weekly basis between 8am-10am and designed using location/allocation modelling. Seasonal fixed-site sampling campaigns captured continuous 24-h measurements over two-week periods. Generalized Additive Models (GAMs), which combined data on pollution concentrations with spatial, temporal, and spatiotemporal predictor variables were used to model and predict concentration surfaces. Annual models for PM2.5, NO2, O3 as well as seven of the smallest size fractions in the UFP range, had high out of sample predictive accuracy (range r2: 0.54-0.86). Varying spatial patterns were observed across UFP size ranges measured as Particle Number Counts (PNC). The monthly spatiotemporal models for PM2.5 (r2 = 0.49), BC (r2 = 0.27), BrC (r2 = 0.29), and PNC (r2 = 0.49) had moderate or moderate-low out of sample predictive accuracy. We conducted a sensitivity analysis and found that the minimum number of 'n visits' (mobile monitoring sessions) required to model annually representative air pollution concentrations was between 24 and 32 visits dependent on the pollutant. This study provides a single source of exposure models for a comprehensive set of air pollutants in Quebec City, Canada. These exposure models will feed into epidemiological research on the health impacts of ambient UFPs and other pollutants.

Item Type: Article
Additional Information: © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Air pollution, Canada, Land use regression, Mobile monitoring, Spatiotemporal, UFP, Environmental Sciences
SGUL Research Institute / Research Centre: Academic Structure > Population Health Research Institute (INPH)
Journal or Publication Title: Environ Pollut
ISSN: 1873-6424
Language: eng
Dates:
DateEvent
20 June 2024Published
10 June 2024Published Online
8 June 2024Accepted
Publisher License: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
PubMed ID: 38866318
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/116604
Publisher's version: https://doi.org/10.1016/j.envpol.2024.124353

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