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A parent-school initiative to assess and predict air quality around a heavily trafficked school

Kumar, P., Omidvarborna, H. and Yao, R. ORCID: (2023) A parent-school initiative to assess and predict air quality around a heavily trafficked school. Science of the Total Environment, 861. 160587. ISSN 0048-9697

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To link to this item DOI: 10.1016/j.scitotenv.2022.160587


Many primary schools in the UK are situated in close proximity to heavily-trafficked roads, yet long-term air pollution monitoring around such schools to establish factors affecting the variability of exposure is limited. We co-designed a study to monitor particulate matter in different size fractions (PM , PM , PM ), gaseous pollutants (NO , O and CO) and meteorological parameters (ambient temperature, relative humidity) over a period of one year. The period included phases of national COVID-19 lockdown and its subsequent easing and removal. Statistical analysis was used to assess the diurnal patterns, pollution hotspots and underlying factors driving changes. A pollution episode was observed early in January 2021, owing to new year celebration fireworks, when the daily average PM was around three-times the World Health Organisation limit. PM and NO exceeded the threshold limits on 15 and 10 days, respectively, as the lockdown eased and the school reopened, despite the predominant wind direction often being away from the school towards the roads. The peak concentration levels for all pollutants occurred during morning drop-off hours; however, some weekends showed higher or comparable concentrations to those during weekdays. The strong disproportional Pearson correlation between CO and temperature demonstrated the possible contribution of local sources through biomass burning. The impact of lifting restrictions after removing the weather impact showed that the average pollution levels were low in the beginning and increased by up to 22.7 % and 4.2 % for PM and NO , respectively, with complete easing of lockdown. The Prophet algorithm was implemented to develop a prediction model using an NO dataset that performed moderately (R , 0.48) for a new monthly dataset. This study was able to build a local air pollution database at a school gate, which enabled an understanding of the air pollution variability across the year and allowed evidence-based mitigation strategies to be devised.

Item Type:Article
Divisions:Science > School of the Built Environment > Construction Management and Engineering
ID Code:109434
Uncontrolled Keywords:COVID-19 lockdown, Citizen science, School gate, De-weathering, Air pollution


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