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

Prashant Kumar; Hamid Omidvarborna; Runming Yao

doi:10.1016/j.scitotenv.2022.160587

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

Sci Total Environ. 2023 Feb 25:861:160587. doi: 10.1016/j.scitotenv.2022.160587. Epub 2022 Dec 5.

Authors

Prashant Kumar¹, Hamid Omidvarborna², Runming Yao³

Affiliations

¹ Global Centre for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, Surrey, United Kingdom; Institute for Sustainability, University of Surrey, Guildford GU2 7XH, Surrey, United Kingdom. Electronic address: P.Kumar@surrey.ac.uk.
² Global Centre for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, Surrey, United Kingdom.
³ School of The Built Environment, University of Reading, RG6 6DF, United Kingdom; Joint International Research Laboratory of Green Buildings and Built Environments (Ministry of Education), School of the Civil Engineering, Chongqing University, Chongqing 400045, China.

PMID: 36470381
DOI: 10.1016/j.scitotenv.2022.160587

Abstract

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 (PM₁, PM_2.5, PM₁₀), gaseous pollutants (NO₂, O₃ 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_2.5 was around three-times the World Health Organisation limit. PM_2.5 and NO₂ 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_2.5 and NO₂, respectively, with complete easing of lockdown. The Prophet algorithm was implemented to develop a prediction model using an NO₂ dataset that performed moderately (R², 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.

Keywords: Air pollution; COVID-19 lockdown; Citizen science; De-weathering; School gate.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
COVID-19* / epidemiology
Communicable Disease Control
Environmental Monitoring
Environmental Pollutants* / analysis
Humans
Nitrogen Dioxide / analysis
Particulate Matter / analysis

Substances

Air Pollutants
Nitrogen Dioxide
Particulate Matter
Environmental Pollutants