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A parent-school initiative to assess and predict air quality around a heavily trafficked school.
Kumar, Prashant; Omidvarborna, Hamid; Yao, Runming.
  • Kumar P; 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, United Kingdom; Institute for Sustainability, University of Surrey, Guildford GU2 7XH, Surrey, United Kingdom. Electronic address: P.Kumar@surrey.ac.uk.
  • Omidvarborna H; 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, United Kingdom.
  • Yao R; 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.
Sci Total Environ ; : 160587, 2022 Dec 02.
Article in English | MEDLINE | ID: covidwho-2242377
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, PM2.5, 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 when, owing to new year celebration fireworks, the daily average PM2.5 was around three-times the World Health Organisation limit. PM2.5 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 PM2.5 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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Sci Total Environ Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Sci Total Environ Year: 2022 Document Type: Article