Published November 27, 2023
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A methodological framework for estimating ambient PM2.5 particulate matter concentrations in the UK.

Creators

  • 1. Department of Atmospheric Pollution, National Centre for Environment Health, Health Institute Carlos III. Ctra. Majadahonda a Pozuelo km 2.2, Madrid 28220, Spain.
  • 2. Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan; Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan.
  • 3. Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan.
  • 4. School of Architecture, Building and Civil Engineering, Loughborough University, Leicestershire LE11 3TU, United Kingdom.
  • 5. Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan.
  • 6. Neotectonics and Natural Hazards Institute, RWTH Aachen University, Aachen 52056, Germany; UNESCO Chair on Coastal Geo-Hazard Analysis, Research Institute for Earth Sciences, Tehran 13185-1494, Iran; Water, Sediment, Hazards, and Earth-surface Dynamics (waterSHED) Lab, Department of Geoscience, University of Calgary, Calgary Alberta T2N 1N4, Canada.
  • 7. RWTH Aachen University
  • 8. University of Calgary
  • 9. Department of Climatology, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar 9617976487, Iran.
  • 10. Laser Chemistry Research Group, Department of Analytical Chemistry, Faculty of Chemistry, Complutense University of Madrid, Plaza de Ciencias 1, Madrid 28040, Spain; Analytical Chemistry Department, FCNET, University of Panama, University City, University Mail, Panama 4, Panama City 3366, Panama.
  • 11. Complutense University of Madrid
  • 12. Laser Chemistry Research Group, Department of Analytical Chemistry, Faculty of Chemistry, Complutense University of Madrid, Plaza de Ciencias 1, Madrid 28040, Spain.
  • 13. 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, United Kingdom.
  • 14. Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan. Electronic address: jong.kim@nu.edu.kz.

Description

Scientific evidence sustains PM2.5 particles' inhalation may generate harmful impacts on human beings' health; therefore, their monitoring in ambient air is of paramount relevance in terms of public health. Due to the limited number of fixed stations within the air quality monitoring networks, development of methodological frameworks to model ambient air PM2.5 particles is primordial to providing additional information on PM2.5 exposure and its trends. In this sense, this work aims to offer a global easily-applicable tool to estimate ambient air PM2.5 as a function of meteorological conditions using a multivariate analysis. Daily PM2.5 data measured by 84 fixed monitoring stations and meteorological data from ERA5 (ECMWF Reanalysis v5) reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach. Data from January 2017 to December 2020 were employed to build a mathematical expression that related the dependent variable (PM2.5) to predictor ones (sea-level pressure, planetary boundary layer height, temperature, precipitation, wind direction and speed), while 2021 data tested the model. Evaluation indicators evidenced a good performance of model (maximum values of RMSE, MAE and MAPE: 1.80 µg/m3, 3.24 µg/m3, and 20.63%, respectively), compiling the current legislation's requirements for modelling ambient air PM2.5 concentrations. A retrospective analysis of meteorological features allowed estimating ambient air PM2.5 concentrations from 2000 to 2021. The highest PM2.5 concentrations relapsed in the Mid- and Southlands, while Northlands sustained the lowest concentrations.
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