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A methodological framework for estimating ambient PM2.5 particulate matter concentrations in the UK.

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Title: A methodological framework for estimating ambient PM2.5 particulate matter concentrations in the UK.
Authors: Galán-Madruga, David1 (AUTHOR), Broomandi, Parya1,2,3 (AUTHOR), Satyanaga, Alfrendo2 (AUTHOR), Jahanbakhshi, Ali4 (AUTHOR), Bagheri, Mehdi3 (AUTHOR), Fathian, Aram5,6,7 (AUTHOR), Sarvestan, Rasoul8 (AUTHOR), Cárdenas-Escudero, J.9,10 (AUTHOR), Cáceres, J.O.9 (AUTHOR), Kumar, Prashant11,12 (AUTHOR), Kim, Jong Ryeol1,2 (AUTHOR) jong.kim@nu.edu.kz
Source: Journal of Environmental Sciences (Elsevier). Apr2025, Vol. 150, p676-691. 16p.
Subject Terms: *Air quality monitoring stations, *Particulate matter, Atmospheric boundary layer, Meteorological stations, Spring
Abstract: • Mathematical equations clarified the association between PM 2.5 and meteorology. • Suggested model to predict PM 2.5 levels was tested in UK and Madrid City. • Compiling current legislation's requirements, the model performed well. • Historical PM 2.5 concentrations were estimated in the UK from 2000 to 2021. • Lowest PM 2.5 levels occurred in Northlands, particularly in summer and autumn. • Highest seasonally PM 2.5 levels were in spring and winter. Scientific evidence sustains PM 2.5 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 PM 2.5 particles is primordial to providing additional information on PM 2.5 exposure and its trends. In this sense, this work aims to offer a global easily-applicable tool to estimate ambient air PM 2.5 as a function of meteorological conditions using a multivariate analysis. Daily PM 2.5 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 (PM 2.5) 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 PM 2.5 concentrations. A retrospective analysis of meteorological features allowed estimating ambient air PM 2.5 concentrations from 2000 to 2021. The highest PM 2.5 concentrations relapsed in the Mid- and Southlands, while Northlands sustained the lowest concentrations. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Environmental Sciences (Elsevier) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A methodological framework for estimating ambient PM2.5 particulate matter concentrations in the UK.
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  Data: <searchLink fieldCode="AR" term="%22Galán-Madruga%2C+David%22">Galán-Madruga, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Broomandi%2C+Parya%22">Broomandi, Parya</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Satyanaga%2C+Alfrendo%22">Satyanaga, Alfrendo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jahanbakhshi%2C+Ali%22">Jahanbakhshi, Ali</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bagheri%2C+Mehdi%22">Bagheri, Mehdi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fathian%2C+Aram%22">Fathian, Aram</searchLink><relatesTo>5,6,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sarvestan%2C+Rasoul%22">Sarvestan, Rasoul</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cárdenas-Escudero%2C+J%2E%22">Cárdenas-Escudero, J.</searchLink><relatesTo>9,10</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cáceres%2C+J%2EO%2E%22">Cáceres, J.O.</searchLink><relatesTo>9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kumar%2C+Prashant%22">Kumar, Prashant</searchLink><relatesTo>11,12</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Jong+Ryeol%22">Kim, Jong Ryeol</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jong.kim@nu.edu.kz</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Environmental+Sciences+%28Elsevier%29%22">Journal of Environmental Sciences (Elsevier)</searchLink>. Apr2025, Vol. 150, p676-691. 16p.
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  Data: *<searchLink fieldCode="DE" term="%22Air+quality+monitoring+stations%22">Air quality monitoring stations</searchLink><br />*<searchLink fieldCode="DE" term="%22Particulate+matter%22">Particulate matter</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+boundary+layer%22">Atmospheric boundary layer</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorological+stations%22">Meteorological stations</searchLink><br /><searchLink fieldCode="DE" term="%22Spring%22">Spring</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: • Mathematical equations clarified the association between PM 2.5 and meteorology. • Suggested model to predict PM 2.5 levels was tested in UK and Madrid City. • Compiling current legislation's requirements, the model performed well. • Historical PM 2.5 concentrations were estimated in the UK from 2000 to 2021. • Lowest PM 2.5 levels occurred in Northlands, particularly in summer and autumn. • Highest seasonally PM 2.5 levels were in spring and winter. Scientific evidence sustains PM 2.5 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 PM 2.5 particles is primordial to providing additional information on PM 2.5 exposure and its trends. In this sense, this work aims to offer a global easily-applicable tool to estimate ambient air PM 2.5 as a function of meteorological conditions using a multivariate analysis. Daily PM 2.5 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 (PM 2.5) 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 PM 2.5 concentrations. A retrospective analysis of meteorological features allowed estimating ambient air PM 2.5 concentrations from 2000 to 2021. The highest PM 2.5 concentrations relapsed in the Mid- and Southlands, while Northlands sustained the lowest concentrations. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Environmental Sciences (Elsevier) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.jes.2023.11.019
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        Text: English
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