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Non-linearity of secondary pollutant formation estimated from emissions data and measured precursor-secondary pollutant relationships

In order to predict the impacts of reductions in air pollutant emissions, it is important to know whether secondary pollutant concentrations will decline in direct proportion to the reduction in their precursor, referred to as linearity. Trends in airborne concentrations of nitrate, sulfate, and SOC...

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Autores principales: Harrison, Roy M., Beddows, David C. S., Tong, Chengxu, Damayanti, Seny
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471023/
https://www.ncbi.nlm.nih.gov/pubmed/36120117
http://dx.doi.org/10.1038/s41612-022-00297-9
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author Harrison, Roy M.
Beddows, David C. S.
Tong, Chengxu
Damayanti, Seny
author_facet Harrison, Roy M.
Beddows, David C. S.
Tong, Chengxu
Damayanti, Seny
author_sort Harrison, Roy M.
collection PubMed
description In order to predict the impacts of reductions in air pollutant emissions, it is important to know whether secondary pollutant concentrations will decline in direct proportion to the reduction in their precursor, referred to as linearity. Trends in airborne concentrations of nitrate, sulfate, and SOC at sites in southern England are compared with emissions and concentration trends for sulfur dioxide (SO(2)), oxides of nitrogen (NO(x)), and non-methane VOC, and show some increased ratios of concentrations to emissions, strongly suggestive of non-linearity in the primary-secondary pollutant relationships for nitrate, but not the other pollutants. Analysis of a further 20-year dataset from the AGANET network shows a decline of nitrate concentrations significantly lower than that of NO(x) emissions and ambient NO(x) concentrations. For sulfate, the decline lies between that of emissions and airborne concentrations of SO(2). Back trajectory analysis and Potential Source Contribution Function mapping for 2014–2018 show that the highest concentrations of secondary constituents in southern England are associated with air masses originating in mainland Europe, with 42% of sulfate, 55% of nitrate, and 35% of SOC estimated to be associated with air masses entering the UK from the European mainland.
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spelling pubmed-94710232022-09-14 Non-linearity of secondary pollutant formation estimated from emissions data and measured precursor-secondary pollutant relationships Harrison, Roy M. Beddows, David C. S. Tong, Chengxu Damayanti, Seny NPJ Clim Atmos Sci Article In order to predict the impacts of reductions in air pollutant emissions, it is important to know whether secondary pollutant concentrations will decline in direct proportion to the reduction in their precursor, referred to as linearity. Trends in airborne concentrations of nitrate, sulfate, and SOC at sites in southern England are compared with emissions and concentration trends for sulfur dioxide (SO(2)), oxides of nitrogen (NO(x)), and non-methane VOC, and show some increased ratios of concentrations to emissions, strongly suggestive of non-linearity in the primary-secondary pollutant relationships for nitrate, but not the other pollutants. Analysis of a further 20-year dataset from the AGANET network shows a decline of nitrate concentrations significantly lower than that of NO(x) emissions and ambient NO(x) concentrations. For sulfate, the decline lies between that of emissions and airborne concentrations of SO(2). Back trajectory analysis and Potential Source Contribution Function mapping for 2014–2018 show that the highest concentrations of secondary constituents in southern England are associated with air masses originating in mainland Europe, with 42% of sulfate, 55% of nitrate, and 35% of SOC estimated to be associated with air masses entering the UK from the European mainland. Nature Publishing Group UK 2022-09-14 2022 /pmc/articles/PMC9471023/ /pubmed/36120117 http://dx.doi.org/10.1038/s41612-022-00297-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Harrison, Roy M.
Beddows, David C. S.
Tong, Chengxu
Damayanti, Seny
Non-linearity of secondary pollutant formation estimated from emissions data and measured precursor-secondary pollutant relationships
title Non-linearity of secondary pollutant formation estimated from emissions data and measured precursor-secondary pollutant relationships
title_full Non-linearity of secondary pollutant formation estimated from emissions data and measured precursor-secondary pollutant relationships
title_fullStr Non-linearity of secondary pollutant formation estimated from emissions data and measured precursor-secondary pollutant relationships
title_full_unstemmed Non-linearity of secondary pollutant formation estimated from emissions data and measured precursor-secondary pollutant relationships
title_short Non-linearity of secondary pollutant formation estimated from emissions data and measured precursor-secondary pollutant relationships
title_sort non-linearity of secondary pollutant formation estimated from emissions data and measured precursor-secondary pollutant relationships
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471023/
https://www.ncbi.nlm.nih.gov/pubmed/36120117
http://dx.doi.org/10.1038/s41612-022-00297-9
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