Spatial heterogeneity of air pollution statistics in Europe
Abstract Air pollution is one of the leading causes of death globally, and continues to have a detrimental effect on our health. In light of these impacts, an extensive range of statistical modelling approaches has been devised in order to better understand air pollution statistics. However, the tim...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-16109-2 |
| _version_ | 1811293850010910720 |
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| author | Hankun He Benjamin Schäfer Christian Beck |
| author_facet | Hankun He Benjamin Schäfer Christian Beck |
| author_sort | Hankun He |
| collection | DOAJ |
| description | Abstract Air pollution is one of the leading causes of death globally, and continues to have a detrimental effect on our health. In light of these impacts, an extensive range of statistical modelling approaches has been devised in order to better understand air pollution statistics. However, the time-varying statistics of different types of air pollutants are far from being fully understood. The observed probability density functions (PDFs) of concentrations depend very much on the spatial location and on the pollutant substance. In this paper, we analyse a large variety of data from 3544 different European monitoring sites and show that the PDFs of nitric oxide (NO), nitrogen dioxide ( $$NO_2$$ N O 2 ) and particulate matter ( $$PM_{10}$$ P M 10 and $$PM_{2.5}$$ P M 2.5 ) concentrations generically exhibit heavy tails and are asymptotically well approximated by q-exponential distributions with a given width parameter $$\lambda$$ λ . We observe that the power-law parameter q and the width parameter $$\lambda$$ λ vary widely for the different spatial locations. For each substance, we find different patterns of parameter clouds in the $$(q, \lambda )$$ ( q , λ ) plane. These depend on the type of pollutants and on the environmental characteristics (urban/suburban/rural/traffic/industrial/background). This means the effective statistical physics description of air pollution exhibits a strong degree of spatial heterogeneity. |
| first_indexed | 2024-04-13T05:07:39Z |
| format | Article |
| id | doaj.art-dd65cc9086e14783bfbd2b5d90b4d4bb |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T05:07:39Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-dd65cc9086e14783bfbd2b5d90b4d4bb2022-12-22T03:01:07ZengNature PortfolioScientific Reports2045-23222022-07-0112111210.1038/s41598-022-16109-2Spatial heterogeneity of air pollution statistics in EuropeHankun He0Benjamin Schäfer1Christian Beck2School of Mathematical Sciences, Queen Mary University of LondonSchool of Mathematical Sciences, Queen Mary University of LondonSchool of Mathematical Sciences, Queen Mary University of LondonAbstract Air pollution is one of the leading causes of death globally, and continues to have a detrimental effect on our health. In light of these impacts, an extensive range of statistical modelling approaches has been devised in order to better understand air pollution statistics. However, the time-varying statistics of different types of air pollutants are far from being fully understood. The observed probability density functions (PDFs) of concentrations depend very much on the spatial location and on the pollutant substance. In this paper, we analyse a large variety of data from 3544 different European monitoring sites and show that the PDFs of nitric oxide (NO), nitrogen dioxide ( $$NO_2$$ N O 2 ) and particulate matter ( $$PM_{10}$$ P M 10 and $$PM_{2.5}$$ P M 2.5 ) concentrations generically exhibit heavy tails and are asymptotically well approximated by q-exponential distributions with a given width parameter $$\lambda$$ λ . We observe that the power-law parameter q and the width parameter $$\lambda$$ λ vary widely for the different spatial locations. For each substance, we find different patterns of parameter clouds in the $$(q, \lambda )$$ ( q , λ ) plane. These depend on the type of pollutants and on the environmental characteristics (urban/suburban/rural/traffic/industrial/background). This means the effective statistical physics description of air pollution exhibits a strong degree of spatial heterogeneity.https://doi.org/10.1038/s41598-022-16109-2 |
| spellingShingle | Hankun He Benjamin Schäfer Christian Beck Spatial heterogeneity of air pollution statistics in Europe Scientific Reports |
| title | Spatial heterogeneity of air pollution statistics in Europe |
| title_full | Spatial heterogeneity of air pollution statistics in Europe |
| title_fullStr | Spatial heterogeneity of air pollution statistics in Europe |
| title_full_unstemmed | Spatial heterogeneity of air pollution statistics in Europe |
| title_short | Spatial heterogeneity of air pollution statistics in Europe |
| title_sort | spatial heterogeneity of air pollution statistics in europe |
| url | https://doi.org/10.1038/s41598-022-16109-2 |
| work_keys_str_mv | AT hankunhe spatialheterogeneityofairpollutionstatisticsineurope AT benjaminschafer spatialheterogeneityofairpollutionstatisticsineurope AT christianbeck spatialheterogeneityofairpollutionstatisticsineurope |