On the Correlations between Particulate Matter: Comparison between Annual/Monthly Concentrations and PM<sub>10</sub>/PM<sub>2.5</sub>
Annual concentration is a key element to assess the air quality of an area for long-time exposure effects. Nonetheless, obtaining annual concentrations from sensors is costly since it needs to have a year of measurements for each required pollutant. To overcome this issue, several strategies are stu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/2/385 |
| _version_ | 1797622447658237952 |
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| author | Xavier Jurado Nicolas Reiminger Loïc Maurer José Vazquez Cédric Wemmert |
| author_facet | Xavier Jurado Nicolas Reiminger Loïc Maurer José Vazquez Cédric Wemmert |
| author_sort | Xavier Jurado |
| collection | DOAJ |
| description | Annual concentration is a key element to assess the air quality of an area for long-time exposure effects. Nonetheless, obtaining annual concentrations from sensors is costly since it needs to have a year of measurements for each required pollutant. To overcome this issue, several strategies are studied to assess annual particulate matter concentration from monthly data, with their pros and cons depending on the risk acceptance and measurement campaign costs. When applied on a French dataset, the error spans from 12–14% with one month of measurement to 4–6% for six months of measurement for PM<sub>10</sub> and PM<sub>2.5</sub>, respectively. A relationship between the mean relative error and 95th percentile relative error is provided with an R<sup>2</sup> of 0.99. The relationship between PM<sub>10</sub> and PM<sub>2.5</sub> was also investigated and improved compared to previous work by considering the seasonality and influence on emission reaching a mean relative error of 12%. Thus, this study provides tools for urban planners, engineers, researchers, and public authorities for improved monitoring of annual air pollution at a lower cost for particulate matter. |
| first_indexed | 2024-03-11T09:10:30Z |
| format | Article |
| id | doaj.art-e11451de3fed47c0bc62acf900aa43fe |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T09:10:30Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-e11451de3fed47c0bc62acf900aa43fe2023-11-16T19:04:07ZengMDPI AGAtmosphere2073-44332023-02-0114238510.3390/atmos14020385On the Correlations between Particulate Matter: Comparison between Annual/Monthly Concentrations and PM<sub>10</sub>/PM<sub>2.5</sub>Xavier Jurado0Nicolas Reiminger1Loïc Maurer2José Vazquez3Cédric Wemmert4AIR&D, 32 rue Wimpheling, 67000 Strasbourg, FranceAIR&D, 32 rue Wimpheling, 67000 Strasbourg, FranceDepartment of Effect-Directed Analysis, Helmholtz Centre for Environmental Research—UFZ, Permoserstraße 15, 04318 Leipzig, GermanyICUBE Laboratory, CNRS/University of Strasbourg, 67000 Strasbourg, FranceICUBE Laboratory, CNRS/University of Strasbourg, 67000 Strasbourg, FranceAnnual concentration is a key element to assess the air quality of an area for long-time exposure effects. Nonetheless, obtaining annual concentrations from sensors is costly since it needs to have a year of measurements for each required pollutant. To overcome this issue, several strategies are studied to assess annual particulate matter concentration from monthly data, with their pros and cons depending on the risk acceptance and measurement campaign costs. When applied on a French dataset, the error spans from 12–14% with one month of measurement to 4–6% for six months of measurement for PM<sub>10</sub> and PM<sub>2.5</sub>, respectively. A relationship between the mean relative error and 95th percentile relative error is provided with an R<sup>2</sup> of 0.99. The relationship between PM<sub>10</sub> and PM<sub>2.5</sub> was also investigated and improved compared to previous work by considering the seasonality and influence on emission reaching a mean relative error of 12%. Thus, this study provides tools for urban planners, engineers, researchers, and public authorities for improved monitoring of annual air pollution at a lower cost for particulate matter.https://www.mdpi.com/2073-4433/14/2/385particulate matterair pollutionannual concentration assessmentmonthly measurementssensor data analysis |
| spellingShingle | Xavier Jurado Nicolas Reiminger Loïc Maurer José Vazquez Cédric Wemmert On the Correlations between Particulate Matter: Comparison between Annual/Monthly Concentrations and PM<sub>10</sub>/PM<sub>2.5</sub> Atmosphere particulate matter air pollution annual concentration assessment monthly measurements sensor data analysis |
| title | On the Correlations between Particulate Matter: Comparison between Annual/Monthly Concentrations and PM<sub>10</sub>/PM<sub>2.5</sub> |
| title_full | On the Correlations between Particulate Matter: Comparison between Annual/Monthly Concentrations and PM<sub>10</sub>/PM<sub>2.5</sub> |
| title_fullStr | On the Correlations between Particulate Matter: Comparison between Annual/Monthly Concentrations and PM<sub>10</sub>/PM<sub>2.5</sub> |
| title_full_unstemmed | On the Correlations between Particulate Matter: Comparison between Annual/Monthly Concentrations and PM<sub>10</sub>/PM<sub>2.5</sub> |
| title_short | On the Correlations between Particulate Matter: Comparison between Annual/Monthly Concentrations and PM<sub>10</sub>/PM<sub>2.5</sub> |
| title_sort | on the correlations between particulate matter comparison between annual monthly concentrations and pm sub 10 sub pm sub 2 5 sub |
| topic | particulate matter air pollution annual concentration assessment monthly measurements sensor data analysis |
| url | https://www.mdpi.com/2073-4433/14/2/385 |
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