Evaluating Ultrafine Particles and PM2.5 in Microenvironments with Health Perspectives: Variability in Concentrations and Pollutant Interrelationships
Abstract Regulation has been applied to the fine particles (PM2.5) but not to particle number concentrations (PNC). We use a mobile platform to measure PNC and PM2.5 in four microenvironments (diesel plume, urban freeway, urban street, and rural freeway). A total of 38661 pairs of measurements in tw...
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Springer
2023-07-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.230046 |
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| author | Sheng Xiang Shaojun Zhang Yu Ting Yu Hui Wang Ye Deng Qinwen Tan Zihang Zhou Ye Wu |
| author_facet | Sheng Xiang Shaojun Zhang Yu Ting Yu Hui Wang Ye Deng Qinwen Tan Zihang Zhou Ye Wu |
| author_sort | Sheng Xiang |
| collection | DOAJ |
| description | Abstract Regulation has been applied to the fine particles (PM2.5) but not to particle number concentrations (PNC). We use a mobile platform to measure PNC and PM2.5 in four microenvironments (diesel plume, urban freeway, urban street, and rural freeway). A total of 38661 pairs of measurements in two years (winter 2018 and autumn 2020) are used to evaluate variability in the pollutant concentrations and their interrelationships. Source-discerned total PNC (PNCtot) and temporal-adjusted PM2.5 (ΔPM2.5) are calculated and evaluated. Results showed that the average PNCtot in winter (4.8 × 104 pt cm−3) were over two times higher than autumn (0.36 × 104−0.56 × 104 pt cm−3). Moreover, the traffic emissions (PNCd,tr) contribute 30% of the PNC throughout the study while solid fuel burning (PNCd,sfb) could be a major contributor only in winter (29%). Seasonal variability in PNCd,tr and PNCd,sfb was found, with 2–3 times higher median PNCd,tr and 7 times higher median PNCd,sfb in winter compared to autumn. Similarly, PM2.5 in winter (109 µg m−3) was 3–5 times higher than autumn, while ΔPM2.5 (40 µg m−3) was 3–6 times higher. In winter, the PM2.5 and ΔPM2.5 showed higher concentrations in urban street and rural freeway similar to PNCd,sfb but opposite to the trend of PNCtot and PNCd,tr. The correlation coefficient (R2) is investigated as three combinations (i.e., PNCtot vs. PM2.5, PNCd,tr vs. ΔPM2.5, PNCd,sfb vs. ΔPM2.5). Here, the R2 showed a comparable seasonal trend (winter lower than autumn) and similar magnitude as the literature, but no strong correlation (R2 < 0.15) was found. This stresses the fact that mitigation measures of PM2.5 do not necessarily reduce PNC and monitoring networks evaluate PM2.5 exposure are unlikely to represent PNC exposure. The concentration ratios in the three combinations are found to vary with microenvironments and seasons. This variability implies that control policies should be diversified with pollutant types and energy usage of the city. |
| format | Article |
| id | doaj-art-97a4b37b8bba411cbaa69b9d99d610bf |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | Springer |
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| series | Aerosol and Air Quality Research |
| spelling | doaj-art-97a4b37b8bba411cbaa69b9d99d610bf2025-02-09T12:23:22ZengSpringerAerosol and Air Quality Research1680-85842071-14092023-07-0123911910.4209/aaqr.230046Evaluating Ultrafine Particles and PM2.5 in Microenvironments with Health Perspectives: Variability in Concentrations and Pollutant InterrelationshipsSheng Xiang0Shaojun Zhang1Yu Ting Yu2Hui Wang3Ye Deng4Qinwen Tan5Zihang Zhou6Ye Wu7School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua UniversitySchool of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua UniversitySchool of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua UniversitySchool of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua UniversityChengdu Academy of Environmental SciencesChengdu Academy of Environmental SciencesChengdu Academy of Environmental SciencesSchool of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua UniversityAbstract Regulation has been applied to the fine particles (PM2.5) but not to particle number concentrations (PNC). We use a mobile platform to measure PNC and PM2.5 in four microenvironments (diesel plume, urban freeway, urban street, and rural freeway). A total of 38661 pairs of measurements in two years (winter 2018 and autumn 2020) are used to evaluate variability in the pollutant concentrations and their interrelationships. Source-discerned total PNC (PNCtot) and temporal-adjusted PM2.5 (ΔPM2.5) are calculated and evaluated. Results showed that the average PNCtot in winter (4.8 × 104 pt cm−3) were over two times higher than autumn (0.36 × 104−0.56 × 104 pt cm−3). Moreover, the traffic emissions (PNCd,tr) contribute 30% of the PNC throughout the study while solid fuel burning (PNCd,sfb) could be a major contributor only in winter (29%). Seasonal variability in PNCd,tr and PNCd,sfb was found, with 2–3 times higher median PNCd,tr and 7 times higher median PNCd,sfb in winter compared to autumn. Similarly, PM2.5 in winter (109 µg m−3) was 3–5 times higher than autumn, while ΔPM2.5 (40 µg m−3) was 3–6 times higher. In winter, the PM2.5 and ΔPM2.5 showed higher concentrations in urban street and rural freeway similar to PNCd,sfb but opposite to the trend of PNCtot and PNCd,tr. The correlation coefficient (R2) is investigated as three combinations (i.e., PNCtot vs. PM2.5, PNCd,tr vs. ΔPM2.5, PNCd,sfb vs. ΔPM2.5). Here, the R2 showed a comparable seasonal trend (winter lower than autumn) and similar magnitude as the literature, but no strong correlation (R2 < 0.15) was found. This stresses the fact that mitigation measures of PM2.5 do not necessarily reduce PNC and monitoring networks evaluate PM2.5 exposure are unlikely to represent PNC exposure. The concentration ratios in the three combinations are found to vary with microenvironments and seasons. This variability implies that control policies should be diversified with pollutant types and energy usage of the city.https://doi.org/10.4209/aaqr.230046Mobile monitoringParticulate matterUrban microenvironmentsAir qualityExposure |
| spellingShingle | Sheng Xiang Shaojun Zhang Yu Ting Yu Hui Wang Ye Deng Qinwen Tan Zihang Zhou Ye Wu Evaluating Ultrafine Particles and PM2.5 in Microenvironments with Health Perspectives: Variability in Concentrations and Pollutant Interrelationships Aerosol and Air Quality Research Mobile monitoring Particulate matter Urban microenvironments Air quality Exposure |
| title | Evaluating Ultrafine Particles and PM2.5 in Microenvironments with Health Perspectives: Variability in Concentrations and Pollutant Interrelationships |
| title_full | Evaluating Ultrafine Particles and PM2.5 in Microenvironments with Health Perspectives: Variability in Concentrations and Pollutant Interrelationships |
| title_fullStr | Evaluating Ultrafine Particles and PM2.5 in Microenvironments with Health Perspectives: Variability in Concentrations and Pollutant Interrelationships |
| title_full_unstemmed | Evaluating Ultrafine Particles and PM2.5 in Microenvironments with Health Perspectives: Variability in Concentrations and Pollutant Interrelationships |
| title_short | Evaluating Ultrafine Particles and PM2.5 in Microenvironments with Health Perspectives: Variability in Concentrations and Pollutant Interrelationships |
| title_sort | evaluating ultrafine particles and pm2 5 in microenvironments with health perspectives variability in concentrations and pollutant interrelationships |
| topic | Mobile monitoring Particulate matter Urban microenvironments Air quality Exposure |
| url | https://doi.org/10.4209/aaqr.230046 |
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