Journals / Atmospheric Pollution Research / 2020 / Cilt: 11 - Sayı: 6

Spatiotemporal variations of PM2.5 concentration at the neighborhood level in five Chinese megacities

Pages
190–202
Publication Date
2022-11-06
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results showed that the within-city differences in PM2.5 concentration gradually increased from 2015 to 2017, and the differences were greater at a lower pollution level.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:A thorough understanding of the spatiotemporal variations of PM2.5 concentrations is crucial for mitigating PM2.5 pollution with effective measures; however, few studies have investigated this issue based on the neighborhood scale.
📊
Key Findings & Data:This study investigated the spatiotemporal variations of PM2.5 concentrations in 40 sites from five megacities by using hourly PM2.5 concentrations derived from air quality monitoring stations.
💡
Conclusion & Impact:Results showed that the within-city differences in PM2.5 concentration gradually increased from 2015 to 2017, and the differences were greater at a lower pollution level.
🔬Design:Research Article
📍Scope:2015 Verileri
⚙️Variables:Domain Parameters

Abstract

A thorough understanding of the spatiotemporal variations of PM2.5 concentrations is crucial for mitigating PM2.5 pollution with effective measures; however, few studies have investigated this issue based on the neighborhood scale. This study investigated the spatiotemporal variations of PM2.5 concentrations in 40 sites from five megacities by using hourly PM2.5 concentrations derived from air quality monitoring stations. Two relative indicators—the range and rate of change in PM2.5 concentration—were calculated to exclude the background levels of PM2.5 in different cities. Therefore, the differences between both the regions and between the sites within the same city were taken into consideration. Results showed that the within-city differences in PM2.5 concentration gradually increased from 2015 to 2017, and the differences were greater at a lower pollution level. Neighborhood-level PM2.5 concentration fluctuated mainly between 80% and 120% of each city's overall PM2.5 level. Combined with urban structure, the distribution of PM2.5 concentration during the four seasons presented three spatial patterns: the PM2.5 concentration was higher in the transition area, the PM2.5 concentration was higher in the core area than other areas, and the PM2.5 concentration had a relatively even distribution. Furthermore, the regional differences in the variations of PM2.5 concentrations depended on the PM2.5 concentrations differences among the cities; and the higher the PM2.5 pollution level, the greater was the observed regional difference. In addition, the range and rate of change in PM2.5 concentration had no significant differences among most sites at different pollution levels

🔖

Cite This Article

CHEN, M., YANG, B., DAI, F. (2022). Spatiotemporal variations of PM2.5 concentration at the neighborhood level in five Chinese megacities. Atmospheric Pollution Research, Cilt: 11 - Sayı: 6, 190–202. https://ajindex.com/articles/648806

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Aslam, M.Y., Krishna, K.R., Beig, G., Tinmaker, M.I.R., Chate, D.M., 2017. Diurnal evolution of urban heat island and its impact on air quality by using ground observations (SAFAR) over New Delhi. Open J. Air Pollut. 6, 52–64.
  2. Boarnet, M.G., Houston, D., Edwards, R., Princevac, M., Ferguson, G., Pan, H., Bartolome, C., 2011. Fine particulate concentrations on sidewalks in five Southern California cities. Atmos. Environ. 45, 4025–4033.
  3. Chen, M., Dai, F., Yang, B., Zhu, S., 2019a. Effects of neighborhood green space on PM2.5 mitigation: evidence from five megacities in China. Build. Environ. 156, 33–45.
  4. Chen, M., Dai, F., Yang, B., Zhu, S., 2019b. Effects of urban green space morphological pattern on variation of PM2.5 concentration in neighborhoods of five Chinese megacities. Build. Environ. 158, 1–15.
  5. Chen, X., Feng, J., 2019. Health effects of built environment based on a comparison of walkability and air pollution: a case study of Nanjing City. Prog. Geogr. 38 (2), 296–304.
  6. Chen, Z., Chen, D., Xie, X., Cai, J., Zhuang, Y., Cheng, N., He, B., Gao, B., 2019c. Spatial self-aggregation effects and national division of city-level PM2.5 concentrations in China based on spatio-temporal clustering. J. Clean. Prod. 207, 875–881.
  7. Cheng, Y., Lee, S.C., Gao, Y., Cui, L., Deng, W., Cao, J., Shen, Z., Sun, J., 2015. Real-time measurements of PM2.5, PM10-2.5, and BC in an urban street canyon. Particuology 20, 134–140.
  8. Cheng, Z., Luo, L., Wang, S., Wang, Y., Sharma, S., Shimadera, H., Wang, X., Bressi, M., de Miranda, R.M., Jiang, J., Zhou, W., Fajardo, O., Yan, N., Hao, J., 2016. Status and characteristics of ambient PM2.5 pollution in global megacities. Environ. Int. 89–90, 212–221.
  9. Chu, N., Kadane, J.B., Davidson, C.I., 2009. Identifying likely PM2.5 sources on days of elevated concentration: a simple statistical approach. Environ. Sci. Technol. 43 (7), 2407–2411.
  10. Cui, M., Lu, H., Etyemezian, V., Su, Q., 2019. Quantifying the emission potentials of fugitive dust sources in Nanjing, East China. Atmos. Environ. 2017, 129–135.
  11. Dawson, J.P., Adams, P.J., Pandis, S.N., 2007. Sensitivity of PM2.5 to climate in the eastern US: a modeling case study. Atmos. Chem. Phys. 7 (16), 4295–4309.
  12. Edussuriya, R., Chan, A., Ye, A., 2011. Urban morphology and air quality in dense residential environments in Hong Kong. Part I: district-level analysis. Atmos. Environ. 45 (27), 4789–4803.
  13. Fang, K., Wang, T., He, J., Wang, T., Xie, X., Tang, Y., Shen, Y., Xu, A., 2020. The distribution a