Journals / Atmospheric Pollution Research / 2019 / Cilt: 10 - Sayı: 1

Spatio-temporal variation of wind influence on distribution offineparticulate matter and its precursor gases

Pages
53–64
DOI
—
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AI Summary & Key Takeaways

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

Our results showed that the influence of wind on the concentration of particulatematter and its precursor gases varies spatially over Beijing.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Frequent occurrence of severe haze in Beijing indicates the importance of effective decisions and clear goals toreduce the concentration of particulate matter in the atmosphere.
📊
Key Findings & Data:Our method suggested that both local emissionsources offine particulate matter and its precursor gas (NO2) and transported SO2from regional industriesshould be reduced for the remediation of severe air pollution scenarios, especially in the Beijing urban area.
💡
Conclusion & Impact:Our results showed that the influence of wind on the concentration of particulatematter and its precursor gases varies spatially over Beijing.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

Frequent occurrence of severe haze in Beijing indicates the importance of effective decisions and clear goals toreduce the concentration of particulate matter in the atmosphere. To achieve this goal, elucidation of the ele-ments governingfine particulate matter concentrations should be considered as one of the priorities. In thispaper, we proposed a statistical model based on physical understanding of the diffusion of air pollutants by windto investigate the sensitivity offine particulate matter and its precursor gases concentrations to wind speed anddirection. By using our method, it is possible to determine the major wind directions that have large impact onair quality for a particular area. Our results showed that the influence of wind on the concentration of particulatematter and its precursor gases varies spatially over Beijing. Our method suggested that both local emissionsources offine particulate matter and its precursor gas (NO2) and transported SO2from regional industriesshould be reduced for the remediation of severe air pollution scenarios, especially in the Beijing urban area. Ourapproach serves as a good complement for commonly used approaches for source apportionment analysiswithout exploiting complicated methods. The method shows its feasibility in identifying air pollutant emissionsources based on wind patterns and can be used in other regions as an aid in air pollution controlling programs.

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Cite This Article

KARİMAN, H., Lİ, Q., Lİ, C., CHEN, G., MO, Y., WU, C., FAN, J. (2019). Spatio-temporal variation of wind influence on distribution offineparticulate matter and its precursor gases. Atmospheric Pollution Research, Cilt: 10 - Sayı: 1, 53–64. https://ajindex.com/articles/351565

References (11)

  1. Bei, N., Wu, J., Elser, M., Feng, T., Cao, J., El-Haddad, I., Li, X., Huang, R., Li, Z., Long, X.,Xing, L., Zhao, S., Tie, X., Prévôt, A.S.H., Li, G., 2017. Impacts of meteorologicaluncertainties on the haze formation in Beijing–Tianjin–Hebei (Bth) during winter-time: a case study. Atmos. Chem. Phys. 17, 14579–14591.
  2. Carslaw, D.C., Ropkins, K., 2012. Open air—an R Package for air quality data analysis.Environ. Model. Software 27–28, 52–61.
  3. Caumo, S., Vicente, A., Custódio, D., Alves, C., Vasconcellos, P., 2017. Organic com-pounds in particulate and gaseous phase collected in the neighbourhood of an in-dustrial complex in São paulo (Brazil). Air Qual. Atmos. Health 11, 271–283.
  4. Chen, J., 2014. Impact of relative humidity and water soluble constituents of Pm2.5 onvisibility impairment in beijing, China. Aerosol Air Qual. Res. 14, 260–268.
  5. Cheng, Y., Engling, G., He, K.B., Duan, F.K., Ma, Y.L., Du, Z.Y., Liu, J.M., Zheng, M.,Weber, R.J., 2013. Biomass burning contribution to beijing aerosol. Atmos. Chem.Phys. 13, 7765–7781.
  6. Custodio, D., Cerqueira, M., Alves, C., Nunes, T., Pio, C., Esteves, V., Frosini, D., Lucarelli,F., Querol, X., 2016. A one-year record of carbonaceous components and major ionsin aerosols from an urban kerbside location in Oporto, Portugal. Sci. Total Environ.562, 822–833.
  7. Finlayson-Pitts, B., Pitts, N.J., 2000. Chemistry of Upper and Lower Atmosphere-theory,Experience and Applications. Academic press.Gebhart, K.A., Schichtel, B.A., Barna, M.G., 2012. Directional biases in back trajectoriescaused by model and input data. J. Air Waste Manag. Assoc. 55, 1649–1662.
  8. Guo, S., Hu, M., Zamora, M.L., Peng, J., Shang, D., Zheng, J., Du, Z., Wu, Z., Shao, M.,Zeng, L., Molina, M.J., Zhang, R., 2014. Elucidating severe urban haze formation inChina. Proc. Natl. Acad. Sci. U. S. A. 111, 17373–17378.
  9. Harris, J.M., Draxler, R.R., Oltmans, S.J., 2005. Trajectory model sensitivity to differencesin input data and vertical transport method. J. Geophys. Res.: Atmos. 110 n/a-n/a.He, Q., Li, C., Geng, F., Lei, Y., Li, Y., 2012. Study on long-term aerosol distribution overthe land of east China using modis data. Aerosol and Air Quality Research 12,304–319.
  10. HildaHuang, X.H., Bian, J., Ng Man, W., Louei, K.K.P., Yu, Z.J., 2014. Characterization ofPm2.5 major components and source investigation in suburban Hong Kong: a oneyear monitoring study. Aerosol and Air Quality Research 14, 237–250.
  11. Huang, Q., Cheng, S., Perozzi, R.E., Perozzi, E.F., 2012. Use of a mm5–camx–