Advertisement

Advances in Atmospheric Sciences

, Volume 35, Issue 9, pp 1145–1159 | Cite as

Source Contributions to PM2.5 under Unfavorable Weather Conditions in Guangzhou City, China

  • Nan Wang
  • Zhenhao Ling
  • Xuejiao Deng
  • Tao Deng
  • Xiaopu Lyu
  • Tingyuan Li
  • Xiaorong Gao
  • Xi Chen
Original Paper

Abstract

Historical haze episodes (2013–16) in Guangzhou were examined and classified according to synoptic weather systems. Four types of weather systems were found to be unfavorable, among which “foreside of a cold front” (FC) and “sea high pressure” (SP) were the most frequent (>75% of the total). Targeted case studies were conducted based on an FC-affected event and an SP-affected event with the aim of understanding the characteristics of the contributions of source regions to fine particulate matter (PM2.5) in Guangzhou. Four kinds of contributions—namely, emissions outside Guangdong Province (super-region), emissions from the Pearl River Delta region (PRD region), emissions from Guangzhou–Foshan–Shenzhen (GFS region), and emissions from Guangzhou (local)—were investigated using the Weather Research and Forecasting–Community Multiscale Air Quality model. The results showed that the source region contribution differed with different weather systems. SP was a stagnant weather condition, and the source region contribution ratio showed that the local region was a major contributor (37%), while the PRD region, GFS region and the super-region only contributed 8%, 2.8% and 7%, respectively, to PM2.5 concentrations. By contrast, FC favored regional transport. The super-region became noticeable, contributing 34.8%, while the local region decreased to 12%. A simple method was proposed to quantify the relative impact of meteorology and emissions. Meteorology had a 35% impact, compared with an impact of -18% for emissions, when comparing the FC-affected event with that of the SP. The results from this study can provide guidance to policymakers for the implementation of effective control strategies.

Key words

WRF Community Multiscale Air Quality model source contribution unfavorable weather system fine particulate matter 

摘要

我们收集、调研了2013~2016年期间广州的灰霾日,并将其按照主导天气型进行了分类,得到四类不利的天气型. 其中,“冷锋前部型”(FC)和“海上高压型”(SH)发生的频率是最高的(发生概率大于75%). 因此,为了了解广州PM2.5可能的贡献来源,我们开展了针对性的案例研究来解析FC和SH下PM2.5的来源可能. 通过利用WRF-CMAQ模式,解析了不利天气条件下四类潜在的来源贡献,即,广东省以外的贡献(超远距离输送)、珠三角区域的贡献、广州-佛山-深圳的贡献(广佛深)输送和广州本地的贡献. 结果表明,不同天气型主导的条件下PM2.5的污染来源也不同. “海上高压型”是一种静稳的天气,解析表明本地排放是占据主导的贡献(37%),珠三角区域的贡献、广佛深的贡献和外省的贡献仅占据8%,2.8%和7%. 相反,“冷锋前部型”是利于区域输送的天气,外省的贡献(超远距离输送)变得十分显著,贡献量为34.8%,而在这类天气条件下,本地的贡献将降低为12%. 此外,我们提出了一个简化的方法来评估排放和天气条件的影响. 同“海上高压型”相比,天气条件在“冷锋前部型”的影响为35%,而排放的贡献为-18%. 本文研究成果有助于政府相关部门减排施测.

关键词

WRF-CMAQ PM2.5 来源解析 不利天气条件 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This study was supported by the National Key R&D Program of China: Task 3 (Grant No. 2016 YFC0202000); Guangzhou Science and Technology Plan (Grant No. 201604020028); National Natural Science Foundation of China (Grant No. 41775037 and 41475105); Science and Technology Innovative Research Team Plan of Guangdong Meteorological Bureau (Grant No. 201704); Guangdong Natural Science Foundation- Major Research Training Project (2015A030308014); and a science and technology study project of Guangdong Meteorological Bureau (Grant No. 2015Q03). The author also thanks Tsinghua University for providing MEIC.

Supplementary material

376_2018_7212_MOESM1_ESM.pdf (1.4 mb)
Electronic Supplementary Material to: Source Contributions to PM2.5 under UnfavorableWeather Conditions in Guangzhou City, China

References

  1. Andersson, A., J. J. Deng, K. Du, M. Zheng, C. Q. Yan, M. Sköld, and ö. Gustafsson, 2015: Regionally-varying combustion sources of the January 2013 severe haze events over eastern China. Environ. Sci. Technol., 49, 2038–2043,  https://doi.org/10.1021/es503855e CrossRefGoogle Scholar
  2. Cao, J. J., Q. Y. Wang, J. C. Chow, J. G. Watson, X. X. Tie, Z. X. Shen, P. Wang, and Z. S. An, 2012: Impacts of aerosol compositions on visibility impairment in Xi’an, China. Atmos. Environ., 59, 559–566,  https://doi.org/10.1016/j.atmosenv.2012.05.036 CrossRefGoogle Scholar
  3. Chan, C. Y., and L. Y., Chan, 2000: Effect of meteorology and air pollutant transport on ozone episodes at a subtropical coastal Asian city, Hong Kong. J Geophys Res: Atmos 105: 20707–24,  https://doi.org/10.1029/2000JD900140 CrossRefGoogle Scholar
  4. Che, W. W., J. Y. Zheng, S. S. Wang, L. J. Zhong, and A. Lau, 2011: Assessment of motor vehicle emission control policies using Model-3/CMAQ model for the Pearl River Delta region, China. Atmos. Environ., 45, 1740–1751,  https://doi.org/10.1016/j.atmosenv.2010.12.050 CrossRefGoogle Scholar
  5. Chen, D. S., S. Y. Cheng, L. Liu, T. Chen, and X. R. Guo, 2007: An integrated MM5–CMAQ modeling approach for assessing trans-boundary PM10 contribution to the host city of 2008 Olympic summer games—Beijing, China. Atmos. Environ., 41, 1237–1250,  https://doi.org/10.1016/j.atmosenv.2006.09.045 CrossRefGoogle Scholar
  6. Cui, H. Y., W. H. Chen, W. Dai, H. Liu, X. M. Wang, and K. B. He, 2015: Source apportionment of PM2.5 in Guangzhou combining observation data analysis and chemical transport model simulation. Atmos. Environ., 116, 262–271,  https://doi.org/10.1016/j.atmosenv.2015.06.054 CrossRefGoogle Scholar
  7. Deng, T., D. Wu, X. J. Deng, H. B. Tan, F. Li, and B. T. Liao, 2014: A vertical sounding of severe haze process in Guangzhou area. Science China Earth Sciences, 57, 2650–2656,  https://doi.org/10.1007/s11430-014-4928-y CrossRefGoogle Scholar
  8. Deng, X. J, X. X. Tie, D. Wu, X. J., Zhou, X. Y. Bi, H. B. Tan et al., 2008: Long-term trend of visibility and its characterizations in the Pearl River Delta (PRD) region, China. Atmospheric Environment, 42 1424–1435,  https://doi.org/10.1016/j.atmosenv.2007.11.025 CrossRefGoogle Scholar
  9. Dockery, D. W., and Coauthors, 1993: An association between air pollution and mortality in six U.S. cities. New England Journal of Medicine, 329(24), 1753–1759,  https://doi.org/10.1056/NEJM199312093292401 CrossRefGoogle Scholar
  10. Gao B., H. Guo, X.-M. Wang, X.-Y. Zhao, Z.-H. Ling, Z. Zhang, and T.-Y. Liu, 2012: Polycyclic aromatic hydrocarbons in PM2.5 in Guangzhou, southern China: Spatiotemporal patterns and emission sources. Journal of hazardous materials, 239: 78–87,  https://doi.org/10.1016/j.jhazmat.2012.07.068 CrossRefGoogle Scholar
  11. Gao, B., H. Guo, X. M. Wang, X. Y. Zhao, Z. H. Ling, Z. Zhang, and T. Y. Liu, 2013: Tracer-based source apportionment of polycyclic aromatic hydrocarbons in PM2.5 in Guangzhou, southern China, using positive matrix factorization (PMF). Environmental Science and Pollution Research, 20, 2398–2409,  https://doi.org/10.1007/s11356-012-1129-0 CrossRefGoogle Scholar
  12. Guenther, A., T. Karl, P. Harley, C. Wiedinmyer, P. I. Palmer, and C. Geron, 2006: Estimates of global terrestrial isoprene emissions using MEGAN (model of emissions of gases and aerosols from nature). Atmos. Chem. Phys., 6, 3181–3210,  https://doi.org/10.5194/acp-6-3181-2006 CrossRefGoogle Scholar
  13. Guo, H., A. J. Ding, K. L. So, G. Ayoko, Y. S. Li, and W. T. Hung, 2009: Receptor modeling of source apportionment of Hong Kong aerosols and the implication of urban and regional contribution. Atmos. Environ., 43, 1159–1169,  https://doi.org/10.1016/j.atmosenv.2008.04.046 CrossRefGoogle Scholar
  14. He, H., X. X. Tie, Q. Zhang, X. G. Liu, Q. Gao, X. Li, and Y. Gao, 2015: Analysis of the causes of heavy aerosol pollution in Beijing, China: A case study with the WRF-Chem model. Particuology, 20, 32–40,  https://doi.org/10.1016/j.partic.2014.06.004 CrossRefGoogle Scholar
  15. He, K. B., 2012: Multi-resolution emission inventory for China (MEIC): model framework and 1990–2010 anthropogenic emissions. Proceedings of International Global Atmospheric Chemistry Conference, Beijing, China, AGU.Google Scholar
  16. Huang, R. J., and Coauthors, 2014: High secondary aerosol contribution to particulate pollution during haze events in China. Nature, 514, 218–222,  https://doi.org/10.1038/nature13774 CrossRefGoogle Scholar
  17. Huang, J. P., J. C. H. Fung, and A. K. H. Lau 2006: Integrated processes analysis and systematic meteorological classification of ozone episodes in Hong Kong. J. Geophys. Res., 111, D20309,  https://doi.org/10.1029/2005JD007012 CrossRefGoogle Scholar
  18. Hyslop, N. P., 2009: Impaired visibility: The air pollution people see. Atmos. Environ., 43, 182–195,  https://doi.org/10.1016/j.atmosenv.2008.09.067 CrossRefGoogle Scholar
  19. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I Contribution to the Fifth Assessment Report of the International Panel on Climate Change. Cambridge University Press.Google Scholar
  20. Jiang, F., T. J. Wang, T. T. Wang, M. Xie, and H. Zhao, 2008: Numerical modeling of a continuous photochemical pollution episode in Hong Kong using WRF–chem. Atmos. Environ., 42, 8717–8727,  https://doi.org/10.1016/j.atmosenv.2008.08.034 CrossRefGoogle Scholar
  21. Kemball-Cook, S., D. Parrish, T. Ryerson, U. Nopmongcol, J. Johnson, E. Tai, and G. Yarwood, 2009: Contributions of regional transport and local sources to ozone exceedances in Houston and Dallas: Comparison of results from a photochemical grid model to aircraft and surface measurements. J. Geophys. Res., 114,  https://doi.org/10.1029/2008JD010248
  22. Li Y. M., X. J. Deng, T. Deng, Z. M. Lao, G. R. Xia. 2016: Haze characteristics associated with meteorological factors in Zhongshan during 2000~2014. China Environmental Science. 36(6), 1638–1644. (in Chinese)Google Scholar
  23. Li Y. M., S. J. Fan, R. W. Zhang. 2011: Study on air pollution meteorology over the Pearl River Delta during the autumn of 2008. China Environmental Science, 31(10), 1585–1591. (in Chinese)Google Scholar
  24. Li, Y., A. K. H. Lau, J. C. H. Fung, H. Ma, and Y. Tse, 2013: Systematic evaluation of ozone control policies using an Ozone Source Apportionment method. Atmos. Environ., 76, 136–146,  https://doi.org/10.1016/j.atmosenv.2013.02.033 CrossRefGoogle Scholar
  25. Li, Y., A. K. H. Lau, J. C. H. Fung, J. Y. Zheng, L. J. Zhong, and P. K. K. Louie, 2012: Ozone source apportionment (OSAT) to differentiate local regional and super-regional source contributions in the Pearl River Delta region, China. J. Geophys. Res., 117, D15305,  https://doi.org/10.1029/2011JD017340 Google Scholar
  26. Liu, J. W., and Coauthors, 2014: Source apportionment using radiocarbon and organic tracers for PM2.5 carbonaceous aerosols in Guangzhou, South China: Contrasting local- and regional-scale haze events. Environ. Sci. Technol., 48, 12002–12011,  https://doi.org/10.1021/es503102w CrossRefGoogle Scholar
  27. Louie, P. K. K., J. C. Chow, L. W. A. Chen, J. G. Watson, G. Leung, and D. W. M. Sin, 2005: PM2.5 chemical composition in Hong Kong: Urban and regional variations. Science of the Total Environment, 338, 267–281,  https://doi.org/10.1016/j.scitotenv.2004.07.021 CrossRefGoogle Scholar
  28. Mai, J. H., T. Deng, L. L. Yu, X. J. Deng, H. B. Tan, S. Q. Wang, and X. T. Liu, 2016: A modeling study of impact of emission control strategies on PM2.5 reductions in Zhongshan, China, using WRF-CMAQ. Advances in Meteorology, 2016, 5836070,  https://doi.org/10.1155/2016/5836070 CrossRefGoogle Scholar
  29. Pope III, C. A., M. Ezzati, and D. W. Dockery, 2009: Fineparticulate air pollution and life expectancy in the United States. New England Journal of Medicine, 360, 376–386,  https://doi.org/10.1056/NEJMsa0805646 CrossRefGoogle Scholar
  30. Qiu, J. H., H. Q. Wang, X. J. Zhou, and D. R. Lu, 1985: Experimental study of remote sensing of atmospheric aerosol size distribution by combined solar extinction and forward scattering method. Adv. Atmos. Sci., 2(3), 307–315,  https://doi.org/10.1007/BF02677246 CrossRefGoogle Scholar
  31. Streets, D. G., and Coauthors, 2007: Air quality during the 2008 Beijing Olympic Games. Atmos. Environ., 41, 480–492,  https://doi.org/10.1016/j.atmosenv.2006.08.046 CrossRefGoogle Scholar
  32. Saskia Buchholza, Jürgen Junka, Andreas Krein et al, 2010: Air pollution characteristics associated with mesoscale atmospheric patterns in northwest continental Europe. Atmospheric Environment. 44 (2010) 5183–5190,  https://doi.org/10.1016/j.atmosenv.2010.08.053 Google Scholar
  33. Tan, J. H., J. C. Duan, K. B. He, Y. L. Ma, F. K. Duan, Y. Chen, and J. M. Fu, 2009: Chemical characteristics of PM2.5 during a typical haze episode in Guangzhou. Journal of Environmental Sciences, 21, 774–781,  https://doi.org/10.1016/S1001-0742(08)62340-2 CrossRefGoogle Scholar
  34. Tan, H. B., Y. Yin, X. S. Gu, F. Li, P. W. Chan, H. B. Xu, X. J. Deng, and Q. L. Wan, 2013: An observational study of the hygroscopic properties of aerosols over the Pearl River Delta region. Atmos. Environ., 77, 817–826,  https://doi.org/10.1016/j.atmosenv.2013.05.049 CrossRefGoogle Scholar
  35. Tao, J., J. J. Cao, R. J. Zhang, L. H. Zhu, T. Zhang, S. Shi, and C. Y. Chan, 2012: Reconstructed light extinction coefficients using chemical compositions of PM2.5 in winter in Urban Guangzhou, China. Adv. Atmos. Sci., 29, 359–368,  https://doi.org/10.1007/s00376-011-1045-0 CrossRefGoogle Scholar
  36. Tie, X. X., D. Wu, and G. Brasseur, 2009: Lung cancer mortality and exposure to atmospheric aerosol particles in Guangzhou, China. Atmos. Environ., 43, 2375–2377,  https://doi.org/10.1016/j.atmosenv.2009.01.036 CrossRefGoogle Scholar
  37. Wang, N., H. Guo, F. Jiang, Z. H. Ling, and T. Wang, 2015: Simulation of ozone formation at different elevations in mountainous area of Hong Kong using WRF-CMAQ model. Science of the Total Environment, 505, 939–951,  https://doi.org/10.1016/j.scitotenv.2014.10.070 CrossRefGoogle Scholar
  38. Wang, N. X. P. Lyu, X. J. Deng, H. Guo, T. Deng, Y. Li, C. Q. Yin, and S. Q. Wang, 2016: Assessment of regional air quality resulting from emission control in the Pearl River Delta region, southern China. Science of the Total Environment, 573, 1554–1565,  https://doi.org/10.1016/j.scitotenv.2016.09.013 CrossRefGoogle Scholar
  39. Wang, X. M., G. Carmichael, D. L. Chen, Y. H. Tang, and T. J. Wang, 2005: Impacts of different emission sources on air quality during March 2001 in the Pearl River Delta (PRD) region. Atmos. Environ., 39, 5227–5241,  https://doi.org/10.1016/j.atmosenv.2005.04.035 CrossRefGoogle Scholar
  40. Wang, X. M., F. Chen, Z. Y. Wu, M. G. Zhang, M. Tewari, A. Guenther, and C. Wiedinmyer, 2009: Impacts of weather conditions modified by urban expansion on surface ozone: Comparison between the Pearl River Delta and Yangtze River Delta regions. Adv. Atmos. Sci., 26(5), 962–972,  https://doi.org/10.1007/s00376-009-8001-2 CrossRefGoogle Scholar
  41. Wang, X. Y., and Coauthors, 2010: Process analysis and sensitivity study of regional ozone formation over the Pearl River Delta, China, during the PRIDE-PRD2004 campaign using the Community Multiscale Air Quality modeling system. Atmos. Chem. Phys., 10, 4423–4437,  https://doi.org/10.5194/acp-10-4423-2010 CrossRefGoogle Scholar
  42. Watson, J. G., 2002: Visibility: Science and regulation. Journal of the Air & Waste Management Association, 52, 628–713,  https://doi.org/10.1080/10473289.2002.10470813 CrossRefGoogle Scholar
  43. Wu, D. W., J. C. H. Fung, T. Yao, and A. K. H. Lau, 2013: A study of control policy in the Pearl River Delta region by using the particulate matter source apportionment method. Atmos. Environ., 76, 147–161,  https://doi.org/10.1016/j.atmosenv.2012.11.069 CrossRefGoogle Scholar
  44. Wu, D., X. J. Deng, X. Y. Bi, F. Li, H. B. Tan, and G. L. Liao, 2007; Study on the visibility reduction caused by atmospheric haze in Guangzhou area. Journal of Tropical Meteorology, 13, 77–80.Google Scholar
  45. Wu, D., X. X. Tie, C. C. Li, Z. M. Ying, A. K. H. Lau, J. Huang, X. J. Deng, and X. Y. Bi, 2005: An extremely low visibility event over the Guangzhou region: A case study. Atmos. Environ., 39, 6568–6577,  https://doi.org/10.1016/j.atmosenv.2005.07.061 CrossRefGoogle Scholar
  46. Xiao, R., and Coauthors, 2011: Characterization and source apportionment of submicron aerosol with aerosol mass spectrometer during the PRIDE-PRD 2006 campaign. Atmos. Chem. Phys., 11, 6911–6929,  https://doi.org/10.5194/acp-11-6911-2011 CrossRefGoogle Scholar
  47. Xing, J., and Coauthors, 2011: Modeling study on the air quality impacts from emission reductions and atypical meteorological conditions during the 2008 Beijing Olympics. Atmos. Environ., 45, 1786–1798,  https://doi.org/10.1016/j.atmosenv.2011.01.025 CrossRefGoogle Scholar
  48. Zhang, H. L., J. Y. Li, Q. Ying, J. Z. Yu, D. Wu, Y. Cheng, K. B. He, and J. K. Jaing, 2012: Source apportionment of PM2.5 nitrate and sulfate in China using a source-oriented chemical transport model. Atmos. Environ., 62, 228–242,  https://doi.org/10.1016/j.atmosenv.2012.08.014 CrossRefGoogle Scholar
  49. Zhang, Q., and Coauthors, 2009: Asian emissions in 2006 for the NASA INTEX-B mission. Atmos. Chem. Phys., 9, 5131–5153,  https://doi.org/10.5194/acp-9-5131-2009 CrossRefGoogle Scholar
  50. Zhao, B., and Coauthors, 2017: Enhanced PM2.5 pollution in China due to aerosol-cloud interactions. Sci. Rep., 7, 4453,  https://doi.org/10.1038/s41598-017-04096-8 CrossRefGoogle Scholar
  51. Zheng, M., and Coauthors, 2011: Sources of excess urban carbonaceous aerosol in the Pearl River Delta Region, China. Atmos. Environ., 45, 1175–1182,  https://doi.org/10.1016/j.atmosenv.2010.09.041 CrossRefGoogle Scholar
  52. Zhong, L. J., and Coauthors, 2013: The Pearl River Delta regional air quality monitoring network–regional collaborative efforts on joint air quality management. Aerosol and Air Quality Research, 13, 1582–1597,  https://doi.org/10.4209/aaqr.2012.L10.0276 Google Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Nan Wang
    • 1
  • Zhenhao Ling
    • 2
  • Xuejiao Deng
    • 1
  • Tao Deng
    • 1
  • Xiaopu Lyu
    • 3
  • Tingyuan Li
    • 4
  • Xiaorong Gao
    • 5
  • Xi Chen
    • 6
  1. 1.Institute of Tropical and Marine MeteorologyChina Meteorological AdministrationGuangzhouChina
  2. 2.School of Atmospheric SciencesSun Yat-sen UniversityGuangzhouChina
  3. 3.Department of Civil and Environmental EngineeringThe Hong Kong Polytechnic UniversityHong KongChina
  4. 4.Ecological Meteorology CenterGuangdong Provincial Meteorological BureauGuangzhouChina
  5. 5.Guangzhou Meteorological ObservatoryGuangzhouChina
  6. 6.School of Environmental Science and EngineeringSun Yat-sen UniversityGuangzhouChina

Personalised recommendations