Advertisement

Environmental Chemistry Letters

, Volume 17, Issue 4, pp 1839–1847 | Cite as

Evidence for regional heterogeneous atmospheric particulate matter distribution in China: implications for air pollution control

  • Rui FengEmail author
  • Hui-jun ZhengEmail author
Original Paper

Abstract

China has suffered from severe nationwide air quality degradation for decades. PM2.5, the atmospheric particulate matter with an aerodynamic equivalent diameter of less than 2.5 μm, is the most concerning atmospheric pollutant for heath. Pollution control policies are commonly applied nationwide, but atmospheric pollution may vary from one area to another, thus suggesting the need for different, adapted policies. However, there is little knowledge on pollution distribution in China. Therefore, here we used recurrent neural network and random forest models to analyze the wintertime regional PM2.5 patterns in four most polluted cities of China, which are Beijing, Shanghai, Guangzhou and Chengdu, from December 2014 to February 2019. We find that different megacities in China have completely different PM2.5 patterns, which remained unchanged during the past 6 years. CO plays a predominant role in shaping PM2.5 nationwide, and the importance of CO varies from region to region. Therefore, different regional PM2.5 control policies should be carried out for better regulation. Furthermore, we demonstrate that PM2.5 is not strongly linked with time series, inferring that PM2.5 concentrations at a given date are not linked with previous PM2.5 concentrations. This finding suggests that the chemical reaction equilibrium may get reversed and that the rate of chemical reactions of PM2.5 is faster than we normally think.

Keywords

PM2.5 Recurrent neural network Random forest Feature importance 

Notes

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Nos. 51390493 and 51476144).

Supplementary material

10311_2019_890_MOESM1_ESM.docx (20 kb)
Supplementary material 1 (DOCX 21 kb)

References

  1. Abbas I, Badran G, Verdin A, Ledoux F, Roumié M, Courcot D (2018) Polycyclic aromatic hydrocarbon derivatives in airborne particulate matter: sources, analysis and toxicity. Environ Chem Lett 16(2):439–475.  https://doi.org/10.1007/s10311-017-0697-0 CrossRefGoogle Scholar
  2. Abián M, Millera A, Bilbao R, Alzueta M (2019) Effect of CO2 atmosphere and presence of NOx (NO and NO2) on the moist oxidation of CO. Fuel 236:615–621.  https://doi.org/10.1016/j.fuel.2018.09.054 CrossRefGoogle Scholar
  3. Bai K, Ma M, Chang N, Gao W (2019) Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data. J Environ Manage 233:530–542.  https://doi.org/10.1016/j.jenvman.2018.12.071 CrossRefGoogle Scholar
  4. Chen F, Fan Z, Qiao Z, Cui Y, Zhang M, Zhao X, Li X (2017) Does temperature modify the effect of PM10 on mortality? A systematic review and meta-analysis. Environ Pollut 224:326–335.  https://doi.org/10.1016/j.envpol.2017.02.012 CrossRefGoogle Scholar
  5. Chen L, Li F, Wei Y, Li G, Shen K, He H (2019) High cadmium adsorption on nanoscale zero-valent iron coated Eichhornia crassipes biochar. Environ Chem Lett 17(1):589–594.  https://doi.org/10.1007/s10311-018-0811-y CrossRefGoogle Scholar
  6. Dang X, Shan Z (2018) Dust pollution and control with leather waste. Environ Chem Lett 16(2):427–437.  https://doi.org/10.1007/s10311-017-0683-6 CrossRefGoogle Scholar
  7. Dubey S, Shri M, Gupta A, Rani V, Chakrabarty D (2018) Toxicity and detoxification of heavy metals during plant growth and metabolism. Environ Chem Lett 16(4):1169–1192.  https://doi.org/10.1007/s10311-018-0741-8 CrossRefGoogle Scholar
  8. Feng R, Wang Q, Huang C, Liang J, Luo K, Fan J, Zheng H (2018) Ethylene, xylene, toluene and hexane are major contributors of atmospheric ozone in Hangzhou, China, prior to the 2022 Asian games. Environ Chem Lett.  https://doi.org/10.1007/s10311-018-00846-w CrossRefGoogle Scholar
  9. Feng R, Wang Q, Huang C, Liang J, Luo K, Fan J, Cen K (2019) Investigation on air pollution control strategy in Hangzhou for post-G20/pre-Asian-games period (2018–2020). Atmos Pollut Res 10(1):197–208.  https://doi.org/10.1016/j.apr.2018.07.006 CrossRefGoogle Scholar
  10. Gao P, Yao D, Qian Y, Zhong S, Zhang L, Xue G, Jia H (2018) Factors controlling the formation of persistent free radicals in hydrochar during hydrothermal conversion of rice straw. Environ Chem Lett 16(4):1463–1468.  https://doi.org/10.1007/s10311-018-0757-0 CrossRefGoogle Scholar
  11. Ge P, Liang Y, Cheng Y, Sun K, Liu R (2018) A new persistent luminescent composite for tracing toxic air particulate matter. Environ Chem Lett 16(4):1487–1492.  https://doi.org/10.1007/s10311-018-0761-4 CrossRefGoogle Scholar
  12. Gui K, Che H, Wang Y, Wang H, Zhang L, Zhao H, Zheng Y, Sun T, Zhang X (2019) Satellite-derived PM2.5 concentration trends over Eastern China from 1998 to 2016: relationships to emissions and meteorological parameters. Environ Pollut 247:1125–1133.  https://doi.org/10.1016/j.envpol.2019.01.056 CrossRefGoogle Scholar
  13. Guo P, Yu S, Wang L, Li P, Li Z, Mehmood K, Chen X, Liu W, Zhu Y, Yu X, Alapaty K, Lichtfouse E, Rosenfeld D, Seinfeld J (2019) High-altitude and long-range transport of aerosols causing regional severe haze during extreme dust storms explains why afforestation does not prevent storms. Environ Chem Lett.  https://doi.org/10.1007/s10311-019-00858-0 CrossRefGoogle Scholar
  14. Hadley O (2017) Background PM2.5 source apportionment in the remote Northwestern United States. Atmos Environ 167:298–308.  https://doi.org/10.1016/j.atmosenv.2017.08.030 CrossRefGoogle Scholar
  15. Hu Y, Zhang R, Fang D (2019) Quaternary phosphonium cationic ionic liquid/porous metal–organic framework as an efficient catalytic system for cycloaddition of carbon dioxide into cyclic carbonates. Environ Chem Lett 17(1):501–508.  https://doi.org/10.1007/s10311-018-0793-9 CrossRefGoogle Scholar
  16. Kamińska J (2018) The use of random forests in modelling short-term air pollution effects based on traffic and meteorological conditions: a case study in Wrocław. J Environ Manage 217:164–174.  https://doi.org/10.1016/j.jenvman.2018.03.094 CrossRefGoogle Scholar
  17. Kroupnov A, Pogosbekian M (2018) DFT calculation-based study of the mechanism for CO2 formation in the interaction of CO and NO2 molecules. Chem Phys Lett 710:90–95.  https://doi.org/10.1016/j.cplett.2018.08.077 CrossRefGoogle Scholar
  18. Li P, Wang L, Guo P, Yu S, Mehmood K, Wang S, Liu W, Seinfeld J, Zhang Y, Wong D, Alapaty K, Pleim J, Mathur R (2017a) High reduction of ozone and particulate matter during the 2016 G-20 summit in Hangzhou by forced emission controls of industry and traffic. Environ Chem Lett 15(4):709–715.  https://doi.org/10.1007/s10311-017-0642-2 CrossRefGoogle Scholar
  19. Li C, Dong F, Feng L, Zhao J, Zhang T, Cizmas L, Sharma V (2017b) Bacterial community structure and microorganism inactivation following water treatment with ferrate(VI) or chlorine. Environ Chem Lett 15(3):525–530.  https://doi.org/10.1007/s10311-017-0623-5 CrossRefGoogle Scholar
  20. Li K, Jacob D, Liao H, Shen L, Zhang Q, Bates K (2019a) Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China. Proc Natl Acad Sci USA 116(2):422–427.  https://doi.org/10.1073/pnas.1812168116 CrossRefGoogle Scholar
  21. Li H, Yan D, Zhang Z, Lichtfouse E (2019b) Prediction of CO2 absorption by physical solvents using a chemoinformatics-based machine learning model. Environ Chem Lett.  https://doi.org/10.1007/s10311-019-00874-0 CrossRefGoogle Scholar
  22. Li T, Guo Y, Liu Y, Wang J, Wang Q, Sun Z, He M, Shi X (2019c) Estimating mortality burden attributable to short-term PM2.5 exposure: a national observational study in China. Environ Int 125:245–251.  https://doi.org/10.1016/j.envint.2019.01.073 CrossRefGoogle Scholar
  23. Liu Z, Sullivan C (2019) Prediction of weather induced background radiation fluctuation with recurrent neural networks. Radiat Phys Chem 155:275–280.  https://doi.org/10.1016/j.radphyschem.2018.03.005 CrossRefGoogle Scholar
  24. Liu S, Zhang Y, Jiang H, Wang X, Zhang T, Yao Y (2018) High CO2 adsorption by amino-modified bio-spherical cellulose nanofibres aerogels. Environ Chem Lett 16(2):605–614.  https://doi.org/10.1007/s10311-017-0701-8 CrossRefGoogle Scholar
  25. Liu D, Wang Q, Wu J, Liu Y (2019) A review of sorbents for high-temperature hydrogen sulfide removal from hot coal gas. Environ Chem Lett 17(1):259–276.  https://doi.org/10.1007/s10311-018-0792-x CrossRefGoogle Scholar
  26. Lu J, Li X, Zhao Y, Ma H, Wang L, Wang X, Yu Y, Shen T, Xu H, Zhang Y (2018) CO2 capture by ionic liquid membrane absorption for reduction of emissions of greenhouse gas. Environ Chem Lett.  https://doi.org/10.1007/s10311-018-00822-4 CrossRefGoogle Scholar
  27. Mehmood K, Chang S, Yu S, Wang L, Li P, Li Z, Liu W, Rosenfeld D, Seinfeld J (2018) Spatial and temporal distributions of air pollutant emissions from open crop straw and biomass burnings in China from 2002 to 2016. Environ Chem Lett 16(1):301–309.  https://doi.org/10.1007/s10311-017-0675-6 CrossRefGoogle Scholar
  28. Mukherjee A, Agrawal M (2017) World air particulate matter: sources, distribution and health effects. Environ Chem Lett 15(2):283–309.  https://doi.org/10.1007/s10311-017-0611-9 CrossRefGoogle Scholar
  29. Mukherjee A, Agrawal M (2018) Air pollutant levels are 12 times higher than guidelines in Varanasi, India: sources and transfer. Environ Chem Lett 16(3):1009–1016.  https://doi.org/10.1007/s10311-018-0706-y CrossRefGoogle Scholar
  30. Naus S, Röckmann T, Popa M (2019) The isotopic composition of CO in vehicle exhaust. Atmos Environ 177:132–142.  https://doi.org/10.1016/j.atmosenv.2018.01.015 CrossRefGoogle Scholar
  31. Nidheesh P (2018) Removal of organic pollutants by peroxicoagulation. Environ Chem Lett 16(4):1283–1292.  https://doi.org/10.1007/s10311-018-0752-5 CrossRefGoogle Scholar
  32. Norbäck D, Lu C, Zhang Y, Li B, Zhao Z, Huang C, Zhang X, Qian H, Sun Y, Wang J, Liu W, Sundell J, Deng Q (2019) Sources of indoor particulate matter (PM) and outdoor air pollution in China in relation to asthma, wheeze, rhinitis and eczema among pre-school children: synergistic effects between antibiotics use and PM10 and second hand smoke. Environ Int 125:252–260.  https://doi.org/10.1016/j.envint.2019.01.036 CrossRefGoogle Scholar
  33. Ou M, Zhang Z, Wen Y, Yang H, Gu J, Xu X (2019) Cytotoxic study in the treatment of tetracycline by using magnetic Fe3O4–PAMAM–antibody complexes. Environ Chem Lett 17(1):543–549.  https://doi.org/10.1007/s10311-018-0803-y CrossRefGoogle Scholar
  34. Prashantha Kumar TKM, Mandlimath TR, Sangeetha P et al (2018) Nanoscale materials as sorbents for nitrate and phosphate removal from water. Environ Chem Lett 16(2):389–400.  https://doi.org/10.1007/s10311-017-0682-7 CrossRefGoogle Scholar
  35. Razmjoo A, Xanthopoulos P, Zheng Q (2017) Online feature importance ranking based on sensitivity analysis. Expert Syst Appl 85:397–406.  https://doi.org/10.1016/j.eswa.2017.05.016 CrossRefGoogle Scholar
  36. Rogula-Kopiec P, Rogula-Kozłowska W, Pastuszka J, Mathews B (2019) Air pollution of beauty salons by cosmetics from the analysis of suspensed particulate matter. Environ Chem Lett 17(1):551–558.  https://doi.org/10.1007/s10311-018-0798-4 CrossRefGoogle Scholar
  37. Sanjurjo-Sánchez J, Alves C (2017) Geologic materials and gamma radiation in the built environment. Environ Chem Lett 15(4):561–589.  https://doi.org/10.1007/s10311-017-0643-1 CrossRefGoogle Scholar
  38. Saraga D, Tolis E, Maggos T, Vasilakos C, Bartzis J (2019) PM2.5 source apportionment for the port city of Thessaloniki, Greece. Sci Total Environ 650(2):2337–2354.  https://doi.org/10.1016/j.scitotenv.2018.09.250 CrossRefGoogle Scholar
  39. Satpathy R (2019) Quantitative structure activity relationship methods for the prediction of the toxicity of pollutants. Environ Chem Lett 17(1):123–128.  https://doi.org/10.1007/s10311-018-0780-1 CrossRefGoogle Scholar
  40. Shen G, Du W, Zhuo S, Yu J, Tao S (2019) Improving regulations on residential emissions and non-criteria hazardous contaminants—insights from a field campaign on ambient PM and PAHs in North China Plain. Environ Sci Policy 92:201–206.  https://doi.org/10.1016/j.envsci.2018.12.003 CrossRefGoogle Scholar
  41. Shou Y, Huang Y, Zhu X, Liu C, Hu Y, Wang H (2019) A review of the possible associations between ambient PM2.5 exposures and the development of Alzheimer’s disease. Ecotoxicol Environ Saf 174:344–352.  https://doi.org/10.1016/j.ecoenv.2019.02.086 CrossRefGoogle Scholar
  42. Sun J, Shen Z, Zhang L et al (2019) Chemical source profiles of urban fugitive dust PM2.5 samples from 21 cities across China. Sci Total Environ 649:1045–1053.  https://doi.org/10.1016/j.scitotenv.2018.08.374 CrossRefGoogle Scholar
  43. Tai C, Zhang S, Wang J, Yin Y, Shi J, Wu H, Mao Y (2017) Solar-induced generation of singlet oxygen and hydroxyl radical in sewage wastewaters. Environ Chem Lett 15(3):515–523.  https://doi.org/10.1007/s10311-017-0625-3 CrossRefGoogle Scholar
  44. Tan Z, Lu K, Dong H, Hu M, Li X, Liu Y, Lu S, Shao M, Su R, Wang H, Wu Y, Wahner A, Zhang Y (2018) Explicit diagnosis of the local ozone production rate and the ozone-NOx-VOC sensitivities. Sci Bull 63(16):1067–1076.  https://doi.org/10.1016/j.scib.2018.07.001 CrossRefGoogle Scholar
  45. Tang C, Duan C, Yu C, Song Y, Chai L, Xiao R, Wei Z (2017) Removal of nitrogen from wastewaters by anaerobic ammonium oxidation (ANAMMOX) using granules in upflow reactors. Environ Chem Lett 15(2):311–328.  https://doi.org/10.1007/s10311-017-0607-5 CrossRefGoogle Scholar
  46. Tang Y, Ren H, Yang P, Li H, Zhang J, Qu C, Chen G (2019) Treatment of fracturing fluid waste by Fenton reaction using transition metal complexes catalyzes oxidation of hydroxypropyl guar gum at high pH. Environ Chem Lett 17(1):559–564.  https://doi.org/10.1007/s10311-018-0805-9 CrossRefGoogle Scholar
  47. Tanner P (2009) Vehicle-related ammonia emissions in Hong Kong. Environ Chem Lett 7(1):37–40.  https://doi.org/10.1007/s10311-007-0131-0 CrossRefGoogle Scholar
  48. Tilt B (2019) China’s air pollution crisis: science and policy perspectives. Environ Sci Policy 92:275–280.  https://doi.org/10.1016/j.envsci.2018.11.020 CrossRefGoogle Scholar
  49. Wang Y, Li M, Wan X, Sun Y, Cheng K, Zhao X, Zheng Y, Yang G, Wang L (2018a) Spatiotemporal analysis of PM2.5 and pancreatic cancer mortality in China. Environ Res 164:132–139.  https://doi.org/10.1016/j.envres.2018.02.026 CrossRefGoogle Scholar
  50. Wang W, Han C, Xie F (2018b) Efficient mercury recovery from mercuric-thiosulfate solutions by ultraviolet photolysis. Environ Chem Lett 16(3):1049–1054.  https://doi.org/10.1007/s10311-018-0716-9 CrossRefGoogle Scholar
  51. Wang H, Gao Z, Ren J, Liu Y, Chang L, Cheung K, Feng Y, Li Y (2019a) An urban-rural and sex differences in cancer incidence and mortality and the relationship with PM2.5 exposure: an ecological study in the southeastern side of Hu line. Chemosphere 216:766–773.  https://doi.org/10.1016/j.chemosphere.2018.10.183 CrossRefGoogle Scholar
  52. Wang Y, Chen J, Wang Q, Qin Q, Ye J, Han Y, Li L, Zhen W, Zhi Q, Zhang Y, Cao J (2019b) Increased secondary aerosol contribution and possible processing on polluted winter days in China. Environ Int 127:78–84.  https://doi.org/10.1016/j.envint.2019.03.021 CrossRefGoogle Scholar
  53. Wang S, Li Z, Du Z, Li J, Cheng F (2019c) Preparation of a PASi-P(AM-ADB) hybrid flocculant and efficiently removal bio-refractory organics from coking wastewater. Environ Chem Lett 17(1):509–514.  https://doi.org/10.1007/s10311-018-0796-6 CrossRefGoogle Scholar
  54. Wu Y, Wang P, Yu S, Wang L, Li P, Li Z, Mehmood K, Liu W, Wu J (2018) Residential emissions predicted as a major source of fine particulate matter in winter over the Yangtze River Delta, China. Environ Chem Lett 16(3):1117–1127.  https://doi.org/10.1007/s10311-018-0735-6 CrossRefGoogle Scholar
  55. Xiao D, Fang F, Zheng J, Pain C, Navon I (2019) Machine learning-based rapid response tools for regional air pollution modeling. Atmos Environ 199:463–473.  https://doi.org/10.1016/j.atmosenv.2018.11.051 CrossRefGoogle Scholar
  56. Xu M, Ge C, Qin Y, Gu T, Lou D, Li Q, Hu L, Feng J, Huang P, Tan J (2019a) Prolonged PM2.5 exposure elevates risk of oxidative stress-driven nonalcoholic fatty liver disease by triggering increase of dyslipidemia. Free Radic Biol Med 130:542–556.  https://doi.org/10.1016/j.freeradbiomed.2018.11.016 CrossRefGoogle Scholar
  57. Xu M, Wu J, Yang G, Zhang X, Peng H, Yu X, Xiao Y, Qi H (2019b) Biochar addition to soil highly increases P retention and decreases the risk of phosphate contamination of waters. Environ Chem Lett 17(1):533–541.  https://doi.org/10.1007/s10311-018-0802-z CrossRefGoogle Scholar
  58. Yang J, Zhang B (2018) Air pollution and healthcare expenditure: implication for the benefit of air pollution control in China. Environ Int 120:443–455.  https://doi.org/10.1016/j.envint.2018.08.011 CrossRefGoogle Scholar
  59. Yang X, Lu X, Wu L, Zhang J, Huang Y, Li X (2017) Pd nanoparticles entrapped in TiO2 nanotubes for complete butane catalytic combustion at 130 °C. Environ Chem Lett 15(3):421–426.  https://doi.org/10.1007/s10311-017-0608-4 CrossRefGoogle Scholar
  60. Yao L, Wang D, Fu Q, Qiao L, Wang H, Li L, Sun W, Li Q, Wang L, Yang X, Zhao Z, Kan H, Xian A, Wang G, Xiao H, Chen J (2019) The effects of firework regulation on air quality and public health during the Chinese Spring Festival from 2013 to 2017 in a Chinese megacity. Environ Int 126:96–106.  https://doi.org/10.1016/j.envint.2019.01.037 CrossRefGoogle Scholar
  61. Yi Y, Zhou X, Xue L, Wang W (2018a) Air pollution: formation of brown, lighting-absorbing, secondary organic aerosols by reaction of hydroxyacetone and methylamine. Environ Chem Lett 16(3):1083–1088.  https://doi.org/10.1007/s10311-018-0727-6 CrossRefGoogle Scholar
  62. Yi H, Yang K, Tang X, Liu X, Zhao S, Gao F, Huang Y, Yang Z, Wang J, Shi Y (2018b) Effects of preparation conditions on the performance of simultaneous desulfurization and denitrification over SiO2–MnOx composites. J Clean Prod 189:627–634.  https://doi.org/10.1016/j.jclepro.2018.04.044 CrossRefGoogle Scholar
  63. Yu S (2019) Fog geoengineering to abate local ozone pollution at ground level by enhancing air moisture. Environ Chem Lett 17(2):565–580.  https://doi.org/10.1007/s10311-018-0809-5 CrossRefGoogle Scholar
  64. Yue W, Tong L, Liu X, Weng X, Chen X, Wang D, Dudley S, Weir E, Ding W, Lu Z, Xu Y, Chen Y (2019) Short term PM2.5 exposure caused a robust lung inflammation, vascular remodeling, and exacerbated transition from left ventricular failure to right ventricular hypertrophy. Redox Biol 22:101161.  https://doi.org/10.1016/j.redox.2019.101161 CrossRefGoogle Scholar
  65. Zeng Y, Cao Y, Qiao X, Seyler B, Tang Y (2019) Air pollution reduction in China: recent success but great challenge for the future. Sci Total Environ 663:329–337.  https://doi.org/10.1016/j.scitotenv.2019.01.262 CrossRefGoogle Scholar
  66. Zhang H, Yao Y (2017) Vermiculite addition to soil decreases N water pollution by over 30%. Environ Chem Lett 15(3):507–513.  https://doi.org/10.1007/s10311-017-0631-5 CrossRefGoogle Scholar
  67. Zhang T, Li G, Wang W, Du Y, Li C, Lv J (2012) Theoretical studies on atmospheric reactions of CH2FO2 with HO2 and HO2·H2O complex. Comput Theor Chem 991:13–21.  https://doi.org/10.1016/j.comptc.2012.03.016 CrossRefGoogle Scholar
  68. Zhang H, Wang Y, Park T, Deng Y (2017) Quantifying the relationship between extreme air pollution events and extreme weather events. Atmos Res 188:64–79.  https://doi.org/10.1016/j.atmosres.2016.11.010 CrossRefGoogle Scholar
  69. Zhang H, Liu R, Ning T, Lal R (2018a) Higher CO2 absorption using a new class of calcium hydroxide (Ca(OH)2) nanoparticles. Environ Chem Lett 16(3):1095–1100.  https://doi.org/10.1007/s10311-018-0729-4 CrossRefGoogle Scholar
  70. Zhang X, Ding Z, Yang J, Cizmas L, Lichtfouse E, Sharma V (2018b) Efficient microwave degradation of humic acids in water using persulfate and activated carbon. Environ Chem Lett 16(3):1069–1075.  https://doi.org/10.1007/s10311-018-0721-z CrossRefGoogle Scholar
  71. Zhang L, Long R, Chen H, Geng J (2019a) A review of China’s road traffic carbon emissions. J Clean Prod 207:569–581.  https://doi.org/10.1016/j.jclepro.2018.10.003 CrossRefGoogle Scholar
  72. Zhang Z, Kuramochi H, Osako M (2019b) Predicted distribution of 16 short-chain chlorinated paraffins in air, water, soils and sediments. Environ Chem Lett 17(1):515–520.  https://doi.org/10.1007/s10311-018-0787-7 CrossRefGoogle Scholar
  73. Zhao K, He T, Wu S, Wang S, Dai B, Yang Q, Lei Y (2019) Research on video classification method of key pollution sources based on deep learning. J Vis Commun Image Represent 59:283–291.  https://doi.org/10.1016/j.jvcir.2019.01.015 CrossRefGoogle Scholar
  74. Zhou M, Zhang J, Sun C (2018) Easier removal of nonylphenol and naphthalene pollutants in wet weather revealed by Markov chains modeling. Environ Chem Lett 16(3):1089–1093.  https://doi.org/10.1007/s10311-018-0728-5 CrossRefGoogle Scholar
  75. Zou B, You J, Lin Y, Duan X, Zhao X, Fang X, Campen M, Li X (2019) Air pollution intervention and life-saving effect in China. Environ Int 125:529–541.  https://doi.org/10.1016/j.envint.2018.10.045 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Clean Energy UtilizationZhejiang UniversityHangzhouPeople’s Republic of China
  2. 2.China New Building Materials Design and Research InstituteChina National Building Materials Group CorporationHangzhouPeople’s Republic of China
  3. 3.Department of Intensive Care UnitSir Run Run Shaw Hospital, Zhejiang University School of MedicineHangzhouPeople’s Republic of China

Personalised recommendations