Appreciating the role of big data in the modernization of environmental governance

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References

[1] Almuhtaram H, Zamyadi A, Hofmann R (2021). Machine learning for anomaly detection in cyanobacterial fluorescence signals. Water Research, 197: 117073

[2] Di Q, Kloog I, Koutrakis P, Lyapustin A, Wang Y, Schwartz J (2016). Assessing PM_2.5 exposures with high spatiotemporal resolution across the continental United States. Environmental Science & Technology, 50(9): 4712–4721

[3] Eyre R, de Luca F, Simini F (2020). Social media usage reveals recovery of small businesses after natural hazard events. Nature Communications, 11(1): 1629

[4] Jones S G (1995). CyberSociety: Computer-Mediated Communication and Community. Thousand Oaks, CA: Sage Publications

[5] Kirilenko A P, Stepchenkova S O (2014). Public microblogging on climate change: One year of Twitter worldwide. Global Environmental Change, 26: 171–182

[6] Liu M, Bi J, Yang J, Qu S, Wang J (2021). Social media never shake the role of trust building in relieving public risk perception. Journal of Cleaner Production, 282: 124442

[7] Liu R, Ma Z, Liu Y, Shao Y, Zhao W, Bi J (2020). Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach. Environment International, 142: 105823

[8] Liu X, Hu W (2019). Attention and sentiment of Chinese public toward green buildings based on Sina Weibo. Sustainable Cities and Society, 44: 550–558

[9] Lombard M A, Bryan M S, Jones D K, Bulka C, Bradley P M, Backer L C, Focazio M J, Silverman D T, Toccalino P, Argos M, Gribble M O, Ayotte J D (2021). Machine learning models of arsenic in private wells throughout the conterminous United States as a tool for exposure assessment in human health studies. Environmental Science & Technology, 55(8): 5012–5023

[10] Lu W (2019). Big data analytics to identify illegal construction waste dumping: A Hong Kong study. Resources, Conservation and Recycling, 141: 264–272

[11] Moore F C, Obradovich N (2020). Using remarkability to define coastal flooding thresholds. Nature Communications, 11(1): 530

[12] Reid C E, Jerrett M, Petersen M L, Pfister G G, Morefield P E, Tager I B, Raffuse S M, Balmes J R (2015). Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning. Environmental Science & Technology, 49(6): 3887–3896

[13] Vu T, Shi Z, Cheng J, Zhang Q, He K, Wang S, Harrison R M (2019). Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique. Atmospheric Chemistry and Physics, 19(17): 11303–11314

[14] Wang Y, Hu J, Zhu J, Li J, Qin M, Liao H, Chen K, Wang M (2021). Health burden and economic impacts attributed to PM_2.5 and O₃ in China from 2010 to 2050 under different representative concentration pathway scenarios. Resources, Conservation and Recycling, 173(38): 105731

[15] Wen S, Wang Q, Li Y M, Zhu L, Lü H, Lei S H, Ding X L, Miao S (2018). Remote sensing identification of urban black-odor water bodies based on high-resolution images: A case study in Nanjing. Environmental Science, 39(1): 57–67 (in Chinese) PMID:29965666

[16] Wu Z, Zhang Y, Chen Q, Wang H (2021). Attitude of Chinese public towards municipal solid waste sorting policy: A text mining study. Science of the Total Environment, 756: 142674

[17] Yang H, Huang K, Zhang K, Weng Q, Zhang H, Wang F (2021a). Predicting heavy metal adsorption on soil with machine learning and mapping global distribution of soil adsorption capacities. Environmental Science & Technology, 55(20): 14316–14328

[18] Yang J, Wen Y, Wang Y, Zhang S, Pinto J P, Pennington E A, Wang Z, Wu Y, Sander S P, Jiang J H, Hao J, Yung Y L, Seinfeld J H (2021b). From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model. Proceedings of the National Academy of Sciences of the United States of America, 118(26): e2102705118

[19] Zhong S, Zhang K, Bagheri M, Burken J G, Gu A, Li B, Ma X, Marrone B L, Ren Z J, Schrier J, Shi W, Tan H, Wang T, Wang X, Wong B M, Xiao X, Yu X, Zhu J J, Zhang H (2021). Machine learning: New ideas and tools in environmental science and engineering. Environmental Science & Technology, 55(19): 12741–12754

[20] Zhu D, Zhou Q, Liu M, Bi J (2021). Non-optimum temperature-related mortality burden in China: Addressing the dual influences of climate change and urban heat islands. Science of the Total Environment, 782: 146760

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