Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

doi:10.1007/s11783-023-1738-5

Front. Environ. Sci. Eng.

2023, Vol. 17

Issue (11) : 138 https://doi.org/10.1007/s11783-023-1738-5

RESEARCH ARTICLE

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Jin Xue^1,², Fangting Wang^1,², Kun Zhang^1,², Hehe Zhai^1,², Dan Jin³, Yusen Duan³(

), Elly Yaluk^1,², Yangjun Wang^1,², Ling Huang^1,², Yuewu Li³, Thomas Lei⁴, Qingyan Fu³, Joshua S. Fu⁵, Li Li^1,²(

)

¹. School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
². Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
³. Shanghai Environmental Monitoring Center, Shanghai 200235, China
⁴. Institute of Science and Environment, University of Saint Joseph, Macao 999078, China
⁵. Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USA

Download: PDF(5849 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

● A novel integrated machine learning method to analyze O₃ changes is proposed.

● Various factors affecting long-term changes of O₃ in Shanghai are quantified.

● Meteorological, photochemical, and regional background O₃ are well separated.

Surface ozone (O₃) is influenced by regional background and local photochemical formation under favorable meteorological conditions. Understanding the contribution of these factors to changes in O₃ is crucial to address the issue of O₃ pollution. In this study, we propose a novel integrated method that combines random forest, principal component analysis, and Shapley additive explanations to distinguish observed O₃ into meteorologically affected ozone (O_{3_MET}), chemically formed from local emissions (O_{3_LC}), and regional background ozone (O_{3_RBG}). Applied to three typical stations in Shanghai during the warm season from 2013 to 2021, the results indicate that O_{3_RBG} in Shanghai was 48.8 ± 0.3 ppb, accounting for 79.6%–89.4% at different sites, with an overall declining trend of 0.018 ppb/yr. O_{3_LC} at urban and regional sites ranged from 5.9–9.0 ppb and 8.9–14.6 ppb, respectively, which were significantly higher than the contributions of 2.5–7.4 ppb at an upwind background site. O_{3_MET} can be categorized into those affecting O₃ photochemical generation and those changing O₃ dispersion conditions, with absolute contributions to O₃ ranging from 13.4–19.0 ppb and 13.1–13.7 ppb, respectively. We found that the O₃ rebound in 2017, compared to 2013, was primarily influenced by unfavorable O₃ dispersion conditions and unbalanced emission reductions; while the O₃ decline in 2021, compared to 2017, was primarily influenced by overall favorable meteorological conditions and further emissions reduction. These findings highlight the challenge of understanding O₃ change due to meteorology and regional background, emphasizing the need for systematic interpretation of the different components of O₃.

Keywords Ozone Integrated method Machine learning

Corresponding Author(s): Yusen Duan,Li Li

About author:

^* These authors contributed equally to this work.

Issue Date: 15 November 2023

Cite this article:

Jin Xue,Fangting Wang,Kun Zhang, et al. Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method[J]. Front. Environ. Sci. Eng., 2023, 17(11): 138.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1738-5
https://academic.hep.com.cn/fese/EN/Y2023/V17/I11/138

Fig.1 Framework of the Integrated Method.

Fig.2 Time series of O₃, maximum daily 8 hour average ozone (MDA8 O₃) and NO₂ at PT (blue), DSL (red), PDHN (green). In the left figure, the thick line is the 360-day sliding average; the thin line is the daily average. In the right figure; the shaded area is the 95% confidence interval of corresponding data.

Fig.3 Bivariate polar plot and wind rose at PT, DSL and PDHN.

Fig.4 Comparisons of the major meteorological factors among the three observational sites.

Fig.5 Meteorological contributions to O₃ at PT, DSL and PDHN. The bands beside the lines are the 95% confidence intervals in panel (a). Base is the base value of the SHAP approach, i.e. the average of the SHAP value in panels (b–d).

Fig.6 Contributions of O_{3_RBG} at PT, DSL and PDHN. In panel (a), the upper and lower boundaries of the box represent the 25th percentile and 75th percentile, and the notches represent the confidence interval around the median. The line and hollow triangles inside boxes represent the average and median values of corresponding data, respectively. The error lines above and below the box represent the 25th percentile value minus 1.5 * IQR (interquartile range) and the 75th percentile value plus 1.5 * IQR (interquartile range), respectively. In panel (b), the error line indicates the 95% confidence interval of the corresponding data.

Fig.7 Contributions of O_{3_LC} at (a) PT, (b) DSL and (c) PDHN. In panels (a–c), the upper and lower boundaries of the box represent the 25th percentile and 75th percentile, and the notches represent the confidence interval around the median. The line and hollow triangles inside boxes represent the average and median values of corresponding data, respectively. The error lines above and below the box represent the 25th percentile value minus 1.5 * IQR (interquartile range) and the 75th percentile value plus 1.5 * IQR (interquartile range), respectively. In panels (d–f), the error line indicates the 95% confidence interval of the corresponding data.

Fig.8 Quantification of influencing factors on O₃ changes during the warm season from 2013 to 2021 in Shanghai.

Fig.9 Variation of influencing factors on O₃ for typical years at PT (a), DSL (b) and PDHN (c).

1	O A Alduchov, R E Eskridge. (1996). Improved Magnus form approximation of saturation vapor pressure. Journal of Applied Meteorology, 35(4): 601–609 https://doi.org/10.1175/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2
2	E Austin, J Xiang, T R Gould, J H Shirai, S Yun, M G Yost, T V Larson, E Seto. (2021). Distinct ultrafine particle profiles associated with aircraft and roadway traffic. Environmental Science & Technology, 55(5): 2847–2858 https://doi.org/10.1021/acs.est.0c05933
3	S R Berlin, A O Langford, M Estes, M Dong, D D Parrish. (2013). Magnitude, decadal changes, and impact of regional background ozone transported into the greater Houston, Texas, area. Environmental Science & Technology, 47(24): 13985–13992 https://doi.org/10.1021/es4037644
4	L Camalier, W Cox, P Dolwick. (2007). The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmospheric Environment, 41(33): 7127–7137 https://doi.org/10.1016/j.atmosenv.2007.04.061
5	L Chen, J Zhu, H Liao, Y Yang, X Yue. (2020). Meteorological influences on PM2.5 and O3 trends and associated health burden since China’s clean air actions. Science of the Total Environment, 744: 140837 https://doi.org/10.1016/j.scitotenv.2020.140837
6	W Chen, A B Guenther, M Shao, B Yuan, S Jia, J Mao, F Yan, P Krishnan, X Wang. (2022). Assessment of background ozone concentrations in China and implications for using region-specific volatile organic compounds emission abatement to mitigate air pollution. Environmental Pollution, 305: 119254 https://doi.org/10.1016/j.envpol.2022.119254
7	Q Dai, L Hou, B Liu, Y Zhang, C Song, Z Shi, P K Hopke, Y Feng. (2021). Spring festival and COVID-19 lockdown: disentangling PM sources in major Chinese cities. Geophysical Research Letters, 48(11): e2021GL093403
8	R Dang, H Liao, Y Fu. (2021). Quantifying the anthropogenic and meteorological influences on summertime surface ozone in China over 2012–2017. Science of the Total Environment, 754: 142394 https://doi.org/10.1016/j.scitotenv.2020.142394
9	J Ding, Q Dai, W Fan, M Lu, Y Zhang, S Han, Y Feng. (2023). Impacts of meteorology and precursor emission change on O3 variation in Tianjin, China from 2015 to 2021. Journal of Environmental Sciences-China, 126: 506–516 https://doi.org/10.1016/j.jes.2022.03.010
10	D Gao, M Xie, J Liu, T Wang, C Ma, H Bai, X Chen, M Li, B Zhuang, S Li. (2021). Ozone variability induced by synoptic weather patterns in warm seasons of 2014–2018 over the Yangtze River Delta region, China. Atmospheric Chemistry and Physics, 21(8): 5847–5864 https://doi.org/10.5194/acp-21-5847-2021
11	J González-Pardo, S Ceballos-Santos, R Manzanas, M Santibáñez, I Fernández-Olmo. (2022). Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: a case-study for urban traffic sites in Spain. Science of the Total Environment, 823: 153786 https://doi.org/10.1016/j.scitotenv.2022.153786
12	S K Grange, D C Carslaw. (2019). Using meteorological normalisation to detect interventions in air quality time series. Science of the Total Environment, 653: 578–588 https://doi.org/10.1016/j.scitotenv.2018.10.344
13	S K Grange, D C Carslaw, A C Lewis, E Boleti, C Hueglin. (2018). Random forest meteorological normalisation models for Swiss PM10 trend analysis. Atmospheric Chemistry and Physics, 18(9): 6223–6239 https://doi.org/10.5194/acp-18-6223-2018
14	L R F Henneman, H A Holmes, J A Mulholland, A G Russell. (2015). Meteorological detrending of primary and secondary pollutant concentrations: method application and evaluation using long-term (2000–2012) data in Atlanta. Atmospheric Environment, 119: 201–210 https://doi.org/10.1016/j.atmosenv.2015.08.007
15	R M Hirsch, J R Slack, R A Smith. (1982). Techniques of trend analysis for monthly water quality data. Water Resources Research, 18(1): 107–121 https://doi.org/10.1029/WR018i001p00107
16	L Hou, Q Dai, C Song, B Liu, F Guo, T Dai, L Li, B Liu, X Bi, Y Zhang. et al.. (2022). Revealing drivers of haze pollution by explainable machine learning. Environmental Science & Technology Letters, 9(2): 112–119 https://doi.org/10.1021/acs.estlett.1c00865
17	C Hu, P Kang, D A Jaffe, C Li, X Zhang, K Wu, M Zhou. (2021). Understanding the impact of meteorology on ozone in 334 cities of China. Atmospheric Environment, 248: 118221 https://doi.org/10.1016/j.atmosenv.2021.118221
18	Y Huang, J Chen, Q Duan, Y Feng, R Luo, W Wang, F Liu, S Bi, J Lee. (2022). A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning. Frontiers of Environmental Science & Engineering, 16(3): 38 https://doi.org/10.1007/s11783-021-1472-9
19	I Joliffe, B Morgan. (1992). Principal component analysis and exploratory factor analysis. Statistical Methods in Medical Research, 1(1): 69–95 https://doi.org/10.1177/096228029200100105
20	A O Langford, C J Senff, R M Banta, R M Hardesty, R J II Alvarez, S P Sandberg, L S Darby. (2009). Regional and local background ozone in Houston during Texas air quality study 2006. Journal of Geophysical Research, 114(D7): D00F12 https://doi.org/10.1029/2008JD011687
21	J Lelieveld, J S Evans, M Fnais, D Giannadaki, A Pozzer. (2015). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525(7569): 367–371 https://doi.org/10.1038/nature15371
22	C Li, Q Zhu, X Jin, R C Cohen. (2022). Elucidating contributions of anthropogenic volatile organic compounds and particulate matter to ozone trends over China. Environmental Science & Technology, 56(18): 12906–12916 https://doi.org/10.1021/acs.est.2c03315
23	K Li, D J Jacob, H Liao, Y Qiu, L Shen, S Zhai, K H Bates, M P Sulprizio, S Song, X Lu. et al.. (2021a). Ozone pollution in the North China Plain spreading into the late-winter haze season. Proceedings of the National Academy of Sciences of the United States of America, 118(10): e2015797118 https://doi.org/10.1073/pnas.2015797118
24	K Li, D J Jacob, H Liao, L Shen, Q Zhang, K H Bates. (2019). Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China. Proceedings of the National Academy of Sciences of the United States of America, 116(2): 422–427 https://doi.org/10.1073/pnas.1812168116
25	K Li, D J Jacob, L Shen, X Lu, I De Smedt, H Liao. (2020). Increases in surface ozone pollution in China from 2013 to 2019: anthropogenic and meteorological influences. Atmospheric Chemistry and Physics, 20(19): 11423–11433 https://doi.org/10.5194/acp-20-11423-2020
26	X B Li, G Fan, S Lou, B Yuan, X Wang, M Shao. (2021b). Transport and boundary layer interaction contribution to extremely high surface ozone levels in eastern China. Environmental Pollution, 268: 115804 https://doi.org/10.1016/j.envpol.2020.115804
27	C Lin, A K H Lau, J C H Fung, Y Song, Y Li, M Tao, X Lu, J Ma, X Q Lao. (2021). Removing the effects of meteorological factors on changes in nitrogen dioxide and ozone concentrations in China from 2013 to 2020. Science of the Total Environment, 793: 148575 https://doi.org/10.1016/j.scitotenv.2021.148575
28	Y Liu, T Wang. (2020a). Worsening urban ozone pollution in China from 2013 to 2017 – Part 1: The complex and varying roles of meteorology. Atmospheric Chemistry and Physics, 20(11): 6305–6321 https://doi.org/10.5194/acp-20-6305-2020
29	Y Liu, T Wang. (2020b). Worsening urban ozone pollution in China from 2013 to 2017 – Part 2: The effects of emission changes and implications for multi-pollutant control. Atmospheric Chemistry and Physics, 20(11): 6323–6337 https://doi.org/10.5194/acp-20-6323-2020
30	M Lovrić, K Pavlović, M Vuković, S K Grange, M Haberl, R Kern. (2021). Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning. Environmental Pollution, 274: 115900 https://doi.org/10.1016/j.envpol.2020.115900
31	W Lu, W Huo, H Gulina, C Pan. (2022). Development of machine learning multi-city model for municipal solid waste generation prediction. Frontiers of Environmental Science & Engineering, 16(9): 119 https://doi.org/10.1007/s11783-022-1551-6
32	X Lu, L Zhang, Y Chen, M Zhou, B Zheng, K Li, Y Liu, J Lin, T M Fu, Q Zhang. (2019). Exploring 2016–2017 surface ozone pollution over China: source contributions and meteorological influences. Atmospheric Chemistry and Physics, 19(12): 8339–8361 https://doi.org/10.5194/acp-19-8339-2019
33	S M Lundberg, G Erion, H Chen, A Degrave, J M Prutkin, B Nair, R Katz, J Himmelfarb, N Bansal, S I Lee. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1): 56–67 https://doi.org/10.1038/s42256-019-0138-9
34	S M LundbergS I (2017) Lee. A Unified Approach to Interpreting Model Predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA. Red Hook, NY: Curran Associates Inc, 4768–4777
35	S Mousavinezhad, Y Choi, A Pouyaei, M Ghahremanloo, D L Nelson. (2021). A comprehensive investigation of surface ozone pollution in China, 2015–2019: Separating the contributions from meteorology and precursor emissions. Atmospheric Research, 257: 105599 https://doi.org/10.1016/j.atmosres.2021.105599
36	R Ni, J Lin, Y Yan, W Lin. (2018). Foreign and domestic contributions to springtime ozone over China. Atmospheric Chemistry and Physics, 18(15): 11447–11469 https://doi.org/10.5194/acp-18-11447-2018
37	M Pathakoti, T Santhoshi, M Aarathi, D V Mahalakshmi, A L Kanchana, J Srinivasulu, R S S Shekhar, V K Soni, S M V R Sai, P Raja. (2021). Assessment of spatio-temporal climatological trends of ozone over the Indian region using machine learning. Spatial Statistics, 43: 100513 https://doi.org/10.1016/j.spasta.2021.100513
38	L Qu, S Liu, L Ma, Z Zhang, J Du, Y Zhou, F Meng. (2020). Evaluating the meteorological normalized PM2.5 trend (2014–2019) in the “2+26” region of China using an ensemble learning technique. Environmental Pollution, 266: 115346 https://doi.org/10.1016/j.envpol.2020.115346
39	S K Sahu, S Liu, S Liu, D Ding, J Xing. (2021). Ozone pollution in China: Background and transboundary contributions to ozone concentration & related health effects across the country. Science of the Total Environment, 761: 144131 https://doi.org/10.1016/j.scitotenv.2020.144131
40	P K Sen. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324): 1379–1389 https://doi.org/10.1080/01621459.1968.10480934
41	Z Shi, C Song, B Liu, G Lu, J Xu, T Van Vu, R J R Elliott, W Li, W J Bloss, R M Harrison. (2021). Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances, 7(3): eabd6696 https://doi.org/10.1126/sciadv.abd6696
42	C Song, S Becagli, D C S Beddows, J Brean, J Browse, Q Dai, M Dall’osto, V Ferracci, R M Harrison, N Harris. et al.. (2022). Understanding sources and drivers of size-resolved aerosol in the high Arctic Islands of Svalbard using a receptor model coupled with machine learning. Environmental Science & Technology, 56(16): 11189–11198 https://doi.org/10.1021/acs.est.1c07796
43	A F Stein, R R Draxler, G D Rolph, B J B Stunder, M D Cohen, F Ngan. (2015). NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bulletin of the American Meteorological Society, 96(12): 2059–2077 https://doi.org/10.1175/BAMS-D-14-00110.1
44	L G Suciu, R J Griffin, C A Masiello. (2017). Regional background O3 and NOx in the Houston–Galveston–Brazoria (TX) region: a decadal-scale perspective. Atmospheric Chemistry and Physics, 17(11): 6565–6581 https://doi.org/10.5194/acp-17-6565-2017
45	G Tang, Y Liu, X Huang, Y Wang, B Hu, Y Zhang, T Song, X Li, S Wu, Q Li. et al.. (2021). Aggravated ozone pollution in the strong free convection boundary layer. Science of the Total Environment, 788: 147740 https://doi.org/10.1016/j.scitotenv.2021.147740
46	M X Tang, X F Huang, T L Sun, Y Cheng, Y Luo, Z Chen, X Y Lin, L M Cao, Y H Zhai, L Y He. (2022). Decisive role of ozone formation control in winter PM2.5 mitigation in Shenzhen, China. Environmental Pollution, 301: 119027 https://doi.org/10.1016/j.envpol.2022.119027
47	T V Vu, Z Shi, J Cheng, Q Zhang, K He, S Wang, R M Harrison. (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 https://doi.org/10.5194/acp-19-11303-2019
48	F T Wang, K Zhang, J Xue, L Huang, Y J Wang, H Chen, S Y Wang, J S Fu, L Li. (2022a). Understanding regional background ozone by multiple methods: a case study in the Shandong region, China, 2018–2020. Journal of Geophysical Research. Atmospheres, 127(22): e2022JD036809 https://doi.org/10.1029/2022JD036809
49	P Wang, H Guo, J Hu, S H Kota, Q Ying, H Zhang. (2019). Responses of PM2.5 and O3 concentrations to changes of meteorology and emissions in China. Science of the Total Environment, 662: 297–306 https://doi.org/10.1016/j.scitotenv.2019.01.227
50	T Wang, L Xue, P Brimblecombe, Y F Lam, L Li, L Zhang. (2017). Ozone pollution in China: a review of concentrations, meteorological influences, chemical precursors, and effects. Science of the Total Environment, 575: 1582–1596 https://doi.org/10.1016/j.scitotenv.2016.10.081
51	W Wang, D D Parrish, S Wang, F Bao, R Ni, X Li, S Yang, H Wang, Y Cheng, H Su. (2022b). Long-term trend of ozone pollution in China during 2014–2020: distinct seasonal and spatial characteristics and ozone sensitivity. Atmospheric Chemistry and Physics, 22(13): 8935–8949 https://doi.org/10.5194/acp-22-8935-2022
52	Y Wang, Y Wen, Y Wang, S Zhang, K M Zhang, H Zheng, J Xing, Y Wu, J Hao. (2020). Four-month changes in air quality during and after the COVID-19 lockdown in six megacities in China. Environmental Science & Technology Letters, 7(11): 802–808 https://doi.org/10.1021/acs.estlett.0c00605
53	S N Wren, C M Mihele, G Lu, Z Jiang, D Wen, K Hayden, R L Mittermeier, R M Staebler, S G Cober, J R Brook. (2020). Improving insights on air pollutant mixtures and their origins by enhancing local monitoring in an area of intensive resource development. Environmental Science & Technology, 54(23): 14936–14945 https://doi.org/10.1021/acs.est.0c06055
54	Q Wu, T Li, S Zhang, J Fu, B C Seyler, Z Zhou, X Deng, B Wang, Y Zhan. (2022). Evaluation of NOx emissions before, during, and after the COVID-19 lockdowns in China: A comparison of meteorological normalization methods. Atmospheric Environment, 278: 119083 https://doi.org/10.1016/j.atmosenv.2022.119083
55	J Xu, X Tie, W Gao, Y Lin, Q Fu. (2019). Measurement and model analyses of the ozone variation during 2006 to 2015 and its response to emission change in megacity Shanghai, China. Atmospheric Chemistry and Physics, 19(14): 9017–9035 https://doi.org/10.5194/acp-19-9017-2019
56	L Yang, H Luo, Z Yuan, J Zheng, Z Huang, C Li, X Lin, P K K Louie, D Chen, Y Bian. (2019). Quantitative impacts of meteorology and precursor emission changes on the long-term trend of ambient ozone over the Pearl River Delta, China, and implications for ozone control strategy. Atmospheric Chemistry and Physics, 19(20): 12901–12916 https://doi.org/10.5194/acp-19-12901-2019
57	P Yang, H Yang, J Sardans, C Tong, G Zhao, J Peñuelas, L Li, Y Zhang, L Tan, K P Chun. et al.. (2020). Large spatial variations in diffusive CH4 fluxes from a subtropical coastal reservoir affected by sewage discharge in Southeast China. Environmental Science & Technology, 54(22): 14192–14203 https://doi.org/10.1021/acs.est.0c03431
58	H Yin, X Lu, Y Sun, K Li, M Gao, B Zheng, C Liu. (2021). Unprecedented decline in summertime surface ozone over eastern China in 2020 comparably attributable to anthropogenic emission reductions and meteorology. Environmental Research Letters, 16(12): 124069 https://doi.org/10.1088/1748-9326/ac3e22
59	K Zhang, Z Liu, X Zhang, Q Li, A Jensen, W Tan, L Huang, Y Wang, J De Gouw, L Li. (2022). Insights into the significant increase of ozone during COVID-19 in a typical urban city of China. Atmospheric Chemistry and Physics, 22(7): 4853–4866 https://doi.org/10.5194/acp-22-4853-2022
60	Y Zhang, T V Vu, J Sun, J He, X Shen, W Lin, X Zhang, J Zhong, W Gao, Y Wang. et al.. (2020). Significant changes in chemistry of fine particles in wintertime Beijing from 2007 to 2017: impact of clean air actions. Environmental Science & Technology, 54(3): 1344–1352 https://doi.org/10.1021/acs.est.9b04678
61	W Zhou, L Lei, A Du, Z Zhang, Y Li, Y Yang, G Tang, C Chen, W Xu, J Sun. et al.. (2022). Unexpected increases of severe haze pollution during the post COVID-19 period: effects of emissions, meteorology, and secondary production. Journal of Geophysical Research: Atmospheres, 127(3): e2021JD035710 https://doi.org/10.1029/2021JD035710
62	Q Zhu, A Gu, D Li, T Zhang, L Xiang, M He. (2021). Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm. Frontiers of Environmental Science & Engineering, 15(6): 136 https://doi.org/10.1007/s11783-021-1430-6

[1]

FSE-23036-OF-XJ_suppl_1

Download

[1]	Qiannan Duan, Pengwei Yan, Yichen Feng, Qianru Wan, Xiaoli Zhu. Machine learning assisted adsorption performance evaluation of biochar on heavy metal[J]. Front. Environ. Sci. Eng., 2024, 18(5): 55-.
[2]	Yanpeng Huang, Chao Wang, Yuanhao Wang, Guangfeng Lyu, Sijie Lin, Weijiang Liu, Haobo Niu, Qing Hu. Application of machine learning models in groundwater quality assessment and prediction: progress and challenges[J]. Front. Environ. Sci. Eng., 2024, 18(3): 29-.
[3]	Wiley Helm, Shifa Zhong, Elliot Reid, Thomas Igou, Yongsheng Chen. Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual prediction[J]. Front. Environ. Sci. Eng., 2024, 18(2): 17-.
[4]	Yang Zhang, Mei Lei, Kai Li, Tienan Ju. Spatial prediction of soil contamination based on machine learning: a review[J]. Front. Environ. Sci. Eng., 2023, 17(8): 93-.
[5]	Ling Qi, Zhige Tian, Nan Jiang, Fangyuan Zheng, Yuchen Zhao, Yishuo Geng, Xiaoli Duan. Collaborative control of fine particles and ozone required in China for health benefit[J]. Front. Environ. Sci. Eng., 2023, 17(8): 92-.
[6]	Zhongyao Liang, Yaoyang Xu, Gang Zhao, Wentao Lu, Zhenghui Fu, Shuhang Wang, Tyler Wagner. Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning[J]. Front. Environ. Sci. Eng., 2023, 17(6): 76-.
[7]	Yirong Hu, Wenjie Du, Cheng Yang, Yang Wang, Tianyin Huang, Xiaoyi Xu, Wenwei Li. Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique[J]. Front. Environ. Sci. Eng., 2023, 17(5): 55-.
[8]	Rui Liang, Chao Chen, Akash Kumar, Junyu Tao, Yan Kang, Dong Han, Xianjia Jiang, Pei Tang, Beibei Yan, Guanyi Chen. State-of-the-art applications of machine learning in the life cycle of solid waste management[J]. Front. Environ. Sci. Eng., 2023, 17(4): 44-.
[9]	Yuanxin Zhang, Fei Li, Chaoqiong Ni, Song Gao, Shuwei Zhang, Jin Xue, Zhukai Ning, Chuanming Wei, Fang Fang, Yongyou Nie, Zheng Jiao. Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network[J]. Front. Environ. Sci. Eng., 2023, 17(2): 21-.
[10]	Min Cheng, Zhiyuan Zhang, Shihui Wang, Kexin Bi, Kong-qiu Hu, Zhongde Dai, Yiyang Dai, Chong Liu, Li Zhou, Xu Ji, Wei-qun Shi. A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning[J]. Front. Environ. Sci. Eng., 2023, 17(12): 148-.
[11]	Weishuai Li, Jingang Huang, Zhuoer Shi, Wei Han, Ting Lü, Yuanyuan Lin, Jianfang Meng, Xiaobing Xu, Pingzhi Hou. Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated sludge fermentation[J]. Front. Environ. Sci. Eng., 2023, 17(11): 135-.
[12]	Haoyang Xian, Pinjing He, Dongying Lan, Yaping Qi, Ruiheng Wang, Fan Lü, Hua Zhang, Jisheng Long. Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning[J]. Front. Environ. Sci. Eng., 2023, 17(10): 121-.
[13]	Tienan Ju, Mei Lei, Guanghui Guo, Jinglun Xi, Yang Zhang, Yuan Xu, Qijia Lou. A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification[J]. Front. Environ. Sci. Eng., 2023, 17(1): 8-.
[14]	Wenjing Lu, Weizhong Huo, Huwanbieke Gulina, Chao Pan. Development of machine learning multi-city model for municipal solid waste generation prediction[J]. Front. Environ. Sci. Eng., 2022, 16(9): 119-.
[15]	Yicai Huang, Jiayuan Chen, Qiannan Duan, Yunjin Feng, Run Luo, Wenjing Wang, Fenli Liu, Sifan Bi, Jianchao Lee. A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning[J]. Front. Environ. Sci. Eng., 2022, 16(3): 38-.

Viewed

Full text

Abstract

Cited

Shared

Discussed