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Zhang L, Wang L, Ji D, Xia Z, Nan P, Zhang J, Li K, Qi B, Du R, Sun Y, Wang Y, Hu B. Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 922:171295. [PMID: 38417501 DOI: 10.1016/j.scitotenv.2024.171295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/23/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
Megacity Hangzhou, located in eastern China, has experienced severe O3 pollution in recent years, thereby clarifying the key drivers of the formation is essential to suppress O3 deterioration. In this study, the ensemble machine learning model (EML) coupled with Shapley additive explanations (SHAP), and positive matrix factorization were used to explore the impact of various factors (including meteorology, chemical components, sources) on O3 formation during the whole period, pollution days, and typical persistent pollution events from April to October in 2021-2022. The EML model achieved better performance than the single model, with R2 values of 0.91. SHAP analysis revealed that meteorological conditions had the greatest effects on O3 variability with the contribution of 57 %-60 % for different pollution levels, and the main drivers were relative humidity and radiation. The effects of chemical factors on O3 formation presented a positive response to volatile organic compounds (VOCs) and fine particulate matter (PM2.5), and a negative response to nitrogen oxides (NOx). Oxygenated compounds (OVOCs), alkenes, and aromatic of VOCs subgroups had higher contribution; additionally, the effects of PM2.5 and NOx were also important and increased with the O3 deterioration. The impact of seven emission sources on O3 formation in Hangzhou indicated that vehicle exhaust (35 %), biomass combustion (16 %), and biogenic emissions (12 %) were the dominant drivers. However, for the O3 pollution days, the effects of biomass combustion and biogenic emissions increased. Especially in persistent pollution events with highest O3 concentrations, the magnitude of biogenic emission effect elevated significantly by 156 % compared to the whole situations. Our finding revealed that the combination of the EML model and SHAP analysis could provide a reliable method for rapid diagnosis of the cause of O3 pollution at different event scales, supporting the formulation of control measures.
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Affiliation(s)
- Lei Zhang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Lili Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Zhejiang Key Laboratory of Ecological and Environmental Big Data (2022P10005), Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Dan Ji
- Suichang Meteorological Bureau, Suichang 323000, China
| | - Zheng Xia
- Zhejiang Key Laboratory of Ecological and Environmental Big Data (2022P10005), Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China; Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Hangzhou 310012, China
| | - Peifan Nan
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
| | - Jiaxin Zhang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Ke Li
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Bing Qi
- Hangzhou Meteorological Bureau, Hangzhou 310051, China
| | - Rongguang Du
- Hangzhou Meteorological Bureau, Hangzhou 310051, China
| | - Yang Sun
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yuesi Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Bo Hu
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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Nath SJ, Girach IA, Harithasree S, Bhuyan K, Ojha N, Kumar M. Urban ozone variability using automated machine learning: inference from different feature importance schemes. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:393. [PMID: 38520559 DOI: 10.1007/s10661-024-12549-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/16/2024] [Indexed: 03/25/2024]
Abstract
Tropospheric ozone is an air pollutant at the ground level and a greenhouse gas which significantly contributes to the global warming. Strong anthropogenic emissions in and around urban environments enhance surface ozone pollution impacting the human health and vegetation adversely. However, observations are often scarce and the factors driving ozone variability remain uncertain in the developing regions of the world. In this regard, here, we conducted machine learning (ML) simulations of ozone variability and comprehensively examined the governing factors over a major urban environment (Ahmedabad) in western India. Ozone precursors (NO2, NO, CO, C5H8 and CH2O) from the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis and meteorological parameters from the ERA5 (European Centre for Medium-Range Weather Forecast's (ECMWF) fifth-generation reanalysis) were included as features in the ML models. Automated ML (AutoML) fitted the deep learning model optimally and simulated the daily ozone with root mean square error (RMSE) of ~2 ppbv reproducing 84-88% of variability. The model performance achieved here is comparable to widely used ML models (RF-Random Forest and XGBoost-eXtreme Gradient Boosting). Explainability of the models is discussed through different schemes of feature importance, including SAGE (Shapley Additive Global importancE) and permutation importance. The leading features are found to be different from different feature importance schemes. We show that urban ozone could be simulated well (RMSE = 2.5 ppbv and R2 = 0.78) by considering first four leading features, from different schemes, which are consistent with ozone photochemistry. Our study underscores the need to conduct science-informed analysis of feature importance from multiple schemes to infer the roles of input variables in ozone variability. AutoML-based studies, exploiting potentials of long-term observations, can strongly complement the conventional chemistry-transport modelling and can also help in accurate simulation and forecast of urban ozone.
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Affiliation(s)
- Sankar Jyoti Nath
- Centre for Environment and Energy Development, Ranchi, 834001, India
| | - Imran A Girach
- Space Applications Centre, Indian Space Research Organisation, Ahmedabad, 380015, India.
| | - S Harithasree
- Physical Research Laboratory, Ahmedabad, 380009, India
- Indian Institute of Technology, Gandhinagar, 382055, Gujarat, India
| | - Kalyan Bhuyan
- Centre for Atmospheric Studies, Dibrugarh University, Dibrugarh, 786004, India
| | - Narendra Ojha
- Physical Research Laboratory, Ahmedabad, 380009, India.
| | - Manish Kumar
- Centre for Environment and Energy Development, Ranchi, 834001, India
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Kalbande R, Kumar B, Maji S, Yadav R, Atey K, Rathore DS, Beig G. Machine learning based quantification of VOC contribution in surface ozone prediction. CHEMOSPHERE 2023; 326:138474. [PMID: 36958496 DOI: 10.1016/j.chemosphere.2023.138474] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/14/2023] [Accepted: 03/20/2023] [Indexed: 06/18/2023]
Abstract
The prediction of surface ozone is essential attributing to its impact on human and environmental health. Volatile organic compounds (VOCs) are crucial in driving ozone concentration; particularly in urban areas where VOC limited regimes are prominent. The limited measurements of VOCs, however, hinder assessing the VOC-ozone relationship. This work applies machine learning (ML) algorithms for temporal forecasting of surface ozone over a metropolitan city in India. The availability of continuous VOCs measurement data along with meteorology and other pollutants during 2014-2016 makes it possible to deduce the influence of various input parameters on surface ozone prediction. After evaluating the best ML model for ozone prediction, simulations were carried out using varied input combinations. The combination with isoprene, meteorology, NOx, and CO (Isop + MNC) was the best with RMSE 4.41 ppbv and MAPE 6.77%. A season-wise comparison of simulations having all data, only meteorological data and Isop + MNC as input showed that Isop + MNC simulation gives the best results during the summer season (RMSE: 5.86 ppbv, MAPE: 7.05%). This shows the increased ability of the model to capture ozone peaks (high ozone during summer) relatively better when isoprene data is used. The overall results highlight that using all available data doesn't necessarily give best prediction results; also critical thinking is essential when evaluating the model results.
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Affiliation(s)
- Ritesh Kalbande
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; Mohanlal Sukhadia University, Udaipur, India.
| | - Bipin Kumar
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
| | - Sujit Maji
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
| | - Ravi Yadav
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; NUS Environmental Research Insitute, National University of Singapore, Singapore.
| | - Kaustubh Atey
- Indian Institute of Science Education and Research, Pune, India.
| | | | - Gufran Beig
- National Institute of Advanced Studies, Indian Institute of Science Campus, Bangalore, India.
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Wang L, Zhao Y, Shi J, Ma J, Liu X, Han D, Gao H, Huang T. Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 318:120798. [PMID: 36464118 DOI: 10.1016/j.envpol.2022.120798] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/24/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Ground-level ozone (O3) formation depends on meteorology, precursor emissions, and atmospheric chemistry. Understanding the key drivers behind the O3 formation and developing an accurate and efficient method for timely assessing the O3-VOCs-NOx relationships applicable in different O3 pollution events are essential. Here, we developed a novel machine learning ensemble model coupled with a Shapley additive explanation algorithm to predict the O3 formation regime and derive O3 formation sensitivity curves. The algorithm was tested for O3 events during the COVID-19 lockdown, a sandstorm event, and a heavy O3 pollution episode (maximum hourly O3 concentration >200 μg/m3) from 2019 to 2021. We show that increasing O3 concentrations during the COVID-19 lockdown and the heavy O3 pollution event were mainly caused by the photochemistry subject to local air quality and meteorological conditions. Influenced by the sandstorm weather, low O3 levels were mainly attributable to weak sunlight and low precursor levels. O3 formation sensitivity curves demonstrate that O3 formation in the study area was in a VOCs-sensitive regime. The VOCs-specific O3 sensitivity curves can also help make hybrid and timely strategies for O3 abatement. The results demonstrate that machine learning driven by observational data has the potential to be a very useful tool in predicting and interpreting O3 formation.
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Affiliation(s)
- Li Wang
- Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, 730000, China
| | - Yuan Zhao
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.
| | - Jinsen Shi
- Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, 730000, China
| | - Jianmin Ma
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Xiaoyue Liu
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Dongliang Han
- Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, 730000, China
| | - Hong Gao
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Tao Huang
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
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Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm. Sci Rep 2022; 12:5646. [PMID: 35383223 PMCID: PMC8983660 DOI: 10.1038/s41598-022-09619-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/25/2022] [Indexed: 12/03/2022] Open
Abstract
Surface ozone (O\documentclass[12pt]{minimal}
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\begin{document}$$_3$$\end{document}3) is primarily formed through complex photo-chemical reactions in the atmosphere, which are non-linearly dependent on precursors. Even though, there have been many recent studies exploring the potential of machine learning (ML) in modeling surface ozone, the inclusion of limited available ozone precursors information has received little attention. The ML algorithm with in-situ NO information and meteorology explains 87% (R\documentclass[12pt]{minimal}
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\begin{document}$$^{2}$$\end{document}2 = 0.87) of the ozone variability over Munich, a German metropolitan area, which is 15% higher than a ML algorithm that considers only meteorology. The ML algorithm trained for the urban measurement station in Munich can also explain the ozone variability of the other three stations in the same city, with R\documentclass[12pt]{minimal}
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\begin{document}$$^{2}$$\end{document}2 = 0.88, 0.91, 0.63. While the same model robustly explains the ozone variability of two other German cities’ (Berlin and Hamburg) measurement stations, with R\documentclass[12pt]{minimal}
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\begin{document}$$^{2}$$\end{document}2 ranges from 0.72 to 0.84, giving confidence to use the ML algorithm trained for one location to other locations with sparse ozone measurements. The inclusion of satellite O\documentclass[12pt]{minimal}
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\begin{document}$$_3$$\end{document}3 precursors information has little effect on the ML model’s performance.
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