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Wu J, Chen X, Li R, Wang A, Huang S, Li Q, Qi H, Liu M, Cheng H, Wang Z. A novel framework for high resolution air quality index prediction with interpretable artificial intelligence and uncertainties estimation. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 357:120785. [PMID: 38583378 DOI: 10.1016/j.jenvman.2024.120785] [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: 09/08/2023] [Revised: 02/02/2024] [Accepted: 03/27/2024] [Indexed: 04/09/2024]
Abstract
Accurate air quality index (AQI) prediction is essential in environmental monitoring and management. Given that previous studies neglect the importance of uncertainty estimation and the necessity of constraining the output during prediction, we proposed a new hybrid model, namely TMSSICX, to forecast the AQI of multiple cities. Firstly, time-varying filtered based empirical mode decomposition (TVFEMD) was adopted to decompose the AQI sequence into multiple internal mode functions (IMF) components. Secondly, multi-scale fuzzy entropy (MFE) was applied to evaluate the complexity of each IMF component and clustered them into high and low-frequency portions. In addition, the high-frequency portion was secondarily decomposed by successive variational mode decomposition (SVMD) to reduce volatility. Then, six air pollutant concentrations, namely CO, SO2, PM2.5, PM10, O3, and NO2, were used as inputs. The secondary decomposition and preliminary portion were employed as the outputs for the bidirectional long short-term memory network optimized by the snake optimization algorithm (SOABiLSTM) and improved Catboost (ICatboost), respectively. Furthermore, extreme gradient boosting (XGBoost) was applied to ensemble each predicted sub-model to acquire the consequence. Ultimately, we introduced adaptive kernel density estimation (AKDE) for interval estimation. The empirical outcome indicated the TMSSICX model achieved the best performance among the other 23 models across all datasets. Moreover, implementing the XGBoost to ensemble each predicted sub-model led to an 8.73%, 8.94%, and 0.19% reduction in RMSE, compared to SVM. Additionally, by utilizing SHapley Additive exPlanations (SHAP) to assess the impact of the six pollutant concentrations on AQI, the results reveal that PM2.5 and PM10 had the most notable positive effects on the long-term trend of AQI. We hope this model can provide guidance for air quality management.
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Affiliation(s)
- Junhao Wu
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200062, China
| | - Xi Chen
- School of Geographic Sciences, East China Normal University, Shanghai, 200241, China; Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, 200241, China; Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai, 200241, China.
| | - Rui Li
- School of Geographic Sciences, East China Normal University, Shanghai, 200241, China
| | - Anqi Wang
- Department of Mathematics, The University of Manchester, Manchester, M13 9PL, UK
| | - Shutong Huang
- School of Geographic Sciences, East China Normal University, Shanghai, 200241, China
| | - Qingli Li
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, 200241, China
| | - Honggang Qi
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Min Liu
- School of Geographic Sciences, East China Normal University, Shanghai, 200241, China; Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, 200241, China
| | - Heqin Cheng
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200062, China.
| | - Zhaocai Wang
- College of Information, Shanghai Ocean University, Shanghai, 201306, China.
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Natarajan SK, Shanmurthy P, Arockiam D, Balusamy B, Selvarajan S. Optimized machine learning model for air quality index prediction in major cities in India. Sci Rep 2024; 14:6795. [PMID: 38514669 PMCID: PMC10958024 DOI: 10.1038/s41598-024-54807-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 02/16/2024] [Indexed: 03/23/2024] Open
Abstract
Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.
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Affiliation(s)
- Suresh Kumar Natarajan
- School of Computer Science and Engineering, Jain (Deemed-to-be University), Bengaluru, Karnataka, India
| | - Prakash Shanmurthy
- School of Computer Science and Engineering and Information Science, Presidency University, Bengaluru, Karnataka, India
| | | | | | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK.
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Ma X, Zou B, Deng J, Gao J, Longley I, Xiao S, Guo B, Wu Y, Xu T, Xu X, Yang X, Wang X, Tan Z, Wang Y, Morawska L, Salmond J. A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023. ENVIRONMENT INTERNATIONAL 2024; 183:108430. [PMID: 38219544 DOI: 10.1016/j.envint.2024.108430] [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: 09/03/2023] [Revised: 11/26/2023] [Accepted: 01/04/2024] [Indexed: 01/16/2024]
Abstract
Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.
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Affiliation(s)
- Xuying Ma
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China; College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China.
| | - Jun Deng
- College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China; Shaanxi Key Laboratory of Prevention and Control of Coal Fire, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Jay Gao
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
| | - Ian Longley
- National Institute of Water and Atmospheric Research, Auckland 1010, New Zealand
| | - Shun Xiao
- School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yarui Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Tingting Xu
- School of Software Engineering, Chongqing University of Post and Telecommunications, Chongqing 400065, China
| | - Xin Xu
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Xiaosha Yang
- Shandong Nova Fitness Co., Ltd., Baoji, Shaanxi 722404, China
| | - Xiaoqi Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Zelei Tan
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Yifan Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, Queensland 4000, Australia.
| | - Jennifer Salmond
- School of Environment, Faculty of Science, University of Auckland, Auckland 1010, New Zealand
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Gokul T, Kumar KR, Veeramanikandan V, Arun A, Balaji P, Faggio C. Impact of Particulate Pollution on Aquatic Invertebrates. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2023; 100:104146. [PMID: 37164218 DOI: 10.1016/j.etap.2023.104146] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/16/2023] [Accepted: 04/23/2023] [Indexed: 05/12/2023]
Abstract
A serious global problem, air pollution poses a risk to both human and environmental health. It contains hazardous material like heavy metals, nanoparticles, and others that can create an impact on both land and marine environments. Particulate pollutants, which can enter water systems through a variety of ways, including precipitation and industrial runoff, can have a particularly adverse influence on aquatic invertebrates. Once in the water, these particles can harm aquatic invertebrates physically, physiologically, and molecularly, resulting in developmental problems and multi-organ toxicity. Further research at the cellular and molecular levels in numerous locations of the world is necessary to completely understand the impacts of particle pollution on aquatic invertebrates. Understanding how particle pollution affects aquatic invertebrates is vital as the significance of ecotoxicological studies on particulate contaminants increases. This review gives a comprehensive overview of the current understanding of how particle pollution affects aquatic invertebrates.
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Affiliation(s)
- Tamilselvan Gokul
- PG and Research Centre in Zoology, Vivekananda College, Tiruvedakam (West), Madurai, TN, India
| | - Kamatchi Ramesh Kumar
- PG and Research Centre in Zoology, Vivekananda College, Tiruvedakam (West), Madurai, TN, India
| | | | - Alagarsamy Arun
- Department of Microbiology, Alagappa University, Karaikudi, TN, India
| | - Paulraj Balaji
- PG and Research Centre in Biotechnology, MGR College, Hosur, TN, India.
| | - Caterina Faggio
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Italy.
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