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Wang W, Liu B, Tian Q, Xu X, Peng Y, Peng S. Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 350:124053. [PMID: 38677458 DOI: 10.1016/j.envpol.2024.124053] [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: 11/09/2023] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 04/29/2024]
Abstract
Dust pollution from storage and handling of materials in dry bulk ports seriously affects air quality and public health in coastal cities. Accurate prediction of dust pollution helps identify risks early and take preventive measures. However, there remain challenges in solving non-stationary time series and selecting relevant features. Besides, existing studies rarely consider impacts of port operations on dust pollution. Therefore, a hybrid approach based on data decomposition and deep learning is proposed to predict dust pollution from dry bulk ports. Port operational data is specially integrated into input features. A secondary decomposition and recombination (SDR) strategy is presented to reduce data non-stationarity. A dual-stage attention-based sequence-to-sequence (DA-Seq2Seq) model is employed to adaptively select the most relevant features at each time step, as well as capture long-term temporal dependencies. This approach is compared with baseline models on a dataset from a dry bulk port in northern China. The results reveal the advantages of SDR strategy and integrating operational data and show that this approach has higher accuracy than baseline models. The proposed approach can mitigate adverse effects of dust pollution from dry bulk ports on urban residents and help port authorities control dust pollution.
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Affiliation(s)
- Wenyuan Wang
- State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116023, China
| | - Bochi Liu
- State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116023, China
| | - Qi Tian
- State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116023, China
| | - Xinglu Xu
- State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116023, China
| | - Yun Peng
- State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116023, China
| | - Shitao Peng
- Tianjin Research Institute for Water Transport Engineering, Ministry of Transport, Tianjin, 300456, China.
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Deng Y, Xu T, Sun Z. A hybrid multi-scale fusion paradigm for AQI prediction based on the secondary decomposition. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:32694-32713. [PMID: 38658513 DOI: 10.1007/s11356-024-33346-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 04/12/2024] [Indexed: 04/26/2024]
Abstract
With rapid industrialization and urbanization, air pollution has become an increasingly severe problem. As a key indicator of air quality, accurate prediction of the air quality index (AQI) is essential for policymakers to establish effective early warning management mechanisms and adjust living plans. In this work, a hybrid multi-scale fusion prediction paradigm is proposed for the complex AQI time series prediction. First, an initial decomposition and integration of the original data is performed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sample entropy (SE). Then, the subsequences, divided into high-frequency and low-frequency groups, are applied to different processing methods. Among them, the variational mode decomposition (VMD) is chosen to perform a secondary decomposition of the high-frequency sequence groups and integrated by using K-means clustering with sample entropy. Finally, multi-scale fusion training of sequence prediction results with different frequencies by using long short-term memory (LSTM) yields more accurate results with R2 of 0.9715, RMSE of 2.0327, MAE of 0.0154, and MAPE of 0.0488. Furthermore, validation of the AQI datasets acquired from four different cities demonstrates that the new paradigm is more robust and generalizable as compared to other baseline methods. Therefore, this model not only holds potential value in developing AQI prediction models but also serves as a valuable reference for future research on AQI control strategies.
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Affiliation(s)
- Yufan Deng
- School of Business, Shandong University, Weihai, 264209, People's Republic of China
| | - Tianqi Xu
- School of Business, Shandong University, Weihai, 264209, People's Republic of China
| | - Zuoren Sun
- School of Business, Shandong University, Weihai, 264209, People's Republic of China.
- Institute of Blue and Green Development, Shandong University, Weihai, 264209, People's Republic of China.
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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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Shi G, Leung Y, Zhang J, Zhou Y. Modeling the air pollution process using a novel multi-site and multi-scale method with adaptive utilization of spatio-temporal information. CHEMOSPHERE 2024; 349:140799. [PMID: 38052313 DOI: 10.1016/j.chemosphere.2023.140799] [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: 08/15/2023] [Revised: 11/15/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023]
Abstract
This study focuses on modeling air quality with an adaptive utilization of spatio-temporal information from multiple air quality monitoring stations under a multi-scale framework. To this end, it is necessary to consider different strategies to combine different methods to decompose the given series and to fuse multi-site information. Based on a systematic comparative analysis, we propose a novel multi-scale and multi-site modeling method named the multivariate empirical mode decomposition and spatial cosine-attention-based long short-term memory (MEMD-SCA-LSTM). The MEMD-SCA-LSTM first employs MEMD to decompose the multi-site air quality series into the scale-aligned components and then models the components at different scales. The high-frequency components are modeled by a novel SCA-LSTM, which employs LSTM with residual blocks to extract the temporal information and utilizes an attention mechanism based on the cosine similarity to adaptively extract interactions among different sites. Other relatively regular components are modeled by the LSTM. Empirical study on PM2.5 in Hong Kong has demonstrated the effectiveness of fusing multi-site information using the spatial attention (SA) mechanism under the multi-scale framework with MEMD. The proposed MEMD-SCA-LSTM can improve the one-day ahead modeling performance with the mean absolute error and the root mean square error reduced over 10%, compared to the baseline modeling methods. For the two-day and three-day ahead performance, the MEMD-SCA-LSTM is still the best one. Furthermore, by visualizing the attention weights, we illustrate that our proposed SCA-LSTM can overcome some limitations of the traditional attention mechanisms and that the attention weights exhibit more informative patterns which could be used to analysis the transport of air pollutant between sites. The proposed modeling method is a general method, which is feasible and applicable to other pollutants in other cities or regions.
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Affiliation(s)
- Guang Shi
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China; School of Computer Science, Xi'an Polytechnic University, Xi'an, 710048, Shaanxi, China
| | - Yee Leung
- Institute of Future Cities, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Jiangshe Zhang
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Yu Zhou
- Institute of Future Cities, The Chinese University of Hong Kong, Shatin, Hong Kong, China; School of Urban & Regional Science and Institute for Global Innovation and Development, East China Normal University, Shanghai, 200241, China.
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Sarkar N, Gupta R, Keserwani PK, Govil MC. Air Quality Index prediction using an effective hybrid deep learning model. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 315:120404. [PMID: 36240962 DOI: 10.1016/j.envpol.2022.120404] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/27/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
Environmentalism has become an intrinsic part of everyday life. One of the greatest challenge to the environment's long-term existence is the air pollution. Delhi, the capital of India, has experienced decreasing of air quality for several years. The poor air quality has a significant impact on the lives of individuals. Air Quality Index (AQI) prediction can help to its beneficiaries in taking safeguards about their health before moving to any polluted area. In this study, a variety of data forecasting approaches is evaluated to predict the AQI value for Particulate Matter (PM2.5) μm at a particular area of Delhi and several error-prone strategies such as R-Squared (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) methods are catalogued. In the proposed approach two deep learning models like Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are combined to predict the AQI of the environment. Several stand alone machine learning (ML) and deep learning (DL) models such as LSTM, Linear-Regression (LR), GRU, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are also trained on the same dataset to compare their performances with the proposed hybrid (LSTM-GRU) model and it is found that the proposed hybrid model shows supremacy in the performance with the MAE value 36.11 and R2 value 0.84.
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Affiliation(s)
- Nairita Sarkar
- Computer Science and Engineering Department, National Institute of Technology Sikkim, South Sikkim, Ravangla, Sikkim, India.
| | - Rajan Gupta
- Computer Science and Engineering Department, National Institute of Technology Sikkim, South Sikkim, Ravangla, Sikkim, India.
| | - Pankaj Kumar Keserwani
- Computer Science and Engineering Department, National Institute of Technology Sikkim, South Sikkim, Ravangla, Sikkim, India.
| | - Mahesh Chandra Govil
- Computer Science and Engineering Department, National Institute of Technology Sikkim, South Sikkim, Ravangla, Sikkim, India.
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