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Liao Q, Zhu M, Wu L, Wang D, Wang Z, Zhang S, Cao W, Pan X, Li J, Tang X, Xin J, Sun Y, Zhu J, Wang Z. Probing the capacity of a spatiotemporal deep learning model for short-term PM 2.5 forecasts in a coastal urban area. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 950:175233. [PMID: 39102955 DOI: 10.1016/j.scitotenv.2024.175233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/22/2024] [Accepted: 07/31/2024] [Indexed: 08/07/2024]
Abstract
Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.
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Affiliation(s)
- Qi Liao
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China
| | - Mingming Zhu
- Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
| | - Lin Wu
- Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Carbon Neutrality Research Center (CNRC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Dawei Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Zixi Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Si Zhang
- Carbon Neutrality Research Center (CNRC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wudi Cao
- Carbon Neutrality Research Center (CNRC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Xiaole Pan
- Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Jie Li
- Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Xiao Tang
- Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Jinyuan Xin
- Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yele Sun
- Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiang Zhu
- Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; International Center for Climate and Environment Science (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Zifa Wang
- Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Urban Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
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Caporale GM, Carmona-González N, Alberiko Gil-Alana L. Atmospheric pollution in Chinese cities: Trends and persistence. Heliyon 2024; 10:e38211. [PMID: 39386778 PMCID: PMC11462000 DOI: 10.1016/j.heliyon.2024.e38211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2024] [Revised: 09/18/2024] [Accepted: 09/19/2024] [Indexed: 10/12/2024] Open
Abstract
This paper applies fractional integration to investigate the behaviour of various pollutants in seven Chinese cities. The objective is to establish if the series exhibit long memory and if time trends are statistically significant over the sample period going from January 2014 to November 2022. The results suggest that the steps recently taken by the Chinese authorities to reduce emissions and improve air quality have already had some effect: in most cases the air pollutant series are in the stationary range, with mean reversion occurring and shocks only having temporary effects, and there are significant downward trends indicating a decline over time in the degree of pollution in China. It is also interesting that in the most recent period, the Zero-Covid policy of the Chinese authorities has led to a further fall. On the whole, it would appear that the action plan adopted by the Chinese government is bringing the expected environmental benefits and therefore it is to be hoped that such policies will continue to be implemented and extended to improve air quality even further.
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Wang S, Sun Y, Gu H, Cao X, Shi Y, He Y. A deep learning model integrating a wind direction-based dynamic graph network for ozone prediction. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174229. [PMID: 38917895 DOI: 10.1016/j.scitotenv.2024.174229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 06/11/2024] [Accepted: 06/21/2024] [Indexed: 06/27/2024]
Abstract
Ozone pollution is an important environmental issue in many countries. Accurate forecasting of ozone concentration enables relevant authorities to enact timely policies to mitigate adverse impacts. This study develops a novel hybrid deep learning model, named wind direction-based dynamic spatio-temporal graph network (WDDSTG-Net), for hourly ozone concentration prediction. The model uses a dynamic directed graph structure based on hourly changing wind direction data to capture evolving spatial relationships between air quality monitoring stations. It applied the graph attention mechanism to compute dynamic weights between connected stations, thereby aggregating neighborhood information adaptively. For temporal modeling, it utilized a sequence-to-sequence model with attention mechanism to extract long-range temporal dependencies. Additionally, it integrated meteorological predictions to guide the ozone forecasting. The model achieves a mean absolute error of 6.69 μg/m3 and 18.63 μg/m3 for 1-h prediction and 24-h prediction, outperforming several classic models. The model's IAQI accuracy predictions at all stations are above 75 %, with a maximum of 81.74 %. It also exhibits strong capabilities in predicting severe ozone pollution events, with a 24-h true positive rate of 0.77. Compared to traditional static graph models, WDDSTG-Net demonstrates the importance of incorporating short-term wind fluctuations and transport dynamics for data-driven air quality modeling. In principle, it may serve as an effective data-driven approach for the concentration prediction of other airborne pollutants.
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Affiliation(s)
- Shiyi Wang
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yiming Sun
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Haonan Gu
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Xiaoyong Cao
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China; Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Yao Shi
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yi He
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China; Institute of Zhejiang University-Quzhou, Quzhou 324000, China; Department of Chemical Engineering, University of Washington, Seattle 98915, USA.
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4
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Wang Q, Liu H, Li Y, Li W, Sun D, Zhao H, Tie C, Gu J, Zhao Q. Predicting plateau atmospheric ozone concentrations by a machine learning approach: A case study of a typical city on the southwestern plateau of China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 363:125071. [PMID: 39368623 DOI: 10.1016/j.envpol.2024.125071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2024] [Revised: 09/15/2024] [Accepted: 10/02/2024] [Indexed: 10/07/2024]
Abstract
Atmospheric ozone (O3) has been placed on the priority control pollutant list in China's 14th Five-Year Plan. Due to their unique meteorological conditions, plateau regions contain high concentrations of atmospheric O3. However, traditional experimental methods for determining O3 concentrations using automatic monitoring stations cannot predict O3 trends. In this study, two machine learning models (a nonlinear auto-regressive model with external inputs (NARX) and a temporal convolution network (TCN)) were developed to predict O3 concentrations in a plateau area in the Kunming region by considering the effects of meteorological parameters, air quality parameters, and volatile organic compounds (VOCs). The plateau O3 prediction accuracy of the machine learning models was found to be much higher than those of numerical models that served as a comparison. The O3 values predicted by the machine learning models closely matched the actual monitoring data. The temporal distribution of plateau O3 displayed a high all-day peak from February to May. A correlation analysis between O3 concentrations and feature parameters demonstrated that humidity is the feature with the highest absolute correlation (-0.72), and was negatively correlated with O3 concentrations during all test periods. VOCs and temperatures were also found to have high positive correlation coefficients with O3 during periods of significant O3 pollution. After negating the effects of meteorological parameters, the predicted O3 concentrations decreased significantly, whereas they increased in the absence of NOx. Although individual VOCs were found to greatly affect the O3 concentration, the total VOC (TVOC) concentration had a relatively small effect. The proposed machine learning model was demonstrated to predict plateau O3 concentrations and distinguish how different features affect O3 variations.
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Affiliation(s)
- Qiyao Wang
- School of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, Yunnan province, P.R. China, 650031
| | - Huaying Liu
- School of Chemical Engineering, Kunming University of Science and Technology, Kunming, Yunnan province, P.R. China, 650031
| | - Yingjie Li
- School of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, Yunnan province, P.R. China, 650031.
| | - Wenjie Li
- School of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, Yunnan province, P.R. China, 650031
| | - Donggou Sun
- School of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, Yunnan province, P.R. China, 650031
| | - Heng Zhao
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden, 11428.
| | - Cheng Tie
- Yunnan Center of Environmental and Ecological Monitoring, Kunming, Yunnan province, P.R. China, 650034
| | - Jicang Gu
- Yunnan Center of Environmental and Ecological Monitoring, Kunming, Yunnan province, P.R. China, 650034
| | - Qilin Zhao
- Yunnan Center of Environmental and Ecological Monitoring, Kunming, Yunnan province, P.R. China, 650034
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5
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Dong J, Zhang Y, Hu J. Short-term air quality prediction based on EMD-transformer-BiLSTM. Sci Rep 2024; 14:20513. [PMID: 39227685 PMCID: PMC11372107 DOI: 10.1038/s41598-024-67626-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 07/15/2024] [Indexed: 09/05/2024] Open
Abstract
Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015-0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction.
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Affiliation(s)
- Jie Dong
- Fudan University, Shanghai, 200433, China
- Zhongnan University of Economics and Law, Wuhan, 430073, China
| | - Yaoli Zhang
- Zhongnan University of Economics and Law, Wuhan, 430073, China
| | - Jiang Hu
- School of Economics and Management, Hubei University of Automotive Technology, Shiyan, 442002, China.
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6
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Rautela KS, Goyal MK. Transforming air pollution management in India with AI and machine learning technologies. Sci Rep 2024; 14:20412. [PMID: 39223178 PMCID: PMC11369276 DOI: 10.1038/s41598-024-71269-7] [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: 03/11/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024] Open
Abstract
A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural-urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28-30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.
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Affiliation(s)
- Kuldeep Singh Rautela
- Department of Civil Engineering, Indian Institute of Technology Indore, Simrol, Indore, 453552, Madhya Pradesh, India
| | - Manish Kumar Goyal
- Department of Civil Engineering, Indian Institute of Technology Indore, Simrol, Indore, 453552, Madhya Pradesh, India.
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7
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Zhou S, Wang W, Zhu L, Qiao Q, Kang Y. Deep-learning architecture for PM 2.5 concentration prediction: A review. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 21:100400. [PMID: 38439920 PMCID: PMC10910069 DOI: 10.1016/j.ese.2024.100400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 03/06/2024]
Abstract
Accurately predicting the concentration of fine particulate matter (PM2.5) is crucial for evaluating air pollution levels and public exposure. Recent advancements have seen a significant rise in using deep learning (DL) models for forecasting PM2.5 concentrations. Nonetheless, there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM2.5 prediction models. Here we extensively reviewed those DL-based hybrid models for forecasting PM2.5 levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examined the similarities and differences among various DL models in predicting PM2.5 by comparing their complexity and effectiveness. We categorized PM2.5 DL methodologies into seven types based on performance and application conditions, including four types of DL-based models and three types of hybrid learning models. Our research indicates that established deep learning architectures are commonly used and respected for their efficiency. However, many of these models often fall short in terms of innovation and interpretability. Conversely, models hybrid with traditional approaches, like deterministic and statistical models, exhibit high interpretability but compromise on accuracy and speed. Besides, hybrid DL models, representing the pinnacle of innovation among the studied models, encounter issues with interpretability. We introduce a novel three-dimensional evaluation framework, i.e., Dataset-Method-Experiment Standard (DMES) to unify and standardize the evaluation for PM2.5 predictions using DL models. This review provides a framework for future evaluations of DL-based models, which could inspire researchers to standardize DL model usage in PM2.5 prediction and improve the quality of related studies.
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Affiliation(s)
- Shiyun Zhou
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Wei Wang
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Long Zhu
- College of Water Sciences, Beijing Normal University, Beijing 100875, China
| | - Qi Qiao
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yulin Kang
- Institute of Environmental Information, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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8
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Iwaszenko S, Smolinski A, Grzanka M, Skowronek T. Airborne particulate matter measurement and prediction with machine learning techniques. Sci Rep 2024; 14:18999. [PMID: 39152189 PMCID: PMC11329646 DOI: 10.1038/s41598-024-70152-9] [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: 04/07/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024] Open
Abstract
Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated.
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Affiliation(s)
- Sebastian Iwaszenko
- Central Mining Institute - National Research Institute, Plac Gwarkow 1, 40-166, Poland, Katowice
| | - Adam Smolinski
- Central Mining Institute - National Research Institute, Plac Gwarkow 1, 40-166, Poland, Katowice.
| | - Marcin Grzanka
- eGminy Sp. z o.o., Cieszyńska 365, 43-300, Bielsko Biała, Poland
| | - Tomasz Skowronek
- Central Mining Institute - National Research Institute, Plac Gwarkow 1, 40-166, Poland, Katowice
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9
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Pande CB, Kushwaha NL, Alawi OA, Sammen SS, Sidek LM, Yaseen ZM, Pal SC, Katipoğlu OM. Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 351:124040. [PMID: 38685551 DOI: 10.1016/j.envpol.2024.124040] [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: 02/05/2024] [Revised: 04/01/2024] [Accepted: 04/22/2024] [Indexed: 05/02/2024]
Abstract
This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.
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Affiliation(s)
- Chaitanya Baliram Pande
- Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang, 43000, Malaysia; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.
| | - Nand Lal Kushwaha
- Department of Soil and Water Engineering, Punjab Agricultural University, Ludhiana, Punjab, 141004, India; Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Omer A Alawi
- Department of Thermofluids, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru, Malaysia
| | - Saad Sh Sammen
- Department of Civil Engineering, College of Engineering, Diyala University, Diyala Governorate, Iraq
| | - Lariyah Mohd Sidek
- Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang, 43000, Malaysia
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, 713104, India
| | - Okan Mert Katipoğlu
- Faculty of Engineering and Architecture, Department of Civil Engineering, Erzincan Binali Yıldırım University, 24100, Erzincan, Turkey
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10
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Shen Y, Yang X, Liu H, Li Z. Advancing mortality rate prediction in European population clusters: integrating deep learning and multiscale analysis. Sci Rep 2024; 14:6255. [PMID: 38491097 PMCID: PMC10942990 DOI: 10.1038/s41598-024-56390-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
Accurately predicting population mortality rates is crucial for effective retirement insurance and economic policy formulation. Recent advancements in deep learning time series forecasting (DLTSF) have led to improved mortality rate predictions compared to traditional models like Lee-Carter (LC). This study focuses on mortality rate prediction in large clusters across Europe. By utilizing PCA dimensionality reduction and statistical clustering techniques, we integrate age features from high-dimensional mortality data of multiple countries, analyzing their similarities and differences. To capture the heterogeneous characteristics, an adaptive adjustment matrix is generated, incorporating sequential variation and spatial geographical information. Additionally, a combination of graph neural networks and a transformer network with an adaptive adjustment matrix is employed to capture the spatiotemporal features between different clusters. Extensive numerical experiments using data from the Human Mortality Database validate the superiority of the proposed GT-A model over traditional LC models and other classic neural networks in terms of prediction accuracy. Consequently, the GT-A model serves as a powerful forecasting tool for global population studies and the international life insurance field.
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Affiliation(s)
- Yuewen Shen
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China
| | - Xinhao Yang
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China.
| | - Hao Liu
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China
| | - Ze Li
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215000, China
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11
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Tao C, Jia M, Wang G, Zhang Y, Zhang Q, Wang X, Wang Q, Wang W. Time-sensitive prediction of NO 2 concentration in China using an ensemble machine learning model from multi-source data. J Environ Sci (China) 2024; 137:30-40. [PMID: 37980016 DOI: 10.1016/j.jes.2023.02.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/02/2023] [Accepted: 02/13/2023] [Indexed: 11/20/2023]
Abstract
Nitrogen dioxide (NO2) poses a critical potential risk to environmental quality and public health. A reliable machine learning (ML) forecasting framework will be useful to provide valuable information to support government decision-making. Based on the data from 1609 air quality monitors across China from 2014-2020, this study designed an ensemble ML model by integrating multiple types of spatial-temporal variables and three sub-models for time-sensitive prediction over a wide range. The ensemble ML model incorporates a residual connection to the gated recurrent unit (GRU) network and adopts the advantage of Transformer, extreme gradient boosting (XGBoost) and GRU with residual connection network, resulting in a 4.1%±1.0% lower root mean square error over XGBoost for the test results. The ensemble model shows great prediction performance, with coefficient of determination of 0.91, 0.86, and 0.77 for 1-hr, 3-hr, and 24-hr averages for the test results, respectively. In particular, this model has achieved excellent performance with low spatial uncertainty in Central, East, and North China, the major site-dense zones. Through the interpretability analysis based on the Shapley value for different temporal resolutions, we found that the contribution of atmospheric chemical processes is more important for hourly predictions compared with the daily scale predictions, while the impact of meteorological conditions would be ever-prominent for the latter. Compared with existing models for different spatiotemporal scales, the present model can be implemented at any air quality monitoring station across China to facilitate achieving rapid and dependable forecast of NO2, which will help developing effective control policies.
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Affiliation(s)
- Chenliang Tao
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Man Jia
- Shandong Provincial Eco-environment Monitoring Center, Jinan 250101, China
| | - Guoqiang Wang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Yuqiang Zhang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Qingzhu Zhang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China.
| | - Xianfeng Wang
- Shandong Provincial Eco-environment Monitoring Center, Jinan 250101, China.
| | - Qiao Wang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Wenxing Wang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China
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12
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Mohammadpour A, Keshtkar M, Samaei MR, Isazadeh S, Mousavi Khaneghah A. Assessing water quality index and health risk using deterministic and probabilistic approaches in Darab County, Iran; A machine learning for fluoride prediction. CHEMOSPHERE 2024; 352:141284. [PMID: 38336038 DOI: 10.1016/j.chemosphere.2024.141284] [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: 07/23/2023] [Revised: 12/16/2023] [Accepted: 01/21/2024] [Indexed: 02/12/2024]
Abstract
The present study employed deterministic and probabilistic approaches to determine the Water Quality Index (WQI) and assess health risks associated with water consumption in Darab County, Iran. Additionally, pollution levels were predicted using a machine-learning algorithm. The study's findings indicate that certain physicochemical parameters of water in some locations exceeded permissible limits (WHO or EPA), with 79.00 % of total hardness (TH) and 21.74 % of Total dissolved solids (TDS) levels exceeding standard values. The WQI for drinking water was determined to be 94.56 % using the deterministic approach, and 98.4 % of samples included the excellent and good categories according to the WQI classification system using the probabilistic approach. Fluoride (F) exhibited the most substantial impact on WQI values. The Artificial Neural Network (ANN) analysis findings suggest that the pH, nitrate (NO3), and TDS are the most significant factors affecting the prediction of F concentration in water. Multivariate analysis demonstrated that anthropogenic, especially agriculture and geogenic factors, contributed to the water quality in this area. The health risk assessment (HRA) using deterministic methods revealed that water consumption posed a relatively high risk in certain areas. However, Monte Carlo simulation demonstrated that the 5th and 95th percentiles of Hazard Index (HI) for children, teenagers, and adults were within limits of (0.14-2.38), (0.09-1.29), and (0.10-1.00) respectively, with a certainty level of 70 %, 91 %, and 95 %. Interactive indices revealed that the intake of IR and NO3-IR in children, BW and F-BW in teenagers, and NO3 and NO3-IR in adults significantly impacted health risks. Based on these findings, augmenting water treatment processes, regulating fluoride concentrations, and advocating for sustainable agricultural practices complemented by continuous monitoring is imperative.
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Affiliation(s)
- Amin Mohammadpour
- Department of Environmental Health Engineering, School of Public Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mahsa Keshtkar
- Department of Environmental Health Engineering, School of Public Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Environmental Health Engineering, School of Public Health, Hormozgan University of Medical Sciences, Hormozgan, Iran
| | - Mohammad Reza Samaei
- Department of Environmental Health Engineering, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran.
| | | | - Amin Mousavi Khaneghah
- Department of Fruit and Vegetable Product Technology, Prof. Wacław Dąbrowski Institute of Agricultural and Food Biotechnology - State Research Institute, Warsaw, Poland; Food Health Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
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13
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Liu X, Zhang X, Wang R, Liu Y, Hadiatullah H, Xu Y, Wang T, Bendl J, Adam T, Schnelle-Kreis J, Querol X. High-Precision Microscale Particulate Matter Prediction in Diverse Environments Using a Long Short-Term Memory Neural Network and Street View Imagery. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:3869-3882. [PMID: 38355131 PMCID: PMC10902834 DOI: 10.1021/acs.est.3c06511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
In this study, we propose a novel long short-term memory (LSTM) neural network model that leverages color features (HSV: hue, saturation, value) extracted from street images to estimate air quality with particulate matter (PM) in four typical European environments: urban, suburban, villages, and the harbor. To evaluate its performance, we utilize concentration data for eight parameters of ambient PM (PM1.0, PM2.5, and PM10, particle number concentration, lung-deposited surface area, equivalent mass concentrations of ultraviolet PM, black carbon, and brown carbon) collected from a mobile monitoring platform during the nonheating season in downtown Augsburg, Germany, along with synchronized street view images. Experimental comparisons were conducted between the LSTM model and other deep learning models (recurrent neural network and gated recurrent unit). The results clearly demonstrate a better performance of the LSTM model compared with other statistically based models. The LSTM-HSV model achieved impressive interpretability rates above 80%, for the eight PM metrics mentioned above, indicating the expected performance of the proposed model. Moreover, the successful application of the LSTM-HSV model in other seasons of Augsburg city and various environments (suburbs, villages, and harbor cities) demonstrates its satisfactory generalization capabilities in both temporal and spatial dimensions. The successful application of the LSTM-HSV model underscores its potential as a versatile tool for the estimation of air pollution after presampling of the studied area, with broad implications for urban planning and public health initiatives.
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Affiliation(s)
- Xiansheng Liu
- Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain
| | - Xun Zhang
- Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China
- State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
| | - Rui Wang
- Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China
| | - Ying Liu
- Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer Science and Engineering, Beijing Technology and Business University, Beijing 100048, China
| | | | - Yanning Xu
- School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266525, China
| | - Tao Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Fudan University, Shanghai 200433, China
| | - Jan Bendl
- University of the Bundeswehr Munich, Faculty for Mechanical Engineering, Institute of Chemical and Environmental Engineering, 85577 Neubiberg, Germany
| | - Thomas Adam
- University of the Bundeswehr Munich, Faculty for Mechanical Engineering, Institute of Chemical and Environmental Engineering, 85577 Neubiberg, Germany
- Joint Mass Spectrometry Center, Cooperation Group Comprehensive Molecular Analytics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, Neuherberg 85764, Germany
| | - Jürgen Schnelle-Kreis
- Joint Mass Spectrometry Center, Cooperation Group Comprehensive Molecular Analytics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, Neuherberg 85764, Germany
| | - Xavier Querol
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain
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14
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Chen Y, Huang L, Xie X, Liu Z, Hu J. Improved prediction of hourly PM 2.5 concentrations with a long short-term memory and spatio-temporal causal convolutional network deep learning model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:168672. [PMID: 38016563 DOI: 10.1016/j.scitotenv.2023.168672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 11/30/2023]
Abstract
Accurate prediction of particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) is important for environmental management and human health protection. In recent years, many efforts have been devoted to develop air quality predictions using the machine learning and deep learning techniques. In this study, we propose a deep learning model for short-term PM2.5 predictions. The salient feature of the proposed model is that the convolution in the model architecture is causal, where the output of a time step is only convolved with components of the same or earlier time step from the previous layer. The model also weighs the spatial correlation between multiple monitoring stations. Through temporal and spatial correlation analysis, relevant information is screened from the monitoring stations with a strong relationship with the target station. Information from the target and related sites is then taken as input and fed into the model. A case study is conducted in Nanjing, China from January 1, 2020 to December 31, 2020. Using historical air quality and meteorological data from nine monitoring stations, the model predicts PM2.5 concentrations for the next hour. The experimental results show that the predicted PM2.5 concentrations are consistent with observation, with correlation coefficient (R2) and Root Mean Squared Error (RMSE) of our model are 0.92 and 6.75 μg/m3. Additionally, to better understand the factors affecting PM2.5 levels in different seasons, a machine learning algorithm based on Principal Component Analysis (PCA) is used to analyze the correlations between PM2.5 and its influencing factors. By identifying the main factors affecting PM2.5 and optimizing the input of the predictive model, the application of PCA in the model further improves the prediction accuracy, with decrease of up to 17.2 % in RMSE and 38.6 % in mean absolute error (MAE). The deep learning model established in this study provide a valuable tool for air quality management and public health protection.
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Affiliation(s)
- Yinsheng Chen
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Lin Huang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Xiaodong Xie
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Zhenxin Liu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
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15
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Lin MD, Liu PY, Huang CW, Lin YH. The application of strategy based on LSTM for the short-term prediction of PM 2.5 in city. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167892. [PMID: 37852485 DOI: 10.1016/j.scitotenv.2023.167892] [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: 08/05/2023] [Revised: 09/28/2023] [Accepted: 10/15/2023] [Indexed: 10/20/2023]
Abstract
Many cities have long suffered from the events of fine particulate matter (PM2.5) pollutions. The Taiwanese Government has long strived to accurately predict the short-term hourly concentration of PM2.5 for the warnings on air pollution. Long Short-Term Memory neural network (LSTM) based on deep learning improves the prediction accuracy of daily PM2.5 concentration but PM2.5 prediction for next hours still needs to be improved. Therefore, this study proposes innovative Application-Strategy-based LSTM (ASLSTM) to accurately predict the short-term hourly PM2.5 concentrations, especially for the high PM2.5 predictions. First, this study identified better spatiotemporal input feature of a LSTM for obtaining this Better LSTM (BLSTM). In doing so, BLSTM trained by appropriate datasets could accurately predict the next hourly pollution concentration. Next, the application strategy was applied on BLSTM to construct ASLSTM. Specifically, from a timeline perspective, ASLSTM concatenates several BLSTMs to predict the concentration of PM2.5 at the following next several hours during which the predicted outputs of BLSTM at this time t was selected and included as the inputs of the next BLSTM at the next time t + 1, and the oldest input used as BLSTM at the time t was removed. The result demonstrated that BLSTM were trained by the dataset collected from 2008 to 2010 at Dali measurement station because there is a relatively large amount of data on high PM2.5 concentration in this dataset. Besides, a comparison of the performance of the ASLSTM with that of the LSTM was made to validate this proposed ASLSTM, especially for the range of higher PM2.5 concentration that people concerned. More importantly, the feasibility of this proposed application strategy and the necessity of optimizing the input parameters of LSTM were validated. In summary, this ASLSTM could accurately predict the short-term PM2.5 in Taichung city.
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Affiliation(s)
- Min-Der Lin
- Department of Environmental Engineering, National Chung Hsing University, 145 Xingda Rd., Taichung 402, Taiwan
| | - Ping-Yu Liu
- General Education Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
| | - Chi-Wei Huang
- Department of Environmental Engineering, National Chung Hsing University, 145 Xingda Rd., Taichung 402, Taiwan
| | - Yu-Hao Lin
- Department of Environmental Engineering, National Chung Hsing University, 145 Xingda Rd., Taichung 402, Taiwan.
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16
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Wang Z, Wu X, Wu Y. A spatiotemporal XGBoost model for PM 2.5 concentration prediction and its application in Shanghai. Heliyon 2023; 9:e22569. [PMID: 38058450 PMCID: PMC10696222 DOI: 10.1016/j.heliyon.2023.e22569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 12/08/2023] Open
Abstract
This paper innovatively constructed an analytical and forecasting framework to predict PM2.5 concentration levels for 16 municipal districts in Shanghai. By means of XGBoost parameters adjustment, empirical mode decomposition, and model fusion, improvements are made on XGBoost prediction accuracy and stability so that prediction deviation at extreme points can be avoided. The main findings of this paper can be summarized as follows: 1) Compared with the original model, the goodness of fit of the modified XGBoost model on the test set increased by 17 %, and the root mean square error decreased by 28 %; 2) The variation of PM2.5 concentration in Shanghai has a significant seasonal (cyclical) effect, and its fluctuation period is 3 months (a quarter). In winter, the frequency of extreme value points is significantly higher than that in other seasons; 3) In terms of spatial distribution, the PM2.5 concentration in the central city of Shanghai is higher than that in the rural areas, and the PM2.5 concentration gradually decreases from center city to the surrounding areas. The innovation and contribution of this paper can be summarized as follows: 1) EEMD algorithm verified by SSA was used to decompose the original model without reconstructing all subsequences and get the best weighing among each part of the hybrid model by using variable weight assignment; 2) The city was cut into pieces according to administrative districts in avoid of the duplicate analysis when utilizing advised Kriging interpolation; 3) IDW method was applied to verified Kriging interpolation to increase the accuracy; 4) The latitude and longitude were innovatively converted into the arc length of the corresponding spherical surface; 5) Hierarchical analysis method was used to obtain the order of importance among the PM2.5 monitoring stations, which could improve the accuracy and achieve dimension reduction.
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Affiliation(s)
- Zidong Wang
- School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
| | - Xianhua Wu
- School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
| | - You Wu
- School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
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17
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Feng Y, Kim JS, Yu JW, Ri KC, Yun SJ, Han IN, Qi Z, Wang X. Spatiotemporal informer: A new approach based on spatiotemporal embedding and attention for air quality forecasting. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 336:122402. [PMID: 37597418 DOI: 10.1016/j.envpol.2023.122402] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/01/2023] [Accepted: 08/16/2023] [Indexed: 08/21/2023]
Abstract
Accurate prediction of air pollution is essential for public health protection. Air quality, however, is difficult to predict due to the complex dynamics, and its accurate forecast still remains a challenge. This study suggests a spatiotemporal Informer model, which uses a new spatiotemporal embedding and spatiotemporal attention, to improve AQI forecast accuracy. In the first phase of the proposed forecast mechanism, the input data is transformed by the spatiotemporal embedding. Next, the spatiotemporal attention is applied to extract spatiotemporal features from the embedded data. The final forecast is obtained based on the attention tensors. In the proposed forecast model, the input is a 3-dimensional data that consists of air quality data (AQI, PM2.5, O3, SO2, NO2, CO) and geographic information, and the output is a multi-positional, multi-temporal data that shows the AQI forecast result of all the monitoring stations in the study area. The proposed forecast model was evaluated by air quality data of 34 monitoring stations in Beijing, China. Experiments showed that the proposed forecast model could provide highly accurate AQI forecast: the average of MAPE values for from 1 h to 20 h ahead forecast was 11.61%, and it was much smaller than other models. Moreover, the proposed model provided a highly accurate and stable forecast even at the extreme points. These results demonstrated that the proposed spatiotemporal embedding and attention techniques could sufficiently capture the spatiotemporal correlation characteristics of air quality data, and that the proposed spatiotemporal Informer could be successfully applied for air quality forecasting.
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Affiliation(s)
- Yang Feng
- School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin, 300384, China
| | - Ju-Song Kim
- School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin, 300384, China; Department of Mathematics, University of Science, Pyongyang, 999091, Democratic People's Republic of Korea
| | - Jin-Won Yu
- School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin, 300384, China; Department of Mathematics, University of Science, Pyongyang, 999091, Democratic People's Republic of Korea
| | - Kuk-Chol Ri
- Department of Foreign Languages and Literature, Kim Il Sung University, Pyongyang, 950001, Democratic People's Republic of Korea; School of Foreign Languages, Tianjin University, Tianjin, 300350, China
| | - Song-Jun Yun
- Department of Mathematics, University of Science, Pyongyang, 999091, Democratic People's Republic of Korea
| | - Il-Nam Han
- Department of Mathematics, University of Science, Pyongyang, 999091, Democratic People's Republic of Korea
| | - Zhanfeng Qi
- School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin, 300384, China
| | - Xiaoli Wang
- School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin, 300384, China.
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18
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Ameri R, Hsu CC, Band SS, Zamani M, Shu CM, Khorsandroo S. Forecasting PM 2.5 concentration based on integrating of CEEMDAN decomposition method with SVM and LSTM. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 266:115572. [PMID: 37837695 DOI: 10.1016/j.ecoenv.2023.115572] [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: 06/08/2023] [Revised: 09/28/2023] [Accepted: 10/09/2023] [Indexed: 10/16/2023]
Abstract
With urbanization and increasing consumption, there is a growing need to prioritize sustainable development across various industries. Particularly, sustainable development is hindered by air pollution, which poses a threat to both living organisms and the environment. The emission of combustion gases containing particulate matter (PM 2.5) during human and social activities is a major cause of air pollution. To mitigate health risks, it is crucial to have accurate and reliable methods for forecasting PM 2.5 levels. In this study, we propose a novel approach that combines support vector machine (SVM) and long short-term memory (LSTM) with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast PM 2.5 concentrations. The methodology involves extracting Intrinsic mode function (IMF) components through CEEMDAN and subsequently applying different regression models (SVM and LSTM) to forecast each component. The Naive Evolution algorithm is employed to determine the optimal parameters for combining CEEMDAN, SVM, and LSTM. Daily PM 2.5 concentrations in Kaohsiung, Taiwan from 2019 to 2021 were collected to train models and evaluate their performance. The performance of the proposed model is evaluated using metrics such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2) for each district. Overall, our proposed model demonstrates superior performance in terms of MAE (1.858), MSE (7.2449), RMSE (2.6682), and (0.9169) values compared to other methods for 1-day ahead PM 2.5 forecasting. Furthermore, our proposed model also achieves the best performance in forecasting PM 2.5 for 3- and 7-day ahead predictions.
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Affiliation(s)
- Rasoul Ameri
- Department of Information Management, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Chung-Chian Hsu
- Department of Information Management, International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou, Taiwan.
| | - Shahab S Band
- Department of Information Management, International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou, Taiwan; Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Taiwan.
| | - Mazdak Zamani
- Department of Computer Science, New York University, 251 Mercer, New York, NY 10012, USA
| | - Chi-Min Shu
- Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan
| | - Sajad Khorsandroo
- Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USA
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19
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Gao Z, Chen J, Wang G, Ren S, Fang L, Yinglan A, Wang Q. A novel multivariate time series prediction of crucial water quality parameters with Long Short-Term Memory (LSTM) networks. JOURNAL OF CONTAMINANT HYDROLOGY 2023; 259:104262. [PMID: 37944201 DOI: 10.1016/j.jconhyd.2023.104262] [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: 08/06/2023] [Revised: 10/03/2023] [Accepted: 10/27/2023] [Indexed: 11/12/2023]
Abstract
Intelligent prediction of water quality plays a pivotal role in water pollution control, water resource protection, emergency decision-making for sudden water pollution incidents, tracking and evaluation of water quality changes in river basins, and is crucial to ensuring water security. The primary methodology employed in this paper for water quality prediction is as follows: (1) utilizing the comprehensive pollution index method and Mann-Kendall (MK) trend analysis method, an assessment is made of the pollution status and change trend within the basin, while simultaneously extracting the principal water quality parameters based on their respective pollution share rates; (2) employing the spearman method, an analysis is conducted to identify the influential factors impacting each key parameter; (3) subsequently, a water quality parameter prediction model, based on Long Short-Term Memory (LSTM) analysis, is constructed using the aforementioned driving factor analysis outcomes. The developed LSTM model in this study showed good prediction performance. The average coefficient of determination (R2) of the prediction of crucial water quality parameters such as total nitrogen (TN) and dissolved oxygen (DO) reached 0.82 and 0.86 respectively. Additionally, the error analysis of WQI prediction results showed that >75% of the prediction errors were in the range of 0-0.15. The comparative analysis revealed that the LSTM model outperforms both the random forest (RF) model in time series prediction and demonstrates superior robustness and applicability compared to the AutoRegressive Moving Average with eXogenous inputs model (ARMAX). Hence, the model developed in this study offers valuable technical assistance for water quality prediction and early warning systems, particularly in economically disadvantaged regions with limited monitoring capabilities. This contribution facilitates resource optimization and promotes sustainable development.
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Affiliation(s)
- Zhenyu Gao
- Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Jinyue Chen
- Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China; Shenzhen Research Institute of Shandong University, Shenzhen 518057, China.
| | - Guoqiang Wang
- Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China; Innovation Research Center of Satellite Application, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Shilong Ren
- Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Lei Fang
- Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China
| | - A Yinglan
- Innovation Research Center of Satellite Application, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Qiao Wang
- Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China
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20
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Chen C, Qiu A, Chen H, Chen Y, Liu X, Li D. Prediction of Pollutant Concentration Based on Spatial-Temporal Attention, ResNet and ConvLSTM. SENSORS (BASEL, SWITZERLAND) 2023; 23:8863. [PMID: 37960562 PMCID: PMC10647283 DOI: 10.3390/s23218863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/24/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
Accurate and reliable prediction of air pollutant concentrations is important for rational avoidance of air pollution events and government policy responses. However, due to the mobility and dynamics of pollution sources, meteorological conditions, and transformation processes, pollutant concentration predictions are characterized by great uncertainty and instability, making it difficult for existing prediction models to effectively extract spatial and temporal correlations. In this paper, a powerful pollutant prediction model (STA-ResConvLSTM) is proposed to achieve accurate prediction of pollutant concentrations. The model consists of a deep learning network model based on a residual neural network (ResNet), a spatial-temporal attention mechanism, and a convolutional long short-term memory neural network (ConvLSTM). The spatial-temporal attention mechanism is embedded in each residual unit of the ResNet to form a new residual neural network with the spatial-temporal attention mechanism (STA-ResNet). Deep extraction of spatial-temporal distribution features of pollutant concentrations and meteorological data from several cities is carried out using STA-ResNet. Its output is used as an input to the ConvLSTM, which is further analyzed to extract preliminary spatial-temporal distribution features extracted from the STA-ResNet. The model realizes the spatial-temporal correlation of the extracted feature sequences to accurately predict pollutant concentrations in the future. In addition, experimental studies on urban agglomerations around Long Beijing show that the prediction model outperforms various popular baseline models in terms of accuracy and stability. For the single-step prediction task, the proposed pollutant concentration prediction model performs well, exhibiting a root-mean-square error (RMSE) of 9.82. Furthermore, even for the pollutant prediction task of 1 to 48 h, we performed a multi-step prediction and achieved a satisfactory performance, being able to achieve an average RMSE value of 13.49.
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Affiliation(s)
- Cai Chen
- School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China; (C.C.); (X.L.)
- Chinese Academy of Surveying and Mapping, Beijing 100830, China
| | - Agen Qiu
- Chinese Academy of Surveying and Mapping, Beijing 100830, China
| | - Haoyu Chen
- Jiangsu Provincial Surveying and Mapping Engineering Institute, Nanjing 210013, China;
| | - Yajun Chen
- China Electronics Standardization Institute, Beijing 100007, China;
| | - Xu Liu
- School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China; (C.C.); (X.L.)
| | - Dong Li
- School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China; (C.C.); (X.L.)
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21
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Wen C, Lin X, Ying Y, Ma Y, Yu H, Li X, Yan J. Dioxin emission prediction from a full-scale municipal solid waste incinerator: Deep learning model in time-series input. WASTE MANAGEMENT (NEW YORK, N.Y.) 2023; 170:93-102. [PMID: 37562201 DOI: 10.1016/j.wasman.2023.08.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/02/2023] [Accepted: 08/04/2023] [Indexed: 08/12/2023]
Abstract
The immeasurability of real-time dioxin emissions is the principal limitation to controlling and reducing dioxin emissions in municipal solid waste incineration (MSWI). Existing methods for dioxin emissions prediction are based on machine learning with inadequate dioxin datasets. In this study, the deep learning models are trained through larger online dioxin emissions data from a waste incinerator to predict real-time dioxin emissions. First, data are collected and the operating data are preprocessed. Then, the dioxin emission prediction performance of the machine learning and deep learning models, including long short-term memory (LSTM) and convolutional neural networks (CNN), with normal input and time-series input are compared. We evaluate the applicability of each model and find that the performance of the deep learning models (LSTM and CNN) has improved by 36.5% and 30.4%, respectively, in terms of the mean square error (MSE) with the time-series input. Moreover, through feature analysis, we find that temperature, airflow, and time dimension are considerable for dioxin prediction. The results are meaningful for optimizing the control of dioxins from MSWI.
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Affiliation(s)
- Chaojun Wen
- Polytechnic Institute, Zhejiang University, Hangzhou 310027, China
| | - Xiaoqing Lin
- Polytechnic Institute, Zhejiang University, Hangzhou 310027, China; State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China.
| | - Yuxuan Ying
- State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China
| | - Yunfeng Ma
- State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China
| | - Hong Yu
- State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China
| | - Xiaodong Li
- State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China; Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province, Jiaxing Research Institute, Zhejiang University, Jiaxing 314031, China
| | - Jianhua Yan
- State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang University, Hangzhou 310027, China
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22
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Ramírez-Montañez JA, Rangel-Magdaleno JDJ, Aceves-Fernández MA, Ramos-Arreguín JM. Modeling of Particulate Pollutants Using a Memory-Based Recurrent Neural Network Implemented on an FPGA. MICROMACHINES 2023; 14:1804. [PMID: 37763967 PMCID: PMC10537238 DOI: 10.3390/mi14091804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/04/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023]
Abstract
The present work describes the training and subsequent implementation on an FPGA board of an LSTM neural network for the modeling and prediction of the exceedances of criteria pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM10 and PM2.5). Understanding the behavior of pollutants and assessing air quality in specific geographical regions is crucial. Overexposure to these pollutants can cause harm to both natural ecosystems and living organisms, including humans. Therefore, it is essential to develop a solution that can accurately evaluate pollution levels. One potential approach is to implement a modified LSTM neural network on an FPGA board. This implementation obtained an 11% improvement compared to the original LSTM network, demonstrating that the proposed architecture is able to maintain its functionality despite reducing the number of neurons in its initial layers. It shows the feasibility of integrating a prediction network into a limited system such as an FPGA board, but easily coupled to a different system. Importantly, this implementation does not compromise the prediction accuracy for both 24 h and 72 h time frames, highlighting an opportunity for further enhancement and refinement.
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Affiliation(s)
| | - Jose de Jesús Rangel-Magdaleno
- Digital Systems Group, Electronics Department, National Institute for Astrophysics, Optics and Electronics, Puebla 72840, Mexico
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23
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Liu H, Han Q, Sun H, Sheng J, Yang Z. Spatiotemporal adaptive attention graph convolution network for city-level air quality prediction. Sci Rep 2023; 13:13335. [PMID: 37587186 PMCID: PMC10432486 DOI: 10.1038/s41598-023-39286-0] [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: 03/28/2023] [Accepted: 07/22/2023] [Indexed: 08/18/2023] Open
Abstract
Air pollution is a leading cause of human diseases. Accurate air quality predictions are critical to human health. However, it is difficult to extract spatiotemporal features among complex spatiotemporal dependencies effectively. Most existing methods focus on constructing multiple spatial dependencies and ignore the systematic analysis of spatial dependencies. We found that besides spatial proximity stations, functional similarity stations, and temporal pattern similarity stations, the shared spatial dependencies also exist in the complete spatial dependencies. In this paper, we propose a novel deep learning model, the spatiotemporal adaptive attention graph convolution model, for city-level air quality prediction, in which the prediction of future short-term series of PM2.5 readings is preferred. Specifically, we encode multiple spatiotemporal dependencies and construct complete spatiotemporal interactions between stations using station-level attention. Among them, we design a Bi-level sharing strategy to extract shared inter-station relationship features between certain stations efficiently. Then we extract multiple spatiotemporal features with multiple decoders, which it is extracted from the complete spatial dependencies between stations. Finally, we fuse multiple spatiotemporal features with a gating mechanism for multi-step predictions. Our model achieves state-of-the-art experimental results in several real-world datasets.
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Affiliation(s)
- Hexiang Liu
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China
- Institute of Systems Engineering, Academy of Military Sciences, Beijing, 100089, China
| | - Qilong Han
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China
| | - Hui Sun
- Institute of Systems Engineering, Academy of Military Sciences, Beijing, 100089, China
| | - Jingyu Sheng
- Institute of Systems Engineering, Academy of Military Sciences, Beijing, 100089, China
| | - Ziyu Yang
- Institute of Systems Engineering, Academy of Military Sciences, Beijing, 100089, China.
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24
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Lu Y, Li K. Multistation collaborative prediction of air pollutants based on the CNN-BiLSTM model. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:92417-92435. [PMID: 37490250 DOI: 10.1007/s11356-023-28877-z] [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: 02/02/2023] [Accepted: 07/16/2023] [Indexed: 07/26/2023]
Abstract
The development of industry has led to serious air pollution problems. It is very important to establish high-precision and high-performance air quality prediction models and take corresponding control measures. In this paper, based on 4 years of air quality and meteorological data from Tianjin, China, the relationships between various meteorological factors and air pollutant concentrations are analyzed. A hybrid deep learning model consisting of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) is proposed to predict pollutant concentrations. In addition, a Bayesian optimization algorithm is applied to obtain the optimal combination of hyperparameters for the proposed deep learning model, which enhances the generalization ability of the model. Furthermore, based on air quality data from multiple stations in the region, a multistation collaborative prediction method is designed, and the concept of a strongly correlated station (SCS) is defined. The predictive model is modified using the idea of SCS and is used to predict the pollutant concentration in Tianjin. The coefficient of determination R2 of PM2.5, PM10, SO2, NO2, CO, and O3 are 0.89, 0.84, 0.69, 0.83, 0.92, and 0.84, respectively. The results show that our model is capable of dealing with air pollutant prediction with satisfactory accuracy.
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Affiliation(s)
- Yanan Lu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, 200433, China.
| | - Kun Li
- School of Economics and Management, Tiangong University, Tianjin, 300387, China
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25
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Yan X, Zuo C, Li Z, Chen HW, Jiang Y, He B, Liu H, Chen J, Shi W. Cooperative simultaneous inversion of satellite-based real-time PM 2.5 and ozone levels using an improved deep learning model with attention mechanism. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 327:121509. [PMID: 36967005 DOI: 10.1016/j.envpol.2023.121509] [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: 02/23/2023] [Revised: 02/28/2023] [Accepted: 03/22/2023] [Indexed: 06/18/2023]
Abstract
Ground-level fine particulate matter (PM2.5) and ozone (O3) are air pollutants that can pose severe health risks. Surface PM2.5 and O3 concentrations can be monitored from satellites, but most retrieval methods retrieve PM2.5 or O3 separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014-2021, we found a strong relationship between PM2.5 and O3 with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM2.5 inversion deep neural Network (SOPiNet), which allows for daily real-time monitoring and full coverage of PM2.5 and O3 simultaneously at a spatial resolution of 5 km. SOPiNet employs the multi-head attention mechanism to better capture the temporal variations in PM2.5 and O3 based on previous days' conditions. Applying SOPiNet to MODIS data over China in 2022, using 2019-2021 to construct the network, we found that simultaneous retrievals of PM2.5 and O3 improved the performance compared with retrieving them independently: the temporal R2 increased from 0.66 to 0.72 for PM2.5, and from 0.79 to 0.82 for O3. The results suggest that near-real time satellite-based air quality monitoring can be improved by simultaneous retrieval of different but related pollutants. The codes of SOPiNet and its user guide are freely available online at https://github.com/RegiusQuant/ESIDLM.
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Affiliation(s)
- Xing Yan
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China
| | - Chen Zuo
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China
| | - Zhanqing Li
- Department of Atmospheric and Oceanic Science and ESSIC, University of Maryland, College Park, MD, 20740, USA
| | - Hans W Chen
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden; Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, 41296, Sweden.
| | - Yize Jiang
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China
| | - Bin He
- College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China
| | - Huiming Liu
- Satellite Environment Center, Ministry of Environmental Protection, Beijing, 100094, China
| | - Jiayi Chen
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China
| | - Wenzhong Shi
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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26
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Li Y, Sha Z, Tang A, Goulding K, Liu X. The application of machine learning to air pollution research: A bibliometric analysis. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 257:114911. [PMID: 37154080 DOI: 10.1016/j.ecoenv.2023.114911] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/27/2023] [Accepted: 04/10/2023] [Indexed: 05/10/2023]
Abstract
Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.
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Affiliation(s)
- Yunzhe Li
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
| | - Zhipeng Sha
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
| | - Aohan Tang
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China.
| | - Keith Goulding
- Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UK
| | - Xuejun Liu
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
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27
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Li W, Jiang X. Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition. Sci Rep 2023; 13:4665. [PMID: 36949097 PMCID: PMC10031189 DOI: 10.1038/s41598-023-31569-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 03/14/2023] [Indexed: 03/24/2023] Open
Abstract
A model with high accuracy and strong generalization performance is conducive to preventing serious pollution incidents and improving the decision-making ability of urban planning. This paper proposes a new neural network structure based on seasonal-trend decomposition using locally weighted scatterplot smoothing (Loess) (STL) and a dependency matrix attention mechanism (DMAttention) based on cosine similarity to predict the concentration of air pollutants. This method uses STL for series decomposition, temporal convolution, a bidirectional long short-term memory network (TCN-BiLSTM) for feature learning of the decomposed series, and DMAttention for interdependent moment feature emphasizing. In this paper, the long short-term memory network (LSTM) and the gated recurrent unit network (GRU) are set as the baseline models to design experiments. At the same time, to test the generalization performance of the model, short-term forecasts in hours were performed using PM2.5, PM10, SO2, NO2, CO, and O3 data. The experimental results show that the model proposed in this paper is superior to the comparison model in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). The MAPE values of the 6 kinds of pollutants are 6.800%, 10.492%, 9.900%, 6.299%, 4.178%, and 7.304%, respectively. Compared with the baseline LSTM and GRU models, the average reduction is 49.111% and 43.212%, respectively.
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Affiliation(s)
- Wenlin Li
- School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China
| | - Xuchu Jiang
- School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China.
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28
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Gong J, Ding L, Lu Y, Qiong Zhang, Yun Li, Beidi Diao. Scientometric and multidimensional contents analysis of PM 2.5 concentration prediction. Heliyon 2023; 9:e14526. [PMID: 36950620 PMCID: PMC10025157 DOI: 10.1016/j.heliyon.2023.e14526] [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: 12/29/2022] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 03/13/2023] Open
Abstract
The foundation for the environmental department to take suitable measures and make a significant contribution towards improving air quality is the precise and dependable prediction of PM2.5 concentration. It is essential to review the development process and hotspots of PM2.5 concentration prediction studies over the past 20 years (2000-2021) comprehensively and quantitatively. This study used detailed bibliometric methods and CiteSpace software to visually analyze the PM2.5 pollution level. The outcomes found that the prediction research phases of PM2.5 can be broadly divided into three phases and enter the rapid growth phase after 2017. Five categories of keywords are clustered, and the forecasting data and forecasting methods are typical cluster representatives. Then, the construction and processing of PM2.5 concentration prediction datasets, the prediction methods and technical processes, and the determination of the prediction spatial-temporal scales are the main content of the analysis. In the future, it is necessary to concentrate on multi-source data fusion for PM2.5 concentration prediction at multiple spatial-temporal scales and focus on technology integration and innovative applications in forecasting models, especially the optimal use of deep machine learning methods to improve prediction accuracy and practical application conversion.
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Affiliation(s)
- Jintao Gong
- The Library, Ningbo Polytechnic, Ningbo 315800, China
| | - Lei Ding
- Research Center of Industrial Economy Around Hangzhou Bay, Ningbo Polytechnic, Ningbo 315800, China
- Corresponding author. Industrial Economic Research Center Around Hangzhou Bay, Ningbo Polytechnic; 1069 Xinda Road, 315800, Ningbo, China. ;
| | - Yingyu Lu
- Research Center of Industrial Economy Around Hangzhou Bay, Ningbo Polytechnic, Ningbo 315800, China
| | - Qiong Zhang
- Research Center of Industrial Economy Around Hangzhou Bay, Ningbo Polytechnic, Ningbo 315800, China
| | - Yun Li
- Research Center of Industrial Economy Around Hangzhou Bay, Ningbo Polytechnic, Ningbo 315800, China
| | - Beidi Diao
- School of Economics and Management, China University of Mining and Technology, No.1 Daxue Road, 221116, Xuzhou, China
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29
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A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic. Sci Rep 2023; 13:1015. [PMID: 36653488 PMCID: PMC9848720 DOI: 10.1038/s41598-023-28287-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as - 25.88 in Wuhan and - 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.
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30
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Zhang K, Yang X, Cao H, Thé J, Tan Z, Yu H. Multi-step forecast of PM 2.5 and PM 10 concentrations using convolutional neural network integrated with spatial-temporal attention and residual learning. ENVIRONMENT INTERNATIONAL 2023; 171:107691. [PMID: 36516675 DOI: 10.1016/j.envint.2022.107691] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
Accurate and reliable forecasting of PM2.5 and PM10 concentrations is important to the public to reasonably avoid air pollution and for the governmental policy responses. However, the prediction of PM2.5 and PM10 concentrations has great uncertainty and instability because of the dynamics of atmospheric flows, making it difficult for a single model to efficiently extract the spatial-temporal dependences. This paper reports a robust forecasting system to achieve accurate multi-step ahead forecasting of PM2.5 and PM10 concentrations. First, correlation analysis is adopted to screen the spatial information on pollution and meteorology that may facilitate the prediction of concentrations in a target city. Then, a spatial-temporal attention mechanism is used to assign weights to original inputs from both space and time dimensions to enhance the essential information. Subsequently, the residual-based convolutional neural network with feature extraction capabilities is employed to model the refined inputs. Finally, five accuracy metrics and two additional statistical tests are applied to comprehensively assess the performance of the proposed forecasting system. In addition, experimental studies of three major cities in the Yangtze River Delta urban agglomeration region indicate that the forecasting system outperforms various prevalent baseline models in terms of accuracy and stability. Quantitatively, the proposed STA-ResCNN model reduces root mean square error by 5.595 %-15.247 % and 6.827 %-16.906 % for the average of 1-4 h ahead predictions in three major cities of PM2.5 and PM10, respectively, compared to baseline models. The applicability and generalization of the proposed forecasting system are further verified by the extended applications in the other 23 cities in the entire region. The results prove that the forecasting system is promising in the early warning, regional prevention, and control of air pollution.
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Affiliation(s)
- Kefei Zhang
- School of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China; Key Laboratory of Coal Processing and Efficient Utilization, Ministry of Education, Xuzhou, Jiangsu 221116, China
| | - Xiaolin Yang
- School of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China; Key Laboratory of Coal Processing and Efficient Utilization, Ministry of Education, Xuzhou, Jiangsu 221116, China
| | - Hua Cao
- School of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China; Key Laboratory of Coal Processing and Efficient Utilization, Ministry of Education, Xuzhou, Jiangsu 221116, China
| | - Jesse Thé
- Department of Mechanical & Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada; Lakes Environmental Research Inc., 170 Columbia St. W. Suite 1, Waterloo, Ontario N2L 3L3, Canada
| | - Zhongchao Tan
- Department of Mechanical & Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada; Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
| | - Hesheng Yu
- School of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China; Key Laboratory of Coal Processing and Efficient Utilization, Ministry of Education, Xuzhou, Jiangsu 221116, China; Department of Mechanical & Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada.
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31
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Bermejo L, Gil-Alana LA, del Río M. Time trends and persistence in PM 2.5 in 20 megacities: evidence for the time period 2018-2020. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:5603-5620. [PMID: 35978243 PMCID: PMC9894978 DOI: 10.1007/s11356-022-22512-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
The degree of persistence in daily data for PM2.5 in 20 relevant megacities such as Bangkok, Beijing, Mumbai, Calcutta, Canton, Dhaka, Delhi, Jakarta, London, Los Angeles, Mexico City, Moscow, New York, Osaka. Paris, Sao Paulo, Seoul, Shanghai, Tientsin, and Tokyo is examined in this work. The analysis developed is based on fractional integration techniques. Specifically, the differentiation parameter is used to measure the degree of persistence in the series under study, which collects data on daily measurements carried out from January 1, 2018, to December 31, 2020. The results obtained show that the estimated values for the differentiation parameter are restricted to the interval (0, 1) in all cases, which allows us to conclude that there is a mean reverting pattern and, therefore, transitory effects of shocks.
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Affiliation(s)
| | - Luis A. Gil-Alana
- Department of Economics, Faculty of Economics, University of Navarra, E31008 Pamplona, Spain
- University Francisco de Vitoria, Madrid, Spain
| | - Marta del Río
- Faculty of Economics, Universidad Villanueva, Madrid, Spain
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32
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Huang G, Zhao X, Lu Q. A new cross-domain prediction model of air pollutant concentration based on secure federated learning and optimized LSTM neural network. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:5103-5125. [PMID: 35974279 DOI: 10.1007/s11356-022-22454-6] [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: 05/31/2022] [Accepted: 08/05/2022] [Indexed: 06/15/2023]
Abstract
As air pollution worsens, the fast prediction of air pollutant concentration becomes increasingly important for public health. This paper proposes a new cross-domain prediction model of air pollutant concentration based on federated learning (FL), differential privacy laplace mechanism (DPLA) and long and short-term memory network optimized by sparrow search algorithm (SSA-LSTM), named FL-DPLA-SSA-LSTM. Firstly, with FL, SSA-LSTM is used as local training model for each city and predicts air pollutant concentration. Secondly, DPLA is used to add noise to the local model parameters, which can protect local data security. Then, the global model is updated by using the federated averaging algorithm (FedAvg). Lastly, FL is used to share global model for all cities, which can safely and quickly cross-domain predict air pollutant concentration. For data set, it is taken from hourly air pollutants and meteorological data from 12 cities in the Fenhe River and Weihe River Plains in China in 2020. The experimental results show that the prediction performance of the proposed model is significantly better than all comparison models. FedAvg updating with local model parameters with DPLA noise has little effect on the performance of the global model and even exceeds that of the global model. The calculation time of FL-DPLA-SSA-LSTM model is reduced by 99.95% compared with that of not using FL-DPLA machine learning model. It is proved that the model is high sharing and high safety, which greatly improves the training efficiency and has better generalization ability. It is significant for joint air pollution prevention and control and environmental protection.
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Affiliation(s)
- Guangqiu Huang
- School of Management, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
| | - Xixuan Zhao
- School of Management, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
| | - Qiuqin Lu
- School of Management, Xi'an University of Architecture and Technology, Xi'an, 710055, China
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Ayus I, Natarajan N, Gupta D. Comparison of machine learning and deep learning techniques for the prediction of air pollution: a case study from China. ASIAN JOURNAL OF ATMOSPHERIC ENVIRONMENT 2023; 17:4. [PMCID: PMC10214349 DOI: 10.1007/s44273-023-00005-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 09/07/2023]
Abstract
The adverse effect of air pollution has always been a problem for human health. The presence of a high level of air pollutants can cause severe illnesses such as emphysema, chronic obstructive pulmonary disease (COPD), or asthma. Air quality prediction helps us to undertake practical action plans for controlling air pollution. The Air Quality Index (AQI) reflects the degree of concentration of pollutants in a locality. The average AQI was calculated for the various cities in China to understand the annual trends. Furthermore, the air quality index has been predicted for ten major cities across China using five different deep learning techniques, namely, Recurrent Neural Network (RNN), Bidirectional Gated Recurrent unit (Bi-GRU), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network BiLSTM (CNN-BiLSTM), and Convolutional BiLSTM (Conv1D-BiLSTM). The performance of these models has been compared with a machine learning model, eXtreme Gradient Boosting (XGBoost) to discover the most efficient deep learning model. The results suggest that the machine learning model, XGBoost, outperforms the deep learning models. While Conv1D-BiLSTM and CNN-BiLSTM perform well among the deep learning models in the estimation of the air quality index (AQI), RNN and Bi-GRU are the least performing ones. Thus, both XGBoost and neural network models are capable of capturing the non-linearity present in the dataset with reliable accuracy.
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Affiliation(s)
- Ishan Ayus
- Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha India
| | - Narayanan Natarajan
- Department of Civil Engineering, Dr. Mahalingam College of Engineering and Technology, Tamil Nadu, Pollachi, 642003 India
| | - Deepak Gupta
- Department of Computer Science & Engineering, MNNIT Allahabad, Prayagraj, 211004 India
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Ju J, Liu K, Liu F. Prediction of SO 2 Concentration Based on AR-LSTM Neural Network. Neural Process Lett 2022; 55:1-19. [PMID: 36590992 PMCID: PMC9789735 DOI: 10.1007/s11063-022-11119-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/10/2022] [Indexed: 12/25/2022]
Abstract
Sulphur dioxide is one of the most common air pollutants, forming acid rain and other harmful substances in the atmosphere, which can further damage our ecosystem and cause respiratory diseases in humans. Therefore, it is essential to monitor the concentration of sulphur dioxide produced in industrial processes in real-time to predict the concentration of sulphur dioxide emissions in the next few hours or days and to control them in advance. To address this problem, we propose an AR-LSTM analytical forecasting model based on ARIMA and LSTM. Based on the sensor's time series data set, we preprocess the data set and then carry out the modeling and analysis work. We analyze and predict the proposed analysis and prediction model in two data sets and conduct comparative experiments with other comparison models based on the three evaluation indicators of R2, RMSE and MAE. The results demonstrated the effectiveness of the AR-LSTM analytical prediction model; Finally, a forecasting exercise was carried out for emissions in the coming weeks using our proposed AR-LSTM analytical forecasting model.
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Affiliation(s)
- Jie Ju
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358 China
| | - Ke’nan Liu
- Huawei Technologies Co., Ltd., Shenzhen, China
| | - Fang’ai Liu
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358 China
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Chen S, Xu Z, Wang X, Zhang C. Ambient air pollutants concentration prediction during the COVID-19: A method based on transfer learning. Knowl Based Syst 2022; 258:109996. [PMID: 36277675 PMCID: PMC9576259 DOI: 10.1016/j.knosys.2022.109996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 09/17/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
Research on the correlation analysis between COVID-19 and air pollution has attracted increasing attention since the COVID-19 pandemic. While many relevant issues have been widely studied, research into ambient air pollutant concentration prediction (APCP) during COVID-19 is still in its infancy. Most of the existing study on APCP is based on machine learning methods, which are not suitable for APCP during COVID-19 due to the different distribution of historical observations before and after the pandemic. Therefore, to fulfill the predictive task based on the historical observations with a different distribution, this paper proposes an improved transfer learning model combined with machine learning for APCP during COVID-19. Specifically, this paper employs the Gaussian mixture method and an optimization algorithm to obtain a new source domain similar to the target domain for further transfer learning. Then, several commonly used machine learning models are trained in the new source domain, and these well-trained models are transferred to the target domain to obtain APCP results. Based on the real-world dataset, the experimental results suggest that, by using the improved machine learning methods based on transfer learning, our method can achieve the prediction with significantly high accuracy. In terms of managerial insights, the effects of influential factors are analyzed according to the relationship between these influential factors and prediction results, while their importance is ranked through their average marginal contribution and partial dependence plots.
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Affiliation(s)
- Shuixia Chen
- Business School, Sichuan University, Chengdu 610064, China
| | - Zeshui Xu
- Business School, Sichuan University, Chengdu 610064, China
| | - Xinxin Wang
- Business School, Sichuan University, Chengdu 610064, China
| | - Chenxi Zhang
- Business School, Sichuan University, Chengdu 610064, China
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Bhimavarapu U, Sreedevi M. An enhanced loss function in deep learning model to predict PM2.5 in India. INTELLIGENT DECISION TECHNOLOGIES 2022. [DOI: 10.3233/idt-220111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Fine particulate matter (PM2.5) is one of the major air pollutants and is an important parameter for measuring air quality levels. High concentrations of PM2.5 show its impact on human health, the environment, and climate change. An accurate prediction of fine particulate matter (PM2.5) is significant to air pollution detection, environmental management, human health, and social development. The primary approach is to boost the forecast performance by reducing the error in the deep learning model. So, there is a need to propose an enhanced loss function (ELF) to decrease the error and improve the accurate prediction of daily PM2.5 concentrations. This paper proposes the ELF in CTLSTM (Chi-Square test Long Short Term Memory) to improve the PM2.5 forecast. The ELF in the CTLSTM model gives more accurate results than the standard forecast models and other state-of-the-art deep learning techniques. The proposed ELFCTLSTM reduces the prediction error of by a maximum of 10 to 25 percent than the state-of-the-art deep learning models.
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Affiliation(s)
- Usharani Bhimavarapu
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
| | - M. Sreedevi
- Department of CSE, Amrita Sai Institute of Science and Technology, Paritala, Andhra Pradesh, India
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Lee W, Lim YH, Ha E, Kim Y, Lee WK. Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:88318-88329. [PMID: 35834079 PMCID: PMC9281380 DOI: 10.1007/s11356-022-21768-9] [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: 01/17/2022] [Accepted: 06/27/2022] [Indexed: 04/16/2023]
Abstract
Environmental exposure constantly changes with time and various interactions that can affect health outcomes. Machine learning (ML) or deep learning (DL) algorithms have been used to solve complex problems, such as multiple exposures and their interactions. This study developed predictive models for cause-specific mortality using ML and DL algorithms with the daily or hourly measured meteorological and air pollution data. The ML algorithm improved the performance compared to the conventional methods, even though the optimal algorithm depended on the adverse health outcomes. The best algorithms were extreme gradient boosting, ridge, and elastic net, respectively, for non-accidental, cardiovascular, and respiratory mortality with daily measurement; they were superior to the generalized additive model reducing a mean absolute error by 4.7%, 4.9%, and 16.8%, respectively. With hourly measurements, the ML model tended to outperform the conventional models, even though hourly data, instead of daily data, did not enhance the performance in some models. The proposed model allows a better understanding and development of robust predictive models for health outcomes using multiple environmental exposures.
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Affiliation(s)
- Woojoo Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Youn-Hee Lim
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Eunhee Ha
- Department of Occupational and Environmental Medicine, Ewha Medical Research Center, College of Medicine, Ewha Woman's University, Seoul, Republic of Korea
| | - Yoenjin Kim
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Won Kyung Lee
- Department of Prevention and Management, Inha University Hospital, School of Medicine, Inha University, Incheon, Republic of Korea.
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38
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Dual-channel spatial–temporal difference graph neural network for PM$$_{2.5}$$ forecasting. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08036-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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39
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Masood A, Ahmad K. Data-driven predictive modeling of PM 2.5 concentrations using machine learning and deep learning techniques: a case study of Delhi, India. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 195:60. [PMID: 36326946 DOI: 10.1007/s10661-022-10603-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
The present study intends to use machine learning (ML) and deep learning (DL) models to forecast PM2.5 concentration at a location in Delhi. For this purpose, multi-layer feed-forward neural network (MLFFNN), support vector machine (SVM), random forest (RF) and long short-term memory networks (LSTM) have been applied. The air pollutants, e.g., CO, Ozone, PM10, NO, NO2, NOx, NH3, SO2, benzene, toluene, as well as meteorological parameters (temperature, wind speed, wind direction, rainfall, evaporation, humidity, pressure, etc.), have been used as inputs in the present study. Moreover, this is one of the first papers that employ aerodynamic roughness coefficient as an input parameter for the prediction of PM2.5 concentration. The result of the study shows that the LSTM model with index of agreement (IA) 0.986, root mean square error (RMSE) 21.510, Nash-Sutcliffe efficiency index (NSE) 0.945, (coefficient of determination)R2 0.945, and (correlation coefficient)R 0.972 is the best performing technique for the prediction of PM2.5 followed by MLFFNN, SVM, and RF models. The sensitivity analysis for the LSTM model reported that PM10, wind speed, NH3, and benzene are the key influencing parameters for the estimation of PM2.5. The findings in this work suggest that the LSTM could advance in PM2.5 forecasting and thus would be useful for developing fine-scale, state-of-the-art air pollution forecasting models.
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Affiliation(s)
- Adil Masood
- Department of Civil Engineering, Jamia Millia Islamia University, New Delhi, 110025, India.
| | - Kafeel Ahmad
- Department of Civil Engineering, Jamia Millia Islamia University, New Delhi, 110025, India
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40
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A probabilistic forecasting approach for air quality spatio-temporal data based on kernel learning method. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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41
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He M, Che J, Jiang Z, Zhao W, Wan B. A novel decomposition-denoising ANFIS model based on singular spectrum analysis and differential evolution algorithm for seasonal AQI forecasting. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-222920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Understanding and forecasting air quality index (AQI) plays a vital role in guiding the reduction of air pollution and helping social sustainable development. By combining fuzzy logic with decomposition techniques, ANFIS has become an important means to analyze the data resources, uncertainty and fuzziness. However, few studies have paid attention to the noise of decomposed subseries. Therefore, this paper presents a novel decomposition-denoising ANFIS model named SSADD-DE-ANFIS (Singular Spectrum Analysis Decomposition and Denoising-Differential Evolution-Adaptive Neuro-Fuzzy Inference System). This method uses twice SSA to decompose and denoise the AQI series, respectively, then fed the subseries obtained after the decomposition and denoising into the constructed ANFIS for training and predicting, and the parameters of ANFIS are optimized using DE. To investigate the prediction performance of the proposed model, twelve models are included in the comparisons. The experimental results of four seasons show that: the RMSE of the proposed SSADD-DE-ANFIS model is 1.400628, 0.63844, 0.901987 and 0.634114, respectively, which is 19.38%, 21.27%, 20.43%, 21.27% and 87.36%, 88.12%, 88.97%, 88.71% lower than that of the single SSA decomposition and SSA denoising. Diebold-Mariano test is performed on all the prediction results, and the test results show that the proposed model has the best prediction performance.
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Affiliation(s)
- Mingjun He
- School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, China
| | - Jinxing Che
- School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, China
| | - Zheyong Jiang
- School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China
- Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, China
| | - Weihua Zhao
- School of Science, Nantong University, Nantong, Jiangsu, China
| | - Bingrong Wan
- School of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China
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42
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PM2.5 forecasting for an urban area based on deep learning and decomposition method. Sci Rep 2022; 12:17565. [PMID: 36266317 PMCID: PMC9584903 DOI: 10.1038/s41598-022-21769-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 09/30/2022] [Indexed: 01/13/2023] Open
Abstract
Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.
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Guo Q, Ren M, Wu S, Sun Y, Wang J, Wang Q, Ma Y, Song X, Chen Y. Applications of artificial intelligence in the field of air pollution: A bibliometric analysis. Front Public Health 2022; 10:933665. [PMID: 36159306 PMCID: PMC9490423 DOI: 10.3389/fpubh.2022.933665] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/11/2022] [Indexed: 01/25/2023] Open
Abstract
Background Artificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution. Methods All literature on the application of AI to air pollution was searched from the Web of Science database. CiteSpace 5.8.R1 was used to analyze countries/regions, institutions, authors, keywords and references cited, and to reveal hot spots and frontiers of AI in atmospheric pollution. Results Beginning in 1994, publications on AI in air pollution have increased in number, with a surge in research since 2017. The leading country and institution were China (N = 524) and the Chinese Academy of Sciences (N = 58), followed by the United States (N = 455) and Tsinghua University (N = 33), respectively. In addition, the United States (0.24) and the England (0.27) showed a high degree of centrality. Most of the identified articles were published in journals related to environmental science; the most cited journal was Atmospheric Environment, which reached nearly 1,000 citations. There were few collaborations among authors, institutions and countries. The hot topics were machine learning, air pollution and deep learning. The majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods, low-cost air quality sensors, indoor air quality, and thermal comfort. Conclusion Researches in the field of AI and air pollution are expanding rapidly in recent years. The majority of scholars are from China and the United States, and the Chinese Academy of Sciences is the dominant research institution. The United States and the England contribute greatly to the development of the cooperation network. Cooperation among research institutions appears to be suboptimal, and strengthening cooperation could greatly benefit this field of research. The prediction of air pollutant concentrations, particularly PM2.5, low-cost air quality sensors, and thermal comfort are the current research hotspot.
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Affiliation(s)
- Qiangqiang Guo
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Mengjuan Ren
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Shouyuan Wu
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Yajia Sun
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Jianjian Wang
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Qi Wang
- Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada,McMaster Health Forum, McMaster University, Hamilton, ON, Canada
| | - Yanfang Ma
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China
| | - Xuping Song
- School of Public Health, Lanzhou University, Lanzhou, China,Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China,Lanzhou University Institute of Health Data Science, Lanzhou, China,World Health Organization Collaborating Center for Guideline Implementation and Knowledge Translation, Lanzhou, China,*Correspondence: Xuping Song
| | - Yaolong Chen
- School of Public Health, Lanzhou University, Lanzhou, China,Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China,Lanzhou University Institute of Health Data Science, Lanzhou, China,World Health Organization Collaborating Center for Guideline Implementation and Knowledge Translation, Lanzhou, China,Yaolong Chen
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Yang H, Zhao J, Li G. A new hybrid prediction model of PM 2.5 concentration based on secondary decomposition and optimized extreme learning machine. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:67214-67241. [PMID: 35524096 DOI: 10.1007/s11356-022-20375-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 04/18/2022] [Indexed: 06/14/2023]
Abstract
As air pollution worsens, the prediction of PM2.5 concentration becomes increasingly important for public health. This paper proposes a new hybrid prediction model of PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), amplitude-aware permutation entropy (AAPE), variational mode decomposition improved by marine predators algorithm (MPA-VMD), and extreme learning machine optimized by chimp optimization algorithm (ChOA-ELM), named CEEMDAN-AAPE-MPA-VMD-ChOA-ELM. Firstly, CEEMDAN is used to decompose the original data, and AAPE is used to quantify the complexity of all IMF components. Secondly, MPA-VMD is used to decompose the IMF component with the maximum AAPE. Lastly, ChOA-ELM is used to predict all IMF components, and all prediction results are reconstructed to obtain the final prediction results. The proposed model combines the advantages of secondary decomposition technique, feature analysis, and optimization algorithm, which can predict PM2.5 concentration accurately. PM2.5 concentrations at hourly intervals collected from March 1, 2021, to March 31, 2021, in Shanghai and Shenyang, China, are used for experimental study and DM test. The experimental results in Shanghai show that the RMSE, MAE, MAPE, and R2 of the proposed model are 1.0676, 0.7685, 0.0181, and 0.9980 respectively, which is better than all comparison models at 90% confidence level. In Shenyang, the RMSE, MAE, MAPE, and R2 of the proposed model are 1.4399, 1.1258, 0.0389, and 0.9976, respectively, which is better than all comparison models at 95% confidence level.
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Affiliation(s)
- Hong Yang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China.
| | - Junlin Zhao
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China
| | - Guohui Li
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China
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45
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Xu S, Li W, Zhu Y, Xu A. A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks. Sci Rep 2022; 12:14434. [PMID: 36002466 PMCID: PMC9402967 DOI: 10.1038/s41598-022-17754-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/30/2022] [Indexed: 12/03/2022] Open
Abstract
In recent years, air pollution has become a factor that cannot be ignored, affecting human lives and health. The distribution of high-density populations and high-intensity development and construction have accentuated the problem of air pollution in China. To accelerate air pollution control and effectively improve environmental air quality, the target of our research was cities with serious air pollution problems to establish a model for air pollution prediction. We used the daily monitoring data of air pollution from January 2016 to December 2020 for the respective cities. We used the long short term memory networks (LSTM) algorithm model to solve the problem of gradient explosion in recurrent neural networks, then used the particle swarm optimization algorithm to determine the parameters of the CNN-LSTM model, and finally introduced the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) decomposition to decompose air pollution and improve the accuracy of model prediction. The experimental results show that compared with a single LSTM model, the CEEMDAN-CNN-LSTM model has higher accuracy and lower prediction errors. The CEEMDAN-CNN-LSTM model enables a more precise prediction of air pollution, and may thus be useful for sustainable management and the control of air pollution.
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Affiliation(s)
- Shenyi Xu
- School of Statistics and Mathematics, Zhejiang Gongshang University, No.18 Xuezheng Street, Xiasha Higher Education Park, Hangzhou, Zhejiang, China
| | - Wei Li
- School of Statistics and Mathematics, Zhejiang Gongshang University, No.18 Xuezheng Street, Xiasha Higher Education Park, Hangzhou, Zhejiang, China
| | - Yuhan Zhu
- School of Statistics and Mathematics, Zhejiang Gongshang University, No.18 Xuezheng Street, Xiasha Higher Education Park, Hangzhou, Zhejiang, China.,Collaborative Innovation Center of Statistical Data Engineering, Technology & Application, Zhejiang Gongshang University, Hangzhou, China
| | - Aiting Xu
- School of Statistics and Mathematics, Zhejiang Gongshang University, No.18 Xuezheng Street, Xiasha Higher Education Park, Hangzhou, Zhejiang, China. .,Collaborative Innovation Center of Statistical Data Engineering, Technology & Application, Zhejiang Gongshang University, Hangzhou, China.
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46
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Faraji M, Nadi S, Ghaffarpasand O, Homayoni S, Downey K. An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155324. [PMID: 35452742 DOI: 10.1016/j.scitotenv.2022.155324] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/20/2022] [Accepted: 04/12/2022] [Indexed: 06/14/2023]
Abstract
This study proposes a new model for the spatiotemporal prediction of PM2.5 concentration at hourly and daily time intervals. It has been constructed on a combination of three-dimensional convolutional neural network and gated recurrent unit (3D CNN-GRU). The performance of the proposed model is boosted by learning spatial patterns from similar air quality (AQ) stations while maintaining long-term temporal dependencies with simultaneous learning and prediction for all stations over different time intervals. 3D CNN-GRU model was applied to air pollution observations, especially PM2.5 level, collected from several AQ stations across the city of Tehran, the capital of Iran, from 2016 to 2019. It could achieve promising results compared to the methods such as LSTM, GRU, ANN, SVR, and ARIMA, which are recently introduced in the literature; it estimates 84% (R2 = 0.84) and 78% (R2 = 0.78) of PM2.5 concentration variations for the next hour and the following day, respectively.
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Affiliation(s)
- Marjan Faraji
- Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, HezarJerib St., Isfahan 81746-73441, Iran.
| | - Saeed Nadi
- Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada.
| | - Omid Ghaffarpasand
- School of Geography, Earth, and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK.
| | - Saeid Homayoni
- Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec, QC G1K 9A9, Canada.
| | - Kay Downey
- School of Geography, Earth, and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
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47
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Prediction of Monthly PM2.5 Concentration in Liaocheng in China Employing Artificial Neural Network. ATMOSPHERE 2022. [DOI: 10.3390/atmos13081221] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Fine particulate matter (PM2.5) affects climate change and human health. Therefore, the prediction of PM2.5 level is particularly important for regulatory planning. The main objective of the study is to predict PM2.5 concentration employing an artificial neural network (ANN). The annual change in PM2.5 in Liaocheng from 2014 to 2021 shows a gradual decreasing trend. The air quality in Liaocheng during lockdown and after lockdown periods in 2020 was obviously improved compared with the same periods of 2019. The ANN employed in the study contains a hidden layer with 6 neurons, an input layer with 11 parameters, and an output layer. First, the ANN is used with 80% of data for training, then with 10% of data for verification. The value of correlation coefficient (R) for the training and validation data is 0.9472 and 0.9834, respectively. In the forecast period, it is demonstrated that the ANN model with Bayesian regularization (BR) algorithm (trainbr) obtained the best forecasting performance in terms of R (0.9570), mean absolute error (4.6 μg/m3), and root mean square error (6.6 μg/m3), respectively. The ANN model has produced accurate results. These results prove that the ANN is effective in monthly PM2.5 concentration predicting due to the fact that it can identify nonlinear relationships between the input and output variables.
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48
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Yu F, Hu X. Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects. JOURNAL OF HAZARDOUS MATERIALS 2022; 432:128730. [PMID: 35338937 DOI: 10.1016/j.jhazmat.2022.128730] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 06/14/2023]
Abstract
Microplastics (MPs, sizes <5 mm) have been found to be widely distributed in various environments, such as marine, freshwater, terrestrial and atmospheric systems. Machine learning provides a potential solution for evaluating the ecological risks of MPs based on big data. Compared with traditional models, data-driven machine learning can accelerate the realization of the control of hazardous MPs and reduce the impact of MPs at both local and global scales. However, there are some urgent issues that should be resolved. For example, lack of MP databases and incomparable literatures causing the current MP data cannot fully support big data research. Therefore, it is imperative to formulate a set of standard and universal MP collection and testing protocols. For machine learning, predictions of large-scale MP distribution and the corresponding environmental risks remain lacking. To accelerate studies of MPs in the future, the methods and theories achieved for other particle pollutants, such as nanomaterials and aerosols, can be referenced. Beyond predication alone, the improvement of causality and interpretability of machine learning deserves attention in the studies of MP risks. Overall, this perspective paper provides insights for the development of machine learning methods in research on the environmental risks of MPs.
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Affiliation(s)
- Fubo Yu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Xiangang Hu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education)/Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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49
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Yin G, Chen X, Zhu H, Chen Z, Su C, He Z, Qiu J, Wang T. A novel interpolation method to predict soil heavy metals based on a genetic algorithm and neural network model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 825:153948. [PMID: 35219652 DOI: 10.1016/j.scitotenv.2022.153948] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/13/2022] [Accepted: 02/13/2022] [Indexed: 06/14/2023]
Abstract
To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R2) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs.
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Affiliation(s)
- Guangcai Yin
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Xingling Chen
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Hanghai Zhu
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Zhiliang Chen
- Research center for eco-environment restoration technology, South China Institute of Environmental Science, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510006, China
| | - Chuanghong Su
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China
| | - Zechen He
- Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Jinrong Qiu
- Research center for eco-environment restoration technology, South China Institute of Environmental Science, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510006, China
| | - Tieyu Wang
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China.
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50
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Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai. ATMOSPHERE 2022. [DOI: 10.3390/atmos13060959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction.
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