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Gholami H, Mohammadifar A, Behrooz RD, Kaskaoutis DG, Li Y, Song Y. Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 342:123082. [PMID: 38061429 DOI: 10.1016/j.envpol.2023.123082] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/11/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023]
Abstract
Total suspended particulates (TSP), as a key pollutant, is a serious threat for air quality, climate, ecosystems and human health. Therefore, measurements, prediction and forecasting of TSP concentrations are necessary to mitigate their negative effects. This study applies the gated recurrent unit (GRU) deep learning model to predict TSP concentrations in Zabol, Iran, during the dust period of the 120-day wind (3 June - 4 October 2014). Three uncertainty quantification (UQ) techniques consisting of the blackbox metamodel, heteroscedastic regression and infinitesimal jackknife were applied to quantify the uncertainty associated with GRU model. Permutation feature importance measure (PFIM), based on the game theory, was employed for the interpretability of the predictive model's outputs. A total of 80 TSP samples were collected and were randomly divided as training (70%) and validation (30%) datasets, while eight variables were used in the TSP prediction model. Our findings showed that GRU performed very well for TSP prediction (with r and Nash Sutcliffe coefficient (NSC) values above 0.99 for both datasets, and RMSE of 57 μg m-3 and 73 μg m-3 for training and validation datasets, respectively). Among the three UQ techniques, the infinitesimal jackknife was the most accurate one, while all the observed and predicted TSP values fell within the continence limitation estimated by the model. PFIM plots showed that wind speed and air humidity were the most and least important variables, respectively, impacting the predictive model's outputs. This is the first attempt of using an interpretable DL model for TSP prediction modelling, recommending that future research should involve aspects of uncertainty and interpretability of the predictive models. Overall, UQ and interpretability techniques have a key role in reducing the impact of uncertainties during optimization and decision making, resulting in better understanding of sophisticated mechanisms related to the predictive model.
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Affiliation(s)
- Hamid Gholami
- Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.
| | - Aliakbar Mohammadifar
- Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran
| | - Reza Dahmardeh Behrooz
- Department of Environmental Science, Faculty of Natural Resources, University of Zabol, P.O. Box 98615-538, Zabol, Iran
| | - Dimitris G Kaskaoutis
- Department of Chemical Engineering, University of Western Macedonia, Kozani, 50100, Greece
| | - Yue Li
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; Laoshan Laboratory, Qingdao, 266061, China
| | - Yougui Song
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; Laoshan Laboratory, Qingdao, 266061, China.
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Global temperature reconstruction of equipment based on the local temperature image using TRe-GAN. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Sakunkoo P, Thonglua T, Sangkham S, Jirapornkul C, Limmongkon Y, Daduang S, Tessiri T, Rayubkul J, Thongtip S, Maneenin N, Pimonsree S. Human health risk assessment of PM 2.5-bound heavy metal of anthropogenic sources in the Khon Kaen Province of Northeast Thailand. Heliyon 2022; 8:e09572. [PMID: 35711987 PMCID: PMC9193878 DOI: 10.1016/j.heliyon.2022.e09572] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/21/2022] [Accepted: 05/24/2022] [Indexed: 11/24/2022] Open
Abstract
The study aimed to assess the human health risk of PM2.5-bound heavy metals from anthropogenic sources in Khon Kaen Province, Thailand between December 2020 and February 2021. According to the findings, the geometric mean concentration of PM2.5 in the university area, residential area, industrial zone, and the agricultural zone was 32.78 μg/m3, 50.25 μg/m3, 44.48 μg/m3, and 29.53 μg/m3, respectively. The results showed that the estimated human health risk assessment, in terms of non-carcinogenic risks among children and adults in an urban area (residential and university), industrial zone, and the agricultural area, was of hazard index (HI) value of >1.0 indicating a greater chance of chronic effects occurring. This study showed that exposure to PM2.5-bound heavy metal may increase the likelihood that lasting effects will result in a very high carcinogenic risk (CR) in children in residential areas, and an industrial zone with total carcinogenic risk (TCR) values of 0.23×101, and 0.12×101, respectively while resulting in a high TCR of 3.34×10−2 and 4.11×10−2 within the university areas and agricultural zone, respectively. In addition, health risk assessments among adults demonstrate high TCR values of 4.40×10−1 (residential area), 2.28×10−1 (industrial zone), and 7.70×10−3 (agricultural zone), thus indicating a potential health risk to adults living in these areas while the university area was very low effects on carcinogenic risk (CR≤10−8) for adults. Therefore, lowering the risk of exposure to PM2.5 via the respiratory tract, for example, wearing a mask outside is a very effective self-defense strategy for people within and around the study site. This data study strongly supports the implementation of the air pollutant emission source reduction measures control and health surveillance.
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Affiliation(s)
- Pornpun Sakunkoo
- Department of Environmental Health Occupational Health and Safety, Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen, 40002, Thailand
- Occupational Health Safety and Environmental Epidemiology Group (OHSEE), Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen 40002, Thailand
| | - Theerachai Thonglua
- Department of Environmental Health Occupational Health and Safety, Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen, 40002, Thailand
| | - Sarawut Sangkham
- Department of Environmental Health, School of Public Health, University of Phayao, Muang District, Phayao, 56000, Thailand
- Corresponding author at: Department of Environmental Health, School of Public Health, University of Phayao, Muang District, Phayao, 56000, Thailand.
| | - Chananya Jirapornkul
- Occupational Health Safety and Environmental Epidemiology Group (OHSEE), Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen 40002, Thailand
- Department of Epidemiology and Biostatistics, Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen, 40002, Thailand
| | - Yuparat Limmongkon
- Department of Environmental Health Occupational Health and Safety, Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen, 40002, Thailand
- Occupational Health Safety and Environmental Epidemiology Group (OHSEE), Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen 40002, Thailand
| | - Sakda Daduang
- Faculty of Pharmaceutical Sciences, Khon Kaen University, Muang District, Khon Kaen, 40002, Thailand
| | - Thanee Tessiri
- Faculty of Pharmaceutical Sciences, Khon Kaen University, Muang District, Khon Kaen, 40002, Thailand
| | - Jetnapis Rayubkul
- Occupational Health Safety and Environmental Epidemiology Group (OHSEE), Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen 40002, Thailand
- Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen, 40002, Thailand
| | - Sakesun Thongtip
- Department of Environmental Health, School of Public Health, University of Phayao, Muang District, Phayao, 56000, Thailand
- Atmospheric Pollution and Climate Change Research Unit, School of Energy and Environment, University of Phayao, Muang District, Phayao, 56000, Thailand
| | - Naowarat Maneenin
- Occupational Health Safety and Environmental Epidemiology Group (OHSEE), Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen 40002, Thailand
- Department of Epidemiology and Biostatistics, Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen, 40002, Thailand
- Corresponding author at: Department of Epidemiology and Biostatistics, Faculty of Public Health, Khon Kaen University, Muang District, Khon Kaen, 40002, Thailand.
| | - Sittichai Pimonsree
- Atmospheric Pollution and Climate Change Research Unit, School of Energy and Environment, University of Phayao, Muang District, Phayao, 56000, Thailand
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Zhang P, Yang L, Ma W, Wang N, Wen F, Liu Q. Spatiotemporal estimation of the PM 2.5 concentration and human health risks combining the three-dimensional landscape pattern index and machine learning methods to optimize land use regression modeling in Shaanxi, China. ENVIRONMENTAL RESEARCH 2022; 208:112759. [PMID: 35077716 DOI: 10.1016/j.envres.2022.112759] [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: 07/31/2021] [Revised: 01/05/2022] [Accepted: 01/16/2022] [Indexed: 06/14/2023]
Abstract
PM2.5 pollution endangers human health and urban sustainable development. Land use regression (LUR) is one of the most important methods to reveal the temporal and spatial heterogeneity of PM2.5, and the introduction of characteristic variables of geographical factors and the improvement of model construction methods are important research directions for its optimization. However, the complex non-linear correlation between PM2.5 and influencing indicators is always unrecognized by the traditional regression model. The two-dimensional landscape pattern index is difficult to reflect the real information of the surface, and the research accuracy cannot meet the requirements. As such, a novel integrated three-dimensional landscape pattern index (TDLPI) and machine learning extreme gradient boosting (XGBOOST) improved LUR model (LTX) are developed to estimate the spatiotemporal heterogeneity in the fine particle concentration in Shaanxi, China, and health risks of exposure and inhalation of PM2.5 were explored. The LTX model performed well with R2 = 0.88, RMSE of 8.73 μg/m3 and MAE of 5.85 μg/m3. Our findings suggest that integrated three-dimensional landscape pattern information and XGBOOST approaches can accurately estimate annual and seasonal variations of PM2.5 pollution The Guanzhong Plain and northern Shaanxi always feature high PM2.5 values, which exhibit similar distribution trends to those of the observed PM2.5 pollution. This study demonstrated the outstanding performance of the LTX model, which outperforms most models in past researches. On the whole, LTX approach is reliable and can improve the accuracy of pollutant concentration prediction. The health risks of human exposure to fine particles are relatively high in winter. Central part is a high health risk area, while northern area is low. Our study provides a new method for atmospheric pollutants assessing, which is important for LUR model optimization, high-precision PM2.5 pollution prediction and landscape pattern planning. These results can also contribute to human health exposure risks and future epidemiological studies of air pollution.
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Affiliation(s)
- Ping Zhang
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China; Shaanxi Key Laboratory of Land Consolidation, Xi'an, 710075, China.
| | - Lianwei Yang
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China
| | - Wenjie Ma
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China
| | - Ning Wang
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China
| | - Feng Wen
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China.
| | - Qi Liu
- School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an, 710048, China; The First Institute of Photogrammetry and Remote Sensing, MNR, Xi'an, 710054, China.
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Zaini N, Ean LW, Ahmed AN, Malek MA. A systematic literature review of deep learning neural network for time series air quality forecasting. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:4958-4990. [PMID: 34807385 DOI: 10.1007/s11356-021-17442-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.
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Affiliation(s)
- Nur'atiah Zaini
- Institute of Sustainable Energy, Universiti Tenaga Nasional, Selangor, Malaysia.
| | - Lee Woen Ean
- Institute of Sustainable Energy, Universiti Tenaga Nasional, Selangor, Malaysia
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure, Universiti Tenaga Nasional, Selangor, Malaysia
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Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale. REMOTE SENSING 2021. [DOI: 10.3390/rs13224545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Directly establishing the relationship between satellite data and PM2.5 concentration through deep learning methods for PM2.5 concentration estimation is an important means for estimating regional PM2.5 concentration. However, due to the lack of consideration of uncertainty in deep learning methods, methods based on deep learning have certain overfitting problems in the process of PM2.5 estimation. In response to this problem, this paper designs a deep Bayesian PM2.5 estimation model that takes into account multiple scales. The model uses a Bayesian neural network to describe key parameters a priori, provide regularization effects to the neural network, perform posterior inference through parameters, and take into account the characteristics of data uncertainty, which is used to alleviate the problem of model overfitting and to improve the generalization ability of the model. In addition, different-scale Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data and ERA5 reanalysis data were used as input to the model to strengthen the model’s perception of different-scale features of the atmosphere, as well as to further enhance the model’s PM2.5 estimation accuracy and generalization ability. Experiments with Anhui Province as the research area showed that the R2 of this method on the independent test set was 0.78, which was higher than that of the DNN, random forest, and BNN models that do not consider the impact of the surrounding environment; moreover, the RMSE was 19.45 μg·m−3, which was also lower than the three compared models. In the experiment of different seasons in 2019, compared with the other three models, the estimation accuracy was significantly reduced; however, the R2 of the model in this paper could still reach 0.66 or more. Thus, the model in this paper has a higher accuracy and better generalization ability.
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Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167326] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO2, SO2, CO, O3) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m−3) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m−3) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R2 = 0.46–0.76. The validation analysis reveals that the RF model (R2 = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks.
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