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Fan Y, Sun N, Lv S, Jiang H, Zhang Z, Wang J, Xie Y, Yue X, Hu B, Ju B, Yu P. Prediction of developmental toxic effects of fine particulate matter (PM 2.5) water-soluble components via machine learning through observation of PM 2.5 from diverse urban areas. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174027. [PMID: 38906297 DOI: 10.1016/j.scitotenv.2024.174027] [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: 03/25/2024] [Revised: 06/09/2024] [Accepted: 06/13/2024] [Indexed: 06/23/2024]
Abstract
The global health implications of fine particulate matter (PM2.5) underscore the imperative need for research into its toxicity and chemical composition. In this study, zebrafish embryos exposed to the water-soluble components of PM2.5 from two cities (Harbin and Hangzhou) with differences in air quality, underwent microscopic examination to identify primary target organs. The Harbin PM2.5 induced dose-dependent organ malformation in zebrafish, indicating a higher level of toxicity than that of the Hangzhou sample. Harbin PM2.5 led to severe deformities such as pericardial edema and a high mortality rate, while the Hangzhou sample exhibited hepatotoxicity, causing delayed yolk sac absorption. The experimental determination of PM2.5 constituents was followed by the application of four algorithms for predictive toxicological assessment. The random forest algorithm correctly predicted each of the effect classes and showed the best performance, suggesting that zebrafish malformation rates were strongly correlated with water-soluble components of PM2.5. Feature selection identified the water-soluble ions F- and Cl- and metallic elements Al, K, Mn, and Be as potential key components affecting zebrafish development. This study provides new insights into the developmental toxicity of PM2.5 and offers a new approach for predicting and exploring the health effects of PM2.5.
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Affiliation(s)
- Yang Fan
- Department of Medical Oncology of the Second Affiliated Hospital, Department of Toxicology, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Nannan Sun
- Hangzhou SanOmics AI Co., Ltd, Hangzhou 311103, China
| | - Shenchong Lv
- Department of Medical Oncology of the Second Affiliated Hospital, Department of Toxicology, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Hui Jiang
- Department of Medical Oncology of the Second Affiliated Hospital, Department of Toxicology, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Ziqing Zhang
- Department of Medical Oncology of the Second Affiliated Hospital, Department of Toxicology, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Junjie Wang
- Department of Medical Oncology of the Second Affiliated Hospital, Department of Toxicology, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Yiyi Xie
- Department of Medical Oncology of the Second Affiliated Hospital, Department of Toxicology, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Xiaomin Yue
- Department of Biophysics, Zhejiang University School of Medicine, Hangzhou 310058, China; Department of Neurology of the Fourth Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Baolan Hu
- College of Environmental Resource Sciences, Zhejiang University, Hangzhou 310058, China.
| | - Bin Ju
- Hangzhou SanOmics AI Co., Ltd, Hangzhou 311103, China.
| | - Peilin Yu
- Department of Medical Oncology of the Second Affiliated Hospital, Department of Toxicology, Zhejiang University School of Medicine, Hangzhou 310058, China.
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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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Houdou A, Khomsi K, Monache LD, Hu W, Boutayeb S, Belyamani L, Abdulla F, Al-Delaimy WK, Khalis M. Predicting Particulate Matter (PM10) Levels in Morocco: A 5-Day Forecast Using the Analog Ensemble Method. RESEARCH SQUARE 2024:rs.3.rs-4619478. [PMID: 39149506 PMCID: PMC11326415 DOI: 10.21203/rs.3.rs-4619478/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Accurate prediction of Particulate Matter (\({PM}_{10}\)) levels, an indicator of natural pollutants such as those resulting from dust storms, is crucial for public health and environmental planning. This study aims to provide accurate forecasts of \({PM}_{10}\) over Morocco for five days. The Analog Ensemble (AnEn) and the Bias Correction (AnEnBc) techniques were employed to post-process \({PM}_{10}\) forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) global atmospheric composition forecasts, using CAMS reanalysis data as a reference. The results show substantial prediction improvements: the Root Mean Square Error (RMSE) decreased from 63.83 \(\mu g/{m}^{3}\) in the original forecasts to 44.73 \(\mu g/{m}^{3}\) with AnEn and AnEnBc, while the Mean Absolute Error (MAE) reduced from 36.70 \(\mu g/{m}^{3}\) to 24.30 \(\mu g/{m}^{3}\). Additionally, the coefficient of determination (\({R}^{2}\)) increased more than twofold from 29.11-65.18%, and the Pearson correlation coefficient increased from 0.61 to 0.82. This is the first use of this approach for Morocco and the Middle East and North Africa and has the potential for translation into early and more accurate warnings of \({PM}_{10}\) pollution events. The application of such approaches in environmental policies and public health decision making can minimize air pollution health impacts.
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Verma A, Ranga V, Vishwakarma DK. BREATH-Net: a novel deep learning framework for NO 2 prediction using bi-directional encoder with transformer. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:340. [PMID: 38436748 DOI: 10.1007/s10661-024-12455-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/12/2024] [Indexed: 03/05/2024]
Abstract
Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO2) is a prevalent atmospheric pollutant that can potentially exacerbate respiratory ailments and cardiovascular disorders and contribute to cancer development. The present study introduces a novel approach for monitoring and predicting Delhi's nitrogen dioxide concentrations by leveraging satellite data and ground data from the Sentinel 5P satellite and monitoring stations. The research gathers satellite and monitoring data over 3 years for evaluation. Exploratory data analysis (EDA) methods are employed to comprehensively understand the data and discern any discernible patterns and trends in nitrogen dioxide levels. The data subsequently undergoes pre-processing and scaling utilizing appropriate techniques, such as MinMaxScaler, to optimize the model's performance. The proposed forecasting model uses a hybrid architecture of the Transformer and BiLSTM models called BREATH-Net. BiLSTM models exhibit a strong aptitude for effectively managing sequential data by adeptly capturing dependencies in both the forward and backward directions. Conversely, transformers excel in capturing extensive relationships over extended distances in temporal data. The results of this study will illustrate the proposed model's efficacy in predicting the levels of NO2 in Delhi. If effectively executed, this model can significantly enhance strategies for controlling urban air quality. The findings of this research show a significant improvement of RMSE = 9.06 compared to other state-of-the-art models. This study's primary objective is to contribute to mitigating respiratory health issues resulting from air pollution through satellite data and deep learning methodologies.
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Affiliation(s)
- Abhishek Verma
- Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Bawana Road, Delhi, 110042, India.
| | - Virender Ranga
- Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Bawana Road, Delhi, 110042, India.
| | - Dinesh Kumar Vishwakarma
- Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Bawana Road, Delhi, 110042, India
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Peng Z, Zhang B, Wang D, Niu X, Sun J, Xu H, Cao J, Shen Z. Application of machine learning in atmospheric pollution research: A state-of-art review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 910:168588. [PMID: 37981149 DOI: 10.1016/j.scitotenv.2023.168588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/07/2023] [Accepted: 11/12/2023] [Indexed: 11/21/2023]
Abstract
Machine learning (ML) is an artificial intelligence technology that has been used in atmospheric pollution research due to their powerful fitting ability. In this review, 105 articles related to ML and the atmospheric pollution research are critically reviewed. Applications of ML in the prediction of atmospheric pollution (mainly particulate matters) are systematically described, including the principle of prediction, influencing factors and improvement measures. Researchers can improve the accuracy of the prediction model through three main aspects, namely considering the geographical features of the study area into the model, introducing the physical characteristics of pollutants, matching and optimizing ML models. And by using interpretable ML tools, researchers are able to understand the mechanism of the model and gain in-depth information. Then, the state-of-art applications of ML in the source apportionment of atmospheric particulate matter and the effect of atmospheric pollutants on human health are also described. In addition, the advantages and disadvantages of the current applications of ML in atmospheric pollution research are summarized, and the application perspective of ML in this field is elucidated. Given the scarcity of source apportionment applications and human health research, standardized research methods and specialized ML methods are required in atmospheric pollution research to connect these two disciplines.
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Affiliation(s)
- Zezhi Peng
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Bin Zhang
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Diwei Wang
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xinyi Niu
- School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jian Sun
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
| | - Hongmei Xu
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Junji Cao
- Key Lab of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710049, China
| | - Zhenxing Shen
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
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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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Ismael BH, Khaleel F, Ibrahim SS, Khaleel SR, AlOmar MK, Masood A, Aljumaily MM, Alsalhy QF, Mohd Razali SF, Al-Juboori RA, Hameed MM, Alsarayreh AA. Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques. MEMBRANES 2023; 13:900. [PMID: 38132904 PMCID: PMC10744684 DOI: 10.3390/membranes13120900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023]
Abstract
Vacuum membrane distillation (VMD) has attracted increasing interest for various applications besides seawater desalination. Experimental testing of membrane technologies such as VMD on a pilot or large scale can be laborious and costly. Machine learning techniques can be a valuable tool for predicting membrane performance on such scales. In this work, a novel hybrid model was developed based on incorporating a spotted hyena optimizer (SHO) with support vector machine (SVR) to predict the flux pressure in VMD. The SVR-SHO hybrid model was validated with experimental data and benchmarked against other machine learning tools such as artificial neural networks (ANNs), classical SVR, and multiple linear regression (MLR). The results show that the SVR-SHO predicted flux pressure with high accuracy with a correlation coefficient (R) of 0.94. However, other models showed a lower prediction accuracy than SVR-SHO with R-values ranging from 0.801 to 0.902. Global sensitivity analysis was applied to interpret the obtained result, revealing that feed temperature was the most influential operating parameter on flux, with a relative importance score of 52.71 compared to 17.69, 17.16, and 14.44 for feed flowrate, vacuum pressure intensity, and feed concentration, respectively.
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Affiliation(s)
- Bashar H. Ismael
- Construction and Projects Department, University of Fallujah, Fallujah 31002, Iraq;
- Department of Civil Engineering, Al-Maarif University College (AUC), Ramadi 31001, Iraq; (F.K.); (M.K.A.); (M.M.A.); (M.M.H.)
| | - Faidhalrahman Khaleel
- Department of Civil Engineering, Al-Maarif University College (AUC), Ramadi 31001, Iraq; (F.K.); (M.K.A.); (M.M.A.); (M.M.H.)
| | - Salah S. Ibrahim
- Membrane Technology Research Unit, Chemical Engineering Department, University of Technology, Alsena’a Street 52, Baghdad 10066, Iraq; (S.S.I.); (S.R.K.)
| | - Samraa R. Khaleel
- Membrane Technology Research Unit, Chemical Engineering Department, University of Technology, Alsena’a Street 52, Baghdad 10066, Iraq; (S.S.I.); (S.R.K.)
| | - Mohamed Khalid AlOmar
- Department of Civil Engineering, Al-Maarif University College (AUC), Ramadi 31001, Iraq; (F.K.); (M.K.A.); (M.M.A.); (M.M.H.)
| | - Adil Masood
- Department of Civil Engineering, Jamia Millia Islamia, New Delhi 110025, India;
| | - Mustafa M. Aljumaily
- Department of Civil Engineering, Al-Maarif University College (AUC), Ramadi 31001, Iraq; (F.K.); (M.K.A.); (M.M.A.); (M.M.H.)
| | - Qusay F. Alsalhy
- Membrane Technology Research Unit, Chemical Engineering Department, University of Technology, Alsena’a Street 52, Baghdad 10066, Iraq; (S.S.I.); (S.R.K.)
| | - Siti Fatin Mohd Razali
- Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia;
- Smart and Sustainable Township Research Centre (SUTRA), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
- Green Engineering and Net Zero Solution (GREENZ), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
| | - Raed A. Al-Juboori
- NYUAD Water Research Center, Abu Dhabi Campus, New York University, Abu Dhabi P.O. Box 129188, United Arab Emirates
- Water and Environmental Engineering Research Group, Department of Built Environment, Aalto University, P.O. Box 15200, FI-00076 Espoo, Finland
| | - Mohammed Majeed Hameed
- Department of Civil Engineering, Al-Maarif University College (AUC), Ramadi 31001, Iraq; (F.K.); (M.K.A.); (M.M.A.); (M.M.H.)
| | - Alanood A. Alsarayreh
- Department of Chemical Engineering, Faculty of Engineering, Mutah University, P.O. Box 7, Karak 61710, Jordan;
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Masood A, Hameed MM, Srivastava A, Pham QB, Ahmad K, Razali SFM, Baowidan SA. Improving PM 2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm. Sci Rep 2023; 13:21057. [PMID: 38030733 PMCID: PMC10687010 DOI: 10.1038/s41598-023-47492-z] [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: 09/05/2023] [Accepted: 11/14/2023] [Indexed: 12/01/2023] Open
Abstract
Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.
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Affiliation(s)
- Adil Masood
- Department of Civil Engineering, Jamia Millia Islamia University, New Delhi, India
| | | | - Aman Srivastava
- Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, 721302, West Bengal, India
| | - Quoc Bao Pham
- Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200, Sosnowiec, Poland
| | - Kafeel Ahmad
- Department of Civil Engineering, Jamia Millia Islamia University, New Delhi, India
| | - Siti Fatin Mohd Razali
- Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
- Smart and Sustainable Township Research Centre (SUTRA), Universiti Kebangsaan Malaysia (UKM), 43600, UKM Bangi, Selangor, Malaysia
- Green Engineering and Net Zero Solution (GREENZ), Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
| | - Souad Ahmad Baowidan
- Information Technology Department Faculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Excellence in Environmental Studies, King Abdulaziz University, Jeddah, Saudi Arabia
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