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Jack C, Parker C, Kouakou YE, Joubert B, McAllister KA, Ilias M, Maimela G, Chersich M, Makhanya S, Luchters S, Makanga PT, Vos E, Ebi KL, Koné B, Waljee AK, Cissé G. Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE 2AT Center study protocol. BMJ Open 2024; 14:e077529. [PMID: 38890141 PMCID: PMC11191804 DOI: 10.1136/bmjopen-2023-077529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 05/03/2024] [Indexed: 06/20/2024] Open
Abstract
INTRODUCTION African cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the complexities of heat-related health impacts in these cities. The objectives are: (1) mapping intraurban heat risk and exposure using health, socioeconomic, climate and satellite imagery data; (2) creating a stratified heat-health forecast model to predict adverse health outcomes; and (3) establishing an early warning system for timely heatwave alerts. The ultimate goal is to foster climate-resilient African cities, protecting disproportionately affected populations from heat hazards. METHODS AND ANALYSIS The research will acquire health-related datasets from eligible adult clinical trials or cohort studies conducted in Johannesburg and Abidjan between 2000 and 2022. Additional data will be collected, including socioeconomic, climate datasets and satellite imagery. These resources will aid in mapping heat hazards and quantifying heat-health exposure, the extent of elevated risk and morbidity. Outcomes will be determined using advanced data analysis methods, including statistical evaluation, machine learning and deep learning techniques. ETHICS AND DISSEMINATION The study has been approved by the Wits Human Research Ethics Committee (reference no: 220606). Data management will follow approved procedures. The results will be disseminated through workshops, community forums, conferences and publications. Data deposition and curation plans will be established in line with ethical and safety considerations.
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Affiliation(s)
- Christopher Jack
- Climate System Analysis Group, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Craig Parker
- Wits Planetary Health Research, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Yao Etienne Kouakou
- University Peleforo Gon Coulibaly, Korhogo, Côte d'Ivoire
- Centre Suisse de Recherches Scientifiques, Abidjan, Côte d'Ivoire
| | - Bonnie Joubert
- National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | | | - Maliha Ilias
- National Heart Lung and Blood Institute, Bethesda, Maryland, USA
| | - Gloria Maimela
- Climate and Health Directorate, Wits Reproductive Health and HIV Institute, Hillbrow, Gauteng, South Africa
| | - Matthew Chersich
- Wits Planetary Health Research, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Public Health and Primary Care, School of Medicine, Trinity College Dublin, Dublin, UK
| | | | - Stanley Luchters
- Centre for Sexual Health and HIV & AIDS Research (CeSHHAR), Harare, Zimbabwe
- Liverpool School of Tropical Medicine, Liverpool, UK
| | - Prestige Tatenda Makanga
- Centre for Sexual Health and HIV & AIDS Research (CeSHHAR), Harare, Zimbabwe
- Surveying and Geomatics Department, Midlands State University, Gweru, Zimbabwe
| | - Etienne Vos
- IBM Research-Africa, Johannesburg, South Africa
| | | | - Brama Koné
- University Peleforo Gon Coulibaly, Korhogo, Côte d'Ivoire
- Centre Suisse de Recherches Scientifiques, Abidjan, Côte d'Ivoire
| | - Akbar K Waljee
- Gastroenterology, University of Michigan, Ann Arbor, Michigan, USA
- Ann Arbor VA Medical Center, VA Center for Clinical Management Research, Ann Arbor, Michigan, USA
| | - Guéladio Cissé
- University Peleforo Gon Coulibaly, Korhogo, Côte d'Ivoire
- Centre Suisse de Recherches Scientifiques, Abidjan, Côte d'Ivoire
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Boudreault J, Campagna C, Chebana F. Revisiting the importance of temperature, weather and air pollution variables in heat-mortality relationships with machine learning. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:14059-14070. [PMID: 38270762 DOI: 10.1007/s11356-024-31969-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: 10/02/2023] [Accepted: 01/07/2024] [Indexed: 01/26/2024]
Abstract
Extreme heat events have significant health impacts that need to be adequately quantified in the context of climate change. Traditionally, heat-health association methods have relied on statistical models using a single air temperature index, without considering other heat-related variables that may influence the relationship and their potentially complex interactions. This study aims to introduce and compare different machine learning (ML) models, which naturally consider interactions between predictors and non-linearities, to re-examine the importance of temperature, weather and air pollution predictors in modeling the heat-mortality relationship. ML approaches based on tree ensembles and neural networks, as well as non-linear statistical models, were used to model the heat-mortality relationship in the two most populated metropolitan areas of the province of Quebec, Canada. The models were calibrated using a comprehensive database of heat-related predictors including various lagged temperature indices, temperature variations, meteorological and air pollution variables. Performance was evaluated based on out-of-sample summer mortality predictions. For the two studied regions, models relying only on lagged temperature indices performed better, or equally well, than models considering more heat-related predictors such as temperature variations, weather and air pollution variables. The temperature index with the best performance differed by region, but both mean temperature and humidex were among the best indices. In terms of modeling approaches, non-linear statistical models were as competent as more advanced ML models for predicting out-of-sample summer mortality. This research validated the current use of non-linear statistical models with the appropriate lagged temperature index to model the heat-mortality relationship. Although ML models have not improved the performance of all-cause mortality modeling, these approaches should continue to be explored, particularly for other health effects that may be more directly linked to heat exposure and, in the future, when more data become available.
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Affiliation(s)
- Jérémie Boudreault
- Centre Eau Terre Environnement, Institut national de la recherche scientifique (INRS), 490 de La Couronne, Quebec, QC, G1K 9A9, Canada.
- Direction de la santé environnementale, au travail et de la toxicologie, Institut national de santé publique du Québec (INSPQ), 945 Avenue Wolfe, Quebec, QC, G1V 5B3, Canada.
| | - Céline Campagna
- Centre Eau Terre Environnement, Institut national de la recherche scientifique (INRS), 490 de La Couronne, Quebec, QC, G1K 9A9, Canada
- Direction de la santé environnementale, au travail et de la toxicologie, Institut national de santé publique du Québec (INSPQ), 945 Avenue Wolfe, Quebec, QC, G1V 5B3, Canada
| | - Fateh Chebana
- Centre Eau Terre Environnement, Institut national de la recherche scientifique (INRS), 490 de La Couronne, Quebec, QC, G1K 9A9, Canada
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Srivastava H, Kumar Das S. Air pollution prediction system using XRSTH-LSTM algorithm. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:125313-125327. [PMID: 37481499 DOI: 10.1007/s11356-023-28393-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/19/2023] [Indexed: 07/24/2023]
Abstract
Globally, there are significant worries about the rise in air pollution (AP) from substances that are harmful to human health, different living forms, and unfavorable environmental imbalances. To overcome the problem, AI-based prediction model is the need of the hour. Therefore, an attempt was made to develop a novel AP prediction system based on Xavier Reptile Switan-h-based Long-Short Term Memory (XRSTH-LSTM), which undergoes fine-tuning at various steps such as pre-processing, attribute extraction, and air-quality index prediction, in order to reduce computational cost and also to increase accuracy as well as precision. The dataset used to train the proposed methodology is Air Quality Data in India (2015-2020), taken from publically available sources Kaggle. The dataset includes information on the AQI and air quality at different stations in numerous Indian cities at hourly and daily intervals. The accuracy has been calculated using MSE, MAPE, RMSE, precision, recall, and F-measure. The robustness of the proposed model is tested using parameters such as negative predicted value and Mathew correlation coefficient. The proposed model is found to efficiently process air quality with an improved accuracy of 98.52% and precision of 99.79%, which is 0.74% higher than the existing state-of-the-art model. The testing findings showed that the proposed approach worked better than the current models and offered a higher rate of accuracy in predicting air pollution.
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Affiliation(s)
- Harshit Srivastava
- Department of Electronics and Communication, National Institute of Technology, Rourkela, 769008, Odisha, India
| | - Santos Kumar Das
- Department of Electronics and Communication, National Institute of Technology, Rourkela, 769008, Odisha, India.
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Neo EX, Hasikin K, Lai KW, Mokhtar MI, Azizan MM, Hizaddin HF, Razak SA, Yanto. Artificial intelligence-assisted air quality monitoring for smart city management. PeerJ Comput Sci 2023; 9:e1306. [PMID: 37346549 PMCID: PMC10280551 DOI: 10.7717/peerj-cs.1306] [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: 01/20/2023] [Accepted: 02/28/2023] [Indexed: 06/23/2023]
Abstract
Background The environment has been significantly impacted by rapid urbanization, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosystems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete measurement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of machine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities. Methods This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions. Results In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5concentration, LSTM performed the best overall high R2values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5,PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.
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Affiliation(s)
- En Xin Neo
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Center of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Mohd Istajib Mokhtar
- Department of Science and Technology Studies, Faculty of Sciences, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Muhammad Mokhzaini Azizan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai, Negeri Sembilan, Malaysia
| | - Hanee Farzana Hizaddin
- Department of Chemical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Sarah Abdul Razak
- Institute of Biological Science, Faculty of Science, Univerisiti Malaya, Kuala Lumpur, Malaysia
| | - Yanto
- Civil Engineering Department, Jenderal Soedirman University, Purwokerto, Indonesia
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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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Neo EX, Hasikin K, Mokhtar MI, Lai KW, Azizan MM, Razak SA, Hizaddin HF. Towards Integrated Air Pollution Monitoring and Health Impact Assessment Using Federated Learning: A Systematic Review. Front Public Health 2022; 10:851553. [PMID: 35664109 PMCID: PMC9160600 DOI: 10.3389/fpubh.2022.851553] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 04/01/2022] [Indexed: 12/12/2022] Open
Abstract
Environmental issues such as environmental pollutions and climate change are the impacts of globalization and become debatable issues among academics and industry key players. One of the environmental issues which is air pollution has been catching attention among industrialists, researchers, and communities around the world. However, it has always neglected until the impacts on human health become worse, and at times, irreversible. Human exposure to air pollutant such as particulate matters, sulfur dioxide, ozone and carbon monoxide contributed to adverse health hazards which result in respiratory diseases, cardiorespiratory diseases, cancers, and worst, can lead to death. This has led to a spike increase of hospitalization and emergency department visits especially at areas with worse pollution cases that seriously impacting human life and health. To address this alarming issue, a predictive model of air pollution is crucial in assessing the impacts of health due to air pollution. It is also critical in predicting the air quality index when assessing the risk contributed by air pollutant exposure. Hence, this systemic review explores the existing studies on anticipating air quality impact to human health using the advancement of Artificial Intelligence (AI). From the extensive review, we highlighted research gaps in this field that are worth to inquire. Our study proposes to develop an AI-based integrated environmental and health impact assessment system using federated learning. This is specifically aims to identify the association of health impact and pollution based on socio-economic activities and predict the Air Quality Index (AQI) for impact assessment. The output of the system will be utilized for hospitals and healthcare services management and planning. The proposed solution is expected to accommodate the needs of the critical and prioritization of sensitive group of publics during pollution seasons. Our finding will bring positive impacts to the society in terms of improved healthcare services quality, environmental and health sustainability. The findings are beneficial to local authorities either in healthcare or environmental monitoring institutions especially in the developing countries.
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Affiliation(s)
- En Xin Neo
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Center of Image and Signal Processing (CISIP), Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Mohd Istajib Mokhtar
- Department of Science and Technology Studies, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Muhammad Mokhzaini Azizan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai, Malaysia
| | - Sarah Abdul Razak
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Hanee Farzana Hizaddin
- Department of Chemical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
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