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Romano D, Novielli P, Diacono D, Cilli R, Pantaleo E, Amoroso N, Bellantuono L, Monaco A, Bellotti R, Tangaro S. Insights from Explainable Artificial Intelligence of Pollution and Socioeconomic Influences for Respiratory Cancer Mortality in Italy. J Pers Med 2024; 14:430. [PMID: 38673057 PMCID: PMC11051343 DOI: 10.3390/jpm14040430] [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: 03/04/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Respiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for crafting policies aimed at mitigating exposure. This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary influencers on the prediction of standard mortality rates (SMRs) attributable to respiratory cancer across Italian provinces, utilizing both environmental and socioeconomic data. By scrutinizing thirteen distinct machine learning algorithms, we endeavor to pinpoint the most accurate model for categorizing Italian provinces as either above or below the national average SMR value for respiratory cancer. Furthermore, employing XAI techniques, we delineate the salient factors crucial in predicting the two classes of SMR. Through our machine learning scrutiny, we illuminate the environmental and socioeconomic factors pertinent to mortality in this disease category, thereby offering a roadmap for prioritizing interventions aimed at mitigating risk factors.
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Affiliation(s)
- Donato Romano
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy; (D.R.); (P.N.)
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
| | - Pierfrancesco Novielli
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy; (D.R.); (P.N.)
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
| | - Roberto Cilli
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento di Farmacia Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento di Biomedicina Traslazionale e Neuroscienze, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy; (D.R.); (P.N.)
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
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Dahu BM, Alaboud K, Nowbuth AA, Puckett HM, Scott GJ, Sheets LR. The Role of Remote Sensing and Geospatial Analysis for Understanding COVID-19 Population Severity: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4298. [PMID: 36901308 PMCID: PMC10002247 DOI: 10.3390/ijerph20054298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/30/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Remote sensing (RS), satellite imaging (SI), and geospatial analysis have established themselves as extremely useful and very diverse domains for research associated with space, spatio-temporal components, and geography. We evaluated in this review the existing evidence on the application of those geospatial techniques, tools, and methods in the coronavirus pandemic. We reviewed and retrieved nine research studies that directly used geospatial techniques, remote sensing, or satellite imaging as part of their research analysis. Articles included studies from Europe, Somalia, the USA, Indonesia, Iran, Ecuador, China, and India. Two papers used only satellite imaging data, three papers used remote sensing, three papers used a combination of both satellite imaging and remote sensing. One paper mentioned the use of spatiotemporal data. Many studies used reports from healthcare facilities and geospatial agencies to collect the type of data. The aim of this review was to show the use of remote sensing, satellite imaging, and geospatial data in defining features and relationships that are related to the spread and mortality rate of COVID-19 around the world. This review should ensure that these innovations and technologies are instantly available to assist decision-making and robust scientific research that will improve the population health diseases outcomes around the globe.
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Affiliation(s)
- Butros M. Dahu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Department of Health Management and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Khuder Alaboud
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- NextGen Biomedical Informatics Center, University of Missouri, Columbia, MO 65211, USA
| | - Avis Anya Nowbuth
- Pan African Organization for Health Education and Research (POHER), Manchester, MO 63011, USA
| | - Hunter M. Puckett
- Department of Health Management and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Grant J. Scott
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Lincoln R. Sheets
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Department of Health Management and Informatics, University of Missouri, Columbia, MO 65211, USA
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Kovács KD, Haidu I. Tracing out the effect of transportation infrastructure on NO 2 concentration levels with Kernel Density Estimation by investigating successive COVID-19-induced lockdowns. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 309:119719. [PMID: 35809708 DOI: 10.1016/j.envpol.2022.119719] [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: 01/27/2022] [Revised: 06/23/2022] [Accepted: 07/01/2022] [Indexed: 06/15/2023]
Abstract
This study aims to investigate the effect of transportation infrastructure on the decrease of NO2 air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO2 air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot-coldspot analysis on the relative change in NO2 air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO2 air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R2 = 0.808). The results showed that the largest decrease in NO2 air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = -0.904, R2 = 0.818). Economic and population predictors also explained with good fit the decrease in NO2 air pollution during the first lockdown: GDP (R2 = 0.511), employees (R2 = 0.513), population density (R2 = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.
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Affiliation(s)
- Kamill Dániel Kovács
- Université de Lorraine, Laboratoire LOTERR-EA7304, Île du Saulcy, 57045 Metz, France.
| | - Ionel Haidu
- Université de Lorraine, Laboratoire LOTERR-EA7304, Île du Saulcy, 57045 Metz, France.
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Al Huraimel K, Alhosani M, Gopalani H, Kunhabdulla S, Stietiya MH. Elucidating the role of environmental management of forests, air quality, solid waste and wastewater on the dissemination of SARS-CoV-2. HYGIENE AND ENVIRONMENTAL HEALTH ADVANCES 2022; 3:100006. [PMID: 37519421 PMCID: PMC9095661 DOI: 10.1016/j.heha.2022.100006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/13/2022] [Accepted: 04/30/2022] [Indexed: 11/29/2022]
Abstract
The increasing frequency of zoonotic diseases is amongst several catastrophic repercussions of inadequate environmental management. Emergence, prevalence, and lethality of zoonotic diseases is intrinsically linked to environmental management which are currently at a destructive level globally. The effects of these links are complicated and interdependent, creating an urgent need of elucidating the role of environmental mismanagement to improve our resilience to future pandemics. This review focused on the pertinent role of forests, outdoor air, indoor air, solid waste and wastewater management in COVID-19 dissemination to analyze the opportunities prevailing to control infectious diseases considering relevant data from previous disease outbreaks. Global forest management is currently detrimental and hotspots of forest fragmentation have demonstrated to result in zoonotic disease emergences. Deforestation is reported to increase susceptibility to COVID-19 due to wildfire induced pollution and loss of forest ecosystem services. Detection of SARS-CoV-2 like viruses in multiple animal species also point to the impacts of biodiversity loss and forest fragmentation in relation to COVID-19. Available literature on air quality and COVID-19 have provided insights into the potential of air pollutants acting as plausible virus carrier and aggravating immune responses and expression of ACE2 receptors. SARS-CoV-2 is detected in outdoor air, indoor air, solid waste, wastewater and shown to prevail on solid surfaces and aerosols for prolonged hours. Furthermore, lack of protection measures and safe disposal options in waste management are evoking concerns especially in underdeveloped countries due to high infectivity of SARS-CoV-2. Inadequate legal framework and non-adherence to environmental regulations were observed to aggravate the postulated risks and vulnerability to future waves of pandemics. Our understanding underlines the urgent need to reinforce the fragile status of global environmental management systems through the development of strict legislative frameworks and enforcement by providing institutional, financial and technical supports.
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Affiliation(s)
- Khaled Al Huraimel
- Division of Consultancy, Research & Innovation (CRI), Sharjah Environment Company - Bee'ah, Sharjah, United Arab Emirates
| | - Mohamed Alhosani
- Division of Consultancy, Research & Innovation (CRI), Sharjah Environment Company - Bee'ah, Sharjah, United Arab Emirates
| | - Hetasha Gopalani
- Division of Consultancy, Research & Innovation (CRI), Sharjah Environment Company - Bee'ah, Sharjah, United Arab Emirates
| | - Shabana Kunhabdulla
- Division of Consultancy, Research & Innovation (CRI), Sharjah Environment Company - Bee'ah, Sharjah, United Arab Emirates
| | - Mohammed Hashem Stietiya
- Division of Consultancy, Research & Innovation (CRI), Sharjah Environment Company - Bee'ah, Sharjah, United Arab Emirates
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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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