Innovative Methods in Remote Sensing for Health Impact Assessment of Environmental Pollution
Keywords:
Environmental Pollution, Health Assessment, Data Analysis, Public Health Impacts, Environmental Policies
Abstract
This study explores innovative remote sensing approaches to assess the impact of environmental pollution on public health. The methodology relies on integrating sensor technologies with data analysis models to determine the relationship between pollution levels and various health outcomes. By analyzing diverse environmental and health data, new analytical tools were developed that enhance the accuracy of health assessments in multiple regions. The results highlight the importance of these approaches in providing reliable information to support environmental decision-making and policies, contributing to enhancing public health and raising awareness of pollution risks. The study suggests the potential application of these techniques in future research, providing opportunities to improve understanding of the impact of the environment on health. These innovations are an important step towards better assessment of pollution risks and enhancing strategies to mitigate its negative impacts on communities.
References
Chen, J., Chen, S., Fu, R., Li, D., Jiang, H., Wang, C., ... & Hicks, B. J. (2022). Remote sensing big data for water environment monitoring: current status, challenges, and future prospects. Earth's Future, 10(2), e2021EF002289. wiley.com
Yang, H., Kong, J., Hu, H., Du, Y., Gao, M., & Chen, F. (2022). A review of remote sensing for water quality retrieval: progress and challenges. Remote Sensing. mdpi.com
Mather, P. M. & Koch, M. (2022). Computer processing of remotely-sensed images. [HTML]
Elachi, C. & Van Zyl, J. J. (2021). Introduction to the physics and techniques of remote sensing. academia.edu
Chuvieco, E. (2020). Fundamentals of satellite remote sensing: An environmental approach. researchgate.net
Sabins Jr, F. F. & Ellis, J. M. (2020). Remote sensing: Principles, interpretation, and applications. [HTML]
Domínguez, E. M., Small, D., & Henke, D. (2021). Deriving digital surface models from geocoded SAR images and back-projection tomography. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4339-4351. ieee.org
Bu, J., Wang, Q., Wang, Z., Fan, S., Liu, X., & Zuo, X. (2024). Land Remote Sensing Applications Using Spaceborne GNSS Reflectometry: A Comprehensive Overview. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. ieee.org
Yu, K., Han, S., Bu, J., An, Y., Zhou, Z., Wang, C., ... & Cheong, J. W. (2022). Spaceborne GNSS reflectometry. Remote Sensing, 14(7), 1605. mdpi.com
Xu, C., Du, X., Fan, X., Yan, Z., Kang, X., Zhu, J., & Hu, Z. (2021). A modular remote sensing big data framework. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11. researchgate.net
Haut, J. M., Paoletti, M. E., Moreno-Álvarez, S., Plaza, J., Rico-Gallego, J. A., & Plaza, A. (2021). Distributed deep learning for remote sensing data interpretation. Proceedings of the IEEE, 109(8), 1320-1349. umbc.edu
Ejemeyovwi, D. O., Achima, B. T., & Ogwu, C. (2023). Remote Sensing Satellite Systems and Capabilities in Mapping Environmental Resources. NIU Journal of Social Sciences. ijhumas.com
Fu, W., Ma, J., Chen, P., & Chen, F. (2020). Remote sensing satellites for digital earth. Manual of digital earth. oapen.org
Liu, D., Chen, B., An, J., Li, C., Liu, G., Shao, J., ... & Wang, Z. L. (2020). Wind-driven self-powered wireless environmental sensors for Internet of Things at long distance. Nano Energy, 73, 104819. google.com
Khan, S., Naushad, M., Govarthanan, M., Iqbal, J., & Alfadul, S. M. (2022). Emerging contaminants of high concern for the environment: Current trends and future research. Environmental Research, 207, 112609. [HTML]
Morin-Crini, N., Lichtfouse, E., Liu, G., Balaram, V., Ribeiro, A. R. L., Lu, Z., ... & Crini, G. (2022). Worldwide cases of water pollution by emerging contaminants: a review. Environmental Chemistry Letters, 20(4), 2311-2338. hal.science
Ukaogo, P. O., Ewuzie, U., & Onwuka, C. V. (2020). Environmental pollution: causes, effects, and the remedies. In Microorganisms for sustainable environment and health (pp. 419-429). Elsevier. [HTML]
Xu, H., Jia, Y., Sun, Z., Su, J., Liu, Q. S., Zhou, Q., & Jiang, G. (2022). Environmental pollution, a hidden culprit for health issues. Eco-Environment & Health, 1(1), 31-45. sciencedirect.com
Intisar, A., Ramzan, A., Sawaira, T., Kareem, A. T., Hussain, N., Din, M. I., ... & Iqbal, H. M. (2022). Occurrence, toxic effects, and mitigation of pesticides as emerging environmental pollutants using robust nanomaterials–A review. Chemosphere, 293, 133538. [HTML]
Siddiqua, A., Hahladakis, J. N., & Al-Attiya, W. A. K. (2022). An overview of the environmental pollution and health effects associated with waste landfilling and open dumping. Environmental Science and Pollution Research, 29(39), 58514-58536. springer.com
Bhatt, P., Pandey, S. C., Joshi, S., Chaudhary, P., Pathak, V. M., Huang, Y., ... & Chen, S. (2022). Nanobioremediation: A sustainable approach for the removal of toxic pollutants from the environment. Journal of Hazardous Materials, 427, 128033. [HTML]
Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., ... & McCulloch, P. (2022). Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. bmj, 377. bmj.com
Kuziemski, M. & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications policy. nih.gov
Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review, 138, 101967. sciencedirect.com
Yakovlev, S., Bazilevych, K., Chumachenko, D., Chumachenko, T., Hulianytskyi, L., Meniailov, I., & Tkachenko, A. (2020). The concept of developing a decision support system for the epidemic morbidity control. academia.edu
Yannis, G., Kopsacheili, A., Dragomanovits, A., & Petraki, V. (2020). State-of-the-art review on multi-criteria decision-making in the transport sector. Journal of traffic and transportation engineering (English edition), 7(4), 413-431. sciencedirect.com
Deveci, M., Mishra, A. R., Gokasar, I., Rani, P., Pamucar, D., & Özcan, E. (2022). A decision support system for assessing and prioritizing sustainable urban transportation in metaverse. IEEE Transactions on Fuzzy Systems, 31(2), 475-484. worktribe.com
Burnett, M. J. (). Environmental monitoring of freshwater ecosystems using telemetered behavioural indicators from free-swimming fish in southern Africa. researchgate.net. researchgate.net
Dore, K. M., Gallagher, C. A., & Mill, A. C. (2023). Telemetry-based assessment of home range to estimate the abundance of invasive green monkeys on St. Kitts. Caribbean Journal of Science. [HTML]
Yuan, F., Fan, C., Farahmand, H., Coleman, N., Esmalian, A., Lee, C. C., ... & Mostafavi, A. (2022). Smart flood resilience: harnessing community-scale big data for predictive flood risk monitoring, rapid impact assessment, and situational awareness. Environmental Research: Infrastructure and Sustainability, 2(2), 025006. iop.org
Ganie, P. A., Posti, R., Kunal, G., Bhat, R. A. H., & Sidiq, M. J. (2024). Principle and Applications of Geographic Information System (GIS) in Coldwater Fisheries Development in India. In Aquaculture and Conservation of Inland Coldwater Fishes (pp. 469-495). Singapore: Springer Nature Singapore. [HTML]
Bhat, R. A. H., & Sidiq, M. J. Parvaiz Ahmad Ganie, Ravindra Posti, Garima Kunal. Aquaculture and Conservation of Inland Coldwater Fishes, 469. [HTML]
Navin, M. S., & Agilandeeswari, L. (2020). Comprehensive review on land use/land cover change classification in remote sensing. Journal of Spectral Imaging, 9. semanticscholar.org
Pande, C. B., Moharir, K. N., & Khadri, S. F. R. (2021). Assessment of land-use and land-cover changes in Pangari watershed area (MS), India, based on the remote sensing and GIS techniques. Applied Water Science. springer.com
Macarringue, L. S., Bolfe, L., & Pereira, P. R. M. (2022). Developments in land use and land cover classification techniques in remote sensing: a review.. embrapa.br
Fahad, K. H., Hussein, S., & Dibs, H. (2020). Spatial-temporal analysis of land use and land cover change detection using remote sensing and GIS techniques. In IOP conference series: materials science and engineering (Vol. 671, No. 1, p. 012046). IOP Publishing. iop.org
Abebe, G., Getachew, D., & Ewunetu, A. (2022). Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia. SN Applied Sciences. springer.com
Mertikas, S. P., Partsinevelos, P., Mavrocordatos, C., & Maximenko, N. A. (2021). Environmental applications of remote sensing. In Pollution assessment for sustainable practices in applied sciences and engineering (pp. 107-163). Butterworth-Heinemann. [HTML]
Avtar, R., Komolafe, A. A., Kouser, A., Singh, D., Yunus, A. P., Dou, J., ... & Kurniawan, T. A. (2020). Assessing sustainable development prospects through remote sensing: A review. Remote sensing applications: Society and environment, 20, 100402. nih.gov
Liu, C., Xing, C., Hu, Q., Wang, S., Zhao, S., & Gao, M. (2022). Stereoscopic hyperspectral remote sensing of the atmospheric environment: Innovation and prospects. Earth-Science Reviews. [HTML]
Song, W., Song, W., Gu, H., & Li, F. (2020). Progress in the remote sensing monitoring of the ecological environment in mining areas. International Journal of Environmental Research and Public Health, 17(6), 1846. mdpi.com
Mukundan, A., Huang, C. C., Men, T. C., Lin, F. C., & Wang, H. C. (2022). Air pollution detection using a novel snap-shot hyperspectral imaging technique. Sensors. mdpi.com
Tan, K., Wang, H., Chen, L., Du, Q., Du, P., & Pan, C. (2020). Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. Journal of hazardous materials, 382, 120987. msstate.edu
Lu, Y., Saeys, W., Kim, M., Peng, Y., & Lu, R. (2020). Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress. Postharvest Biology and Technology. kuleuven.be
Khan, A., Vibhute, A. D., Mali, S., & Patil, C. H. (2022). A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. Ecological Informatics. [HTML]
Estoque, R. C., Ooba, M., Seposo, X. T., Togawa, T., Hijioka, Y., Takahashi, K., & Nakamura, S. (2020). Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators. Nature communications, 11(1), 1581. nature.com
Barboza, E. P., Cirach, M., Khomenko, S., Iungman, T., Mueller, N., Barrera-Gómez, J., ... & Nieuwenhuijsen, M. (2021). Green space and mortality in European cities: a health impact assessment study. The Lancet Planetary Health, 5(10), e718-e730. thelancet.com
Malathi, K., Shruthi, S. N., Madhumitha, N., Sreelakshmi, S., Sathya, U., & Sangeetha, P. M. (2024). Medical Data Integration and Interoperability through Remote Monitoring of Healthcare Devices. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 15(2), 60-72. researchgate.net
Zhao, Q., Yu, L., Du, Z., Peng, D., Hao, P., Zhang, Y., & Gong, P. (2022). An overview of the applications of earth observation satellite data: impacts and future trends. Remote Sensing. mdpi.com
Himeur, Y., Rimal, B., Tiwary, A., & Amira, A. (2022). Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives. Information Fusion. [HTML]
Li, J., Pei, Y., Zhao, S., Xiao, R., Sang, X., & Zhang, C. (2020). A review of remote sensing for environmental monitoring in China. Remote Sensing. mdpi.com
Apostolopoulos, D., & Nikolakopoulos, K. (2021). A review and meta-analysis of remote sensing data, GIS methods, materials and indices used for monitoring the coastline evolution over the last twenty years. European Journal of Remote Sensing, 54(1), 240-265. tandfonline.com
Gyamfi-Ampadu, E. & Gebreslasie, M. (2021). Two decades progress on the application of remote sensing for monitoring tropical and sub-tropical natural forests: a review. Forests. mdpi.com
Dube, T., Rampheri, B. M., & Shoko, C. (2022). GIS and remote sensing analytics: assessment and monitoring. In Fundamentals of Tropical Freshwater Wetlands (pp. 661-678). Elsevier. [HTML]
de Leeuw, G., van der A, R., Bai, J., Xue, Y., Varotsos, C., Li, Z., ... & Zhang, Y. (2021). Air quality over China. Remote Sensing, 13(17), 3542. mdpi.com
Stirnberg, R., Cermak, J., Fuchs, J., & Andersen, H. (2020). Mapping and understanding patterns of air quality using satellite data and machine learning. Journal of Geophysical Research: Atmospheres, 125(4), e2019JD031380. wiley.com
Singh, D., Dahiya, M., Kumar, R., & Nanda, C. (2021). Sensors and systems for air quality assessment monitoring and management: A review. Journal of environmental management, 289, 112510. [HTML]
Safarianzengir, V., Sobhani, B., Yazdani, M. H., & Kianian, M. (2020). Monitoring, analysis and spatial and temporal zoning of air pollution (carbon monoxide) using Sentinel-5 satellite data for health management in Iran, located in the Middle East. Air Quality, Atmosphere & Health, 13, 709-719. [HTML]
Bosveld, F. C., Baas, P., Beljaars, A. C., Holtslag, A. A., de Arellano, J. V. G., & Van De Wiel, B. J. (2020). Fifty years of atmospheric boundary-layer research at Cabauw serving weather, air quality and climate. Boundary-Layer Meteorology, 177, 583-612. springer.com
Huang, L. Y., Hsiang, S. M., & Gonzalez-Navarro, M. (2021). Using satellite imagery and deep learning to evaluate the impact of anti-poverty programs. nber.org
Pokhriyal, N., Zambrano, O., Linares, J., & Hernández, H. (2020). Estimating and forecasting income poverty and inequality in haiti using satellite imagery and mobile phone data. iadb.org
Ali, D. A., Deininger, K., & Wild, M. (2020). Using satellite imagery to create tax maps and enhance local revenue collection. Applied Economics. worldbank.org
Klemmer, K., Yeboah, G., de Albuquerque, J. P., & Jarvis, S. A. (2020). Population mapping in informal settlements with high-resolution satellite imagery and equitable ground-truth. arXiv preprint arXiv:2009.08410. [PDF]
Dinh, M. N., Nygate, J., Thwaites, C. L., & Group, G. G. C. E. V. (2020). New technologies to improve healthcare in low-and middle-income countries: global grand challenges satellite event, Oxford University clinical research unit, Ho Chi Minh City, 17th-18th September 2019. Wellcome Open Research, 5. nih.gov
Ang, M. L. E., Owen, J. R., Gibbins, C. N., Lèbre, É., Kemp, D., Saputra, M. R. U., ... & Lechner, A. M. (2023). Systematic review of GIS and remote sensing applications for assessing the socioeconomic impacts of mining. The Journal of Environment & Development, 32(3), 243-273. sagepub.com
Radutu, A., Vlad Sandru, M. I., Nedelcu, I., & Poenaru, V. (2021). Change detection trends in urban areas with remote sensing and socio-economic diagnosis in Bucharest city. Proceedings of the 21st International Multidisciplinary Scientific GeoConference SGEM. researchgate.net
Murayama, Y., Simwanda, M., & Ranagalage, M. (2021). Spatiotemporal analysis of urbanization using GIS and remote sensing in developing countries. Sustainability. mdpi.com
Sun, Y., Li, Y., Ma, R., Gao, C., & Wu, Y. (2022). Mapping urban socio-economic vulnerability related to heat risk: A grid-based assessment framework by combing the geospatial big data. Urban Climate. [HTML]
Hu, T., Li, N., & Yang, Q. (2024). Evaluating the influence of COVID-19 pandemic on socioeconomic development in Wumeng Mountain area based on multi-source remote sensing data. International Journal of Digital Earth. tandfonline.com
Yang, H., Kong, J., Hu, H., Du, Y., Gao, M., & Chen, F. (2022). A review of remote sensing for water quality retrieval: progress and challenges. Remote Sensing. mdpi.com
Mather, P. M. & Koch, M. (2022). Computer processing of remotely-sensed images. [HTML]
Elachi, C. & Van Zyl, J. J. (2021). Introduction to the physics and techniques of remote sensing. academia.edu
Chuvieco, E. (2020). Fundamentals of satellite remote sensing: An environmental approach. researchgate.net
Sabins Jr, F. F. & Ellis, J. M. (2020). Remote sensing: Principles, interpretation, and applications. [HTML]
Domínguez, E. M., Small, D., & Henke, D. (2021). Deriving digital surface models from geocoded SAR images and back-projection tomography. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4339-4351. ieee.org
Bu, J., Wang, Q., Wang, Z., Fan, S., Liu, X., & Zuo, X. (2024). Land Remote Sensing Applications Using Spaceborne GNSS Reflectometry: A Comprehensive Overview. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. ieee.org
Yu, K., Han, S., Bu, J., An, Y., Zhou, Z., Wang, C., ... & Cheong, J. W. (2022). Spaceborne GNSS reflectometry. Remote Sensing, 14(7), 1605. mdpi.com
Xu, C., Du, X., Fan, X., Yan, Z., Kang, X., Zhu, J., & Hu, Z. (2021). A modular remote sensing big data framework. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11. researchgate.net
Haut, J. M., Paoletti, M. E., Moreno-Álvarez, S., Plaza, J., Rico-Gallego, J. A., & Plaza, A. (2021). Distributed deep learning for remote sensing data interpretation. Proceedings of the IEEE, 109(8), 1320-1349. umbc.edu
Ejemeyovwi, D. O., Achima, B. T., & Ogwu, C. (2023). Remote Sensing Satellite Systems and Capabilities in Mapping Environmental Resources. NIU Journal of Social Sciences. ijhumas.com
Fu, W., Ma, J., Chen, P., & Chen, F. (2020). Remote sensing satellites for digital earth. Manual of digital earth. oapen.org
Liu, D., Chen, B., An, J., Li, C., Liu, G., Shao, J., ... & Wang, Z. L. (2020). Wind-driven self-powered wireless environmental sensors for Internet of Things at long distance. Nano Energy, 73, 104819. google.com
Khan, S., Naushad, M., Govarthanan, M., Iqbal, J., & Alfadul, S. M. (2022). Emerging contaminants of high concern for the environment: Current trends and future research. Environmental Research, 207, 112609. [HTML]
Morin-Crini, N., Lichtfouse, E., Liu, G., Balaram, V., Ribeiro, A. R. L., Lu, Z., ... & Crini, G. (2022). Worldwide cases of water pollution by emerging contaminants: a review. Environmental Chemistry Letters, 20(4), 2311-2338. hal.science
Ukaogo, P. O., Ewuzie, U., & Onwuka, C. V. (2020). Environmental pollution: causes, effects, and the remedies. In Microorganisms for sustainable environment and health (pp. 419-429). Elsevier. [HTML]
Xu, H., Jia, Y., Sun, Z., Su, J., Liu, Q. S., Zhou, Q., & Jiang, G. (2022). Environmental pollution, a hidden culprit for health issues. Eco-Environment & Health, 1(1), 31-45. sciencedirect.com
Intisar, A., Ramzan, A., Sawaira, T., Kareem, A. T., Hussain, N., Din, M. I., ... & Iqbal, H. M. (2022). Occurrence, toxic effects, and mitigation of pesticides as emerging environmental pollutants using robust nanomaterials–A review. Chemosphere, 293, 133538. [HTML]
Siddiqua, A., Hahladakis, J. N., & Al-Attiya, W. A. K. (2022). An overview of the environmental pollution and health effects associated with waste landfilling and open dumping. Environmental Science and Pollution Research, 29(39), 58514-58536. springer.com
Bhatt, P., Pandey, S. C., Joshi, S., Chaudhary, P., Pathak, V. M., Huang, Y., ... & Chen, S. (2022). Nanobioremediation: A sustainable approach for the removal of toxic pollutants from the environment. Journal of Hazardous Materials, 427, 128033. [HTML]
Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., ... & McCulloch, P. (2022). Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. bmj, 377. bmj.com
Kuziemski, M. & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications policy. nih.gov
Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review, 138, 101967. sciencedirect.com
Yakovlev, S., Bazilevych, K., Chumachenko, D., Chumachenko, T., Hulianytskyi, L., Meniailov, I., & Tkachenko, A. (2020). The concept of developing a decision support system for the epidemic morbidity control. academia.edu
Yannis, G., Kopsacheili, A., Dragomanovits, A., & Petraki, V. (2020). State-of-the-art review on multi-criteria decision-making in the transport sector. Journal of traffic and transportation engineering (English edition), 7(4), 413-431. sciencedirect.com
Deveci, M., Mishra, A. R., Gokasar, I., Rani, P., Pamucar, D., & Özcan, E. (2022). A decision support system for assessing and prioritizing sustainable urban transportation in metaverse. IEEE Transactions on Fuzzy Systems, 31(2), 475-484. worktribe.com
Burnett, M. J. (). Environmental monitoring of freshwater ecosystems using telemetered behavioural indicators from free-swimming fish in southern Africa. researchgate.net. researchgate.net
Dore, K. M., Gallagher, C. A., & Mill, A. C. (2023). Telemetry-based assessment of home range to estimate the abundance of invasive green monkeys on St. Kitts. Caribbean Journal of Science. [HTML]
Yuan, F., Fan, C., Farahmand, H., Coleman, N., Esmalian, A., Lee, C. C., ... & Mostafavi, A. (2022). Smart flood resilience: harnessing community-scale big data for predictive flood risk monitoring, rapid impact assessment, and situational awareness. Environmental Research: Infrastructure and Sustainability, 2(2), 025006. iop.org
Ganie, P. A., Posti, R., Kunal, G., Bhat, R. A. H., & Sidiq, M. J. (2024). Principle and Applications of Geographic Information System (GIS) in Coldwater Fisheries Development in India. In Aquaculture and Conservation of Inland Coldwater Fishes (pp. 469-495). Singapore: Springer Nature Singapore. [HTML]
Bhat, R. A. H., & Sidiq, M. J. Parvaiz Ahmad Ganie, Ravindra Posti, Garima Kunal. Aquaculture and Conservation of Inland Coldwater Fishes, 469. [HTML]
Navin, M. S., & Agilandeeswari, L. (2020). Comprehensive review on land use/land cover change classification in remote sensing. Journal of Spectral Imaging, 9. semanticscholar.org
Pande, C. B., Moharir, K. N., & Khadri, S. F. R. (2021). Assessment of land-use and land-cover changes in Pangari watershed area (MS), India, based on the remote sensing and GIS techniques. Applied Water Science. springer.com
Macarringue, L. S., Bolfe, L., & Pereira, P. R. M. (2022). Developments in land use and land cover classification techniques in remote sensing: a review.. embrapa.br
Fahad, K. H., Hussein, S., & Dibs, H. (2020). Spatial-temporal analysis of land use and land cover change detection using remote sensing and GIS techniques. In IOP conference series: materials science and engineering (Vol. 671, No. 1, p. 012046). IOP Publishing. iop.org
Abebe, G., Getachew, D., & Ewunetu, A. (2022). Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia. SN Applied Sciences. springer.com
Mertikas, S. P., Partsinevelos, P., Mavrocordatos, C., & Maximenko, N. A. (2021). Environmental applications of remote sensing. In Pollution assessment for sustainable practices in applied sciences and engineering (pp. 107-163). Butterworth-Heinemann. [HTML]
Avtar, R., Komolafe, A. A., Kouser, A., Singh, D., Yunus, A. P., Dou, J., ... & Kurniawan, T. A. (2020). Assessing sustainable development prospects through remote sensing: A review. Remote sensing applications: Society and environment, 20, 100402. nih.gov
Liu, C., Xing, C., Hu, Q., Wang, S., Zhao, S., & Gao, M. (2022). Stereoscopic hyperspectral remote sensing of the atmospheric environment: Innovation and prospects. Earth-Science Reviews. [HTML]
Song, W., Song, W., Gu, H., & Li, F. (2020). Progress in the remote sensing monitoring of the ecological environment in mining areas. International Journal of Environmental Research and Public Health, 17(6), 1846. mdpi.com
Mukundan, A., Huang, C. C., Men, T. C., Lin, F. C., & Wang, H. C. (2022). Air pollution detection using a novel snap-shot hyperspectral imaging technique. Sensors. mdpi.com
Tan, K., Wang, H., Chen, L., Du, Q., Du, P., & Pan, C. (2020). Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. Journal of hazardous materials, 382, 120987. msstate.edu
Lu, Y., Saeys, W., Kim, M., Peng, Y., & Lu, R. (2020). Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress. Postharvest Biology and Technology. kuleuven.be
Khan, A., Vibhute, A. D., Mali, S., & Patil, C. H. (2022). A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. Ecological Informatics. [HTML]
Estoque, R. C., Ooba, M., Seposo, X. T., Togawa, T., Hijioka, Y., Takahashi, K., & Nakamura, S. (2020). Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators. Nature communications, 11(1), 1581. nature.com
Barboza, E. P., Cirach, M., Khomenko, S., Iungman, T., Mueller, N., Barrera-Gómez, J., ... & Nieuwenhuijsen, M. (2021). Green space and mortality in European cities: a health impact assessment study. The Lancet Planetary Health, 5(10), e718-e730. thelancet.com
Malathi, K., Shruthi, S. N., Madhumitha, N., Sreelakshmi, S., Sathya, U., & Sangeetha, P. M. (2024). Medical Data Integration and Interoperability through Remote Monitoring of Healthcare Devices. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 15(2), 60-72. researchgate.net
Zhao, Q., Yu, L., Du, Z., Peng, D., Hao, P., Zhang, Y., & Gong, P. (2022). An overview of the applications of earth observation satellite data: impacts and future trends. Remote Sensing. mdpi.com
Himeur, Y., Rimal, B., Tiwary, A., & Amira, A. (2022). Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives. Information Fusion. [HTML]
Li, J., Pei, Y., Zhao, S., Xiao, R., Sang, X., & Zhang, C. (2020). A review of remote sensing for environmental monitoring in China. Remote Sensing. mdpi.com
Apostolopoulos, D., & Nikolakopoulos, K. (2021). A review and meta-analysis of remote sensing data, GIS methods, materials and indices used for monitoring the coastline evolution over the last twenty years. European Journal of Remote Sensing, 54(1), 240-265. tandfonline.com
Gyamfi-Ampadu, E. & Gebreslasie, M. (2021). Two decades progress on the application of remote sensing for monitoring tropical and sub-tropical natural forests: a review. Forests. mdpi.com
Dube, T., Rampheri, B. M., & Shoko, C. (2022). GIS and remote sensing analytics: assessment and monitoring. In Fundamentals of Tropical Freshwater Wetlands (pp. 661-678). Elsevier. [HTML]
de Leeuw, G., van der A, R., Bai, J., Xue, Y., Varotsos, C., Li, Z., ... & Zhang, Y. (2021). Air quality over China. Remote Sensing, 13(17), 3542. mdpi.com
Stirnberg, R., Cermak, J., Fuchs, J., & Andersen, H. (2020). Mapping and understanding patterns of air quality using satellite data and machine learning. Journal of Geophysical Research: Atmospheres, 125(4), e2019JD031380. wiley.com
Singh, D., Dahiya, M., Kumar, R., & Nanda, C. (2021). Sensors and systems for air quality assessment monitoring and management: A review. Journal of environmental management, 289, 112510. [HTML]
Safarianzengir, V., Sobhani, B., Yazdani, M. H., & Kianian, M. (2020). Monitoring, analysis and spatial and temporal zoning of air pollution (carbon monoxide) using Sentinel-5 satellite data for health management in Iran, located in the Middle East. Air Quality, Atmosphere & Health, 13, 709-719. [HTML]
Bosveld, F. C., Baas, P., Beljaars, A. C., Holtslag, A. A., de Arellano, J. V. G., & Van De Wiel, B. J. (2020). Fifty years of atmospheric boundary-layer research at Cabauw serving weather, air quality and climate. Boundary-Layer Meteorology, 177, 583-612. springer.com
Huang, L. Y., Hsiang, S. M., & Gonzalez-Navarro, M. (2021). Using satellite imagery and deep learning to evaluate the impact of anti-poverty programs. nber.org
Pokhriyal, N., Zambrano, O., Linares, J., & Hernández, H. (2020). Estimating and forecasting income poverty and inequality in haiti using satellite imagery and mobile phone data. iadb.org
Ali, D. A., Deininger, K., & Wild, M. (2020). Using satellite imagery to create tax maps and enhance local revenue collection. Applied Economics. worldbank.org
Klemmer, K., Yeboah, G., de Albuquerque, J. P., & Jarvis, S. A. (2020). Population mapping in informal settlements with high-resolution satellite imagery and equitable ground-truth. arXiv preprint arXiv:2009.08410. [PDF]
Dinh, M. N., Nygate, J., Thwaites, C. L., & Group, G. G. C. E. V. (2020). New technologies to improve healthcare in low-and middle-income countries: global grand challenges satellite event, Oxford University clinical research unit, Ho Chi Minh City, 17th-18th September 2019. Wellcome Open Research, 5. nih.gov
Ang, M. L. E., Owen, J. R., Gibbins, C. N., Lèbre, É., Kemp, D., Saputra, M. R. U., ... & Lechner, A. M. (2023). Systematic review of GIS and remote sensing applications for assessing the socioeconomic impacts of mining. The Journal of Environment & Development, 32(3), 243-273. sagepub.com
Radutu, A., Vlad Sandru, M. I., Nedelcu, I., & Poenaru, V. (2021). Change detection trends in urban areas with remote sensing and socio-economic diagnosis in Bucharest city. Proceedings of the 21st International Multidisciplinary Scientific GeoConference SGEM. researchgate.net
Murayama, Y., Simwanda, M., & Ranagalage, M. (2021). Spatiotemporal analysis of urbanization using GIS and remote sensing in developing countries. Sustainability. mdpi.com
Sun, Y., Li, Y., Ma, R., Gao, C., & Wu, Y. (2022). Mapping urban socio-economic vulnerability related to heat risk: A grid-based assessment framework by combing the geospatial big data. Urban Climate. [HTML]
Hu, T., Li, N., & Yang, Q. (2024). Evaluating the influence of COVID-19 pandemic on socioeconomic development in Wumeng Mountain area based on multi-source remote sensing data. International Journal of Digital Earth. tandfonline.com
Published
2024-10-21
How to Cite
Harbi, M., Rasheed, H., Raheem, M., & Mohsein, O. (2024). Innovative Methods in Remote Sensing for Health Impact Assessment of Environmental Pollution. Central Asian Journal of Medical and Natural Science, 5(4), 1039-1053. Retrieved from https://cajmns.centralasianstudies.org/index.php/CAJMNS/article/view/2656
Section
Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
