Acknowledgment to the Reviewers of Journal of Environmental & Earth Sciences in 2025
2026-02-06
Integration of Big Data, Electronic Information, and GIS in Atmospheric Monitoring
Authors
-
Zhi-Chen Sun
Jinan Food and Drug Inspection and Testing Center, Jinan 250000, China
-
Tong-Tong Jiang
Jinan Food and Drug Inspection and Testing Center, Jinan 250000, China
-
Xiang-Liang Yan
Jinan Food and Drug Inspection and Testing Center, Jinan 250000, China
DOI:
https://doi.org/10.30564/jees.v8i8.13501Abstract
Monitoring of the atmosphere is experiencing a paradigm shift due to a combination of big data, electronic information technologies, and geographic information systems (GIS). Conventional methods of monitoring, which are largely based on sparse fixed-site stations, usually experience spatial constraints, limited time responsiveness, and challenges in integrating multiple sources. This paper provides a systematic review of how the joint evolution of dense sensing networks, real-time communication systems, big-data analytics, and geospatial platforms is transforming existing atmospheric observation systems into smarter and decision-oriented structures. The paper first presents the technological background of integrated atmospheric monitoring, such as heterogeneous data sources, sensor and communication infrastructure, and GIS-based spatial intelligence. It then examines key processes of data preprocessing and quality control, multi-source fusion, spatiotemporal modeling, visualization, and uncertainty-conscious interpretation. Significant application areas are also examined, among which are urban air quality control, regional pollution monitoring, emergency response, and public health assessment. The current limitations are also critically assessed in the review (including data heterogeneity, unstable calibration, scale and location issues, interoperability, propagation of uncertainties, and poor model interpretability). Based on this assessment, future trends are determined to include explainable artificial intelligence, edge intelligence, digital twin platforms, and more standardized and resilient monitoring architectures. The originality of this review is its integrated approach, which does not consider big data, electronic information, and GIS as independent technical solutions, but rather views them as complementary components and pillars of next-generation atmospheric monitoring. The review serves as a systematic source for the development of more accurate, adaptive, and governance-oriented atmospheric monitoring systems.
Keywords:
Atmospheric Monitoring; Big Data; Electronic Information Technology; GIS; Multi-Source Data FusionReferences
[1] Saxena, V., 2025. Water quality, air pollution, and climate change: Investigating the environmental impacts of industrialization and urbanization. Water, Air, & Soil Pollution. 236(2), 73. DOI: https://doi.org/10.1007/s11270-024-07702-4
[2] Fang, S., Xu, L.D., Zhu, Y., et al., 2014. An integrated system for regional environmental monitoring and management based on Internet of Things. IEEE Transactions on Industrial Informatics. 10(2), 1596–1605. DOI: https://doi.org/10.1109/TII.2014.2302638
[3] Kolios, S., Vorobev, A.V., Vorobeva, G.R., et al., 2017. GIS and Environmental Monitoring: Applications in the Marine, Atmospheric and Geomagnetic Fields, 1st ed. Springer: Cham, Switzerland. pp. 1–174. DOI: https://doi.org/10.1007/978-3-319-53086-4
[4] Wang, S., Zhong, Y., Wang, E., 2019. An integrated GIS platform architecture for spatiotemporal big data. Future Generation Computer Systems. 94, 160–172. DOI: https://doi.org/10.1016/j.future.2018.10.034
[5] Chen, H., Xie, J., Wang, S., et al., 2021. Research on intelligent management system of meteorological archives based on big data framework. Advances in Data Science and Adaptive Analysis. 13, 2142004. DOI: https://doi.org/10.1142/S2424922X21420043
[6] Spiridonov, V., Ćurić, M., Novkovski, N., 2025. The new era of environmental monitoring: Insights into weather, water, and climate. In Atmospheric Perspectives: Unveiling Earth's Environmental Challenges. Springer: Cham, Switzerland. pp. 401–443. DOI: https://doi.org/10.1007/978-3-031-86757-6_14
[7] Sun, Y., Yang, T., Gui, H., et al., 2023. Atmospheric environment monitoring technology and equipment in China: A review and outlook. Journal of Environmental Sciences. 123, 41–53. DOI: https://doi.org/10.1016/j.jes.2022.01.014
[8] Selvam, A.P., Al-Humairi, S.N.S., 2025. Environmental impact evaluation using smart real-time weather monitoring systems: A systematic review. Innovative Infrastructure Solutions. 10(1), 13. DOI: https://doi.org/10.1007/s41062-024-01817-7
[9] Hart, J.K., Martinez, K., 2006. Environmental sensor networks: A revolution in the earth system science? Earth-Science Reviews. 78(3–4), 177–191. DOI: https://doi.org/10.1016/j.earscirev.2006.05.001
[10] Radojevic, M., Bashkin, V.N., 2007. Practical Environmental Analysis, 2nd ed. Royal Society of Chemistry: Cambridge, UK. pp. 1–480. DOI: https://doi.org/10.1039/9781847552662
[11] Selvam, A.P., Al-Humairi, S.N.S., 2023. The impact of IoT and sensor integration on real-time weather monitoring systems: A systematic review. Research Square. DOI: https://doi.org/10.21203/rs.3.rs-3579172/v1
[12] Ghaseminya, M.M., Eslami, E., Shahzadeh Fazeli, S.A., et al., 2025. Advancing cloud virtualization: A comprehensive survey on integrating IoT, edge, and fog computing with FaaS for heterogeneous smart environments. The Journal of Supercomputing. 81(14), 1303. DOI: https://doi.org/10.1007/s11227-025-07799-2
[13] Ficili, I., Giacobbe, M., Tricomi, G., et al., 2025. From sensors to data intelligence: Leveraging IoT, cloud, and edge computing with AI. Sensors. 25(6), 1763. DOI: https://doi.org/10.3390/s25061763
[14] Burrough, P.A., McDonnell, R.A., Lloyd, C.D., 2015. Principles of Geographical Information Systems, 3rd ed. Oxford University Press: Oxford, UK. pp. 1–330.
[15] Lü, G., Batty, M., Strobl, J., et al., 2019. Reflections and speculations on the progress in geographic information systems (GIS): A geographic perspective. International Journal of Geographical Information Science. 33(2), 346–367. DOI: https://doi.org/10.1080/13658816.2018.1533136
[16] Sowmiya Narayanan, K.J., Manimaran, A., 2024. Recent developments in geographic information systems across different application domains: A review. Knowledge and Information Systems. 66(3), 1523–1547. DOI: https://doi.org/10.1007/s10115-023-01969-5
[17] Szafarczyk, A., Agbasi, O.E., 2025. Emerging trends in GIS and remote sensing technologies for environmental monitoring: Innovations, applications, and future directions. Geoinformatica Polonica. 24, 25–41. DOI: https://doi.org/10.4467/21995923GP.25.002.22857
[18] Bazargani, J.S., Sadeghi-Niaraki, A., Choi, S.-M., 2021. A Survey of GIS and IoT Integration: Applications and Architecture. Applied Sciences. 11(21), 10365. DOI: https://doi.org/10.3390/app112110365
[19] DeNicola, L.A., 2007. Techniques of the environmental observer: India's earth remote sensing program in the age of global information [PhD Thesis]. Rensselaer Polytechnic Institute: Troy, NY, USA.
[20] Bibri, S.E., Alexandre, A., Sharifi, A., et al., 2023. Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: An integrated approach to an extensive literature review. Energy Informatics. 6(1), 9. DOI: https://doi.org/10.1186/s42162-023-00259-2
[21] Sarker, S., Jahan, F., 2025. AI-driven MIS applications in environmental risk monitoring: A systematic review of predictive geographic information systems. ASRC Procedia: Global Perspectives in Science and Scholarship. 1(1), 81–97. DOI: https://doi.org/10.63125/pnx77873
[22] Sarker, S., 2025. Role of management information systems in environmental risk assessment: A systematic review of geographic and ecological applications. American Journal of Interdisciplinary Studies. 6(1), 95–126. DOI: https://doi.org/10.63125/k27tnn83
[23] Dentener, F., Stevenson, D., Ellingsen, K., et al., 2006. The global atmospheric environment for the next generation. Environmental Science & Technology. 40(11), 3586–3594. DOI: https://doi.org/10.1021/es0523845
[24] Takamatu, S., 2025. Advances and frontiers in environmental geography: A review of theoretical frameworks, technological support, and practical pathways. Geographical Research Bulletin. 4, 724–757.
[25] van der Zee, E., Scholten, H., 2014. Spatial dimensions of big data: Application of geographical concepts and spatial technology to the Internet of Things. In: Bessis, N., Dobre, C. (Eds.). Big Data and Internet of Things: A Roadmap for Smart Environments. Springer: Cham, Switzerland. pp. 137–168. DOI: https://doi.org/10.1007/978-3-319-05029-4_6
[26] Peng, D., Zhang, H., Wang, L., 2026. The research on information geography: Theoretical construction, methodological innovation, and application frontiers. Resources Data Journal. 5, 83–107. DOI: https://doi.org/10.50908/rdj.5.0_83
[27] Rahmahsari, S., Sekti, B.A., 2026. Integrating GIS and remote sensing in early warning systems for real-time disaster preparedness. In: Guney, İ., Celik, M.A. (Eds.). Interdisciplinary Studies on Global Disasters. IGI Global Scientific Publishing: Hershey, PA, USA. pp. 249–276. DOI: https://doi.org/10.4018/979-8-3373-6274-8.ch009
[28] Scott, G., Rajabifard, A., 2017. Sustainable development and geospatial information: A strategic framework for integrating a global policy agenda into national geospatial capabilities. Geo-spatial Information Science. 20(2), 59–76. DOI: https://doi.org/10.1080/10095020.2017.1325594
[29] Tsatsaris, A., Kalogeropoulos, K., Stathopoulos, N., et al., 2021. Geoinformation technologies in support of environmental hazards monitoring under climate change: An extensive review. ISPRS International Journal of Geo-Information. 10(2), 94. DOI: https://doi.org/10.3390/ijgi10020094
[30] Pranjal, P., Soni, A., 2025. Next-generation geographical information systems: Integrating artificial intelligence, geographic information systems, and satellite imagery for mapping and surveying. In: Burnett, C.M., Pradhan, B. (Eds.). Global Land–Ocean Geospatial Applications: Mapping and Surveying with Remote Sensing and GIS, 1st ed. CRC Press: Boca Raton, FL, USA. pp. 216–233. DOI: https://doi.org/10.1201/9781003422877-10
[31] Dong, J., Liang, X., Du, B., et al., 2025. Applications of geographic information systems in ecological impact assessment: A methods landscape, practical bottlenecks, and future pathways. Sustainability. 17(22), 10358. DOI: https://doi.org/10.3390/su172210358
[32] Zhu, X., 2016. GIS for Environmental Applications: A Practical Approach, 1st ed. Routledge: London, UK. pp. 1–471. DOI: https://doi.org/10.4324/9780203383124
[33] Patanè, G., Spagnuolo, M. (Eds.), 2016. Heterogeneous Spatial Data: Fusion, Modeling, and Analysis for GIS Applications, 1st ed. Morgan & Claypool Publishers: San Rafael, CA, USA. pp. 1–155. DOI: https://doi.org/10.2200/S00711ED1V01Y201603VCP024
[34] Chen, J., Chen, S., 2022. Remote sensing big data for water environment monitoring: Current status, challenges, and future prospects. Earth's Future. 10(2), e2021EF002289. DOI: https://doi.org/10.1029/2021EF002289
[35] Domonkos, P., Tóth, R., Nyitrai, L., 2022. Climate Observations: Data Quality Control and Time Series Homogenization, 1st ed. Elsevier: Amsterdam, The Netherlands. pp. 1–302.
[36] Hollingsworth, A., Engelen, R.J., Textor, C., et al., 2008. Toward a monitoring and forecasting system for atmospheric composition: The GEMS project. Bulletin of the American Meteorological Society. 89(8), 1147–1164. DOI: https://doi.org/10.1175/2008BAMS2355.1
[37] Niculae, A.-M., Bâra, A., 2025. Data integration techniques for building a comprehensive air quality dataset. In: Ciurea, C., Pocatilu, P., Filip, F.G. (Eds.). Proceedings of 23rd International Conference on Informatics in Economy (IE 2024). Springer: Singapore. pp. 3–13. DOI: https://doi.org/10.1007/978-981-96-0161-5_1
[38] Purba, R.A., Bedogni, L., 2025. Assessing benefits and limitations of multiple data sources for environmental monitoring. In Proceedings of the 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 10–13 January 2025; pp. 1–6. DOI: https://doi.org/10.1109/CCNC54725.2025.10975904
[39] Hertel, O., Ellermann, T., Palmgren, F., et al., 2007. Integrated air-quality monitoring—combined use of measurements and models in monitoring programmes. Environmental Chemistry. 4(2), 65–74. DOI: https://doi.org/10.1071/EN06077
[40] Klausen, J., Volosciuk, C., Tarasova, O., et al., 2021. Benefits of atmospheric composition monitoring and international data exchange. Available from: https://wmo.int/media/magazine-article/benefits-of-atmospheric-composition-monitoring-and-international-data-exchange (cited 10 May 2026).
[41] Yang, C., Clarke, K., Shekhar, S., et al., 2020. Big spatiotemporal data analytics: A research and innovation frontier. International Journal of Geographical Information Science. 34(6), 1075–1088. DOI: https://doi.org/10.1080/13658816.2019.1698743
[42] Meng, X., Pang, K., Yin, Z., et al., 2021. Grid-based spatiotemporal modeling of ambient ozone to assess human exposure using environmental big data. Atmospheric Pollution Research. 12(12), 101216. DOI: https://doi.org/10.1016/j.apr.2021.101216
[43] Hayat, H., Griffiths, T., Brennan, D., et al., 2019. The state-of-the-art of sensors and environmental monitoring technologies in buildings. Sensors. 19(17), 3648. DOI: https://doi.org/10.3390/s19173648
[44] Vermesan, O., Friess, P. (Eds.), 2013. Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems, 1st ed. River Publishers: Gistrup, Denmark. pp. 1–362.
[45] Fascista, A., 2022. Toward integrated large-scale environmental monitoring using WSN/UAV/crowdsensing: A review of applications, signal processing, and future perspectives. Sensors. 22(5), 1824. DOI: https://doi.org/10.3390/s22051824
[46] Merzougui, S.E., 2025. Investigation of Enabling Mechanisms for Green Driving via 5G-and-Beyond Mobile Networks [PhD Thesis]. University of Padua: Padua, Italy.
[47] Shen, Z., Jin, J., Tan, C., et al., 2024. A survey of next-generation computing technologies in space-air-ground integrated networks. ACM Computing Surveys. 56(1), 1–40. DOI: https://doi.org/10.1145/3606018
[48] Oloruntoba, O., 2025. Architecting resilient multi-cloud database systems: Distributed ledger technology, fault tolerance, and cross-platform synchronization. International Journal of Research Publication and Reviews. 6(2), 2358–2376. DOI: https://doi.org/10.55248/gengpi.6.0225.0918
[49] Liang, H., Zhang, Z., Hu, C., et al., 2024. A survey on spatio-temporal big data analytics ecosystem: Resource management, processing platform, and applications. IEEE Transactions on Big Data. 10(2), 174–193. DOI: https://doi.org/10.1109/TBDATA.2023.3342619
[50] Briggs, D., 2005. The role of GIS: Coping with space (and time) in air pollution exposure assessment. Journal of Toxicology and Environmental Health, Part A. 68(13–14), 1243–1261. DOI: https://doi.org/10.1080/15287390590936094
[51] Matějíček, L., Engst, P., Jaňour, Z., 2006. A GIS-based approach to spatio-temporal analysis of environmental pollution in urban areas: A case study of Prague's environment extended by LIDAR data. Ecological Modelling. 199(3), 261–277. DOI: https://doi.org/10.1016/j.ecolmodel.2006.05.018
[52] Yu, H., 2006. Spatio-temporal GIS design for exploring interactions of human activities. Cartography and Geographic Information Science. 33(1), 3–19. DOI: https://doi.org/10.1559/152304006777323136
[53] Qiang, Y., Van de Weghe, N., 2019. Re-arranging space, time and scales in GIS: Alternative models for multi-scale spatio-temporal modeling and analyses. ISPRS International Journal of Geo-Information. 8(2), 72. DOI: https://doi.org/10.3390/ijgi8020072
[54] Jankowski, P., 2009. Towards participatory geographic information systems for community-based environmental decision making. Journal of Environmental Management. 90(6), 1966–1971. DOI: https://doi.org/10.1016/j.jenvman.2007.08.028
[55] Sadeghi-Niaraki, A., Jelokhani-Niaraki, M., Choi, S.-M., 2020. A volunteered geographic information-based environmental decision support system for waste management and decision making. Sustainability. 12(15), 6012. DOI: https://doi.org/10.3390/su12156012
[56] Chu, Y.-C., 2007. A multi-layered system architecture for environmental monitoring data management—Taiwan's experience. In Proceedings of the 21st International Conference on Informatics for Environmental Protection (EnviroInfo 2007), Warsaw, Poland, 12–14 September 2007; pp. 611–618.
[57] Song, H., Raj, A., Hajebi, S., et al., 2013. Model-based cross-layer monitoring and adaptation of multilayer systems. Science China Information Sciences. 56(8), 1–15. DOI: https://doi.org/10.1007/s11432-013-4915-5
[58] Ye, Y., Wang, Q., Wang, J., 2021. Green city air monitoring and architectural digital art design based on IoT embedded system. Environmental Technology & Innovation. 23, 101717. DOI: https://doi.org/10.1016/j.eti.2021.101717
[59] Franchi, F., Graziosi, F., Di Fina, E., et al., 2024. A survey of cloud-enabled GIS solutions toward edge computing: Challenges and perspectives. IEEE Open Journal of the Communications Society. 5, 312–331. DOI: https://doi.org/10.1109/OJCOMS.2023.3344198
[60] Eleiche, M.A., 2011. Network Analysis Methods for Mobile GIS [PhD Thesis]. University of West Hungary: Sopron, Hungary.
[61] Xu, M., 2025. Enhancing GIS models for sustainable development in human settlements using intelligent IoT infrastructure and human–machine interaction. Results in Engineering. 27, 106713. DOI: https://doi.org/10.1016/j.rineng.2025.106713
[62] Hu, Q., Li, Z., Wang, L., et al., 2019. Rainfall spatial estimations: A review from spatial interpolation to multi-source data merging. Water. 11(3), 579. DOI: https://doi.org/10.3390/w11030579
[63] Fattorini, F., 2024. Multi-source data analysis of the ocean–meteorology coupling mechanism from the perspective of satellite observations. Satellite Oceanography and Meteorology. 5(1), 11–16.
[64] Wang, H., Wang, M., Jiang, P., et al., 2025. Research on time series interpolation and reconstruction of multi-source remote sensing AOD product data using machine learning methods. Atmosphere. 16(6), 655. DOI: https://doi.org/10.3390/atmos16060655
[65] Jurczyk, A., Szturc, J., Otop, I., et al., 2020. Quality-based combination of multi-source precipitation data. Remote Sensing. 12(11), 1709. DOI: https://doi.org/10.3390/rs12111709
[66] Monks, P.S., Archibald, A.T., Colette, A., et al., 2015. Tropospheric ozone and its precursors from the urban to the global scale: From air quality to short-lived climate forcer. Atmospheric Chemistry and Physics. 15(15), 8889–8973. DOI: https://doi.org/10.5194/acp-15-8889-2015
[67] von Schneidemesser, E., Monks, P.S., Allan, J.D., et al., 2015. Chemistry and the linkages between air quality and climate change. Chemical Reviews. 115(10), 3856–3897.
[68] Hertwig, D., Grimmond, S., Kotthaus, S., et al., 2021. Variability of physical meteorology in urban areas at different scales: Implications for air quality. Faraday Discussions. 226, 149–172. DOI: https://doi.org/10.1039/D0FD00098A
[69] Clemitshaw, K.C., 2004. A review of instrumentation and measurement techniques for ground-based and airborne field studies of gas-phase tropospheric chemistry. Critical Reviews in Environmental Science and Technology. 34(1), 1–108. DOI: https://doi.org/10.1080/10643380490265117
[70] Ghamisi, P., Rasti, B., Yokoya, N., et al., 2019. Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art. IEEE Geoscience and Remote Sensing Magazine. 7(1), 6–39. DOI: https://doi.org/10.1109/MGRS.2018.2890023
[71] Chang, N.-B., Bai, K., Imen, S., et al., 2018. Multisensor satellite image fusion and networking for all-weather environmental monitoring. IEEE Systems Journal. 12(2), 1341–1357. DOI: https://doi.org/10.1109/JSYST.2016.2565900
[72] Dalla Mura, M., Prasad, S., Pacifici, F., et al., 2015. Challenges and opportunities of multimodality and data fusion in remote sensing. Proceedings of the IEEE. 103(9), 1585–1601. DOI: https://doi.org/10.1109/JPROC.2015.2462751
[73] Jongen, H.J., Steeneveld, G.-J., Grimmond, S., et al., 2025. Bridging balances: Water and energy in the urban climate. In Proceedings of the 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 July 2025. DOI: https://doi.org/10.5194/icuc12-36
[74] Liu, P., Chen, Y., Liang, X., et al., 2026. A graph neural network for small-area estimation: Integrating spatial regularisation, heterogeneous spatial units, and Bayesian inference. International Journal of Geographical Information Science. 40(7), 2358–2396. DOI: https://doi.org/10.1080/13658816.2025.2597971
[75] Fan, H., Zhao, C., Yang, Y., 2020. A comprehensive analysis of the spatiotemporal variation of urban air pollution in China during 2014–2018. Atmospheric Environment. 220, 117066. DOI: https://doi.org/10.1016/j.atmosenv.2019.117066
[76] Kolovos, A., Skupin, A., Jerrett, M., et al., 2010. Multi-perspective analysis and spatiotemporal mapping of air pollution monitoring data. Environmental Science & Technology. 44(17), 6738–6744.
[77] Huang, H., Chen, T.K., Kamalanathan, S., et al., 2025. Spatiotemporal analysis of urban air pollution dynamics: Linking emission sources, meteorological influences, and public health risks through long-term monitoring. Applied Ecology and Environmental Research. 23(6), 10559–10589. DOI: https://doi.org/10.15666/aeer/2306_1055910589
[78] Li, L., Qian, J., Ou, C.-Q., et al., 2014. Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001–2011. Environmental Pollution. 190, 75–81. DOI: https://doi.org/10.1016/j.envpol.2014.03.020
[79] Maurya, A., Kumar, A., 2024. The role of GIS in the study of sustainable development and environmental management. International Journal for Multidisciplinary Research. 6(6), 1–13. DOI: https://doi.org/10.36948/ijfmr.2024.v06i06.32879
[80] Korchenko, O.H., 2019. GIS and remote sensing as important tools for assessment of environmental pollution. In Proceedings of the 19th International Multidisciplinary Scientific GeoConference SGEM 2019, Albena, Bulgaria, 30 June–6 July 2019; pp. 297–304. DOI: https://doi.org/10.5593/sgem2019/2.1/S07.039
[81] Kulawiak, M., Prospathopoulos, A., Perivoliotis, L., et al., 2010. Interactive visualization of marine pollution monitoring and forecasting data via a web-based GIS. Computers & Geosciences. 36(8), 1069–1080. DOI: https://doi.org/10.1016/j.cageo.2010.02.008
[82] Kamińska, I.A., Ołdak, A., Turski, W.A., 2004. Geographical information system (GIS) as a tool for monitoring and analysing pesticide pollution and its impact on public health. Annals of Agricultural and Environmental Medicine. 11(2), 181–184.
[83] Jensen, S.S., 1999. A Geographic Approach to Modelling Human Exposure to Traffic Air Pollution Using GIS [PhD Thesis]. Roskilde University: Roskilde, Denmark.
[84] Kochanek, A., Generowicz, A., Zacłona, T., 2025. The role of geographic information systems in environmental management and the development of renewable energy sources: A review approach. Energies. 18(17), 4740. DOI: https://doi.org/10.3390/en18174740
[85] Karimi, H.A. (Ed.), 2014. Big Data: Techniques and Technologies in Geoinformatics. CRC Press: Boca Raton, FL, USA. pp. 1–312. DOI: https://doi.org/10.1201/b16524
[86] Wang, X., Yu, S., Wen, Z., et al., 2023. Application of modern GIS and remote sensing technology based on big data analysis in intelligent agriculture. Journal of the Indian Society of Remote Sensing. 51(9), 1891–1901. DOI: https://doi.org/10.1007/s12524-022-01512-z
[87] Zhou, B., Zhang, S., Xue, R., et al., 2023. A review of space–air–ground integrated remote sensing techniques for atmospheric monitoring. Journal of Environmental Sciences. 123, 3–14. DOI: https://doi.org/10.1016/j.jes.2021.12.008
[88] Woo, J.H., An, S.M., Hong, K., et al., 2016. Integration of CFD-based virtual sensors to a ubiquitous sensor network to support micro-scale air quality management. Journal of Environmental Informatics. 27(2), 85–97. DOI: https://doi.org/10.3808/jei.201500314
[89] Sokhi, R.S., Moussiopoulos, N., Baklanov, A., et al., 2022. Advances in air quality research: Current and emerging challenges. Atmospheric Chemistry and Physics. 22(7), 4615–4703. DOI: https://doi.org/10.5194/acp-22-4615-2022
[90] Villa, T.F., Gonzalez, F., Miljievic, B., et al., 2016. An overview of small unmanned aerial vehicles for air quality measurements: Present applications and future prospectives. Sensors. 16(7), 1072. DOI: https://doi.org/10.3390/s16071072
[91] Kadaverugu, R., Sharma, A., Matli, C., et al., 2019. High-resolution urban air quality modeling by coupling CFD and mesoscale models: A review. Asia-Pacific Journal of Atmospheric Sciences. 55(4), 539–556. DOI: https://doi.org/10.1007/s13143-019-00110-3
[92] Apte, J.S., Manchanda, C., 2024. High-resolution urban air pollution mapping. Science. 385(6707), 380–385. DOI: https://doi.org/10.1126/science.adq3678
[93] Lu, Y., Wang, Y., Wang, L., et al., 2019. Provincial analysis and zoning of atmospheric pollution in China from the atmospheric transmission and the trade transfer perspective. Journal of Environmental Management. 249, 109377. DOI: https://doi.org/10.1016/j.jenvman.2019.109377
[94] Ma, J., Yan, Y., Kong, S., et al., 2024. Effectiveness of inter-regional collaborative emission reduction for ozone mitigation under local-dominated and transport-affected synoptic patterns. Environmental Science and Pollution Research. 31(39), 51774–51789. DOI: https://doi.org/10.1007/s11356-024-34656-1
[95] Zhang, C., Luo, S., Zhao, W., et al., 2021. Impacts of meteorological factors, VOC emissions, and inter-regional transport on summer ozone pollution in Yuncheng. Atmosphere. 12(12), 1661. DOI: https://doi.org/10.3390/atmos12121661
[96] Abily, M., Gourbesville, P., de Carvalho Filho, E., et al., 2020. ANYWHERE: Enhancing emergency management and response to extreme weather and climate events. In: Gourbesville, P., Caignaert, G. (Eds.). Advances in Hydroinformatics: SimHydro 2019—Models for Extreme Situations and Crisis Management. Springer: Singapore. pp. 29–37. DOI: https://doi.org/10.1007/978-981-15-5436-0_3
[97] Chen, D., Liu, Z., Wang, L., et al., 2013. Natural disaster monitoring with wireless sensor networks: A case study of data-intensive applications upon low-cost scalable systems. Mobile Networks and Applications. 18(5), 651–663. DOI: https://doi.org/10.1007/s11036-013-0456-9
[98] Ceccato, P., Fernandes, K., Ruiz, D., et al., 2014. Climate and environmental monitoring for decision making. Earth Perspectives. 1(1), 16. DOI: https://doi.org/10.1186/2194-6434-1-16
[99] Xu, Z., Ran, Y., Rao, Z., 2022. Design and integration of air pollutants monitoring system for emergency management in construction site based on BIM and edge computing. Building and Environment. 211, 108725. DOI: https://doi.org/10.1016/j.buildenv.2021.108725
[100] Liu, H.-Y., Bartonova, A., Pascal, M., et al., 2012. Approaches to integrated monitoring for environmental health impact assessment. Environmental Health. 11(1), 88. DOI: https://doi.org/10.1186/1476-069X-11-88
[101] Croner, C.M., Sperling, J., Broome, F.R., 1996. Geographic information systems (GIS): New perspectives in understanding human health and environmental relationships. Statistics in Medicine. 15(18), 1961–1977. DOI: https://doi.org/10.1002/(SICI)1097-0258(19960930)15:18<1961::AID-SIM408>3.0.CO;2-L
[102] Jia, P., Cheng, X., Xue, H., et al., 2017. Applications of geographic information systems (GIS) data and methods in obesity-related research. Obesity Reviews. 18(4), 400–411. DOI: https://doi.org/10.1111/obr.12495
[103] Kelly, F.J., Fuller, G.W., Walton, H.A., et al., 2012. Monitoring air pollution: Use of early warning systems for public health. Respirology. 17(1), 7–19. DOI: https://doi.org/10.1111/j.1440-1843.2011.02065.x
[104] Briggs, D.J., 2008. A framework for integrated environmental health impact assessment of systemic risks. Environmental Health. 7(1), 61. DOI: https://doi.org/10.1186/1476-069X-7-61
[105] Wang, F., 2020. Why public health needs GIS: A methodological overview. Annals of GIS. 26(1), 1–12. DOI: https://doi.org/10.1080/19475683.2019.1702099
[106] Ozdenerol, E., 2016. Spatial Health Inequalities: Adapting GIS Tools and Data Analysis. CRC Press: Boca Raton, FL, USA. pp. 1–206. DOI: https://doi.org/10.1201/9781315371894
[107] Al-Amri, A.M., 2025. Printed sensors for environmental monitoring: Advancements, challenges, and future directions. Chemosensors. 13(8), 285. DOI: https://doi.org/10.3390/chemosensors13080285
[108] Bagkis, E., Hassani, A., Schneider, P., et al., 2025. Evolving trends in application of low-cost air quality sensor networks: Challenges and future directions. npj Climate and Atmospheric Science. 8(1), 335. DOI: https://doi.org/10.1038/s41612-025-01216-4
[109] Zarrar, H., Dyo, V., 2023. Drive-by air pollution sensing systems: Challenges and future directions. IEEE Sensors Journal. 23(19), 23692–23703. DOI: https://doi.org/10.1109/JSEN.2023.3305779
[110] Rao, P.V., Khan, S.A., Paul, S., et al., 2025. IoT-enabled environmental monitoring systems: Trends, challenges, and future directions. International Journal of Environmental Sciences. 11(17S), 1662–1666. DOI: https://doi.org/10.64252/7c4jqp91
[111] World Meteorological Organization, 2018. Low-Cost Sensors for the Measurement of Atmospheric Composition: Overview of Topic and Future Applications. Lewis, A.C., von Schneidemesser, E., Peltier, R.E. (Eds.). World Meteorological Organization: Geneva, Switzerland. pp. 1–46.
[112] Abdulhussain, S.H., Mahmmod, B.M., Alwhelat, A., et al., 2025. A comprehensive review of sensor technologies in IoT: Technical aspects, challenges, and future directions. Computers. 14(8), 342. DOI: https://doi.org/10.3390/computers14080342
[113] Cui, Y., Liu, F., Jing, X., et al., 2021. Integrating sensing and communications for ubiquitous IoT: Applications, trends, and challenges. IEEE Network. 35(5), 158–167. DOI: https://doi.org/10.1109/MNET.010.2100152
[114] Loidl, M., Wallentin, G., Cyganski, R., et al., 2016. GIS and transport modeling—Strengthening the spatial perspective. ISPRS International Journal of Geo-Information. 5(6), 84. DOI: https://doi.org/10.3390/ijgi5060084
[115] Guidotti, R., Monreale, A., Ruggieri, S., et al., 2018. A survey of methods for explaining black box models. ACM Computing Surveys. 51(5), 1–42. DOI: https://doi.org/10.1145/3236009
[116] Aderemi, I.A., Kehinde, T.O., Okwor, U.D., et al., 2025. Explainable AI for water quality monitoring: A systematic review of transparency, interpretability, and trust. IEEE Sensors Reviews. 2, 419–443. DOI: https://doi.org/10.1109/SR.2025.3595500
[117] Tariq, N., 2025. A Geo AI-based environmental risk assessment framework for chemical exposure and public health vulnerability in U.S. industrial corridors. International Journal of Environmental Sciences. 11(25S), 1279–1300. DOI: https://doi.org/10.64252/h82w8t20
[118] Yordanova, Z., Nozari, H., 2026. Enhancing innovation and resilience in entrepreneurial ecosystems using digital twins and fuzzy optimization. Digital. 6(1), 25. DOI: https://doi.org/10.3390/digital6010025
[119] Vallero, D.A., 2023. Air Pollution Calculations: Quantifying Pollutant Formation, Transport, Transformation, Fate and Risks, 2nd ed. Elsevier: Amsterdam, The Netherlands. pp. 1–659. DOI: https://doi.org/10.1016/C2022-0-02977-9
[120] Seinfeld, J.H., Pandis, S.N., 2016. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 3rd ed. John Wiley & Sons: Hoboken, NJ, USA. pp. 1–1152.
[121] Mikkelsen, T., Nielsen, M., 2003. Modelling of pollutant transport in the atmosphere. In Proceedings of the Workshop on Short-Range Transport and Dispersion Modeling for Decision Making, Otwock-Swierk, Poland, 17–18 November 2003.
[122] Stanek, L.W., Brown, J.S., Stanek, J., et al., 2011. Air pollution toxicology—A brief review of the role of the science in shaping the current understanding of air pollution health risks. Toxicological Sciences. 120(Suppl. 1), S8–S27. DOI: https://doi.org/10.1093/toxsci/kfq367
[123] Sahu, Y., Upadhyay, V.K., 2024. Environmental application of digital twins: A review. In: Talukdar, S., Rahman, A., Bera, S., et al. (Eds.). Environmental Risk and Resilience in the Changing World: Integrated Geospatial AI and Multidimensional Approach. Springer: Cham, Switzerland. pp. 287–295. DOI: https://doi.org/10.1007/978-3-031-62442-1_18
[124] Mazzetto, S., 2024. A review of urban digital twins integration, challenges, and future directions in smart city development. Sustainability. 16(19), 8337. DOI: https://doi.org/10.3390/su16198337
Downloads
How to Cite
Sun, Z.-C., Jiang, T.-T., & Yan, X.-L. (2026). Integration of Big Data, Electronic Information, and GIS in Atmospheric Monitoring. Journal of Environmental & Earth Sciences, 8(8), 928–957. https://doi.org/10.30564/jees.v8i8.13501
Issue
Article Type
Review
License
Copyright © 2026 Zhi-Chen Sun, Tong-Tong Jiang, Xiang-Liang Yan
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
