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All issues Volume 611 (2025) E3S Web Conf., 611 (2025) 01002 References
Open Access
Issue
E3S Web Conf.
Volume 611, 2025
2nd International Symposium on Environmental and Energy Policy (ISEEP 2024)
Article Number 01002
Number of page(s) 11
Section Climate Change, Sustainability, and Coastal Management
DOI https://doi.org/10.1051/e3sconf/202561101002
Published online 24 January 2025
  1. J. Ma et al., “Identification of high impact factors of air quality on a national scale using big data and machine learning techniques,” Elsevier BV, vol. 244, pp. 118955–118955, Jan. 2020, doi: 10.1016/j.jclepro.2019.118955. [Google Scholar]
  2. A. Yerramilli, V. B. R. Dodla, and S. Yerramilli, “Air Pollution, Modeling and GIS based Decision Support Systems for Air Quality Risk Assessment,” 2011. doi: 10.5772/22055. [Google Scholar]
  3. N. M. Donahue, Air Pollution and Air Quality. 2023. Online.. Available: https://www.sciencedirect.com/science/article/pii/B9780128092705000078 [Google Scholar]
  4. N. A. Istiqomah and N. N. N. Marleni, “Particulate air pollution in Indonesia: quality index, characteristic, and source identification,” IOP Publ., vol. 599, pp. 012084–012084, Nov. 2020, doi: 10.1088/1755-1315/599/1/012084. [Google Scholar]
  5. G. Syuhada et al., “Impacts of Air Pollution on Health and Cost of Illness in Jakarta, Indonesia,” Multidiscip. Digit. Publ. Inst., vol. 20, no. 4, pp. 2916–2916, Feb. 2023, doi: 10.3390/ijerph20042916. [Google Scholar]
  6. P. Agista, N. Gusdini, and M. D. Maharani, “ANALISIS KUALITAS UDARA DENGAN INDEKS STANDAR PENCEMAR UDARA (ISPU) DAN SEBARAN KADAR POLUTANNYA DI PROVINSI DKI JAKARTA,” vol. 2, no. 2, pp. 39–57, Sep. 2020, doi: 10.36441/seoi.v2i2.491. [Google Scholar]
  7. A. D. Venter et al., “An air quality assessment in the industrialised western Bushveld Igneous Complex, South Africa,” Acad. Sci. South Afr., vol. 108, no. 9/10, Sep. 2012, doi: 10.4102/sajs.v108i9/10.1059. [Google Scholar]
  8. G. P. O. Reddy, “Geographic Information System: Principles and Applications,” in Springer International Publishing, 2018, pp. 45–62. doi: 10.1007/978-3-319-787114_3. [Google Scholar]
  9. Van Westen, C. J. (2013). Remote sensing and GIS for natural hazards assessment and disaster risk management. Treatise on geomorphology, 3(15), 259–298. [CrossRef] [Google Scholar]
  10. Petropoulos, G. P., Kalivas, D. P., Griffiths, H. M., & Dimou, P. P. (2015). Remote sensing and GIS analysis for mapping spatio-temporal changes of erosion and deposition of two Mediterranean river deltas: The case of the Axios and Aliakmonas rivers, Greece. International Journal of Applied Earth Observation and Geoinformation, 35, 217–228. [CrossRef] [Google Scholar]
  11. Machiwal, D., Cloutier, V., Güler, C., & Kazakis, N. (2018). A review of GISintegrated statistical techniques for groundwater quality evaluation and protection. Environmental Earth Sciences, 77(19), 681. [CrossRef] [Google Scholar]
  12. Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., & Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of the total environment, 739, 140033. [CrossRef] [Google Scholar]
  13. You, W., Lin, L., Wu, L., Ji, Z., Zhu, J., Fan, Y., & He, D. (2017). Geographical information system-based forest fire risk assessment integrating national forest inventory data and analysis of its spatiotemporal variability. Ecological Indicators, 77, 176–184. [CrossRef] [Google Scholar]
  14. M. Pandey, V. Singh, and R. C. Vaishya, “Geomatics Approach for Assessment of respiratory disease Mapping,” in Copernicus Publications, Nov. 2014, pp. 205–211. doi: 10.5194/isprsarchives-xl-8-205-2014. [Google Scholar]
  15. Tella, A., & Balogun, A. L. (2021). GIS-based air quality modelling: Spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms. Environmental Science and Pollution Research, 1–17. [Google Scholar]
  16. C. Bachechi, F. Desimoni, L. Po, and D. M. Casas, “Visual analytics for spatiotemporal air quality data,” Sep. 2020. doi: 10.1109/iv51561.2020.00080. [Google Scholar]
  17. W. Hedgecock, P. Völgyesi, Á. Lédeczi, and X. Koutsoukos, “Dissemination and presentation of high resolution air pollution data from mobile sensor nodes,” Apr. 2010. doi: 10.1145/1900008.1900019. [Google Scholar]
  18. J. Wivou, L. Udawatta, A. S. Alshehhi, E. Alzaabi, A. Albeloshi, and S. Alfalasi, “Air quality monitoring for sustainable systems via drone based technology,” Dec. 2016. doi: 10.1109/iciafs.2016.7946542. [Google Scholar]
  19. F. M. Putra and I. S. Sitanggang, “Classification model of air quality in Jakarta using decision tree algorithm based on air pollutant standard index,” IOP Publ., vol. 528, pp. 012053–012053, Jul. 2020, doi: 10.1088/1755-1315/528/1/012053. [Google Scholar]
  20. R. Taborda, N. Datia, M. Pato, and J. M. Pires, “Exploring air quality using a multiple spatial resolution dashboard — a case study in Lisbon,” Sep. 2020. doi: 10.1109/iv51561.2020.00032. [Google Scholar]
  21. V. Kumar and C. Khosla, “Data Cleaning-A Thorough Analysis and Survey on Unstructured Data,” Jan. 2018. doi: 10.1109/confluence.2018.8442950. [Google Scholar]
  22. A. Livera et al., “Data processing and quality verification for improved photovoltaic performance and reliability analytics,” Wiley, vol. 29, no. 2, pp. 143–158, Oct. 2020, doi: 10.1002/pip.3349. [Google Scholar]
  23. B. S. Freeman, G. W. Taylor, B. Gharabaghi, and J. V. G. Thé, “Forecasting air quality time series using deep learning,” Taylor Francis, vol. 68, no. 8, pp. 866–886, May 2018, doi: 10.1080/10962247.2018.1459956. [Google Scholar]
  24. S. Chairani, “Study on the impact of air quality in agricultural and health sectors,” IOP Publ., vol. 334, pp. 012028–012028, Mar. 2018, doi: 10.1088/1757899x/334/1/012028. [Google Scholar]
  25. M. Sharma, S. Jain, S. Mittal, and T. Sheikh, “Forecasting And Prediction Of Air Pollutants Concentrates Using Machine Learning Techniques: The Case Of India,” IOP Publ., vol. 1022, pp. 012123–012123, Jan. 2021, doi: 10.1088/1757899x/1022/1/012123. [Google Scholar]

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