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Context-Aware Systems and Applications. 10th EAI International Conference, ICCASA 2021, Virtual Event, October 28–29, 2021, Proceedings

Research Article

Applying CoKriging Method for Air Pollution Prediction PM10 in Binh Duong Province

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  • @INPROCEEDINGS{10.1007/978-3-030-93179-7_25,
        author={Nguyen Cong Nhut},
        title={Applying CoKriging Method for Air Pollution Prediction PM10 in Binh Duong Province},
        proceedings={Context-Aware Systems and Applications. 10th EAI International Conference, ICCASA 2021, Virtual Event,  October 28--29, 2021, Proceedings},
        proceedings_a={ICCASA},
        year={2022},
        month={1},
        keywords={Air pollution Cokriging Prediction PM10},
        doi={10.1007/978-3-030-93179-7_25}
    }
    
  • Nguyen Cong Nhut
    Year: 2022
    Applying CoKriging Method for Air Pollution Prediction PM10 in Binh Duong Province
    ICCASA
    Springer
    DOI: 10.1007/978-3-030-93179-7_25
Nguyen Cong Nhut1,*
  • 1: Faculty of Information Technology
*Contact email: ncnhut@ntt.edu.vn

Abstract

Geostatistics is a branch of statistics that focuses on spatial data sets. Geostatistics is used for prediction in many fields such as ore mining, petroleum geology, climate, geography, environment. CoKriging can be seen as a point interpolation, which requires a point map as input and which returns a raster map with estimations and optionally an error map. CoKring is a prediction method for problems with two or more parameters, including one main parameter. Tthe costs of installing new observational stations to observe metropolitan air pollution sources, as PM10 (Particulate Matter), CO (Cacbon monoxit), SO2(Dioxit Sunfua) and NO2(oxit Nito) concentrations are high. In this study, analysis of air pollution of 16 stations monitored in 2018 year was carried out. Geostatistics have been used by many researchers to effectively predict air pollution, ore mining, and groundwater levels.

Keywords
Air pollution Cokriging Prediction PM10
Published
2022-01-06
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-93179-7_25
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