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

Nhut, Nguyen Cong

doi:10.1007/978-3-030-93179-7_25

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…The r 2 provides an indicator of how well the model fits the variance data. However, r 2 is not as strong as the RSS value [8], [9], [10], [11].…”

Section: Resultsmentioning

confidence: 99%

“…where β is the Lagrange multiplier. After differentiating (5) with respect to λ 1 , λ 2 , ..., λ n , and β and set the derivatives equal to zero we find that j=1 λ j C(u i , u j ) − C(u 0 , u i ) − β = 0, i, j = 1, 2, ..., n and i=1 λ i = 1 [8], [9], [10], [11].…”

Section: Data Collection and Analysismentioning

confidence: 99%

“…Analysis of the Spatio-Temporal variation of groundwater levels using geostatistics [7]. Using CoKriging method to predict air pollution [10], [11]. Application of Geostatistics to forecast DO pollutants in water [9].…”

Section: Introductionmentioning

confidence: 99%

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Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam

Cong Nhut

2023

EAI Endorsed Trans Context Aware Syst App

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The COVID-19 pandemic can be considered a human disaster, it has claimed the lives of many people. We only know the number of deaths due to COVID-19 through government statistics, but on days when there are no statistics, how do we know whether people died that day or not? This study aims to predict the number of new deaths per day due to COVID 19 in Vietnam on days when observational data is not available and predict the number of deaths in the future. The study used COVID-19 data from the World Health Organization (WHO). A total of 260 days were collected and the author processed and standardized the data. Based on available data, the author uses Kriging interpolation statistical method to build a forecast model. As a result, the author has selected a prediction model suitable for a highly reliable data set, the regression coefficient and correlation coefficient are close to 1, the error between the model’s prediction results compared to data. There are days when the prediction error is almost zero. The study has built a future forecast map of the number of new deaths per day due to COVID-19. The article concludes that applying the Kriging statistical methodis appropriate for COVID-19 data. This research opens up new research directions for related fields such as earthquakes, mining, groundwater, environment, etc.

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“…The r 2 provides an indicator of how well the model fits the variance data. However, r 2 is not as strong as the RSS value [8], [9], [10], [11].…”

Section: Resultsmentioning

confidence: 99%

Section: Data Collection and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam

Cong Nhut

2023

EAI Endorsed Trans Context Aware Syst App

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“…However, it is not enough to measure air quality at these locations because it is essential for the community and government to know the air conditions in the area where they live. It is closely related to the branch of statistics, namely Geostatistics [11]. Therefore, it is necessary to estimate the concentration of pollutants, especially NO2.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Concentration of No2 With the Cokriging Method in the Capital City of Jakarta

Saifudin,

Faiza,

Puspasari

et al. 2023

BAREKENG: J. Math. & App.

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The increase in transportation activities is the leading cause of the emergence of the air pollution risk, which harms public health. especially in big cities. Nitrogen Dioxide (NO2) gas is one of the critical factors explaining air pollution. NO2 contributes to particle pollution and acid deposits and is a precursor of ozone, the leading cause of photochemical smog. Meanwhile, Sulfur Dioxide (SO2) is another substance that is a source of pollutants and can cause acid rain and aerosolized sulfate particle pollution. Therefore, it is necessary to estimate the concentration of pollutants, especially NO2. One method that can be used in this estimation is Cokriging, which considers secondary variables to record primary variables. Based on the results obtained, the Cokriging analysis shows that the estimation of NO2 content in the five predicted areas, namely Tanjung Priok, Johar Baru, Gelora Bung Karno, Pancoran, Halim Perdanakusuma, has a quality standard value below 80 µppm (quality standard value set by the BMKG). In summary, the research unveils variations in SO2 and NO2 levels across distinct regions in Jakarta, with Monas recording the highest SO2 levels (10 μppm), while Grogol exhibits the lowest NO2 levels (16 μppm). The average SO2 level among all surveyed areas is 5.875 μppm, while NO2 averages 17.375 μppm. These findings emphasize the pressing need to implement measures aimed at preserving air quality within the limits established by government standards. Additionally, Cokriging estimates reveal that the content of NO2 in Tanjung Priok, a coastal area, significantly deviates from estimations in other Jakarta areas and ranks as the highest among the locations studied, with GBK displaying the lowest content.

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Review of air pollutants modeling techniques and methods

Dhumad,

Jasim,

Hamed

2023

3rd International Conference on Smart Cities and Sustainable Planning

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

Cited by 3 publications

References 3 publications

Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam

Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam

Estimating the Concentration of No2 With the Cokriging Method in the Capital City of Jakarta

Review of air pollutants modeling techniques and methods

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