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LONG-TERM AIR QUALITY EVALUATION SYSTEM PREDICTION IN CHINA BASED ON MULTINOMIAL LOGISTIC REGRESSION METHOD. / Хе, Ян; Ци, Дунфан; Буре, Владимир Мансурович.

In: Geography, Environment, Sustainability, Vol. 16, No. 4, 12.2023, p. 164 - 171.

Research output: Contribution to journal › Article › peer-review

Harvard

Хе, Я, Ци, Д & Буре, ВМ 2023, 'LONG-TERM AIR QUALITY EVALUATION SYSTEM PREDICTION IN CHINA BASED ON MULTINOMIAL LOGISTIC REGRESSION METHOD', Geography, Environment, Sustainability, vol. 16, no. 4, pp. 164 - 171. https://doi.org/10.24057/2071-9388-2023-2719

APA

Хе, Я., Ци, Д., & Буре, В. М. (2023). LONG-TERM AIR QUALITY EVALUATION SYSTEM PREDICTION IN CHINA BASED ON MULTINOMIAL LOGISTIC REGRESSION METHOD. Geography, Environment, Sustainability, 16(4), 164 - 171. https://doi.org/10.24057/2071-9388-2023-2719

Vancouver

Хе Я, Ци Д, Буре ВМ. LONG-TERM AIR QUALITY EVALUATION SYSTEM PREDICTION IN CHINA BASED ON MULTINOMIAL LOGISTIC REGRESSION METHOD. Geography, Environment, Sustainability. 2023 Dec;16(4):164 - 171. https://doi.org/10.24057/2071-9388-2023-2719

Author

Хе, Ян ; Ци, Дунфан ; Буре, Владимир Мансурович. / LONG-TERM AIR QUALITY EVALUATION SYSTEM PREDICTION IN CHINA BASED ON MULTINOMIAL LOGISTIC REGRESSION METHOD. In: Geography, Environment, Sustainability. 2023 ; Vol. 16, No. 4. pp. 164 - 171.

BibTeX

@article{c4a8144719b74a97bcbc81c610b68886,
title = "LONG-TERM AIR QUALITY EVALUATION SYSTEM PREDICTION IN CHINA BASED ON MULTINOMIAL LOGISTIC REGRESSION METHOD",
abstract = "The aim of this article evaluate the long-term air quality in China based on the air quality index (AQI) and theair quality composite index (AQCI) though the multinomial logistic regression method. The two developed models employdifferent dependent variables, AQI and AQCI, while maintaining the same controlled variables gross domestic product (GDP),and a primary pollutant. Explicitly, the primary impurity is associated with one or more contaminants among six pollutantfactors: O3, PM2.5, PM10, NO2, SO2, and CO. Model quality verification is an integral part of our analysis. The results are illustrated using real air quality data from China. The developed models were applied to predict AQI and ACQI for the 31 capital citiesin China from 2013 to 2019 annually. All calculations and tests are conducted using R-studio. In summary, both models areable to predict China{\textquoteright}s long-term air quality. A comparison of the AQI and AQCI models using the ROC curve reveals that theAQCI model exhibits greater significance than the AQI model.",
author = "Ян Хе and Дунфан Ци and Буре, {Владимир Мансурович}",
year = "2023",
month = dec,
doi = "10.24057/2071-9388-2023-2719",
language = "English",
volume = "16",
pages = "164 -- 171",
journal = "Geography, Environment, Sustainability",
issn = "2071-9388",
publisher = "Lomonosov Moscow State University, Faculty of Geography",
number = "4",

}

RIS

TY - JOUR

T1 - LONG-TERM AIR QUALITY EVALUATION SYSTEM PREDICTION IN CHINA BASED ON MULTINOMIAL LOGISTIC REGRESSION METHOD

AU - Хе, Ян

AU - Ци, Дунфан

AU - Буре, Владимир Мансурович

PY - 2023/12

Y1 - 2023/12

N2 - The aim of this article evaluate the long-term air quality in China based on the air quality index (AQI) and theair quality composite index (AQCI) though the multinomial logistic regression method. The two developed models employdifferent dependent variables, AQI and AQCI, while maintaining the same controlled variables gross domestic product (GDP),and a primary pollutant. Explicitly, the primary impurity is associated with one or more contaminants among six pollutantfactors: O3, PM2.5, PM10, NO2, SO2, and CO. Model quality verification is an integral part of our analysis. The results are illustrated using real air quality data from China. The developed models were applied to predict AQI and ACQI for the 31 capital citiesin China from 2013 to 2019 annually. All calculations and tests are conducted using R-studio. In summary, both models areable to predict China’s long-term air quality. A comparison of the AQI and AQCI models using the ROC curve reveals that theAQCI model exhibits greater significance than the AQI model.

AB - The aim of this article evaluate the long-term air quality in China based on the air quality index (AQI) and theair quality composite index (AQCI) though the multinomial logistic regression method. The two developed models employdifferent dependent variables, AQI and AQCI, while maintaining the same controlled variables gross domestic product (GDP),and a primary pollutant. Explicitly, the primary impurity is associated with one or more contaminants among six pollutantfactors: O3, PM2.5, PM10, NO2, SO2, and CO. Model quality verification is an integral part of our analysis. The results are illustrated using real air quality data from China. The developed models were applied to predict AQI and ACQI for the 31 capital citiesin China from 2013 to 2019 annually. All calculations and tests are conducted using R-studio. In summary, both models areable to predict China’s long-term air quality. A comparison of the AQI and AQCI models using the ROC curve reveals that theAQCI model exhibits greater significance than the AQI model.

UR - https://ges.rgo.ru/jour/issue/current

U2 - 10.24057/2071-9388-2023-2719

DO - 10.24057/2071-9388-2023-2719

M3 - Article

VL - 16

SP - 164

EP - 171

JO - Geography, Environment, Sustainability

JF - Geography, Environment, Sustainability

SN - 2071-9388

IS - 4

ER -

ID: 115690577