{"title":"基于多项式 Logistic 回归方法的中国空气质量长期评价系统预测","authors":"Y. He, D. Qi, V. M. Bure","doi":"10.24057/2071-9388-2023-2719","DOIUrl":null,"url":null,"abstract":"The aim of this article evaluate the long-term air quality in China based on the air quality index (AQI) and the air quality composite index (AQCI) though the multinomial logistic regression method. The two developed models employ different 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 pollutant factors: O3, PM2.5, PM10, NO2, SO2, and CO. Model quality verification is an integral part of our analysis. The results are illustrate d using real air quality data from China. The developed models were applied to predict AQI and ACQI for the 31 capital cities in China from 2013 to 2019 annually. All calculations and tests are conducted using R-studio. In summary, both models are able to predict China’s long-term air quality. A comparison of the AQI and AQCI models using the ROC curve reveals that the AQCI model exhibits greater significance than the AQI model.","PeriodicalId":37517,"journal":{"name":"Geography, Environment, Sustainability","volume":"38 14","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Long-Term Air Quality Evaluation System Prediction In China Based On Multinomial Logistic Regression Method\",\"authors\":\"Y. He, D. Qi, V. M. Bure\",\"doi\":\"10.24057/2071-9388-2023-2719\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this article evaluate the long-term air quality in China based on the air quality index (AQI) and the air quality composite index (AQCI) though the multinomial logistic regression method. The two developed models employ different 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 pollutant factors: O3, PM2.5, PM10, NO2, SO2, and CO. Model quality verification is an integral part of our analysis. The results are illustrate d using real air quality data from China. The developed models were applied to predict AQI and ACQI for the 31 capital cities in China from 2013 to 2019 annually. All calculations and tests are conducted using R-studio. In summary, both models are able to predict China’s long-term air quality. A comparison of the AQI and AQCI models using the ROC curve reveals that the AQCI model exhibits greater significance than the AQI model.\",\"PeriodicalId\":37517,\"journal\":{\"name\":\"Geography, Environment, Sustainability\",\"volume\":\"38 14\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geography, Environment, Sustainability\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24057/2071-9388-2023-2719\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Agricultural and Biological Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geography, Environment, Sustainability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24057/2071-9388-2023-2719","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
Long-Term Air Quality Evaluation System Prediction In China Based On Multinomial Logistic Regression Method
The aim of this article evaluate the long-term air quality in China based on the air quality index (AQI) and the air quality composite index (AQCI) though the multinomial logistic regression method. The two developed models employ different 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 pollutant factors: O3, PM2.5, PM10, NO2, SO2, and CO. Model quality verification is an integral part of our analysis. The results are illustrate d using real air quality data from China. The developed models were applied to predict AQI and ACQI for the 31 capital cities in China from 2013 to 2019 annually. All calculations and tests are conducted using R-studio. In summary, both models are able to predict China’s long-term air quality. A comparison of the AQI and AQCI models using the ROC curve reveals that the AQCI model exhibits greater significance than the AQI model.
期刊介绍:
Journal “GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY” is founded by the Faculty of Geography of Lomonosov Moscow State University, The Russian Geographical Society and by the Institute of Geography of RAS. It is the official journal of Russian Geographical Society, and a fully open access journal. Journal “GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY” publishes original, innovative, interdisciplinary and timely research letter articles and concise reviews on studies of the Earth and its environment scientific field. This goal covers a broad spectrum of scientific research areas (physical-, social-, economic-, cultural geography, environmental sciences and sustainable development) and also considers contemporary and widely used research methods, such as geoinformatics, cartography, remote sensing (including from space), geophysics, geochemistry, etc. “GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY” is the only original English-language journal in the field of geography and environmental sciences published in Russia. It is supposed to be an outlet from the Russian-speaking countries to Europe and an inlet from Europe to the Russian-speaking countries regarding environmental and Earth sciences, geography and sustainability. The main sections of the journal are the theory of geography and ecology, the theory of sustainable development, use of natural resources, natural resources assessment, global and regional changes of environment and climate, social-economical geography, ecological regional planning, sustainable regional development, applied aspects of geography and ecology, geoinformatics and ecological cartography, ecological problems of oil and gas sector, nature conservations, health and environment, and education for sustainable development. Articles are freely available to both subscribers and the wider public with permitted reuse.