Cargando…
Spatiotemporal evolution characteristics and prediction analysis of urban air quality in China
To describe the spatiotemporal variations characteristics and future trends of urban air quality in China, this study evaluates the spatiotemporal evolution features and linkages between the air quality index (AQI) and six primary pollution indicators, using air quality monitoring data from 2014 to...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10235078/ https://www.ncbi.nlm.nih.gov/pubmed/37264078 http://dx.doi.org/10.1038/s41598-023-36086-4 |
_version_ | 1785052629622063104 |
---|---|
author | Du, Yuanfang You, Shibing Liu, Weisheng Basang, Tsering-xiao Zhang, Miao |
author_facet | Du, Yuanfang You, Shibing Liu, Weisheng Basang, Tsering-xiao Zhang, Miao |
author_sort | Du, Yuanfang |
collection | PubMed |
description | To describe the spatiotemporal variations characteristics and future trends of urban air quality in China, this study evaluates the spatiotemporal evolution features and linkages between the air quality index (AQI) and six primary pollution indicators, using air quality monitoring data from 2014 to 2022. Seasonal autoregressive integrated moving average (SARIMA) and random forest (RF) models are created to forecast air quality. (1) The study’s findings indicate that pollution levels and air quality index values in Chinese cities decline annually, following a “U”-shaped pattern with a monthly variation. The pollutant levels are high in winter and low in spring, and low in summer and rising in the fall (O(3) shows the opposite). (2) The spatial distribution of air quality in Chinese cities is low in the southeast and high in the northwest, and low in the coastal areas and higher in the inland areas. The correlation coefficients between AQI and the pollutant concentrations are as follows: fine particulate matter (PM(2.5)), inhalable particulate matter (PM(10)), carbon monoxide (CO), nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), and ozone (O(3)) values are correlated at 0.89, 0.84, 0.54, 0.54, 0.32, and 0.056, respectively. (3) In terms of short-term AQI predictions, the RF model performs better than the SARIMA model. The long-term forecast indicates that the average AQI value in Chinese cities is expected to decrease by 0.32 points in 2032 compared to the 2022 level of 52.95. This study has some guiding significance for the analysis and prediction of urban air quality. |
format | Online Article Text |
id | pubmed-10235078 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102350782023-06-03 Spatiotemporal evolution characteristics and prediction analysis of urban air quality in China Du, Yuanfang You, Shibing Liu, Weisheng Basang, Tsering-xiao Zhang, Miao Sci Rep Article To describe the spatiotemporal variations characteristics and future trends of urban air quality in China, this study evaluates the spatiotemporal evolution features and linkages between the air quality index (AQI) and six primary pollution indicators, using air quality monitoring data from 2014 to 2022. Seasonal autoregressive integrated moving average (SARIMA) and random forest (RF) models are created to forecast air quality. (1) The study’s findings indicate that pollution levels and air quality index values in Chinese cities decline annually, following a “U”-shaped pattern with a monthly variation. The pollutant levels are high in winter and low in spring, and low in summer and rising in the fall (O(3) shows the opposite). (2) The spatial distribution of air quality in Chinese cities is low in the southeast and high in the northwest, and low in the coastal areas and higher in the inland areas. The correlation coefficients between AQI and the pollutant concentrations are as follows: fine particulate matter (PM(2.5)), inhalable particulate matter (PM(10)), carbon monoxide (CO), nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), and ozone (O(3)) values are correlated at 0.89, 0.84, 0.54, 0.54, 0.32, and 0.056, respectively. (3) In terms of short-term AQI predictions, the RF model performs better than the SARIMA model. The long-term forecast indicates that the average AQI value in Chinese cities is expected to decrease by 0.32 points in 2032 compared to the 2022 level of 52.95. This study has some guiding significance for the analysis and prediction of urban air quality. Nature Publishing Group UK 2023-06-01 /pmc/articles/PMC10235078/ /pubmed/37264078 http://dx.doi.org/10.1038/s41598-023-36086-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Du, Yuanfang You, Shibing Liu, Weisheng Basang, Tsering-xiao Zhang, Miao Spatiotemporal evolution characteristics and prediction analysis of urban air quality in China |
title | Spatiotemporal evolution characteristics and prediction analysis of urban air quality in China |
title_full | Spatiotemporal evolution characteristics and prediction analysis of urban air quality in China |
title_fullStr | Spatiotemporal evolution characteristics and prediction analysis of urban air quality in China |
title_full_unstemmed | Spatiotemporal evolution characteristics and prediction analysis of urban air quality in China |
title_short | Spatiotemporal evolution characteristics and prediction analysis of urban air quality in China |
title_sort | spatiotemporal evolution characteristics and prediction analysis of urban air quality in china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10235078/ https://www.ncbi.nlm.nih.gov/pubmed/37264078 http://dx.doi.org/10.1038/s41598-023-36086-4 |
work_keys_str_mv | AT duyuanfang spatiotemporalevolutioncharacteristicsandpredictionanalysisofurbanairqualityinchina AT youshibing spatiotemporalevolutioncharacteristicsandpredictionanalysisofurbanairqualityinchina AT liuweisheng spatiotemporalevolutioncharacteristicsandpredictionanalysisofurbanairqualityinchina AT basangtseringxiao spatiotemporalevolutioncharacteristicsandpredictionanalysisofurbanairqualityinchina AT zhangmiao spatiotemporalevolutioncharacteristicsandpredictionanalysisofurbanairqualityinchina |