Atmosphere 15(11) pg. 1303.
ISSN/ISBN: 2073-4433 DOI: 10.3390/atmos15111303
Abstract: This study systematically evaluates the reliability of PM2.5 monitoring data across major urban areas, utilizing a comprehensive dataset covering 283 cities in China over a seven-year period. By using Benford’s Law, robust regression analysis, and various machine learning methods, such as Gradient Boosting Trees and Random Forests, the overall reliability of China’s PM2.5 monitoring data is high. These models effectively captured complex patterns and detected anomalies related to both natural environmental and socioeconomic factors, as well as potential data manipulation. Based on the integrated models, the proportion of anomalies in PM2.5 concentration monitoring data across 283 cities in China from 2015 to 2022 was less than 2%, which strongly indicates the overall reliability of China’s PM2.5 concentration monitoring data. Additionally, machine learning models provided a ranking of the importance of different variables affecting PM2.5 concentrations, offering a scientific basis for understanding the driving factors behind the data. The three variables that have the greatest impact on PM2.5 concentrations are population density, average temperature, and relative humidity. By comparing with other related studies, we further validated our findings. Overall, this study provides new methods and perspectives for understanding and evaluating the reliability of PM2.5 data in China, laying a solid foundation for future research.
Bibtex:
@Article{,
AUTHOR = {Duan, Hongyan and Yue, Wenfu and Li, Weidong},
TITLE = {Reliability Assessment of PM2.5 Concentration Monitoring Data: A Case Study of China},
JOURNAL = {Atmosphere},
VOLUME = {15},
YEAR = {2024},
NUMBER = {11},
ARTICLE-NUMBER = {1303},
URL = {https://www.mdpi.com/2073-4433/15/11/1303},
ISSN = {2073-4433},
DOI = {10.3390/atmos15111303},
}
Reference Type: Journal Article
Subject Area(s): Environmental Sciences