College of Chinese Language and Culture, Jinan University, Guangzhou 510610, China.
Division of Engineering, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates.
Int J Environ Res Public Health. 2022 Dec 1;19(23):16115. doi: 10.3390/ijerph192316115.
To understand the temporal variation, spatial distribution and factors influencing the public's sensitivity to air pollution in China, this study collected air pollution data from 2210 air pollution monitoring sites from around China and used keyword-based filtering to identify individual messages related to air pollution and health on Sina Weibo during 2017-2021. By analyzing correlations between concentrations of air pollutants (PM, PM, CO, NO, O and SO) and related microblogs (air-pollution-related and health-related), it was found that the public is most sensitive to changes in PM concentration from the perspectives of both China as a whole and individual provinces. Correlations between air pollution and related microblogs were also stronger when and where air quality was worse, and they were also affected by socioeconomic factors such as population, economic conditions and education. Based on the results of these correlation analyses, scientists can survey public concern about air pollution and related health outcomes on social media in real time across the country and the government can formulate air quality management measures that are aligned to public sensitivities.
为了了解中国公众对空气污染的敏感性的时间变化、空间分布和影响因素，本研究从中国各地的 2210 个空气污染监测站点收集了空气污染数据，并使用基于关键词的过滤方法，从新浪微博上识别出 2017-2021 年与空气污染和健康相关的个人信息。通过分析空气污染物（PM、PM、CO、NO、O 和 SO）浓度与相关微博（与空气污染和健康相关的微博）之间的相关性，发现从全国和各省的角度来看，公众对 PM 浓度变化最为敏感。当空气质量较差时，空气污染与相关微博之间的相关性也更强，而且还受到人口、经济条件和教育等社会经济因素的影响。基于这些相关分析的结果，科学家可以实时在全国范围内通过社交媒体调查公众对空气污染和相关健康结果的关注，政府也可以制定与公众敏感性相匹配的空气质量管理措施。
