Air Quality Statistics and Prediction Based on Urban Agglomerations and Sentiment Analysis of People Under Different Pollutants

Air pollution is the focus of attention. It is affected by time and space. At the same time, people’s attitudes toward air pollution are also worth noting. People with different pollutants may have different sensitivities. In order to compare this characteristic, this paper selects the historical data of air pollution in China’s Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta cities for statistical and emotion analysis. Pollution in the Beijing-Tianjin-Hebei region is higher than other regions, but the improvement in the past few years is relatively high. The air quality in the Pearl River Delta is the best, but the improvement is low, and even there is a downward trend in some periods. The pollution in the spring and winter is generally serious. Then we use the ARIMA model to predict the future trend of various air pollutants, and find that although the overall air pollution shows a downward trend, especially PM2.5, PM10 is much reduced, but ozone has an upward trend and often becomes an important pollution in summer. In order to understand people’s attention to ozone pollution, we crawl the microblog data under the same pollution conditions of ozone and PM2.5 in Beijing for 6 days for emotional analysis, and find that people’s prevention and attention to ozone pollution is less than PM2.5.