Prediction Modeling and Research on the Relationship Between Urban Air Pollutants and Respiratory Diseases
Keywords:Air pollutants, Respiratory diseases, Prediction, Arima.
In recent years, with the development of urbanization, the use of traditional fuels such as oil and coal is increasing, and air pollution is also becoming increasingly serious. In recent years, people have paid more attention to health issues, and the relationship between air pollution and health has gradually become a research hotspot. Based on the data of air pollutant concentration and respiratory diseases in Shijiazhuang, China, this paper analyzes the correlation between air pollutants and respiratory diseases, and finds that a variety of air pollutants will increase the prevalence of acute respiratory diseases, influenza and pneumonia, and acute upper respiratory infection. Then, this paper uses ARIMA model to predict the data of six air pollutants, and uses ridge regression model, Using the predicted air pollutant data, the number of respiratory diseases in urban population is predicted. Finally, this paper provides suggestions on how to prevent diseases for urban residents in the future.
Xu B, Lin B. Do we really understand the development of China's new energy industry?[J]. Energy economics, 2018, 74: 733-745.
Brown J S, Gordon T, Price O, et al. Thoracic and respirable particle definitions for human health risk assessment[J]. Particle and fibre toxicology, 2013, 10(1): 1-12.
Liu J, Yin H, Tang X, et al. Transition in air pollution, disease burden and health cost in China: A comparative study of long-term and short-term exposure[J]. Environmental Pollution, 2021, 277: 116770.
Feng S. Analysis on the Effect of Green Finance on Sustainable Development[J]. International Journal of Science, 2021, 8(9).
Hoerl A E, Kennard R W. Ridge regression: applications to nonorthogonal problems[J]. Technometrics, 1970, 12(1): 69-82.
Benvenuto D, Giovanetti M, Vassallo L, et al. Application of the ARIMA model on the COVID-2019 epidemic dataset[J]. Data in brief, 2020, 29: 105340.
Xu Jian Study on the correlation between outpatient visits and air pollution in Tianjin [D]. University of science and technology of China, 2011
Dai Y H, Zhou W X. Temporal and spatial correlation patterns of air pollutants in Chinese cities[J]. PloS one, 2017, 12(8): e0182724.
Akinwande M O, Dikko H G, Samson A. Variance inflation factor: as a condition for the inclusion of suppressor variable (s) in regression analysis[J]. Open Journal of Statistics, 2015, 5(07): 754.
Mondal P, Shit L, Goswami S. Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices[J]. International Journal of Computer Science, Engineering and Applications, 2014, 4(2): 13.
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