Complex PM_2.5 Pollution and Hospital Admission for Respiratory Diseases over Big Data in Cloud Environment

Abstract

With the establishment of China's national air quality monitoring network, large amounts of monitoring data are available for different kinds of users. How to process and use this big data is a tough problem for users: most users have limited computing power, and new data are collected at every moment. Cloud computing may be an efficient and low-cost way to solve this problem.is paper investigates a problem of a complex system: the impact of PM 2.5 on hospitalization for respiratory diseases. A changepoint detection method based on grey relation analysis was used to solve this problem. Daily air pollution monitoring data and patient data were used in this study. Our results showed that (1) PM 2.5 pollution showed a positive correlation on hospital admission for respiratory disease; (2) most patients went to hospital 2 days after PM 2.5 pollution events; and (3) male, children, and old people were significantly affected by PM 2.5 pollution. Our study is of great significance to help the government formulate suitable policies to reduce the damage caused by PM 2.5 pollution and help hospitals allocate medical resources efficiently.

MiR-217-5p inhibits smog (PM2.5)-induced inflammation and oxidative stress response of mouse lung tissues and macrophages through targeting STAT1

“…Particulate matter 2.5 (PM2.5) is an important composition of smog, which seriously threatens human health. Long-term exposure to PM2.5 has a close association with respiratory diseases [ 1 , 2 ]. The mechanism of damage of PM2.5 to the respiratory system primarily includes inflammatory response [ 3 ], oxidative stress response [ 4 ], immunotoxicity [ 5 ] and genotoxicity [ 6 ].…”