Time series based urban air quality predication
Big Data & Information Analytics(2016)
摘要
Urban air pollution post a great threat to human health, and has been a major concern of many metropolises in developing countries. Lately, a few air quality monitoring stations have been established to inform public the real-time air quality indices based on fine particle matters, eg PM2. 5, in countries suffering from air pollutions. Air quality, unfortunately, is fairly difficult to manage due to multiple complex human activities from driving to smelting. We observe that human activities’ hidden regular pattern offers possibility in predication, and this motivates us to infer urban air condition from the perspective of time series. In this paper, we focus on PM2. 5based urban air quality, and introduce two kinds of time-series methods for real-time and fine-grained air quality prediction, harnessing historical air quality data reported by existing monitoring stations. The methods are evaluated based in the real-life PM2. 5concentration data in the year of 2013 (January-December) in Wuhan, China.1. Introduction. While the atmosphere, a complex natural gaseous system, has been an essential key to support life on earth, air pollution is recognized as a threat to human health as well as to the earth’s ecosystems. Among all those particles in air, particles less than 2.5 micrometers in diameter are called “fine” particles, ie PM2. 5. Tiny size results in its ability to travel deeply into the respiratory tract, reaching the lungs and causing worsen medical conditions such as asthma and heart disease. Sources of fine particles include all types of combustion, including motor vehicles, power plants, residential wood burnSing, forest fires, agricultural burning, and some industrial …
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络