Abstract
Air quality information such as the concentration of PM2.5 is of great significance for human health and city management. It affects the way of traveling, urban planning, government policies and so on. However, in major cities there is typically only a limited number of air quality monitoring stations. In the meantime, air quality varies in the urban areas and there can be large differences, even between closely neighboring regions. In this paper, a random forest approach for predicting air quality (RAQ) is proposed for urban sensing systems. The data generated by urban sensing includes meteorology data, road information, real-time traffic status and point of interest (POI) distribution. The random forest algorithm is exploited for data training and prediction. The performance of RAQ is evaluated with real city data. Compared with three other algorithms, this approach achieves better prediction precision. Exciting results are observed from the experiments that the air quality can be inferred with amazingly high accuracy from the data which are obtained from urban sensing.
Highlights
As urbanization leads to urban community growth, the transportation infrastructure dependent on fossil fuels expands [1]
There are two important factors that affect the performance of a random forest, which are the number of trees and features
With the public data in the urban sensing system, our model predicts the air quality index (AQI) of all the regions in Shenyang based on the AQI published by 11 air quality monitoring stations, meteorology data reported by weather stations, road information and real-time traffic status collected from Baidu
Summary
As urbanization leads to urban community growth, the transportation infrastructure dependent on fossil fuels expands [1]. The popularity in vehicle use gives rise to an increase in traffic related pollutant emissions. Urban air pollution is a major problem in both developed and developing countries, as atmospheric pollutants have a great effect on human health. Numerous illnesses such as lung cancer may be caused by various atmospheric pollutants [2]. Some other serious environmental problems can result from air pollution, such as acid rain and the greenhouse gas effect. SO2 and NO2 are the main causes of acid rain [3], while CO2 and
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