Abstract
Citizens eager to know the local pollution level to prevent from air pollution. The real-time measurement for everywhere is a very expensive way, a statistical model based on artificial neural network is applied in this research. This model can estimate particle pollution level with some influencing factors, including background pollution level, weather conditions, urban morphology and local pollution sources. The monitoring from regulatory monitoring sites is considered as the background level. The field measurements of 20 locations are conducted to feed the output layer of ANN model. The average relative error of prediction compared with measurement is 9.24% for PM10 and 18.90% for PM2.5.
Highlights
In recent years, the air pollution issue has drawn widespread public attention
40 predicting variables, including time periodicity, background pollution level, weather conditions, urban morphology and local pollution sources are considered in this model, it is a spatial interpolation model considering comprehensively the local divergence, including metrological conditions, urban morphologies and emission sources (SC0)
The bias is very small for PM10, but it shows certain negative bias for PM2.5 (Table 1 and Table 2), the positive errors appear in the higher concentration, and the negative bias mainly caused in lower concentration
Summary
Citizens eager to know the local pollution level, i.e. the concentrations of some main air pollutants, which can advise them to make some protection for their outdoor activities [1] or to decide whether or not to apply the natural ventilation for energyefficiently comfortable indoor environment [2]. It is very important data for the risk assessment of some environmental-related diseases [3]. The multiple linear regression (MLR) and the artificial neural network (ANN) are two mainstream approaches.
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