Abstract
ABSTRACT In recent years, the dangers that air pollutants pose to human health and the environment have received widespread attention. Although accurately predicting the air quality is essential to managing pollution and developing control policies, traditional forecasting models have not been able to simulate the seasonal and diurnal variation in air pollutant concentrations. Furthermore, inadequate processing of the available spatio-temporal data has precluded the capture of predictive historical patterns. Therefore, we have developed a staging evolving spiking neural network (eSNN) model named Staging-eSNN that first employs a time series clustering algorithm to distinguish the seasonal from the diurnal variation in the PM2.5 concentration. We then predict the concentrations in Beijing and Shanghai 1, 3, 6, 12 and 24 hours in advance. Various evaluation indicators show that the Staging-eSNN model achieves higher performance than the support vector regression (SVR), random forest (RF) and other eSNN models.
Highlights
Since the reform and opening up, China has made steady progress in the process of urbanization
In order to improve the accuracy of PM2.5 hourly concentration prediction, this paper proposes a staging evolving spiking neural network model, Staging-eSNN, and a whole day’s pollution and meteorological data is used to predict PM2.5 concentration in the 1, 3, 6, 12 and 24 hours in two cities (Beijing and Shanghai)
It should be noted that six indicators used reflect different aspects of prediction capabilities (Table 4); the performance of the Staging-eSNN model in different periods depends on what we focus on
Summary
Since the reform and opening up, China has made steady progress in the process of urbanization. The urbanization rate has increased from 17.92% before the reform and opening up to 58.52% in 2017. Beijing and Shanghai, as two key cities to support China’s sustained economic growth, are experiencing frequent effects of smog while developing rapidly. Smog is usually characterized by high PM2.5 concentration. According to Liu et al (2019), there is a statistically significant correlation between short-term exposure to PM10, PM2.5 and cardiovascular, respiratory mortality. PM2.5 has a negative impact on socioeconomic and climate change (Li et al, 2016)
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