Space-time estimation and prediction method for PM2.5 concentration distribution

Abstract

The invention provides a space-time estimation and prediction method for PM2.5 concentration distribution. The space-time estimation and prediction method for PM2.5 concentration distribution comprises: collecting and correcting fine-grained aerosol optical thickness (AOD), calculating a regression model of fine-grained PM2.5, and predicting fine-grained PM2.5 concentration distribution. By comparing several regression models with a machine learning model, an XGBoost model is determined as an estimation model under the framework, the minimum root mean square error (RMSE) is 32.86 [mu]g / m<3>, and the maximum R2 is 0.71. 10 times of verification and space-time comparison with a traditional time series prediction model, namely a seasonal autoregressive differential moving average (SARIMA) model, are carried out; the prediction precision of ConvLSTM is higher, the total average prediction RMSE is 14.94 [mu]g / m<3>, and the prediction precision of SARIMA is 17.41 [mu]g / m<3>. Moreover, the ConvLSTM is relatively small in fluctuation in time and relatively good in stability, and the spatial difference of prediction precision can be relatively well eliminated in space.

Description

technical field [0001] The present invention relates to the field of environment, in particular, to a method for spatiotemporal estimation and prediction of PM2.5 concentration distribution. Background technique [0002] PM, short for particulate matter, refers to particulate matter in the air with a kinetic equivalent diameter less than or equal to 2.5 microns. A large number of observational studies have shown that the mass concentration of PM2.5 is mainly affected by various pollution sources and meteorological conditions. Heavy air pollution events with PM2.5 as the main pollutant have a major impact on people's daily travel and social activities. Concentrations of fine particulate matter have been shown to be positively associated with cardiopulmonary and respiratory morbidity and mortality. If people live in an environment with excessively high concentrations of air pollutants, acute health risks, such as chronic respiratory diseases and cardiovascular diseases, will...

AI Technical Summary

Problems solved by technology

Studies have shown that PM2.5 can penetrate into the lungs and bronchi, and long-term exposure to PM2.5 can increase the morbidity and mortality of respiratory diseases and cardiovascular diseases; PM2.5 is small in diameter, good in quality, and stays in the atmosphere for a long time. The transmission distance is long, so it will seriously affect the visibility of the atmosphere and have a bad impact on people's daily life and social activities

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