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

A concentration distribution, space-time technology, applied in the environmental field, can solve problems such as long residence time, small diameter of PM2.5, and influence on atmospheric visibility

Active Publication Date: 2020-10-30
HOHAI UNIV
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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,

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

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[0031] The following examples can enable those skilled in the art to understand the present disclosure more fully, but do not limit the present disclosure in any way.

[0032] The concentration of PM2.5 is affected by the topography, emission location, emission rate and meteorological factors in the study area, and has strong nonlinear characteristics. At the same time, there is a potential interdependence between the observed values ​​of PM2.5 in the same distribution area. .5 There is a certain spatial autocorrelation among the variables. In order to improve the prediction accuracy of PM2.5 and ensure the reliability of the algorithm, this paper adopts the convolutional long-term short-term memory (ConvLSTM) model and the improved long-term short-term memory (LSTM) model, and adds convolution operation to the LSTM model to extract spatial features. Predict the temporal and spatial distribution of PM2.5 PM2.5 for the next day or next day.

[0033]When studying and predicting...

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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...

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N20/00G01N15/02
CPCG06F30/27G06N3/049G06N20/00G01N15/0205G06N3/045
Inventor 张光远芮小平逯海玥于光夏范永磊
Owner HOHAI UNIV
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