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首页> 外文期刊>Atmospheric environment >Meteorological parameters and gaseous pollutant concentrations as predictors of daily continuous PM_(2.5) concentrations using deep neural network in Beijing-Tianjin-Hebei, China
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Meteorological parameters and gaseous pollutant concentrations as predictors of daily continuous PM_(2.5) concentrations using deep neural network in Beijing-Tianjin-Hebei, China

机译：气象参数和气态污染物浓度作为北京-天津-河北省深度神经网络预报的连续PM_（2.5）日浓度的指标

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摘要

The deep learning model can simulate the complex nonlinear relationship between PM2.5 and aerosol optical depth (AOD), and has great application potentiality in PM2.5 inversion. However, the underestimation of high PM2.5 concentrations problem is still exist in heavily polluted Beijing-Tianjin-Hebei (JingJinJi) region due to AOD cannot adequately represent the correlation between high PM2.5 concentrations and independent variables and neglected the effects of missing AOD. Thus, the long- and short-term PM2.5 exposure risk estimate was reduced. This work introduces gaseous pollutant data (NO2, SO2, CO, and O-3) related to primary emission and secondary transformation of pollutants as predictors into a deep neural network model to improve the underestimation of high PM2.5 concentrations based on AOD and meteorological factors. We predicted the PM2.5 concentration in the missing AOD areas, generated a daily continuous PM2.5 spatial distribution, and reduced estimated bias due to AOD deficiency. Grid-based 10-fold cross-validation (CV) was used to test the model performance. Results show that daily PM2.5 concentration CV R-2 is 0.87 and the root-mean-square prediction error (RMSE) is 27.11 mu g/m(3). The CV R-2 and RMSE are higher by 0.12 and lower by 9.72 mu g/m(3) than the model without gaseous pollutants (GAS(s)) as predictors. In including the missing AOD, the average concentration of PM2.5 CV R-2 is 0.86 and the RMSE is 16.95 mu g/m(3) in heavy polluted winter; the CV R-2 and RMSE are higher by 0.07 and lower by 3.95 mu g/m(3), respectively, than when the missing AOD was excluded. Prediction results of PM2.5 spatial distribution show that the model has high prediction accuracy and provides a complete and highly accurate spatiotemporal distribution characteristics for long- and short-term PM2.5 exposure studies, and reduces exposure misclassification of PM2.5 in heavily polluted areas.
机译：深度学习模型可以模拟PM2.5与气溶胶光学深度（AOD）之间的复杂非线性关系，在PM2.5反演中具有很大的应用潜力。然而，由于AOD不能充分反映高PM2.5浓度与自变量之间的相关性，并且忽略了AOD缺失的影响，在严重污染的京津冀地区仍然存在低估高PM2.5浓度的问题。 。因此，减少了长期和短期的PM2.5暴露风险估计。这项工作将与污染物的一次排放和二次转化有关的气态污染物数据（NO2，SO2，CO和O-3）作为预测因子，引入到深度神经网络模型中，以改善基于AOD和气象学对高PM2.5浓度的低估因素。我们预测了缺失的AOD区域中的PM2.5浓度，产生了每日连续的PM2.5空间分布，并减少了由于AOD缺乏引起的估计偏差。基于网格的10倍交叉验证（CV）用于测试模型性能。结果表明，每日PM2.5浓度CV R-2为0.87，均方根预测误差（RMSE）为27.11μg / m（3）。与没有气态污染物（GAS）作为预测指标的模型相比，CV R-2和RMSE分别高0.12和低9.72μg / m（3）。包括缺失的AOD，在严重污染的冬季，PM2.5 CV R-2的平均浓度为0.86，RMSE为16.95μg / m（3）；与排除缺失的AOD相比，CV R-2和RMSE分别高0.07和低3.95μg / m（3）。 PM2.5空间分布的预测结果表明，该模型具有较高的预测准确性，可为长期和短期PM2.5暴露研究提供完整而高度准确的时空分布特征，并减少了重污染PM2.5的暴露错误分类地区。

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