| 太原市PM2.5浓度的气象特征影响分析及预报 |
| 摘要点击 3128 全文点击 2681 投稿时间：2022-03-04 修订日期：2022-05-11 |
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| 中文关键词 长短时记忆神经网络(LSTM) 气象特征 机器学习 PM2.5浓度 预报 k均值聚类 太原 |
| 英文关键词 long short term memory(LSTM) meteorological characteristics machine learning PM2.5concentration forecast k-means clustering Taiyuan |
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| 中文摘要 |
| 利用2016~2020年太原市污染物浓度资料、以及国家基准气象观测站的同期地面气象资料，重点分析了太原市PM2.5浓度的变化特征以及湿度、降水、风和混合层厚度等气象条件对PM2.5浓度的影响，同时探讨了污染物浓度变化的成因，建立基于LSTM神经网络的PM2.5浓度预报模型.结果表明，2016~2020年太原市区冬季出现的重污染天数最多，其中2017年冬季出现天数最多为28 d，PM2.5浓度总体呈现出秋冬季节高，春夏季节低，周末PM2.5浓度高于工作日浓度，PM2.5浓度日变化大致呈现双峰型分布，分别出现在09：00左右和23：00至翌日01：00.除相对湿度和冬季气温外，其余气象要素与PM2.5浓度在四季均表现为负相关.影响太原市区PM2.5浓度升高的污染源主要位于其NE-ENE-E方向，西北部地区的相对不明显.汛期当达到中雨（降水量≥10 mm）以上级别的降水都对PM2.5浓度降低有明显效果.大气混合层高度增加对于PM2.5在垂直方向的扩散和稀释非常有利.冬季强偏西北向气流，相对湿度低，地面处于高压控制，混合层高度高，属于最利于PM2.5浓度降低的类簇.采用LSTM模型建模，PM2.5浓度预测的R2高达0.95，显著优于传统树模型和线性回归模型（R2<0.60），预测结果残差接近正态分布，其中84.2%预测结果的绝对误差低于20μg ·m-3，模型的MAE、MAPE和RMSE分别为38.17、17.19%和20.6. |
| 英文摘要 |
| Based on the pollutant concentration data of Taiyuan City from 2016 to 2020 and the surface meteorological data of the national benchmark meteorological observation station in the same period, the variation characteristics of PM2.5 concentration in Taiyuan City and the effects of meteorological conditions such as humidity, precipitation, wind, and mixing layer thickness on PM2.5 concentration were analyzed. At the same time, the causes of pollutant concentration changes were discussed, and the PM2.5 concentration prediction model based on the LSTM neural network was established. The results showed that the number of days of heavy pollution in Taiyuan City from 2016 to 2020 was the highest in winter, of which the maximum number of days in 2017 was 28 days. The PM2.5 concentration was generally high in autumn and winter and low in spring and summer. The PM2.5 concentration on weekends was higher than that on weekdays. The daily variation in PM2.5 concentration roughly presented a bimodal distribution, which appeared around 09:00 and 23:00 to 01:00 the following day. Except for relative humidity and winter temperature, other air pressure, wind speed, and PM concentration showed negative correlations in the four seasons. The pollution sources affecting the increase in PM2.5 concentration in Taiyuan City were mainly located in the NE-ENE-E direction, and the pollution in the northwest was not relatively apparent. In flood season, when the precipitation reached the level of moderate rain (rainfall ≥ 10 mm), it had an obvious effect on the reduction of PM2.5 concentration. The increase in atmospheric mixing layer height was very beneficial to the diffusion and dilution of PM2.5 in the vertical direction. The strong northwest air flow in winter, low relative humidity, high pressure control on the ground, and high height of the mixing layer belonged to the cluster most conducive to the reduction in PM2.5 concentration. Using the LSTM model for modeling, the R2 of PM2.5 concentration prediction was as high as 0.95, which was significantly better than that of the traditional tree model and linear regression model (R2<0.60). The residual of the prediction results was close to the normal distribution, of which the absolute error of 84.2% prediction results was less than 20 μg·m-3, and the MAE, MAPE, and RMSE of the model were 38.17, 17.19%, and 20.6, respectively. |
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