| 基于数学模型的PM2.5月尺度浓度预测研究 |
| Prediction of PM2.5 Monthly Scale Concentration Based on Mathematical Model |
| 投稿时间：2023-08-12 修订日期：2024-03-27 |
| DOI：10.19316/j.issn.1002-6002.2025.01.19 |
| 中文关键词: PM2.5 拟合分析 排放量 决定系数 浓度预测 |
| 英文关键词:PM2.5 fitting analysis emission coefficient of determination concentration prediction |
| 基金项目: |
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| 通讯作者:刘娟* 天津理工大学环境科学与安全工程学院, 天津 300384 |
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| 中文摘要: |
| 精准预测PM2.5浓度能为大气污染治理提供科学依据。为了在明确PM2.5排放量的情况下使PM2.5浓度变化具有可获得性,基于全国各省份2013—2017年PM2.5月均浓度和月排放量历史监测数据,构建两者间的数学关系模型,通过Pearson分析将全国各省份划分为两种类型(分布规律和分布分散),并使用2018—2020年PM2.5月均浓度数据验证数学模型的精度。结果表明:在PM2.5月排放量与月均浓度散点分布规律的19个省份中,11个省份的决定系数(R2)介于0.60~0.90之间,8个省份的R2介于0.50~0.60之间。分布规律省份数学模型精度验证结果显示,14个省份的均方根误差(RMSE)介于6.00~16.00之间,3个省份的RMSE介于16.00~20.00之间。在PM2.5月排放量与月均浓度散点分布分散的10个小组中,5个小组的R2介于0.70~0.90之间,4个小组的R2介于0.60~0.70之间。分布分散省份数学模型精度验证结果显示,8个小组的RMSE介于6.00~16.00之间。因此,通过拟合方法得出的数学模型对于PM2.5浓度预报具有一定的适用性,且可以较准确地预测未来情景中的PM2.5浓度变化。 |
| 英文摘要: |
| Accurate prediction of PM2.5 concentration can provide a scientific basis for air pollution management. In order to have accessibility to the concentration changes in the case of clear PM2.5 emissions,based on the historical monitoring data of monthly average PM2.5 concentration and monthly emissions of each province from 2013 to 2017,a mathematical relationship model was constructed,the provinces were classified into two types by the Pearson analysis of SPSS,and the accuracy of the mathematical model was verified using the monthly average PM2.5 concentration from 2018 to 2020. The results showed that:there were 19 provinces in the category Ⅰ of the scatter distribution of PM2.5 emissions and monthly average concentrations,the coefficient of determination (R2) of 11 provinces ranged from 0. 60 to 0. 90,and the R2 of 8 provinces ranged from 0. 50 to 0. 60,and the root mean square error (RMSE) of 14 provinces ranged from 6. 00 to 16. 00 and the RMSE of 3 provinces ranged from 16. 00 to 20. 00 in the validation of the model's accuracy;and in the dispersed distribution of the category Ⅱ group of provinces was divided into 10 groups,of which 5 groups had R2 between 0. 70 and 0. 90,4 groups had R2 between 0. 60 and 0. 70,and 8 groups had RMSE between 6. 00 and 16. 00 at the time of model accuracy validation. It is therefore concluded that the mathematical model obtained by the fitting method has certain applicability to the prediction of PM2.5 concentration and can better predict the changes in PM2.5 concentrations in future scenarios. |
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