| 基于组合模型的PM2.5浓度预测及其不确定性分析 |
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| 引用本文： | 王学梅,王凤文,陈滔,张庆国,蒋跃林.基于组合模型的PM2.5浓度预测及其不确定性分析[J].环境工程,2020,38(8):229-235. |
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| 作者姓名： | 王学梅 王凤文 陈滔 张庆国 蒋跃林 |
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| 作者单位： | 1. 安徽农业大学 资源与环境学院, 合肥 230036; |
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| 基金项目： | 安徽省教育厅重点科研项目;国家重点基础研究发展计划（973计划） |
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| 摘 要： | 运用GIS软件及克里金（Kriging）插值等方法分析合肥城市圈PM2.5浓度的时空分布，根据合肥市环境监测历史数据、地面气象站点数据及历史气象数据，采用多元回归分析、相关分析等方法，研究合肥市PM2.5浓度的影响因素。结果表明:1）PM2.5 浓度整体变化情况为冬季 > 秋季 > 春季 > 夏季，大部分城市PM2.5浓度峰值出现在1月，之后浓度开始逐渐下降，7月达到最低值，此后浓度逐渐升高。2）PM2.5浓度与CO呈高度正相关，相关系数高达0.875；与PM10、SO2、NO2的相关性也较高；与O3呈负相关。PM2.5浓度与气压、风速、降雨量以及能见度呈负相关，与温度、相对湿度呈强正相关。基于2018—2019年合肥市地面站点PM2.5浓度监测数据，构建预测PM2.5浓度的组合模型:对比三次指数平滑模型，确定模拟退火+遗传+三次指数平滑为优组合模型，拟合度达到95%。通过Kappa及MAPE指数对组合模型不确定性进行分析评价，两者分别为0.654和0.072，说明该模型具有高度稳定性。恰当的预测因子组合和模型不确定性研究有助于模型预测精度的提升和改善，从而为大气环境质量监测和评价提供参考。
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| 关 键 词： | PM2.5浓度 时空分布 预测 组合模型 不确定性分析 |
| 收稿时间： | 2019-11-14 |
PM 2. 5 CONCENTRATION PREDICTION AND UNCERTAINTY ANALYSIS BASED ON A COMPOSITE MODEL |
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| WANG Xue-mei,WANG Feng-wen,CHEN Tao,ZHANG Qing-guo,JIANG Yue-lin.PM 2. 5 CONCENTRATION PREDICTION AND UNCERTAINTY ANALYSIS BASED ON A COMPOSITE MODEL[J].Environmental Engineering,2020,38(8):229-235. |
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| Authors: | WANG Xue-mei WANG Feng-wen CHEN Tao ZHANG Qing-guo JIANG Yue-lin |
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| Affiliation: | 1. School of Resources and Environment, Anhui Agricultural University, Hefei 230036, China;2. Xuancheng Meteorological Bureau, Xuancheng 242000, China;3. School of Technology, Anhui Agricultural University, Hefei 230036, China;4. School of Science, Anhui Agricultural University, Hefei 230036, China |
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| Abstract: | In this paper, GIS software and Kriging interpolation method were used to analyze the spatial and temporal distribution of PM2.5 concentration in Hefei city circle. According to historical environment monitoring data, ground meteorological stations and historical meteorological data of Hefei, multiple regression analysis, correlation analysis and other methods were adopted to study the influencing factors of PM2.5 concentration in Hefei. The results showed that: 1) the overall change of PM2.5 concentration in the above cities was in the order of winter > autumn > spring > summer, and PM2.5 concentration in most cities peaked in January, then gradually declined, reached the lowest value in July, and then gradually increased; 2) PM2.5 concentration showed a highly positive correlation with CO, with the correlation coefficient as high as 0.875. The correlation with PM10, SO2 and NO2 was also high. There was a negative correlation with O3. PM2.5 concentration was negatively correlated with air pressure, wind speed, rainfall and visibility, and strongly positively correlated with temperature and relative humidity. Based on PM2.5 concentration monitoring data of Hefei from 2018 to 2019, a composite model was built to predict PM2.5 concentration, and three times exponential smoothing model was compared to determine that simulated annealing+genetic+three times exponential smoothing was the optimal composite model, with the fitting degree reaching 95%. Kappa and MAPE indexes were used to analyze and evaluate the uncertainty of the composite model. Kappa and MAPE indexes were 0.654 and 0.072 respectively, indicating that the model was highly stable. The proper combination of prediction factors and the study of model uncertainty were helpful to improve the model prediction accuracy, so as to provide theoretical basis and method for the monitoring and evaluation of atmospheric environment quality. |
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