An Improved LUR-based Spatial Distribution Simulation for the Large Area PM2.5 Concentration
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摘要: 针对采用传统土地利用回归（land use regression，LUR）模型进行大气污染物浓度模拟时预测变量信息损失的缺陷，将主成分分析（principle component analysis，PCA）与逐步多元线性回归（stepwise multiple line regression，SMLR）相结合，提出了一种改进的LUR（PCA+SMLR）模型模拟大区域PM2.5浓度空间分布的方法。首先采用相关分析筛选与PM2.5显著相关的预测变量，然后对筛选出的预测变量进行主成分变换（PCA），最后保留所有主成分变量进行SMLR建立回归模型模拟PM2.5浓度。并以京津冀为研究区域进行实验验证，对PCR、SMLR、PCA+SMLR这3种模型的实验结果进行对比分析，结果表明，PCA+SMLR模型可提高预测变量对回归模型的贡献度，调整后R2达0.883，并且其精度检验指标及制图效果皆优于传统的LUR模型，证明了该模型可有效提高PM2.5浓度的模拟精度，对PM2.5区域联防联控具有指导意义。Abstract: There exists the shortage of traditional land use regression (LUR) model in losing information of predictor variables when simulating the air pollutant concentration. An improved model which combined principal component regression (PCR) and stepwise multiple line regression (SMLR)-LUR (PCA+SMLR) was developed to simulate the spatial distribution of PM2.5 in large area. Firstly, the correlation analysis was conducted to screen out effective predictor variables. Secondly, principal component analysis (PCA) was employed to transform effective predictor variables to principle components. Finally, all principal components were used to conduct SMLR to simulate the spatial distribution of PM2.5. Meanwhile, the reliability of the improved model was tested in Beijing-Tianjin-Hebei urban agglomeration. Experimental results of three models (PCR, SMLR and PCA+SMLR) were compared and analyzed. The results indicated that the PCA+SMLR model has an adjusted R2 of 0.883 by improving the contribution of the predictor variables. Besides, it is better than the traditional mo-del for accuracy index and the mapping results. Therefore, it can be concluded that the PCA+SMLR is a promising PM2.5 modeling method and could be very use-ful for air pollution mapping.
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Keywords:
- PM2.5 /
- PCA /
- SMLR /
- contribution /
- spatial distribution
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图 5 PM2.5年均浓度模拟空间分布图
Figure 5. Spatial Distribution of PM2.5 Annual Concentrations Estimated
表 1 模型参数及拟合度对比结果
Table 1 Comparison of Parameterization and Model Fitting for Four Models
模型 参数 Adj_R2 PCR1 P1、P2、P3、P4、P5、P6 0.793 PCR2 P1、P2、P3、P4、P5、P6、P7、P8、P9、
P10、P11、P12、P13、P14、P15、P16、P170.880 SMLR X1、X2、X3、X4、X5 0.832 PCA+SMLR P1、P2、P4、P5、P8、P17 0.883 注：Pi为第i个主成分；X1为气溶胶光学厚度；X2为降水；X3为监测站8 000 m缓冲区内耕地面积占比；X4为监测站8 000 m缓冲区内房屋建筑面积占比；X5为监测站5 000 m缓冲区内的露天采掘场面积占比。 
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表 2 精度检验指标对比结果
Table 2 Comparison of Accuracy Indicators for Four Models
模型 拟合精度 验证精度 RMSE
/μg·m-3MPE
/μg·m-3MRPE
/%RMSE
/μg·m-3MPE
/μg·m-3MRPE
/%PCR1 8.942 6.869 9.920 10.394 7.102 9.300 PCR2 6.248 5.000 6.983 8.780 6.950 10.266 SMLR 8.104 6.225 8.628 9.303 6.628 8.509 PCA+SMLR 6.721 5.278 7.389 7.391 5.912 8.419
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