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- 2018
基于改进LUR模型的大区域PM2.5浓度空间分布模拟
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Abstract:
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
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