利用LUR模型模拟杭州市PM2.5质量浓度空间分布

模拟城市大气污染物浓度空间分布对研究城市空气质量及人体健康至关重要.本研究利用土地利用回归模型(Land Use Regression,LUR),提取包括污染点源因子、交通因子、人口因子、土地利用因子和气象因子等60个预测因子,基于地理加权算法(GWR)建立春、夏、秋、冬四个季节的模型,实现对杭州地区近地表PM_(2.5)质量浓度空间分布的预测.结果表明:基于地理加权回归算法时,检验模型的R2值分别达到0.76(春季)、0.70(夏季)、0.73(秋季)、0.76(冬季),模型能够解释PM_(2.5)浓度值80%以上的变异.每个季度杭州地区PM_(2.5)浓度变化不尽相同,但总体以杭州中部最高,西南部偏低.研究说明基于LUR模型模拟大尺度地区PM_(2.5)质量浓度空间分布是可行的.

Application of LUR models for simulating the spatial distribution of PM2.5 concentration in Hangzhou, China

Quantification of spatial variation of air pollutants in urban areas provides exposure assessment for epidemiological studies. In this paper, Land use regression (LUR) models were used to estimate the spatial distribution of particulate matter (PM_2.5) in Hangzhou City. In total, more than 60 variables for the land use regression models were generated to characterize the road network, land use, meteorology and other factors. The geographical weighted regression algorithm (GWR) was used to build PM_2.5 LUR models for the different seasons. The adjusted R² values for the PM_2.5 LUR models for spring, summer, autumn and winter were 0.76, 0.70, 0.73 and 0.76, respectively. The LUR models explained more than 80% of the spatial variability for PM_2.5. The spatial pattern of PM_2.5 changed with the seasons. The high concentrations of PM_2.5 were more dispersed in the central areas of Hangzhou, and a clear area of low concentrations was evident in the southwest of the study regions. The approach of modeling the spatial distribution of PM_2.5 using LUR models has potential usefulness for exposure assessment in health studies.