College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.
J Environ Sci (China). 2010;22(9):1364-73. doi: 10.1016/s1001-0742(09)60263-1.
Land use regression (LUR) model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression (MLR) equations were established based on the most significant variables for NO2 in heating season (R2 = 0.74), and non-heating season (R2 = 0.61) in the whole study area; and PM10 in heating season (R2 = 0.72), and non-heating season (R2 = 0.49). Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations (resolution is 10 km). Intercepts of MLR equations were 0.050 (NO2, heating season), 0.035 (NO2, non-heating season), 0.068 (PM10, heating season), and 0.092 (PM10, non-heating season) in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 (NO2, heating season), 0.043 (NO2, non-heating season), 0.087 (PM10, heating season), and 0.096 (PM10, non-heating season). These intercept values might imply an area's background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. R2 values increased 0.09 (heating season) and 0.18 (non-heating season) for NO2, and 0.08 (heating season) and 0.04 (non-heating season) for PM10. In terms of R2, LUR model performed more effectively in heating season than non-heating season in the study area and gave a better result for NO2 compared with PM10.
基于环境空气质量监测数据，采用土地利用回归（LUR）模型预测天津地区 NO2 和 PM10 的空间浓度分布。建立了四个多元线性回归（MLR）方程，用于供暖季（整个研究区域的 R2=0.74）和非供暖季（整个研究区域的 R2=0.61）的 NO2 最显著变量，以及供暖季（R2=0.72）和非供暖季（R2=0.49）的 PM10 最显著变量。基于 MLR 方程（分辨率为 10km）获得了 NO2 和 PM10 空间浓度分布图。整个研究区域 MLR 方程的截距分别为 0.050（NO2，供暖季）、0.035（NO2，非供暖季）、0.068（PM10，供暖季）和 0.092（PM10，非供暖季）。在天津地区的中心区域，截距分别为 0.042（NO2，供暖季）、0.043（NO2，非供暖季）、0.087（PM10，供暖季）和 0.096（PM10，非供暖季）。这些截距值可能暗示了一个地区的背景浓度。来自 LUR 模型的预测结果在中心区域比整个研究区域更好。对于 NO2，R2 值增加了 0.09（供暖季）和 0.18（非供暖季），对于 PM10，R2 值增加了 0.08（供暖季）和 0.04（非供暖季）。就 R2 而言，LUR 模型在研究区域的供暖季比非供暖季表现更有效，并且对于 NO2 的预测结果优于 PM10。
