SO2, NO2, and PM10 are the major outdoor air pollutants in China, and most of the cities in China have regulatory monitoring sites for these three air pollutants. In this study, we developed a land use regression (LUR) model using regulatory monitoring data to predict the spatial distribution of air pollutant concentrations in Jinan, China. Traffic, land use and census data, and meteorological and physical conditions were included as candidate independent variables, and were tabulated for buffers of varying radii. SO2, NO2, and PM10 concentrations were most highly correlated with the area of industrial land within a buffer of 0.5 km (R2=0.34), the distance from an expressway (R2=0.45), and the area of residential land within a buffer of 1.5 km (R2=0.25), respectively. Three multiple linear regression (MLR) equations were established based on the most significant variables (p<0.05) for SO2, NO2, and PM10, and R2 values obtained were 0.617, 0.640, and 0.600, respectively. An LUR model can be applied to an area with complex terrain. The buffer radii of independent variables for SO2, NO2, and PM10 were chosen to be 0.5, 2, and 1.5 km, respectively based on univariate models. Intercepts of MLR equations can reflect the background concentrations in a certain area, but in this study the intercept values seemed to be higher than background concentrations. Most of the cities in China have a network of regulatory monitoring sites. However, the number of sites has been limited by the level of financial support available. The results of this study could be helpful in promoting the application of LUR models for monitoring pollutants in Chinese cities.
Li CHENShi-yong DUZhi-peng BAIShao-fei KONGYan YOUBin HANDao-wen HANZhi-yong LI
Advancing the understanding of the spatial aspects of air pollution in the city regional environment is an area where improved methods can be of great benefit to exposure assessment and policy support. We created land use regression （LUR） models for SO2, NO2 and PMI0 for Tianjin, China. Traffic volumes, road networks, land use data, population density, meteorological conditions, physical conditions and satellite-derived greenness, brightness and wetness were used for predicting SOa, NO2 and PMt0 concentrations. We incorporated data on industrial point sources to improve LUR model performance. In order to consider the impact of different sources, we calculated the PSIndex, LSIndex and area of different land use types （agricultural land, industrial land, commercial land, residential land, green space and water area） within different buffer radii （1 to 20 kin）. This method makes up for the lack of consideration of source impact based on the LUR model. Remote sensing-derived variables were significantly correlated with gaseous pollutant concentrations such as SO2 and NO2. R2 values of the multiple linear regression equations for SO2, NO2 and PM10 were 0.78, 0.89 and 0.84, respectively, and the RMSE values were 0.32, 0.18 and 0.21, respectively. Model predictions at validation monitoring sites went well with predictions generally within 15% of measured values. Compared to the relationship between dependent variables and simple variables （such as traffic variables or meteorological condition variables）, the relationship between dependent variables and integrated variables was more consistent with a linear relationship. Such integration has a discernable influence on both the overall model prediction and health effects assessment on the spatial distribution of air pollution in the city region.
Li ChenYuming WangPeiwu LiYaqin JiShaofei KongZhiyong LiZhipeng Bai
PM2.5 samples for 24h were collected during winter in Tianjin, China. The ambient mass concentration and chemical composition of the PM2.5 were determined. Ionic species were analyzed by ion chromatography, while carbonaceous species were determined with the IMPROVE thermal optical reflectance （TOR） method, and inorganic elements were measured by inductively coupled plasma-atomic emission spectrometer. The daily PM2.5 mass concentrations ranged from 48.2 to 319.2 μg/m^3 with an arithmetic average of 144.6 μg/m^3. The elevated PM2.5 in winter was mostly attributed to combustion sources such as vehicle exhaust, heating, cooking and industrial emissions, low wind speeds and high relative humidity （RH）, which were favorable for pollutant accumulation and formation of secondary pollutants. By chemical mass balance, it was estimated that about 89.1% of the PM2.5 mass concentrations were explained by carbonaceous species, secondary particles, crustal matters, sea salt and trace elements. Organic material was the largest contributor, accounting for about 32.7% of the total PM2.5 mass concentrations. SO4^2-, NO3^-, Cl^- and NH4^＋ were four major ions, accounting for 16.6%, 11.5%, 4.7% and 6,0%, respectively, of the total mass of PM2.5.
