文献类型：期刊文献

英文题名：Development of land use regression models to characterise spatial patterns of particulate matter and ozone in urban areas of Lanzhou

作者：Zhou, Tian[1];Fang, Shuya[1];Jin, Limei[2];Li, Xingran[1];Song, Xiaokai[1];Wang, Yufei[1];Zhou, Xiaowen[1];Bai, Yana[3];Ma, Xuying[4]

第一作者：Zhou, Tian

通信作者：Zhou, T[1];Jin, LM[2]

机构：[1]Lanzhou Univ, Coll Atmospher Sci, Lanzhou 730000, Peoples R China;[2]Gansu Univ Chinese Med, Sch Publ Hlth, Lanzhou 730000, Peoples R China;[3]Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou 730000, Peoples R China;[4]Xian Univ Sci & Technol, Coll Geomatics, Xian 710054, Peoples R China

第一机构：Lanzhou Univ, Coll Atmospher Sci, Lanzhou 730000, Peoples R China

通信机构：[1]corresponding author), Lanzhou Univ, Coll Atmospher Sci, Lanzhou 730000, Peoples R China;[2]corresponding author), Gansu Univ Chinese Med, Sch Publ Hlth, Lanzhou 730000, Peoples R China.|[10735e9d5e7087247e71b]甘肃中医药大学公共卫生学院;[10735]甘肃中医药大学;

年份：2024

卷号：55

外文期刊名：URBAN CLIMATE

收录：;Scopus(收录号：2-s2.0-85189540385);WOS:【SCI-EXPANDED(收录号:WOS:001217817900001)】；

基金：This work was supported by the National Science Foundation of China (No.41975019) , Gansu Provincial Science and Technology Program (23JRRA1032) , Scientific Research and Innovation Foundation of Gansu University of Chinese Medicine (No. 2020KCYB-4) .

语种：英文

外文关键词：Land use regression; Mobile monitoring; Particulate matter; Ozone

摘要：There are still many challenges in Land use regression (LUR) application in cities in China due to insufficient air pollutants data. In this study, the LUR models of TSP, PM10, PM4, PM2.5, PM1, and O-3 are developed by basing on the mobile monitoring in 2019 in Lanzhou, China. Our results show that the adjusted-R-2 of six best models are rang of 0.45 similar to 0.87. Referring to adjusted-R-2, the differences in cross-validation-R-2 (CV-R-2) using the training data are less than 9% excluding PM10, and the differences in CV-R-2 using the test data are within 19% in the models of TSP, PM4, and O-3. Overall, the models of TSP, PM4, and O-3 are more robust than that of PM10, PM2.5, and PM1. The O-3 model has a good fit. The spatial patterns of PMs exhibit high concentration in the west, center and east area, and the concentration being higher in the south than in the north. The predicted O-3 concentrations decrease from west to east. All predicted concentrations indicate that there are the highest level and the largest area of air pollutants in Xigu Distinct. These results can provide scientific data for urban planning, land use regulation, prevention and control of air pollution.