Abstract:In order to effectively solve the problem that the traditional monitoring technology cannot obtain the high-resolution spatial distribution of PM2.5 concentration in the city, the Guanzhong plain city group was taken as an example to simulate its PM2.5 spatial distribution status based on the land use regression (LUR) model. The 5 LUR models for spring, summer, autumn, winter and an annual average were built through obtaining PM2.5 concentration data of 54 monitoring stations in the study range and combining the factors such as land use type, meteorology, terrain, vegetation index, population density, traffic and pollution sources. The results showed that the adjusted R2 of each season and annual average of the LUR model reached 0.831 (spring), 0.817 (summer), 0.874 (autumn), 0.857 (winter), 0.90 (annual average), respectively, and better fitting levels occurred for the five models. A cross-examination method was used to carry out the accuracy test, and the average accuracy of the five models reached 80.4%, indicating that the LUR model had good applicability when simulating the spatial distribution of PM2.5 concentration in the Guanzhong plain city group. The simulation results showed that the PM2.5 concentration in each season of the study area was roughly same in spatial distribution with the significant characteristics of high in the east, low in the west, and obvious distribution trends along the altitude. However, there was a clear difference of low in summer and high in winter for the seasonal change of the mean concentration. The results of this study can provide a scientific basis for the prevention and control of PM2.5 pollution in the Guanzhong plain city group, and can also provide new ideas for obtaining the spatial distribution data of PM2.5 concentration within the city. Key words:land use regression model(LUR)/ PM2.5/ Guanzhong plain city group/ monitoring methods/ spatial distribution.
图1研究区及监测站点分布 Figure1.Study area and monitoring site distribution
HOEK G, BEELEN R, HOOGH K D, et al. A review of land-use regression models to assess spatial variation of outdoor air pollution[J]. Atmospheric Environment, 2008, 42(33): 7561-7578. doi: 10.1016/j.atmosenv.2008.05.057
CIOCǎNEA A, DRAGOMIRESCU A. Modular ventilation with twin air curtains for reducing dispersed pollution[J]. Tunnelling and Underground Space Technology, 2013, 37: 180-198. doi: 10.1016/j.tust.2013.03.012
LEE J H, WU C F, HOEK G, et al. LUR models for particulate matters in the Taipei metropolis with high densities of roads and strong activities of industry, commerce and construction[J]. Science of the Total Environment, 2015, 514: 178-184. doi: 10.1016/j.scitotenv.2015.01.091
[11]
MAVKO M E, TANG B, GEORGE L A. A sub-neighborhood scale land use regression model for predicting NO2[J]. Science of the Total Environment, 2008, 398(1/2/3): 68-75.
[12]
KASHIMA S, YORIFUJI T, TSUDA T, et al. Application of land use regression to regulatory air quality data in Japan[J]. Science of the Total Environment, 2009, 407(8): 3055-3062. doi: 10.1016/j.scitotenv.2008.12.038
[13]
TANG R, BLANGIARDO M, GULLIVER J. Using building heights and street configuration to enhance intra urban PM10, NOx, and NO2 land use regression models[J]. Environmental Science&Technology, 2013, 47(20): 11643-11650.
College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China Received Date: 2019-12-27 Accepted Date: 2020-04-24 Available Online: 2020-10-14 Keywords:land use regression model(LUR)/ PM2.5/ Guanzhong plain city group/ monitoring methods/ spatial distribution Abstract:In order to effectively solve the problem that the traditional monitoring technology cannot obtain the high-resolution spatial distribution of PM2.5 concentration in the city, the Guanzhong plain city group was taken as an example to simulate its PM2.5 spatial distribution status based on the land use regression (LUR) model. The 5 LUR models for spring, summer, autumn, winter and an annual average were built through obtaining PM2.5 concentration data of 54 monitoring stations in the study range and combining the factors such as land use type, meteorology, terrain, vegetation index, population density, traffic and pollution sources. The results showed that the adjusted R2 of each season and annual average of the LUR model reached 0.831 (spring), 0.817 (summer), 0.874 (autumn), 0.857 (winter), 0.90 (annual average), respectively, and better fitting levels occurred for the five models. A cross-examination method was used to carry out the accuracy test, and the average accuracy of the five models reached 80.4%, indicating that the LUR model had good applicability when simulating the spatial distribution of PM2.5 concentration in the Guanzhong plain city group. The simulation results showed that the PM2.5 concentration in each season of the study area was roughly same in spatial distribution with the significant characteristics of high in the east, low in the west, and obvious distribution trends along the altitude. However, there was a clear difference of low in summer and high in winter for the seasonal change of the mean concentration. The results of this study can provide a scientific basis for the prevention and control of PM2.5 pollution in the Guanzhong plain city group, and can also provide new ideas for obtaining the spatial distribution data of PM2.5 concentration within the city.
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