A land use regression model incorporating data on industrial point source pollution

A land use regression model for estimating the NO2 concentration in shanghai, China

“…It could be explained by that, comparing with emission data, the number of industries was more accurate and easier to obtain, which may therefore, better reflect the industrial land use and industry intensity. The model R 2 decreased to 0.60 after removing this predictor (results not shown), suggesting that industry was an important emission source of NO 2 and that it might have a large-scale spatial influence in Shanghai, which was consistent with a study in another Chinese city, Tianjin (Chen et al, 2012).…”

“…Recently, several LUR models for NO 2 were attempted in Asian cities, e.g., the LUR model was used in Shizuoka (Kashima et al, 2009), yielding R 2 values of 0.54. The model R 2 values in one Chinese city Tianjin for NO 2 were 0.74 in the heating season, 0.61 in the non-heating season (Chen et al, 2010) and 0.89 for the entire year when industrial indicators were included (Chen et al, 2012). Monitoring data of NO 2 from a regulatory network were used in this study, which is a cost-effective method without additional sampling, and the measurements were continuous in temporal coverage.…”

Land use regression models coupled with meteorology to model spatial and temporal variability of NO2 and PM10 in Changsha, China

“…The simplest method is to recalibrate existing LUR models with a continuous background monitoring station (Gan et al, 2011;Nethery et al, 2008). Another approach is to build several unique models in different time periods Chen et al, 2012).…”

Estimating the spatial distribution of PM2.5 concentration by integrating geographic data and field measurements

“…The location of these environment monitoring stations are shown in Figure 2. The 65 geographic attributes of the 40 environment observed/supplementary sites are calculated using GIS analysis methods according to their impacting extent and distance to PM 2.5 observing sites, including shortest distance to the national-level road (D_NR), provincial level road (D_PR) and to the industrial factories (D_IF), and the number of different industrial factories (1km/2km/3km_IF), percentage of different dust surfaces (1km/2km/3km_DS), and percentage of different land covers (1km/2km/3km_ CD/GL/WL/AF/WB/BL) within the range of certain buffer radius (Sangrador J L T, 2008;Giannadaki D, 2014;Chen L, 2012).…”