Land Use Regression Models Using Satellite Aerosol Optical Depth Observations and 3D Building Data from the Central Cities of Liaoning Province, China

Abstract

Land use regression (LUR) modeling is a promising method for assessing the spatial variation of air pollutant concentrations. We developed an LUR model for air pollutants (SO 2 , NO 2 , and PM 10 ) in the central cities of Liaoning Province using monitoring data collected during 2013. We evaluated whether the addition of annual satellite aerosol optical depth (AOD) observations and fi ve canyon indicators (building height, building coverage ratio, fl oor area ratio, building shape coeffi cient, and high-rise building ratio) improved the LUR models. Out-of-sample "10-fold" cross validation was used to quantify the accuracy of the model predictions. Our results showed that the gross domestic product (GDP) and the distance to the nearest industrial emissions were the common variables for the models. Annual AOD demonstrated weak correlations with air pollutant concentrations because of its instantaneity, low resolution, and limited precision; however, it was useful for improving the coeffi cient of determination (R 2 ) of the LUR models. The full models incorporating the annual AOD data and canyon indicators showed further improvement. The improvements of R 2 were 0.22, 0.19, and 0.39 for SO 2 , NO 2 , and PM 10 , respectively, demonstrating that the consideration of canyon indicators could still be valuable and could be used in LUR models.

Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach

“…Since then LUR along with geographic weighted regression (GWR) models have been used as a computationally cost-effective method for mapping primary and secondary formed pollutants to explain spatio-temporal variation in the concentrations due to geographic factors. Recently interest is surging to develop LUR models for PM 2.5 [9][10][11][12][13] due to the cognizance of its effect on human health [14] and its rising concentration across developing countries [15]. Van Donkelaar et al [16] showed that using land-use in GWR to observed and simulated AOD and ambient PM 2.5 concentrations results in significant PM 2.5 concentration prediction in locations lacking ground observations.…”

Resources and Environmental Carrying Capacity Using RS and GIS

“…2 presents the final AOD distribution. In this study, we used AOD data as an environment indicator, as it has been proven to accurately reflect air quality [21][22][23]. The AOD value of the southeast region of the study area is obviously higher than that of the northwest region, likely due to their different regional landforms.…”

Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs

“…Example datasets are NO 2 vertical column density (VCD) from the Ozone Monitoring Instrument, and aerosol optical depth (AOD) for PM [63]. As with studies in Europe [64][65][66][67][68], studies in China have reported that incorporating satellite-based estimates improved model performance of regional LUR models for NO 2 , PM 10 , and PM 2.5 [41,56]. More temporally-resolved models have also been reported; for example, using satellite AOD and VCDs, linear mixed-effects LUR models have been used to estimate daily average concentration of PM 10 [53] and NO 2 [51].…”

“…The pixel sizes of current instruments are around several hundred km 2 [70]; and thirdly, the availability of some satellite data are subject to meteorological conditions; for example, cloud cover can influence the quality of the retrievals of many variables (e.g., AOD) that are sensitive to atmospheric optics. In addition, satellite data can have systematic seasonal errors [41,56]. For example, Yang et al [41] reported that satellite remote sensing tended to overestimate concentrations of PM 2.5 in summer and underestimate in winter.…”