Land use Regression as Method to Model Air Pollution. Previous Results for Gothenburg/Sweden

Abstract

In the past 20 years, considerable progress has been made to improve urban air quality in the EU. However, road traffic still contributes considerably to the deterioration of urban air quality to below standards, which requires a method to measure properly and model pollution levels resulting from road traffic. In order to visualize the geographical distribution of pollution concentration realistically, we applied the Land Use Regression (LUR) model to the urban area of Gothenburg. The NO 2 concentration was already obtained by 25 samplers through the urban area during 7-20 May, 2001. Predictive variables such as altitude, density, roads types, traffic and land use were estimated by geographic information system in buffers ranging 50 to 500 m-radii. Linear regression (α=5%) between NO 2 and every predictive variable was calculated, and the most robust variables and without collinearity variables were selected to the multivariate regression model. The final formula was applied using Kriging in a grid map to estimate NO 2 levels. The average of measurements was 23.5 μg/m³ (± 6.8 μg/m³) and 180 predictive variables were obtained. The final model explained 59.4% of the variance of NO 2 concentration with presence of altitude and sum of traffic within 150 m around the sampler sites as predictor variables. The correlation measured versus predicted levels of NO 2 was r = 0.77 (p < 0.001). These results highlight the contribution of traffic in air pollution concentration, although the model is not precise in regions outside the urban area (e.g. islands and rural area). Moreover, future analyses should include meteorological data to improve the LUR modelling.

Remote Sensing in Environmental Justice Research—A Review

“…LUR are able to approximate local differences in environmental burdens in dependence of multiple physical variables in the local surroundings spatially exhaustive and with high spatial resolution [105,106]. Thus, LUR approaches were used in various studies to assess local concentrations of ambient air pollution (e.g., [107][108][109][110]), where the independent variables are land use/land cover (LULC), population, and traffic density, as well as built-up structure. As another method to provide a spatial representation of air pollutant exposure, physical atmospheric dispersal models can be deployed on local or regional scales [111,112].…”

Investigating the Effects of the Built Environment on PM2.5 and PM10: A Case Study of Seoul Metropolitan City, South Korea

“…PM 2.5 (Moran's I = −0.22, p = 0.18) and PM 10 (Moran's I = −0.21, p = 0.22) failed to show spatial autocorrelation, indicating that they were influenced more by the neighborhood-level built environment than by each other. Therefore, this study used the OLS regression model, as described previously [10,24,40]. Table 2 shows the descriptive statistics of variables considered in this study.…”

Land Use Change in Coastal Cities during the Rapid Urbanization Period from 1990 to 2016: A Case Study in Ningbo City, China

“…Land use change is regarded as one of the most important consequences of unprecedented human alterations of the earth's land surface [1]. These changes significantly impact ecosystems, climate, and human vulnerability, and are also related to land degradation, soil erosion, biodiversity loss, and environment pollution [2][3][4][5][6]. Land use change has been selected as a core research project of the International Geosphere Biosphere Programme (IGBP), the International Human Dimensions Programme on Global Environmental Change (IHDP), and the World Climate Research Programme (WCRP) [7].…”