Land Use Regression Modeling of PM2.5 Concentrations at Optimized Spatial Scales

Abstract

Though land use regression (LUR) models have been widely utilized to simulate air pollution distribution, unclear spatial scale effects of contributing characteristic variables usually make results study-specific. In this study, LUR models for PM 2.5 in Houston Metropolitan Area, US were developed under scales of 100 m, 300 m, 500 m, 800 m, and 1000-5000 m with intervals of 500 m by employing the idea of statistically optimized analysis. Results show that the annual average PM 2.5 concentration in Houston was significantly influenced by area ratios of open space urban and medium intensity urban at a 100 m scale, as well as of high intensity urban at a 500 m scale, whose correlation coefficients valued −0.64, 0.72, and 0.56, respectively. The fitting degree of LUR model at the optimized spatial scale (adj. R 2 = 0.78) is obviously better than those at any other unified spatial scales (adj. R 2 ranging from 0.19 to 0.65). Differences of PM 2.5 concentrations produced by LUR models with best-, moderate-, weakest fitting degree, as well as ordinary kriging were evident, while the LUR model achieved the best cross-validation accuracy at the optimized spatial scale. Results suggested that statistical based optimized spatial scales of characteristic variables might possibly ensure the performance of LUR models in mapping PM 2.5 distribution.

A Generalized Additive Model Combining Principal Component Analysis for PM2.5 Concentration Estimation

“…According to the previous LUR research findings on the selection of geographical feature characteristics [14,[29][30][31], data collected for LUR modelling in this study contains annual average PM2.5 concentrations, elevation, AOD, climate characteristics (temperature, wind speed, relative humidity, atmospheric pressure, and precipitation), road traffic, land use and cover, industrial plants, and surface dust. The distribution of PM2.5 monitoring sites and the partial basic geographical feature data, within the BTH region during the study period of 1 January 2015 to 31 December 2015, are shown in Figure 1.…”

“…The characteristic values were extracted at a 100-10,000 m (100, 200, 400, 500, 600, 800, 1000, 2000, 3000, 4000, 5000, 6000, 8000, and 10,000 m) buffering radius based on previous findings and experiments [10,14,29]. Moreover, the distance to a nearest road or industrial plant, elevation, the annual averages of AOD, as well as the annual averages of climate characteristics, were also included as the potential predictor variables in this study.…”

“…It is an efficient statistical regression model that estimates air pollution by using ground-level monitoring data as the dependent variable, and uses surrounding land use, Aerosol Optical Depth (AOD), meteorological and other auxiliary data as the independent variables [12]. In recent decades, LUR modelling has been widely used to study the spatial distribution of air pollutants, such as PM 2.5 [13][14][15], PM 10 [16,17], NO 2 [17,18], NO X [18], SO 2 [19], and O 3 [20]. However, most of them depend on presumed linear relationships between the ground-level measured PM 2.5 concentrations and the independent variables, despite the fact that the linear influencing mechanism on PM 2.5 concentration is not always suitable for all independent variables.…”

Optimization of PM2.5 Estimation Using Landscape Pattern Information and Land Use Regression Model in Zhejiang, China

“…The types of land use included woodland, residential, industrial, commercial, urban greenery, transportation, agricultural, bare land, waters, and roads. Buffers were created for 100, 300, 500, 800, 1000, 2000, 3000, 4000, and 5000 m, according to previous research findings [23][24][25]. Using version 4.2 of FRAGSTATS [10,26], We calculated landscape pattern index [27] of different distance buffers for analysis ( Table 1).…”

“…The variables that were highly relevant (R > 0.6) to the selected factor were eliminated, and the variables with correlations different from historical experiences were removed [18]. By comparing the prediction accuracy of forward, backward, and stepwise selection, we found that the prediction accuracy of stepwise selection was higher [25,[28][29][30]. All variables that satisfied the requirements were subjected to stepwise multivariate linear regression along with the PM 2.5 concentration.…”