Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability.

摘要: Abstract This study utilized a long-term satellite-based vegetation index, and considered culture-specific emission sources (temples Chinese restaurants) with Land-use Regression (LUR) modelling to estimate the spatial-temporal variability of PM 2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average concentrations 2006 2012 17 air quality monitoring stations established by Environmental Protection Administration Taiwan were used for model development. measurements 2013 external verification. Monthly Normalized Difference Vegetation Index (NDVI) images coupled buffer analysis assess variations greenness surrounding sites. The distribution temples restaurants included represent contributions incense joss money burning, gas cooking, respectively. Spearman correlation coefficient stepwise regression LUR development, 10-fold cross-validation verification applied verify reliability. results showed strongly negative (r: −0.71 to −0.77) between NDVI while (r: 0.52 0.66) 0.31 0.44) positively correlated concentrations. With adjusted R 2 0.89, cross-validated adj-R 0.90, validated 0.83, high explanatory power resultant was confirmed. Moreover, averaged within 1750 m circular ( p  = 0.06) selected as important predictors during selection procedures. According partial , explained 66% variation dominant variable in developed model. We suggest future studies consider these three factors when establishing models estimating other cities.