Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability. (May 2017)

• Record Type: Journal Article
• Authors: Wu, Chih-Da; Chen, Yu-Cheng; Pan, Wen-Chi; Zeng, Yu-Ting; Chen, Mu-Jean; Guo, Yue Leon; Lung, Shih-Chun Candice
• Is Part Of: Environmental pollution. Volume 224(2017)
• Page Start: 148
• Page End: 157
• Publication Date: 2017-05
• Subjects: Fine particulate matter -- Land-use regression -- Normalized difference vegetation index (NDVI) -- Temple -- Restaurants with Chinese-style cooking; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2017.01.074
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: This study utilized a long-term satellite-based vegetation index, and considered culture-specific emission sources (temples and Chinese restaurants) with Land-use Regression (LUR) modelling to estimate the spatial-temporal variability of PM2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average PM2.5 concentrations from 2006 to 2012 of 17 air quality monitoring stations established by Environmental Protection Administration of Taiwan were used for model development. PM2.5 measurements from 2013 were used for external data verification. Monthly Normalized Difference Vegetation Index (NDVI) images coupled with buffer analysis were used to assess the spatial-temporal variations of greenness surrounding the monitoring sites. The distribution of temples and Chinese restaurants were included to represent the emission contributions from incense and joss money burning, and gas cooking, respectively. Spearman correlation coefficient and stepwise regression were used for LUR model development, and 10-fold cross-validation and external data verification were applied to verify the model reliability. The results showed a strongly negative correlation (r: −0.71 to −0.77) between NDVI and PM2.5 while temples (r: 0.52 to 0.66) and Chinese restaurants (r: 0.31 to 0.44) were positively correlated to PM2.5 concentrations. With the adjusted model R 2 of 0.89, a cross-validated adj-R 2 of 0.90, and external validated R 2 of 0.83, the high explanatory power of the resultant model was confirmed. Moreover, the averaged NDVI within a 1750 m circular buffer ( p < 0.01), the number of Chinese restaurants within a 1750 m buffer ( p < 0.01), and the number of temples within a 750 m buffer ( p = 0.06) were selected as important predictors during the stepwise selection procedures. According to the partial R 2, NDVI explained 66% of PM2.5 variation and was the dominant variable in the developed model. We suggest future studies consider these three factors when establishing LUR models for estimating PM2.5 in other Asian cities. Graphical abstract: Highlights: Vegetation and culture-specific sources were considered in PM2.5 modelling. High explanatory power (adj-R 2 = 0.89) of the resultant model was confirmed. Vegetation was the dominant variable in the developed model. Temple and Chinese restaurant were positively correlated to PM2.5 concentration. Abstract : Vegetation, Chinese restaurants, and temples were important predictors for PM2.5 LUR modelling in Asian cities while vegetation was the dominant variable.