Land Use Regression (LUR) models was developed using fixed site and mobile station data in Dublin Ireland. The uncertainty associated with short term, mobile station data was accounted for using a weighted function, considering the differences in record duration of data across the study. A systematic buffer distance decay curve-based approach was also considered in this study to identify appropriate predictors and their associated buffer distance. The analysis was performed in Dublin city for fine particulate matter (PM2.5) where 10 fixed monitoring stations data were available, with average daily concentration data records of 1–7 years in length. 105 mobile station data were also collected at several locations in the city for durations of 12 h to 3 days. The performance of a developed multiple linear regression based LUR model was evaluated by using the leave-one-out-cross-validation procedure. The model explained variances (R2) of the LUR model were found to be 0.219 for winter and 0.688 for summer months. One of the primary reasons for poor performance of the LUR model was that the relationship between the predictors and the mean PM2.5 concentration were found to be non-linear which cannot be modelled by using multiple linear regression approach. To address this, a weighted support vector regression (WSVR) approach was also considered that can account for non-linearity while developing the LUR model. The model performance in terms of R2 was found to improve to 0.912 for winter and 0.916 for summer using this method. The developed WSVR model can be used to predict PM2.5 concentration at any ungauged locations in the study area with considerable accuracy, which can be the basis for future epidemiological studies.
|Number of pages||13|
|Publication status||Published - 9 Jan 2019|