Development of Land Use Regression models for particulate matter and associated components in a low air pollutant concentration airshed. (November 2016) - Dirgawati, Mila; Heyworth, Jane S.; Wheeler, Amanda J.; McCaul, Kieran A.; Blake, David; Boeyen, Jonathon; Cope, Martin; Yeap, Bu Beng; Nieuwenhuijsen, Mark; Brunekreef, Bert; Hinwood, Andrea
Abstract: Perth, Western Australia represents an area where pollutant concentrations are considered low compared with international locations. Land Use Regression (LUR) models for PM10, PM2.5 and PM2.5 Absorbance (PM2.5 Abs) along with their elemental components: Fe, K, Mn, V, S, Zn and Si were developed for the Perth Metropolitan area in order to estimate air pollutant concentrations across Perth. The most important predictor for PM10 was green spaces. Heavy vehicle traffic load was found to be the strongest predictor for PM2.5 Abs. Traffic variables were observed to be the important contributors for PM10 and PM2.5 elements in Perth, except for PM2.5  V which had distance to coast as the predominant predictor. Open green spaces explained more of the variability in the PM10 elements than for PM2.5 elements, and population density was more important for PM2.5 elements than for PM10 elements. The PM2.5 and PM2.5 Abs LUR models explained 67% and 82% of the variance, respectively, but the PM10 model only explained 35% of the variance. The PM2.5 models for Mn, V, and Zn explained between 70% and 90% of the variability in concentrations. PM10 V, Si, K, S and Fe models explained between 53% and 71% of the variability in respective concentrations. Testing the models using leave one-out cross validation, hold out validation and cross-hold out validation supported the validity of LUR models for PM10, PM2.5 and PM2.5 Abs and their corresponding elements in Metropolitan Perth despite the relatively low concentrations. Highlights: Development of LUR model in area with low air pollutant concentration is possible. Traffics are the main sources of airborne particulate matters and the elements. Most of LUR models explain more than 50% of the pollutant's spatial variability. Lack of specific predictor data for air pollutant limit the models' performance.