Exploring the modelling of Spatio-temporal variations in ambient air pollution within the land use regression framework: Estimation of PM<sub>10</sub> concentrations on a daily basis
Creators: name: Md. Saniul Alam
name: McNabola, Aonghus
Publisher: name: Taylor & Francis
Year: 2015
Subjects: subject: Mathematics
subject: FOS: Mathematics
subject: FOS: Mathematics
subject: Information and Computing Sciences
subject: Biological Sciences
subject: Ecology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Chemistry
subject: Earth and Environmental Sciences
subject: Biotechnology
subject: Engineering

description: ABSTRACTEstimation of daily average exposure to PM10 using the available fixed site monitoring stations (FSMs) in a city poses a great challenge. This is because typically FSMs are limited in number when considering the spatial representativeness of their measurements and also because statistical models of city-wide exposure have yet to be explored in this context. This paper deals with the later aspect of this challenge and extends the widely used Land use regression (LUR) approach to deal with temporal changes in air pollution and the influence of trans-boundary air pollution on short-term variations in PM10. Using the concept of Multiple linear regression (MLR) modelling the average daily concentration of PM10 in two European cities, Vienna and Dublin, were modelled. Models were initially developed using the standard MLR approach in Vienna using the most recently available data. Efforts were subsequently made to i) to assess the stability of model predictions over time; ii) explores the applicability of Non-Parametric Regression (NPR) and Artificial Neural Networks (ANN) to deal with the non-linearity of input variables.The predictive performance of the MLR models of the both cities were demonstrated to be stable over time and to produce similar results. However, NPR and ANN was found to have more improvement in the predictive performance in both cities. Using ANN produced the highest result with daily PM10 exposure predicted at R2 = 66% for Vienna and 51% for Dublin.In addition, two new predictor variables were also assessed for the Dublin model. The variables representing trans-boundary air pollution and peak traffic count were found to account for 6.5% and 12.7% of the variation in average daily PM10 concentration. The variable representing trans-boundary air pollution that was derived from air mass history (from back trajectory analysis) and population density has demonstrated a positive impact on model performance. <b>Implication Statement</b>The implications of this research would suggest that it is possible to produce a model of ambient air quality on a city wide scale using the readily available data. Most European cities typically have a limited FSM network with average daily concentrations of air pollutants as well as available meteorological, traffic and land-use data. This research highlights that using these data in combination with advanced statistical techniques such as NPR or ANNs will produce reasonably accurate predictions of ambient air quality across a city, including temporal variations.Therefore this approach reduces the need for additional measurement data to supplement existing historical records, and enables a lower cost method of air pollution model development for practitioners and policy makers.