Multivariate Receptor Modeling for Temporally Correlated Data by Using MCMC

Multivariate receptor modeling aims to estimate pollution source profiles and the amounts of pollution based on a series of ambient concentrations of multiple chemical species over time. Air pollution data often show temporal dependence due to meteorology and/or background sources. Previous approaches to receptor modeling do not incorporate this dependence. We model dependence in the data using a time series approach so that we can incorporate extra sources of variability in parameter estimation and uncertainty estimation. We estimate parameters using the Markov chain Monte Carlo method, which makes simultaneous estimation of parameters and uncertainties possible. The methods are applied to simulated data and 1990 Atlanta air pollution data. The results show promise towards the goal of accounting for the dependence in the data.