Source apportionment with uncertainty estimates of fine particulate matter in Ostrava, Czech Republic using Positive Matrix Factorization

Abstract A 14-week investigation during a warm and cold seasons was conducted to improve understanding of air pollution sources that might be impacting air quality in Ostrava, the Czech Republic. Fine particulate matter (PM 2.5 ) samples were collected in consecutive 12-h day and night increments during spring and fall 2012 sampling campaigns. Sampling sites were strategically located to evaluate conditions in close proximity of a large steel works industrial complex, as well as away from direct influence of the industrial complex. These samples were analyzed for metals and other elements, organic and elemental (black) carbon, and polycyclic aromatic hydrocarbons (PAHs). The PM 2.5 samples were supplemented with pollutant gases and meteorological parameters. We applied the EPA PMF v5.1 model with uncertainty estimate features to the Ostrava data set. Using the model's bootstrapping procedure and other considerations, six factors were determined to provide the optimum solution. Each model run consisted of 100 iterations to ensure that the solution represents a global minimum. The resulting factors were identified as representing coal (power plants), mixed Cl, crustal, industrial 1 (alkali metals and PAHs), industrial 2 (transition metals), and home heat/transportation. The home heating source is thought to be largely domestic boilers burning low quality fuels such as lignite, wood, and domestic waste. Transportation-related combustion emissions could not be resolved as a separate factor. Uncertainty estimates support the general conclusion that the factors identified as representing coal power and home heat/transportation dominate the percent contribution to fine mass. Apportionment of regulated individual species is also presented.