Spatial and temporal variability of fine particle composition and source types in five cities of Connecticut and Massachusetts

Abstract

To protect public health from PM 2.5 air pollution, it is critical to identify the source types of PM 2.5 mass and chemical components associated with higher risks of adverse health outcomes. Source apportionment modeling using Positive Matrix Factorization (PMF), was used to identify PM 2.5 source types and quantify the source contributions to PM 2.5 in five cities of Connecticut and Massachusetts. Spatial and temporal variability of PM 2.5 mass, components and source contributions were investigated. PMF analysis identified five source types: regional pollution as traced by sulfur, motor vehicle, road dust, oil combustion and sea salt. The sulfur-related regional pollution and traffic source type were major contributors to PM 2.5. Due to sparse ground-level PM 2.5 monitoring sites, current epidemiological studies are susceptible to exposure measurement errors. The higher correlations in concentrations and source contributions between different locations suggest less spatial variability, resulting in less exposure measurement errors. When concentrations and/or contributions were compared to regional averages, correlations were generally higher than between-site correlations. This suggests that for assigning exposures for health effects studies, using regional average concentrations or contributions from several PM 2.5 monitors is more reliable than using data from the nearest central monitor.