Source apportionment of PM 10 in Biga, Canakkale, Turkiye using Positive Matrix Factorization

Abstract

Abstract Particulate matter (PM) is a type of air pollution that poses a risk to human health, the environment, and property. PM10 is particularly significant as the vector of numerous dangerous trace elements due to the possible influence on human health and the ecosystem. Because of these effects, identifying potential sources and quantifying their impact on ambient PM10 concentration is essential for creating efficient control strategies to reach the threshold values. Based on the chemical species information derived from PM samples, receptor modeling has been extensively used for source appointment. In this study, PM10 samples were collected for three different periods (April, May, and June 2021), each lasting 15 days, using semi-automatic dust sampler systems at two sites selected in Biga, Canakkale, Turkiye. The relative contributions of different source types were quantified using EPA PMF (Positive Matrix Factorization) based on the 36 elements as components of PM10. As a result of the analysis, five source types were identified, which include: crustal elements/limestone/calcite quarry (64.9%), coal-fired power plant (11.2%), metal industry (9%), sea salt and ship emissions (8.5%), road traffic emissions and road dust (6.3%). The distribution of source contributions and the conditional probability function analysis matched the known sources' locations.