Seasonal Variations and Health Risk Evaluation of Trace Elements in Atmospheric PM2.5 in Liaocheng, the North China Plain

Atmospheric elements can cause harmful effects on air quality and human health. Despite extensive research on PM2.5, there remains a limited understanding of the seasonal variations, origins, and associated health risks of specific elements in urban areas of the North China Plain. PM2.5 samples across four seasons were collected to investigate the seasonal variations, provenance, and health risks of 18 elements in urban Liaocheng. The concentrations of PM2.5 and total detected elements (TDEs) exhibited distinct seasonal patterns, with the biggest values occurring in winter, followed by spring, autumn, and summer. Fe, Ca, Al, and K were the most plentiful elements throughout the campaign, contributing 72.2% of TDEs. The enhanced concentrations of crustal elements were due to frequent dust storms in spring. Results from positive matrix factorization suggested that the dust source was only identified in spring, accounting for the largest percentage (37.0%), while secondary oxidation made the most significant contribution (34.6%) in summer, facilitated by higher temperatures and stronger sunshine. The relative abundance (41.6%) of biomass burning was highest in autumn, ascribed to intensified agricultural waste burning during the autumn harvest, especially in October. The contribution of coal combustion in cold seasons was substantially greater than in warm seasons, highlighting the role of increased coal burning for house heating in deteriorating air quality. Potential source function analysis showed that elements in Liaocheng originated from local and neighboring regions. The carcinogenic risk from the selected elements was notably stronger for adult males than for adult females and children, while the non-carcinogenic risk was stronger for children than for adults. Overall, these findings provide ponderable insights into the contamination characteristics and sources of elements, which are useful to inform effective measures for improving air quality and aerosol modeling.