Abstract

Particle size is a critical influencing factor in assessing human exposure risk as fine particles are generally more hazardous than larger coarse particles. However, how particle composition influences human health risk is only poorly understood as different studies have different utilised different definitions and as a consequence there is no consensus. Here, with a new methodology taking insights of each size fraction load (%GSFload), metal bioaccessibility, we classify which specific particle size can reliably estimate the human exposure risk of lead and other metals. We then validate these by correlating the metals in each size fraction with those in human blood, hair, crop grain and different anthropogenic sources. Although increasing health risks are linked to metal concentration these increase as particle size decrease, the adjusted-risk for each size fraction differs when %GSFload is introduced to the risk assessment program. When using a single size fraction (250–50 µm, 50–5 µm, 5–1 µm, and < 1 µm) for comparison, the risk may be either over- or under-estimated. However, by considering bulk and adjusting the risk, it would be possible to obtain results that are closer to the real scenarios, which have been validated through human responses and evidence from crops. Fine particle size fractions (< 5 µm) bearing the mineral crystalline or aggregates (CaCO3, Fe3O4, Fe2O3, CaHPO4, Pb5(PO4)3Cl) alter the accumulation, chemical speciation, and fate of metals in soil/dust/sediment from the different sources. Loaded lead in the size fraction of < 50 µm has a significantly higher positive association with the risk-receptor biomarkers (BLLs, Hair Pb, Corn Pb, and Crop Pb) than other size fractions (bulk and 50–250 µm). Thus, we conclude that the < 50 µm fraction would be likely to be recommended as a reliable fraction to include in a risk assessment program. This methodology acts as a valuable instrument for future research undertakings, highlighting the importance of choosing suitable size fractions and attaining improved accuracy in risk assessment results that can be effectively compared.

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