Prioritizing chemicals and data requirements for screening-level exposure and risk assessment

Abstract

Background: Scientists and regulatory agencies strive to identify chemicals that may cause harmful effects to humans and the environment; however, prioritization is challenging because of the large number of chemicals requiring evaluation and limited data and resources.

Objectives: We aimed to prioritize chemicals for exposure and exposure potential and obtain a quantitative perspective on research needs to better address uncertainty in screening assessments.

Methods: We used a multimedia mass balance model to prioritize > 12,000 organic chemicals using four far-field human exposure metrics. The propagation of variance (uncertainty) in key chemical information used as model input for calculating exposure metrics was quantified.

Results: Modeled human concentrations and intake rates span approximately 17 and 15 orders of magnitude, respectively. Estimates of exposure potential using human concentrations and a unit emission rate span approximately 13 orders of magnitude, and intake fractions span 7 orders of magnitude. The actual chemical emission rate contributes the greatest variance (uncertainty) in exposure estimates. The human biotransformation half-life is the second greatest source of uncertainty in estimated concentrations. In general, biotransformation and biodegradation half-lives are greater sources of uncertainty in modeled exposure and exposure potential than chemical partition coefficients.

Conclusions: Mechanistic exposure modeling is suitable for screening and prioritizing large numbers of chemicals. By including uncertainty analysis and uncertainty in chemical information in the exposure estimates, these methods can help identify and address the important sources of uncertainty in human exposure and risk assessment in a systematic manner.

Figure 1

Schematic of the far-field human exposure model simulations highlighting the two human exposure potential metrics (light gray boxes, calculated using unit emission rates) and the two actual human exposure metrics (dark gray boxes, calculated using actual emission rate estimates); ADME, absorption, distribution, metabolism, excretion processes.

Figure 2

Relative ranking of 12,619 organic substances for far-field model estimates of iFs (A), C_Us (B), iR_As (C), and C_As (D). Note that the relative rankings (x-axis) are unique for each metric.

Figure 3

Statistical summary for the CV of model input parameters on the calculated far-field human iFs, C_Us, iR_As, and C_As for the 12,619 substances; HL_BIO–av/mam, HL_BIO–avian/mammalian. Model input parameters are summarized in Table 1. Boxes represent 25th–75th percentiles and whiskers 5th–95th percentiles, vertical lines (|) indicate ranges, lines within boxes are medians, and small squares are means.

Figure 4

Statistical summary of the modeled output Cfs for far-field human iFs, C_Us, iR_As, and C_As for the 12,619 substances. Boxes represent 25th–75th percentiles and whiskers 5th–95th percentiles, crosses (x) indicate ranges, lines within boxes are medians, and small squares are means.