Occupational Health Impacts Due to Exposure to Organic Chemicals over an Entire Product Life Cycle

This article presents an innovative approach to include occupational exposures to organic chemicals in life cycle impact assessment (LCIA) by building on the characterization factors set out in Kijko et al. (2015) to calculate the potential impact of occupational exposure over the entire supply chain of product or service. Based on an economic input-output model and labor and economic data, the total impacts per dollar of production are provided for 430 commodity categories and range from 0.025 to 6.6 disability-adjusted life years (DALY) per million dollar of final economic demand. The approach is applied on a case study assessing human health impacts over the life cycle of a piece of office furniture. It illustrates how to combine monitoring data collected at the manufacturing facility and averaged sector specific data to model the entire supply chain. This paper makes the inclusion of occupational exposure to chemicals fully compatible with the LCA framework by including the supply chain of a given production process and will help industries focus on the leading causes of human health impacts and prevent impact shifting

Impact of Occupational Exposure to Chemicals in Life Cycle Assessment: A Novel Characterization Model Based on Measured Concentrations and Labor Hours

According to Lim et al., based on World Health Organization (WHO) data, hazardous chemicals in the workplace are responsible for over 370 000 premature deaths annually. Despite these high figures, life cycle impact assessment (LCIA) does not yet include a fully operational method to consider occupational impacts in its scope over the entire supply chain. This paper describes a novel approach to account for occupational exposure to chemicals by inhalation in LCA. It combines labor statistics and measured occupational concentrations of chemicals from the OSHA database to calculate operational LCIA characterization factors (i.e., intakes per hour worked and impact intensities for 19 069 organic chemical/sector combinations with confidence intervals across the entire U.S. manufacturing industry). For the seven chemicals that most contribute to the global impact, measured workplace concentrations range between 5 × 10^–4 and 3 × 10³ mg/m³. Carcinogenic impacts range over 4 orders of magnitude, from 1.3 × 10^–8 and up to 3.4 × 10^–4 DALY per blue-collar worker labor hour. The innovative approach set out in this paper assesses health impacts from occupational exposure to chemicals with population exposure to outdoor emissions, making it possible to integrate occupational exposure within LCIA. It broadens the LCIA scope to analyze hotspots and avoid impact shifting

Toxicokinetic Toxicodynamic (TKTD) Modeling of Ag Toxicity in Freshwater Organisms: Whole-Body Sodium Loss Predicts Acute Mortality Across Aquatic Species

ToxicoKinetic ToxicoDynamic (TKTD) models are considered essential tools to further advance acute toxicity prediction of metals for a range of species and exposure conditions, but they are currently underutilized. We present a mechanistic TKTD model for acute toxicity prediction of silver (Ag) in freshwater organisms. In this new approach, we explicitly link relevant TKTD processes to species (physiological) characteristics, which facilitates model application to other untested freshwater organisms. The model quantifies the reduction in whole-body sodium concentration over time as a function of the target site inhibition over time, the target site density and the species-specific sodium turnover rate. Freshwater species are assumed to die instantly when they have lost a critical amount of their initial whole-body sodium concentration. Results show that mortality is significantly related to sodium loss (<i>r</i>² = 0.86) for various aquatic organisms and exposure durations. The model accurately predicts lethal effect concentrations for different freshwater organisms, including <i>Daphnia magna</i>, rainbow trout and juvenile crayfish, and is able to capture the observed size-specific variation of nearly 2 orders of magnitude in empirical LC₅₀s

Physiologically based pharmacokinetic modeling of polyethylene glycol-coated polyacrylamide nanoparticles in rats

<p>Nanoparticles’ health risks depend on their biodistribution in the body. Phagocytosis may greatly affect this distribution but has not yet explicitly accounted for in whole body pharmacokinetic models. Here, we present a physiologically based pharmacokinetic model that includes phagocytosis of nanoparticles to explore the biodistribution of intravenously injected polyethylene glycol-coated polyacrylamide nanoparticles in rats. The model explains 97% of the observed variation in nanoparticles amounts across organs. According to the model, phagocytizing cells quickly capture nanoparticles until their saturation and thereby constitute a major reservoir in richly perfused organs (spleen, liver, bone marrow, lungs, heart and kidneys), storing 83% of the nanoparticles found in these organs 120 h after injection. Key determinants of the nanoparticles biodistribution are the uptake capacities of phagocytizing cells in organs, the partitioning between tissue and blood, and the permeability between capillary blood and tissues. This framework can be extended to other types of nanoparticles by adapting these determinants.</p

Assessing the Importance of Spatial Variability versus Model Choices in Life Cycle Impact Assessment: The Case of Freshwater Eutrophication in Europe

In Life Cycle Impact Assessment (LCIA) both spatial variability and model choices may be influential. In the case of the effect model, the effect factors differ with respect to their assumption of linear/nonlinear responses to increases in environmental stressor levels, and whether or not they account for the current stressor levels in the environment. Here, we derived spatially explicit characterization factors of phosphorus emissions causing eutrophication based on three different effect models (depicted by marginal, linear, and average effect factors) and two freshwater types (lakes and streams) and we performed an analysis of variance (ANOVA) to investigate how the selection of the effect models and the freshwater types influence the impacts of phosphorus emissions to freshwater on heterotrophic species. We found that 56% of the variability of ecological impacts per unit of phosphorus emission was explained, primarily, by the difference between freshwater types and, to a lesser extent, by the difference between effect models. The remaining variability was attributed to the spatial variation between river basins, mainly due to the variability in fate factors. Our study demonstrates the particular importance of accounting for spatial variability and model choices in LCIA

Spatial Variability and Uncertainty of Water Use Impacts from U.S. Feed and Milk Production

This paper addresses water use impacts of agriculture, developing a spatially explicit approach tracing the location of water use and water scarcity related to feed production, transport, and livestock, tracking uncertainties and illustrating the approach with a case study on dairy production in the United States. This approach was developed as a step to bring spatially variable production and impacts into a process-based life cycle assessment (LCA) context. As water resources and demands are spatially variable, it is critical to take into account the location of activities to properly understand the impacts of water use, accounting for each of the main feeds for milk production. At the crop production level, the example of corn grain shows that 59% of water stress associated with corn grain production in the United States is located in Nebraska, a state with moderate water stress and moderate corn production (11%). At the level of milk production, four watersheds account for 78% of the national water stress impact, as these areas have high milk production and relatively high water stress; it is the production of local silage and hay crops that drives water consumption in these areas. By considering uncertainty in both inventory data and impact characterization factors, we demonstrate that spatial variability may be larger than uncertainty, and that not systematically accounting for the two can lead to artificially high uncertainty. Using a nonspatial approach in a spatially variable setting can result in a significant underestimation or overestimation of water impacts. The approach demonstrated here could be applied to other spatially variable processes

Parameterization Models for Pesticide Exposure via Crop Consumption

An approach for estimating human exposure to pesticides via consumption of six important food crops is presented that can be used to extend multimedia models applied in health risk and life cycle impact assessment. We first assessed the variation of model output (pesticide residues per kg applied) as a function of model input variables (substance, crop, and environmental properties) including their possible correlations using matrix algebra. We identified five key parameters responsible for between 80% and 93% of the variation in pesticide residues, namely time between substance application and crop harvest, degradation half-lives in crops and on crop surfaces, overall residence times in soil, and substance molecular weight. Partition coefficients also play an important role for fruit trees and tomato (Kow), potato (Koc), and lettuce (Kaw, Kow). Focusing on these parameters, we develop crop-specific models by parametrizing a complex fate and exposure assessment framework. The parametric models thereby reflect the framework’s physical and chemical mechanisms and predict pesticide residues in harvest using linear combinations of crop, crop surface, and soil compartments. Parametric model results correspond well with results from the complex framework for 1540 substance-crop combinations with total deviations between a factor 4 (potato) and a factor 66 (lettuce). Predicted residues also correspond well with experimental data previously used to evaluate the complex framework. Pesticide mass in harvest can finally be combined with reduction factors accounting for food processing to estimate human exposure from crop consumption. All parametric models can be easily implemented into existing assessment frameworks

Additional file 2: of In vivo biodistribution and physiologically based pharmacokinetic modeling of inhaled fresh and aged cerium oxide nanoparticles in rats

Calculation of the deposition fractions in different regions of the respiratory system. Figure S1. Size distribution of the mass based concentration of CeO2 nanoparticles for four experiments based on the SMPS collected data. (DOCX 57 kb

Additional file 4: of In vivo biodistribution and physiologically based pharmacokinetic modeling of inhaled fresh and aged cerium oxide nanoparticles in rats

Individual organ concentrations of CeO2 nanoparticles for all experiments. (DOCX 39 kb

Additional file 3: Figure S2. of In vivo biodistribution and physiologically based pharmacokinetic modeling of inhaled fresh and aged cerium oxide nanoparticles in rats

Concentrations found in feces in the pilot study. Error bars representing one standard deviation on the means. (DOCX 37 kb