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Using a pretest-posttest quasi-experimental control group design, a learning environment study was conducted to evaluate the environmental literacy of postsecondary, nonscience majors. Data were collected from 183 students taking an introductory environmental science class—a 41-question Environmental Literacy Instrument (ELI) prompted students for responses across four subscales of environmental literacy: Knowledge, Beliefs, Opinions, and Self-Perceptions. Differences between presurvey and postsurvey scores were compared to determine whether a constructivist-based or traditional learning environment improved students' environmental literacy more. Results showed that the constructivist-based curriculum was not a significant factor of influence, suggesting that regardless of which learning environment they were exposed to, participants experienced similar improvements to their environmental literacy across a 16-week semester. Given that the findings were contrary to expectations, and counterindicated by several other learning environment studies as well, a broader investigation as to why the two learning environments produced similar results is warranted.

AbstractIn assessing environmental health risks, the risk characterization step synthesizes information gathered in evaluating exposures to stressors together with dose–response relationships, characteristics of the exposed population, and external environmental conditions. This article summarizes key steps of a cumulative risk assessment (CRA) followed by a discussion of considerations for characterizing cumulative risks. Cumulative risk characterizations differ considerably from single chemical‐ or single source‐based risk characterization. CRAs typically focus on a specific population instead of a pollutant or pollutant source and should include an evaluation of all relevant sources contributing to the exposures in the population and other factors that influence dose–response relationships. Second, CRAs may include influential environmental and population‐specific conditions, involving multiple chemical and nonchemical stressors. Third, a CRA could examine multiple health effects, reflecting joint toxicity and the potential for toxicological interactions. Fourth, the complexities often necessitate simplifying methods, including judgment‐based and semi‐quantitative indices that collapse disparate data into numerical scores. Fifth, because of the higher dimensionality and potentially large number of interactions, information needed to quantify risk is typically incomplete, necessitating an uncertainty analysis. Three approaches that could be used for characterizing risks in a CRA are presented: the multiroute hazard index, stressor grouping by exposure and toxicity, and indices for screening multiple factors and conditions. Other key roles of the risk characterization in CRAs are also described, mainly the translational aspect of including a characterization summary for lay readers (in addition to the technical analysis), and placing the results in the context of the likely risk‐based decisions.