Cumulative risk assessment contains many subcategories of exposure, health, and ecologic risk analyses, and it is important for EPA to examine its research portfolio in this domain carefully to ensure that it is well aligned with the ultimate decision contexts. With the increased use of LCA or life-cycle thinking, identification of combinations of exposures associated with processes or technologies would be increasingly common, and methods to characterize the ecologic and human health implications of combined exposures would be valuable. There are potentially valuable applications of advanced biosciences for evaluating various chemical mixtures rapidly, but they would not capture psychosocial stressors and other prevalent community-scale factors that are of increasing interest to the agency and various stakeholders (Nweke et al. 2011). New epidemiologic methods or application of epidemiologic insights can start to address those factors, but today they are limited in the number of stressors and locations with adequate exposure data and sample size that they can accommodate. Advancing methods along both fronts, ideally in a coordinated and mutually reinforcing manner, would be the most fruitful approach.
As EPA concentrates increasingly on wicked problems and broad mandates related to sustainability, narrowly focused risk assessments that omit complex interactions will be increasingly uninformative and unsupportive of effective preventive decisions. The broad challenge before the agency will involve developing tools and approaches to characterize cumulative effects in complex systems and harnessing insights from multistressor analyses without paralyzing decisions because of analytic complexities or missing data.
Social, Economic, Behavioral, and Decision Sciences
Systems thinking involves acknowledgment, up front, that environmental conditions are substantially determined by the individual and collective interactions that humans have with environmental processes. As discussed in Chapter 2, the human drivers of environmental change include population growth, settlement patterns, land uses, landscape patterns, the structure of the built environment, consumption patterns, the mix and amounts of energy sources, the spatial structure of production, and a host of other relevant variables. Social, economic, behavioral, and decision sciences show that those drivers are not independent of the natural environments in which effects occur, and that there are feedbacks, positive and negative, between human and environmental systems (Diamond 2005; Ostrom 1990; Taylor 2009). Environmental science and engineering also provide technologies for altering the relationships between humans and the environment and tools for predicting environmental change in response to changes in social and economic systems. That knowledge is all essential and useful for informing environmental decisions and policies; however, additional knowledge, skills, and expertise are needed. To make well-informed policies and decisions that are sustainable, it is essential to integrate theories of, evidence on, and tools for understanding how people respond to changes in the environ-