tive science-informed regulation and policy aimed at protecting human health and environmental quality relies on robust approaches to data acquisition and to knowledge generated from the data. For science to inform regulation and policy effectively, a strong problem-formulation step is needed. Once a problem is formulated, EPA scientists can evaluate what types of data are needed and then determine which available tools and technologies are appropriate for gathering the most robust data (see Figure 3-1). As described in detail in this chapter, management and interpretation of “big data” will be a continuing challenge for EPA inasmuch as new technologies are now capable of quickly generating huge amounts of data. Senior statisticians are needed in the agency to help analyze, model, and support the synthesis of that data. In many instances, large amounts of data are directly acquired as a component of hypothesis-driven research. However, many new technologies are also used for discovery-driven research— that is, generating large volumes of data that may not be a derivative of a clear, hypothesis-driven experiment, but nevertheless may yield important new hypotheses. In both instances, the data themselves do not become knowledge that can be applied as solutions to problems until they are analyzed and interpreted and then placed in the context of an appropriate problem or scientific theory. As depicted in Figure 3-1, there are iterations and feedback loops that must exist, particularly between data acquisition and data modeling, analysis, and synthesis.

The generation of knowledge, which can take many forms depending on the question being addressed and the nature of the data, ultimately serves as the basis of science-informed regulation and policy (see Figure 3-1). The committee recognizes that scientific data constitute one—albeit important—input into decision-making processes but alone will not resolve highly complex and uncertain environmental and health problems. Ultimately, environmental and health decisions and solutions will also be based on economic, societal, and other considerations apart from science. They need to take into account the variety and complexities of interactions between humans and the environment. But with better scientific understanding, regulations and other actions can be more effective and can have better and more cost-effective outcomes, such as improved human health and improved quality of ecosystems and the environment.

In accordance with the above discussion, it is imperative that EPA have the capacity and knowledge to take advantage of the latest science and technologies, which are always changing. The remainder of the chapter highlights a number of scientific and technologic advances that will be increasingly important for state-of-the-art, science-informed environmental regulation. It also includes several examples of how emerging science, technologies, and tools are transforming the way in which EPA will use data to address important regulatory issues and decision-making, and they demonstrate the need for a systems approach to addressing these complex problems. The chapter has been organized in parallel to the challenges identified in Chapter 2. The main topics that will be discussed are tools and technologies to address challenges related to 1) chemical exposures, human health, and the environment; 2) air pollution and climate



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