BOX 5.
LEARNING BEFORE DOING:
NEW GOALS FOR ENVIRONMENTAL TECHNOLOGY

For many years, the dominant environmental paradigm has been learning too late. Waste streams from every sector of society have necessitated after-the-fact treatment and remediation, often at tremendous cost and effort. Ozone-destroying chlorofluorocarbons, brain-damaging metals such as mercury and lead, reproductive-system-impairing persistent organic pollutants such as DDT and PCBs are a few familiar examples of learning too late. A new goal for environmental technology is to"learn more before doing."

For example, the development of microarray technology for simultaneously analyzing the total component of genome-encoded messenger RNA holds promise in allowing biologists to evaluate gene expression, protein function, and metabolism at the whole-genome level. Microarray analysis is being adapted to evaluate microbial community diversity and speciation. Research is needed to couple this technology to quantitative models so that it can be used to help understand the likely responses of microorganisms to environmental perturbations, how compounds travel through ecosystems, and how species interact.

In another example, as the rate of synthesis of new chemicals grows, screening compounds early and anticipating possible environmental interactions will be key. Presently we are able to learn about potential environmental impacts as a part of production. Can we use computer simulation modeling together with an increasingly sophisticated understanding of atmospheric, aquatic, and terrestrial systems to"learn more before doing" ? Scientists and engineers would like to explore virtual prototyping, molecular modeling, and retrosynthesis in order to help design environmentally benign production processes and products.

The integration of informatics, molecular biology, robotics, and ecology also has rich potential for environmental technologies that increase efficiency, dematerialization, and recyclability and may drop costs substantially. A new and vigorous fundamental science and engineering research agenda that highlights the promise and the priorities emerging from the intersection of systems and complexity theory, quantitative modeling, and environmentally benign technology development would be a smart investment.