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.