Graduate Student Researcher
University of California, Santa Barbara Santa Barbara, CA 93106-5080
IEE Research Areas:
Runsheng Song’s research interests focus on using predictive models to bridge data gaps in the stage of Life Cycle Inventory (LCI) in the area of Life Cycle Assessment (LCA), especially for new chemicals. The research also examines methods of rapid-throughput testing to determine the toxicities of those new chemicals.
Hundreds of new chemicals are being made every day, and many of them are released into the market. Their environmental implications are often hard to know without conducting detailed laboratory analysis. But the pace of such analysis is too slow to keep up with number of new chemicals being developed. Life Cycle Assessment (LCA) is a powerful tool for quantifying raw material consumption, and the environmental impacts of those new chemicals throughout their life cycles. However, reliable data are usually unavailable and the size of current databases are limited. Runsheng is working on a method to predict the life-cycle inventory and the toxicity of new chemicals based on the characteristics of similar chemicals, others to fill out the data gap in rapid way so that it can support decision-making. In general, statistical predictive models will be developed and existing chemical data will be used as training datasets. Eventually, his works should be useful to LCI database development, to decision makers in deciding whether a new chemical should be made as well as to downstream models that require necessary data inputs.
BE: Environmental Engineering, Huazhong University of Science and Technology
MESM: UC Santa Barbara