The US Environmental Protection Agency (EPA) announced $5,959,842 million in research funding to nine institutions to improve air quality models used to simulate ozone, particulate matter (PM), regional haze, air toxics, and emerging pollutants.
Research supported by these grants will improve air quality models—specifically the component of models that represent how chemicals react in the atmosphere (known as “chemical mechanisms”). This research will advance our understanding of the sources and chemistry of air pollutants and how they move in the atmosphere. It will also inform the development of strategies for improving air quality.
The universities receiving the funding through EPA’s Science to Achieve Results (STAR) Program include:
Colorado State University, to gain insights on how emissions from wildfires and volatile chemical products (for example, personal care products, cleaning agents, and coatings) contribute to the formation of fine particles in the atmosphere.
Columbia University to develop tools that will improve the computational efficiency of chemical mechanisms for use in air quality models.
Harvard University to improve modeling of isoprene, halogen, and mercury chemistry; and increase the computational efficiency of chemical mechanisms in a widely used model to support air quality management.
Massachusetts Institute of Technology to develop a systematic approach towards developing chemical mechanisms for formation of particulate matter from complex organic compounds by using state-of-the science laboratory data.
University of California, Riverside to develop chemical mechanisms for emerging sources of pollutants, such as wildland fires and volatile chemical products, and approaches for increasing the computational efficiency of chemical mechanisms for use in air quality models.
University of Colorado Boulder to incorporate volatile chemical products compounds to current chemical mechanisms to improve air quality model predictions of ozone in US urban areas.
University of Illinois, Urbana to improve the computational efficiency of chemical mechanisms using machine learning algorithms.
University of Maryland to develop software packages using machine learning methods to gain insights on atmospheric chemical processes and increase computational efficiency of chemical mechanisms for use in air quality models.
University of Wisconsin, Madison, to develop and validate a new way of simulating heterogeneous chemistry of dinitrogen pentoxide to improve modeling of ozone and particulate matter.