Improving ground-level air quality prediction by integrating spatiotemporal new observation system datasets and numerical simulations

Develop an Air Quality Generation Platform (AQGP) at GMU as part of the AIST QRS Air Quality Analytics Collaborative Framework (AQACF) network. Using the city of Los Angeles as the demonstration location, the AQGP will downscale MERRA2 data from 50km to high temporal and spatial scale (3km – 111km, and 60min – 5min). The downscaled data will be used as input to WRF-Chem-based numerical model to generate predicted output products. The output products will be validated according to the community baseline prediction by WRF-Chem. The work will address three research challenges: 1) extending the NASA AIST Air Quaity Analytic Center Framework (AQACF) and the Apache Science Data Analytics Platform (SDAP) to facilitate the fusion of selected AQ in situ observations, satellite observations, and numerical simulations, 2) improving the short-term prediction of ground-level regional AQ information using numerical methods in conjunction with machine and deep learning techniques, and 3) automate the generation and delivery of value-added and prediction products to the AQACF for the city of Los Angeles.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

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