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Estimating US Background Ozone Using Data Fusion
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DOI：10.1021/acs.est.0c08625.png)
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摘要
En 中文
US background (US-B) ozone (O-3) is the O-3 that would be present in the absence of US anthropogenic (US-A) emissions. US-B O-3 varies by location and season and can make up a large, sometimes dominant, portion of total O-3. Typically, US-B O-3 is quantified using a chemical transport model (CTM) though results are uncertain due to potential errors in model process descriptions and inputs, and there are significant differences in various model estimates of US-B O-3. We develop and apply a method to fuse observed O-3 with US-B O-3 simulated by a regional CTM (CMAQ). We apportion the model bias as a function of space and time to US-B and US-A O-3. Trends in O3 bias are explored across different simulation years and varying model scales. We found that the CTM US-B O-3 estimate was typically biased low in spring and high in fall across years (2016-2017) and model scales. US-A O-3 was biased high on average, with bias increasing for coarser resolution simulations. With the application of our data fusion bias adjustment method, we estimate a 28% improvement in the agreement of adjusted US-B O-3. Across the four estimates, we found annual mean CTM-simulated US-B O-3 ranging from 30 to 37 ppb with the spring mean ranging from 32 to 39 ppb. After applying the bias adjustment, we found annual mean US-B O-3 ranging from 32 to 33 ppb with the spring mean ranging from 37 to 39 ppb.
Keyword:
CMAQ MODELING SYSTEM
LAND-USE REGRESSION
AIR
TRANSPORT
EMISSIONS
IMPACTS
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期刊
E
IF:
11.3
论文数:
4.3W
被引数:
26.0W
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