Abstract
A data fusion approach is developed to blend ground-based observations and simulated data from the Community Multiscale Air Quality (CMAQ) model. Spatiotemporal information and finer temporal scale variations have been captured by the resulting fields that are provided by both air quality modeling and observations. The approach is applied to daily PM2.5 total mass, five major particulate species (OC, EC, SO4 2−, NO3 −, and NH4 +), and three gaseous pollutants (CO, NOx, NO2) during 2006–2008 over North Carolina (USA). Applying the data fusion method significantly reduces biases in CMAQ fields to almost zero at monitor locations. The results show improvements in capturing spatial and temporal variability with observations, which is important to health and planning studies. The correlation for the cross-validation test decreased from 0.98 (no withholding) to 0.91 (10% random data withholding) when comparing modeled results to observations. If 10% monitor-based withholding is used, the correlation is 0.91 (random 10% of monitors withheld), and the correlation is 0.88 if spatially-specific withholding is used (10% of monitors withheld are grouped spatially). Results from a satellite-retrieved aerosol optical depth (AOD) method were compared with PM2.5 total mass concentration from data fusion, and the data-fusion fields have slightly less overall error; an R2 of 0.95 compared to 0.81 (AOD). Comparing results from an application of the Integrated Mobile Source Indicator method shows that the data fusion fields can be used to estimate mobile source impacts. Overall, the data fusion approach is attractive for providing spatiotemporal pollutant fields for speciated particulate pollutants, as the demand for accurate, fused, air quality model fields is growing.
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Acknowledgements
This publication was made possible by USEPA grant R834799. The work of X. Hu and Y. Liu was supported by NASA Applied Sciences Program (grant numbers NNX11AI53G and NNX14AG01G, Principal Investigator: Liu). Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the US government. Further, US government does not endorse the purchase of any commercial products or services mentioned in the publication.
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Questioner: Sofiev
Question: You mentioned issues in AOD retrievals over coastal areas due to humidity impact. So what is the issue?
Answer: The AOD method is 10 km resolution which is difficult to handle the grid that mix the land and water (coastal areas). The method retrieval doesn’t include RH, although they calibrate during the following steps. RH is related to sea salt aerosol which is important to PM2.5 concentration in coastal areas. Also, the data is incomplete in the day and areas that they can’t retrieve from satellite.
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Huang, R. et al. (2018). Using Air Quality Model-Data Fusion Methods for Developing Air Pollutant Exposure Fields and Comparison with Satellite AOD-Derived Fields: Application over North Carolina, USA. In: Mensink, C., Kallos, G. (eds) Air Pollution Modeling and its Application XXV. ITM 2016. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-57645-9_33
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DOI: https://doi.org/10.1007/978-3-319-57645-9_33
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