Literature DB >> 12144273

Comparison of source apportionment and source sensitivity of ozone in a three-dimensional air quality model.

Alan M Dunker1, Greg Yarwood, Jerome P Ortmann, Gary M Wilson.   

Abstract

The ozone source apportionment technology (OSAT) estimates the contributions of different sources to ozone concentrations using a set of tracers for NOx, total VOCs, and ozone and an indicator that ascribes instantaneous ozone production to NOx or VOCs. These source contributions were compared to first-order sensitivities obtained by the decoupled direct method (DDM) for a three-dimensional simulation of an ozone episode in the Lake Michigan region. The cut-point for the OSAT indicator between VOC- and NOx-sensitive ozone production agrees well with the DDM sensitivities to VOC and NOx. In a ranking of the most important contributors to ozone concentrations >80 ppb, the OSAT and DDM results agreed on four of the top five contributors on average. The spatial distributions of the sensitivities and source contributions are similar, and the OSAT and DDM results for ozone >80 ppb correlate well. However, the source contributions ascribe substantially less relative importance to anthropogenic emissions and greater relative importance to the boundary concentrations than do the sensitivities. In regions where NOx inhibits ozone formation and the sensitivity is negative, the source contribution is small and positive. For the same subdivision of the emissions, the OSAT is 14 times faster than the DDM, but the DDM has greater flexibility in defining which emissions to include and generates results for species other than ozone. The first-order sensitivities explain, on average, 70% of the ozone concentrations.

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Year:  2002        PMID: 12144273     DOI: 10.1021/es011418f

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  3 in total

1.  Air pollutant source characterization using the revised regional haze tracking metric and a photochemical grid model and implications for regional haze planning.

Authors:  Patricia Brewer; Gail Tonnesen; Ralph Morris; Tom Moore; Uarporn Nopmongcol; Debra Miller
Journal:  J Air Waste Manag Assoc       Date:  2019-01-09       Impact factor: 2.235

2.  Evaluating a Space-Based Indicator of Surface Ozone-NO x -VOC Sensitivity Over Midlatitude Source Regions and Application to Decadal Trends.

Authors:  Xiaomeng Jin; Arlene M Fiore; Lee T Murray; Lukas C Valin; Lok N Lamsal; Bryan Duncan; K Folkert Boersma; Isabelle De Smedt; Gonzalo Gonzalez Abad; Kelly Chance; Gail S Tonnesen
Journal:  J Geophys Res Atmos       Date:  2017-10-16       Impact factor: 4.261

3.  Deep Learning for Prediction of the Air Quality Response to Emission Changes.

Authors:  Jia Xing; Shuxin Zheng; Dian Ding; James T Kelly; Shuxiao Wang; Siwei Li; Tao Qin; Mingyuan Ma; Zhaoxin Dong; Carey Jang; Yun Zhu; Haotian Zheng; Lu Ren; Tie-Yan Liu; Jiming Hao
Journal:  Environ Sci Technol       Date:  2020-07-01       Impact factor: 9.028

  3 in total

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