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000844167 0247_ $$2doi$$a10.5194/acp-2018-90
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000844167 1001_ $$0P:(DE-HGF)0$$aFiore, Arlene M.$$b0$$eCorresponding author
000844167 245__ $$aRegional and intercontinental pollution signatures on modeled and measured PAN at northern mid-latitude mountain sites
000844167 260__ $$aKatlenburg-Lindau$$bEGU$$c2018
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000844167 520__ $$aPeroxy acetyl nitrate (PAN) is the most important reservoir species for nitrogen oxides (NOx) in the remote troposphere. Upon decomposition in remote regions, PAN promotes efficient ozone production. We evaluate monthly mean PAN abundances from global chemical transport model simulations (HTAP1) for 2001 with measurements from five northern mid-latitude mountain sites (four European and one North American). The multi-model mean generally captures the observed monthly mean PAN but individual models simulate a factor of ~ 4–8 range in monthly abundances. We quantify PAN source-receptor relationships at the measurement sites with sensitivity simulations that decrease regional anthropogenic emissions of PAN (and ozone) precursors by 20 % from North America (NA), Europe (EU), and East Asia (EA). The HTAP1 models attribute more of the observed PAN at Jungfraujoch (Switzerland) to emissions in NA and EA, and less to EU, than a prior trajectory-based estimate. The trajectory-based and modeling approaches agree that EU emissions play a role in the observed springtime PAN maximum at Jungfraujoch. The signal from anthropogenic emissions on PAN is strongest at Jungfraujoch and Mount Bachelor (Oregon, U.S.A.) during April. In this month, PAN source-receptor relationships correlate both with model differences in regional anthropogenic volatile organic compound (AVOC) emissions and with ozone source-receptor relationships. PAN observations at mountaintop sites can thus provide key information for evaluating models, including links between PAN and ozone production and source-receptor relationships. Establishing routine, long-term, mountaintop measurements is essential given the large observed interannual variability in PAN.
000844167 536__ $$0G:(DE-HGF)POF3-512$$a512 - Data-Intensive Science and Federated Computing (POF3-512)$$cPOF3-512$$fPOF III$$x0
000844167 536__ $$0G:(DE-Juel-1)ESDE$$aEarth System Data Exploration (ESDE)$$cESDE$$x1
000844167 7001_ $$0P:(DE-HGF)0$$aFischer, Emily V.$$b1
000844167 7001_ $$0P:(DE-HGF)0$$aDeolal, Shubha Pandey$$b2
000844167 7001_ $$0P:(DE-HGF)0$$aWild, Oliver$$b3
000844167 7001_ $$0P:(DE-HGF)0$$aJaffe, Dan$$b4
000844167 7001_ $$0P:(DE-HGF)0$$aStaehelin, Johannes$$b5
000844167 7001_ $$0P:(DE-HGF)0$$aClifton, Olivia E.$$b6
000844167 7001_ $$0P:(DE-HGF)0$$aMilly, George P.$$b7
000844167 7001_ $$0P:(DE-HGF)0$$aBergmann, Dan$$b8
000844167 7001_ $$0P:(DE-HGF)0$$aCollins, William$$b9
000844167 7001_ $$0P:(DE-HGF)0$$aDentener, Frank$$b10
000844167 7001_ $$0P:(DE-HGF)0$$aDoherty, Ruth M.$$b11
000844167 7001_ $$0P:(DE-HGF)0$$aDuncan, Bryan N.$$b12
000844167 7001_ $$0P:(DE-HGF)0$$aFischer, Bernd$$b13
000844167 7001_ $$0P:(DE-HGF)0$$aGilge, Stefan$$b14
000844167 7001_ $$0P:(DE-HGF)0$$aHess, Peter G.$$b15
000844167 7001_ $$0P:(DE-HGF)0$$aHorowitz, Larry W.$$b16
000844167 7001_ $$0P:(DE-HGF)0$$aLupu, Alexandru$$b17
000844167 7001_ $$0P:(DE-HGF)0$$aMacKenzie, Ian$$b18
000844167 7001_ $$0P:(DE-HGF)0$$aPark, Rokjin$$b19
000844167 7001_ $$0P:(DE-HGF)0$$aRies, Ludwig$$b20
000844167 7001_ $$0P:(DE-HGF)0$$aSanderson, Michael$$b21
000844167 7001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin$$b22
000844167 7001_ $$0P:(DE-HGF)0$$aShindell, Drew T.$$b23
000844167 7001_ $$0P:(DE-HGF)0$$aSteinbacher, Martin$$b24
000844167 7001_ $$0P:(DE-HGF)0$$aStevenson, David S.$$b25
000844167 7001_ $$0P:(DE-HGF)0$$aSzopa, Sophie$$b26
000844167 7001_ $$0P:(DE-HGF)0$$aZellweger, Christoph$$b27
000844167 7001_ $$0P:(DE-HGF)0$$aZeng, Guang$$b28
000844167 773__ $$0PERI:(DE-600)2069857-4$$a10.5194/acp-2018-90$$p90$$tAtmospheric chemistry and physics / Discussions$$v1$$x1680-7367$$y2018
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