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000838702 1001_ $$0P:(DE-HGF)0$$aChang, Kai-Lan$$b0$$eCorresponding author
000838702 245__ $$aRegional trend analysis of surface ozone observations from monitoring networks in eastern North America, Europe and East Asia
000838702 260__ $$aWashington, DC$$bBioOne$$c2017
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000838702 520__ $$aSurface ozone is a greenhouse gas and pollutant detrimental to human health and crop and ecosystem productivity. The Tropospheric Ozone Assessment Report (TOAR) is designed to provide the research community with an up-to-date observation-based overview of tropospheric ozone’s global distribution and trends. The TOAR Surface Ozone Database contains ozone metrics at thousands of monitoring sites around the world, densely clustered across mid-latitude North America, western Europe and East Asia. Calculating regional ozone trends across these locations is challenging due to the uneven spacing of the monitoring sites across urban and rural areas. To meet this challenge we conducted a spatial and temporal trend analysis of several TOAR ozone metrics across these three regions for summertime (April–September) 2000–2014, using the generalized additive mixed model (GAMM). Our analysis indicates that East Asia has the greatest human and plant exposure to ozone pollution among investigating regions, with increasing ozone levels through 2014. The results also show that ozone mixing ratios continue to decline significantly over eastern North America and Europe, however, there is less evidence for decreases of daytime average ozone at urban sites. The present-day spatial coverage of ozone monitors in East Asia (South Korea and Japan) and eastern North America is adequate for estimating regional trends by simply taking the average of the individual trends at each site. However the European network is more sparsely populated across its northern and eastern regions and therefore a simple average of the individual trends at each site does not yield an accurate regional trend. This analysis demonstrates that the GAMM technique can be used to assess the regional representativeness of existing monitoring networks, indicating those networks for which a regional trend can be obtained by simply averaging the trends of all individual sites and those networks that require a more sophisticated statistical approach.
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000838702 7001_ $$0P:(DE-HGF)0$$aPetropavlovskikh, Irina$$b1
000838702 7001_ $$0P:(DE-Juel1)168529$$aCooper, Owen$$b2
000838702 7001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin$$b3
000838702 7001_ $$0P:(DE-HGF)0$$aWang, Tao$$b4
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