Surface ozone: Trend details, seasonal variations, and interpretation

1989 ◽  
Vol 94 (D15) ◽  
pp. 18289 ◽  
Author(s):  
Walter E. Janach
Keyword(s):  
Seasonal Variations ◽  
Surface Ozone ◽  
Ozone Trend

Uncertainties in surface ozone trend at hohenpeissenberg

1991 ◽  
Vol 25 (2) ◽  
pp. 511-515 ◽  
Author(s):  
P.S. Low ◽  
T.D. Davies ◽  
P.M. Kelly ◽  
R. Reiter
Keyword(s):  
Surface Ozone ◽  
Ozone Trend

Spatial and seasonal variations of surface ozone formation regime and source attributions in the Guanzhong Basin, China

2020 ◽  
Author(s):  
Guohui Li ◽  
Ruonan Wang ◽  
Naifang Bei ◽  
Jiarui Wu ◽  
Suixin Liu ◽  
...  
Keyword(s):  
Seasonal Variations ◽  
Surface Ozone ◽  
Ozone Formation ◽  
Guanzhong Basin

scholarly journals Surface ozone trend details and interpretations in Beijing, 2001–2006

2009 ◽  
Vol 9 (22) ◽  
pp. 8813-8823 ◽  
Author(s):  
G. Tang ◽  
X. Li ◽  
Y. Wang ◽  
J. Xin ◽  
X. Ren
Keyword(s):  
Surface Ozone ◽  
Northern China ◽  
Daily Maximum ◽  
Photochemical Formation ◽  
Ozone Trend ◽  
Reduction Of Nox ◽  
Surrounding Areas ◽  
Local Emissions ◽  
Urban Sites ◽  
Analyze Data

Abstract. Beijing is a megacity situated in the rapidly developing Beijing-Tianjin-Hebei region of northern China. In this study, we analyze data on ozone and nitrogen oxide levels obtained at six urban sites in Beijing between the months of July and September. Our goal is to investigate average trends and interpretations over the 2001–2006 period. Average concentrations of NOx (NOx=NO+NO2), O3, and Ox (Ox=O3+NO2) were 49.2±5.9 ppbv, 26.6±2.8 ppbv, and 60.3±1.9 ppbv, respectively. NOx concentrations decreased linearly at a rate of 3.9±0.5 ppbv/yr after 2002, while ozone concentrations increased at a rate of 1.1±0.5 ppbv/yr during 2001–2006, and Ox concentrations remained nearly constant. The reduction of NOx emissions and elevated non-methane hydrocarbon (NMHCs) emissions may have contributed to the increased O3 concentrations in Beijing. When the contributions from Beijing's urban and surrounding areas were disaggregated via trajectory cluster analysis, daily maximum and average Ox concentrations attributable to Beijing's local emissions increased linearly at rates of 1.3±0.6 ppbv/yr and 0.8±0.6 ppbv/yr, while the Ox concentrations attributable to regional areas decreased linearly at rates of 0.6±0.3 ppbv/yr and 0.5±0.3 ppbv/yr, respectively. The decrease in Ox concentrations of the surrounding areas was found to counteract increasing Beijing urban Ox production, leading to nearly constant Ox concentrations in the Beijing region over the study period. Our results may be helpful for redefining government strategies to control the photochemical formation of air pollutants in the Beijing region. Our conclusions have relevance for developing megacities worldwide.

scholarly journals Observed surface ozone trend in the year 2012 over Nairobi, Kenya

Atmósfera ◽  
2014 ◽  
Vol 27 (4) ◽  
pp. 377-384 ◽  
Author(s):  
Zablon W. Shilenje ◽  
Victor Ongoma
Keyword(s):  
Surface Ozone ◽  
Ozone Trend

scholarly journals A climatology of surface ozone in the extra tropics: cluster analysis of observations and model results

2007 ◽  
Vol 7 (4) ◽  
pp. 12541-12572 ◽  
Author(s):  
O. A. Tarasova ◽  
C. A. M. Brenninkmeijer ◽  
P. Jöckel ◽  
A. M. Zvyagintsev ◽  
G. I. Kuznetsov
Keyword(s):  
Seasonal Variations ◽  
Surface Ozone ◽  
Transport Processes ◽  
Diurnal Variability ◽  
Model Cluster ◽  
Seasonal Maximum ◽  
Polluted Sites ◽  
Summer Maximum ◽  
The One ◽  
Spring Maximum

Abstract. Important aspects of the seasonal variations of surface ozone are discussed. The underlying analysis is based on the long-term (1990–2004) ozone records of Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (EMEP) and the World Data Center of Greenhouse Gases which do have a strong Northern Hemisphere bias. Seasonal variations are pronounced at most of the 114 locations for any time of the day. Seasonal-diurnal variability classification using hierarchical agglomeration clustering reveals 5 distinct clusters: clean/rural, semi-polluted non-elevated, semi-polluted semi-elevated, elevated and polar/remote marine types. For the cluster "clean/rural" the seasonal maximum is observed in April, both for night and day. For those sites with a double maximum or a wide spring-summer maximum, the one in spring appears both for day and night, while the one in summer is more pronounced for daytime and hence can be attributed to photochemical processes. For the spring maximum photochemistry is a less plausible explanation as no dependence of the maximum timing is observed. More probably the spring maximum is caused by dynamical/transport processes. Using data from the 3-D atmospheric chemistry general circulation model ECHAM5/MESSy1 covering the period of 1998–2005 a comparison has been performed for the identified clusters. For the model data four distinct classes of variability are detected. The majority of cases are covered by the regimes with a spring seasonal maximum or with a broad spring-summer maximum (with prevailing summer). The regime with winter–early spring maximum is reproduced by the model for southern hemispheric locations. Background and semi-polluted sites appear in the model in the same cluster. The seasonality in this model cluster is characterized by a pronounced spring (May) maximum. For the model cluster that covers partly semi-elevated semi-polluted sites the role of the photochemical production/destruction seems to be overestimated. Taking into consideration the differences in the data sampling procedure the carried out comparison demonstrates the ability of the model to reproduce the main regimes of surface ozone variability quite well.

scholarly journals A climatology of surface ozone in the extra tropics: cluster analysis of observations and model results

2007 ◽  
Vol 7 (24) ◽  
pp. 6099-6117 ◽  
Author(s):  
O. A. Tarasova ◽  
C. A. M. Brenninkmeijer ◽  
P. Jöckel ◽  
A. M. Zvyagintsev ◽  
G. I. Kuznetsov
Keyword(s):  
Atmospheric Chemistry ◽  
Seasonal Variations ◽  
General Circulation ◽  
Surface Ozone ◽  
Circulation Model ◽  
Transport Processes ◽  
Mixing Ratios ◽  
Seasonal Maximum ◽  
Summer Maximum ◽  
Spring Maximum

Abstract. Important aspects of the seasonal variations of surface ozone are discussed. The underlying analysis is based on the long-term (1990–2004) ozone records of the Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (EMEP) and the World Data Centre of Greenhouse Gases, which provide data mostly for the Northern Hemisphere. Seasonal variations are pronounced at most of the 114 locations at all times of the day. A seasonal-diurnal variations classification using hierarchical agglomeration clustering reveals 6 distinct clusters: clean background, rural, semi-polluted non-elevated, semi-polluted semi-elevated, elevated and polar/remote marine. For the "clean background" cluster the seasonal maximum is observed in March-April, both for night and day. For those sites with a double maximum or a wide spring-summer maximum, the spring maximum appears both for day and night, while the summer maximum is more pronounced for daytime and hence can be attributed to photochemical processes. The spring maximum is more likely caused by dynamical/transport processes than by photochemistry as it is observed in spring for all times of the day. We compare the identified clusters with corresponding data from the 3-D atmospheric chemistry general circulation model ECHAM5/MESSy1 covering the period of 1998–2005. For the model output as for the measurements 6 clusters are considered. The simulation shows at most of the sites a spring seasonal maximum or a broad spring-summer maximum (with higher summer mixing ratios). For southern hemispheric and polar remote locations the seasonal maximum in the simulation is shifted to spring, while the absolute mixing ratios are in good agreement with the measurements. The seasonality in the model cluster covering background locations is characterized by a pronounced spring (April–May) maximum. For the model clusters which cover rural and semi-polluted sites the role of the photochemical production/destruction seems to be overestimated. Taking into consideration the differences in the data sampling procedure, the comparison demonstrates the ability of the model to reproduce the main regimes of surface ozone variations quite well.

scholarly journals Surface ozone trend details and interpretations in Beijing, 2001–2006

2009 ◽  
Vol 9 (2) ◽  
pp. 8159-8185 ◽  
Author(s):  
G. Tang ◽  
X. Li ◽  
Y. Wang ◽  
J. Xin ◽  
X. Ren
Keyword(s):  
Surface Ozone ◽  
Northern China ◽  
Daily Maximum ◽  
Photochemical Formation ◽  
Ozone Trend ◽  
Reduction Of Nox ◽  
Surrounding Areas ◽  
Local Emissions ◽  
Urban Sites ◽  
Analyze Data

Abstract. Beijing is a megacity situated in the rapidly developing Beijing-Tianjin-Hebei region of northern China. In this study, we analyze data on ozone and nitrogen oxide levels obtained at six urban sites in Beijing between the months of July and September. Our goal is to investigate average trends and interpretations over the 2001–2006 period. Average concentrations of NOx (NOx=NO+NO2), O3, and Ox (Ox=O3+NO2) were 49.2±5.9 ppbv, 26.6±2.8 ppbv, and 60.3±1.9 ppbv, respectively. NOx concentrations decreased linearly at a rate of 3.9±0.5 ppbv/yr after 2002, while ozone concentrations increased at a rate of 1.1±0.5 ppbv/yr in a two-year cycle during 2001–2006, and Ox concentrations remained nearly constant. The reduction of NOx emissions and elevated non-methane hydrocarbon (NMHCs) emissions may have contributed to the increased O3 concentrations in Beijing. When the contributions from Beijings urban and surrounding areas were disaggregated via trajectory cluster analysis, daily maximum and average Ox concentrations attributable to Beijing local emissions increased linearly at rates of 1.3±0.6 ppbv/yr and 0.8±0.6 ppbv/yr, while the Ox concentrations attributable to regional areas decreased linearly at rates of 0.6±0.3 ppbv/yr and 0.5±0.3 ppbv/yr, respectively. The decrease in Ox concentrations of surrounding areas was found to counteract increasing Beijing urban Ox production, leading to nearly constant Ox concentrations in the Beijing region over the study period. Our results may be helpful for redefining government strategies to control the photochemical formation of air pollutants in the Beijing region. Our conclusions have relevance for developing megacities worldwide.

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