scholarly journals Supplementary material to "Reanalysis of and attribution to near-surface ozone concentrations in Sweden during 1990–2013"

2017 ◽  
Author(s):  
Camilla Andersson ◽  
Heléne Alpfjord ◽  
Lennart Robertson ◽  
Per Erik Karlsson ◽  
Magnuz Engardt
Keyword(s):  
Surface Ozone ◽  
Near Surface ◽  
Ozone Concentrations ◽  
Supplementary Material

scholarly journals A numerical process study on the rapid transport of stratospheric air down to the surface over western North America and the Tibetan Plateau

2019 ◽  
Vol 19 (9) ◽  
pp. 6535-6549 ◽  
Author(s):  
Bojan Škerlak ◽  
Stephan Pfahl ◽  
Michael Sprenger ◽  
Heini Wernli
Keyword(s):  
Boundary Layer ◽  
Tibetan Plateau ◽  
Surface Ozone ◽  
Vertical Transport ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Air Mass ◽  
Ozone Concentrations ◽  
Rapid Transport ◽  
Upper Level

Abstract. Upper-level fronts are often associated with the rapid transport of stratospheric air along tilted isentropes to the middle or lower troposphere, where this air leads to significantly enhanced ozone concentrations. These plumes of originally stratospheric air can only occasionally be observed at the surface because (i) stable boundary layers prevent an efficient vertical transport down to the surface, and (ii) even if boundary layer turbulence were strong enough to enable this transport, the originally stratospheric air mass can be diluted by mixing, such that only a weak stratospheric signal can be recorded at the surface. Most documented examples of stratospheric air reaching the surface occurred in mountainous regions. This study investigates two such events, using a passive stratospheric air mass tracer in a mesoscale model to explore the processes that enable the transport down to the surface. The events occurred in early May 2006 in the Rocky Mountains and in mid-June 2006 on the Tibetan Plateau. In both cases, a tropopause fold associated with an upper-level front enabled stratospheric air to enter the troposphere. In our model simulation of the North American case, the strong frontal zone reaches down to 700 hPa and leads to a fairly direct vertical transport of the stratospheric tracer along the tilted isentropes to the surface. In the Tibetan Plateau case, however, no near-surface front exists and a reservoir of high stratospheric tracer concentrations initially forms at 300–400 hPa, without further isentropic descent. However, entrainment at the top of the very deep boundary layer (reaching to 300 hPa over the Tibetan Plateau) and turbulence within the boundary layer allows for downward transport of stratospheric air to the surface. Despite the strongly differing dynamical processes, stratospheric tracer concentrations at the surface reach peak values of 10 %–20 % of the imposed stratospheric value in both cases, corroborating the potential of deep stratosphere-to-troposphere transport events to significantly influence surface ozone concentrations in these regions.

scholarly journals IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany

2021 ◽  
Vol 14 (1) ◽  
pp. 1-25
Author(s):  
Felix Kleinert ◽  
Lukas H. Leufen ◽  
Martin G. Schultz
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Surface Ozone ◽  
Training Data ◽  
Horizontal Wind ◽  
High Exposure ◽  
Near Surface ◽  
Reference Models ◽  
Ozone Concentrations ◽  
Single Station

Abstract. The prediction of near-surface ozone concentrations is important for supporting regulatory procedures for the protection of humans from high exposure to air pollution. In this study, we introduce a data-driven forecasting model named “IntelliO3-ts”, which consists of multiple convolutional neural network (CNN) layers, grouped together as inception blocks. The model is trained with measured multi-year ozone and nitrogen oxide concentrations of more than 300 German measurement stations in rural environments and six meteorological variables from the meteorological COSMO reanalysis. This is by far the most extensive dataset used for time series predictions based on neural networks so far. IntelliO3-ts allows the prediction of daily maximum 8 h average (dma8eu) ozone concentrations for a lead time of up to 4 d, and we show that the model outperforms standard reference models like persistence models. Moreover, we demonstrate that IntelliO3-ts outperforms climatological reference models for the first 2 d, while it does not add any genuine value for longer lead times. We attribute this to the limited deterministic information that is contained in the single-station time series training data. We applied a bootstrapping technique to analyse the influence of different input variables and found that the previous-day ozone concentrations are of major importance, followed by 2 m temperature. As we did not use any geographic information to train IntelliO3-ts in its current version and included no relation between stations, the influence of the horizontal wind components on the model performance is minimal. We expect that the inclusion of advection–diffusion terms in the model could improve results in future versions of our model.

Trends in near-surface ozone concentrations in Switzerland: the 1990s

2002 ◽  
Vol 36 (17) ◽  
pp. 2841-2852 ◽  
Author(s):  
Stefan Brönnimann ◽  
Brigitte Buchmann ◽  
Heinz Wanner
Keyword(s):  
Surface Ozone ◽  
Near Surface ◽  
Ozone Concentrations

scholarly journals IntelliO3-ts v1.0: A neural network approach to predict near-surface ozone concentrations in Germany

2020 ◽  
Author(s):  
Felix Kleinert ◽  
Lukas H. Leufen ◽  
Martin G. Schultz
Keyword(s):  
Time Series ◽  
Surface Ozone ◽  
Training Data ◽  
Horizontal Wind ◽  
Daily Maximum ◽  
High Exposure ◽  
Near Surface ◽  
Reference Models ◽  
Ozone Concentrations ◽  
Single Station

Abstract. The prediction of near-surface ozone concentrations is important to support regulatory procedures for the protection of humans from high exposure to air pollution. In this study, we introduce a data-driven forecasting model named IntelliO3-ts, which consists of multiple convolutional neural layers (CNN), grouped together as inception blocks. The model is trained with measured multi-year ozone and nitrogen oxides concentrations of more than 300 German measurement stations in rural environments, and six meteorological variables from the meteorological COSMO reanalysis. This is by far the most extensive dataset used for time series predictions based on neural networks so far. IntelliO3-ts allows predicting daily maximum 8-hour average (dma8eu) ozone concentrations for a lead time of up to four days, and we show that the model outperforms standard reference models like persistence. Moreover, we demonstrate that IntelliO3-ts outperforms climatological reference models for the first two days, while it does not add any genuine value for longer lead times. We attribute this to the limited deterministic information that is contained in the single station time series training data. We applied a bootstrapping technique to analyse the influence of different input variables and found, that the previous day ozone concentrations are of major importance, followed by 2 m temperature. As we did not use any geographic information to train IntelliO3-ts in its current version and included no relation between stations, the influence of the horizontal wind components on the model performance is minimal. We expect that the inclusion of advection-diffusion terms in the model could improve results in future versions of our model.

scholarly journals A numerical process study on the rapid transport of stratospheric air down to the surface over western North America and the Tibetan Plateau

2018 ◽  
Author(s):  
Bojan Škerlak ◽  
Stephan Pfahl ◽  
Michael Sprenger ◽  
Heini Wernli
Keyword(s):  
Boundary Layer ◽  
Tibetan Plateau ◽  
Surface Ozone ◽  
Vertical Transport ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Air Mass ◽  
Ozone Concentrations ◽  
Rapid Transport ◽  
Upper Level

Abstract. Upper-level fronts are often associated with the rapid transport of stratospheric air along tilted isentropes to the middle or lower troposphere, there leading to significantly enhanced ozone concentrations. Only occasionally these plumes of originally stratospheric air can be observed at the surface, because (i) stable boundary layers prevent an efficient vertical transport down to the surface, and (ii) if boundary layer turbulence is strong enough to enable this transport, the originally stratospheric air mass is strongly diluted by mixing such that only a weak stratospheric signal can be recorded at the surface. Most documented examples of stratospheric air reaching the surface are from mountainous regions. This study investigates two such events, using a passive stratospheric air mass tracer in a mesoscale model to explore the processes that enable the transport down to the surface. The events occurred in early May 2006 in the Rocky Mountains and in mid June 2006 on the Tibetan Plateau. In both cases, a tropopause fold associated with an upper-level front enabled stratospheric air to enter the troposphere. In our model simulation of the North American case, the strong frontal zone reaches down to 700 hPa and leads to a fairly direct vertical transport of the stratospheric tracer along the tilted isentropes to the surface. In the Tibetan Plateau case, however, no near-surface front exists and a reservoir of high stratospheric tracer concentrations forms at 300–400 hPa, without further isentropic descent. Entrainment at the top of the very deep boundary layer (reaching to 300 hPa over the Tibetan Plateau) and turbulence within the boundary layer fosters downward transport of stratospheric air to the surface. Interestingly, despite the strongly differing dynamical processes, stratospheric tracer concentrations at the surface reach peak values of 10–20 % in both cases, corroborating the potential of deep stratosphere-to-troposphere transport events to significantly influence surface ozone concentrations in these regions.

Temporal patterns and trends of surface ozone concentrations over Portugal

2021 ◽  
Author(s):  
Carla Gama ◽  
Alexandra Monteiro ◽  
Myriam Lopes ◽  
Ana Isabel Miranda
Keyword(s):  
Air Quality ◽  
Iberian Peninsula ◽  
Human Health ◽  
Temporal Variability ◽  
Urban Areas ◽  
Surface Ozone ◽  
Quality Data ◽  
Ozone Concentrations ◽  
Long Term Study

<p>Tropospheric ozone (O<sub>3</sub>) is a critical pollutant over the Mediterranean countries, including Portugal, due to systematic exceedances to the thresholds for the protection of human health. Due to the location of Portugal, on the Atlantic coast at the south-west point of Europe, the observed O<sub>3</sub> concentrations are very much influenced not only by local and regional production but also by northern mid-latitudes background concentrations. Ozone trends in the Iberian Peninsula were previously analysed by Monteiro et al. (2012), based on 10-years of O<sub>3</sub> observations. Nevertheless, only two of the eleven background monitoring stations analysed in that study are located in Portugal and these two stations are located in Porto and Lisbon urban areas. Although during pollution events O<sub>3</sub> levels in urban areas may be high enough to affect human health, the highest concentrations are found in rural locations downwind from the urban and industrialized areas, rather than in cities. This happens because close to the sources (e.g., in urban areas) freshly emitted NO locally scavenges O<sub>3</sub>. A long-term study of the spatial and temporal variability and trends of the ozone concentrations over Portugal is missing, aiming to answer the following questions:</p><p>-           What is the temporal variability of ozone concentrations?</p><p>-           Which trends can we find in observations?</p><p>-           How were the ozone spring maxima concentrations affected by the COVID-19 lockdown during spring 2020?</p><p>In this presentation, these questions will be answered based on the statistical analysis of O<sub>3</sub> concentrations recorded within the national air quality monitoring network between 2005 and 2020 (16 years). The variability of the surface ozone concentrations over Portugal, on the timescales from diurnal to annual, will be presented and discussed, taking into account the physical and chemical processes that control that variability. Using the TheilSen function from the OpenAir package for R (Carslaw and Ropkins 2012), which quantifies monotonic trends and calculates the associated p-value through bootstrap simulations, O<sub>3</sub> concentration long-term trends will be estimated for the different regions and environments (e.g., rural, urban).  Moreover, taking advantage of the unique situation provided by the COVID-19 lockdown during spring 2020, when the government imposed mandatory confinement and citizens movement restriction, leading to a reduction in traffic-related atmospheric emissions, the role of these emissions on ozone levels during the spring period will be studied and presented.</p><p> </p><p>Carslaw and Ropkins, 2012. Openair—an R package for air quality data analysis. Environ. Model. Softw. 27-28,52-61. https://doi.org/10.1016/j.envsoft.2011.09.008</p><p>Monteiro et al., 2012. Trends in ozone concentrations in the Iberian Peninsula by quantile regression and clustering. Atmos. Environ. 56, 184-193. https://doi.org/10.1016/j.atmosenv.2012.03.069</p>

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