Temporal variations in CO4 and CO concentrations in Saint-Petersburg suburb (Peterhof)

The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident’s outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In order to take the periodicity of pollutants into empirical air quality forecasting models, this study evaluates the temporal variations of air pollutants and develops a novel sequence to sequence model with weekly periodicity to forecast air quality. Two-year observation data from Shanghai roadside air quality monitoring stations are employed to support analyzing and modeling. The results conclude that the fine particulate matter (PM2.5) and carbon monoxide (CO) concentrations show obvious daily and weekly variations, and the temporal patterns are nearly consistent with the periodicity of traffic flow in Shanghai. Compared with PM2.5, the CO concentrations are more affected by traffic variation. The proposed model outperforms the baseline model in terms of accuracy, and presents a higher linear consistency in PM2.5 prediction and lower errors in CO prediction. This study could assist environmental researchers to further improve the technologies for urban air quality forecasting, and serve as tools for supporting policymakers to implement related traffic management and emission control policies.

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Evaluation of UTLS carbon monoxide simulations in GMI and GEOS-Chem chemical transport models using Aura MLS observations

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-5641-2016 ◽

2016 ◽

Vol 16 (9) ◽

pp. 5641-5663 ◽

Cited By ~ 6

Author(s):

Lei Huang ◽

Jonathan H. Jiang ◽

Lee T. Murray ◽

Megan R. Damon ◽

Hui Su ◽

...

Keyword(s):

Carbon Monoxide ◽

Chemical Transport ◽

Temporal Variations ◽

Vertical Transport ◽

Spatial Distributions ◽

Transport Models ◽

Chemical Transport Models ◽

The Tropics ◽

The Impact ◽

Co Concentrations

Abstract. This study evaluates the distribution and variation of carbon monoxide (CO) in the upper troposphere and lower stratosphere (UTLS) during 2004–2012 as simulated by two chemical transport models, using the latest version of Aura Microwave Limb Sounder (MLS) observations. The simulated spatial distributions, temporal variations and vertical transport of CO in the UTLS region are compared with those observed by MLS. We also investigate the impact of surface emissions and deep convection on CO concentrations in the UTLS over different regions, using both model simulations and MLS observations. Global Modeling Initiative (GMI) and GEOS-Chem simulations of UTLS CO both show similar spatial distributions to observations. The global mean CO values simulated by both models agree with MLS observations at 215 and 147 hPa, but are significantly underestimated by more than 40 % at 100 hPa. In addition, the models underestimate the peak CO values by up to 70 % at 100 hPa, 60 % at 147 hPa and 40 % at 215 hPa, with GEOS-Chem generally simulating more CO at 100 hPa and less CO at 215 hPa than GMI. The seasonal distributions of CO simulated by both models are in better agreement with MLS in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH), with disagreements between model and observations over enhanced CO regions such as southern Africa. The simulated vertical transport of CO shows better agreement with MLS in the tropics and the SH subtropics than the NH subtropics. We also examine regional variations in the relationships among surface CO emission, convection and UTLS CO concentrations. The two models exhibit emission–convection–CO relationships similar to those observed by MLS over the tropics and some regions with enhanced UTLS CO.

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Evaluation of UTLS carbon monoxide simulations in GMI and GEOS-Chem chemical transport models using Aura MLS observations

10.5194/acp-2016-111 ◽

2016 ◽

Author(s):

Lei Huang ◽

Jonathan H. Jiang ◽

Lee Murray ◽

Megan Damon ◽

Hui Su ◽

...

Keyword(s):

Carbon Monoxide ◽

Chemical Transport ◽

Temporal Variations ◽

Vertical Transport ◽

Spatial Distributions ◽

Transport Models ◽

Chemical Transport Models ◽

The Tropics ◽

The Impact ◽

Co Concentrations

Abstract. This study evaluates the distribution and variation of carbon monoxide (CO) in the upper troposphere and lower stratosphere (UTLS) during 2004–2012 as simulated by two chemical transport models, using the latest version of Aura Microwave Limb Sounder (MLS) observations. The simulated spatial distributions, temporal variations and vertical transport of CO in the UTLS region are compared with those observed by MLS. We also investigate the impact of surface emissions and deep convection on CO concentrations in the UTLS over different regions, using both model simulations and MLS observations. Global Modeling Initiative (GMI) and GEOS-Chem simulations of UTLS CO both show similar spatial distributions to observations. The global mean CO values simulated by both models agree with MLS observations at 215 hPa and 147 hPa, but are significantly underestimated (> 40 %) at 100 hPa. In addition, the models underestimate the peak CO values by up to 70 % at 100 hPa, 60 % at 147 hPa, and 40 % at 215 hPa, with GEOS-Chem generally simulating more CO at 100 hPa and less CO at 215 hPa than GMI. The seasonal distributions of CO simulated by both models are in better agreement with MLS in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH), with disagreements between model and observations over some enhanced CO regions such as southern Africa. The simulated vertical transport of CO shows better agreement with MLS in the tropics and SH subtropics than NH subtropics. We also examine regional variations in the relationships among surface CO emission, convection and UTLS CO concentrations. The two models exhibit emission-convection-CO relationships similar to those observed by MLS over the tropics and some regions with enhanced UTLS CO.

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Temporal variations of CH4/CO2/CO fluxes in the central Amazon rainforest

10.5194/egusphere-egu2020-16019 ◽

2020 ◽

Author(s):

Shujiro Komiya ◽

Jost Lavric ◽

David Walter ◽

Santiago Botia ◽

Alessandro Araujo ◽

...

Keyword(s):

Amazon Basin ◽

Temporal Variations ◽

Dry Season ◽

Amazon Rainforest ◽

Eddy Covariance Method ◽

Co Concentration ◽

Dry Seasons ◽

Co Concentrations ◽

Wet And Dry Seasons

Amazon rainforests and soils contain large amounts of carbon, which is under pressure from ongoing climate and land use change in the Amazon basin. It is estimated that methane (CH4), an important greenhouse gas, is largely released from the flooded wetlands of the Amazon, but the trends and balances of CH4 in the Amazon rainforest are not yet well understood. In addition, the change in atmospheric CH4 concentration is strongly associated with a change in carbon monoxide (CO) concentration, often caused by the human-induced combustion of biomass that usually peaks during dry season. Understanding the long-term fluctuations in the fluxes of greenhouse gases in the Amazon rainforest is essential for improving our understanding of the carbon balance of the Amazon rainforest.Since March 2012, we have continuously measured atmospheric CO2/CH4/CO concentrations at five levels (79, 53, 38, 24, and 4 m a.g.l.) using two wavelength-scanned cavity ring-down spectroscopy analyzers (G1301 and G1302, Picarro Inc., USA), which are automatically calibrated on site every day. In addition, we measured the CO2 flux by the eddy covariance method at the same tower. We estimated the CO2/CH4/CO fluxes by combining the vertical profile of the CO2/CH4/CO concentrations with the flux gradient method. Our results generally show no major difference in CO2 flux between the wet and dry seasons except for year 2017, when an elevated CO2 uptake was documented during the dry season despite the lowest precipitation between 2014 and 2018. The CH4 flux showed the largest CH4 emission during the dry season in year 2016. Further results will be analyzed and discussed in the presentation.

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