Statistical Analysis of the CO2 and CH4 Annual Cycle on the Northern Plateau of the Iberian Peninsula

Pérez, Isidro A.; Sánchez, M. Luisa; García, M. Ángeles; Pardo, Núria; Fernández-Duque, Beatriz

doi:10.3390/atmos11070769

Cited by 6 publications

(5 citation statements)

References 52 publications

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“…The measured average concentrations for atmospheric CO 2 ranged from 380 to 440 ppm with a mean of 412 ± 3.1 ppm for the vegetation season; and the maximum concentrations for CO 2 were observed in the heating season, in the range of 410-470 ppm with a mean of about 430 ± 3.8 ppm in winter (Figure 3). Thus, the derived seasonal variation in atmospheric CO 2 concentration reaches up to 25-30 ppm, which is consistent with the majority of previous studies [63][64][65][66].…”

Section: Summary Of the Measured Co 2 Concentrationssupporting

confidence: 92%

GIS-Based Approach to Spatio-Temporal Interpolation of Atmospheric CO2 Concentrations in Limited Monitoring Dataset

et al. 2021

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Understanding the magnitude and distribution of the mixes of the near-ground carbon dioxide (CO2) components spatially (related to the surface characteristics) and temporally (over seasonal timescales) is critical to evaluating present and future climate impacts. Thus, the application of in situ measurement approaches, combined with the spatial interpolation methods, will help to explore variations in source contribution to the total CO2 mixing ratios in the urban atmosphere. This study presents the spatial characteristic and temporal trend of atmospheric CO2 levels observed within the city of Wroclaw, Poland for the July 2017–August 2018 period. The seasonal variability of atmospheric CO2 around the city was directly measured at the selected sites using flask sampling with a Picarro G2201-I Cavity Ring-Down Spectroscopy (CRDS) technique. The current work aimed at determining the accuracy of the interpolation techniques and adjusting the interpolation parameters for estimating the magnitude of CO2 time series/seasonal variability in terms of limited observations during the vegetation and non-vegetation periods. The objective was to evaluate how different interpolation methods will affect the assessment of air pollutant levels in the urban environment and identify the optimal sampling strategy. The study discusses the schemes for optimization of the interpolation results that may be adopted in areas where no observations are available, which is based on the kriging error predictions for an appropriate spatial density of measurement locations. Finally, the interpolation results were extended regarding the average prediction bias by exploring additional experimental configurations and introducing the limitation of the future sampling strategy on the seasonal representation of the CO2 levels in the urban area.

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Section: Summary Of the Measured Co 2 Concentrationssupporting

confidence: 92%

GIS-Based Approach to Spatio-Temporal Interpolation of Atmospheric CO2 Concentrations in Limited Monitoring Dataset

et al. 2021

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“…However, even for the simplest cycles, the one harmonic evolution may be considered as an initial approximation, since a six-month period between the maximum and the minimum is difficult to achieve [48,49]. Pérez et al [19] tried different combinations of four harmonics and recommended using the first and second harmonics as a simple enough and complete enough equation to accurately describe CO 2 and CH 4 annual evolution. Moreover, the CO 2 cycle was already fitted by a two-harmonic function and kernel calculations by Pérez et al [50].…”

Section: Fitting Of Daily and Annual Cyclesmentioning

confidence: 99%

“…Finally, the last part of this analysis considers the daily and annual cycles of CO 2 and CH 4 . Although these cycles have previously been described [18,19], their response to wind speed merits a detailed study. Two procedures, one parametric and another nonparametric, are used to smooth and investigate the two cycles.…”

Section: Introductionmentioning

confidence: 99%

Influence of Wind Speed on CO2 and CH4 Concentrations at a Rural Site

Pérez

García

Sánchez

et al. 2021

IJERPH

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Meteorological variables have a noticeable impact on pollutant concentrations. Among these variables, wind speed is typically measured, although research into how pollutants respond to it can be improved. This study considers nine years of hourly CO2 and CH4 measurements at a rural site, where wind speed values were calculated by the METEX model. Nine wind speed intervals are proposed where concentrations, distribution functions, and daily as well as annual cycles are calculated. Contrasts between local and transported concentrations are around 5 and 0.03 ppm for CO2 and CH4, respectively. Seven skewed distributions are applied, and five efficiency criteria are considered to test the goodness of fit, with the modified Nash–Sutcliffe efficiency proving to be the most sensitive statistic. The Gumbel distribution is seen to be the most suitable for CO2, whereas the Weibull distribution is chosen for CH4, with the exponential function being the worst. Finally, daily and annual cycles are analysed, where a gradual decrease in amplitude is observed, particularly for the daily cycle. Parametric and nonparametric procedures are used to fit both cycles. The latter gave the best fits, with the agreement being higher for the daily cycle, where evolution is smoother than for the annual cycle.

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“…Better fits were found for CO2 data as well as for rural data. (Pérez et al, 2020). More specifically, the highest CH4 mixing ratio values reported at the CIBA station are partially due to the greater sensitivity from CH4 emission sources at the monitoring station.…”

Section: Validation Indicators (I II Iii Iv)mentioning

confidence: 89%

“…As regards CO2 and CH4 seasonal behaviour, it should be noted that the lowest mixing ratios for both greenhouse gases were found in the summer season. The minimum mixing ratio values occurred when hemispheric CO2 uptake draws down global CO2 mixing ratios and when CH4 is oxidised by OH radicals through the hemisphere (Cheng et al, 2019;Pérez et al, 2020). Al-Anzi et al (2016) also explained this behaviour because of the higher planetary boundary layer that reinforces pollutant transport and the dispersion process.…”

Section: Co2 and Ch4 Seasonal Evolutionmentioning

confidence: 99%

Analysis of CO2 and CH4 temporal patterns and the Valladolid urban plume influence over the upper Spanish plateau

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1.2. Resumen 5 2. Introduction 9 2.1. An overview 9 2.2. CO2 2.3. CH4 2.4. The importance of accurate CO2 and CH4 mixing ratio measurements 2.5. CIBA station characteristics 2.6. Time series 2.7. Valladolid urban plume analysis 2.8. Motivation of the study 3. Objectives 3.1. General objective 3.2. Specific objectives 4. Material and methods 4.1. Site description 4.2. Monitoring station 4.3. Instrumentation 4.4. Database 4.5. Software employed 4.6. Mathematical equations used 4.7. Statistical techniques 4.8. Graphical summary techniques 4.9. Validation methods 5. List of Original Contributions 6. Original Contributions 6.1. Harmonic function for describing CO2 and CH4 temporal patterns: Original Contribution I 6.2. Kernel functions for describing CO2 and CH4 temporal patterns: Original Contribution II 6.3. Local regression functions for describing CO2 and CH4 temporal patterns: Original Contribution III 101 6.4. Valladolid urban plume influence on the final CO2 and CH4 mixing ratios at CIBA: Original Contribution IV 115 7. General results 133 7.1. CO2 and CH4 temporal analysis 133 7.2. Back-trajectory analysis 136 II 7.3. Validation indicators 8. General discussion 8.1. Temporal pattern evolution 8.2. Mathematical equations for analysing temporal patterns 8.3. Valladolid urban plume analysis 9. Conclusions / Conclusiones 9.1. Conclusions 9.2. Conclusiones 10. References 11. Appendices 11.1. Acronyms 11.2. List of tables and figures 2.1.1.1. The Intergovernmental Panel on Climate Change (IPCC) In 1988 the World Meteorological Organization (WMO) and the United Nations EnvironmentProgram (UNEP) created the Intergovernmental Panel on Climate Change (IPCC) to analyse the problem of global climate change. The IPCC is a group open to all members of the United Nations and WMO (IPCC, 2020a). The function of the IPCC is not to conduct its own research but to analyse, in a comprehensive, objective, open and transparent way, the relevant scientific, technical and socioeconomic information required to understand the scientific elements involved in climate change caused by human activities, its possible repercussions and the possibilities of attenuation and/or adaptation (IPCC, 2020a). In addition, through their evaluations, the IPCC seeks to identify the strength of scientific agreement in different areas and indicates where further research is needed. The IPCC is made up of three different working In 2015, the 21 st Conference of the Parties (COP 21) of the United Nations Framework on Climate Change (UNFCCC) took place in Paris, ending with the adoption of the Paris Agreement by the 195 UNFCCC countries (Boffino et al., 2019). The Paris Agreement establishes the global framework for combating climate change from 2020, promoting a transition towards a low-emission economy in the fight against climate change (Boffino et al., 2019; Pauw et al., 2019). This agreement establishes a plan to reduce emissions of CO2 and other greenhouse gases, aiming to keep global temperature increases to below 2ºC relative to 2.3.1. The CH4 cycle A...

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Statistical Analysis of the CO2 and CH4 Annual Cycle on the Northern Plateau of the Iberian Peninsula

Cited by 6 publications

References 52 publications

GIS-Based Approach to Spatio-Temporal Interpolation of Atmospheric CO2 Concentrations in Limited Monitoring Dataset

GIS-Based Approach to Spatio-Temporal Interpolation of Atmospheric CO2 Concentrations in Limited Monitoring Dataset

Influence of Wind Speed on CO2 and CH4 Concentrations at a Rural Site

Analysis of CO2 and CH4 temporal patterns and the Valladolid urban plume influence over the upper Spanish plateau

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