A Framework for the Datasets of CRDS CO₂ and CH₄ Stable Carbon Isotope Measurements in the Atmosphere

Abstract

Accessible datasets of greenhouse gas (GHG) concentrations help define long-term trends on a global scale and also provide significant information on the characteristic variability of emission sources and sinks. The integration of stable carbon isotope measurements of carbon dioxide (CO₂) and methane (CH₄) can significantly increase the accuracy and reliability of source apportionment efforts, due to the isotopic fractionation processes and fingerprint that characterize each mechanism. Via isotopic parameters such as δ¹³C, the ratio of ¹³C to ¹²C compared to an international standard (VPDB, Vienna Pee Dee Belemnite), it is in fact possible to discriminate, for example, between thermogenic and microbial sources of CH₄, thus ensuring a more detailed understanding of global balances. A number of stations within the Italian consortium of atmospheric observation sites have been equipped with Picarro G2201-i CRDS (Cavity Ring-Down Spectrometry) analyzers capable of measuring the stable carbon isotopic ratios of CO₂ and CH₄, reported as δ¹³C-CO₂ and δ¹³C-CO₂, respectively. The first dataset (Lamezia Terme, Calabria region) of the consortium resulting from these measurements was released, and a second dataset (Potenza, Basilicata region) from another station was also released, relying on the same format to effectively standardize these new types of datasets. This work provides details on the data, format, and methods used to generate these products and describes a framework for the format and processing of similar data products based on CRD spectroscopy.

Dataset:https://doi.org/10.71763/CNR-IMAA/ITINERIS/CNR-ISAC-LT/CARBON_ISOTOPES_LMT_2024.

Dataset License: CC-BY 4.0

1. Summary

The analysis and evaluation of isotopic fingerprints in greenhouse gases (GHGs) provides an unprecedented detail in the characterization of emission sources, as well as the balance between the emission sources themselves and sinks [1]. Several peculiar phenomena affect the relative abundance of isotopologues, thus allowing their measurement to provide information concerning fractionation mechanisms; for this reason, the isotopic “fingerprint” is frequently used as an effective atmospheric tracer [2,3,4,5,6]. These evaluations consequently allow researchers to better understand global budgets, which are affected by anthropic perturbations of natural balances; the effects may vary based on the characteristic of each parameter, and the study of isotopic trends can provide substantial evidence in favor of the models explaining global variability. For instance, carbon dioxide (CO₂) shows trends and isotopic seasonal patterns consistent with anthropogenic emissions attributable to fossil fuel burning [7], while methane (CH₄) is characterized by alternating global growth rates and an isotopic fingerprint indicating notable contributions from microbial sources of emission such as wetlands, waste, and the agricultural sector [8,9]. Isotopic fingerprints in either parameter are measured as deviations per mille (‰) from an international standard, the Vienna Pee Dee Belemnite (VPDB) [10,11], and are reported as δ¹³C.

In Italy, four atmospheric stations operated by three research institutions have been implemented with stable carbon isotope analyzers of CO₂ and CH₄, specifically the Picarro G2201-i CRDS (Cavity Ring-Down Spectrometry) analyzers. Under the ITINERIS (Italian Integrated Environmental Research Infrastructures System) project, the first dataset of continuous atmospheric measurements of δ¹³CO₂ and δ¹³CH₄ in the country was released, based on six months of measurements performed at the Lamezia Terme (code: LMT) World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) Regional observation site in the region of Calabria, which is fully operated by CNR-ISAC (National Research Council of Italy—Institute of Atmospheric Sciences and Climate), between July and December 2024. Specifically, the measurements were performed from CNR-ISAC’s laboratory (Lat: 38.8774 N; Lon: 16.2364 E; Elev: 10 meters above sea level).

The cross-country consortium does not currently have a calibration procedure to correct data from deviations; a second dataset with an identical format [12], released under project ITINERIS by CNR-IMAA (National Research Council of Italy—Institute of Methodologies for Environmental Analysis) Potenza (code: POT) in the neighboring region of Basilicata, features a few months of observations, falling within the time span recommended by the manufacturer for long term calibration procedures, which is four months of operation [13]. The dataset, which introduced 10-minute averages or “blocks” instead of hourly measurements, was consequently used in the first scientific publication from the national consortium aimed at the assessment of stable carbon isotope variability in CO₂ and CH₄; the site is a tall tower with a sampling height of 104 meters above ground level [14]. Two other atmospheric stations, operated by ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) and CNR-ISAC, are also equipped with G2201-i analyzers [15]. Overall, the national consortium includes Lamezia Terme (LMT, in the region of Calabria), Potenza (POT, Basilicata), Monte Cimone (CMN, Emilia-Romagna), and Lampedusa (LMP, Sicily) [14,15].

The present work illustrates the characteristics of the first dataset, with references to the second product released by the consortium sharing an identical format, in the scope of illustrating the framework used under project ITINERIS for the release of data products based on CRDS analyzer measurements.

2. Data Description

The product is based on the hourly averages and their respective standard deviations of key parameters provided by the instrument and used to characterize the isotopic fingerprints of CO₂ and CH₄. The first measurements became available on 2 July 2024; however, the product has been set to start at 00:00UTC on 1 July, thus making the integration with any other dataset more convenient. The first columns provide clear and specific information concerning each hourly aggregated concentration: date in extended format (DD/MM/YYYY), year, month, weekday, day, and hour. The hour_prog parameter is an integer in the 1–4416 range unambiguously defining each hour elapsed between July and December 2024.

The weekday column, set up to match the Monday to Sunday (MON–SUN) format, has been implemented to associate each day with an integer representative of the specific weekday (e.g., 1 for Monday, 7 for Sunday). This parameter allows us to calculate weekly patterns and may be grouped to create broader weekday (MON–FRI, 1–5) and weekend (SAT–SUN, 6–7) categories, which can in turn be used to evaluate the possible weekly cycles. In fact, unlike many natural and anthropogenic phenomena which are affected by daily, seasonal, and yearly cycles, weekly cycles are of anthropic origin only and can be used to differentiate natural sources from their anthropogenic counterparts.

The Q/A status flags and descriptions are used to provide information concerning the status of the instrument and the validity of its measurements. Details concerning these flags and their characteristics are available in

Table 1. Identifiers of the quality assurance flags used for each hourly data in the product, also include a description. The Data Validity column indicates whether each flag can be associated with validated data. Release status indicates whether the flag is used in readily available datasets or other—work in progress—datasets.

Q/A Status	Description	Data Validity	Release Status
ANTE	No measurement in place	No	Released
OP_NORMAL	Validated data	Yes	Released
OP_EXCLUDED	Data excluded for quality assurance purposes	No	Released
OP_NONAF	Data gathered without a Nafion dryer	Limited	Released
NOOP	Instrument not in operation	No	Released
OP_CALIB	Calibration procedure	Other	Planned
OP_EXPER	Experiment	Other	Planned

. Some of the flags described in the table are not part of presently released data products; however, they are being used in the early versions of additional datasets meant to be released in the future.

ANTE (indicating “before”) is solely limited to the hours elapsed between 00:00UTC on 1 July 2024 and 2 July at 13:00UTC and is meant to optimize the interoperability of the ITINERIS dataset with any other record of hourly data.

OP_NORMAL (“Operating—Normal Conditions”) reflects hourly data which have been measured under optimal conditions and have met sufficient quality assurance criteria. The flag is intended to be used as a filter in evaluations (prior to analysis, only measurements with this status flag should be considered, and adequate filters need to be applied).

OP_EXCLUDED (“Operating—Measurements Excluded”) indicates measurements performed when the instrument was in operation; however, they did not reflect the minimum standards of quality assurance. The flag is commonly applied to hourly data affected by power shortages.

OP_NONAF (“Operating—No Nafion Installed”) is a very specific status flag applied for the first set of measurements at the LMT site, during which the instrument was not equipped with a PERMA PURE Nafion dryer meant to reduce H₂O concentrations and possible interferences with measurements. In Table 1, the validity of this flag is “Limited” as it may still be used in data analysis; however, it needs to be properly differentiated from OP_NORMAL. Once installed, the Nafion dried ambient air prior to measurement by the G2201-i. It is worth mentioning that, according to the manual provided by the manufacturer, the guaranteed spec range is within 0–2.4% H₂O, while the operational range is 0–5% H₂O [13]; although these values are above measured water vapor, measurements without a Nafion have been flagged to allow the end user to differentiate these measurements from standard, dried ambient air data.

NOOP (“Not in Operation”) indicates that no measurements were in place, i.e., that the instrument was not operating at the time. Data having this flag are sporadic across the dataset, with the exception of a prolonged period from 21 October to 29 October, due to an extreme weather event which compromised most instruments used at the LMT observation site.

OP_CALIB (“Operating—Calibration”) is presently not featured in released datasets; however, its use is planned in other products meant to be released in the future. It refers to hourly data characterized by prolonged calibration procedures, i.e., the measurement of reference cylinders for the purpose of correcting instrumental drift. These measurements should not be included in evaluations of external/ambient air, as calibration gas from a standard cylinder is measured during this period.

OP_EXPER (“Operating—Experiment in progress”) is also a planned flag which is presently not used in a released dataset. The flag refers to laboratory experiments and similar circumstances that do not involve calibration standards or comparable materials and should, therefore, be excluded from external/ambient air analysis.

The main parameters of the dataset are differentiated by unit of measurement: concentrations are in ppm (parts per million), while water vapor is reported as percentage, and isotopic deltas are shown as deviations per mille (or per mil) from the VPDB standard. The units reflect the raw output from the instrument. The “_sd” suffix indicates the standard deviations calculated for each hourly average and used as an indicatory of measurement stability and data variability.

Unlike other similar instruments such as the G2210-i, the G2201-i does not provide the mole fractions of CO₂ and CH₄. Therefore, these need to be calculated via the sum of ¹²C and ¹³C isotopologues in either parameter, using the HP (High Precision) variant of CH₄ when available, and dry mole fractions. The HP variant is used for standard atmospheric concentrations of CH₄, when they fall within the 1.8–12 ppm range; the alternate mode, HR (High Dynamic Range), is to be used for concentrations between 10 and 500 ppm [13]. The operating ranges reported by the manufacturer overlap in the 10–12 ppm range. Considering the available CH₄ data record at LMT and considering the strong daily cycles observed at the site due to the influence of wind circulation and local geomorphology, all measurements fall in the HP range, which is the one used in the data product. Consequently, the HP variant of CH₄’s isotopic delta is also used instead of its HR variant. It is worth mentioning that the modular nature of the dataset would allow the future implementation of HR values, adequately flagged with a “HR_” prefix.

The CH4_2201 and CO2_2201 parameters in the dataset, therefore, unambiguously identify the mole fractions measured by the instrument, which are distinct from the isotopic deltas. The “_2201” suffix is used to differentiate the employed G2201-i from other instrument such as the Picarro G2401, which measures carbon dioxide (CO), CO₂, and CH₄ mole fractions in ppm, thus allowing to perform direct comparisons between instruments. These comparisons between G2201 and G2401 measurements of mole fractions have been performed at the POT station via the Bland and Altman methodology [16,17], as described in the work by Buono et al. [14]. The G2401 used for this comparison was characterized by the Integrated Carbon Observation System (ICOS) Atmosphere Thematic Centre (ATC).

Data not suitable for analysis (e.g., NOOP) are marked with a value of –999.999 in the data products. Depending on the program/algorithm, users can conveniently convert these values to NAs.

3. Methods and Results

Raw data have been processed in R to generate the hourly averages and their respective standard deviations, which are used as indicators of data stability. An algorithm was set up to exclude all data not yielding the optimal status flags reported by the manufacturer; measurements affected by temperature and pressure warnings, for example, have been excluded from processing [13]. However, as the instrument was subject to power shortages and similar events falling outside the coverage of UPS (Uninterruptible Power Supply), all hourly data have been quality checked manually and flagged when required, as shown in Table 1.

In the case of the CNR-IMAA dataset featuring measurements performed at the tall tower site of Potenza (code: POT) [12], 10-min aggregates have been generated by implementing an additional R algorithm attributing a value between 1 and 6 to each 10-minute block within a given hour. In this case, the column min10_prog indicates the number of elapsed 10-minute blocks, thus being the equivalent of hour_prog in the first hourly dataset from LMT.

Figure 1 shows two Keeling plots [18], one per chemical parameter (A: CO₂; B: CH₄), computed using the dataset. The plots are intended to provide an insight into the dataset and do not constitute a detailed evaluation of the measurements performed at LMT, which will be addressed in future research papers also accounting from uncertainties in slopes and intercepts. The Keeling plot is an effective method to determine sources of emissions, as the y-axis intercept of regression lines identifies the isotopic fingerprint of the emission source. The x-axis represents the reciprocal of the evaluated parameter (e.g., 1/CO₂ ppm⁻¹), while the y-axis shows its respective delta based on the VPDB scale. Seasons, in this plot, have been defined using the standard trimesters seen in climate and GHG studies, adapted to the coverage of the LMT dataset (Winter: December; Fall: September to November; Summer: July and August). The general outline of these plots is based on the results of Buono et al. [14], which relied on a lm (linear regression model in R). It is worth mentioning that results of Keeling plots are susceptible to fitting methods, as evidenced in the literature [19,20,21,22].

From the plots, it is possible to infer that in CO₂’s case (Figure 1A) the majority of peaks are attributable to a source which is compatible with fossil fuel burning, according to the ranges and values provided by a number of studies on stable carbon isotope analyses [23,24], while CH₄ (Figure 1B), which has a lower atmospheric concentration, i.e., ≈2 ppm compared to CO₂’s ≈ 425 ppm, is characterized by a number of fluctuations in data distribution, which can still allow to pinpoint possible microbial/landfill related emission sources which will be investigated in future research.

4. Perspectives

While datasets of greenhouse gas and aerosol concentrations are commonly used across international networks and research infrastructures, data products resulting from the evaluation of continuous stable isotope measurements are not as common [25,26,27,28,29]. In order to measure the stable carbon isotopes of CO₂ and CH₄, ad hoc instruments and methodologies are required. In the context of the Italian consortium of atmospheric observation sites, which presently involves four stations operated by three institutions (ENEA, CNR-ISAC, CNR-IMAA), the implementation of consistent formats and methodologies employed to generate δ¹³C-CO₂ and δ¹³C-CH₄ datasets has become a tool to propose a framework for the management and processing these measures. The consortium presently lacks calibration standards; however, the modular nature of the datasets and the introduction of new quality assurance flags can effectively integrate calibrations at later stages. A standard format for these measurements, such as the one used in the LMT and POT datasets, also allows a new degree of interoperability which in turn can result in more accurate source apportionment efforts, possibly aimed at various scales, based on the characteristics of each observation site [30,31]. The data format and products described in this work, therefore, constitute the fundament upon which future research on stable carbon isotope analyses in Italy will be performed.