Investigation of Carbon Monoxide, Carbon Dioxide, and Methane Source Variability at the WMO/GAW Station of Lamezia Terme (Calabria, Southern Italy) Using the Ratio of Ozone to Nitrogen Oxides as a Proximity Indicator

Abstract

In the field of Atmospheric Sciences, source apportionment and a more detailed understanding of local and remote contributions to observed concentrations of greenhouse gases (GHGs) across international networks, such as the World Meteorological Organization—Global Atmosphere Watch (WMO/GAW), can be achieved via the implementation of new atmospheric tracers. One tool for achieving a more precise understanding of GHG emissions is the evaluation of air mass aging indicators, which can serve as proximity indicators. In this study, the ratio between ozone (O₃) and nitrogen oxides (NO_x) is applied to nine continuous years (2015–2023) of measurements at the Lamezia Terme (LMT) observation site in Calabria, Southern Italy, to differentiate the aging of air masses and identify four distinct categories: LOC (local), N–SRC (near source), R–SRC (remote source), and BKG (atmospheric background). Due to possible overestimation of nitrogen dioxide (NO₂) caused by heated (~300–400 °C) molybdenum converters used in the employed instruments, a correction factor based on a previous study has been integrated to further analyze the results. Additionally, this work introduces a second correction factor based on the local behavior of surface ozone and the diurnal peaks observed during boreal warm seasons in an area characterized by a Mediterranean climate. The results of this study confirm the hypotheses of previous works on local sources of pollution: the LOC category yields the highest concentrations observed at the site, which are in accordance with the northeastern wind sector and anthropogenic sources. R–SRC and BKG are more representative of atmospheric background levels and characterize westerly winds from the Tyrrhenian Sea. N–SRC, as expected, shows an intermediate behavior between local and remote/background levels. Differences in results between standard O₃/NO_x categories and corrected measurements will need to be investigated in future studies.

1. Introduction

In the field of Atmospheric Sciences, for decades, source apportionment and the evaluation of atmospheric tracers aimed at specific sources of emission have been important tools in better understanding atmospheric composition and its changes over time [1,2,3,4,5,6,7,8]. The atmosphere is a complex medium, and the lowermost layer is characterized by turbulent motion and kinetic interaction with the surface, which result in changes in the transport and concentration of greenhouse gases (GHGs) and aerosols [9,10,11,12,13,14,15]. In addition to these complexities, air masses can retain characteristics representative of their “aging”, i.e., whether they are fresh or linked to remote emission sources [16,17,18]. The findings of Parrish et al. (2009) [19] and Morgan et al. (2010) [20] demonstrated that the ratio between ozone (O₃) and nitrogen oxides (NO + NO₂ = NO_x) could be used as a “proximity indicator” to differentiate local emissions from remote/background outputs. A high O₃/NO_x ratio would be attributable to aged air masses, while a low ratio would be linked to local sources of emissions. In the atmosphere, NO_x are an important control factor over O₃ [21,22,23,24,25,26,27,28] and are generally linked to anthropogenic sources such as fossil/biomass burning and fertilizer use in agricultural activities [29,30,31,32,33,34]. O₃ has both anthropogenic and natural origins [35,36] and is divided into two categories: stratospheric O₃, which is beneficial for Earth’s ecosystem due to its capacity to partially screen the surface from solar ultraviolet (UV) radiation [37,38,39], and tropospheric O₃, which poses health and environmental hazards [40,41,42,43,44]. This information, combined with additional atmospheric tracers and indicators of specific emission sources, can provide an accurate understanding of anthropogenic and natural emissions, which in turn can further improve regulations and mitigation strategies at local-to-global scales [45,46,47,48].

Prior to these findings, a study by Steinbacher et al. (2007) [49] demonstrated that heated molybdenum converters (~300–400 °C) used in instruments commonly employed for NO_x measurements can significantly overestimate (~50%) observed NO₂ mole fractions. Specifically, other nitrogen species such as nitric acid (HNO₃), nitrous acid (HONO), nitric acid anhydride (N₂O₅), ethyl nitrate (C₂H₅NO₃), and peroxyacetyl nitrate (C₂H₃NO₅, or PAN) can interfere with regular NO₂ measurements and provide inaccurate readings. In the presence of oxygen (O₂), NO₂ is reduced to NO, and the converter’s surface is oxidized to molybdenum trioxide (MoO₃) or dioxide (MoO₂). Over time, many studies have highlighted the possibility of organic nitrates being reduced to NO under specific conditions [50,51,52,53]. This interference is believed to be amplified in aged air masses, characterized by a high O₃/NO_x ratio [49]. In the literature, the issues posed by the measurement of “true NO_x” concentrations have been the subject of various papers focused on different methodologies and ways to determine the extent of uncertainties in NO_x [54,55,56,57]; these studies are often based on comparisons between different instruments and methods designed to test errors in measurements caused by heated molybdenum converters and similar devices [58,59].

At the Lamezia Terme (code: LMT) WMO/GAW (World Meteorological Organization—Global Atmosphere Watch) observation site in Calabria, Southern Italy, an early study based on preliminary data gathered at the observatory relied on O₃/NO_x ratio thresholds as proximity indicators to differentiate local from remote sources of emissions and also accounted for possible NO₂ overestimation by applying a specific correction factor [60]. In this study, nine years of continuous measurements (2015–2023) have been evaluated to further expand the knowledge gained from preliminary findings and provide the most accurate differentiation between local, intermediate, and remote sources ever performed at the site. This study also proposes a new correction factor based on the local behavior of O₃ at LMT, as described in D’Amico et al. (2024d) [61]. Nine years (2015–2023) of measurements have characterized O₃ at LMT and demonstrated the effect of photochemical activity on warm season peaks in the central Mediterranean region, which are observed from the western sector of LMT. The methodologies used in previous research, combined with those introduced in this study for the first time, are aimed not only at the evaluation of source variability at the LMT site but are also meant to provide new tools for the evaluation of data gathered at sites with similar characteristics.

Three compounds will be evaluated in this paper based on proximity categories: carbon monoxide (CO), carbon dioxide (CO₂), and methane (CH₄). CH₄ was the subject of a detailed multi-year (2016–2022) study assessing its variability and patterns [62], while CO and CO₂ have been assessed for their multi-year variability, primarily with respect to weekly cycles and the influence of anthropogenic activities on seasonal concentrations [63].

Due to its nature as a combustion byproduct, CO is an ideal target gas for assessing anthropogenic emissions, especially those attributable to domestic heating and fuel burning [64,65,66]. Its short lifetime in the atmosphere (~60 days) [67] allows it to be used, in conjunction with the proximity indicators employed in this paper, as an effective tracer of local anthropogenic emissions. The short atmospheric lifetime of CO is expected to clearly differentiate between fresh and aged air masses, as the latter would be significantly depleted in this compound. It is worth mentioning that CO is not a greenhouse gas per se, as it does not contribute to Earth’s radiative forcing perturbation; however, it plays an important role in atmospheric chemistry via its effects on other compounds that do have an impact on the global climate [68,69]. This compound has been on the rise for decades [70], but emission mitigation policies and the optimization of engines have managed to counterbalance this effect [71]; major wildfires, however, are deemed responsible for recent increases in atmospheric CO concentrations [72,73].

CO₂ is the key driver of anthropogenic climate change [74,75,76,77,78], which is amplified by a long atmospheric lifetime (~1.000 years) [79]. Fossil fuel burning is the primary source of anthropogenic CO₂ [80], and the mitigation of these emissions is one of the most notable challenges in climate change policies and regulations. Due to its long lifetime, observed CO₂ may not be susceptible to major changes between evaluated proximity categories, as both aged and fresh air masses would be characterized by similar CO₂ concentrations. However, via proximity categories, a differentiation between fresh anthropogenic outputs and the atmospheric background—which is susceptible to global anthropogenic emissions—should still be possible. Atmospheric CO₂ has been on the rise for decades due to anthropogenic emissions; observatories such as the Mauna Loa site operated by NOAA (National Oceanic and Atmospheric Administration) provide accurate information on the upward trend caused by anthropic activities [81,82].

With an intermediate lifetime between CO and CO₂, CH₄ (~10 years) [83,84,85] is characterized by a Global Warming Potential (GWP) that is nearly two orders of magnitude higher than that of CO₂ [86]. CH₄ is a combustion byproduct [87,88,89] but is most notably released by wetlands and other natural sources [90]. Livestock farming and landfills are among the key anthropogenic emissions of this compound [91]. Like CO₂, CH₄ is characterized by an upward trend driven by anthropogenic emissions; however, its mechanisms and chemistry are affected by an articulated balance of sources and sinks. In fact, the analysis of stable carbon isotopes has allowed for the determination of a shift in the biogenic/anthropogenic balance of atmospheric CH₄ [92]. A multi-year study based on seven years of continuous measurements (2016–2022), described in greater detail the behavior of this compound at the Lamezia Terme (LMT) site [62]; CH₄ concentrations are lower in the western seaside sector and higher in the northeastern continental sector due to the influence of anthropogenic sources. Furthermore, the study demonstrated the occurrence of a Hyperbola Branch Pattern (HBP) linked to the northeastern sector; CH₄ concentrations tend to be higher in the presence of low wind speeds and vice versa, while high wind speeds are generally linked to low CH₄ concentrations.

With the three evaluated compounds being characterized by distinct natural/anthropogenic sources, sinks, and atmospheric lifetimes, this study considers additional factors, such as seasonal variability and the correlation with wind parameters, which—once combined—can provide a more accurate understanding of the balance of emission sources. At the LMT site, several anthropogenic sources of emissions have been considered to explain certain peaks in pollutants observed since the observation site started its data gathering operations in 2015 [60]; however, the preliminary data lacked further characterization and were also limited in coverage. This study relies on a more robust time series to evaluate changes over time and provide additional tools for the characterization of a “multisource” site such as LMT. With specific compounds showing seasonal trends and different emission sources over the course of one year (e.g., summertime CO emissions related to wildfires and their wintertime counterparts linked to domestic heating and other anthropogenic sources), the differentiation between local and remote sources based on proximity categories can provide new insights into the variability of each pollutant.

These findings can also establish the foundation for further analyses of other compounds at the same site, possibly using alternative methodologies such as the O₃/CO [93] and CO/NO_x [94] ratios, which have both shown distinct trends in the context of the Mediterranean Basin. This area is considered a major hotspot for air mass transport processes [95,96,97], climate change [98], and air quality [99]. Ad hoc adjustments and corrections, such as those introduced in this study, which can be based on location-specific characteristics (in LMT’s case, its location in the central Calabrian area of Italy) and/or broader categories (e.g., all coastal sites in international networks of atmospheric measurements), may provide new tools for the detailed characterization of atmospheric mechanisms in the basin.

This work is divided as follows: Section 2 describes the LMT WMO/GAW site and its characteristics; Section 3 describes the instruments, data, and methods used in this work; Section 4 shows the results of this study; and Section 5 and Section 6 discuss the findings and conclude the paper, respectively.

2. Characteristics of the Lamezia Terme WMO/GAW Observation Site

Located in the southern Italian region of Calabria and operated by CNR-ISAC (National Research Council of Italy—Institute of Atmospheric Sciences and Climate), the Lamezia Terme (code: LMT; Lat: 38°52.605′ N; Lon: 16°13.946′ E; Alt: 6 m ASL) World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) regional observation site is situated 600 m from the Tyrrhenian coast of the region, at the narrowest point of the entire Italian peninsula (Figure 1). The distance between the western (Tyrrhenian) and eastern (Ionian) coasts is ≈32 km, and the entire area—known as the Catanzaro isthmus, named after the regional capital located on the eastern coast—separates the Sila Massif and coastal chain (Catena Costiera) in the north from the Serre Massif in the south. The station started data gathering operations in 2015.

The orographic configuration of the area, combined with LMT’s location in the westernmost part of the isthmus, results in the site being affected by a well-defined wind pattern. Federico et al. (2010a) [100] demonstrated the importance of breezes as a key regulating factor in local wind circulation and climate: breeze regimes vary depending on the season, and minor changes in wind orientations are also observed. However, the W-WSW/NE-ENE axis dominates throughout the entire year as a result of local orography and wind channeling through the Marcellinara gap in the Catanzaro isthmus (Figure 1). In Federico et al. (2010b) [101], two years of data have been assessed and modeled to provide a more accurate understanding of wind regimes. The study demonstrated that during parts of fall, spring, and summer, diurnal breezes are the result of a combination of large-scale and local flows, while in November–February, diurnal circulation is linked to large-scale forcing. Nocturnal flows are linked to nocturnal breeze circulation.

Data from a short campaign, limited to measurements taken during summer 2009, were used to analyze Planetary Boundary Layer (PBL) variability at the site [102,103]. The characterization of near-surface wind patterns was the subject of additional studies that used wind lidars and other instruments to characterize the behavior of local winds at various altitude thresholds [104].

Figure 1. (A): Location and coordinates of Lamezia Terme (LMT) in the Mediterranean Basin, shown on a EMODnet Digital Elevation Model (DEM) of Europe [105]. (B): Details of the area where LMT is located, with a focus on known sources of emissions. The A2 highway is labeled as E45 (European route 45), which is a major infrastructure connecting Norway to Italy via other countries. E848 (European route 848) connects the main E45 infrastructure with the Ionian coast of central Calabria, where the regional capital of Catanzaro is located. SS280 is a state highway (Strada Statale) with a track generally parallel to that of the A2. In addition to the landfill shown on the map, the second landfill is located close to the Station, E45, and E848 labels but is excluded from this graph to optimize visualization. The Highway label refers to a point where the distance to the LMT is ≈4.2 km. Agricultural and livestock farms are spread over the area.

Figure 1. (A): Location and coordinates of Lamezia Terme (LMT) in the Mediterranean Basin, shown on a EMODnet Digital Elevation Model (DEM) of Europe [105]. (B): Details of the area where LMT is located, with a focus on known sources of emissions. The A2 highway is labeled as E45 (European route 45), which is a major infrastructure connecting Norway to Italy via other countries. E848 (European route 848) connects the main E45 infrastructure with the Ionian coast of central Calabria, where the regional capital of Catanzaro is located. SS280 is a state highway (Strada Statale) with a track generally parallel to that of the A2. In addition to the landfill shown on the map, the second landfill is located close to the Station, E45, and E848 labels but is excluded from this graph to optimize visualization. The Highway label refers to a point where the distance to the LMT is ≈4.2 km. Agricultural and livestock farms are spread over the area.

In Cristofanelli et al. (2017) [60], preliminary data from LMT and other stations in southern Italy were used to assess local and remote sources contributing to observed atmospheric concentrations of select gases. The study, which relied on the O₃/NO_x ratio as a proximity indicator, showed that local (LOC) concentrations of CH₄ at the site were higher than their remote counterparts and indicated livestock farming in the area may be a possible cause for these emissions. In addition to these findings, other sources of pollution were identified in the preliminary study: Lamezia Terme International Airport (IATA: SUF; ICAO: LICA), located 3 km north of the observatory, and the A2/E45 highway (Figure 1).

A study performed on data gathered during the first COVID-19 lockdown of 2020, when strict regulations were introduced by the Italian government to stop, or reduce, most anthropic activities [106,107], assessed the variability of greenhouse gases and aerosols via a comparison of pre-lockdown, lockdown, and post-lockdown concentrations [93]. The study showed radical changes in the behavior of NO_x in particular, thus demonstrating that the peaks observed at LMT during rush hours were attributable to vehicular traffic, as hypothesized in a previous study [60].

More complexity in the behavior of gases and aerosols at the site was described following a 2024 campaign that paired surface observations of these parameters with Planetary Boundary Layer Height (PBLH) variability measured using a Lufft CHM 15k Nimbus ceilometer (Fellbach, Germany) [15]. The ceilometer data, in conjunction with wind parameters, allowed researchers to define four distinct wind regimes at the site, each with unique characteristics in terms of GHG and aerosol concentrations.

Due to its location in the central Mediterranean (Figure 1), LMT is exposed to frequent Saharan dust events from Africa [108] and open fire emissions from sources ranging from regional to continental [109]. In particular, the latter contribute CO anomalies during the summer season, which are distinct from the peaks observed in winter, attributed to domestic heating and other forms of biomass burning.

3. Instruments, Datasets, and Methodologies

At Lamezia Terme (LMT), key data on wind (speed and direction) were gathered by a Vaisala WXT520 (Vantaa, Finland) weather station. The WXT520 measures wind speed (WS, in m/s) and direction (WD, in degrees) using ultrasounds transmitted between three transducers on a horizontal plane. Wind from a given direction and at a given speed alters the time required for ultrasound pulses to travel between the transducers; the instrument measures the deviation from a standard travel time and calculates WS and WD with a precision of ±0.3 m/s (at 10 m/s) and ±3°, respectively. In addition to WS and WD, the instrument measures meteorological parameters such as temperature and relative humidity. Further details on Vaisala WXT520 measurements and data processing at LMT are available in D’Amico et al. (2024c) [110].

The mole fractions of carbon monoxide (CO, ppb), carbon dioxide (CO₂, ppm), and methane (CH₄, ppb) at the site have been measured using a Picarro G2401 Cavity Ring-Down Spectrometry (CRDS). CRD spectrometry allows for the measurement, with a high degree of precision, of greenhouse gases in the atmosphere [111]. With one measurement every 5 s, the reported precision of the G2401 is ±1 ppb. In accordance with NOAA procedures, the Picarro G2401 operating at LMT is subject to frequent calibration using cylinders based on WMO standards. In addition to WMO cylinders, other target cylinders are used for quality assurance purposes. Details on G2401 measurements and data processing at LMT are available in D’Amico et al. (2024a) [62] and D’Amico et al. (2024c) [110].

Nitrogen oxides (NO, NO₂) have been measured using a Thermo Scientific 42i-TL (Franklin, MA, USA). The instrument operates on the principle of chemiluminescence (CL), in which NO in ambient air reacts with O₃ produced by the instrument itself to release NO₂ in an excited state and molecular oxygen (O₂) [112]. The reported precision of the 42i-TL model is ±0.4 ppb. More details on NO_x measurements at LMT are available in Cristofanelli et al. (2017) [60] and D’Amico et al. (2024c) [110]. The instrument is equipped with a molybdenum converter, which can overestimate NO₂, as described in Section 1.

The concentrations of surface ozone (O₃) have been measured using a Thermo Scientific 49i (Franklin, MA, USA), which operates as a photometric analyzer with a detection limit of 1 ppb. The 49i uses Lambert’s Law, combined with O₃’s absorption of ultraviolet light at a wavelength of precisely 254 nanometers. Via a two-cell mechanism, sampled ambient air and a reference air depleted of O₃ by a scrubber are used to calculate O₃ concentrations. A detailed description of these measurements at LMT is available in D’Amico et al. (2024d) [61].

Table 1. Yearly and total coverage rates of instruments and datasets, shown as percentages (%) compared to the actual number of hours elapsed between 2015 and 2023. Please note that both 2016 and 2020 were leap years, with an additional 24 h. The “Preliminary” column refers to the combined Thermo 42i-TL and 49i dataset with applicable O₃/NO_x ratios. “Proximity” refers to the subset of this set featuring valid Picarro G2401 data. “Proximity Meteo” also includes Vaisala WXT520 data.

Year	Hours	G2401	T42i	T49i	WXT520	Prelim.	Proximity	Prox. Meteo
2015	8760	94.73%	92.73%	92.14%	95.9%	92.12%	87.87%	86.99%
2016	8784	94.95%	95.91%	96.17%	96.34%	94.17%	89.9%	88.22%
2017	8760	99.57%	96.39%	95.93%	93.8%	95.65%	95.27%	90.67%
2018	8760	94%	98.11%	98.13%	77.05%	97.95%	92.32%	73.34%
2019	8760	97.6%	96.78%	94.21%	98.59%	94.18%	93.28%	93.26%
2020	8784	93.8%	94.23%	98.52%	99.98%	94%	89.13%	89.12%
2021	8760	97.78%	87.14%	91.65%	99.74%	78.91%	77.84%	77.83%
2022	8760	83.89%	69%	85.22%	90.11%	68.97%	59.15%	58.04%
2023	8760	66.76%	81.86%	82.12%	96.3%	81.82%	58.61%	57.35%
	78,888 1	91.45% 2	90.24% 2	92.68% 2	94.20% 2	88.64% 2	82.60% 2	79.42% 2

¹ Sum. ² Average value.

shows the coverage of all four instruments throughout the observation period (2015–2023) and the applicability of proximity indicators to the analyzed parameters. Two years (2017 and 2019) yield a combined “Proximity Meteo” coverage rate of 90% or higher, while three years (2015, 2016, and 2020) fall within the 80–90% range. However, two years (2020 and 2023) yield rates lower than 60%, thus showing the limitations of this methodology, as it requires multiple instruments to operate at the same time.

Proximity categories have been calculated using the same thresholds from the previous study, which was aimed at LMT and other southern Italian stations [60]. The local (LOC) category is defined by an O₃/NO_x ratio lower than or equal to 10; the near source (N–SRC) is set with a 10 < O₃/NO_x ≤ 50 ratio; for remote source (R–SRC) emissions, the 50 < O₃/NO_x ≤ 100 ratio is used; finally, for background (BKG) air masses, the category is defined by a ratio greater than 100.

Corrections for remote source (R–SRC) and atmospheric background (BKG) categories had already been calculated in the previous study according to the findings of Steinbacher et al. (2007) [49], which indicated an overestimation of NO₂ in aged air masses. Consequently, for the R–SRC and BKG thresholds, NO₂ concentrations have been divided by two to generate the “corrected” R–SRC_cor and BKG_cor subcategories according to the methodology outlined in Cristofanelli et al. (2017) [60].

Following the analysis of surface ozone at the LMT site performed by D’Amico et al. (2024d) [61], which demonstrated the occurrence of diurnal peaks of O₃ linked to warm seasons and westerly winds from the Tyrrhenian Sea, in this study, an additional correction is proposed and consequently applied. The purpose of this correction is to compensate for the peaks of O₃ linked to heavy photochemical activity under specific circumstances, which would cause fresh air masses to be categorized as more aged instead. For this purpose, “ecor” (for “enhanced correction”) divided O₃ concentrations by two when all of the following conditions were satisfied: (a) measurements occurring during a warm season, i.e., Spring and Summer (March to August); (b) diurnal hours, i.e., between 10:00 and 16:00 UTC; (c) westerly winds, i.e., wind directions between 240 and 300° N. An R 4.4.2 algorithm based on the dplyr package/library was used to apply the correction under the circumstances specified above.

Table 2. Percentage (%) of hours falling into the six proximity categories (including the two corrected remote source, R–SRC, and background, BKG, sets), calculated from the “Preliminary” dataset shown in Table 1. To ease visualization and comparisons between coverage rates observed throughout the years, the same “Preliminary” column from Table 1 has been added to this table.

Year	Prelim.	Standard				Corrected
		LOC	N–SRC	R–SRC	BKG	R–SRCcor	BKGcor	R–SRCecor	BKGecor
2015	92.12%	48.97%	30.54%	10.75%	9.71%	2.78%	17.68%	6.24%	13.39%
2016	94.17%	30.75%	46.78%	19.1%	3.32%	5.06%	17.38%	6.66%	13.75%
2017	95.65%	36.65%	45.49%	16.95%	0.89%	4.6%	13.24%	7.49%	8.67%
2018	97.95%	42.82%	47.72%	9.17%	0.26%	3.36%	6.07%	3.5%	5.62%
2019	94.18%	42%	45.99%	11.92%	0.07%	3.84%	8.15%	5%	5.46%
2020	94%	37.48%	39.28%	15.74%	7.48%	2.51%	20.7%	6.12%	16.6%
2021	78.91%	34.7%	40.72%	19.39%	4.55%	4.09%	19.86%	9.18%	13.61%
2022	68.97%	44.75%	42.75%	11.51%	0.95%	2.33%	10.14%	6.27%	5.16%
2023	81.82%	33.31%	34.96%	26.15%	5.35%	4.43%	27.07%	10.86%	19.51%
	88.64% 1	39.05% 1	41.58% 1	15.63% 1	3.62% 1	3.67% 1	15.59% 1	6.81% 1	11.31% 1

¹ Average.

reports the rate of hours in each category by year, as subsets of the “Preliminary” dataset shown in Table 1, which does not account for Picarro G2401 and Vaisala WXT520 coverages.

As evidenced by the previous work [60], the implementation of a correction meant to compensate for molybdenum converters’ overestimation of NO₂ results in a reduction in R–SRC and an increase in BKG. The O₃ correction applied in this study exhibits the opposite behavior, although the corrected R–SRC data are still lower compared to their standard counterparts. Direct comparisons between standard and corrected values are shown in

Table 3. Comparison between R–SRC/BKG data and their corrected values based on the standard NO₂/2 correction seen in Cristofanelli et al. (2017) [60] and the select O₃/2 correction applied in this study, based on the findings of D’Amico et al. (2024d) [61]. The differences between the standard values seen in Table 2 and the corrected values from the same table, as well as the differences between the two correction types, are reported here.

Year	Standard—cor		Standard—ecor		cor—ecor
	R–SRC	BKG	R–SRC	BKG	R–SRC	BKG
2015	−7.97%	+7.97%	−4.51%	+3.68%	+3.46%	−4.29%
2016	−14.04%	+14.06%	−12.44%	+10.43%	+1.6%	−3.63%
2017	−12.35%	+12.35%	−9.46%	+7.78%	+2.89%	−4.57%
2018	−5.81%	+5.81%	−5.67%	+5.36%	+0.14%	−0.45%
2019	−8.08%	+8.08%	−6.92%	+5.39%	+1.16%	−2.69%
2020	−13.23%	+13.22%	−9.62%	+9.12%	+3.61%	−4.1%
2021	−15.3%	+15.31%	−10.21%	+9.06%	+5.09%	−6.25%
2022	−9.18%	+9.19%	−5.24%	+4.21%	+3.94%	−4.98%
2023	−21.72%	+21.72%	−15.29%	+14.16%	+6.43%	−7.56%
	−11.96% 1	+11.97% 1	−8.82% 1	+7.69% 1	+3.15% 1	−4.28% 1

¹ Average.

; in

Table 4. Number of hours falling into each proximity categories, also accounting for the northeastern (0–90° N) and western (240–300° N) wind corridors at the LMT observation site. “Total” refers to all wind directions, including those falling outside the two ranges.

Categories	Wind Corridors
	Total	Northeast	West
LOC	24,649	15,657	1597
N–SRC	26,258	6505	12,539
R–SRC	9320	603	7302
BKG	2328	104	1873
R–SRCcor	2584	153	1892
BKGcor	10,804	612	8090
R–SRCecor	4692	153	4003
BKGecor	7901	612	5190

, standard and corrected categories are reported based on their wind corridor attribution, which in turn is heavily influenced by the patterns of the Catanzaro isthmus.

Proximity category attribution at LMT is therefore influenced by wind corridors and exhibits different behaviors depending on whether standard or corrected categories are used. From the key statistics on the applicability of the method, it is also possible to assess its limitations. For the ratio to be used, two instruments (in this case, the Thermo 42i-TL and 49i) need to operate at the same time and provide validated data; once defined, a proximity category requires at least one more instrument (e.g., Picarro G2401) to operate in conjunction with O₃ and NO_x data gathering, thus likely reducing the number of data. Should wind data be integrated (e.g., Vaisala WXT520), a further potential reduction of the available dataset would occur. In this study, Table 1 shows the limits of proximity categorization and the general limitations of this methodology. Thermo 42i and 49i had total hourly coverage (between 2015 and 2023) of 90.24% and 92.68%, respectively; however, when their validated data are combined to calculate the proximity categories (LOC, N–SRC, R–SRC, BKG), the total coverage is reduced to 88.64%. The integration of Picarro G2401 data on CO, CO₂, and CH₄, as well as that of Vaisala WXT520 for wind data, reduced the coverage to 82.60% and 79.42%, respectively. Each instrument is characterized by high coverage rates throughout the observation period; however, the G2401 has limitations in 2022 (83.89%) and 2023 (66.76%), which affect the entire dataset. The Thermo instruments have high coverage rates except for 2022 (T42i, 69%). Vaisala data generally have very high rates; however, 2018 is characterized by a significant reduction (77.05%).

Reductions in the coverage rate of a specific instrument do not have a major impact on the total coverage if the gaps in the available dataset are shared between instruments; however, when the gaps do not overlap, the reduction is propagated in the final result. One example is the year 2015, which has a combined rate of 86.99% despite all instruments having rates above 90%. Two out of nine years yield a combined coverage rate lower than 60%, five years have a rate of 80% or higher, and only two years exceed the 90% threshold. These figures further demonstrate the limitations of this method and its consequent applicability. It is also worth mentioning that a further limitation of this methodology is related to the absence of well-defined, or even approximate, buffer zones and/or distance thresholds attributable to each air mass aging type. The categories described in this paper and the previous work [60] refer to air masses affected by emissions and photochemical processes at various distances; however, the leading literature in the field lacks accurate spatial resolution [19,20]. Future studies and additional atmospheric tracers are expected to narrow down these ranges and provide more precise correlations between peaks in the concentration of a given pollutant and its emission sources.

In this study, both the standard and corrected values are used; however, the latter (especially “ecor”) are excluded from a number of graphs to optimize visualization. Seasonal averages were calculated by assigning the following months to each season, in accordance with previous research [60,61,62]: January, February, and December to Winter (JFD); March, April, and May to Spring (MAM); June, July, and August to Summer (JJA); and September, October, and November to Fall (SON). Weekly analyses are based on the methodology described in D’Amico et al. (2024b) [63]. Monthly aggregations were also calculated. All data were processed in R 4.4.2, and plots showing the results of these evaluations were generated using the following packages and libraries: ggplot2, ggpubr, and tidyverse. Polar plots were computed in MATLAB2016a using the datasets processed in R. Statistical evaluations of linear correlations were calculated in Jamovi 2.6.22, while their quadratic counterparts were computed in R 4.4.2.

4. Results

4.1. Wind Roses Based on Proximity Categories

In previous works, descriptions of the LMT site showed a wind rose in which the two W/NE corridors can be seen [61,62]. In this study, wind roses based on proximity categories have been generated to show the correlation between wind direction/speed and distinct proximity categories (Figure 2).

The plotted wind roses allow us to determine, at a glance, how the local (LOC) and background (BKG) conditions are closely related to local wind patterns. The corrected proximity categories also allow us to visualize different distributions on a per-corridor basis, as indicated in Table 4. From these plots, it is also possible to highlight the presence of a sector in the 30–60° N range, which is totally absent in BKG.

4.2. CO, CO₂, and CH₄ Concentration Variability by Category

Using the four standard proximity categories and the corrected (cor, ecor) concentrations, CO (ppb), CO₂ (ppm), and CH₄ (ppb) data have been aggregated on a per-category basis to assess possible differences. The results, shown in

Table 5. Average concentrations (±1σ) of CO (ppb), CO₂ (ppm), and CH₄ (ppb) per category.

Type	Category	CO (ppb)	CO2 (ppm)	CH4 (ppb)
Standard	LOC	170.36 ± 68.34	449.05 ± 217.56	2120.14 ± 187.49
Standard	N–SRC	126.49 ± 29.35	416.99 ± 53.22	1960.65 ± 67.33
Standard	R–SRC	108.72 ± 19.29	411.78 ± 8.54	1940.77 ± 43.06
Standard	BKG	103.88 ± 21.01	409.17 ± 7.85	1930.95 ± 42.39
Corrected	R–SRCcor	110.21 ± 18.67	410.81 ± 8.38	1935.42 ± 41.15
Corrected	BKGcor	107.19 ± 19.94	411.39 ± 8.49	1939.72 ± 43.53
Corrected	R–SRCecor	108.68 ± 19.57	410.87 ± 8.91	1939.12 ± 42.55
Corrected	BKGecor	106.95 ± 19.84	411.75 ± 8.12	1939.93 ± 43.51

, are based on the “Proximity” dataset, which combines CO, CO₂, CH₄, O₃, and NO_x measurements from three distinct instruments (Picarro G2401, Thermo 49i, and Thermo 42i-TL).

Due to seasonal influences on the concentration of each parameter [62,63,110], the results have been aggregated on a seasonal basis, as shown in

Table 6. Average concentrations (±1σ) of CO (ppb), CO₂ (ppm), and CH₄ (ppb), divided by category and season. Fall and Winter concentrations of “ecor” categories are omitted, as they are identical to their “cor” counterparts (the “ecor” algorithm is limited to the Spring and Summer seasons, i.e., March through August).

Category	Season	CO (ppb)	CO2 (ppm)	CH4 (ppb)
LOC	Fall	143.80 ± 48.42	468.57 ± 395.02	2125.00 ± 165.83
	Spring	172.36 ± 54.17	442.21 ± 25.07	2103.86 ± 182.11
	Summer	144.90 ± 50.36	456.22 ± 26.16	2135.63 ± 176.26
	Winter	217.32 ± 82.18	427.62 ± 15.99	2115.87 ± 218.65
N–SRC	Fall	115.18 ± 23.05	418.40 ± 104.13	1963.13 ± 65.08
	Spring	134.05 ± 25.49	418.27 ± 9.64	1964.99 ± 66.35
	Summer	112.36 ± 25.35	415.26 ± 13.34	1961.09 ± 89.99
	Winter	141.45 ± 32.36	415.51 ± 6.99	1952.57 ± 42.87
R–SRC	Fall	107.46 ± 17.21	412.35 ± 7.03	1955.85 ± 36.57
	Spring	115.24 ± 16.47	414.62 ± 7.44	1947.55 ± 41.44
	Summer	103.24 ± 20.49	408.61 ± 8.99	1927.19 ± 44.45
	Winter	116.77 ± 15.99	416.62 ± 6.14	1954.99 ± 33.49
BKG	Fall	102.84 ± 10.85	409.53 ± 6.78	1932.98 ± 35.15
	Spring	116.12 ± 16.96	407.51 ± 8.08	1912.62 ± 44.08
	Summer	97.82 ± 22.30	409.51 ± 7.78	1937.85 ± 40.86
	Winter	118.30 ± 12.96	412.61 ± 9.06	1935.33 ± 45.84
R–SRCcor	Fall	106.27 ± 16.24	410.08 ± 7.14	1945.30 ± 36.28
	Spring	117.80 ± 16.14	414.03 ± 7.71	1945.93 ± 42.82
	Summer	104.52 ± 19.25	407.33 ± 8.43	1918.53 ± 38.25
	Winter	117.73 ± 17.04	415.99 ± 5.64	1951.84 ± 33.81
BKGcor	Fall	106.78 ± 16.43	412.38 ± 6.96	1953.80 ± 37.42
	Spring	114.77 ± 16.62	413.18 ± 8.11	1940.09 ± 44.24
	Summer	101.45 ± 21.37	409.13 ± 8.76	1931.98 ± 44.60
	Winter	116.63 ± 15.31	416.38 ± 6.81	1953.83 ± 35.54
R–SRCecor	Fall	-	-	-
	Spring	115.28 ± 16.81	414.14 ± 7.49	1949.40 ± 42.19
	Summer	104.52 ± 20.48	408.65 ± 9.46	1930.89 ± 43.18
	Winter	-	-	-
BKGecor	Fall	-	-	-
	Spring	114.88 ± 16.27	412.86 ± 8.43	1934.99 ± 45.01
	Summer	99.40 ± 21.52	409.30 ± 8.07	1930.83 ± 44.99
	Winter	-	-	-

.

From the concentrations reported in the table, it can be inferred that LOC yields higher values, and a progressive transition from LOC to BKG results in lower thresholds as air mass aging increases. Additionally, the uncorrected BKG value is generally lower than the two corrected versions, indicating a possible anthropogenic influence.

4.3. Assessment of Daily Cycles

At LMT, studies have shown the presence of daily cycles that are the result of local wind circulation, seasonal changes, and atmospheric chemistry [60,61,62,110]. Of the three compounds assessed in this study, only methane was subjected to a detailed daily cycle analysis [62]. In this subsection, proximity categories are introduced as a new variable to assess their influences on the daily cycles of CO (Figure 3), CO₂ (Figure 4), and CH₄ (Figure 5) on a seasonal basis. In order to optimize visualization, “ecor” data are featured in the R–SRC_cor (C) and BKG_cor (D) plots as dashed lines.

The y axis scales in these figures are fixed and are meant to highlight differences in observed values on a per-category basis. Supplementary Materials Figures S1A–S3F show adjusted vertical scales and other features meant to highlight the variability of the observed parameters. The SM content also shows the uncorrected R–SRC and BKG values.

All three compounds show a clear influence of the daily cycle (which largely depends on local wind circulation patterns) on the LOC category, while all remaining categories are less affected; from LOC, it is also possible to infer distinct seasonal trends. N–SRC shows a weak tendency toward the LOC cycle during nighttime hours.

4.4. Weekly Cycle Variability

In the multi-year assessment of methane at LMT based on seven years of data (2016–2022) [62], weekly evaluations were performed. These evaluations were further expanded in D’Amico et al. (2024b) [63], which introduced new methods for weekly analyses. In this study, weekly cycles have been assessed to test the influences of anthropogenic emissions, especially in the cases of LOC and N–SRC. The results are shown in Figure 6 (CO), Figure 7 (CO₂), and Figure 8 (CH₄). As noted in Section 4.3, “ecor” values are featured in the R–SRC_cor (C) and BKG_cor (D) plots as dashed lines.

Supplementary Materials Figure S4A–6F show adjusted vertical scales and include the uncorrected R–SRC and BKG data.

Contrary to expectations, no clear weekly cycles are reported, which is significant, especially for LOC, which, as a proximity category, should be more subject to weekly changes in anthropic activities and their emissions. In fact, a previous study on the weekly assessment of gases and aerosols at LMT showed the presence of weekly cycles and seasonal variability [63]. However, LOC shows seasonal differences in terms of concentrations and, as expected, higher values throughout the week.

4.5. Seasonal Variability by Wind Sector

In Section 4.1, wind roses showing the distribution of speeds and directions based on the main proximity categories are shown. In Figure 9 (carbon monoxide, ppb), Figure 10 (carbon dioxide, ppm), and Figure 11 (methane, ppb), bivariate plots show seasonal changes based on observed wind directions and speeds, without accounting for proximity categories. Following the method used in D’Amico et al. (2025a) [113], which aimed at evaluating sulfur dioxide (SO₂) concentrations at LMT, these plots are designed to highlight concentrations exceeding the third quartile threshold for each compound. The following values are used: CO, 160 ppb; CO₂, 430 ppm; and CH₄, 2050 ppb.

Seasonal changes in the distribution of pollutants and their relationship with wind speeds can be inferred from these plots and will be subject to statistical evaluation in Section 4.6. High values, exceeding the third quartile (75th percentile) of each compound, are clearly linked to the northeastern sector of LMT, which is more exposed to anthropic influence.

4.6. Correlations with Wind Speed by Corridor

The multi-year evaluations of CH₄ [62] and surface O₃ [61] were also based on correlations with wind speeds along the main corridors (western/seaside at 240–300° N and northeastern/continental at 0–90° N), which are the result of local circulation. In addition to these corridors, total data, each representative of distinct anthropogenic influences (including those falling outside the 0–90° N and 240–300° N wind direction ranges), were evaluated. This analysis allowed us to identify the Hyperbola Branch Pattern (HBP) of methane from the northeastern sector [62], which was not observed in surface O₃ [61].

In this study, the three wind corridor categories are assessed based on a differentiation by proximity category. Nine years (2015–2023) of CO (Figure 12), CO₂ (Figure 13), and CH₄ (Figure 14) concentration data are shown below.

In order to evaluate the linear correlation between wind speed and the mole fractions of observed compounds, Pearson’s Correlation Coefficient (PCC) was used for the main proximity categories (LOC, N–SRC, R–SRC, BKG), as well as the corrected categories described in this work (R–SRC_cor, BKG_cor, R–SRC_ecor, BKG_ecor). The PCC [114,115] and the respective p-values are shown in

Table 7. Results of the statistical evaluation based on Pearson’s Correlation Coefficient (PCC), a quadratic regression for which the coefficient of determination is shown (R²), along with the respective p-values. The linear and quadratic evaluations are aimed at testing the correlation between wind speeds and the mole fractions of CO, CO₂, and CH₄ in three different wind corridors: western, defined as the 240–300° N range; northeastern, defined as 0–90° N; and total, which includes all wind directions, including those falling outside the previous categories. This table specifically refers to the local (LOC) proximity category.

LOC
Parameter	Statistics	Total WS (m/s)	Northeast WS (m/s)	West WS (m/s)
		n = 24,649	n = 15,657	n = 1597
CO (ppb)	PCC	−0.051	−0.051	−0.096
	p-value	<0.001	<0.001	<0.001
	R2	0.004	0.007	0.014
	p-value	<0.001	<0.001	<0.001
CO2 (ppm)	PCC	−0.066	−0.087	−0.056
	p-value	<0.001	<0.001	0.026
	R2	0.000	<0.001	0.017
	p-value	0.26	0.776	<0.001
CH4 (ppb)	PCC	−0.251	−0.286	−0.178
	p-value	<0.001	<0.001	<0.001
	R2	0.001	0.004	0.061
	p-value	<0.001	<0.001	<0.001

(LOC),

Table 8. Results of the statistical evaluation based on Pearson’s Correlation Coefficient (PCC), a quadratic regression for which the coefficient of determination is shown (R²), along with the respective p-values. The linear and quadratic evaluations are aimed at testing the correlation between wind speeds and the mole fractions of CO, CO₂, and CH₄ under three different wind corridors: western, defined as the 240–300° N range; northeastern, defined as 0–90° N; and total, which includes all wind directions, including those falling outside the previous categories. This table specifically refers to the near source (N–SRC) proximity category.

N–SRC
Parameter	Statistics	Total WS (m/s)	Northeast WS (m/s)	West WS (m/s)
		n = 26,258	n = 6505	n = 12,539
CO (ppb)	PCC	−0.051	−0.036	−0.072
	p-value	<0.001	0.004	<0.001
	R2	0.018	0.090	0.005
	p-value	<0.001	<0.001	<0.001
CO2 (ppm)	PCC	−0.059	−0.079	−0.033
	p-value	<0.001	<0.001	<0.001
	R2	0.001	0.024	0.011
	p-value	<0.001	<0.001	<0.001
CH4 (ppb)	PCC	−0.224	−0.296	−0.1
	p-value	<0.001	<0.001	<0.001
	R2	0.007	0.051	0.010
	p-value	<0.001	<0.001	<0.001

(N–SRC),

Table 9. Results of the statistical evaluation, based on Pearson’s Correlation Coefficient (PCC) and related p-values, aimed to test the correlation between wind speeds and the mole fractions of CO, CO₂, and CH₄ in three different wind corridors: western, defined as the 240–300° N range; northeastern, defined as 0–90° N; and total, which includes all wind directions, including those falling outside the previous categories. This table specifically refers to the remote source (R–SRC) proximity category.

R–SRC
Parameter	Statistics	Total WS (m/s)	Northeast WS (m/s)	West WS (m/s)
		n = 9320	n = 603	n = 7302
CO (ppb)	PCC	−0.040	−0.011	−0.056
	p-value	<0.001	0.784	<0.001
	R2	0.049	0.291	0.010
	p-value	<0.001	<0.001	<0.001
CO2 (ppm)	PCC	−0.019	−0.298	0.035
	p-value	0.064	<0.001	0.003
	R2	0.001	0.162	0.001
	p-value	<0.001	<0.001	0.001
CH4 (ppb)	PCC	0.009	−0.243	0.036
	p-value	0.369	<0.001	0.002
	R2	0.004	0.144	0.014
	p-value	<0.001	<0.001	<0.001

(R–SRC),

Table 10. Results of the statistical evaluation based on Pearson’s Correlation Coefficient (PCC), a quadratic regression for which the coefficient of determination is shown (R²), and the respective p-values. The linear and quadratic evaluations are aimed at testing the correlation between wind speeds and the mole fractions of CO, CO₂, and CH₄ in three different wind corridors: western, defined as the 240–300° N range; northeastern, defined as 0–90° N; and total, which includes all wind directions, including those falling outside the previous categories. This table specifically refers to the atmospheric background (BKG) proximity category.

BKG
Parameter	Statistics	Total WS (m/s)	Northeast WS (m/s)	West WS (m/s)
		n = 2328	n = 104	n = 1873
CO (ppb)	PCC	−0.034	0.189	−0.038
	p-value	0.098	0.052	0.099
	R2	0.004	0.268	0.010
	p-value	0.004	<0.001	<0.001
CO2 (ppm)	PCC	−0.048	−0.572	−0.008
	p-value	0.021	<0.001	0.717
	R2	0.002	0.050	0.016
	p-value	0.067	0.027	<0.001
CH4 (ppb)	PCC	−0.009	−0.490	0.006
	p-value	0.667	<0.001	0.803
	R2	0.001	0.094	0.023
	p-value	0.162	<0.001	<0.001

(BKG),

Table 11. Results of the statistical evaluation, based on Pearson’s Correlation Coefficient (PCC) and related p-values, aimed to test the correlation between wind speeds and the mole fractions of CO, CO₂, and CH₄ in three different wind corridors: western, defined as the 240–300° N range; northeastern, defined as 0–90° N; and total, which includes all wind directions, including those falling outside the previous categories. This table specifically refers to the remote source, corrected (R–SRC_cor) proximity category.

R–SRCcor
Parameter	Statistics	Total WS (m/s)	Northeast WS (m/s)	West WS (m/s)
		n = 2584	n = 153	n = 1892
CO (ppb)	PCC	0.1	0.197	−0.002
	p-value	<0.001	0.014	0.921
	R2	0.036	0.251	0.004
	p-value	<0.001	<0.001	0.020
CO2 (ppm)	PCC	0.090	0.191	0.004
	p-value	<0.001	0.017	0.862
	R2	0.009	0.086	0.005
	p-value	<0.001	<0.001	0.003
CH4 (ppb)	PCC	0.092	0.193	0.006
	p-value	<0.001	0.016	0.798
	R2	0.011	0.151	0.004
	p-value	<0.001	<0.001	0.020

(R–SRC_cor),

Table 12. Results of the statistical evaluation based on Pearson’s Correlation Coefficient (PCC), a quadratic regression for which the coefficient of determination is shown (R²), and the respective p-values. The linear and quadratic evaluations are aimed at testing the correlation between wind speeds and the mole fractions of CO, CO₂, and CH₄ in three different wind corridors: western, defined as the 240–300° N range; northeastern, defined as 0–90° N; and total, which includes all wind directions, including those falling outside the previous categories. This table specifically refers to the atmospheric background, corrected (BKG_cor) proximity category.

BKGcor
Parameter	Statistics	Total WS (m/s)	Northeast WS (m/s)	West WS (m/s)
		n = 10,804	n = 612	n = 8090
CO (ppb)	PCC	0.068	0.082	−0.008
	p-value	<0.001	0.041	0.497
	R2	0.023	0.225	0.002
	p-value	<0.001	<0.001	<0.001
CO2 (ppm)	PCC	0.060	0.075	−0.004
	p-value	<0.001	0.064	0.7
	R2	0.003	0.089	<0.001
	p-value	<0.001	<0.001	0.222
CH4 (ppb)	PCC	0.061	0.070	−0.003
	p-value	<0.001	0.084	0.761
	R2	0.004	0.093	<0.001
	p-value	<0.001	<0.001	0.078

(BKG_cor),

Table 13. Results of the statistical evaluation, based on Pearson’s Correlation Coefficient (PCC) and related p-values, aimed to test the correlation between wind speeds and the mole fractions of CO, CO₂, and CH₄ in three different wind corridors: western, defined as the 240–300° N range; northeastern, defined as 0–90° N; and total, which includes all wind directions, including those falling outside the previous categories. This table specifically refers to the remote source, enhanced corrected (R–SRC_ecor) proximity category.

R–SRCecor
Parameter	Statistics	Total WS (m/s)	Northeast WS (m/s)	West WS (m/s)
		n = 4692	n = 153	n = 4003
CO (ppb)	PCC	0.049	0.197	−0.006
	p-value	<0.001	0.014	0.684
	R2	0.012	0.251	0.014
	p-value	<0.001	<0.001	<0.001
CO2 (ppm)	PCC	0.044	0.191	0
	p-value	0.003	0.017	0.991
	R2	0.002	0.086	<0.001
	p-value	0.001	<0.001	0.224
CH4 (ppb)	PCC	0.046	0.193	0
	p-value	0.002	0.016	0.979
	R2	0.005	0.151	<0.001
	p-value	<0.001	<0.001	0.261

(R–SRC_ecor), and

Table 14. Results of the statistical evaluation based on Pearson’s Correlation Coefficient (PCC), a quadratic regression for which the coefficient of determination is shown (R²), and the respective p-values. The linear and quadratic evaluations are aimed at testing the correlation between wind speeds and the mole fractions of CO, CO₂, and CH₄ in three different wind corridors: western, defined as the 240–300° N range; northeastern, defined as 0–90° N; and total, which includes all wind directions, including those falling outside the previous categories. This table specifically refers to the atmospheric background, enhanced corrected (BKG_ecor) proximity category.

BKGecor
Parameter	Statistics	Total WS (m/s)	Northeast WS (m/s)	West WS (m/s)
		n = 7901	n = 612	n = 5190
CO (ppb)	PCC	0.084	0.082	0.005
	p-value	<0.001	0.041	0.695
	R2	0.031	0.225	<0.001
	p-value	<0.001	<0.001	0.917
CO2 (ppm)	PCC	0.074	0.075	0.007
	p-value	<0.001	0.064	0.617
	R2	0.006	0.089	0.006
	p-value	<0.001	<0.001	<0.001
CH4 (ppb)	PCC	0.076	0.070	0.009
	p-value	<0.001	0.084	0.537
	R2	0.007	0.093	0.004
	p-value	<0.001	<0.001	<0.001

(BKG_ecor). The same tables also feature an evaluation of quadratic regressions. The number of hourly measurements per category/sector is also shown in Table 4.

Many of the tested correlations are statistically significant (p-value < 0.05) but yield low linear PCC values and quadratic R². The categorization by wind sector also results in different behaviors depending on whether the measured mole fractions of CO, CO₂, and CH₄ are linked to westerly (seaside) or northeastern (continental) winds. As reported in a previous study, CH₄ is susceptible to an inverse correlation with wind speed from the northeast, which was interpreted in D’Amico et al. (2024a) [62] as an indicator of anthropogenic influence.

4.7. Infra-Annual and Multi-Year Variability

As described in Section 1, the three analyzed compounds are characterized by different atmospheric lifetimes, balances between anthropogenic and natural sources, and multi-year trends. In fact, CO₂ and CH₄ are known to be increasing, while CO trends are affected by the mitigation of specific anthropogenic emissions, although peaks linked to extreme wildfires are common. In Figure 15, monthly aggregates are used to show the infra-annual variability based on proximity categories in greater detail.

The final step of these assessments is the aggregation of data throughout the entire observation period (2015–2023), differentiated by proximity category, to verify multi-year trends. In Figure 16, monthly aggregates have been used.

All compounds show seasonal trends over the course of the observation period and, more importantly, show increasing (CO₂, CH₄) and oscillating (CO) behaviors that are consistent with expectations. LOC concentrations are systematically higher than those of all other categories, thus indicating that LOC is representative of air masses influenced by anthropogenic sources at LMT.

5. Discussion

At the Lamezia Terme (code: LMT) World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) regional station located in the southern Italian region of Calabria, an investigation based on the ratio between surface ozone and nitrogen oxides (O₃/NO_x) as an air mass aging and proximity indicator (Figure 17) has allowed for an unprecedented characterization of local-to-remote sources of CO, CO₂, and CH₄ at the site. This study is based on nine continuous years of measurements (2015–2023) and expands on the preliminary data analyzed by Cristofanelli et al. (2017) [60], which used the same method to assess the distribution of emission sources in the area. This methodology is affected by NO_x measurement issues [54,55,56,57,58,59]; based on the findings of Steinbacher et al. (2007) [49], the previous study applied a correction factor for NO₂ [60].

As a coastal site located in a central Mediterranean region (Figure 1A), LMT is subject to warm boreal summer seasons; in addition, this site is affected by well-defined wind circulation patterns oriented along a western–northeastern axis [100,101]. These characteristics have a direct impact on the measurement of gases, aerosols, and other pollutants, as western seaside winds generally yield lower concentrations compared to northeastern continental winds, which are enriched in anthropogenic outputs [62]. This was particularly verified for CH₄ following a multi-year (2016–2022) analysis of the compound’s behavior at LMT.

Very high O₃/NO_x ratios linked to westerly winds and warm seasons have already been reported in a previous study on proximity categories [60], but these ratios were not considered for an actual correction of surface O₃ in the ratio used as a proximity indicator. While NO_x has not been subject to a detailed cyclic and multi-year analysis at LMT, the behavior of surface O₃ has been described in detail in D’Amico et al. (2024d) [61] using nine years of data (2015–2023) that match the dataset used in this study. During warm seasons, the influence of photochemical activity on O₃ concentrations is tangible and may lead to further overestimation, i.e., some of the fresh air masses may be categorized as aged. In this study, a correction factor of O₃/2 has been applied under specific conditions where the age of gathered air masses could be altered by ozone overproduction: westerly winds (240–300° N), warm boreal seasons (March–August), and diurnal measurements (10:00–16:00 UTC). The applied conditions are in accordance with findings in the literature that show enhanced surface ozone formation in the presence of solar radiation and NO_x [116,117], while also considering LMT’s characteristics with respect to wind corridor orientation and previous findings on O₃ patterns in the area [61].

Table 2 shows the implementation of proximity categories and their corrected versions, while Table 3 compares changes in their frequency rates, i.e., whether the implementation of a given correction increases or reduces the frequency of a category. In accordance with the findings of Cristofanelli et al. (2017) [60], the frequency of data peaks at LOC/N–SRC and shows minima at BKG, with R–SRC yielding intermediate coverage. In two (2015, 2022) out of nine years, LOC is the most common category; the remaining seven years show that N–SRC is the most represented category at LMT. BKG has the lowest rates, with three consecutive years (2017–2019) yielding coverages below 1%. The year 2022 is also affected by a coverage slightly lower than the 1% threshold, at 0.95%. Following the implementation of the first correction (cor), BKG_cor rates significantly increased and peaked in 2020 (20.7%) and 2023 (27.07%). Conversely, R–SRC_cor rates dropped to the 2.33–5.06% interval. These results show the importance of assessing corrections and proximity categories over multiple years of data, as focusing on preliminary results alone does not provide accurate information on the applicability of corrections and their implications.

The ”ecor” correction introduced in this study, as shown in Table 3, systematically reduces R–SRC_ecor in favor of BKG_ecor, thus somehow counterbalancing the effects of the first correction on the available dataset. BKG_ecor has higher rates compared to R–SRC_ecor in all years except for 2022 (6.27% vs. 5.16%). In 2019, the two rates are nearly identical (5% vs. 5.46%). Considering that, overall, N–SRC has a higher frequency compared to LOC, BKG_ecor yielding higher frequency rates than R–SRC_ecor may not be representative of the effectiveness of the correction.

In addition to changes in the coverage of specific proximity categories based on the implementation of corrections for NO₂ and O₃, a key factor in this analysis is the attribution of proximity categories to specific wind corridors, i.e., northeastern (continental) and western (seaside). In Table 4, northeastern winds are prominent in LOC, which is consistent with the enhanced anthropogenic influence over LMT measurements from that sector due to the presence of various emission sources (Figure 1). Conversely, BKG is rare from the same sector, as only favorable conditions would allow air masses not influenced by anthropogenic emissions during their transport from the Ionian to the Tyrrhenian coasts of Calabria to be measured at LMT. When these corridors are considered, both the “cor” and “ecor” corrections increase the number of BKG measurements linked to both the northeastern and western sectors. In particular, the increase in measurements of northeastern winds explains why BKG values are lower than their BKG_cor and BKG_ecor counterparts (Table 5), indicating that the uncorrected O₃/NO_x methodology can measure atmospheric background concentrations under specific conditions. The presence of a 30–60° N gap in the uncorrected BKG category (Figure 2) also indicates differences in the distribution of emission sources in the northeastern sector, as the 30–60° N range is oriented toward the closest urban settlements in the municipality of Lamezia Terme (Figure 1B).

The implementation of wind speeds and directions provides a better understanding of primary corrections in the dataset. In fact, in Figure 2, LOC is linked to generally low wind speeds and a consistent contribution from the northeastern sector, which is in accordance with the findings of D’Amico et al. (2024a) [62] regarding the correlation between CH₄ peaks and low wind speeds from the northeast. N–SRC is more subjected to high wind exposure from the west and has reduced coverage in the 30–60° N range, which is compatible with the location of downtown Lamezia Terme (Figure 1B) and various sources of anthropogenic pollution. The uncorrected R–SRC and BKG exhibit distinct characteristics: the former is linked to the highest wind speeds observed from the west and generally high speeds from the northeast, while the latter shows a clear gap in the 30–60° N range, which effectively acts as a blind spot for a number of anthropogenic sources (notably, downtown Lamezia Terme, Figure 1B), and moderate speeds from the westerly sector. Considering the local orography, the data aligned with the 60° N direction for BKG may indicate winds channeled through the Marcellinara gap, possibly coming directly from the Ionian Sea with reduced anthropogenic influences along the way.

With the implementation of the primary correction, R–SRC_cor shows a new blind spot in the same range described for uncorrected BKG and generally reduces its peak westerly speeds, as well as the total amount of data from the northeast. BKG_cor increases its frequency in the western sector and partially fills the previous gap of 30–60° N. More hourly measurements in the 30–60° N range effectively result in higher concentrations of CO, CO₂, and CH₄ and demonstrate the influence of anthropogenic emissions in that range.

The evaluation of the absolute concentrations of all gases based on proximity categories, shown in Table 5, provides a more detailed understanding of the balance between local and remote sources. LOC is linked to the highest peaks of all compounds, and the averaged concentrations decrease throughout the entire progression from LOC to BKG, with the latter being considered representative of the atmospheric background. Both types of corrected categories (cor, ecor) do not show true minima in BKG concentrations: BKG values of CO, CO₂, and CH₄ are systematically lower than their BKG_cor and BKG_ecor counterparts. R–SRC categories, however, do not show a prevailing trend. These results may indicate that uncorrected BKG, which has very low frequencies at the site, may have concentrations representative of relatively unpolluted atmospheric background conditions, while BKG_cor and BKG_ecor, with their higher frequency rates, may have retained more polluted values, which increased the average. This is also consistent with the results shown in Table 4, which indicate changes between uncorrected and corrected values in the representativeness of each wind sector at LMT.

With seasons being considered, as shown in Table 6, LOC still retains the highest peaks, and differences in the interplay of various sources can be observed. For CO, the contribution of domestic heating and other forms of biomass burning leads to an absolute peak of 217.32 ± 82.18 ppb. The summer season, which is characterized by open fire emissions [109], has a lower average for LOC, as well as other proximity categories. This is likely due to wildfire emissions being linked to punctual bursts in concentration during the summer, while winter concentrations are believed to remain constant throughout the entire season. Biomass burning, in this case, is an example of an emission source whose rate and intensity depend on seasonal cycles and alternating natural/anthropic activity; LOC and N–SRC would therefore be more representative of fresh air masses affected by biomass burning. In fact, the BKG minimum at 97.82 ± 22.30 ppb could be considered representative of background summertime levels characterized by the absence of wildfire-related peaks. Another important factor in CO variability by category is the lifetime of this compound, which is the shortest among the three compounds evaluated in this study. The shorter lifetime in the atmosphere would lead to a progressive reduction in average concentrations from LOC to BKG, with the latter yielding remote outputs of combustion subject to transport. With respect to CO₂, the absolute peak is reported in LOC fall seasons, at 468.57 ± 395.02 ppm, while the winter LOC counterpart yields the lowest value for the category, at 427.62 ± 15.99 ppm. CO₂ is characterized by a long atmospheric lifetime and may not be subject to substantial differentiation between proximity categories, as even LOC would retain residual CO₂ from long-range transport and atmospheric buildup over time, with the latter being a significant factor in anthropogenic climate change. The LOC peak observed in fall could be a combination of residual wildfire emissions near the end of the warm season (September–October) and anthropic activities that follow the end of summertime vacations in the country. Due to the rural nature of the area where LMT is located, the low LOC value observed in winter would be mostly due to fossil fuel burning nearby (e.g., the E45/A2 highway). CH₄ confirms the peaks observed for LOC in the study based on preliminary data [60]; however, the seasonal variability in LOC is not in accordance with the general variability observed in D’Amico et al. (2024a) [62], which linked CH₄ peaks to the winter season. In fact, LOC CH₄ concentrations peak in summer (2135.63 ± 176.26 ppb) and fall (2125 ± 165.83 ppb), while in the case of N–SRC, winter has the lowest concentration of the category at 1952.57 ± 42.87 ppb compared to the spring peak of 1964.99 ± 66.35 ppb. If agriculture, manure, and livestock are indeed responsible for LOC peaks at LMT, one possibility would be changes in the CH₄ outputs of livestock and agricultural sources based on seasonal changes, such as different diets [118,119,120,121]. Another possibility would be seasonal changes in the balance of emission rates and natural sinks of CH₄, which in turn would affect the relative concentrations observed for the LOC category. The analysis of the CH₄ budget at scales ranging from global to local has been the core subject of many works, and some of the mechanisms driving this budget are not well understood; hence, there is a need to implement new methodologies and tracers to better assess the variability of this compound and medium- to long-term trends [92,122,123,124]. In the case of LMT, LOC is likely affected by livestock farming and landfills, while the N–SRC could be affected by biomass burning and other mechanisms occurring on a larger scale compared to the western area of the Catanzaro isthmus.

We evaluated the daily cycle, which is one of LMT’s peculiarities due to local wind circulation and the behavior of aerosols and gases at the site [15,61,62,110]. As seen in Figure 3 (CO), Figure 4 (CO₂), and Figure 5 (CH₄), the “proper” daily cycle, as defined in previous research, is limited to the LOC categories, while the other categories (especially R–SRC and BKG) show flat patterns and a general progression from LOC peaks to BKG minima. This finding shows that daily trends are more closely related to local wind patterns and fresh air masses than expected and do not apply to all circumstances. CO and CO₂ were not subject to detailed multi-year and cyclic analyses; therefore, CH₄ is more representative of the differences reported between general variability (also accounting for seasons) and proximity category-dependent variability. CO’s LOC (Figure 3A) shows seasonal trends in accordance with the other findings, as winter concentrations dominate and report nighttime peaks compatible with domestic heating, while summer is characterized by a flat pattern that is compatible with sporadic bursts, which are hereby attributed to wildfires. CO₂ shows a well-defined fall anomaly in LOC (Figure 4A) and N–SRC (Figure 4B), as shown by the findings described above. The LOC daily cycle of CH₄ shown in Figure 5A is similar to the uncategorized cycle described in D’Amico et al. (2024a) [62]. The N–SRC behavior seen in Figure 5B shows a similar trend, characterized by heavy smoothing and a flat pattern.

The observation site of LMT served as a proving ground in the Italian peninsula for weekly analyses [61,62,63]. The weekly cycle has been assessed in a number of studies under the assumption that no natural phenomenon would discriminate between weekdays (MON–SUN), while anthropic activities change over the course of a standard week and may vary with seasonal differences, as evidenced by a previous study on weekly patterns [63]. In this evaluation, it was initially assumed that the LOC and N–SRC categories would show well-defined differences between MON–FRI (weekdays latu sensu) and SAT–SUN (weekends); due to the proximity to anthropogenic sources, weekly patterns should be noticeable, while R–SRC and BKG would combine various outputs over longer time scales, thus “hiding” the weekly cycle.

Contrary to expectations, despite differences reported in absolute concentrations between LOC and BKG in Figure 6 (CO), Figure 7 (CO₂), and Figure 8 (CH₄), no relevant weekly cycle was observed. The unexpected absence of a weekly pattern was previously reported for O₃ [61]: at LMT, the Ozone Weekend Effect (OWE) has proven to be absent, indicating the interplay of both rural and urban characteristics at the site in terms of NO_x and O₃ emissions. In a purely urban site, O₃ concentrations would increase during weekends due to lower NO_x emissions from anthropogenic sources such as ground transportation [125,126,127,128]. The absence of a significant weekday/weekend difference at LMT suggests rural characteristics and may therefore explain why LOC does not show the expected weekly pattern. The only evidence of a weekly trend is reported for CO₂ in LOC (Figure 7A) and, at a lower rate, in N–SRC (Figure 7B), and they are both limited to fall concentrations. The nature of these CO₂ anomalies during the fall season requires additional investigation. The absence of a LOC weekly cycle would be explained, in the case of CH₄, by emissions from the agricultural sector (including livestock), which are not expected to change over the course of a standard week. This is also consistent with the prevailing rural nature of the areas surrounding LMT in the southwestern sector of the Lamezia Terme municipality. LOC CO and CO₂ may be explained by a balance between industrial and transportation-related emissions during MON–FRI, and domestic emissions during SAT–SUN. These hypotheses require further investigation in future studies that account for additional atmospheric tracers. As shown in Figure 9 (CO), Figure 10 (CO₂), and Figure 11 (CH₄), the interplay between wind direction, speeds, and gas concentrations at LMT is dependent on local circulation, so a number of anthropogenic and natural sources, other than those mentioned in this study (Figure 1B), could be considered.

By analyzing wind corridors and related speeds, some of the hypotheses raised in previous works can be validated. In Figure 12 (CO), Figure 13 (CO₂), and Figure 14 (CH₄), the Hyperbola Branch Pattern (HBP) hypothesis, first proposed by D’Amico et al. (2024a) [62], is now validated by chemical data. In fact, the pattern and influence of anthropogenic emissions linked to low wind speeds were first assumed based on the “exposure” of ambient air to anthropogenic pollution: with low wind speeds, ambient air from the northeastern sector is enriched in pollutants, while high speeds do not allow sufficient time to trigger the same effect. In addition to these figures, the linear and quadratic statistical evaluations shown in Table 7, Table 8, Table 9, Table 10, Table 11, Table 12, Table 13 and Table 14 provide more details on the wind sector-dependent relationship between wind speeds and measured mole fractions of CO, CO₂, and CH₄. Wind speed is negatively correlated with most pollutants, suggesting that higher wind speeds spread emissions over wide areas, thus reducing mole fractions measured at LMT. In the case of LOC (Table 7), CH₄ shows a high reported linear correlation (PCC = −0.286, p-value < 0.001) with wind speed from the northeastern sector, which is consistent with the HBP pattern described in previous studies and also underscores the importance of local sources, such as livestock farming [60,62]. A lower, but still significant correlation (PCC = −0.178, p-value < 0.001) is linked to westerly winds, which may be due to wind inversion patterns linked to the daily cycle and the breeze regime [15]. For N–SRC (Table 8), the correlation with the northeastern sector is even higher (PCC = 0.296), while the western counterpart is significantly lower (PCC = 0.1); both correlations are statistically significant (p-value < 0.001); the difference between LOC and N–SRC may indicate distinct influences of local wind inversion patterns, i.e., northeastern winds headed toward the Tyrrhenian Sea that cross the shoreline and are transported back to LMT during wind direction inversion on the W/NE axis. For R–SRC (Table 9), in addition to a weaker northeastern correlation of CH₄, CO₂ shows a northeastern peak in PCC (−0.298) that lacks a western counterpart (0.035); this is interpreted as specific eastern synoptic conditions allowing remote outputs to be channeled through the Catanzaro isthmus toward the LMT observatory. R–SRC_cor (Table 11) and R–SRC_ecor (Table 13) both reduce the PCC to 0.191, thus indicating that the peak may be attributable to NO₂ and O₃ overestimation. An even higher correlation of CO₂ (PCC = −0572; p-value < 0.001) occurs at BKG (Table 10) for northeastern winds, possibly indicating an opposite synoptic regime that is more representative of central Mediterranean background levels; however, BKG_cor (Table 12) and BKG_ecor (Table 14) do not corroborate this finding, as both their PCC values are 0.075. CH₄ at BKG shows a northeastern PCC of −0.49 (p-value < 0.001), which also does not align with the cor and ecor counterparts (PCC = 0.070). These discrepancies may be attributable, in addition to the factors altering the O₃/NO_x ratio described in this article, to the different sample sizes (104 hourly measurements for BKG, 612 measurements for BKG_cor and BKG_ecor) and their susceptibility to correlations that are not corroborated by a higher number of measurements.

This research concludes with the infra-annual (Figure 15) and multi-year (Figure 16) analyses of the three gases. Monthly aggregations allow us to define infra-annual variability in greater detail, and a clear difference between LOC and other categories is shown for all gases. CO₂ is characterized by a median (summer) peak that does not align with the general trend of this compound, as summer concentrations are generally lower due to photosynthesis activity [129]. However, many pollutants can interfere with the photosynthesis process and thus reduce CO₂ uptake by the biosphere [130]. The local behavior of CO₂ would then require further investigation via the implementation of additional atmospheric tracers capable of differentiating between natural and anthropogenic sources. Figure 16 shows multi-year variability in accordance with the general trends described in Section 1: CO₂ and CH₄ are increasing globally, as confirmed at LMT by R–SRC and BKG variability between 2015 and 2023, while LOC concentrations are more susceptible to local sources. CO shows major fluctuations that are not linked to a well-defined increase or decrease; however, the influence of wildfire emissions [72,73] and fossil fuel emission mitigation strategies and policies [70,71] can impact the variability observed for BKG and R–SRC values at LMT.

Overall, this study has provided an unprecedented level of detail on the balance between local and remote sources of emissions at LMT, a site characterized by many peculiarities. The findings of this study regarding the atmospheric background levels shown in Figure 16 could be complemented by other measurements, such as the deviations from the Vienna Pee Dee Belemnite (VPDB) standard [131] of the carbon-13 to carbon-12 ratio (¹³C/¹²C) in CH₄ (δ¹³C-CH₄) and CO₂ (δ¹³C-CO₂), which serve as effective tracers of anthropogenic and natural emissions [129,132,133,134]. The LMT observation site is part of the developing cross-country network of continuous carbon isotope measurements, together with Lampedusa (LMP) in the Strait of Sicily, Potenza (POT) in the neighboring region of Basilicata, and Mt. Cimone (CMN) in the northern region of Emilia-Romagna [135]. The first preliminary results from this network, covering the period between July and December 2024, show the potential of carbon isotope measurements as an efficient tool for discriminating between emission sources [136]. The implementation of proximity indicators could differentiate background levels of δ¹³C-CH₄ and δ¹³C-CO₂ from those affected by anthropogenic emissions, and vice versa, characteristic isotopic fingerprints representative of the atmospheric background could be used to validate indicators based on the O₃/NO_x ratio and their respective corrections, including the “enhanced correction” (ecor) introduced in this study. One possibility would be to integrate the BKG categories defined in this study with other methods aimed at the detection of atmospheric background levels in the central Mediterranean that do not rely on carbon isotope fractionation [137], thereby verifying the applicability of the O₃/NO_x ratio for this purpose, with the goal of improving it even further via more precise correction factors. Future research based on these integrated methodologies could therefore increase the accuracy of atmospheric measurements aimed at background and unpolluted levels and define the applicability of corrections to other stations across the globe.

6. Conclusions

At the Lamezia Terme (LMT) World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) observation site in Calabria, Southern Italy, the ratio between surface ozone and nitrogen oxides (O₃/NO_x) has been used as a proximity indicator to assess, with unprecedented detail for the site, the source attribution among local (LOC), near source (N–SRC), remote source (R–SRC), and background (BKG) concentrations of carbon monoxide (CO), carbon dioxide (CO₂), and methane (CH₄). This study expands on the findings of previous research based on preliminary data gathered at the site by using nine full years (2015–2023) of measurements.

The implementation of these methodologies faces numerous challenges, such as coverage rates (multiple instruments need to operate at the same time) and the potential overestimation of NO₂ by certain instruments. A current limitation of this methodology is the absence of well-defined spatial boundaries for each category, which prevents accurate emission source attribution. Previous work used a correction factor for NO₂, which is also applied and expanded in this research; furthermore, this study introduces a new correction factor for O₃, based on the cyclic and multi-year assessment of this compound at the LMT site, which highlighted peaks in photochemical activity during boreal warm seasons in the central Mediterranean region.

The results of this study confirm a number of hypotheses raised in previous works regarding the peaks observed at the site: CH₄ peaks are attributed to the LOC category, indicating local sources of emission such as agriculture and livestock farming; local emissions with increased concentrations of CO, CO₂, and CH₄ are linked to low wind speeds from the northeastern sector, which is subject to anthropogenic pollution; and seasonal variabilities show changes in the nature of sources of emission (e.g., summertime open fires and wintertime biomass burning for purposes such as domestic heating). In addition to the findings and the validation of hypotheses detailed in previous works, a number of observations, such as weekly trends and the interplay of local anthropogenic and natural sources, have not shown the expected differentiation. The absence of weekly trends for the local proximity category is interpreted in light of the prevailing rural nature of the area surrounding the LMT observation site in the westernmost sector of the Catanzaro isthmus. Furthermore, the behavior of CO₂ under specific conditions raises questions regarding the balances of emissions and sinks in the area. Statistical correlations on a per-category basis have confirmed a number of hypotheses from previous works on LMT measurements and also highlighted the susceptibility of the O₃/NO_x ratio and its corrections to categorizations based on wind sectors and the relative orientation with respect to known sources of anthropogenic emissions. These discrepancies require further investigation and the implementation of additional atmospheric tracers, such as carbon isotope ratios in CO₂ and CH₄ (δ¹³C-CO₂ and δ¹³C-CH₄), which would allow for better constraints on specific emission sources with enhanced spatial resolution.

This study also underscores the importance of making ad hoc corrections to the measurements taken at a given observation site, accounting for the site’s characteristic wind circulation patterns, climate, and variability in the concentrations of atmospheric gases. The same corrections could be applied to atmospheric stations that share the same key peculiarities.