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Article

Multimethodological Approach for the Evaluation of Tropospheric Ozone’s Regional Photochemical Pollution at the WMO/GAW Station of Lamezia Terme, Italy

by
Francesco D’Amico
1,2,*,
Giorgia De Benedetto
1,
Luana Malacaria
1,
Salvatore Sinopoli
1,
Arijit Dutta
3,
Teresa Lo Feudo
1,*,
Daniel Gullì
1,
Ivano Ammoscato
1,
Mariafrancesca De Pino
1 and
Claudia Roberta Calidonna
1,*
1
Institute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy, Area Industriale Comparto 15, 88046 Lamezia Terme, Catanzaro, Italy
2
Department of Biology, Ecology and Earth Sciences (DiBEST), University of Calabria, Via Pietro Bucci Cubo 15B, 87036 Rende, Cosenza, Italy
3
Department of Computer Engineering, Modeling, Electronics and Systems (DiMES), University of Calabria, Via Pietro Bucci Cubo 42C, 87036 Rende, Cosenza, Italy
*
Authors to whom correspondence should be addressed.
AppliedChem 2025, 5(2), 10; https://doi.org/10.3390/appliedchem5020010
Submission received: 4 April 2025 / Revised: 13 May 2025 / Accepted: 14 May 2025 / Published: 20 May 2025
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Abstract

:
The photochemical production of tropospheric ozone (O3) is very closely linked to seasonal cycles and peaks in solar radiation occurring during warm seasons. In the Mediterranean Basin, which is a hotspot for climate and air mass transport mechanisms, boreal warm seasons cause a notable increase in tropospheric O3, which unlike stratospheric O3 is not beneficial for the environment. At the Lamezia Terme (code: LMT) World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) station located in Calabria, Southern Italy, peaks of tropospheric O3 were observed during boreal summer and spring seasons, and were consequently linked to specific wind patterns compatible with increased photochemical activity in the Tyrrhenian Sea. The finding resulted in the introduction of a correction factor for O3 in the O3/NOx (ozone to nitrogen oxides) ratio “Proximity” methodology for the assessment of air mass aging. However, some of the mechanisms driving O3 patterns and their correlation with other parameters at the LMT site remain unknown, despite the environmental and health hazards posed by tropospheric O3 in the area. In general, the issue of ozone photochemical pollution in the region of Calabria, Italy, is understudied. In this study, the behavior of O3 at the site is assessed with remarkable detail using nine years (2015–2023) of data and correlations with surface temperature and solar radiation. The evaluations demonstrate non-negligible correlations between environmental factors, such as temperature and solar radiation, and O3 concentrations, driven by peculiar patterns in local wind circulation. The northeastern sector of LMT, partly neglected in previous works, yielded higher statistical correlations with O3 than expected. The findings of this study also indicate, for central Calabria, the possibility of heterogeneities in O3 exposure due to local geomorphology and wind patterns. A case study of very high O3 concentrations reported during the 2015 summer season is also reported by analyzing the tendencies observed during the period with additional methodologies and highlighting drivers of photochemical pollution on larger scales, also demonstrating that near-surface concentrations result from specific combinations of multiple factors.

1. Introduction

Photochemical processes are a key driver in atmospheric chemistry [1], as well as in regular chemical dynamics [2]. Ozone (O3) is a reactive and oxidant gas, heavily influenced by photochemistry, discovered by Christian Friedrich Schönbein in the nineteenth century [3,4]. Following its discovery, O3 was proven to increase with altitude and show distinct behavior based on vertical gradients, thus leading to a well-defined differentiation between tropospheric O3 and stratospheric O3, with the latter being beneficial for the environment [5] as it reduces the impact of solar radiation on terrestrial ecosystems [6,7,8] while the former poses health issues to living organisms [9,10,11,12,13,14]. Stratospheric O3 depletion caused by anthropogenic emissions has been the core issue of environmental policies for years, up until the implementation of adequate mitigation measures [15,16,17,18,19,20].
Tropospheric O3 has a concentration of a few dozen ppb (parts per billion), while stratospheric O3 peaks at 20–30 km above ground level with concentrations of ≈10 ppm (parts per million) [21,22,23,24,25,26]. STT (stratosphere-to-troposphere transport) events may lead to SI (stratospheric intrusion) phenomena, and therefore increase tropospheric O3 under specific conditions [27,28,29,30,31,32].
The photochemical production of O3 has been the subject of research for decades [33,34,35,36,37,38,39]. Significant correlations between O3 production and the presence of other compounds in the atmosphere, such as VOCs (Volatile Organic Compounds), have also been reported across the globe [40,41,42,43,44,45].
Among the drivers of O3 production in the atmosphere are photo-oxidation mechanisms triggered by nitrogen oxides (NO + NO2 = NOx) and affecting the above-mentioned VOCs; NMVOCs (Non-Methane VOCs) and NOx may be the result of natural activities and anthropogenic emissions; however, it is worth mentioning that the mechanisms leading to O3 production may vary in nature [46,47] due to chemical reactions—specifically, titration—resulting in NO2 increases from NO [48,49]. Two of the main carbon compounds present in the atmosphere, CO (carbon monoxide) and CH4 (methane), are also connected to tropospheric O3 production [50]. Intense solar radiation, which in the context of the Mediterranean Basin is primarily linked with boreal summer seasons, is known to intensify O3 production [46]; specifically, several works have highlighted the exposure of southern European regions to these processes, thus sparking notable interest on the topic of regional photochemical pollution in the Mediterranean [51,52,53,54,55,56,57,58,59,60].
Increases in tropospheric O3 concentrations may be the result of the interplay of local-to-remote mechanisms, as air mass transport and photochemistry can result in tropospheric O3 increases far from the initial emission sources of O3 precursors; these effects have been observed over notable distances, crossing entire oceans [61], but are most notably reported on a continental scale [53,54,59,60,62,63]. The effects may be amplified in regions such as the Mediterranean, which is a known hotspot for climate, air quality, and air mass transport processes [64,65,66,67,68]. The Mediterranean is also known to be sensitive to various stresses, such as air pollution and water shortage [69,70]. The area is also characterized by differences between its eastern and western sectors that affect air circulation patterns [71,72].
These heterogeneities also result in peculiar sources of NOx and VOC emissions that drive O3 production and its consequent chemical reactions in the atmosphere [73,74,75,76] as well as the transport of air masses enriched in O3 [53,54,62,77]. Overall, the combination of the above-mentioned factors has allowed the Mediterranean Basin to be defined as a major hotspot for the study and evaluation of tropospheric O3, especially considering the reported differences in O3 variability between the eastern and western sectors of the basin itself [51,54,56,59,60,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94].
In addition to the studies aimed at O3 patterns and variability over wide areas in the Mediterranean Basin, research has also focused specifically on the Italian peninsula [95,96,97]. At the World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) observation site of Lamezia Terme (code: LMT) in the southern region of Calabria, previous research has evidenced peaks in O3 concentrations attributable to enhanced photochemical activity [98]. The peaks have been considered in subsequent research based on the ratio of O3 to NOx (nitrogen oxides) as a proximity and air mass aging indicator, by halving the reported concentration of O3 under specific conditions in order to compensate for photochemical production peaks [99]. This factor supplemented another correction meant to compensate for possible NO2 overestimation caused by the presence of heated molybdenum converters in NOx analyzers [100]. At LMT, the O3/NOx ratio has allowed the determination, with unprecedented accuracy, of the balance of local, intermediate, and remote contributions to local measurements [98,100]. However, the mechanisms driving O3 increases are yet to be fully characterized at the site, especially with respect to environmental factors such as ground temperature and solar radiation. Furthermore, research efforts aimed at a detailed evaluation of O3 in Italy are not equally distributed across the country, thus leaving entire areas not adequately characterized in terms of O3 risks and hazards for human health and the environment [101,102,103,104,105,106]. The peninsular region of Calabria, for example, is presently understudied in terms of photochemical pollution and related risks. A more detailed understanding of these mechanisms can therefore integrate present-day knowledge on O3 behavior in the central Mediterranean, provide regulators and policymakers with additional tools necessary to issue warnings and alerts, and also contribute towards the enhancement of the O3/NOx methodology, which in turn is necessary to better evaluate medium- to long-term tendencies and variabilities of CO2 (carbon dioxide), CO, CH4, and other atmospheric compounds linked to anthropogenic emissions [107]. This work is divided as follows: Section 2 will describe the LMT site and applicable methodologies of this study; Section 3 provides the results of this assessment; Section 4 and Section 5 discuss the results and conclude the paper, respectively.

2. The Lamezia Terme Station and Employed Methods

2.1. The LMT WMO/GAW Observation Site in Calabria, Southern Italy

Located 600 m from the Tyrrhenian coastline of Calabria (Italy), the Lamezia Terme (code: LMT; Lat: 8°52.605′ N; Lon: 16°13.946′ E; Elev: 6 m AGL) (Figure 1A) observation site is part of the World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) network and is fully operated by the National Research Council of Italy—Institute of Atmospheric Sciences and Climate (CNR-ISAC). The observation site is located in the industrial area of the Lamezia Terme municipality, within the province of Catanzaro. With a distance of ≈32 km between the Tyrrhenian and Ionian coasts of the region, LMT is located in the westernmost sector of the narrowest point in the entire Italian peninsula, the Catanzaro isthmus. The isthmus effectively separates the Sant’Eufemia plain, where the observation is located, from southern (Serre Massif) and northern (Coastal Chain, Sila Massif) mountain ranges [108,109]. As a result of this peculiar geomorphological framework, near-surface winds are channeled through the isthmus [110], exploiting the depression caused by tectonics [111,112,113,114,115,116,117]. In the early Quaternary period, the isthmus accommodated a tidal strait directly connecting the two seas, as evidenced by local outcrops showing structures linked to 3D and 2D dunes [118,119,120].
The Calabrian Arc uplift triggered marine regression and transgression cycles [121,122,123,124,125,126,127], which combined with sea level variations induced by alternating interglacial and glacial periods [128,129,130], thus leading to the present-day configuration.
Appliedchem 05 00010 g001aAppliedchem 05 00010 g001b
Figure 1. (A) Location of the LMT observation site in Southern Italy, shown on an EUMETSAT [131] map. (B) Digital Terrain Model (DTM) of central Calabria showing the main geomorphological features of the sector. (C) Wind rose showing near-surface wind circulation at LMT during the observation period (2015–2023).
Figure 1. (A) Location of the LMT observation site in Southern Italy, shown on an EUMETSAT [131] map. (B) Digital Terrain Model (DTM) of central Calabria showing the main geomorphological features of the sector. (C) Wind rose showing near-surface wind circulation at LMT during the observation period (2015–2023).
Appliedchem 05 00010 g001aAppliedchem 05 00010 g001b
The unique geomorphological and orographic framework of the Catanzaro isthmus (Figure 1B) compared to the rest of the Italian peninsula allows near-surface wind circulation to be well oriented on a preferential W/NE axis as observed by the LMT observatory itself (Figure 1C). Wind patterns are influenced by seasonal variability, breeze regimes, and eastern/western synoptic conditions, and have also shown vertical gradients reflecting large-scale circulation in the area and local orography [132,133,134,135,136,137]. Breeze regimes were proven to be a key regulator of local climate and wind circulation, with seasonal variations also being reported; however, a primary W-WSW/NE-ENE axis dominates near-surface circulation, as a consequence of local orography. When considering the 850 hPa layer, however, large-scale flows in the area dominate with a preferred NW direction [132]. During most of the cold months, specifically between November and February, large-scale forcing regulates diurnal wind circulation patterns; the March–October period is characterized by nighttime flows connected to nocturnal breeze regimes, and diurnal breezes resulting from both large-scale and local flows [133].
LMT started data-gathering operations in 2015. A preliminary study by Cristofanelli et al. (2017) provided the first data on O3 concentrations in the area, also accounting for the implementation of proximity categories based on the O3/NOx ratio [100]. This early work allowed the evaluation of local sources of pollution, such as livestock farming in the Sant’Eufemia plain where LMT is located, as well as transportation contributions from the A2 highway and the Lamezia Terme International Airport (IATA: SUF; ICAO: LICA) located 2 km north of LMT. Consequently, an evaluation of several years of CH4 data (2016–2022) reported that northeastern–continental winds were enriched in anthropogenic outputs, while western–seaside winds generally yielded lower concentrations; higher concentrations were reported in winter, and lower concentrations during the summer [138].
The multi-year cyclic analysis of O3 (2015–2023) relied on a larger dataset to assess O3 variability in the area with greater detail, and demonstrated the presence of westerly peaks in concentrations linked to warm seasons, thus showing opposite behavior to CH4 [98].
Due to its location in the central Mediterranean, LMT is subject to frequent dust episodes [139] and wildfire emissions [140], which peaked during the 2021 Mediterranean crisis due to large wildfires in the region of Calabria itself [141], as well as other locations in Italy and various countries overlooking the Mediterranean Basin [142]. Over the course of its operational history, the LMT station has been subject to comparative analyses aimed at multiple southern Italian atmospheric observatories, also accounting for aerosol optical properties and the influence of anthropic activities on measurements [143,144]. A study exploiting the strict restrictions applied by the Italian government during the first COVID-19 lockdown of 2020 [145,146] allowed researchers to pinpoint local emission sources with greater accuracy and validate previous hypotheses on said sources by evaluating circumstances of exceptionally low anthropogenic emissions [147].

2.2. Surface Measurements, Available Satellite Products, and Data Processing

At the LMT station, surface O3 mole fractions in ppb (parts per billion) have been measured by a Thermo Scientific 49i (Franklin, MA, USA), an instrument operating as a photometric analyzer [98]. The 49i model, operating with a precision of ±0.1 ppb O3 and a flow rate of 1–3 L per minute (L/min), performs its measurements based on Beer–Lambert’s Law and, specifically, O3’s absorption of ultraviolet (UV) light at a wavelength of 254 nanometers (nm). Atmospheric O3 mole fractions are calculated from a comparison between the absorption occurring in a primary standard depleted in O3 by a scrubber, and the UV absorption at 254 nm in sampled air. The 49i instrument gathers ambient air and splits it into two distinct gas flows: the reference or standard gas used for the comparison passes through a pressure regulator, an O3 scrubber, and the standard solenoid valve; ambient air flows through the pressure regulator, the ozonator, and the manifold to the sample solenoid valve. During measurements, the solenoid valves alternate the standard and ambient air streams between two cells, designated A and B, every 10 s. When one cell (for example, A) contains standard gas, the other (B) contains ambient air and vice versa. The 49i model measures UV light intensities in both cells; these measurements are interrupted for several seconds when the solenoid valves alternate between the two air flows. The interruption is necessary to ensure optimal flushing of both cells and prevent residual air from the previous flow from affecting new measurements. The final output of ambient air O3 mole fractions is calculated, as per Beer–Lambert’s Law, via the ratio of measured UV light intensities in ambient air and standard, O3-depleted, air. For the purpose of this research, hourly aggregates of O3 measurements in ppb have been used.
Surface measurements of downward solar radiation (W/m2) at the LMT site were performed by a Kipp & Zonen CNR4 (Delft, The Netherlands) radiometer. The instrument operates via two pyranometers and two pyrogeometers to measure downward (0.31–2.8 μm) and upward (4.5–42 μm) irradiance, respectively. As per the Baseline Surface Radiation Network (BSRN) standard, the uncertainty in these measurements is in the 1% range [148]. Previously gathered data at LMT have been studied for early-stage characterization of the site and a number of correlations with readily available products [149,150].
Key meteorological data on the surface were gathered by a Vaisala WXT520 (Vantaa, Finland) weather station. Wind directions (WDs, in degrees) and speeds (WSs, in meters per second) are measured via ultrasounds transmitted on a horizontal plane between three transducers. Travel times of ultrasound pulses between transducers are altered by specific wind directions and speeds, which are measured by the instrument with a precision of ±3° and ±0.3 m/s, respectively. Temperature is measured by two reference capacitors and an RC oscillator; specifically, the instrument measures the capacitance of its sensors against both capacitors with a precision of ±0.3 °C. The instrument gathers data on a per-minute basis, then aggregates them, for the purpose of this research, to generate hourly means.
The main details and specifications of the employed instruments for surface measurements of O3, solar radiation, and temperature are shown in Table 1.
Several tools, methodologies and algorithms allow us to estimate, with various degrees of accuracy, solar radiation at specific coordinates over select time spans [151,152,153,154,155,156,157]. The case study presented in this work (Section 3.5) has been evaluated via the retrieval of DSSF (Downward Surface Shortwave Flux) products from the MSG-SEVIRI instrument by LSA-SAF (Land Surface Analysis Satellite Application Facility) [158]. DSSF refers to radiative energy falling in the 0.3–4.0 µm wavelength range reaching the surface; this parameter is widely used in environmental monitoring, oftentimes making up for the absence of local surface solar radiation instruments [159,160]. An LSA-SAF algorithm estimates DSSF values from three shortwave SEVIRI channels, yielding a spatial resolution of 3 km [161]. Due to the influence of factors such as cloud coverage, the algorithm has been optimized and upgraded at various intervals to improve the final product and its reliability [162,163]. A previous study aimed at the LMT observation site validated these products via a direct comparison with solar radiation observed at the station; however, a difference in the range of 55–87 W/m2 was reported [149]. Such differences had already been reported in similar studies [164,165].
For the case study described in Section 3.5 and, specifically, for an evaluation of tropospheric O3’s concentrations on a regional scale, OMI products were also used. OMI is an advanced instrument used by the Aura spacecraft by NASA’s EOS (Earth Observing System) [166]. The instrument depolarizes incoming light and splits it into two channels: VIS (visible, 350–500 nm) and UV (ultraviolet, 270–380 nm) [167]. With a viewing angle of 114° ensuring a wide swath of 2600 km, OMI’s ground pixel at nadir is 13 × 24 km [166]. Although OMI products cannot be directly correlated with surface measurements, they allow us to describe the evolution of tropospheric O3 variability over a wide area in the central Mediterranean, including transport phenomena.
During the 2015–2023 study period, each instrument used at LMT was characterized by a specific coverage rate in terms of hourly aggregates. In this work, data from multiple instruments have been compared, so the resulting datasets are influenced by maintenance issues and quality assurance checks. In Table 2, the main data concerning coverage rates between 2015 and 2023 are reported. The low coverage rate reported for solar radiation measurements in 2018 is due to a single, major malfunction affecting the CNR4 instrument from 1 January to 30 September.
Statistics and correlation factors between evaluated parameters have been computed in Jamovi v. 2.6.22.0. Specifically, Spearman’s Rank Correlation Coefficient (SR) and Pearson’s Correlation Coefficient (PCC) have been used to test correlations between O3, temperature, and solar radiation [168,169,170,171].
Air mass aging and proximity categories [172,173] have been used based on the findings of previous studies on LMT measurements, which exploited the ratio of O3 to NOx as a tool to differentiate local and anthropogenic-influenced air masses from their remote counterparts, more representative of atmospheric background levels [99,100]. These proximity categories were defined as follows: atmospheric background (BKG) air masses are defined by O3/NOx > 100; R-SRC (remote source) air masses are characterized by a ratio in the 50 < O3/NOx ≤ 100 range; N-SRC (near-source) air masses are defined as yielding a ratio of 10 < O3/NOx ≤ 50; local emissions and air masses (LOC) have an O3/NOx ratio lower than or equal to the threshold of 10. In addition to the four main categories, four additional proximity categories were introduced in previous studies on LMT measurements [99,100]: the standard correction (“cor”) of R-SRC and BKG, which is meant to compensate for NO2 overestimation caused by the presence of heated molybdenum converters in employed instruments and consequent uncertainties in the measurement of total NOx [174,175,176,177,178]; and the enhanced correction (“ecor”), also applied to R-SRC and BKG, is meant to account for the effects of the previous correction as well as increased photochemical production of O3 during warm seasons [98].
Jarque–Bera tests [179,180,181] on each of the evaluated parameters have been performed in R to test the normality of available O3, temperature, and solar radiation datasets. The tests yielded p-values consistently below 0.001, thus allowing us to reject the null hypothesis that the data are normally distributed.

3. Results

3.1. Seasonal and Monthly Behaviors of Evaluated Parameters Through the Year

Previous works on O3 concentrations measured at LMT have been based on seasonal patterns, with each season being linked to a specific trimester (e.g., summer for June, July, and August) [98,100,138]. Considering temperature and solar radiation’s influence on O3, this study introduces two broad “warm” (May–September) and “cold” (October–April) seasons, meant to be more representative of differences affecting the regional photochemical pollution of O3.
The wind roses shown in Figure 2 report the distribution of hourly aggregated O3 concentrations measured at LMT based on seasonality and wind speed/direction.
During the course of a year, the main parameters analyzed in this work (O3, temperature, and solar radiation) are subject to seasonal patterns and the interplay of multiple factors. With this work considering broader warm and cold seasons, the conventional season–trimester model used in previous research is replaced by a categorization more representative of the combined variability of all observed parameters. This variability can be noticed with greater detail by considering monthly averages, as shown in Figure 3.
Overall, although April yields generally high O3 concentrations and solar radiation values, its temperature falls below the average of the broader warm season considered in this study. Conversely, October has a relatively high mean temperature, but low O3 and solar radiation.

3.2. Correlations Between O3, Solar Radiation, and Temperature at the Site

The evaluation of correlations between physical and chemical parameters has been widely used in previous works based on multi-year LMT datasets [110,182]. However, this was not directly applied to O3 [98]. In this section of the work, the three main parameters are tested using Pearson’s Correlation Coefficient (PCC) and Spearman’s Rank (SR), as well as their respective p-values, to test these correlations. Table 3, Table 4 and Table 5 provide the results of statistical evaluations, divided on a seasonal basis. Figure 4, Figure 5 and Figure 6 show density distribution plots based on HDRs (High-Density Regions) and the probability of density distributions [183], which are very effective at showing areas reflecting specific percentages of distribution in a dataset [184].
The results indicate the presence of a number of statistically relevant correlations, which are investigated further depending on their season.
Finally, the cold season is considered. The results indicate non-negligible correlation factors which require additional evaluations and analyses.

3.3. Daily Cycle Variability

As a coastal Mediterranean site, LMT is heavily influenced by daily cycle variability, which is the result of alternating wind patterns and local orography [110]. These patterns have a direct impact on the concentrations of gases [100,138], including O3 [98] and aerosols [137]. Unlike other parameters, O3 was reported to peak during diurnal hours, which are generally more influenced by westerly winds and also reflect increased photochemical activity, especially during warm seasons [98,99]. Figure 7 shows the behavior of key measured parameters during the standard daily cycle, differentiated by season.
This analysis of the daily cycle shows that seasonal differences in solar radiation and temperature are very well defined; however, O3 has a hybrid behavior, with diurnal hours yielding higher warm-season concentrations, while early morning concentrations are reportedly higher during the cold season.

3.4. Correlations Based on O3/NOx Ratio Proximity Categories

The O3/NOx ratio has been applied to LMT datasets to differentiate local, intermediate, and remote air masses, each with unique characteristics in terms of anthropogenic influences [99,100]. Using the standard and corrected proximity categories, new correlations have been tested to verify differences based on air mass aging, anthropic activities, and environmental factors. These categories are defined based on the description provided in Section 2 [99,100]. The results are shown in Table 6.
With differences being reported in the statistical significance of correlation statistics and the influence of seasonality and wind corridors, additional combinations of factors have been tested for the warm (Table 7) and cold (Table 8) seasons.
Data ellipses set on a normal distribution with an 80% confidence interval [185] have been used to group data by proximity category, thus allowing us to visualize differences between proximity and air mass aging categories. This methodology is on the same data categorization used in a previous study on LMT data gathered during the first COVID-19 lockdown in the country [145,146], which showed a peculiar pattern based on governmental restrictions on anthropic activities [147]. The resulting plots are shown in Figure 8 (warm season) and Figure 9 (cold season). In order to optimize visualization, only the four main proximity categories are shown.
From the computed data ellipses, it is possible to notice that the LOC category is systematically linked to lower O3 concentrations under all circumstances, and is very well differentiated from the others. Following the same methodology applied to previous studies [99,100], average concentrations of O3 have been calculated depending on proximity categories, both standard and corrected. The results are reported in Table 9 and Figure 10.
The differences between proximity and air mass aging categories were further investigated using the Kruskal–Wallis [186] and the pairwise Wilcoxon rank sum [187,188] tests, as the data were proven not to have a normal distribution.
The Kruskal–Wallis test was performed on O3, temperature, and solar radiation based on standard, corrected (“cor”), and enhanced corrected (“ecor”) proximity categories, consistently yielding p-values lower than 0.001, a result indicating the statistical significance of the reported differences between categories. The pairwise Wilcoxon test (also known as the Mann–Whitney U test) yielded p-values lower than 0.001 for all evaluated pairs, with the exception of R–SRC/BKG for solar radiation (p = 0.002) (which is still statistically significant), R–SRCcor/BKGcor for O3 (p = 0.6), and R–SRCcor/BKGcor for temperature (p = 0.002) (still significant).
From this evaluation, it is inferred that local sources of emissions are not responsible for high O3 concentrations measured at the site, as the concentrations tend to increase in the atmospheric background. Furthermore, local O3 concentrations are higher during cold seasons, while the warm seasons yield the highest remote source and atmospheric background concentrations. This behavior is opposite to that observed for CO, CO2, and CH4 at LMT [99], thus highlighting the peculiarity of O3’s variability compared to other gases.

3.5. Case Study: 10–15 May 2015

A case study (CS) was selected based on the top 0.5% O3 concentrations measured at LMT during the observation period (2015–2023). Between 11 and 15 May 2015, very high concentrations of surface O3, leaning towards the threshold of 80 ppb, were reported at the site. In the CS, 10 May was also included to show key parameters prior to the increase in O3. Surface measurements were integrated from MSG SEVIRI and OMI products, as described in Section 2.2.
SEVIRI products are dependent on the nature of the surface and provide no results over the sea. For this reason, the Stromboli volcanic island, located ≈86 km W-SW from LMT, has been selected as a representative of downward solar radiation conditions in the nearest sector of the Tyrrhenian Sea. The Ionian point has been selected based on the ION1 location from a previous study, as it has been demonstrated to be well representative of conditions on the Ionian coast on the opposite side of the LMT observation site [182]. DDSF products referring to LMT’s location have also been selected for direct correlation with SWdown measurements at the station. The relative positions of Stromboli and “Ionian” compared to LMT, as well as a wind rose of near-surface wind circulation during that period, are both shown in Figure 11.
Surface O3 concentrations, temperatures, and solar radiation data have been correlated with solar radiation provided by MSG SEVIRI products. The results of these correlations are shown in Table 10.
Tendencies observed during the CS are shown in Figure 12 using dual-scale plots comparing surface O3 with other parameters.
Finally, available OMI products have been used to evaluate the CS on a large scale and monitor its evolution over time. OMI data are shown in Figure 13; data from 11 May referring to LMT and other regions of Calabria are not available. Overall, an air mass transport phenomenon from the west is reported. These products cannot be directly compared with surface measurements at LMT; however, they have the advantage of showing tropospheric O3 variability over a wide area, with a focus on the Tyrrhenian.

4. Discussion

At the Lamezia Terme (LMT) WMO/GAW station in Calabria, Southern Italy, nine years of continuous measurements of surface ozone (O3, in ppb), temperature (°C), and downward solar radiation (W/m2) have been analyzed in conjunction with other parameters, such as sectors defined by specific wind directions, to assess the influence of regional photochemical pollution of O3. The station’s position in the central Mediterranean (Figure 1A), combined with peculiar near-surface wind patterns influenced by local orography and the geomorphological characteristics of the Catanzaro isthmus (Figure 1B,C), the narrowest point in the entire Italian peninsula [110,132,133], all result in the presence of multiple factors that affect the diffusion of O3 in the area. This study constitutes the first attempt in the region to characterize O3 variability using multiple environmental factors and parameters, as the previous studies in the region were either limited to short-term field testing of new methodologies [189,190], affected by short observation spans [191,192], or lacked the implementation of temperature, solar radiation, and satellite products [98].
The implementation of methodologies relying on the combination of data from more instruments (Table 1 and Table 2) is heavily affected by each instrument’s maintenance issues and data availability. In this case, in order to properly correlate O3 with solar radiation, temperature, and a given wind sector, four instruments need to operate at the same time. The coverage rates shown in Table 2 clearly indicate that years affected by instrumental issues (e.g., the CNR4 in 2018 due to a major malfunction from 1 January to 30 September) consequently affect the applicability of select methodologies. However, combined coverage rates are in the 80–90% range, thus allowing the evaluation of statistically relevant correlations based on hourly aggregated data.
Unlike previous studies based on a well-defined division of quarters in seasons (e.g., September, October, and November for fall), this work has considered two broader, extended seasons: a warm season (five months, May–September), and a cold season (seven months, October–April). In Figure 2, notable differences in O3 patterns and distribution can be noticed between both seasons; these differences can also be inferred by the monthly patterns shown in Figure 3, which underline peculiar variabilities of O3, temperature and solar radiation across the standard year, but still identify May–September as the ideal “warm season” to test regional photochemical pollution. Months falling outside the warm season are either typical of boreal winter climate (e.g., January, December) [147] or show some, but not all, of the characteristics of warm periods (e.g., April, October).
The first results of PCC and SR correlations between O3 and other parameters are shown in Table 3. In this case, solar radiation data have been categorized to discriminate positive downward radiation, linked to diurnal hours, from negative radiation, which is typically nocturnal. This differentiation is therefore intended as an effective analysis of all-time and diurnal conditions, with only the latter being susceptible to peaks caused by photochemical activity in the central Mediterranean. PCC correlations between O3 and radiation are in the 0.174–0.502 range, thus indicating the presence of a non-negligible linear correlation. The highest PCC factor (0.502) was reported when including all radiation data, including negative values, thus indicating a broad correlation between O3 and downward radiation itself. Conversely, the lowest rate of 0.174 is reported on positive radiation values from the west. SR factors are generally higher than their PCC counterparts, with a peak at 0.592 that is representative of a monotonic relationship. All p-values are lower than 0.001, so the tested correlations are statistically significant. A correlation with temperature is also evidenced by PCC factors ranging from 0.071 to 0.362. The strongest correlation is reported for all data, including negative values of radiation (0.362), suggesting a moderate positive relationship between temperature and O3. The weakest correlation is reported for positive-only data from the western sector (0.071), indicating a minimal effect, under those circumstances, in the direction of the Tyrrhenian Sea. SR factors follow a similar pattern, with values in the 0.106–0.352 range. Overall, radiation yields higher correlation values compared to temperature. All p-values are <0.001 also in this case; therefore, the correlations are statistically significant. Figure 4 allows us to visualize these results, with higher density levels in the distribution indicating well-defined combinations of O3 with temperature or radiation. These results provide the first significant evidence of an active correlation between tested parameters at the LMT site, as hypothesized in a previous study [99].
With the correlations accounting for all data yielding relevant results, additional correlations were tested based on seasonality. The warm season (Table 4) has yielded statistically significant PCC correlation factors in the 0.174–0.528 range, and a notable SR value of 0.605. Both the high PCC and SR values are linked to all radiation data and, as in the previous case, the lowest correlation is found for westerly, positive-only radiation data (PCC = 0.174; SR = 0.173). All correlations are statistically significant. When temperature is considered, an unexpected negative correlation is reported for the western sector, up to −0.132, while the highest is once again linked to “All Rad.” (PCC = 0.343; SR = 0.316). Temperature correlation factors are therefore lower compared to those of radiation. The increase in correlation between O3 and radiation can also be inferred by Figure 4A, which shows a density level at higher radiation thresholds that was absent when considering all data; Figure 4B shows, compared to the previous case, a concentration of density levels linked to a narrower temperature range.
With the warm season evaluated, the cold season was also subject to statistical analysis (Table 5). O3 and radiation yielded statistically significant PCC values in the 0.269–0.459 range, with the peak linked to the northeastern sector, with positive-only radiation data (PCC = 0.459; SR = 0.444). Conversely, the weakest correlation is reported when considering all radiation data from the western sector (PCC = 0.269; SR = 0.270). The correlation factors are lower, in this case, compared to their warm-season counterparts. The northeastern peak may be an indicator of anthropogenic influence, as demonstrated by previous research [99,100,138]. Specifically, northeastern peaks are compatible with NO2 photolysis/photodissociation and O3 production in the troposphere [193,194,195]. When considering the temperature, a lack of direct correlation is observed on the western sector (PCC = −0.033; SR = −0.001), while the strongest positive correlation is reported from the northeast (PCC = 0.402; SR = 0.397). This pattern is similar to that observed for radiation, and has also yielded statistically significant results. This finding demonstrates that, although clear seasonal tendencies in surface O3 are reported at the site and result in summertime peaks from the western–seaside sector [98], O3 production during cold seasons is more linked to temperatures and radiation than initially expected. The density levels shown in Figure 6 for the cold season do not show the same distribution observed in the warm season. While these results clearly indicate that absolute peaks in O3 concentrations at LMT are linked to the Tyrrhenian Sea sector [98], the continental (northeastern) and Ionian Sea sectors show non negligible correlation factors with O3, thus indicating that regional photochemical pollution is subject to a higher number of driving factors than initially expected. In several studies on LMT data variability, the northeastern sector (Figure 1B) was correlated with polluted air masses [100,107,138]; however, it was later proven that, under exceptional conditions, air masses minimally influenced by anthropogenic emissions can be channeled through the Catanzaro isthmus, thus showing characteristics similar to their western counterparts [99]. This finding, initially attributed to CO, CO2, and CH4, can now be extended to O3.
More information can be inferred from the daily cycle, which is one of LMT’s main characteristics in terms of data variability [98,99,138,182]. Figure 7 shows that downward radiation (Figure 7B) and temperatures (Figure 7C) are well differentiated depending on seasonality, while O3 (Figure 7A) shows a peculiar pattern. In fact, diurnal hours yield higher concentrations during the warm season, in accordance with previous findings [98], but the concentrations tend to be identical at 23:00 and 00:00, thus indicating a standard, background or “zero” condition that is unaffected by the seasonal patterns of anthropic activity and atmospheric chemistry. Between 01:00 and 05:00, cold-season concentrations are higher, thus indicating an anthropogenic footprint; considering that nocturnal winds are generally attributed to the northeastern–continental wind corridor, this may explain the higher-than-expected correlation factors observed from that sector. The inversion patterns that occur from 06:00 onwards and drive the daily cycle at LMT are also known to heavily affect the concentration of pollutants, especially those of anthropogenic origin [137,147].
At LMT, the daily cycle is heavily influenced by different wind regimes (breeze, not-complete breeze, synoptic winds), and inversions occurring when a particular regime is replaced by another (e.g., the transition between eastern and western synoptic) frequently result in the precipitation of pollutants that would otherwise be subject to air mass transport at higher altitudes [137,142,147]. Inversions normally occur in the morning and in the late afternoon, and result in well-defined peaks in pollutants which are not present in the next few hours: in the case of O3, this pattern is absent, as the increase is linked to diurnal photochemical activity if maintained up until the next inversion, thus leading to a buildup of O3 concentrations which could be potentially hazardous.
A further assessment of the factors regulating O3 production and diffusion in the region was performed using the O3/NOx ratio as a proximity indicator. This methodology had previously been applied to preliminary data gathered at LMT [100], and was later implemented using a larger dataset [99], as well as additional correction factors based on previous research on O3 variability at the site. Although the method is affected by limitations, such as data availability (in order to be applicable, it requires four instruments operating at the same time) and uncertainties in NOx measurements caused by specific instruments [178], it has been used to determine the differences between local anthropogenic outputs and atmospheric background concentrations [99,100]. A similar approach, although limited to one month of measurements and based on a different attribution (three proximity categories instead of four, and no correction factors), was previously applied to evaluate O3 variability in Northern Italy [196]. Table 6 shows the results of key correlations between O3 and temperature/radiation. Some of the correlations are not statistically significant, and are also affected by data availability and the characteristics of each category (LOC (local) is more common than BKG (background)). In the case of LOC, it is assumed that photochemical production and temperatures would not have sufficient time to affect O3 concentrations prior to measurements: in fact, the correlations are negligible (≈0) except for the SR of O3/radiation at 0.183. N-SRC (near-source), which is an intermediate category between LOC and atmospheric background levels, has statistically significant correlations in the −0.108–0.243 range. R-SRC (remote source), which is meant to be more influenced by photochemical pollution, yields statistically relevant correlations in the −0.083–0.218 range, while BKG yields the −0.130–0.221 range, which is not always significant. R-SRC and BKG, which are linked to aged air masses, have been subject over time to the implementation of correction factors. When considering the standard (“cor”) correction, neither R-SRCcor nor BKGcor shows particular differences; however, the amount of data changes considerably due to the different criteria used for each category. No major differences are reported for the enhanced (“ecor”) corrections, as R-SRCecor and BKGecor do not show substantial differences in terms of correlation statistics. For these reasons, it is required to introduce additional filters, such as seasonal patterns and select wind corridors.
More detailed evaluations have been computed, with the results differentiated by season (Table 7, warm; Table 8, cold). Furthermore, plots featuring data ellipses have been generated based on a methodology previously applied to data gathered during the first COVID-19 lockdown in Italy [147]; these plots are also categorized depending on seasons (Figure 8, warm; Figure 9, cold).
The statistical evaluations shown in Table 7 allow us to report the peculiar behavior of O3 based on proximity categories, wind corridor, and seasons. Unexpectedly, temperature yields low correlation factors from the western sector (up to −0.334 for BKG’s SR value), where peaks in O3 are observed at LMT [98]. Conversely, environmental factors are reported from the northeast (up to 0.769 for R-SRCcor and R-SRCecor’s SR). LOC constitutes an exception, with relatively high (up to 0.442) correlations with radiation from the western sector. These differences highlight the importance of large-scale air mass transport [56] and local wind patterns, as northeastern winds that follow the inversion in the daily cycle can channel the results of O3 photochemical production from the Eastern Mediterranean sector, although these correlations do not indicate peaks in absolute concentrations from the northeast. Table 8 (cold season) shows different characteristics, with radiation being positively correlated (up to 0.595 for R-SRCcor and R-SRCecor’s SR) with the northeastern sector, while the western sector yields low correlation rates (up to −0.11 for N-SRC’s PCC and SR). These results, although apparently not in accordance with previous research on O3 variability at the site [98], actually demonstrate the presence of active wind pattern control [110] and possibly peplospheric influences [137] on the surface concentrations of O3 observed at the site. From this, it is inferred that the northeastern sector should be considered in local photochemical pollution, although the absolute O3 concentrations from the sector do not reach the same peaks observed at the Tyrrhenian coast. Peaks in correlations with the northeast may also indicate greater influence from near-surface wind circulation, as higher altitudes tend to result in prevailing westerly wind, in accordance with large-scale circulation in the area [110,132,133].
Remarkable differences are reported between standard and corrected R-SRC/BKG proximity categories. Correlation factors vary (e.g., in the northeastern sector, the correlation between O3 and radiation has a PCC for BKG of −0.102, and an SR for R-SRCecor of 0.479). A previous study demonstrated that these differences are attributable to the correction factors’ selection of data depending on wind sectors, as each category applies specific restrictions and filters leading to more (or less) data being considered from a specific wind corridor [99].
With the data ellipses shown in Figure 8 and Figure 9 reporting systematically lower concentrations for the LOC proximity category, with N-SRC showing intermediate behavior and R-SRC/BKG being nearly identical, averaged concentrations on a per-category basis have been calculated, also accounting for their standard deviations as seen in previous research [99,100]. The results are shown in Table 9 and report the same behavior inferred from data ellipses. Notably, an inversion in data variability can be noticed in the transition from LOC to BKG: cold-season LOC is slightly higher (21.39 ± 10.50) than its warm-season counterpart (21.28 ± 9.42); however, from N-SRC onward, warm-season concentrations are systematically higher than their cold-season counterparts. These results are also reported in Figure 10. These differences also demonstrate that LOC is more influenced by anthropogenic emissions, especially during cold seasons, when such activities are more common in the area [138,147]; however, during warm seasons, photochemical pollution prevails and results in higher concentrations representative of the atmospheric background. The significance and importance of these results are further corroborated by Kruskal–Wallis [186] and Mann–Whitney U (pairwise Wilcoxon rank sum) [187,188] tests aimed at standard/corrected proximity categories, O3, temperature, and solar radiation, which have provided the first direct evidence of statistically very relevant (p < 0.001) differences between proximity categories in a multi-year study on LMT measurements, as they were not performed in a previous study aimed at CO, CO2, and CH4 [99]. The pairwise Wilcoxon test showed the statistical significance of pairs of proximity categories with respect to measured parameters, with the exception of O3 (p = 0.6) in the R–SRCcor/BKGcor pair. Considering that the uncorrected (R–SRC/BKG) and enhanced corrected (ecor, R–SRCecor/BKGecor) pairs have yielded statistically relevant results (p < 0.001), this finding is the first evidence of the effectiveness of the “ecor” correction as an effective factor counterbalancing the overestimation of O3 in the O3/NOx ratio linked to local peaks in photochemical pollution [99]. Therefore, in addition to the assessment of O3 pollution, the findings can contribute to future improvements of the O3/NOx ratio “Proximity” methodology as an effective indicator of local-to-remote air masses [99,100], and enhance source apportionment efforts aimed at parameters other than O3.
A case study (CS) integrating additional methodologies in addition to surface measurements performed at LMT was selected by filtering the top 0.5% of all measured O3 concentrations at the site. Between 11 and 15 May 2015, concentrations nearing the threshold of 80 ppb were reported at the site. The CS also includes 10 May to evaluate the status quo prior to measuring O3 peaks. The CS was assessed on a regional scale using MSG SEVIRI and OMI products; the former were computed from specific coordinates, matching those of the LMT observatory (LMT SAT) and two other points, the Stromboli volcanic island in the western direction, and the “Ionian” point in the eastern direction, which is based on the “ION1” point used in previous research as representative of conditions in the eastern coast of the Catanzaro isthmus [182]. These locations are shown in Figure 11A.
Hourly data of integrated surface and satellite measurements have been tested for correlations, and the results are shown in Table 10. All correlations are statistically significant (p < 0.001) and yield high values in the 0.412–0.65 range. The tested correlations therefore imply that the high O3 concentrations observed during the CS are at least partially attributable to photochemical production in the central Mediterranean. In detail, hourly data (Figure 12) allow us to highlight the evolution of the entire CS: on 10 May, cloud cover across the area prevented further photochemical production, and O3 concentrations remained stable in the 50–60 ppb range (Figure 12A); temperatures were also stable at ≈20 °C, and the wind regime at LMT had a strong W-SW component (Figure 12C), in contrast with the leading NE direction of the CS period (Figure 11B). Without cloud cover, on 11 May, surface O3 began to rise, and all three locations were subject to substantial increases in downward solar radiation (Figure 12A); temperatures remained mostly stable, although a slight increase was observed (Figure 12B). From 12 May, both temperatures and wind directions began to follow a full daily cycle, with wind directions in particular closely matching the trends observed in surface O3 (Figure 12C), while temperatures experienced daily oscillations closely related to O3 variability (Figure 12B). On 12, 13, and 15 May, increases in O3 had the exact same rate of solar radiation (Figure 12A). On May 14th, O3 concentrations remained in the 50–70 ppb range, thus allowing a buildup due to photochemical production and air mass transport to ultimately lead to the maximum values observed on the 15th, around the 80 ppb threshold, while solar radiation values began to drop (Figure 12A). During the entire CS, differences between downward radiation measured at the surface and those inferred by SEVIRI were in the order of 50 W/m2, which is in accordance with past studies on the assessment of a possible bias between the two methods [149,164,165]. Overall, the integration of satellite and surface data concerning the CS has highlighted the interplay of numerous factors contributing to photochemical pollution in the area. The OMI products shown in Figure 13 provide additional details on the phenomenon, with an eastward movement of air masses enriched in O3 between 10 and 12 May (Figure 13A–C), and residual concentrations in the following days contributed to the buildup and the local 15 May (Figure 13F) peak, as concentrations on a regional scale were lower. OMI products do not allow a direct comparison with surface measurements; however, their spatial resolution has allowed us to evaluate the CS over a wider area and assess its evolution over time; previous studies on LMT data relied on similar methodologies to provide a more comprehensive understanding of central Mediterranean atmospheric dynamics, with a focus on the Tyrrhenian [197,198]. Although OMI products do not show any data concerning the region of Calabria on May the 11th (Figure 13B), a transport phenomenon from the west to the east in the early days of the CS can be noticed. The phenomenon could also be related to stratospheric transport into the troposphere (STT), i.e., stratospheric intrusions (SIs) of O3, as evidenced in a cross-study between other Italian atmospheric observatories, including Mt. Curcio (code: CUR; Lat: 39.31° N; Lon: 16.42° E; Elev: 1796 m a.s.l.), located 52 km N-NE from LMT. In fact, an increase in the number of SI phenomena was observed in May 2015 at CUR [96] and would mark the first report of an SI at LMT.
The observed behavior on regional and larger scales further demonstrates that the number of factors influencing photochemical pollution in central Calabria require monitoring and additional evaluations; in fact, specific combinations of wind direction, solar radiation, and time of day have led to surface O3 concentrations close to the limits reported as hazardous for human health as per the national regulation on air quality [199]. With different methodologies being implemented to evaluate O3 photochemical pollution in Southern Italy [200], these findings constitute a new fundament for exposure mitigation policies and the implementation of early warning systems for individuals affected by medical conditions, who are particularly susceptible to high O3 concentrations in the lower troposphere [201,202], and the summertime peaks observed in the central Mediterranean [203] may increase health hazards. These results may also be used to assess indoor hazards, as wind circulation is known to regulate, in the LMT area, the indoor concentrations of pollutants [204].

5. Conclusions

Nine years (2015–2023) of continuous measurements of surface ozone (O3) at the Lamezia Terme (LMT) WMO/GAW observation site in Calabria, Southern Italy, have been correlated with surface measurements of temperature and downward solar radiation to assess the extent of regional photochemical pollution in the area. Previous works reported peaks in O3 concentrations from the western–seaside sector of LMT during the summer; however, the correlations with physical parameters linked to the northeastern–continental wind corridor at LMT have yielded notable results, thus indicating that the factors driving photochemical O3 production are heavily influenced by local wind patterns. Unlike previous works, this research has considered broader warm (May–September) and cold (October–April) seasons, deemed more representative of seasonal differences in photochemical O3 compared to standard trimesters used as seasons in previous research.
Analysis of O3 variability has shown that anthropic influence has a non-negligible effect on pollution from the northeastern sector, which is generally more affected by anthropogenic emissions. By integrating proximity and air mass aging categories based on the ratio of O3 with nitrogen oxides (NOx), the local influence of anthropogenic emissions during cold seasons has been reported, while the atmospheric background is more influenced by photochemical activity linked to warm seasons. The statistical significance of concentration differences based on proximity and air mass aging categories has yielded positive results, thus indicating that the high O3 levels reported during warm seasons are related to photochemical activity in the central Mediterranean area. Specifically, only LOC (local) air masses are depleted in O3 while N–SRC (near source) to BKG (atmospheric background) air masses yield concentrations greater than that of LOC by a factor of two, which is a unique pattern among those observed at LMT using this methodology.
Finally, the analysis of a case study (CS) selected from the top 0.5% O3 concentrations has allowed us to determine the complexity and interplay of several factors influencing O3 peaks at the LMT site, with implications on the broader topic of regional photochemical pollution in the region, which is understudied despite the high potential for O3 exposure in the area. The results clearly indicate that assessing photochemical pollution and its hazards for the environment requires the integration of multiple instruments and methodologies, as local responses—and, consequently, environmental and health hazards—may vary depending on orography and near-surface wind circulation. Specifically, from these results, it is possible to infer that specific combinations of wind direction, solar radiation, and temperature can lead to potentially hazardous O3 levels that would not otherwise be reached under standard conditions. Exposure mitigation policies and regulations would therefore need to consider all these factors to implement adequate warnings to the population.

Author Contributions

Conceptualization, F.D.; methodology, F.D., T.L.F. and A.D.; software, F.D., G.D.B., A.D. and T.L.F.; validation, F.D., G.D.B., L.M., S.S., A.D., T.L.F., D.G. and I.A.; formal analysis, F.D. and T.L.F.; investigation, F.D., T.L.F. and A.D.; resources, M.D.P. and C.R.C.; data curation, F.D., G.D.B., L.M., S.S., A.D., D.G. and I.A.; writing—original draft preparation, F.D.; writing—review and editing, F.D., G.D.B., L.M., S.S., A.D., T.L.F., D.G., I.A., M.D.P. and C.R.C.; visualization, F.D. and T.L.F.; supervision, C.R.C.; project administration, C.R.C.; funding acquisition, M.D.P. and C.R.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by AIR0000032—ITINERIS, the Italian Integrated Environmental Research Infrastructures System (D.D. n. 130/2022—CUP B53C22002150006) under the EU—Next Generation EU PNRR—Mission 4 “Education and Research”—Component 2: “From research to business”—Investment 3.1: “Fund for the realization of an integrated system of research and in-novation infrastructures”. It was also funded by ECS_00000009—Tech4You, Technologies for climate change adaptation and quality of life improvement (MUR n. 3277 30/12/2021 CUP B83C22003980006) under the EU-Next Generation EU PNRR—Mission 4 “Education and Research”—Component 2: “From research to business”—Investment 1.5, NextGenerationEU.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Surface data are presently not available as they are subject to other ongoing research.

Acknowledgments

The authors would like to thank the editorial board for their support and acknowledge the efforts of the four anonymous reviewers who helped expand and improve the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 2. Warm (top) and cold (bottom) variability of O3 concentrations measured at LMT based on wind directions and speeds.
Figure 2. Warm (top) and cold (bottom) variability of O3 concentrations measured at LMT based on wind directions and speeds.
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Figure 3. Monthly variability of O3 ((A), ppb), solar radiation ((B), W/m2), and temperature ((C), °C) at the LMT site. O3 data are differentiated per wind corridor: continental (0–90 °N); seaside (240–300 °N), and total, accounting for all wind directions, including those falling outside the continental and seaside filters.
Figure 3. Monthly variability of O3 ((A), ppb), solar radiation ((B), W/m2), and temperature ((C), °C) at the LMT site. O3 data are differentiated per wind corridor: continental (0–90 °N); seaside (240–300 °N), and total, accounting for all wind directions, including those falling outside the continental and seaside filters.
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Figure 4. Density distribution plots of O3 with solar radiation ((A), W/m2) and temperature ((B), °C). These plots consider all available data.
Figure 4. Density distribution plots of O3 with solar radiation ((A), W/m2) and temperature ((B), °C). These plots consider all available data.
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Figure 5. Density distribution plots of O3 with solar radiation ((A), W/m2) and temperature ((B), °C). These plots consider the warm season (May–September) only.
Figure 5. Density distribution plots of O3 with solar radiation ((A), W/m2) and temperature ((B), °C). These plots consider the warm season (May–September) only.
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Figure 6. Density distribution plots of O3 with solar radiation ((A), W/m2) and temperature ((B), °C). These plots consider the cold season (October–April) only.
Figure 6. Density distribution plots of O3 with solar radiation ((A), W/m2) and temperature ((B), °C). These plots consider the cold season (October–April) only.
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Figure 7. Daily cycle of O3 ((A), ppb), solar radiation ((B), W/m2), and temperature ((C), °C) differentiated by season. Shaded areas indicate intervals within one standard deviation (±1σ) from the average.
Figure 7. Daily cycle of O3 ((A), ppb), solar radiation ((B), W/m2), and temperature ((C), °C) differentiated by season. Shaded areas indicate intervals within one standard deviation (±1σ) from the average.
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Figure 8. Data ellipses following a normal distribution and an 80% confidence interval showing the variability of O3, during warm seasons, with solar radiation (A) and temperature (B).
Figure 8. Data ellipses following a normal distribution and an 80% confidence interval showing the variability of O3, during warm seasons, with solar radiation (A) and temperature (B).
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Figure 9. Data ellipses following a normal distribution and an 80% confidence interval showing the variability of O3 during cold seasons, with solar radiation (A) and temperature (B).
Figure 9. Data ellipses following a normal distribution and an 80% confidence interval showing the variability of O3 during cold seasons, with solar radiation (A) and temperature (B).
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Figure 10. Average O3 concentrations based on standard (LOC, N-SRC, R-SRC, BKG), corrected (R-SRCcor, BKGcor), and enhanced corrected (R-SRCecor, BKGecor) proximity categories. Shaded areas indicate intervals within one standard deviation (±1σ) from the average.
Figure 10. Average O3 concentrations based on standard (LOC, N-SRC, R-SRC, BKG), corrected (R-SRCcor, BKGcor), and enhanced corrected (R-SRCecor, BKGecor) proximity categories. Shaded areas indicate intervals within one standard deviation (±1σ) from the average.
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Figure 11. (A): location of Stromboli and the “Ionian” point compared to the LMT observatory. (B): wind rose showing near-surface circulation during the case study.
Figure 11. (A): location of Stromboli and the “Ionian” point compared to the LMT observatory. (B): wind rose showing near-surface circulation during the case study.
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Figure 12. Dual-axis plots showing the variability of hourly O3 data with surface/DSSF solar radiation ((A), W/m2), surface temperature ((B), °C), and surface wind direction ((C), °N).
Figure 12. Dual-axis plots showing the variability of hourly O3 data with surface/DSSF solar radiation ((A), W/m2), surface temperature ((B), °C), and surface wind direction ((C), °N).
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Figure 13. OMI products showing available data concerning the evolution of the 11–15 May 2015 case study, also accounting for 10 May to assess the tropospheric distribution of O3 prior to the event. On 11 May, OMI data did not cover the region of Calabria. (A): May 10th; (B): May 11th; (C): May 12th; (D): May 13th; (E): May 14th; (F): May 15th.
Figure 13. OMI products showing available data concerning the evolution of the 11–15 May 2015 case study, also accounting for 10 May to assess the tropospheric distribution of O3 prior to the event. On 11 May, OMI data did not cover the region of Calabria. (A): May 10th; (B): May 11th; (C): May 12th; (D): May 13th; (E): May 14th; (F): May 15th.
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Table 1. Main details of the surface measurements used in this study and their respective instruments. “UoM” refers to the unit of measurement. “Frequency” refers to the minimum time interval between measurements.
Table 1. Main details of the surface measurements used in this study and their respective instruments. “UoM” refers to the unit of measurement. “Frequency” refers to the minimum time interval between measurements.
ParameterInstrument ManufacturerUoMFrequencyPrecision
Ozone49iThermo ScientificppbMinute±0.1 ppb
Solar RadiationCNR4Kipp & ZonenW/m2Minute1%
TemperatureWXT520Vaisala°CMinute±0.3 °C
Table 2. Coverage rates (%) of each dataset compared to the total number of elapsed hours. OZR refers to the dataset including valid ozone and radiation measurements. “Combined” refers to the dataset featuring valid measurements of all three instruments. Please note that 2016 and 2020 are both leap years, with an extra 24 h each.
Table 2. Coverage rates (%) of each dataset compared to the total number of elapsed hours. OZR refers to the dataset including valid ozone and radiation measurements. “Combined” refers to the dataset featuring valid measurements of all three instruments. Please note that 2016 and 2020 are both leap years, with an extra 24 h each.
YearHoursOzoneRadiationMeteoOZRCombined
2015876092.13%99.62%95.90%91.76%90.15%
2016878496.17%98.58%96.35%95.15%93.03%
2017876095.94%96.97%93.80%93.07%91.29%
2018876098.13%25.15%77.05%24.04%24.01%
2019876094.19%98.57%98.60%94.16%94.13%
2020878498.51%100%99.99%98.51%98.50%
2021876091.16%99.91%99.75%91.16%90.99%
2022876085.23%99.95%90.11%85.23%81.99%
2023876081.95%85.97%96.30%68.69%67.35%
78,888 192.60% 289.41% 294.20% 282.42% 281.27% 2
1 Total. 2 Average.
Table 3. Correlation matrix showing the result of PCC (Pearson’s Correlation Coefficient) and SR (Spearman’s Rank) evaluations between O3 (ppb), solar radiation (W/m2), and temperature (°C). “All” columns refer to all available measurements of solar radiation at the site, while “Pos.” (positive) columns are restricted to measurements with positive SWdown values. “NE” refers to the northeastern sector of LMT (wind directions in the 0–90 °N range), while “W” refers to the western sector (240–300 °N). This table considers all data.
Table 3. Correlation matrix showing the result of PCC (Pearson’s Correlation Coefficient) and SR (Spearman’s Rank) evaluations between O3 (ppb), solar radiation (W/m2), and temperature (°C). “All” columns refer to all available measurements of solar radiation at the site, while “Pos.” (positive) columns are restricted to measurements with positive SWdown values. “NE” refers to the northeastern sector of LMT (wind directions in the 0–90 °N range), while “W” refers to the western sector (240–300 °N). This table considers all data.
ParameterStatisticsSurface O3 (ppb) [All Seasons]
All Rad.Pos. Rad.All NEAll WPos. NEPos. W
Solar Radiation (W/m2)df65,02331,98222,80624,281613918,493
PCC0.5020.4520.4450.1950.4830.174
PCC p-value<0.001<0.001<0.001<0.001<0.001<0.001
SR0.5920.450.5510.3060.4590.297
SR p-value<0.001<0.001<0.001<0.001<0.001<0.001
Temperature (°C)df70,05831,66424,94926,410613918,493
PCC0.3620.2680.2560.0960.2730.071
PCC p-value<0.001<0.001<0.001<0.001<0.001<0.001
SR0.3520.2590.220.1490.2430.106
SR p-value<0.001<0.001<0.001<0.001<0.001<0.001
Table 4. Seasonal (warm) correlation matrix showing the result of PCC (Pearson’s Correlation Coefficient) and SR (Spearman’s Rank) evaluations between O3 (ppb), solar radiation (W/m2), and temperature (°C). “All” columns refer to all available measurements of solar radiation at the site, while “Pos.” (positive) columns are restricted to measurements with positive SWdown values. “NE” refers to the northeastern sector of LMT (wind directions in the 0–90 °N range), while “W” refers to the western sector (240–300 °N).
Table 4. Seasonal (warm) correlation matrix showing the result of PCC (Pearson’s Correlation Coefficient) and SR (Spearman’s Rank) evaluations between O3 (ppb), solar radiation (W/m2), and temperature (°C). “All” columns refer to all available measurements of solar radiation at the site, while “Pos.” (positive) columns are restricted to measurements with positive SWdown values. “NE” refers to the northeastern sector of LMT (wind directions in the 0–90 °N range), while “W” refers to the western sector (240–300 °N).
ParameterStatisticsSurface O3 (ppb) [Warm Season]
All Rad.Pos. Rad.All NEAll WPos. NEPos. W
Solar Radiation (W/m2)df27,77315,556 7111 12,7041684 10,589
PCC0.5280.4010.492 0.213 0.523 0.174
PCC p-value<0.001<0.001<0.001<0.001<0.001<0.001
SR0.6050.357 0.45 0.218 0.4890.173
SR p-value<0.001<0.001<0.001<0.001<0.001<0.001
Temperature (°C)df29,117 15,408 761013,431168410,589
PCC 0.343 0.103 0.265 −0.098 0.251 −0.111
PCC p-value<0.001 <0.001 <0.001<0.001 <0.001 <0.001
SR 0.316 0.041 0.169 −0.115 0.19 −0.132
SR p-value<0.001<0.001<0.001<0.001<0.001<0.001
Table 5. Seasonal (cold) correlation matrix showing the result of PCC (Pearson’s Correlation Coefficient) and SR (Spearman’s Rank) evaluations between O3 (ppb), solar radiation (W/m2), and temperature (°C). “All” columns refer to all available measurements of solar radiation at the site, while “Pos.” (positive) columns are restricted to measurements with positive SWdown values. “NE” refers to the northeastern sector of LMT (wind directions in the 0–90 °N range), while “W” refers to the western sector (240–300 °N).
Table 5. Seasonal (cold) correlation matrix showing the result of PCC (Pearson’s Correlation Coefficient) and SR (Spearman’s Rank) evaluations between O3 (ppb), solar radiation (W/m2), and temperature (°C). “All” columns refer to all available measurements of solar radiation at the site, while “Pos.” (positive) columns are restricted to measurements with positive SWdown values. “NE” refers to the northeastern sector of LMT (wind directions in the 0–90 °N range), while “W” refers to the western sector (240–300 °N).
ParameterStatisticsSurface O3 (ppb) [Cold Season]
All Rad.Pos. Rad.All NEAll WPos. NEPos. W
Solar Radiation (W/m2)df37,24816,424 15,668 11,547 4448 7878
PCC 0.44 0.428 0.421 0.269 0.459 0.302
PCC p-value<0.001 <0.001<0.001 <0.001<0.001<0.001
SR 0.554 0.417 0.586 0.27 0.444 0.326
SR p-value<0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Temperature (°C)df 40,939 16,254 17,312 12,948 4448 7878
PCC 0.355 0.166 0.402 −0.033 0.338 0.209
PCC p-value<0.001 <0.001 <0.001 <0.001 <0.001 <0.001
SR 0.351 0.147 0.397 −0.001 0.315 0.229
SR p-value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Table 6. Correlation matrix showing the results of PCC and SR evaluations of hourly O3 concentrations, temperature, and solar radiation based on the four main proximity categories (LOC, local; N-SRC, near source; R-SRC, remote source; BKG, atmospheric background), as well as the corrected (“cor”) and enhanced corrected (“ecor”) categories of R-SRC and BKG.
Table 6. Correlation matrix showing the results of PCC and SR evaluations of hourly O3 concentrations, temperature, and solar radiation based on the four main proximity categories (LOC, local; N-SRC, near source; R-SRC, remote source; BKG, atmospheric background), as well as the corrected (“cor”) and enhanced corrected (“ecor”) categories of R-SRC and BKG.
O3 by Cat. (ppb)StatisticsTemp.
(°C)		Radiation (W/m2)
LOCdf27,292 27,292
PCC −0.057−0.008
PCC p-value <0.0010.209
SR 0.0840.183
SR p-value<0.001<0.001
N-SRCdf29,18329,183
PCC−0.1080.107
PCC p-value<0.001<0.001
SR0.1150.243
SR p-value<0.001<0.001
R-SRCdf10,85210,852
PCC−0.0830.179
PCC p-value<0.001<0.001
SR0.1310.218
SR p-value<0.001<0.001
BKGdf25362536
PCC−0.0100.221
PCC p-value0.616<0.001
SR−0.1300.210
SR p-value<0.001<0.001
R-SRCcordf25842584
PCC−0.0770.147
PCC p-value<0.001<0.001
SR0.1530.224
SR p-value<0.001<0.001
BKGcordf10,80410,804
PCC−0.0610.197
PCC p-value<0.001<0.001
SR0.0780.216
SR p-value<0.001<0.001
R-SRCecordf46944694
PCC−0.0420.131
PCC p-value0.004<0.001
SR0.0260.167
SR p-value0.070<0.001
BKGecordf79017901
PCC0.0930.188
PCC p-value<0.001<0.001
SR0.1100.195
SR p-value<0.001<0.001
Table 7. Correlation matrix of hourly O3 concentrations with temperature and radiation using PCC and SR statistics, differentiated by proximity category and wind sectors. These data refer to the warm season.
Table 7. Correlation matrix of hourly O3 concentrations with temperature and radiation using PCC and SR statistics, differentiated by proximity category and wind sectors. These data refer to the warm season.
O3 by Cat. (ppb)StatisticsAll DataNortheastWest
Temp.
(°C)		Radiation (W/m2)Temp.
(°C)		Radiation (W/m2)Temp.
(°C)		Radiation (W/m2)
LOCdf10,19110,1915668 5668437437
PCC−0.118−0.0320.108−0.020 0.0410.173
PCC p-value<0.0010.001<0.0010.1350.397<0.001
SR0.1010.083 0.0490.1470.0450.442
SR p-value<0.001<0.001 <0.001 <0.0010.343<0.001
N-SRCdf11,61211,6121590159055625562
PCC−0.1070.0240.3030.024−0.0570.027
PCC p-value<0.0010.009<0.0010.340<0.0010.048
SR0.0380.1670.2420.259−0.0730.152
SR p-value<0.001<0.001<0.001<0.001<0.001<0.001
R-SRCdf7084708413313353985398
PCC−0.0670.0060.6550.313−0.0750.085
PCC p-value<0.0010.590<0.001<0.001<0.001<0.001
SR−0.0780.0720.6540.357−0.0870.119
SR p-value<0.001<0.001<0.001<0.001<0.001<0.001
BKGdf19501950222216281628
PCC−0.0110.0890.344−0.102−0.3290.154
PCC p-value0.629<0.0010.10.635<0.001<0.001
SR−0.3540.1000.6080.120−0.3340.159
SR p-value<0.001<0.0010.0020.575<0.001<0.001
R-SRCcordf16171617414111921192
PCC−0.0590.0050.7440.340−0.0310.068
PCC p-value0.0180.830<0.0010.0260.2770.02
SR−0.0220.0820.7690.479−0.060.12
SR p-value0.381<0.001<0.001<0.0010.038<0.001
BKGcordf7417741711411458345834
PCC−0.0490.0280.5870.237−0.1610.106
PCC p-value<0.0010.015<0.0010.010<0.001<0.001
SR0.1660.0750.6100.200−0.170.129
SR p-value<0.001<0.001<0.0010.031<0.001<0.001
R-SRCecordf35153515414130893089
PCC−0.049−0.0040.7440.340−0.1310.037
PCC p-value0.0040.793<0.0010.026<0.0010.041
SR−0.1210.0470.7690.479−0.1510.064
SR p-value<0.0010.005<0.0010.001<0.001<0.001
BKGecordf4876487611411432943294
PCC−0.0620.0380.5870.237−0.1820.15
PCC p-value<0.0010.007<0.0010.010<0.001<0.001
SR−0.1790.1000.6100.200−0.1920.181
SR p-value<0.001<0.001<0.0010.031<0.001<0.001
Table 8. Correlation matrix of hourly O3 concentrations with temperature and radiation using PCC and SR statistics, differentiated by proximity category and wind sectors. These data refer to the cold season.
Table 8. Correlation matrix of hourly O3 concentrations with temperature and radiation using PCC and SR statistics, differentiated by proximity category and wind sectors. These data refer to the cold season.
O3 by Cat. (ppb)StatisticsAll DataNortheastWest
Temp.
(°C)		Radiation (W/m2)Temp.
(°C)		Radiation (W/m2)Temp.
(°C)		Radiation (W/m2)
LOCdf17,099 17,09910,89210,89214,23814,238
PCC0.0220.0060.234−0.0160.0270.077
PCC p-value0.0040.439<0.0010.0880.001<0.001
SR0.1890.2440.1950.315−0.0050.158
SR p-value<0.001<0.001<0.001<0.0010.555<0.001
N-SRCdf17,56917,5695272 527277937793
PCC−0.0150.1810.1630.124−0.110.167
PCC p-value0.051<0.001<0.001<0.001<0.001<0.001
SR0.0340.2920.1230.331−0.110.231
SR p-value<0.001<0.001<0.001<0.001<0.001<0.001
R-SRCdf3766376652252225642564
PCC−0.0200.3610.2840.5860.1130.215
PCC p-value0.220<0.001<0.001<0.001<0.001<0.001
SR0.1160.3590.3170.5770.1040.227
SR p-value<0.001<0.001<0.001<0.001<0.001<0.001
BKGdf5845848383539539
PCC0.0070.5560.6350.4670.1900.563
PCC p-value0.866<0.001<0.001<0.001<0.001<0.001
SR0.2070.5080.6930.4810.2110.527
SR p-value<0.001<0.001<0.001<0.001<0.001<0.001
R-SRCcordf965965110110699699
PCC0.0130.2990.2120.5730.1360.219
PCC p-value0.695<0.0010.025<0.001<0.001<0.001
SR0.1310.3150.2130.5950.1310.252
SR p-value<0.001<0.0010.024<0.001<0.001<0.001
BKGcordf3385338549549522562256
PCC−0.0220.4090.3370.5710.1260.262
PCC p-value0.194<0.001<0.001<0.001<0.001<0.001
SR0.1430.3980.4060.5610.1310.257
SR p-value<0.001<0.001<0.001<0.001<0.001<0.001
R-SRCecordf11771177110110911911
PCC0.0330.3710.2120.5730.1470.306
PCC p-value0.255<0.0010.025<0.001<0.001<0.001
SR0.1520.4040.2130.5950.1470.353
SR p-value<0.001<0.0010.024<0.001<0.001<0.001
BKGecordf3023302349549518941894
PCC−0.0440.3530.3370.5710.1380.145
PCC p-value0.015<0.001<0.001<0.001<0.001<0.001
SR0.1380.3250.4060.5610.1380.107
SR p-value<0.001<0.001<0.001<0.001<0.001<0.001
Table 9. Average O3 concentrations and their respective standard deviations calculated on a per-category basis, and differentiated by season.
Table 9. Average O3 concentrations and their respective standard deviations calculated on a per-category basis, and differentiated by season.
CategoryAverage Surface O3 (ppb) ± 1σ
All DataWarm SeasonCold Season
LOC21.35 ± 10.1121.28 ± 9.4221.39 ± 10.50
N-SRC42.08 ± 8.2444.05 ± 8.7640.77 ± 7.59
R-SRC46.42 ± 7.0847.81 ± 7.1343.80 ± 6.20
BKG47.73 ± 8.5848.52 ± 8.8445.07 ± 7.06
R-SRCcor46.74 ± 7.1348.17 ± 7.3444.35 ± 6.05
BKGcor46.64 ± 7.4747.91 ± 7.5843.86 ± 6.41
R-SRCecor47.27 ± 7.2247.90 ± 7.3445.42 ± 6.53
BKGecor46.04 ± 7.4947.87 ± 7.7243.09 ± 6.03
Table 10. Correlation of hourly surface O3 with parameters measured at the LMT site (temperature, solar radiation—LMT OBS), and DSSF products (LMT SAT, Stromboli, Ionian).
Table 10. Correlation of hourly surface O3 with parameters measured at the LMT site (temperature, solar radiation—LMT OBS), and DSSF products (LMT SAT, Stromboli, Ionian).
StatisticsCS Parameters
LMT SAT (W/m2)LMT OBS (W/m2)Temp.
(°C)		Stromboli (W/m2)Ionian (W/m2)
Surface O3 (ppb)df86 142 14287 87
PCC 0.4560.51 0.617 0.4630.464
PCC p-value <0.001<0.001<0.001<0.001<0.001
SR 0.4420.65 0.65 0.4370.412
SR p-value<0.001<0.001<0.001<0.001<0.001
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D’Amico, F.; De Benedetto, G.; Malacaria, L.; Sinopoli, S.; Dutta, A.; Lo Feudo, T.; Gullì, D.; Ammoscato, I.; De Pino, M.; Calidonna, C.R. Multimethodological Approach for the Evaluation of Tropospheric Ozone’s Regional Photochemical Pollution at the WMO/GAW Station of Lamezia Terme, Italy. AppliedChem 2025, 5, 10. https://doi.org/10.3390/appliedchem5020010

AMA Style

D’Amico F, De Benedetto G, Malacaria L, Sinopoli S, Dutta A, Lo Feudo T, Gullì D, Ammoscato I, De Pino M, Calidonna CR. Multimethodological Approach for the Evaluation of Tropospheric Ozone’s Regional Photochemical Pollution at the WMO/GAW Station of Lamezia Terme, Italy. AppliedChem. 2025; 5(2):10. https://doi.org/10.3390/appliedchem5020010

Chicago/Turabian Style

D’Amico, Francesco, Giorgia De Benedetto, Luana Malacaria, Salvatore Sinopoli, Arijit Dutta, Teresa Lo Feudo, Daniel Gullì, Ivano Ammoscato, Mariafrancesca De Pino, and Claudia Roberta Calidonna. 2025. "Multimethodological Approach for the Evaluation of Tropospheric Ozone’s Regional Photochemical Pollution at the WMO/GAW Station of Lamezia Terme, Italy" AppliedChem 5, no. 2: 10. https://doi.org/10.3390/appliedchem5020010

APA Style

D’Amico, F., De Benedetto, G., Malacaria, L., Sinopoli, S., Dutta, A., Lo Feudo, T., Gullì, D., Ammoscato, I., De Pino, M., & Calidonna, C. R. (2025). Multimethodological Approach for the Evaluation of Tropospheric Ozone’s Regional Photochemical Pollution at the WMO/GAW Station of Lamezia Terme, Italy. AppliedChem, 5(2), 10. https://doi.org/10.3390/appliedchem5020010

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