Next Article in Journal
Validating Circular End-of-Life Strategies for Domestic Post-Consumer Materials in the Latin American Region: A Life Cycle Assessment Approach
Previous Article in Journal
Global Warming and Fish Diversity Changes in the Po River (Northern Italy)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Environments
Volume 11
Issue 10
10.3390/environments11100227
environments-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Cyclic and Multi-Year Characterization of Surface Ozone at the WMO/GAW Coastal Station of Lamezia Terme (Calabria, Southern Italy): Implications for Local Environment, Cultural Heritage, and Human Health

by
Francesco D’Amico
1,2,*,
Daniel Gullì
1,
Teresa Lo Feudo
1,
Ivano Ammoscato
1,
Elenio Avolio
1,
Mariafrancesca De Pino
1,
Paolo Cristofanelli
3,
Maurizio Busetto
3,
Luana Malacaria
1,
Domenico Parise
1,†,
Salvatore Sinopoli
1,
Giorgia De Benedetto
1 and
Claudia Roberta Calidonna
1,*
1
Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Area Industriale Comp. 15, I-88046 Lamezia Terme, Catanzaro, Italy
2
Department of Biology, Ecology and Earth Sciences, University of Calabria, Via Bucci Cubo 15B, I-87036 Rende, Cosenza, Italy
3
Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Via P. Gobetti 101, I-40129 Bologna, Italy
*
Authors to whom correspondence should be addressed.
†
Current address: Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Str. Prv. Lecce-Monteroni km 1.2, I-73100 Lecce, Italy.
Environments 2024, 11(10), 227; https://doi.org/10.3390/environments11100227
Submission received: 25 September 2024 / Revised: 9 October 2024 / Accepted: 15 October 2024 / Published: 17 October 2024
(This article belongs to the Special Issue Advances in Urban Air Pollution: 2nd Edition)
Browse Figures
Versions Notes

Abstract

:
Unlike stratospheric ozone (O3), which is beneficial for Earth due to its capacity to screen the surface from solar ultraviolet radiation, tropospheric ozone poses a number of health and environmental issues. It has multiple effects that drive anthropogenic climate change, ranging from pure radiative forcing to a reduction of carbon sequestration potential in plants. In the central Mediterranean, which itself represents a hotspot for climate studies, multi-year data on surface ozone were analyzed at the Lamezia Terme (LMT) WMO/GAW coastal observation site, located in Calabria, Southern Italy. The site is characterized by a local wind circulation pattern that results in a clear differentiation between Western-seaside winds, which are normally depleted in pollutants and GHGs, and Northeastern-continental winds, which are enriched in these compounds. This study is the first detailed attempt at evaluating ozone concentrations at LMT and their correlations with meteorological parameters, providing new insights into the source of locally observed tropospheric ozone mole fractions. This research shows that surface ozone daily and seasonal patterns at LMT are “reversed” compared to the patterns observed by comparable studies applied to other parameters and compounds, thus confirming the general complexity of anthropogenic emissions into the atmosphere and their numerous effects on atmospheric chemistry. These observations could contribute to the monitoring and verification of new regulations and policies on environmental protection, cultural heritage preservation, and the mitigation of human health hazards in Calabria.

1. Introduction

Ozone (O3) is a reactive gas both anthropogenic and natural in origin [1,2]. Natural ozone is the result of the interaction between ultraviolet (UV) solar radiation and molecular oxygen (O2) in the atmosphere [3,4,5,6]. Tropospheric ozone, which regulates oxidation capacity in the lower atmosphere [7,8], is also a product of photochemical reactions involving common anthropogenic pollutants such as volatile organic compounds (VOC) and nitrogen oxides (NOx) [1,9,10,11]. CO (carbon monoxide), which is a typical product of combustion processes [12,13], is indirectly involved in the increase of tropospheric ozone (O3) [14]. Unlike other gases, which retain identical properties regardless of their altitude, ozone is differentiated into two distinct categories: stratospheric ozone in the so called “ozone layer”, considered beneficial, protects life on Earth via a partial screening from solar UV radiation and is susceptible to pollution-induced depletion [15,16,17]; tropospheric ozone, however, poses numerous health hazards [18,19,20,21,22,23] and has a well-determined impact on vegetation due to its high phytotoxicity [24,25,26,27,28,29,30]. The Tropospheric Ozone Assessment Report (TOAR) is an international effort aimed at assessing the hazards of tropospheric O3. Phase I is now complete (2014–2019), while Phase II (2020–2024) is currently in progress [31].
The impact on vegetation has been shown to affect the carbon sequestration potential of trees, which would normally help counterbalance anthropogenic carbon emissions [32]. In addition to these effects, tropospheric ozone contributes to anthropogenic radiative forcing, which is a driver of climate change [33,34,35,36,37]. Tropospheric ozone has also been confirmed to be a factor in reduced cultural heritage preservation [38,39] due to its corrosive effects on a number of materials [40,41,42].
Stratospheric and tropospheric ozone are not independent of each other: via a phenomenon defined as Stratosphere–to–Troposphere Transport (STT), dry air masses rich in O3 move from the stratosphere to the troposphere and contribute significantly to the balances of low Earth atmosphere chemistry [43,44,45,46,47,48,49,50,51,52]. STT has been confirmed to be influenced by climate change, a finding that has several implications considering the health hazards posed by tropospheric ozone [53,54]. More broadly, Stratosphere–troposphere exchange (STE) is a key driver in changes to the chemical composition of the lower atmosphere [55,56,57,58]. Several studies have relied on a number of atmospheric tracers to discriminate ozone-rich air masses depending on their origin, i.e., whether it was tropospheric or stratospheric [52,59,60,61,62].
In the context of Europe, ozone has been widely studied due to its health and climate hazards [63,64,65]. Research on European tropospheric ozone trends and patterns oftentimes focused on the central Mediterranean hotspot [66,67,68,69,70]. Many studies were specifically aimed at the Italian peninsula [71,72,73]. The literature on this field of atmospheric research has also been based on the integration of historical data from key observatories [74,75].
Several research papers on ozone trends have also been aimed at air quality indicators [76,77], plus the environmental response to ozone concentrations, with a focus on local flora [78,79,80,81]. Global research on tropospheric ozone has found evidence—as early as the 1970s—of patterns linked to anthropic activities and anthropogenic emissions in urban areas. The so called Ozone Weekend Effect (OWE) was first reported by Cleveland et al. (1974) [82] in New York. For years, the OWE has been studied and reported in various urban areas across the globe [83,84]. The typical OWE pattern in urban and polluted areas is higher O3 concentrations observed during weekends which are caused by reduced anthropogenic emissions of NOx (NO + NO2) and other pollutants that normally act as ozone sinks in the atmosphere; without these additional anthropogenic outputs, ozone concentrations tend to increase during weekends [82,85,86].
At the WMO/GAW (World Meteorological Organization—Global Atmosphere Watch) of Lamezia Terme (code: LMT) in Calabria, Southern Italy, recent monoparametric [87] and multiparametric [88,89] studies have assessed the influence of anthropogenic emissions on weekly cycles, under the assumption that natural mechanisms and trends normally result in daily, seasonal, and yearly patterns but not weekly patterns, which are restricted to anthropic activities alone. An analysis accounting for weekly variations in aerosol concentrations at LMT and in two other Southern Italian observation sites was also performed by Donateo et al. (2020) [90]. For this reason, multi-year data on tropospheric ozone mole fractions are hereby investigated and characterized at the LMT observation site for the purpose of evaluating the presence of these cycles and their implications. Recent research at the LMT station has also found evidence of specific correlations between observed parameters and wind data (direction, speed) [87,89], which are also applicable to ozone in order to integrate standard evaluations. In fact, the importance of correlating observed tropospheric ozone peaks with specific wind corridors has been recently highlighted by Shen et al. (2024) [91].
This research paper is divided as follows: Section 2 will describe the observation site of Lamezia Terme-LMT, including its characteristics and a summary of past records; Section 3 will report the results of the analyses on tropospheric ozone performed at LMT; Section 4 and Section 5 will respectively discuss these findings and conclude the paper.

2. The LMT Observation Site and O3-Meteo Datasets

2.1. The Lamezia Terme (LMT) WMO/GAW Station

Part of the WMO/GAW (World Meteorological Organization–Global Atmosphere Watch) network, the Lamezia Terme (code: LMT; Lat: 38.87630° N; Lon: 16.23220° E) observation site is located in the homonym municipality in Calabria, Southern Italy (Figure 1). The station is approximately 600 m from the Tyrrhenian coastline of Calabria, at an elevation of 6 m ASL. LMT, which is fully operated by the National Research Council of Italy–Institute of Atmospheric Sciences and Climate (CNR-ISAC), has been gathering data for nearly a decade on a number of key meteorological and chemical parameters.
Previous studies, both on the short-term [92] and medium-to-long-term data series [87], highlighted the importance of local wind circulation on data gathered at LMT. In fact, the site is characterized by two distinct wind axes: a Western-seaside corridor normally yielding lower concentrations, and a Northeastern-continental corridor that is normally linked to the highest peaks observed at LMT [87,89]. Said wind circulation is due to the orography of the Catanzaro isthmus, a narrow region between the Tyrrhenian and Ionian coasts of the peninsula. In fact, the Lamezia Terme International Airport (IATA: SUF; ICAO: LICA), located 3 km north of the observatory, has a 10/28 (100–280° N) runway orientation, and local air traffic is constantly influenced by wind circulation.
In the past, studies on local wind circulation have demonstrated that it is clearly dominated by a breeze regime, which also plays a relevant role in local climate regulation [93,94]. These studies reported seasonal changes both in terms of wind directions and speeds at low altitudes, though wind circulation retains a dominant W-WSW/NE-ENE orientation throughout the entire calendar year; when considering the 850 hPa layer, however, large-scale circulation in the NW direction is observed [93]. Large-scale forcing was demonstrated to be the main driver of daytime circulation in November and during the winter season, while nighttime flows are related to nocturnal breezes. During part of the fall season, as well as summer and spring, large-scale and local flows dominate daytime breezes [94].
At LMT, research studies on breeze and local circulation also relied on a characterization of wind profiles at several altitudes in the 10–300 m range, performed via a Zephir lidar 300 [95]. Consequently, short campaigns aimed at the characterization of the local PBL (Planetary Boundary Layer) were performed via a combination of multiple techniques and instruments [96,97].
Locally, multiple sources of pollution have been identified in several studies. In fact, livestock and agricultural farming, landfills, urban traffic, the A2 highway (part of E45, a European route), and the above-mentioned international airport have all been mentioned in previous research as local emission sources [87,92]. Moreover, the first Italian lockdown period of 2020 has also been recently analyzed as a proving ground to assess local and remote anthropogenic pollution sources detected at LMT [89]. Additional processes able to affect LMT atmospheric composition observations, related to the station’s location in the context of the Mediterranean basin, are summertime open fire emissions [98] and Saharan dust events [99].

2.2. Instruments and Datasets

Surface ozone (O3) mole fractions in ppb (parts per billion) were measured continuously via a Thermo Scientific 49i (Franklin, MA, USA) instrument, which is a photometric analyzer. Model 49i, which has a lower detection limit of 1 ppb of O3, relies on Beer–Lambert’s Law and ozone’s characteristic absorption of UV at a wavelength of 254 nm to operate. Specifically, ozone mole fractions are measured via a comparison between the absorption of UV light at 254 nm in sampled ambient air and the absorption that occurs in a primary standard. The two gas flows are split in the system: one stream passes through a pressure regulator, an ozone scrubber, and the reference solenoid valve and becomes the reference gas, while the other stream flows through a pressure regulator, Ozonator, and manifold to the sample solenoid valve in order to become the sample gas. Flow rates are in the 1–3 L/min range. Solenoid valves alternate the two gas streams (reference, I0, and sample, I) between two cells, A and B, every 10 s. When cell A contains sample gas, cell B contains reference gas, and vice versa. UV light intensities in each cell are measured, and whenever the solenoid valves alternate between cells, such intensities are ignored for several seconds in order to allow optimal flushing of the cells themselves. Following that, ozone mole fractions are calculated via the ratio between the UV light intensity of the sample and that of the reference gas. At the LMT observation site, following an identical procedure applied to other stations in Southern Italy [92], Thermo 49i instruments are calibrated on a yearly basis via direct comparison with a traveling calibrator (Thermo 49iPS, serial number #1425162559).
A Vaisala WXT520 (Vantaa, Finland) instrument was used to gather key meteorological data (temperature, wind speed, and direction). The WXT520 measures temperature in Celsius degrees (°C) with a precision of 0.3 °C via an RC oscillator and two reference capacitors; the capacitance of both sensors is measured, and a microprocessor performs temperature dependency compensations for pressure and humidity. Ultrasonic transducers on a horizontal plane are used to gather data on wind speed and direction. Wind speed is measured with a precision of 3 m per second, while wind direction has a precision of 3 degrees.
As described in Section 2.1, the LMT observation site is set to a daily cycle, which results in a clear differentiation between Western-seaside and Northeastern-continental winds, which in turn results in differences in a number of parameters [87]. Figure 2 shows a wind rise of hourly data highlighting the two corridors.
Data were aggregated on hourly, daily, seasonal, or yearly basis, depending on each analysis and evaluation. As seen in previous studies on data from the same observation site [87,92], seasons were divided as follows: Winter (January, February, December); Spring (March, April, May); Summer (June, July, August); Fall (September, October, November).
Details on hourly data coverage are shown in Table 1. Coverage (%) is grouped by the following category/instrument: Overall, most years are characterized by coverage rates above 90%, even when accounting for the “Combined” dataset, which has a lower coverage rate compared to the others due to its stricter conditions. Vaisala WXT520 coverage is greater than that of the Thermo Scientific 49i during the entire observation period (2015–2023) at LMT.
All available datasets have been analyzed in R 4.4.0 via ggplot2, ggpubr, open air [100,101], tidyverse, and dplyr packages and their libraries. Evaluations based on surface ozone mole fractions alone will refer to the “Ozone” dataset, and those based on meteorological data (wind speed and direction, temperature) will refer to the “Meteo” dataset. The “Combined” dataset, due to its lower coverage rate, is used only when data evaluations specifically require combining O3 mole fractions with temperature and/or wind data.

3. Results

3.1. Observed Daily Cycles

As described in Section 2, the LMT observation site is mostly affected by two distinct local wind circulation corridors, which result in a daily cycle. Ozone is the second parameter observed at LMT to be subject to a multi-year evaluation of said cycle, with the first being methane [87]. In that case, however, the analysis was limited to seven years of observations. Figure 3 shows daily cycles of ozone and key meteorological data, with the exception of wind speed, which is assessed separately.
Compared to methane, the daily cycle is reversed, with diurnal hours yielding higher concentrations. This finding was already shown in Cristofanelli et al. (2017) [92] using preliminary data from LMT but lacked a multi-year analysis and a direct comparison with seasonal changes in daily temperatures.

3.2. Influence of Wind Direction and Speed

In addition to the wind rose seen in Figure 2, representative of nine years of local wind circulation at LMT, seasonal percentile roses of surface ozone were evaluated and are shown in Figure 4. These plots, which integrate the daily cycle seen in Section 3.1, also show how the highest peaks in ozone are generally linked to spring and summer Western-seaside sectors: shaded areas show the distribution of percentiles; the distance of each percentile area from the center of the rose is representative of surface ozone mole fractions observed from each direction. Supplementary Materials S1-A–S1-I cover each year between 2015 and 2023.
D’Amico et al. (2024a) [87] showed that methane concentrations at LMT from the Northeastern-continental sector follow a hyperbola branch pattern (HBP), with low wind speeds correlated with high concentrations and, vice versa, high wind speeds yielding very low mole fractions. The same pattern was not observed from the Western-seaside air corridor. An identical evaluation was applied to nine years of continuous surface ozone records at LMT: Figure 5A,B refer to the W and NE sectors, respectively, while Figure 5C considers all data, including those falling outside the W and NE filters. Previous research on LMT ozone data did not account for direct correlations between ozone concentrations and wind speeds [92], thus making this evaluation the first of this kind for Lamezia Terme. Supplementary Materials S2-A1–S2-I3 cover the entire 2015–2023 period.
The observed pattern is different from that shown for methane in D’Amico et al. (2024a) [87], however, this evaluation still shows the importance of discriminating data using the two main wind directions as filters to highlight differences between Western and Northeastern corridors.

3.3. Assessment of OWE (Ozone Weekend Effect)

As described in Section 1, areas affected by pollution (or lack thereof) are subject to weekly cycles of ozone, specifically the OWE (Ozone Weekend Effect). For the first time, the OWE phenomenon was tested at Lamezia Terme-LMT (Figure 6), and this constitutes the first evaluation of this kind across all observation sites in Southern Italy, with the exception of a study based on data gathered in the Southern Italian region of Apulia during the 2005 summer season [102].
Is it worth noting that this evaluation had to rely on two different datasets: With wind direction filters implemented (Figure 6A,B), the “Combined” dataset was used; in the case of total data, applied regardless of wind direction (Figure 6C), the “Ozone” dataset was used instead. As shown in Table 1, the “Ozone” dataset has a higher coverage rate compared to the others and also accounts for wind directions other than the Western-seaside and Northeastern-continental corridors. Supplementary Materials S3-A1–S3-I3 cover each year of the entire 2015–2023 observation period.
The weekly analysis of averaged surface ozone mole fractions has not resulted in a clear difference between specific days of the week or weekday/weekdays. Although a well-defined weekday/weekend threshold ratio does not seem to exist in the literature to assess the OWE, research studies on the phenomenon reported considerable differences between weekend and weekday concentrations, which are not seen in LMT data [82,83,84,85,86].

3.4. Multi-Year Variability

This section was aimed specifically at the characterization of inter-annual variability of ozone at LMT. Figure 7A shows yearly averaged values of ozone concentrations, as well as the respective first and third-quartile trends. The years 2022 and 2023 have a lower coverage rate and are therefore excluded from this graph, following the multi-year evaluation of methane performed by D’Amico et al. (2024a) [87]. Figure 7B,C are aimed at monthly variability, both throughout the standard year and the entire observation period.

4. Discussion

For the first time, nine years of gathered data were used to evaluate the daily, weekly, seasonal, and yearly variability of tropospheric ozone at Lamezia Terme (LMT), which is a coastal WMO/GAW station in Calabria, Southern Italy. The much longer time span used for these evaluations, compared to a previous study on the same compound [92], has allowed a more detailed characterization of its local patterns.
Wind circulation at LMT (Figure 2) was confirmed to affect locally gathered data on surface ozone mole fractions (Figure 3A–C). However, unlike methane, which was recently analyzed in a study spanning seven years of data [87], ozone’s pattern is reversed, with diurnal-seaside winds yielding the highest concentrations and nocturnal-continental winds yielding the lowest concentrations. Daily and seasonal patterns were already characterized by Cristofanelli et al. (2017) [92] using data from the first measurements at LMT; however, the preliminary findings were not correlated with hourly temperatures and their changes throughout the four seasons (Figure 3B,C) in a region characterized by a Mediterranean climate.
With respect to the seasonal-daily cycle (Figure 3B,C), the general pattern observed in Cristofanelli et al. (2017) [92] using preliminary data is confirmed; however, many differences can be reported using the longer dataset: During the winter season, diurnal concentrations are nearly identical to those observed during the fall, while in the previous study there was a minor gap between the two; during nocturnal hours, winter concentrations exceed summer and fall mole fractions in the early morning and follow a similar pattern, although less prominent, between 21:00 and 23:00.
Comparisons between surface ozone mole fractions, wind directions (Figure 4), and speeds (Figure 5) were performed on LMT data for the first time. These graphs have shown patterns that are unlike those observed for methane in a study based on 2016–2022 data [87]. The Western-seaside and Northeastern-continental corridors are not as differentiated for ozone as they are for methane, and intermediate ozone concentrations are the only ones linked to very high wind speeds, approaching the maximum observed value of ≈17 m/s, which could be linked to remote sources. Low wind speeds, generally linked with nearby sources, result in the lowest values observed from the Northeastern-continental sector, which are consistent with local influences.
A weekly assessment of ozone concentrations was also performed (Figure 6), following findings from recent research on LMT data that found weekly patterns in a number of parameters [87,88,89]. These studies assume that a weekly cycle would be the result of anthropic activities, while daily, seasonal, and yearly cycles are both anthropogenic and natural in origin. Ozone has been known for decades to be affected by a weekly cycle in areas polluted by anthropogenic emissions via the OWE (Ozone Weekend Effect) [82,85,86]. In this study, the first OWE evaluation was performed on LMT data, which is also the first detailed evaluation of this scale that is performed across the entire Southern Italian network of observation sites. A previous study performed, in fact, an OWE evaluation in the region of Apulia using a limited amount of data gathered during the summer 2005 season [102]. That study, however, did not consider wind directions, which were used as filters in this research meant to differentiate observed ozone mole fractions by corridor (Figure 6A,B). Overall, although seasonal differences in terms of absolute concentrations are reported, no clear weekly pattern is present: when reported, the OWE results in notable differences between weekday and weekend O3 mole fractions [82,83,84,85,86], which are not observed at LMT.
It is worth mentioning that higher spring concentrations compared to their summer counterparts, especially in the afternoon hours, in addition to local photochemistry, might also be due to tropospheric influence through large-scale subsidence, which in the Western Mediterranean seems to be more pronounced during spring in contrast to the summer maxima observed in the Eastern Mediterranean due to the same phenomenon [103,104].
The final evaluation of this research study was aimed at multi-year variability (Figure 7). Of the nine years considered in this study (2015–2023), two of them have a lower coverage rate (2022–2023) and therefore are excluded from our assessment reported in Figure 7A. On a global scale, tropospheric and stratospheric ozone concentrations are expected to vary in the future due to a combination of more effective regulations and policies meant to mitigate NOx and CO emissions, feedback effects caused by global warming, and coordinate-dependent phenomena that would affect ozone variability at low vs. high latitudes [105]. At the Lamezia Terme WMO/GAW station in Calabria, the multi-year trend is affected by a dip in 2020, a year affected by reduced CO and NOx emissions attributable to the first Italian COVID-19 lockdown period [89]. However, on a global scale, the atmospheric chemistry of tropospheric ozone has resulted in different responses to COVID-19 lockdowns, with both increases [106] and decreases [107,108,109] in concentrations. In urban or polluted areas, which are limited in VOC (volatile organic compounds), O3 reduction is controlled by NOx emissions, while in remote areas and the free troposphere, O3 reduction is more closely tied to reductions in photochemical processes [108,109]. At LMT, the two effects likely combine, and although a 2020 dip is present (Figure 7A), the overall reduction in surface ozone was not relevant at the site.
The standard seasonal cycle at LMT (Figure 7B), differentiated by wind corridor, highlights a major dip in summer concentrations from the continental-Northeastern sector, probably related to nighttime NO titration effects [110,111,112], while the Western-seaside sector reports a surge in mole fractions that is attributed to photochemical reactions. Specifically—although additional atmospheric chemistry models would be required to verify their exact contribution—air masses from the Western-seaside sector are believed to be affected by atmospheric background processes, with photochemical reactions influencing polluted air masses in particular. In addition to NO titration, dry deposition of ozone might result in lower levels of “continental” air masses if compared with the corresponding “seaside” ones.
When the overall monthly trends are considered and extra details are added to multi-year variability (Figure 7C), the differentiation between wind corridors is very well defined and corroborates the distinct nature of processes affecting surface ozone observations at LMT (photochemical influence from the Tyrrhenian Sea and anthropogenic-induced sinks from mainland Italy). Figure 7C further demonstrates that surface ozone follows—on a regular basis throughout the entire observation period 2015–2023—an opposite pattern compared to other parameters subject to multi-year characterization at LMT, such as methane [87].
Overall, the findings presented in this paper can potentially serve as a new tool for regulators and policymakers in Calabria on the topics of environmental protection, cultural heritage preservation, and mitigation of human health hazards. In the field of cultural heritage preservation, in the context of the Mediterranean and, specifically, in Italy, recent papers have highlighted the need for proper mitigation efforts against climate change’s consequences on cultural heritage [113,114,115,116]. This study is aimed at data gathered from a station on the Tyrrhenian coast of Calabria, but the observations are very closely related to synoptic flows, which are at least applicable to the whole Catanzaro isthmus and, possibly, to the central Tyrrhenian coast of the region, thus integrating air quality monitoring performed by local authorities. The preferred direction of the highest surface ozone mole fraction could therefore be used in cultural heritage preservation to pinpoint parts (e.g., the walls of a historical building directly facing west) more likely to be exposed to ozone-driven corrosion and act accordingly. Similarly, the detection of ozone peaks linked to specific patterns could also be used as a tool to introduce new outdoor and indoor policies aimed at air quality and the mitigation of human health hazards.
Finally, considering the exposure of LMT to Saharan dust events [99] and previous research on the correlation between such events and tropospheric ozone concentrations [117], this study could also constitute the foundation of future assessments on these events in the central Mediterranean region.

5. Conclusions

Using nine years (2015–2023) of continuous hourly data, surface O3 (ozone) was characterized at the WMO/GAW observation site of Lamezia Terme (LMT) in Calabria, Southern Italy. This study, which constitutes so far the longest data series used for a monoparameter cycle and trend analysis at this station, allowed us to improve our knowledge of local surface ozone mole fractions. LMT data gathering is heavily influenced by local wind circulation, with two main corridors: a Western-seaside direction, which is normally depleted in pollutants and greenhouse gases, and a Northeastern-continental direction, which is enriched in these parameters. The highest ozone mole fractions are linked to westerly winds during the spring and summer seasons, an opposite pattern compared to other compounds. The cross-analysis of surface ozone concentrations and wind speeds has constrained the lowest values to nearby sources located in the Northeastern sector, attributable to local anthropogenic activities. High-speed winds are solely linked to intermediate concentrations, thus indicating remote sources. The most detailed study—in terms of dataset robustness—on the OWE (Ozone Weekend Effect) aimed at a Southern Italian station was performed. Unlike other parameters observed at LMT, which are clearly affected by weekly patterns, ozone remains mostly unaffected. An additional evaluation was aimed at multi-year variability, and a 2020 dip—possibly linked to the first Italian COVID-19 lockdown—was observed. Previous research had already demonstrated a reduction in NOx and CO local emissions during that period, and these findings significantly contribute to further characterization of COVID-19 lockdowns as proving grounds for the assessment of anthropogenic emissions in exceptional circumstances.
Overall, the findings of this study are a step forward in the characterization of the LMT site and, broadly, of patterns affecting the central Mediterranean area. Considering that tropospheric ozone poses hazards to human health and also threatens cultural heritage due to its corrosive potential, these findings could provide new tools for policymakers and regulators alike to mitigate ozone-related risks for the environment.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/environments11100227/s1. Figure S1-A–S1-I: smoothed seasonal percentile roses of surface ozone at LMT, divided by year (2015–2023); Figure S2-A1–S2-I3: correlations between wind speeds and ozone mole fractions, divided by sector and referred to the entire observation period (2015–2023); Figure S3-A1–S-I3: weekly distribution of surface ozone averages, divided by sector and referred to the entire observation period (2015–2023).

Author Contributions

Conceptualization, F.D. and C.R.C.; methodology, F.D., C.R.C., D.G., T.L.F. and P.C.; software, F.D.; validation, C.R.C., I.A., D.G., T.L.F., P.C. and M.B.; formal analysis, F.D.; investigation, F.D.; data curation, F.D., I.A., D.G., E.A., T.L.F., P.C., M.B., L.M., D.P., S.S. and G.D.B.; writing—original draft preparation, F.D.; writing—review and editing, F.D., C.R.C., I.A., D.G., E.A., T.L.F., M.D.P., P.C., M.B., L.M., D.P., S.S. and G.D.B.; visualization, F.D., C.R.C., D.G., E.A. and T.L.F.; supervision, C.R.C. and P.C.; funding acquisition, C.R.C. and M.D.P. 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 innovation infrastructures”.

Data Availability Statement

The datasets presented in this article are not readily available because they are part of other ongoing studies.

Acknowledgments

The authors would like to thank the editorial board for their support and assistance. They would also like to thank the two anonymous reviewers who contributed to expanding and improving the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Staehelin, J.; Thudium, J.; Buheler, R.; Volz-Thomas, A.; Graber, W. Trends in surface ozone concentrations at Arosa (Switzerland). Atmos. Environ. 1994, 28, 75–78. [Google Scholar] [CrossRef]
  2. Smyshlyaev, S.P.; Galin, V.Y.; Blakitnaya, P.A.; Jakovlev, A.R. Numerical Modeling of the Natural and Manmade Factors Influencing Past and Current Changes in Polar, Mid-Latitude and Tropical Ozone. Atmosphere 2020, 11, 76. [Google Scholar] [CrossRef]
  3. Crutzen, P.J. Photochemical reactions initiated by and influencing ozone in unpolluted tropospheric air. Tellus 1974, 26, 47–57. [Google Scholar] [CrossRef]
  4. Fishman, J.; Solomon, S.; Crutzen, P.J. Observational and theoretical evidence in support of a significant in-situ photochemical source of tropospheric ozone. Tellus 1979, 31, 432–446. [Google Scholar] [CrossRef]
  5. Logan, J.A. Tropospheric ozone: Seasonal behavior, trends, and anthropogenic influence. J. Geophys. Res.-Atmos. 1985, 90, 10463–10482. [Google Scholar] [CrossRef]
  6. Lelieveld, J.; Dentener, F.J. What controls tropospheric ozone? J. Geophys. Res.-Atmos. 2000, 105, 3531–3551. [Google Scholar] [CrossRef]
  7. Monks, P.S. Gas-phase radical chemistry in the troposphere. Chem. Soc. Rev. 2005, 34, 376–395. [Google Scholar] [CrossRef]
  8. Pillar-Little, E.A.; Guzman, M.I. An Overview of Dynamic Heterogeneous Oxidations in the Troposphere. Environments 2018, 5, 104. [Google Scholar] [CrossRef]
  9. Yenger, J.J.; Klonecky, A.A.; Levy, H., II; Moxim, W.J.; Carmichael, G.R. An evaluation of chemistry’s role in the winter-spring ozone maximum found in the northern midlatitude free troposphere. J. Geophys. Res. 1999, 104, 3655–3667. [Google Scholar] [CrossRef]
  10. Sahu, L.K. Volatile organic compounds and their measurements in the troposphere. Curr. Sci. 2012, 102, 1645–1649. Available online: http://www.jstor.org/stable/24084821 (accessed on 31 August 2024).
  11. Atkinson, R. Atmospheric chemistry of VOCs and NOx. Atmos. Environ. 2000, 34, 2063–2101. [Google Scholar] [CrossRef]
  12. Edwards, D.P.; Emmons, L.K.; Hauglustaine, D.A.; Chu, D.A.; Gille, J.C.; Kaufman, Y.J.; Pétron, G.; Yurganov, L.N.; Giglio, L.; Deeter, M.N.; et al. Observations of carbon monoxide and aerosols from the Terra satellite: Northern Hemisphere variability. J. Geophys. Res. Atmos. 2004, 109, D2420. [Google Scholar] [CrossRef]
  13. Zheng, B.; Chevallier, F.; Ciais, P.; Yin, Y.; Deeter, M.N.; Worden, H.M.; Wang, Y.; Zhang, Q.; He, K. Rapid decline in carbon monoxide emissions and export from East Asia between years 2005 and 2016. Environ. Res. Lett. 2018, 13, 044007. [Google Scholar] [CrossRef]
  14. Marenco, A. Variations of CO and O3 in the troposphere: Evidence of O3 photochemistry. Atmos. Environ. 1986, 20, 911–918. [Google Scholar] [CrossRef]
  15. Solomon, S. Stratospheric ozone depletion: A review of concepts and history. Rev. Geophys. 1999, 37, 275–316. [Google Scholar] [CrossRef]
  16. Andersen, S.B.; Weatherhead, E.C.; Stevermer, A.; Austin, J.; Brühl, C.; Fleming, E.L.; De Grandpré, J.; Grewe, V.; Isaksen, I.; Pitari, G.; et al. Comparison of recent modeled and observed trends in total column ozone. J. Geophys. Res.-Atmos. 2006, 111, 4428. [Google Scholar] [CrossRef]
  17. Egorova, T.; Rozanov, E.; Arsenovic, P.; Sukhodolov, T. Ozone Layer Evolution in the Early 20th Century. Atmosphere 2020, 11, 169. [Google Scholar] [CrossRef]
  18. De Marco, A. Assessment of present and future risk to Italian forests and human health: Modelling and mapping. Environ. Pollut. 2009, 157, 1407–1412. [Google Scholar] [CrossRef]
  19. Palli, D.; Sera, F.; Giovannelli, L.; Masala, G.; Grechi, D.; Bendinelli, B.; Caini, S.; Dolara, P. Environmental ozone exposure and oxidative DNA damage in adult residents in Florence, Italy. Environ. Pollut. 2009, 157, 1521–1525. [Google Scholar] [CrossRef]
  20. Nuvolone, D.; Petri, D.; Voller, F. The effects of ozone on human health. Environ. Sci. Pollut. Res. Int. 2018, 25, 8074–8088. [Google Scholar] [CrossRef]
  21. Malashock, D.A.; DeLang, M.N.; Becker, J.S.; Serre, M.L.; West, J.J.; Chang, K.-L.; Cooper, O.R.; Anenberg, S.C. Estimates of ozone concentrations and attributable mortality in urban, peri-urban and rural areas worldwide in 2019. Environ. Res. Lett. 2022, 17, 054023. [Google Scholar] [CrossRef]
  22. Olstrup, H.; Åström, C.; Orru, H. Daily Mortality in Different Age Groups Associated with Exposure to Particles, Nitrogen Dioxide and Ozone in Two Northern European Capitals: Stockholm and Tallinn. Environments 2022, 9, 83. [Google Scholar] [CrossRef]
  23. Donzelli, G.; Suarez-Varela, M.M. Tropospheric Ozone: A Critical Review of the Literature on Emissions, Exposure, and Health Effects. Atmosphere 2024, 15, 779. [Google Scholar] [CrossRef]
  24. Fuhrer, J.; Skärby, L.; Ashmore, M.R. Critical levels for ozone effects on vegetation in Europe. Environ. Pollut. 1997, 97, 91–106. [Google Scholar] [CrossRef]
  25. Matyssek, R.; Innes, J. Ozone—A Risk Factor for Trees and Forests in Europe? Water Air Soil Pollut. 1999, 116, 199–226. [Google Scholar] [CrossRef]
  26. Krupa, S.; McGrath, M.T.; Andersen, C.P.; Booker, F.L.; Burkey, K.O.; Chappelka, A.H.; Chevone, B.I.; Pell, E.J.; Zilinskas, B.A. Ambient Ozone and Plant Health. Plant Dis. 2001, 85, 4–12. [Google Scholar] [CrossRef]
  27. Paoletti, E. Ozone impacts on forests. CAB Rev. 2007, 2, 13. [Google Scholar] [CrossRef]
  28. Fagnano, M.; Maggio, A.; Fumagalli, I. Crops’ responses to ozone in Mediterranean environments. Environ. Pollut. 2009, 157, 1438–1444. [Google Scholar] [CrossRef]
  29. Carrari, E.; De Marco, A.; Laschi, A.; Badea, O.; Dalstein-Richier, L.; Fares, S.; Leca, S.; Marchi, E.; Sicard, P.; Popa, I.; et al. Economic and Life Cycle Analysis of Passive and Active Monitoring of Ozone for Forest Protection. Environments 2021, 8, 104. [Google Scholar] [CrossRef]
  30. Conte, A.; Otu-Larbi, F.; Alivernini, A.; Hoshika, Y.; Paoletti, E.; Ashworth, K.; Fares, S. Exploring new strategies for ozone-risk assessment: A dynamic-threshold case study. Environ. Pollut. 2021, 287, 117620. [Google Scholar] [CrossRef]
  31. International Global Atmospheric Chemistry. Tropospheric Ozone Assessment Report (TOAR): Global Metrics for Climate Change, Human Health and Crop/Ecosystem Research. Available online: https://igacproject.org/activities/TOAR (accessed on 12 September 2024).
  32. Fares, S.; Vargas, R.; Detto, M.; Goldstein, A.H.; Karlik, J.; Paoletti, E.; Vitale, M. Tropospheric ozone reduces carbon assimilation in trees: Estimates from analysis of continuous flux measurements. Glob. Chang. Biol. 2013, 19, 2427–2443. [Google Scholar] [CrossRef]
  33. Kiehl, J.T.; Schneider, T.L.; Portmann, R.W.; Solomon, S. Climate forcing due to tropospheric and stratospheric ozone. J. Geophys. Res. 1999, 104, 31239–31254. [Google Scholar] [CrossRef]
  34. Berntsen, T.K.; Myhre, G.; Stordal, F.; Isaksen, I.S.A. Time evolution of tropospheric ozone and its radiative forcing. J. Geophys. Res. 2000, 105, 8915–8930. [Google Scholar] [CrossRef]
  35. Myhre, G.; Karlsdóttir, S.; Isaksen, I.S.A.; Stordal, F. Radiative forcing due to changes in tropospheric ozone in the period 1980 to 1996. J. Geophys. Res. 2000, 105, 28935–28942. [Google Scholar] [CrossRef]
  36. Hauglustaine, D.A.; Brasseur, G.P. Evolution of tropospheric ozone under anthropogenic activities and associated radiative forcing of climate. J. Geophys. Res. 2001, 106, 32337–32360. [Google Scholar] [CrossRef]
  37. Moore, F.C. Climate Change and Air Pollution: Exploring the Synergies and Potential for Mitigation in Industrializing Countries. Sustainability 2009, 1, 43–54. [Google Scholar] [CrossRef]
  38. Screpanti, A.; De Marco, A. Corrosion of cultural heritage buildings in Italy: A role for ozone? Environ. Pollut. 2009, 157, 1513–1520. [Google Scholar] [CrossRef]
  39. Wang, X.; Li, H.; Wang, Y.; Zhao, X. Quantifying the Potential Co-Benefit of Air Quality Improvement on Cultural Heritage in China. Sustainability 2023, 15, 8709. [Google Scholar] [CrossRef]
  40. Massey, S.W. The effects of ozone and NOx on the deterioration of calcareous stone. Sci. Total Environ. 1999, 227, 109–121. [Google Scholar] [CrossRef]
  41. Coyle, M.; Smith, R.I.; Stedman, J.R.; Weston, K.J.; Fowler, D. Quantifying the spatial distribution of surface ozone concentration in the UK. Atmos. Environ. 2002, 36, 1013–1024. [Google Scholar] [CrossRef]
  42. Ivaskova, M.; Kotes, P.; Brodnan, M. Air Pollution as an Important Factor in Construction Materials Deterioration in Slovak Republic. Procedia Eng. 2015, 108, 131–138. [Google Scholar] [CrossRef]
  43. Holton, J.R.; Haynes, P.H.; McIntyre, M.E.; Douglass, A.R.; Rood, R.B.; Pfister, L. Stratosphere-troposphere exchange. Rev. Geophys. 1995, 33, 403–439. [Google Scholar] [CrossRef]
  44. Roelofs, G.-J.; Lelieveld, J. Model study of the influence of cross-tropopause O3 transports on tropospheric O3 levels. Tellus Ser. B 1997, 49, 38–55. [Google Scholar] [CrossRef]
  45. Stohl, A.; Bonasoni, P.; Cristofanelli, P.; Collins, W.; Feichter, J.; Frank, A.; Forster, C.; Gerasopoulos, E.; Gäggeler, H.; James, P.; et al. Stratosphere-troposphere exchange: A review, and what we have learned from STACCATO. J. Geophys. Res.-Atmos. 2003, 108, 8516. [Google Scholar] [CrossRef]
  46. Cristofanelli, P.; Bonasoni, P.; Tositti, L.; Bonafè, U.; Calzolari, F.; Evangelisti, F.; Sandrini, S.; Stohl, A. A 6-year analysis of stratospheric intrusions and their influence on ozone at Mt. Cimone (2165 m above sea level). J. Geophys. Res.-Atmos. 2006, 111, D03306. [Google Scholar] [CrossRef]
  47. Knowland, K.E.; Ott, L.E.; Duncan, B.N.; Wargan, K. Stratospheric Intrusion-Influenced Ozone Air Quality Exceedances Investigated in the NASA MERRA-2 Reanalysis. Geophys. Res. Lett. 2017, 44, 10691–10701. [Google Scholar] [CrossRef]
  48. Young, P.J.; Naik, V.; Fiore, A.M.; Gaudel, A.; Guo, J.; Lin, M.Y.; Neu, J.L.; Parrish, D.D.; Rieder, H.E.; Schnell, J.L.; et al. Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends. Elem. Sci. Anth. 2018, 6, 10. [Google Scholar] [CrossRef]
  49. Akritidis, D.; Katragkou, E.; Zanis, P.; Pytharoulis, I.; Melas, D.; Flemming, J.; Inness, A.; Clark, H.; Plu, M.; Eskes, H. A deep stratosphere-to-troposphere ozone transport event over Europe simulated in CAMS global and regional forecast systems: Analysis and evaluation. Atmos. Chem. Phys. 2018, 18, 15515–15534. [Google Scholar] [CrossRef]
  50. Akritidis, D.; Pozzer, A.; Zanis, P. On the impact of future climate change on tropopause folds and tropospheric ozone. Atmos. Chem. Phys. 2019, 19, 14387–14401. [Google Scholar] [CrossRef]
  51. Abalos, M.; Orbe, C.; Kinnison, D.E.; Plummer, D.; Oman, L.D.; Jöckel, P.; Morgenstern, O.; Garcia, R.R.; Zeng, G.; Stone, K.A.; et al. Future trends in stratosphere-to-troposphere transport in CCMI models. Atmos. Chem. Phys. 2020, 20, 6883–6901. [Google Scholar] [CrossRef]
  52. Trisolino, P.; Putero, D.; Arduini, J.; Amendola, S.; Calzolari, F.; Cristofanelli, P. Influence of deep stratosphere-to-troposphere transport on atmospheric carbon dioxide and methane at the Mt. Cimone WMO/GAW global station (2165 m a.s.l., Italy): A multi-year (2015–2022) investigation. Atmos. Res. 2024, 310, 107627. [Google Scholar] [CrossRef]
  53. von Schneidemesser, E.; Monks, P.S.; Allan, J.D.; Bruhwiller, L.; Forster, P.; Fowler, D.; Lauer, A.; Morgan, W.T.; Paasonen, P.; Righi, M.; et al. Chemistry and Linkages between Air Qualirt and Climate Change. Chem. Rev. 2015, 115, 3856–3897. [Google Scholar] [CrossRef]
  54. Szopa, S.; Naik, V.; Adhikary, B.; Artaxo, P.; Berntsen, T.; Collins, W.D.; Fuzzi, S.; Gallardo, L.; Kiendler-Scharr, A.; Klimont, Z.; et al. Short-Lived Climate Forcers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021; pp. 817–922. [Google Scholar] [CrossRef]
  55. Butchart, N.; Scaife, A. Removal of chlorofluorocarbons by increased mass exchange between the stratosphere and troposphere in a changing climate. Nature 2001, 410, 799–802. [Google Scholar] [CrossRef]
  56. Collins, W.J.; Derwent, R.G.; Garnier, B.; Johonson, C.E.; Sanderson, M.G.; Stevenson, D.S. Effect of stratosphere-troposphere exchange on the future tropospheric ozone trend. J. Geophys. Res. 2003, 108, 8528. [Google Scholar] [CrossRef]
  57. Land, C.; Feichter, J. Stratosphere-troposphere exchange in a changing climate simulated with the general circulation model MAECHAM4. J. Geophys. Res. 2003, 108, 8523. [Google Scholar] [CrossRef]
  58. Sudo, K.; Masaaki, Y.; Akimoto, H. Future changes in stratosphere-troposphere exchange and their impacts on future tropospheric ozone simulations. Geophys. Res. Lett. 2003, 30, 2256. [Google Scholar] [CrossRef]
  59. Trickl, T.; Bärtsch-Ritter, N.; Eisele, H.; Furger, M.; Mücke, R.; Sprenger, M.; Stohl, A. High-ozone layers in the middle and upper troposphere above Central Europe: Potential import from the stratosphere along the subtropical jet stream. Atmos. Chem. Phys. 2011, 11, 9343–9366. [Google Scholar] [CrossRef]
  60. Bonasoni, P.; Evangelisti, F.; Bonafe, U.; Ravegnani, F.; Calzolari, F.; Stohl, A.; Tositti, L.; Tubertini, O.; Colombo, T. Stratospheric ozone intrusion episodes recorded at Mt. Cimone during the VOTALP project: Case studies. Atmos. Environ. 2000, 34, 1355–1365. [Google Scholar] [CrossRef]
  61. Colombo, T.; Santaguida, R.; Capasso, A.; Calzolari, F.; Evangelisti, F.; Bonasoni, P. Biospheric influence on carbon dioxide measurements in Italy. Atmos. Environ. 2000, 34, 4963–4969. [Google Scholar] [CrossRef]
  62. Langford, A.O.; Senff, C.J.; Alvarez, R.J., II; Aikin, K.C.; Baidar, S.; Bonin, T.A.; Brewer, W.A.; Brioude, J.; Brown, S.S.; Burley, J.D.; et al. The Fires, Asian, and Stratospheric Transport–Las Vegas Ozone Study (FAST-LVOS). Atmos. Chem. Phys. 2022, 22, 1707–1737. [Google Scholar] [CrossRef]
  63. Cristofanelli, P.; Bonasoni, P. Background ozone in the southern Europe and Mediterranean area: Influence of the transport processes. Environ. Pollut. 2009, 157, 1399–1406. [Google Scholar] [CrossRef]
  64. Chevalier, A.; Gheusi, F.; Delmas, R.; Ordóñez, C.; Sarrat, C.; Zbinden, R.; Thouret, V.; Athier, G.; Cousin, J.-M. Influence of altitude on ozone levels and variability in the lower troposphere: A ground-based study for western Europe over the period 2001–2004. Atmos. Chem. Phys. 2007, 7, 4311–4326. [Google Scholar] [CrossRef]
  65. Zvyagintsev, A.M.; Kakadzhanova, G.; Kruchenitskii, G.M.; Tarasova, O.A. Periodic variability of surface ozone concentration over western and central Europe from observational data. Russ. Meteorol. Hydrol. 2008, 33, 159–166. [Google Scholar] [CrossRef]
  66. Nolle, M.; Ellul, R.; Heinrich, G.; Güsten, H. A long-term study of background ozone concentrations in the Central Mediterranean-Diurnal and seasonal variations on the island of Gozo. Atmos. Environ. 2002, 36, 1391–1402. [Google Scholar] [CrossRef]
  67. Gerasopoulos, E.; Kouvarakis, G.; Vrekoussis, M.; Donoussis, C.; Mihalopoulos, N.; Kanakidou, M. Photochemical ozone production in the Eastern Mediterranean. Atmos. Environ. 2006, 40, 3057–3069. [Google Scholar] [CrossRef]
  68. Kalabokas, P.D.; Mihalopoulos, N.; Ellul, R.; Kleanthous, S.; Repapis, C.C. An investigation of the meteorological and photochemical factors influencing the background rural and marine surface ozone levels in the Central and Eastern Mediterranean. Atmos. Environ. 2008, 42, 7894–7906. [Google Scholar] [CrossRef]
  69. Cristofanelli, P.; Fierli, F.; Graziosi, F.; Steinbacher, M.; Couret, C.; Calzolari, F.; Roccato, F.; Landi, T.; Putero, D.; Bonasoni, P. Decadal O3 variability at the Mt. Cimone WMO/GAW global station (2165 m a.s.l., Italy) and comparison with two high-mountain “reference” sites in Europe. Elem. Sci. Anth. 2020, 8, 42. [Google Scholar] [CrossRef]
  70. Matasović, B.; Saliba, M.; Muscat, R.; Grima, M.; Ellul, R. Long-Term Tropospheric Ozone Data Analysis 1997–2019 at Giordan Lighthouse, Gozo, Malta. Atmosphere 2023, 14, 1446. [Google Scholar] [CrossRef]
  71. Di Carlo, P.; Pitari, G.; Mancini, E.; Gentile, S.; Pichelli, E.; Visconti, G. Evolution of surface ozone in central Italy based on observations and statistical model. J. Geophys. Res.-Atmos. 2007, 112, D10316. [Google Scholar] [CrossRef]
  72. Cristofanelli, P.; Di Carlo, P.; Aruffo, E.; Apadula, F.; Bencardino, M.; D’Amore, F.; Bonasoni, P.; Putero, D. An Assessment of Stratospheric Intrusions in Italian Mountain Regions Using STEFLUX. Atmosphere 2018, 9, 413. [Google Scholar] [CrossRef]
  73. Guaita, P.R.; Marzuoli, R.; Gerosa, G.A. A regional scale flux-based O3 risk assessment for winter wheat in northern Italy, and effects of different spatio-temporal resolutions. Environ. Pollut. 2023, 333, 121860. [Google Scholar] [CrossRef]
  74. Volz, A.; Kley, D. Evaluation of the Montsouris series of ozone measurements made in the nineteenth century. Nature 1988, 332, 240–242. [Google Scholar] [CrossRef]
  75. Anfossi, D.; Sandroni, S.; Viarengo, S. Tropospheric ozone in the nineteenth century: The Moncalieri series. J. Geophys. Res.-Atmos. 1991, 96, 17349–17352. [Google Scholar] [CrossRef]
  76. Paoletti, E.; De Marco, A.; Racalbuto, S. Why Should We Calculate Complex Indices of Ozone Exposure? Results from Mediterranean Background Sites. Environ. Monit. Assess. 2007, 128, 19–30. [Google Scholar] [CrossRef]
  77. Mills, G.; Pleijel, H.; Malley, C.S.; Sinha, B.; Cooper, O.R.; Schultz, M.G.; Neufeld, H.S.; Simpson, D.; Sharps, K.; Feng, Z.; et al. Tropospheric Ozone Assessment Report: Present-day tropospheric ozone distribution and trends relevant to vegetation. Elem Sci Anth. 2018, 6, 47. [Google Scholar] [CrossRef]
  78. Nali, C.; Pucciarello, C.; Lorenzini, G. Mapping Ozone Critical Levels for Vegetation in Central Italy. Water Air Soil Pollut. 2002, 141, 337–347. [Google Scholar] [CrossRef]
  79. Nali, C.; Crocicchi, L.; Lorenzini, G. Plants as indicators of urban air pollution (ozone and trace elements) in Pisa, Italy. J. Environ. Monit. 2004, 6, 636–645. [Google Scholar] [CrossRef]
  80. Paoletti, E. Impact of ozone on Mediterranean forests: A review. Environ. Pollut. 2006, 144, 463–474. [Google Scholar] [CrossRef]
  81. Nali, C.; Balducci, E.; Frati, L.; Paoli, L.; Loppi, S.; Lorenzini, G. Integrated biomonitoring of air quality with plants and lichens: A case study on ambient ozone from central Italy. Chemosphere 2007, 67, 2169–2176. [Google Scholar] [CrossRef]
  82. Cleveland, W.S.; Graedel, T.E.; Kleiner, B.; Warner, J.L. Sunday and Workday Variations in Photochemical Air Pollutants in New Jersey and New York. Science 1974, 186, 1037–1038. [Google Scholar] [CrossRef]
  83. Hernández-Paniagua, I.Y.; Lopez-Farias, R.; Piña-Mondragón, J.J.; Pichardo-Corpus, J.A.; Delgadillo-Ruiz, O.; Flores-Torres, A.; García-Reynoso, A.; Ruiz-Suárez, L.G.; Mendoza, A. Increasing Weekend Effect in Ground-Level O3 in Metropolitan Areas of Mexico during 1988–2016. Sustainability 2018, 10, 3330. [Google Scholar] [CrossRef]
  84. Sicard, P.; Paoletti, E.; Agathokleous, E.; Araminienė, V.; Proietti, C.; Coulibaly, F.; De Marco, A. Ozone weekend effect in cities: Deep insights for urban air pollution control. Environ. Res. 2020, 191, 110193. [Google Scholar] [CrossRef]
  85. Lebron, F. A comparison of weekend–weekday ozone and hydrocarbon concentrations in the Baltimore-Washington metropolitan area. Atmos. Environ. 1975, 9, 861–863. [Google Scholar] [CrossRef]
  86. Elkus, B.; Wilson, K.R. Photochemical air pollution: Weekend-weekday differences. Atmos. Environ. 1977, 11, 509–515. [Google Scholar] [CrossRef]
  87. D’Amico, F.; Ammoscato, I.; Gullì, D.; Lo Feudo, T.; De Pino, M.; Cristofanelli, P.; Malacaria, L.; Parise, D.; Sinopoli, S.; De Benedetto, G.; et al. Integrated analysis of methane cycles and trends at the WMO/GAW station of Lamezia Terme (Calabria, Southern Italy). Atmosphere 2024, 15, 946. [Google Scholar] [CrossRef]
  88. D’Amico, F.; Ammoscato, I.; Gullì, D.; Lo Feudo, T.; De Pino, M.; Cristofanelli, P.; Malacaria, L.; Parise, D.; Sinopoli, S.; De Benedetto, G.; et al. Anthropic-induced variability of greenhouse gases and aerosols at the WMO/GAW coastal site of Lamezia Terme (Calabria, Southern Italy): Towards a new method to assess the weekly distribution of gathered data. Sustainability 2024, 16, 8175. [Google Scholar] [CrossRef]
  89. D’Amico, F.; Ammoscato, I.; Gullì, D.; Lo Feudo, T.; De Pino, M.; Cristofanelli, P.; Malacaria, L.; Parise, D.; Sinopoli, S.; De Benedetto, G.; et al. Trends in CO, CO2, CH4, BC, and NOx during the first 2020 COVID-19 lockdown: Source insights from the WMO/GAW station of Lamezia Terme (Calabria, Southern Italy). Sustainability 2024, 16, 8229. [Google Scholar] [CrossRef]
  90. Donateo, A.; Lo Feudo, T.; Marinoni, A.; Calidonna, C.R.; Contini, D.; Bonasoni, P. Long-term observations of aerosol optical properties at three GAW regional sites in the Central Mediterranean. Atmos. Res. 2020, 241, 104976. [Google Scholar] [CrossRef]
  91. Shen, Y.; Liu, J.; Chen, Z.; Yang, M.; Shu, L.; Gai, C.; Jiang, Y. Influence of Wind Flows on Surface O3 Variation over a Coastal Province in Southeast China. Atmosphere 2024, 15, 262. [Google Scholar] [CrossRef]
  92. Cristofanelli, P.; Busetto, M.; Calzolari, F.; Ammoscato, I.; Gullì, D.; Dinoi, A.; Calidonna, C.R.; Contini, D.; Sferlazzo, D.; Di Iorio, T.; et al. Investigation of reactive gases and methane variability in the coastal boundary layer of the central Mediterranean basin. Elem. Sci. Anth. 2017, 5, 12. [Google Scholar] [CrossRef]
  93. Federico, S.; Pasqualoni, L.; De Leo, L.; Bellecci, C. A study of the breeze circulation during summer and fall 2008 in Calabria, Italy. Atmos. Res. 2010, 97, 1–13. [Google Scholar] [CrossRef]
  94. Federico, S.; Pasqualoni, L.; Sempreviva, A.M.; De Leo, L.; Avolio, E.; Calidonna, C.R.; Bellecci, C. The seasonal characteristics of the breeze circulation at a coastal Mediterranean site in South Italy. Adv. Sci. Res. 2010, 4, 47–56. [Google Scholar] [CrossRef]
  95. Gullì, D.; Avolio, E.; Calidonna, C.R.; Lo Feudo, T.; Torcasio, R.C.; Sempreviva, A.M. Two years of wind-lidar measurements at an Italian Mediterranean Coastal Site. In European Geosciences Union General Assembly 2017, EGU—Division Energy, Resources & Environment, ERE. Energy Procedia 2017, 125, 214–220. [Google Scholar] [CrossRef]
  96. Avolio, E.; Federico, S.; Miglietta, M.M.; Lo Feudo, T.; Calidonna, C.R.; Sempreviva, A.M. Sensitivity analysis of WRF model PBL schemes in simulating boundary-layer variables in southern Italy: An experimental campaign. Atmos. Res. 2017, 192, 58–71. [Google Scholar] [CrossRef]
  97. Lo Feudo, T.; Calidonna, C.R.; Avolio, E.; Sempreviva, A.M. Study of the Vertical Structure of the Coastal Boundary Layer Integrating Surface Measurements and Ground-Based Remote Sensing. Sensors 2020, 20, 6516. [Google Scholar] [CrossRef]
  98. Malacaria, L.; Parise, D.; Lo Feudo, T.; Avolio, E.; Ammoscato, I.; Gullì, D.; Sinopoli, S.; Cristofanelli, P.; De Pino, M.; D’Amico, F.; et al. Multiparameter detection of summer open fire emissions: The case study of GAW regional observatory of Lamezia Terme (Southern Italy). Fire 2024, 7, 198. [Google Scholar] [CrossRef]
  99. Calidonna, C.R.; Avolio, E.; Gullì, D.; Ammoscato, I.; De Pino, M.; Donateo, A.; Lo Feudo, T. Five Years of Dust Episodes at the Southern Italy GAW Regional Coastal Mediterranean Observatory: Multisensors and Modeling Analysis. Atmosphere 2020, 11, 456. [Google Scholar] [CrossRef]
  100. Carslaw, D.C.; Ropkins, K. Openair—An R package for air quality data analysis. Environ. Model. Softw. 2012, 27–28, 52–61. [Google Scholar] [CrossRef]
  101. Carslaw, D.C. The Openair Manual—Open-Source Tools for Analysing Air Pollution Data. Manual for Version 2.6-6, 2019, University of York. Available online: http://www.openair-project.org/Downloads/OpenAirManual.aspx (accessed on 25 August 2024).
  102. Schipa, I.; Tanzarella, A.; Mangia, C. Differences between weekend and weekday ozone levels over rural and urban sites in Southern Italy. Environ. Monit. Assess. 2009, 156, 509–523. [Google Scholar] [CrossRef]
  103. Gaudel, A.; Cooper, O.R.; Ancellet, G.; Barret, B.; Boynard, A.; Burrows, J.P.; Clerbaux, C.; Coheur, P.-F.; Cuesta, J.; Cuevas, E.; et al. Tropospheric Ozone Assessment Report: Present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation. Elem. Sci. Anth. 2018, 6, 39. [Google Scholar] [CrossRef]
  104. Kalabokas, P.; Jensen, N.R.; Roveri, M.; Hjorth, J.; Eremenko, M.; Cuesta, J.; Dufour, G.; Foret, G.; Beekmann, M. A study of the influence of tropospheric subsidence on spring and summer surface ozone concentrations at the JRC Ispra station in northern Italy. Atmos. Chem. Phys. 2020, 20, 1761–1885. [Google Scholar] [CrossRef]
  105. Karagodin-Doyennel, A.; Rozanov, E.; Sukhodolov, T.; Egorova, T.; Sedlacek, J.; Peter, T. The future ozone trends in changing climate simulated with SOCOLv4. Atmos. Chem. Phys. 2023, 23, 4801–4817. [Google Scholar] [CrossRef]
  106. Sicard, P.; De Marco, A.; Agathokleous, E.; Feng, Z.; Xu, X.; Paoletti, E.; Diéguez Rodriguez, J.J.; Calatayud, C. Amplified ozone pollution in cities during the COVID-19 lockdown. Sci. Total Environ. 2020, 735, 139542. [Google Scholar] [CrossRef]
  107. Pey, J.; Cerro, J.C. Reasons for the observed tropospheric ozone weakening over south-western Europe during COVID-19: Strict lockdown versus the new normal. Sci. Total Environ. 2022, 833, 155162. [Google Scholar] [CrossRef]
  108. Cristofanelli, P.; Arduni, J.; Serva, F.; Calzolari, F.; Bonasoni, P.; Busetto, M.; Maione, M.; Sprenger, M.; Trisolino, P.; Putero, D. Negative ozone anomalies at a high mountain site in northern Italy during 2020: A possible role of COVID-19 lockdowns? Environ. Res. 2021, 16, 074029. [Google Scholar] [CrossRef]
  109. Putero, D.; Cristofanelli, P.; Chang, K.-L.; Dufour, G.; Beachley, G.; Couret, C.; Effertz, P.; Jaffe, D.A.; Kubistin, D.; Lynch, J.; et al. Fingerprints of the COVID-19 economic downturn and recovery on ozone anomalies at high-elevation sites in North American and western Europe. Atmos. Chem. Phys. 2023, 23, 15693–15709. [Google Scholar] [CrossRef]
  110. Li, Y.S.; Yin, S.S.; Yu, S.J.; Bai, L.; Wang, X.D.; Lu, X.; Ma, S.L. Characteristics of ozone pollution and the sensitivity to precursors during early summer in central plain. China. J. Environ. Sci. 2021, 99, 354–368. [Google Scholar] [CrossRef]
  111. Qiu, S.; Du, R.; Tang, G.; Zang, K.; Lin, Y.; Chen, Y.; Qing, X.; Li, J.; Xiong, H.; Jiang, K.; et al. Characteristics of Surface Ozone and Nitrogen Oxides over a Typical City in the Yangtze River Delta, China. Atmosphere 2023, 14, 487. [Google Scholar] [CrossRef]
  112. Real, E.; Couvidat, F.; Chantreux, A.; Megaritis, A.; Valastro, G.; Colette, A. Assessing the Robustness of Ozone Chemical Regimes to Chemistry-Transport Model Configurations. Atmosphere 2024, 15, 532. [Google Scholar] [CrossRef]
  113. Cantatore, E.; Fatiguso, F. An Energy-Resilient Retrofit Methodology to Climate Change for Historic Districts. Application in the Mediterranean Area. Sustainability 2021, 13, 1422. [Google Scholar] [CrossRef]
  114. Urbina Leonor, L.M.; Sosa Echeverría, R.; Perez, N.A.; Vega, E.; Kahl, J.D.W.; Solano Murillo, M.; Soto Ayala, R. Importance of Atmospheric Sciences in Stone Heritage Conservation Study in Italy and Mexico. Sustainability 2023, 15, 5321. [Google Scholar] [CrossRef]
  115. Bostenaru Dan, M.; Ibric, A.; Popescu, M.; Crăciun, C. Architectural Heritage and Archetypal Landscape Approaches Facing Environmental Hazards. Sustainability 2024, 16, 1505. [Google Scholar] [CrossRef]
  116. López Campos, L.I.; Prestileo, F.; Stella, E.M.; Mascitelli, A.; Aruffo, E.; Chiacchiaretta, P.; Di Carlo, P.; Dietrich, S. Heritage Resilience and Identity: Lesson from Trabocchi Coast about Climate Change Adaptation Strategies. Sustainability 2024, 16, 5848. [Google Scholar] [CrossRef]
  117. Duchi, R.; Cristofanelli, P.; Landi, T.C.; Arduini, J.; Bonafe’, U.; Bourcier, L.; Busetto, M.; Calzolari, F.; Marinoni, A.; Putero, D.; et al. Long-term (2002–2012) investigation of Saharan dust transport events at Mt. Cimone GAW global station, Italy (2165 m a.s.l.). Elem. Sci. Anth. 2016, 4, 85. [Google Scholar] [CrossRef]
Environments 11 00227 g001aEnvironments 11 00227 g001b
Figure 1. (A) Location of Lamezia Terme’s observation site (LMT) in the Mediterranean basin. (B) DEM (Digital Elevation Model) shows the location of LMT in central Calabria and the key orographic features of the Catanzaro isthmus that play a major role in local wind circulation. Additional maps and details showing the observation site itself and local emission sources are available in D’Amico et al. (2024a, 2024b, 2024c) [87,88,89].
Figure 1. (A) Location of Lamezia Terme’s observation site (LMT) in the Mediterranean basin. (B) DEM (Digital Elevation Model) shows the location of LMT in central Calabria and the key orographic features of the Catanzaro isthmus that play a major role in local wind circulation. Additional maps and details showing the observation site itself and local emission sources are available in D’Amico et al. (2024a, 2024b, 2024c) [87,88,89].
Environments 11 00227 g001aEnvironments 11 00227 g001b
Environments 11 00227 g002
Figure 2. Wind rose of frequency counts and wind speed thresholds, based on hourly data gathered at LMT between 2015 and 2023. Each bar has an angle of 8 degrees. Calm refers to instances of 0 m/s, that have never occurred (0%) during the observation period.
Figure 2. Wind rose of frequency counts and wind speed thresholds, based on hourly data gathered at LMT between 2015 and 2023. Each bar has an angle of 8 degrees. Calm refers to instances of 0 m/s, that have never occurred (0%) during the observation period.
Environments 11 00227 g002
Environments 11 00227 g003
Figure 3. Main characteristics of daily patterns as observed at the LMT observation site between 2015 and 2023. All data refer to hourly aggregations. (A) Ozone mole fractions are grouped on a yearly basis (2022 and 2023 are excluded due to their lower coverage rate, as shown in Table 1). (B) Average hourly concentrations of ozone, differentiated by season. (C) Seasonal changes in temperatures.
Figure 3. Main characteristics of daily patterns as observed at the LMT observation site between 2015 and 2023. All data refer to hourly aggregations. (A) Ozone mole fractions are grouped on a yearly basis (2022 and 2023 are excluded due to their lower coverage rate, as shown in Table 1). (B) Average hourly concentrations of ozone, differentiated by season. (C) Seasonal changes in temperatures.
Environments 11 00227 g003
Environments 11 00227 g004
Figure 4. Smoothed seasonal percentile rose plots showing hourly variations in ozone concentration thresholds by wind direction. Shaded areas refer to percentiles, while the radius refers to observed mole fractions in ppb.
Figure 4. Smoothed seasonal percentile rose plots showing hourly variations in ozone concentration thresholds by wind direction. Shaded areas refer to percentiles, while the radius refers to observed mole fractions in ppb.
Environments 11 00227 g004
Environments 11 00227 g005
Figure 5. Correlation between wind speeds and ozone mole fractions, divided by sector. (A) Western-seaside (240–300° N); (B) Northeastern-continental (0–90° N); (C) total data.
Figure 5. Correlation between wind speeds and ozone mole fractions, divided by sector. (A) Western-seaside (240–300° N); (B) Northeastern-continental (0–90° N); (C) total data.
Environments 11 00227 g005
Environments 11 00227 g006
Figure 6. Evaluation of the OWE (Ozone Weekend Effect) based on hourly ozone data gathered at LMT, differentiated by weekdays. The dotted horizontal line represents average concentrations. (A) Western-seaside (240–300° N); (B) Northeastern-continental (0–90° N); (C) total data.
Figure 6. Evaluation of the OWE (Ozone Weekend Effect) based on hourly ozone data gathered at LMT, differentiated by weekdays. The dotted horizontal line represents average concentrations. (A) Western-seaside (240–300° N); (B) Northeastern-continental (0–90° N); (C) total data.
Environments 11 00227 g006
Environments 11 00227 g007aEnvironments 11 00227 g007b
Figure 7. (A) Multi-year variability of surface ozone mole fractions at LMT. The years 2022 and 2023 are not shown due to their lower coverage rate. (B) yearly cycle with monthly averages differentiated by wind corridor. (C) differentiated monthly averages referring to the entire observation period (2015–2023).
Figure 7. (A) Multi-year variability of surface ozone mole fractions at LMT. The years 2022 and 2023 are not shown due to their lower coverage rate. (B) yearly cycle with monthly averages differentiated by wind corridor. (C) differentiated monthly averages referring to the entire observation period (2015–2023).
Environments 11 00227 g007aEnvironments 11 00227 g007b
Table 1. Coverage of “Ozone” and “Meteo” datasets with respect to the total number of hours throughout the entire observation period 2015–2023. The “Combined” dataset refers to hours where both instruments were operating at the same time. Both 2016 and 2020 were leap years.
Table 1. Coverage of “Ozone” and “Meteo” datasets with respect to the total number of hours throughout the entire observation period 2015–2023. The “Combined” dataset refers to hours where both instruments were operating at the same time. Both 2016 and 2020 were leap years.
YearHoursOzone (%)Meteo (%)Combined (%)
2015876092.1395.990.50
2016878496.1696.3493.85
2017876095.9293.891.30
2018876098.1277.0575.73
2019876094.298.5994.16
2020878498.599.9898.48
2021876091.1599.7490.98
2022876085.2290.1181.98
2023876081.9596.380.55
Total78,888 192.59 294.2 288.61 2
1 Sum. 2 Average.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

D’Amico, F.; Gullì, D.; Lo Feudo, T.; Ammoscato, I.; Avolio, E.; De Pino, M.; Cristofanelli, P.; Busetto, M.; Malacaria, L.; Parise, D.; et al. Cyclic and Multi-Year Characterization of Surface Ozone at the WMO/GAW Coastal Station of Lamezia Terme (Calabria, Southern Italy): Implications for Local Environment, Cultural Heritage, and Human Health. Environments 2024, 11, 227. https://doi.org/10.3390/environments11100227

AMA Style

D’Amico F, Gullì D, Lo Feudo T, Ammoscato I, Avolio E, De Pino M, Cristofanelli P, Busetto M, Malacaria L, Parise D, et al. Cyclic and Multi-Year Characterization of Surface Ozone at the WMO/GAW Coastal Station of Lamezia Terme (Calabria, Southern Italy): Implications for Local Environment, Cultural Heritage, and Human Health. Environments. 2024; 11(10):227. https://doi.org/10.3390/environments11100227

Chicago/Turabian Style

D’Amico, Francesco, Daniel Gullì, Teresa Lo Feudo, Ivano Ammoscato, Elenio Avolio, Mariafrancesca De Pino, Paolo Cristofanelli, Maurizio Busetto, Luana Malacaria, Domenico Parise, and et al. 2024. "Cyclic and Multi-Year Characterization of Surface Ozone at the WMO/GAW Coastal Station of Lamezia Terme (Calabria, Southern Italy): Implications for Local Environment, Cultural Heritage, and Human Health" Environments 11, no. 10: 227. https://doi.org/10.3390/environments11100227

APA Style

D’Amico, F., Gullì, D., Lo Feudo, T., Ammoscato, I., Avolio, E., De Pino, M., Cristofanelli, P., Busetto, M., Malacaria, L., Parise, D., Sinopoli, S., De Benedetto, G., & Calidonna, C. R. (2024). Cyclic and Multi-Year Characterization of Surface Ozone at the WMO/GAW Coastal Station of Lamezia Terme (Calabria, Southern Italy): Implications for Local Environment, Cultural Heritage, and Human Health. Environments, 11(10), 227. https://doi.org/10.3390/environments11100227

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop