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Article

Air Quality Assessment in Six Major Greek Cities with an Emphasis on the Athens Metropolitan Region

by
Konstantinos Dimitriou
1,* and
Nikolaos Mihalopoulos
1,2
1
Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Palaia Penteli, 15236 Athens, Greece
2
Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, 70013 Crete, Greece
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(9), 1074; https://doi.org/10.3390/atmos15091074
Submission received: 5 August 2024 / Revised: 1 September 2024 / Accepted: 3 September 2024 / Published: 5 September 2024
(This article belongs to the Special Issue The Drivers and Impacts of Climate Change Over the Eastern Mediterranean)
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Abstract

:
To assess the impact of air pollution on human health in multiple urban areas in Greece, hourly concentrations of common air pollutants (CO, NO2, O3, SO2, PM10, and PM2.5) from 11 monitoring stations in six major Greek cities (Athens, Thessaloniki, Patra, Volos, Ioannina, and Kozani), were used to implement the U.S. EPA’s Air Quality Index (AQI) during a seven-year period (2016–2022). In Athens, the capital city of Greece, hourly PM10 and PM2.5 concentrations were also studied in relation to the prevailing wind patterns, while major PM10 episodes exceeding the official daily EU limit (50 μg/m3) were analyzed using the Potential Source Contribution Function (PSCF) in terms of the air mass origin. According to the AQI results, PM10 and PM2.5 were by far the most hazardous pollutants associated with moderate and unhealthy conditions in all the studied areas. In addition, in Athens, Thessaloniki, and Patra, where the benzene levels were also studied, a potential inhalation cancer risk (>1.0 × 10−6) was detected. In Athens, Saharan dust intrusions were associated with downgraded air quality, whilst regional transport and the accumulation of local emissions triggered increased PM10 and PM2.5 levels in traffic sites, especially during cold periods. Our study highlights the need for the development of early warning systems and emission abatement strategies for PM pollution in Greece.

1. Introduction

The Eastern Mediterranean is a region with high aerosol load, from both natural and anthropogenic sources, and diminishing water resources, which is exacerbated by high aerosol load and climatic change [1,2]. In addition, the Eastern Mediterranean is a hot spot of the ozone, with levels exceeding 100 μg/m3 on a 24 h basis, during the warm period (April to October; [3,4]). The relationship between air pollution levels and health impacts has been well-established in the Eastern Mediterranean basin, primarily indicating the strong effect of increased particle concentrations. For example, a 10 μg/m3 increase in PM2.5 (particles with an aerodynamic diameter smaller than 2.5 μm) levels in Limassol (Cyprus) was associated with a 3.07% increase in all-cause mortality [5], while each 10 μg/m3 increase in PM10 (particles with an aerodynamic diameter smaller than 10 μm) levels during desert dust outbreaks in Nicosia (Cyprus) was related to a 2.43% increment in daily cardiovascular mortality [6]. Moreover, Ref. [7] concluded that a large number of hospital admissions for respiratory diseases in Istanbul (Turkey) was associated with the corresponding PM10, PM2.5, and NO2 levels.
Many previous studies have also verified the impact of air pollution on human health in Greece. More specifically, in Athens, the capital city of Greece, which incorporates approximately 50% of the country’s population, increased PM10 and PM2.5 concentrations have been associated with enhanced mortality [8,9,10,11], whilst the effects were stronger after 2008, probably due to the ageing of the vehicular fleet and the increase in biomass burning for household heating [9]. Therefore, the impact of the financial crisis that struck Greece in 2008 was highlighted. In Thessaloniki, the second largest city in Greece, a 10 μg/m3 increment in PM2.5 concentrations increased cardiovascular mortality by 1.10% [5], whilst an augmentation of the PM10 levels by 10 μg/m3 triggered a 2.3% and 2% increment in the total and cardiorespiratory mortality, respectively [12]. Furthermore, in the medium-sized city of Volos, the number of hospital admissions for respiratory disease increased by 25% in years with an annual mean PM10 concentration exceeding the corresponding annual European Union (EU) limit (40 μg/m3; [13]), whilst 90% of the excessive admissions occurred when the daily PM10 levels were on average up to 60 μg/m3 [14]. Moreover, two modelling studies carried out in 1035 municipalities across Greece also indicated that an interquartile range increase in PM2.5 and NO2 concentrations was associated with enhancements in cause-specific mortality from cardiovascular, respiratory, and neurological diseases, etc. [15,16]. Finally, as regards the health impacts of tropospheric O3 in Greece, [17] underlined that during summertime, the O3 concentration remained high even during night hours at the suburban stations around Athens and, thus, may cause respiratory diseases, especially in people over 55 years of age. Furthermore, a 10 μg/m3 increase in O3 levels in Thessaloniki was related to a 3.9% and 5.3% increase in all-cause and cardiorespiratory mortality, respectively, whilst the increment in all-cause mortality was more evident (4.4%) among the elderly [12].
Air Quality Indexes (AQIs) are internationally used tools for the evaluation of air quality based on air pollution levels [18,19,20]. In Greece, various AQIs, defined for specific pollutants or a mixture of several pollutants, have been applied previously. For example, a simple concentration breakpoint scale for O3, marking air quality from “good” to “severe” [21], was utilized by [22] in Athens, indicating respiratory symptoms in children when O3 concentrations exceeded the highest breakpoint limits [23], who applied the AQI of the United States Environmental Protection Agency (U.S. EPA), based on CO, NO2, O3, and PM10 concentrations, in Athens from 2014 to 2018, concluded that PM10 is the most crucial pollutant, whilst the AQI for O3 increased significantly during the warm period due to photochemistry. Moreover, Ref. [24], who also implemented the AQI of the U.S. EPA for PM2.5 at a suburban site in Athens, reported a deterioration in air quality during March 2013 in comparison with March 2008, thus the effect of the financial recession that started at 2008 emerged again. In the city of Thessaloniki, a daily Air Quality Stress Index (AQSI), based on a mixture of pollutants (CO, NO2, O3, SO2, and PM10), revealed degraded air quality in the city center on 43% of the days, whereas in the suburbs, a satisfactory level of air quality was estimated on 54% of the days [25]. The daily AQSI was also applied in the industrialized Thriassion plain, located to the west of Athens, during two heat waves in summer 2007, indicating a moderately to heavily stressed air quality [26]. Finally, Ref. [27] applied the Common Air Quality Index (CAQI) in Thessaloniki, which indicated unhealthier conditions at urban sites, in accordance with [25], whilst Ref. [28] revealed a shift to higher CAQI classes during heat waves in Athens, Thessaloniki, and Volos, especially in the city centers.
In the present research, an air quality assessment in six major, continental Greek cities was attempted, based on long-term (2016–2022) measurements of regulated air pollutants, combined with the U.S. EPA’s daily AQI. To our knowledge, this is the first time that an AQI has been applied simultaneously in six cities in Greece, with various population characteristics and emission profiles; therefore, comparisons among the different regions were facilitated. It must be highlighted that some of the medium-sized cities that are included in this research were not adequately studied in the past concerning air quality, thus new valuable information for the inhabitants of those areas have been produced. In addition, PM2.5 concentrations, which were absent from most of the existing literature on air quality in Greece, were analyzed in all of the selected sampling sites. Special emphasis was given to the PM levels in the Athens metropolitan region, which were also studied in terms of wind patterns and air mass trajectories. Finally, a risk assessment related to benzene exposure was also performed at all the sites with available measurements. Therefore, the main objectives of this study were: (a) to assess the air quality impact, simultaneously, in multiple urban areas in Greece, with different characteristics based on long-term measurements; (b) to implement the U.S. EPA’s AQI across Greece, with the inclusion of PM2.5 concentrations; (c) to identify the impact of local and regional emissions in smaller urban areas that have not been sufficiently studied; and (d) to identify the influence of atmospheric circulation on the PM levels in Athens. The results of this research can be used to assess the most important pollutants to create abatement strategies, to improve the communication with the public in relation to air quality issues, and also to create early warning systems for unhealthy air pollution events.

2. Data and Methodology

2.1. Air Pollution Data

Hourly concentrations of common air pollutants (CO, NO2, O3, SO2, PM10, and PM2.5) and C6H6 were measured during a seven-year period (2016–2022) at 11 public monitoring stations (Table 1) in six major, continental Greek cities, namely Athens, Thessaloniki, Patra, Volos, Ioannina, and Kozani (Figure 1). All air pollution concentration data were downloaded from the database of the Greek Ministry of Environment and Energy (GMEE; https://ypen.gov.gr/perivallon/poiotita-tis-atmosfairas/; accessed on 18 February 2024) in μg/m3, except for CO (mg/m3).
In this research, a primary focus was given to the study of particulates, thus the selection of the 11 monitoring stations and of the studied time period (2016–2022) was based on the existence of simultaneous PM10 and PM2.5 measurements in the database of the GMEE. Athens is the capital city of Greece, with a total population exceeding 3.5 million, and it is one of the major urban centers in southeastern Europe; hence, five stations reflecting background, traffic, and industrial emissions, in urban and suburban areas, were studied (Table 1). Thessaloniki is the second largest city in Greece, incorporating a population of approximately 1.1 million, thus an urban/traffic and a suburban/background station, with available PM10 and PM2.5 measurements, were selected from the city’s air quality monitoring network (Table 1). Finally, Patra, Volos, Ioannina, and Kozani, are smaller urban agglomerations, with 0.3 million, 0.12 million, 0.07 million, and 0.04 million inhabitants, respectively, and, therefore, only one station with adequate PM10 and PM2.5 measurements existed in each city and was included in the present research (Table 1). It should be highlighted that the high-altitude cities of Ioannina and Kozani in northwestern Greece (Figure 1, Table 1) are strongly affected by emissions from winter biomass burning [29,30] and lignite power plants/extraction mines [31,32], respectively, therefore the study of air quality in these regions is of primary interest.
The public network of air pollution monitoring stations in the Athens metropolitan region is supervised by the GMEE, whereas all the other stations are operated by the corresponding regional administrations of Greece. At all the stations, non-dispersive infrared absorption, chemiluminescence, ultraviolet (UV) absorption, UV fluorescence, beta-ray attenuation, and gas chromatography methods are used for the detection of CO, NO2, O3, SO2, particulates (PM10 and PM2.5), and benzene, respectively, except for the ELE station, where PM10 and PM2.5 concentrations are monitored with gravimetric sensors. Therefore, the sampling and analysis procedures among all the stations are generally uniform and, thus, facilitate comparisons. In addition, the wide variety of station types (Table 1) will provide information regarding air quality in areas with different characteristics.

2.2. Methodology

2.2.1. Calculation of Daily Air Quality Index for Common Pollutants

In order to estimate the air quality in relation to each one of the six studied common pollutants, the daily AQI of the U.S. EPA was calculated separately for CO, NO2, O3, SO2, PM10, and PM2.5. According to Equation (1) [18,20,33,34], the implemented AQI allocates the days with available data for each pollutant (p) in seven pollution categories, based on the daily mean concentration of SO2, PM10, and PM2.5; the maximum hourly concentration of O3 and NO2; and the maximum 8 h mean concentration of CO, which is symbolized as Cp. The lowest and highest AQI limits (AQIlo and AQIhi, respectively) and Cp breakpoints (BPlo and BPhi, respectively) for each pollution category can be found in Table 2 and characterize the air quality from good (Category 1) to severe (Category 7). All the Cp breakpoints were obtained from [33], except for the BPlo and BPhi values for PM2.5, which were not included in the work by [33] and, thus, were acquired from the study by [34]. The AQI calculations were performed independently, during cold (16 October–15 April) and warm (16 April–15 October) periods [35], in order to identify possible seasonal variations in air quality conditions in continental Greece.
A Q I = A Q I h i − A Q I l o B P h i − B P l o C P − B P l o + A Q I l o

2.2.2. Estimation of Inhalation Cancer Risk (ICR) for Benzene

Since benzene levels are not included in the AQI calculations and benzene is a solvent with a well-known carcinogenic impact, the dimensionless ICR for benzene was used to express one person’s probability of developing cancer in his/her lifetime, due to the inhalational exposure to this carcinogenic substance. In this study, the estimation of the ICR was performed using the case of an average adult, through the implementation of Equation (2) [36,37,38]; according to which, CDI (mg kg−1 day−1) is the chronic daily intake of benzene through the respiratory route and SF (mg−1 kg day) is the slope factor of the dose–response curve. According to the [39], the SF for benzene is equal to 1.5 × 10−2 mg−1 kg day, whilst the CDI was estimated by Equation (3) [36,37,38]; according to which, C (mg/m3) is the average concentration of benzene in the studied area, BR is the breathing rate of an average adult (20 m3/day, [40]), and BW is the body weight of an average adult (70 kg, [40]). The benzene concentrations were available only at the PIR, ELE, AGS, and PAT stations (Table 1), therefore the ICR calculation was performed only for these four areas.
I C R = C D I × S F
C D I = C × B R B W

2.2.3. Pollution Roses and the Conditional Probability Function (CPF)

In Athens, the capital city of Greece, hourly wind speed (m/s) and direction (°) measurements, for the years 2016–2022, were also performed by a wind sensor, operated by the GMEE. The wind sensor is mounted on a meteorological mast, situated on a tall building’s roof, in the center of Athens (longitude: 23.73°, latitude: 38.00°, altitude: 105 m) and, hence, due to its elevation, it is not affected by the city’s structure and, therefore, is ideal for the identification of general atmospheric circulation patterns.
Wind direction observations were combined with pollutant concentrations from all the available stations in Athens in order to produce pollution roses, showing the frequency of occurrence of distinct pollution levels in relation to winds blowing from specific sectors (every 22.5°). Moreover, based on Equation (4), CPF plots were also created [41,42,43,44]. In Equation (4), nip is the total number of hourly concentrations of pollutant (p) corresponding to wind sector (i), whilst mip is the number of hourly concentrations of pollutant (p) in sector (i) exceeding a designated high-concentration limit. Therefore, the CPF provides the episode probability (%) for pollutant (p) in each wind sector (i). Finally, Pearson correlation coefficients (PCCs) were also computed among the hourly pollutant concentration and wind speed measurements, aiming to identify possible wind dispersion and accumulation effects in Athens. All wind analyses were performed separately, during cold and warm periods, and only for PM10 and PM2.5, because as was indicated by the AQI and ICR analyses, particulates pose a major threat to the Greek population in comparison with the other pollutants during all seasons and, therefore, they had to be further investigated.
C P F i p = m i p n i p × 100

2.2.4. Potential Source Contribution Function (PSCF)

The PSCF method [45,46,47,48] is used to analyze the observed concentrations of pollutants in a certain area, in relation to the gridded residence time of the corresponding incoming air masses, in order to identify potential source regions of pollutants transported into the studied receptor site. In this research, in addition to the examination of hourly PM10 and PM2.5 concentrations in Athens in relation to the prevailing wind patterns, the PSCF method was also implemented to identify the atmospheric circulation patterns associated with the increased likelihood of major, daily PM10 episodes, during cold and warm periods. The daily concentration limit for PM10 episodes was set to 50 μg/m3, in accordance with the EU regulations [13], whilst the same approach was not used for PM2.5, as no official daily limit for PM2.5 is currently in force across the EU. In order to achieve homogenous and comparable results for all the sampling sites in Athens, only 1645 days with available daily PM10 concentrations at all the Athenian stations were studied. For each one of the 1645 selected days, twenty-four, 72 h, backward air mass trajectories were produced by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (https://www.arl.noaa.gov/HYSPLIT/; accessed from 11–18 April 2024) of the National Oceanic and Atmospheric Administration (NOAA) [49,50]. Each day’s trajectories reached Athens (latitude: 37.97°, longitude: 23.72°) from 00:00 to 23:00 local time, in order to correspond with the PM10 measurements, whilst the arrival height of the air masses was defined at 500 m above ground level (AGL) to ensure the detection of potential long-range transport influences in the studied areas, while avoiding surface roughness effects that may trigger misleading results [51]. The implementation of the PSCF method was performed on a 0.5° × 0.5° resolution grid [45,46,47,48] over a wide area (latitude: 20° north to 70° north, longitude: 15° west to 55° east), whilst the incorporated weight function was carried out in accordance with [52,53,54,55].

3. Results and Discussion

3.1. Air Quality Assessment in the Six Studied Greek Cities

3.1.1. Concentrations of Regulated Air Pollutants

At all the stations with available CO measurements, a clear seasonal variation in the CO concentrations was detected (Figure S1a; Table S1), indicating increased values during the cold period. Enhanced emissions, especially from heating, decreased the boundary layer and a decrease in photochemistry can account for this behavior. Peak CO levels were observed at the PIR station, due to the greater volume of vehicles circulating in the Athens metropolitan region in comparison with the smaller Greek cities. However, the levels in other cities with much smaller populations, such as Patra or even Ioannina, were also significantly high, highlighting the impact of wood burning on air quality [56,57,58]. Similarly, maximum NO2 concentrations were also recorded for the Athenian sampling sites (PIR and ARI) with heavy traffic, whereas the lowest levels were recorded for the suburban background sites, AGP, THR, and PAO (Figure S1b; Table S1). Again, the impact of heating is clearly visible in the non-traffic sites (IOA, PAT). Contrariwise, O3 was disintegrated at the sites with traffic, due to the reaction with vehicular NO emissions, which leads to the formation of NO2, as was highlighted by the negative correlations among O3 and NO2 (Table S2). Therefore, peak O3 concentrations were observed during the warm period in the background sites of all the studied cities and, especially, in the suburban background sites in Athens and Thessaloniki, namely AGP, THR, and PAO (Figure S1c; Table S1). It is interesting to note that in the urban background locations, the levels of ozone during the warm period are in very good agreement with the levels observed in regional background locations in the Eastern Mediterranean [3,4], highlighting the regional character of this pollutant and the “hot spot” character of the Eastern Mediterranean in terms of ozone levels. In regard to the levels of SO2, the highest concentrations were measured at the PIR, ELE, and IOA stations (Figure S1d; Table S1). At the PIR station, SO2 levels are probably enriched by shipping emissions [59,60] from the port of Piraeus, one of the largest commercial marine hotspots in the Mediterranean, whilst the ELE station is affected by SO2 emissions from the industrialized Thriassion plain, which among others includes chemical and cement plants, as well as oil refineries. Moreover, the SO2 levels at the IOA station were mainly enhanced during the cold period (Figure S1d; Table S1), thus reflecting the effect of heating emissions [56,57,58,61]. Finally, with respect to particulate pollution levels, increased concentrations of PM10 and PM2.5 were indicated at all the urban traffic sites during the cold period (Figure S1e,f; Table S1), therefore the effect of vehicular emissions was highlighted. High correlations among CO, NO2, PM10, and PM2.5 in the sites with traffic signify vehicular emissions as the main source of particulate pollution in these areas (Table S2). However, increased levels of particulate pollution were also observed during the cold period by the urban background station IOA, where the highest average PM2.5 concentration among all the stations was recorded (Figure S1f; Table S1). Thus, the impact of biomass burning emissions as household heating during the cold period in the city of Ioannina was underlined and highlights the importance of this source of pollution, since the levels of PM2.5 exceeded, by about 50%, those observed in Athens, which has 50 times the amount of inhabitants and vehicles. Finally, regarding PM2.5, it is worth noting the small variance in the average levels throughout Greece during the warm period (Figure S1f; Table S1), independent of the population and site location, indicating a significant impact from regional sources, such as dust intrusions from the Sahara Desert and biomass burning in the Balkans and surrounding areas of the Black Sea [55].

3.1.2. The AQI for Common Pollutants

Regarding the levels of common gaseous pollutants, the implementation of the AQI for CO, NO2, and SO2, during cold and warm periods, clearly indicated that these pollutants do not pose a threat to human health in Greece. Indeed, their concentrations during the vast majority of days at all the stations remained within AQI Category 1 (Table 3), reflecting good air quality. In addition, good air quality conditions were also observed for O3 during the cold period (Table 3), whereas during the warm period, the levels of O3 increased due to solar radiation affluence (Table S1) and therefore, only at background stations where the dissipating effect of NO was weaker, 7.1% (IOA)–20.2% (THR) of the days were characterized by moderate air quality (AQI Category 2), whilst rare cases of higher AQI categories were also observed (Table 3).
Nevertheless, according to the AQI results, airborne particulates (PM10 and PM2.5) are by far the most crucial pollutants associated with the deterioration of air quality in the studied, continental Greek cities. More specifically, moderate air quality in terms of PM10 and PM2.5 was detected in large fractions of the days at urban and suburban background stations (Table 3). In addition, the prevalence of moderate daily air quality in relation to PM10 and PM2.5 was the most common situation in regard to stations with traffic and industrial activities, such as the PIR, ARI, ELE, AGS, PAT, and VOL stations (Table 3), whilst daily episodes of unhealthy conditions for sensitive groups (AQI Category 3) and for the entire population (AQI Category 4) also occurred (Table 3) at the same sampling sites, due to local pollution sources. However, it should be noted that at all the non-Athenian sites, the proportion of days with good air quality in terms of PM2.5 increased substantially during the warm period (Table 3), probably due to the reduction in automobile fuel combustion emissions and the cessation of biomass burning emissions for household heating. Nevertheless, in Athens, the proportion of days with good air quality in relation to PM2.5 increased slightly during the warm period at sampling sites with traffic and industrial activities (PIR, ARI, and ELE) only, whereas in the suburban/background sites (AGP and THR), the level of PM2.5 was decreased, possibly due to enhanced dust resuspension (Table 3). The only day during the warm period that the AQI for PM2.5 reached Category 3 at the VOL station was 9 August 2021, due to the smoke plumes from the severe forest fire in the northern part of the neighboring island of Euboea. Indeed, according to satellite imagery and air mass trajectories, the smoke plumes from the same forest fire also affected the atmosphere in Athens for several days [62,63], causing an increment in the AQI for PM2.5 to Categories 3 (at AGP and PIR stations) and 4 (at ARI station) on 4 August 2021 and Category 3 on 6 August 2021 (at AGP and ARI stations). It must be underlined that the AQI results for PM2.5 in Athens on 4 August 2021 were also further intensified by the simultaneous effect of another peri-urban forest fire, which ignited on 3 August 2021 in the suburban area of Varympompi, located to the north of the Athens basin [62]. Ref. [63] also reported that wildfire smoke in Athens during August 2021 doubled the aerosol penetration inside the human respiratory system, in comparison with the exposure associated with background PM10 and PM2.5 levels. The above results clearly highlight the impact of peri-urban fires during the warm period, which are expected to increase in the future due to climate change [62].
The most frequent occurrence of days with unhealthy conditions for sensitive groups and for the total population in terms of PM2.5 was indicated during the cold period at the IOA urban/background station, where the corresponding AQI Categories 3 and 4 summarized 18.4% and 13.2% of the days, respectively (Table 3). This finding was attributed to biomass burning emissions from woodstoves and fireplaces, which in the high-altitude city of Ioannina are widely used in households for heating purposes. Refs. [29,30,57,58] have also previously reported that the increased levels of PM2.5 in the medium-sized city of Ioannina are associated with biomass burning emissions, especially during cold winter days, when stagnant atmospheric conditions and a shallow planetary boundary layer prevail. In addition, a few individual PM10 episodes triggering hazardous (AQI Category 6) and severe (AQI Category 7) air quality conditions were detected at all the stations in Athens and also at the PAT station in Patra (Table 3). All these episodes were connected to major Saharan dust intrusions, which affected Athens on 23 March 2016 and 26 March 2018, and Patra on 17 April 2018 (Figure S2). During all these dust episodes, the AQI for PM2.5 was also increased and indicated unhealthy air quality conditions for the entire population (AQI Category 4), except for the ELE station, where during the second dust episode (26 March 2018) the air quality was unhealthy only for sensitive groups (AQI Category 3). Dust aerosols are mainly dominated by coarse particles (particles with an aerodynamic diameter higher than 2.5 μm) and, thus, have a stronger impact on PM10 than on PM2.5 [64,65], therefore these findings are justified. Two previous studies conducted in Greece [66,67] have also reported that the deposited PM mass in the human respiratory system increased substantially during Sahara dust events.

3.1.3. ICR for Benzene

Benzene is a volatile organic compound, which is released into the air by multiple anthropogenic sources, such as traffic, fossil fuel combustion, industrial activities, and biomass burning [68,69]. Moreover, benzene is a substance well-known for its carcinogenic effects [36,70,71]. The benzene levels at the four examined locations in three cities (Athens, Thessaloniki, and Patra) are depicted in Table S1 and Figure S3. The benzene levels present clear seasonal variations at all the stations, with increased values during the cold period compared to the warm period (factor of 1.6–2.8), due to local sources (especially vehicular emissions and wood burning), a decreased boundary layer, and reduced photochemistry. The increment ranged from 0.7 μg/m3 at the ELE station to 2.3 μg/m3 at the AGS and PAT stations (Table S1). Ref. [72] have also reported increased levels of benzene during autumn and winter for multiple monitoring sites in Europe, whilst such concentrations in Athens were the highest among all the sites with urban backgrounds and traffic. It is also interesting to note that the amplitude of benzene (max/min) between the sites remains almost the same during the warm and the cold period (Table S1). This is due to the local rather than regional character of benzene, originating mainly from its relatively small lifetime of a few days [72].
In this study, the estimated ICR for benzene at the PIR, ELE, AGS, and PAT stations was found to be equal to 1.2 × 10−5, 3.6 × 10−6, 1.6 × 10−5, and 1.2 × 10−5, respectively, therefore the ICR at all stations exceeded the limit of 1.0 × 10−6, which indicates a potential health risk, but remained below 1.0 × 10−4, which suggests a definite health risk [36]. The estimated cancer risks from benzene exposure around Europe were also in the same range, according to [72]. However, the levels of benzene and the corresponding ICR were much lower at the suburban/industrial ELE station, in comparison with the urban/traffic-related stations, PIR, AGS, and PAT (Table S1; Figure S3), thus the role of traffic emissions in relation to benzene concentrations was highlighted, as was also indicated by the high correlations among CO, NO2, and C6H6 (Table S2). The yearly measurements of benzene from cities impacted by wood burning are missing from Table S1. The measurements performed during wintertime in Ioannina [56] reported values exceeding 7 μg/m3, highlighting the need for more systematic observations, as benzene is a well-known byproduct of wood burning.

3.2. The Effect of Wind Patterns and Air Mass Origin on PM Concentrations in Athens

3.2.1. Pollution Roses and CPF

The produced pollution roses (Figure S4) clearly revealed that the prevailing winds over Athens, during the studied period, mainly blew from northeast and southwest directions. During the cold and warm period, an increased likelihood of high, hourly PM10 and PM2.5 concentrations at suburban stations (AGP, ELE, and THR), exceeding 30 μg/m3 and 20 μg/m3, respectively, was linked with southern and southwest wind directions (Figure S4) and was interpreted as a marker of dust intrusions from the Sahara Desert [73] and the transport of particles from polluted industrial zones in Piraeus, Votanikos, and the Saronicos Gulf [74]. Particles from major cities situated in North Africa may also have been transported into Greece. On the contrary, at the urban/traffic-related sites (ARI and PIR), the increased PM10 and PM2.5 concentrations were more homogenously distributed in terms of the different wind sectors (Figure S4), hence the dominance of local sources emerged. The stronger negative correlations observed between the wind speed and PM levels at the urban/traffic-related sites (Table 4), in comparison with the suburban/industrial site (ELE) and, especially, the suburban/background sites (AGP and THR), during the cold and warm period, also indicated the enhanced accumulation of particle emissions in the urban/traffic-related sites under the influence of weak airflows, thus highlighting the contribution of local sources of pollution.
Since no official hourly concentration limit is currently in force for PM10 and PM2.5 according to EU regulations, the hourly concentration limits for the implementation of the CPF were set at 30 μg/m3 and 20 μg/m3 for PM10 and PM2.5, respectively, based on the produced pollution roses. The results of the CPF plots for the suburban stations (AGP, ELE, and THR), during both the cold and warm period, clearly revealed higher probabilities for exceedances of the designated PM10 and PM2.5 limits under the influence of south and southwest winds (Figure 2), therefore an apparent impact of aerosol transportation from these directions was pointed out. This finding is normally expected in regard to the suburban/background stations AGP and THR, where reduced local PM emissions take place; however, the effect of aerosol transportation was also apparent at the ELE suburban/industrial station, probably due to technological evolutions, which have decreased the impact of local industrial emissions. In addition, at the urban/traffic-related sites (ARI and PIR), a high probability of the occurrence of hourly PM10 and PM2.5 concentrations above the defined limits was combined with multiple wind sectors (Figure 2). Therefore, the triggering of elevated hourly particulate levels in the studied urban areas of Athens with heavy traffic was not dependent on the wind direction and, thus, the main role of accumulation on local emissions was again verified.

3.2.2. PSCF Results

In total, the produced backward air mass trajectories were constituted by 2,882,040 hourly trajectory points (1645 days × 24 trajectories per day × 73 trajectory points per trajectory), which were mainly distributed above Europe, North Africa, the Mediterranean Sea, and the northeast Atlantic. The studied PSCF grid was carefully selected in order to cover those areas and, therefore, included 2,863,691 (99.4%) of the total trajectory points, thus ensuring reliable results. Out of the 1645 studied days, 829 (1,436,164 trajectory points) and 816 (1,427,527 trajectory points) days belonged to the cold and warm period, respectively, hence a sufficient amount of data were available for the distinct implementation of the PSCF technique for both seasons.
In general, the air mass circulation patterns associated with the generation of daily PM10 episodes at the background (AGP and THR) and the industrial (ELE) sampling sites in Athens were strongly homogenous (Figure 3), whilst uniform results were also detected for the two sites with heavy traffic (ARI and PIR) (Figure 4). As regards the background sites, rare episodes of average daily PM10 concentrations exceeding 50 μg/m3 were almost exclusively associated with southern and southwestern airflows during the cold and warm period (Figure 3). Hence, at the background sites, where the influence from local sources is weaker, dust intrusions from North Africa (Egypt, Libya, Tunisia, Algeria, and Morocco) were proven to be the main cause of PM10 exceedances (Figure 3). Contrariwise, at the two sites with heavy traffic, a higher probability of daily PM10 episodes in comparison with the background sites was detected by the PSCF analysis (Figure 4), due to the impact of local emissions. Air masses travelling across North Africa were again related to daily PM10 episodes, however the origin of the dust plumes was primarily isolated over Libya and Tunisia, especially during the cold period (Figure 4a,b). Ref. [66] have also previously identified dust intrusions in Athens from various North African regions and have reported different PM size distributions and chemical content with respect to the origin of the corresponding air masses (Algeria–Tunisia vs. Libya–Egypt). Moreover, various studies have also indicated desert areas in Libya as the main source of dust outbreaks towards Greece, triggered by cyclonic activity over the Mediterranean Sea and the North African coast [73,75].
In addition, during the cold period, an increased likelihood of daily PM10 episodes at the two sites with heavy traffic was also connected to short-range slow-moving air masses from northern directions (Figure 4a,b), favoring atmospheric stagnation and the accumulation of local emissions and also the transportation of aerosols from continental Greece [76,77]. The buildup of local PM10 emissions and/or the addendum of PM10 contributions from regional anthropogenic sources (e.g., biomass burning) at the sites with heavy traffic during the cold period was found to induce daily PM10 episodes over the EU limit. Nevertheless, during the warm period, when the influence of local/regional emissions in Athens was reduced, PM10 episodes at the two sites with heavy traffic were more clearly related to dust intrusions from the Sahara Desert (Figure 4).

4. Conclusions

In this study, a simultaneous long-term analysis of air quality levels in multiple urban areas in Greece was performed to assess its impact on human health. The relationship between air pollution and increased mortality rates in Greece has been well-documented by previous studies, however air quality levels in medium-sized Greek cities have not been adequately analyzed. In this context, the main objectives of the present research were to evaluate air quality in several urban areas, with different populations and various emission sources, to compare the air quality levels among the areas with different characteristics and to assess the impact of the emissions in smaller cities that have not been sufficiently studied.
The hourly concentrations of six regulated pollutants (CO, NO2, O3, SO2, PM10, and PM2.5), measured at 11 sampling sites, in 6 cities, in continental Greece (Athens, Thessaloniki, Patra, Volos, Ioannina, and Kozani), during the 7-year period, 2016–2022, were used for the calculation of daily AQIs, aiming to evaluate air quality conditions. The AQIs for CO, NO2, and SO2 at all the stations showed that these pollutants have a negligible impact on human heath, whilst only at background stations, during the warm period, approximately up to 20% of the days were characterized by moderate air quality in relation to O3. Moreover, the AQIs for PM10 and PM2.5 indicated large fractions of days with moderate air quality, especially at stations with heavy traffic and industrial activities, whilst episodes of unhealthy conditions for sensitive groups, as well as for the whole population, also occurred. Therefore, an increase in mortality due to respiratory and cardiovascular diseases associated with PM and O3 exposure is expected, especially among the most vulnerable cohorts of the population, such as the elderly.
Sahara dust and wood burning for heating were identified as the two main sources responsible for the worsening of air quality. Thus, at all the non-Athenian sites, the proportion of days with good air quality in terms of PM2.5 increased substantially in the warm period, due to the decrease in dust episodes and the cessation of biomass burning emissions for household heating. Finally, at the four stations where benzene measurements were available, the calculated ICR indicated a potential health risk for the exposed population; however, a definite health risk was not pointed out, even at the sites with heavy traffic, where the highest ICR occurred. Nevertheless, continuous measurements of BTEX (Benzene, Toluene, Ethylbenzene, and Xylenes) at other Greek locations, especially those impacted by wood burning, is clearly needed.
In Athens, the capital city of Greece, hourly wind speed and direction measurements were also available from an elevated roof station, ideal for the identification of long-range transportation of pollutants. The pollution roses and CPF plots that were created for PM10 and PM2.5 at the background and industrial sites clearly indicated the advection of Saharan dust and anthropogenic particles from south–southwest directions, whereas the increased hourly PM10 and PM2.5 concentrations at the sites with heavy traffic were attributed primarily to the accumulation of local emissions. The determination of wind patterns, affecting the hourly PM10 and PM2.5 concentrations in Athens, was supplemented by a PSCF analysis for the identification of major daily PM10 episodes (≥50 μg/m3) in relation to the air mass origin. The PSCF method was applied only for PM10, because no official daily concentration limit is, up to now, in force for PM2.5, according to EU regulations. At the background and industrial sites, the infrequent emergence of daily PM10 episodes, during the cold and warm period, was almost exclusively related to dust intrusions from the Sahara Desert, which in extreme cases may trigger severe air quality conditions for the population, according to the calculated AQI. Similarly, dust intrusions were also strongly associated with the emergence of daily PM10 episodes at the sites with heavy traffic during all the seasons. However, during the cold period, the influence of short-range continental air masses was also associated with a high probability of exceeding the EU daily PM10 limit at the sites with heavy traffic, due to regional transport in those areas and the accumulation of local emissions.
The findings in this study can help the Greek authorities to design an early warning system, aimed at reducing the exposure of the country’s population to excessive levels of air pollutants and to minimize the related cases of premature mortality [16,78]. In addition, our results may also contribute to the effective adoption of emission abatement measures in the studied areas, especially for local sources of pollutants, such as wood burning.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos15091074/s1, Table S1: Average pollutant concentrations at the 11 studied stations during cold and warm period; Figure S1: Monthly average concentrations of (a) CO, (b) NO2, (c) O3, (d) SO2, (e) PM10 and (f) PM2.5; Table S2: Pearson correlation coefficients among hourly pollutant concentrations at (a) AGP (b) ARI (c) PIR (d) THR (e) ELE (f) AGS (g) PAO (h) PAT (i) VOL (j) IOA (k) KOZ stations; Figure S2: Major Saharan dust intrusions affecting Greece on (a) 23/3/2016 (b) 26/3/2018 and (c) 17/4/2018 (Source: Dust Regional Atmospheric Model (Dream), http://www.seevccc.rs/?p=14, accessed on 2 March 2024); Figure S3: Monthly average concentrations of benzene; Figure S4: Pollution roses for PM10 and PM2.5 for all the stations in Athens during (a) cold period and (b) warm period.

Author Contributions

Conceptualization, K.D. and N.M.; data curation, K.D.; formal analysis, K.D.; funding acquisition, N.M.; methodology, K.D.; supervision, N.M.; visualization, K.D.; writing—original draft, K.D.; writing—review and editing, N.M. All authors have read and agreed to the published version of the manuscript.

Funding

The publication was funded by the project “Climate Change, Health and Energy: Impacts and Interactions-ENACT” PRIORITY AXIS 3: “RESEARCH/IMPLEMENTATION” of the Financial Program of the Green Fund “NATURAL ENVIRONMENT & INNOVATIVE ACTIONS 2023”. Budget 199,080.00 Funding: Green Fund Beneficiary: National Observatory of Athens.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original data presented in the study are openly available in the database of the Greek Ministry of Environment and Energy at https://ypen.gov.gr/perivallon/poiotita-tis-atmosfairas/, accessed on 2 March 2024.

Acknowledgments

We would like to thank the Greek Ministry of Environment and Energy for the free provision of the air pollution and meteorological data that were used in this research. In addition, the authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model used in this publication.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Atmosphere 15 01074 g001
Figure 1. Satellite image of Greece. The locations of the six studied cities are marked with blue dots.
Figure 1. Satellite image of Greece. The locations of the six studied cities are marked with blue dots.
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Atmosphere 15 01074 g002aAtmosphere 15 01074 g002b
Figure 2. CPF plots depicting PM10 and PM2.5 episode probabilities (%) in relation to distinct wind sectors for all the stations in Athens during (a) the cold period and (b) the warm period.
Figure 2. CPF plots depicting PM10 and PM2.5 episode probabilities (%) in relation to distinct wind sectors for all the stations in Athens during (a) the cold period and (b) the warm period.
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Figure 3. PSCF maps for PM10 exceedances (≥50 μg/m3) detected at background and industrial stations in Athens during (a–c) the cold period (left column) and (d–f) the warm period (right column).
Figure 3. PSCF maps for PM10 exceedances (≥50 μg/m3) detected at background and industrial stations in Athens during (a–c) the cold period (left column) and (d–f) the warm period (right column).
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Figure 4. PSCF maps for PM10 exceedances (≥50 μg/m3) detected at traffic stations in Athens during (a,b) the cold period (left column) and (c,d) the warm period (right column).
Figure 4. PSCF maps for PM10 exceedances (≥50 μg/m3) detected at traffic stations in Athens during (a,b) the cold period (left column) and (c,d) the warm period (right column).
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Table 1. Characteristics of air pollution monitoring stations.
Table 1. Characteristics of air pollution monitoring stations.
Station NameCityAbbreviationType of StationLongitude (°)Latitude (°)Altitude (m)Pollutant Availability
Agia ParaskeviAthensAGPSuburban/Background23.8238.00290NO2, O3, PM10, PM2.5
AristotelousAthensARIUrban/Traffic23.7337.9975NO2, SO2, PM10, PM2.5
Piraeus 1AthensPIRUrban/Traffic23.6537.944CO, NO2, O3, SO2, PM10, PM2.5, C6H6
ThrakomakedonesAthensTHRSuburban/Background23.7638.14550NO2, O3, PM10, PM2.5
ElefsinaAthensELESuburban/Industrial23.5438.0520NO2, O3, SO2, PM10, PM2.5, C6H6
Agia SofiaThessalonikiAGSUrban/Traffic22.9540.6312CO, NO2, O3, SO2, PM10, PM2.5, C6H6
PanoramaThessalonikiPAOSuburban/Background23.0340.59363NO2, O3, PM10, PM2.5
Patra 2PatraPATUrban/Traffic21.7338.258CO, NO2, SO2, PM10, PM2.5, C6H6
Volos 1VolosVOLUrban/Traffic22.9439.3731PM10, PM2.5
Ioannina 2IoanninaIOAUrban/Background20.8539.67481CO, NO2, O3, SO2, PM10, PM2.5
KozaniKozaniKOZUrban/Background21.7940.29675PM10, PM2.5
Table 2. Lowest and highest AQI limits and Cp breakpoints for each pollution category.
Table 2. Lowest and highest AQI limits and Cp breakpoints for each pollution category.
Air Quality CategoryAQICO Max 8 h
(mg/m3)		NO2 Max Hourly
(μg/m3)		O3 Max Hourly
(μg/m3)		PM10 Mean Daily
(μg/m3)		PM2.5 Mean Daily
(μg/m3)		SO2 Mean Daily
(μg/m3)
NumberDescription
1Good0–500 ≤ Cp < 4.70 ≤ Cp < 1520 ≤ Cp < 1370 ≤ Cp < 180 ≤ Cp < 120 ≤ Cp < 30
2Moderate51–1004.7 ≤ Cp < 10152 ≤ Cp < 200137 ≤ Cp < 18018 ≤ Cp < 7512 ≤ Cp < 35.430 ≤ Cp < 125
3Unhealthy for sensitive groups101–15010 ≤ Cp < 13200 ≤ Cp < 262180 ≤ Cp < 23675 ≤ Cp < 12435.4 ≤ Cp < 55.4125 ≤ Cp < 194
4Unhealthy151–20013 ≤ Cp < 16262 ≤ Cp < 326236 ≤ Cp < 294124 ≤ Cp < 17255.4 ≤ Cp < 150.4194 ≤ Cp < 264
5Very unhealthy201–30016 ≤ Cp < 32326 ≤ Cp < 646294 ≤ Cp < 582172 ≤ Cp < 206150.4 ≤ Cp < 250.4264 ≤ Cp < 524
6Hazardous301–40032 ≤ Cp < 43646 ≤ Cp < 806582 ≤ Cp < 726206 ≤ Cp < 245250.4 ≤ Cp < 350.4524 ≤ Cp < 698
7Severe401–50043 ≤ Cp < 54806 ≤ Cp < 966726 ≤ Cp < 870245 ≤ Cp < 294350.4 ≤ Cp < 500.4698 ≤ Cp < 872
Table 3. Distribution (%) of days with available CO, NO2, O3, SO2, PM10, and PM2.5 concentrations at the 11 studied sampling sites in terms of the AQI categories.
Table 3. Distribution (%) of days with available CO, NO2, O3, SO2, PM10, and PM2.5 concentrations at the 11 studied sampling sites in terms of the AQI categories.
StationAQI CategoryCONO2O3SO2PM10PM2.5
ColdWarmColdWarmColdWarmColdWarmColdWarmColdWarm
AGP1 10010099.077.7 69.846.668.056.6
2 001.020.1 29.45331.743
3 0001.7 0.50.30.10.4
4 0000.5 0.10.10.20
5 0000 0000
6 0000 0000
7 0000 0.2000
StationAQI categoryCONO2O3SO2PM10PM2.5
coldwarmcoldwarmcoldwarmcoldwarmcoldwarmcoldwarm
ARI1 99.699.5 1001009.23.919.326.9
2 0.40.4 0086.094.768.272.6
3 00.1 004.51.310.60.4
4 00 000.10.11.90.1
5 00 000000
6 00 000000
7 00 000.2000
StationAQI categoryCONO2O3SO2PM10PM2.5
coldwarmcoldwarmcoldwarmcoldwarmcoldwarmcoldwarm
PIR199.510099.895.199.996.910099.96.72.034.439.2
20.500.24.60.13.100.188.996.257.560.0
30000.300004.11.87.30.8
4000000000.200.80
5000000000000
6000000000000
7000000000.1000
StationAQI categoryCONO2O3SO2PM10PM2.5
coldwarmcoldwarmcoldwarmcoldwarmcoldwarmcoldwarm
THR1 10010097.675.4 68.948.855.047.2
2 002.320.2 30.550.844.652.4
3 000.13.5 0.40.40.20.4
4 0000.6 000.20
5 0000.3 0000
6 0000 0.1000
7 0000 0.1000
StationAQI categoryCONO2O3SO2PM10PM2.5
coldwarmcoldwarmcoldwarmcoldwarmcoldwarmcoldwarm
ELE1 10010098.879.199.998.925.514.825.728.3
2 001.218.60.11.174.084.871.670.4
3 0002.2000.30.42.51.1
4 0000.100000.20.2
5 0000000000
6 0000000.1000
7 0000000.1000
StationAQI categoryCONO2O3SO2PM10PM2.5
coldwarmcoldwarmcoldwarmcoldwarmcoldwarmcoldwarm
AGS199.610010010099.999.399.71003.72.49.225.7
20.40000.10.70.3085.797.070.574.2
30000000010.40.616.30.1
4000000000.204.00
5000000000000
6000000000000
7000000000000
StationAQI categoryCONO2O3SO2PM10PM2.5
coldwarmcoldwarmcoldwarmcoldwarmcoldwarmcoldwarm
PAO1 10010099.784.9 56.457.049.380.4
2 000.315.1 43.443.050.019.6
3 0000 0.200.70
4 0000 0000
5 0000 0000
6 0000 0000
7 0000 0000
StationAQI categoryCONO2O3SO2PM10PM2.5
coldwarmcoldwarmcoldwarmcoldwarmcoldwarmcoldwarm
PAT110010010099.9 99.91007.511.218.661.6
20000.1 0.1090.788.174.738.0
30000 001.50.45.90.2
40000 000.30.10.80.2
50000 0000.100
60000 000000
70000 0000.100
StationAQI categoryCONO2O3SO2PM10PM2.5
coldwarmcoldwarmcoldwarmcoldwarmcoldwarmcoldwarm
VOL1 8.319.013.863.9
2 89.180.969.336.0
3 2.50.113.70.1
4 0.103.20
5 0000
6 0000
7 0000
StationAQI categoryCONO2O3SO2PM10PM2.5
coldwarmcoldwarmcoldwarmcoldwarmcoldwarmcoldwarm
IOA110010010010099.492.310010017.048.112.563.2
200000.67.10073.051.555.536.8
3000000.6007.60.418.40
4000000002.2013.20
5000000000.200.40
6000000000000
7000000000000
StationAQI categoryCONO2O3SO2PM10PM2.5
coldwarmcoldwarmcoldwarmcoldwarmcoldwarmcoldwarm
KOZ1 63.766.755.672.9
2 36.333.344.427.1
3 0000
4 0000
5 0000
6 0000
7 0000
Table 4. Pearson correlation coefficients among PM10 and PM2.5 concentrations and wind speed at the five stations in Athens during the cold and warm period.
Table 4. Pearson correlation coefficients among PM10 and PM2.5 concentrations and wind speed at the five stations in Athens during the cold and warm period.
StationPM10PM2.5
ColdWarmColdWarm
AGP0.059 *−0.052 *−0.081 *−0.193 *
ARI−0.317 *−0.195 *−0.434 *−0.360 *
PIR−0.264 *−0.148 *−0.356 *−0.284 *
THR0.041 *0.006−0.062 *−0.050 *
ELE−0.075 *−0.082 *−0.262 *−0.187 *
* Correlation is significant at the 0.01 level.
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Dimitriou, K.; Mihalopoulos, N. Air Quality Assessment in Six Major Greek Cities with an Emphasis on the Athens Metropolitan Region. Atmosphere 2024, 15, 1074. https://doi.org/10.3390/atmos15091074

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Dimitriou K, Mihalopoulos N. Air Quality Assessment in Six Major Greek Cities with an Emphasis on the Athens Metropolitan Region. Atmosphere. 2024; 15(9):1074. https://doi.org/10.3390/atmos15091074

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Dimitriou, Konstantinos, and Nikolaos Mihalopoulos. 2024. "Air Quality Assessment in Six Major Greek Cities with an Emphasis on the Athens Metropolitan Region" Atmosphere 15, no. 9: 1074. https://doi.org/10.3390/atmos15091074

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