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Article

Variability of Air Pollutant Concentrations and Their Relationships with Meteorological Parameters during COVID-19 Lockdown in Western Macedonia

by
Paraskevi Begou
1,
Vasilios Evagelopoulos
2,* and
Nikolaos D. Charisiou
2
1
Laboratory of Meteorology, Department of Physics, University of Ioannina, 45110 Ioannina, Greece
2
Department of Chemical Engineering, University of Western Macedonia, 50100 Kozani, Greece
*
Author to whom correspondence should be addressed.
Atmosphere 2023, 14(9), 1398; https://doi.org/10.3390/atmos14091398
Submission received: 13 July 2023 / Revised: 21 August 2023 / Accepted: 2 September 2023 / Published: 4 September 2023
(This article belongs to the Special Issue Recent Advances in Air Quality Management)
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Abstract

:
The lockdown implemented to tackle the spread of the COVID-19 pandemic had a positive impact on air quality. Globally, studies have shown that air pollutant levels decreased temporally during the restriction measures. In this study, we evaluated the impact of COVID-19 restrictions on the air quality of Western Macedonia, Greece, using the concentrations of PM2.5 and PM10 along with meteorological data from the Air Quality Monitoring Stations (AQMS) operated by the Lignite Center of Western Macedonia. In Western Macedonia, previous studies have identified a general reduction in air pollutant levels during the last decade due to the coal phase-out plan for power generation. During the lockdown, the levels of PM2.5 and PM10 decreased further. The reduced emissions from the local mining activities and lignite-fired power plant electricity generation, as well as the weather conditions, seem to contribute to improving air quality.

1. Introduction

The coronavirus SARS-CoV-2 that causes COVID-19 first emerged in Wuhan in 2019 and spread rapidly across the globe, causing serious public health issues. It was declared a pandemic by the World Health Organization (WHO) in March 2020. In order to manage the pandemic, countries across the world implemented a range of stringent government policies, including stay-at-home restrictions, face coverings, restrictions and cancellation of events and public gatherings, school and workplace closures, and restrictions on international and domestic travel and public transport [1]. Consequently, the pandemic affected labour markets, enterprises, industries, and business activities in general. As economic activities were largely disrupted, electricity consumption, which is an indicator of economic growth, dropped during the lockdown periods [2,3]. Based on International Energy Agency (IEA) reports related to COVID-19 pandemic impacts on the energy system, global electricity demand growth dropped by 2% in 2020, while in the first quarter of 2020, the demand dropped by more than 3% due to stringent lockdowns in China and the mild winter in the northern hemisphere [2,3]. In the European Union (EU), the drop in electricity demand was not only related to the lockdowns but also due to higher renewable energy production [3].
In Greece, the Independent Power Transmission Operator (IPTO or ADMIE) reports confirm the general decreasing trends in energy sectors [4]. As for December 2020, the monthly energy bulletins of the IPTO recorded a drop in energy demand in comparison to the corresponding month of the previous year, especially among high-voltage consumers [4]. In addition, the production from mines decreased significantly [4]. These declining trends in lignite production are mainly consequences of the energy transition and the phase-out of coal-powered electricity production, but they were also augmented by the pandemic.
In addition to the impacts on energy systems and electricity consumption, the pandemic had a great impact on air quality, which is a consequence of strict pandemic-driven policies that are interrelated with economic growth. In February 2020, NASA and European Space Agency (ESA) pollution monitoring satellites detected significant decreases in mean tropospheric nitrogen dioxide (NO2) density over China compared to a reference period [5]. The reduction in NO2 and air pollutant concentrations was initially evident near Wuhan, China, but eventually spread across the globe. Scientific studies from India, China, Europe, and the US have confirmed that pandemic restrictions improved air quality [6,7]. In general, significant reductions have occurred mainly in NOX concentrations, as demonstrated by ground-based and satellite observations, while for PM concentrations the reductions have been less pronounced, either variable or unevenly distributed [6,7]. The decrease in air pollutant levels appears to be a combination of meteorology and a reduction in anthropogenic emissions [6,7]. On the other hand, in the majority of the studies analysed, the O3 concentrations were found to be higher in 2020 during the lockdown compared to 2019 levels [6]. This can be explained by the significant drop in NO2 concentrations during the same period, which favoured the formation of O3 [6].
In Greece, Varotsos et al. (2021) studied the levels of NO2, O3, PM2.5, and PM10 concentrations during the lockdown period in Athens, Thessaloniki, Volos, and Larissa based on the observations recorded at the air quality monitoring stations belonging to the National Atmospheric Pollution Monitoring Network (NAPM) [8]. The results showed that in most cases, the change in air pollutants is not statistically significant, while long-range transport seems to be an important mechanism for particle pollution episodes [8]. Similarly, Avdoulou et al. (2023) showed that PM10 concentrations are highly affected by weather conditions as well as the long-range transport of African dust [9].
Scientific studies regarding the effects of lockdown on ambient air quality mainly focus on urban areas where the major human mobility restriction policies were implemented [7,8,9]. Additionally, areas with industrial, electricity generation, and coal-mining activities experienced a temporary reduction in their operations because of lockdown, with a possible impact on air quality [10,11]. In Poland, where coal dominates electricity production, its share in power production during the pandemic was reduced by 24% [10]. Filonchyk et al. (2021) analysed the PM2.5, PM10, SO2, and NO2 concentrations in Polish cities during March–April 2020 and attributed their reductions to the reduced activity of power plants, among other restriction policies [10]. Arregocés et al. (2021) studied the effects of lockdown on particle air pollution at the Cerrejón mine, which is Latin America’s largest open-pit coal mine, and found increasing trends in their concentrations mainly attributed to weather conditions [11]. Ranjan et al. (2020) investigated the changes in aerosol optical depth (AOD) level during lockdown phases over urban and mining regions in India. Primarily, they reported positive AOD in different coalfields throughout the lockdown periods due to on-going coal mining operations, while negative AOD anomalies were reported in a minor number of mines when operating at reduced capacity [12].
In this study, we focus on Western Macedonia, Greece, which is largely dominated by lignite mining, lignite-fired power plants, and district heating systems [13,14,15,16,17,18,19,20,21,22,23,24,25,26]. These activities are a significant part of the local and national economies but also contribute to environmental pollution in the region. As demonstrated by previous studies, the air pollution in Western Macedonia is highly correlated with the activities in the lignite centre of the region and the power generation [13,14,15,16,17,18,19,20,21,22,23,24,25,26]. Skoulidou et al. (2021) studied the NOX emissions over Western Macedonia based on observations from satellite instruments as well as surface measurements of NO2 from the AQMS operating in the region [27]. Their analysis revealed a strong decrease in emissions and concentrations in the summers of 2018 and 2019, which is supported by similar decreases in the energy production of the power plants [27]. In addition, studies that used PM concentration data from the ground-based AQMS reported an overall improvement in air quality and downward trends in PM concentrations over the decade 2011–2020 [22]. These trends follow the gradual decrease in both lignite production and total excavation volumes that are consequences of the goal of complete decarbonisation and the Just Transition Development Plan of lignite areas in Greece [21,23,26].
In this study, we analyse the impact of COVID-19 restriction measures on air quality in a “coal region” in the era of energy transition based on air pollution data from a dense network of AQMS in the Lignite Center of Western Macedonia. The specific research objectives are: (1) to identify the temporal and spatial distribution of both PM2.5 and PM10 in the region pre-, during, and post-lockdown periods; (2) to unveil the contribution of coal phase-out policies to air pollutant concentrations; and (3) to find the associations of air pollutants with the meteorological parameters.

2. Materials and Methods

2.1. Study Location

Western Macedonia, located in north-western Greece, is divided into the regional units of Florina, Grevena, Kastoria, and Kozani, with a total population of 255,056 inhabitants. The area is mostly composed of mountainous and semi-mountainous land, and it also has the largest concentration of surface water in the country. The economy of Western Macedonia is heavily based on the extraction and use of lignite in thermal power plants [21,22,23,24]. These operations began in September 1956, when LIPTOL SA (Ptolemaida Lignite Mines) signed an agreement with the German company KHD regarding the construction of the first thermal power station operating on lignite [28]. For decades, the exploitation of lignite deposits for power generation covered the majority of the electricity production in Greece. As an example, in the years 2001–2004, the lignite production in Western Macedonia exceeded 55 million tons per year [24]. For the period 2010–2022, the lignite production is shown in Figure 1.

2.2. Data

In this study, we used the data from PM2.5, PM10, NO, NO2, NOX, and SO2 concentrations registered on an hourly basis from ten Air Quality Monitoring Stations (AQMS) in the region under study for a 13-year period (from the 1 January 2010 to the 31 December 2022). The concentrations of PM10 and PM2.5 were detected by Grimm Aerosol Technik, model EDM180 [18]. At each AQMS, the air temperature (°C), relative humidity (%), wind speed (m/s), and wind direction are measured. The spatial coverage of AQMS includes different locations in the region. Most of the AQMS are located in villages and towns in proximity to the power stations.
The location of the AQMS and their coordinates are presented in Figure 2 and Table 1, respectively. The measurements are available for all AQMS over the 13-year period, except for the AQMS S3, where measurements are available for the period from the 1 January 2010 to the 28 November 2019. So, the AQMS S3 was excluded from the analysis.

2.3. Statistical Analysis

For the statistical analysis of the air pollution and meteorological data, we used the packages “PerformanceAnalytics”, “openair”, and “sjPlot” in R software. The statistical characteristics and temporal variations in the air pollutant concentrations at 9 AQMS were studied over the years 2019–2022. Descriptive statistics is a classical statistical analysis dealing with any data set and providing summarising information on the characteristics and distribution of values. The box and whisker plots were calculated in order to explore the characteristics of the data, such as the lower quartile (25%), upper quartile (75%), median, mean, and maximum and minimum values. So, the dispersion of the data can be easily compared between different data sets. For this purpose, we used the “PerformanceAnalytics” package.
The temporal variations in air pollutant concentrations on a daily and monthly basis were investigated with the “openair” package [29]. The “TimeVariation” and “TimePlot” functions included in this package were utilized. The output of the “TimeVariation” function shows graphs with the mean and the 95% confidence intervals in the mean. The “TimePlot” function is designed to plot the time series of data with the ability to average the data by different averaging periods and choose the time intervals with the “SelectByDate” function. These functions are particularly useful to analyse the daily and monthly air pollutant concentrations during the selected periods of COVID-19 restrictions. These methods have been implemented in the literature to analyse and compare the average concentration in the previous months or in the corresponding months of the previous years without COVID-19 social distancing measures [30].
A correlation matrix, which produces the relationship between all data pairs, was carried out in order to find the associations among the air pollutants and meteorological variables at selected AQMS. The correlation matrix is a row-by-column arrangement of a set of correlation coefficients [31]. The correlation coefficients demonstrate the relationship between each pair of variables. For the purpose of this study, we used the “sjp.corr” function of the “sjPlot” package.
In this study, the Pearson correlation coefficient was chosen as a correlation computation method. The Pearson correlation coefficient is a measure of the strength of the linear relationship between two variables, and its values range from −1 to +1. If there is a perfect linear relationship with a positive slope between the two variables, r = 1, while if there is a perfect linear relationship with a negative slope between the two variables, r = −1. A correlation coefficient of 0 means that there is no linear relationship between the variables [29]. The significance level was tested with the p value.

3. Results

3.1. PM2.5 Concentrations at the AQMS in Western Macedonia

Figure 3 and Figure 4 illustrate the box plots of the annual mean PM2.5 and PM10 concentrations as registered by the AQMS in the region of Western Macedonia Lignite Center for the years 2019, 2020, 2021, and 2022. The concentrations of PM2.5 and PM10 varied among the AQMS. Notably, AQMS S7 and S8 registered the highest PM2.5 values. As for the years 2020 and 2021, when the lockdown restrictions were implemented, there was a general decrease in the mean, median, and interquartile range of PM2.5 concentrations at all the AQMS, except for AQMS S6 and S10, where a slight increase was detected in 2020. Similarly, the mean, median, and interquartile range of PM10 concentrations generally decreased in 2020 at all the AQMS except for AQMS S6.
In Figure 3, the box plots of PM2.5 concentrations for the years 2019, 2020, 2021, and 2022 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 are shown. In 2019, the mean annual PM2.5 concentrations were 13.7 μg/m3, 12.8 μg/m3, 17.0 μg/m3, 12.7 μg/m3, 11.9 μg/m3, 22.9 μg/m3, 30.5 μg/m3, 11.9 μg/m3, and 12.5 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. In 2020, the mean PM2.5 concentrations were 12.9 μg/m3, 11.9 μg/m3, 15.2 μg/m3, 11.6 μg/m3, 12.8 μg/m3, 18.9 μg/m3, 28.0 μg/m3, 10.2 μg/m3, and 12.9 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. A minor decreasing trend followed in the year 2021. In 2021, the mean PM2.5 concentrations were 12.8 μg/m3, 10.6 μg/m3, 13.7 μg/m3, 10.3 μg/m3, 12.2 μg/m3, 20.5 μg/m3, 23.3 μg/m3, 10.8 μg/m3, and 11.0 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. In 2022, a slight increase in PM2.5 concentrations was detected, which is not apparent for all AQMS. Specifically, the mean PM2.5 concentrations in 2022 were 12.0 μg/m3, 11.7 μg/m3, 14.9 μg/m3, 11.6 μg/m3, 12.0 μg/m3, 18.2 μg/m3, 26.6 μg/m3, 11.4 μg/m3, and 12.0 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively.
As we mentioned above, the AQMS S7 and S8 were constantly recording the highest values. The AQMS S8 was located in Meliti for the years under study after its relocation from Vevi in 2018, while the AQMS S7 was located in Florina. In both areas, district heating networks are currently not available. This means that traditional heating systems such as fireplaces and wood-burning stoves are prevalent, which are, of course, major sources of air pollution in these areas [12]. It is well known that combustion appliances that burn fossil fuels, wood, or biomass emit a large number of air pollutants (CO, PM, NOX, volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs)) as well as CO2 emissions [32,33].
Given that the amount of energy needed for heating applications depends on the prevailing weather conditions and that, due to its geographic location, the cold period in Western Macedonia is longer in comparison to the rest of the country, the region is the most energy-consuming region in Greece in terms of heating degree days [34]. According to Zoras et al. (2007), the climatological monthly mean temperature in Kozani and Florina ranged from values below 3 °C in January to almost 24 °C in July, while the precipitation amounts were higher in November and December [16].
In contrast to Florina, Kozani, Ptolemaida, Amyntaio, and Filotas are equipped with district heating networks that utilise the thermal load of the neighbouring lignite-fired power stations [35]. The district energy systems have the advantage of producing heat and/or power with limited environmental impacts and reduced CO2 and other GHG (greenhouse gas) emissions, as well as having economic benefits because they are highly efficient systems and minimise energy waste [36]. Pitoska et al. (2021) conducted a questionnaire survey in the city of Ptolemaida and found that the participants recognised the high efficiency of district heating in reducing pollutants and protecting the environment [35].
Figure 4 displays the box plots of PM10 concentrations for the years 2019, 2020, 2021, and 2022 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10. In 2019, the mean annual PM10 concentrations were 22.9 μg/m3, 21.1 μg/m3, 26.4 μg/m3, 21.2 μg/m3, 18.2 μg/m3, 29.9 μg/m3, 40.1 μg/m3, 22.2 μg/m3, and 27.0 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. In 2020, the mean PM10 concentrations were 19.5 μg/m3, 19.8 μg/m3, 22.4 μg/m3, 20.0 μg/m3, 19.3 μg/m3, 25.9 μg/m3, 36.5 μg/m3, 17.4 μg/m3, and 23.1 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. In most of the AQMS, there was a decrease in annual mean and median PM10 concentrations in 2021, followed by elevated values in 2022. Specifically, the mean PM10 concentrations in 2021 were 19.5 μg/m3, 18.3 μg/m3, 20.0 μg/m3, 18.6 μg/m3, 20.0 μg/m3, 28.9 μg/m3, 33.8 μg/m3, 19.0 μg/m3, and 20.7 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. The mean PM10 concentrations in 2022 were 19.3 μg/m3, 20.9 μg/m3, 22.4 μg/m3, 19.5 μg/m3, 18.4 μg/m3, 26.9 μg/m3, 32.6 μg/m3, 22.4 μg/m3, and 18.5 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively.
In general, the air pollutants in the region of Western Macedonia exhibit clear seasonality patterns, with their highest values during the winter months and their lowest values during the summer period. The great variations in the air pollutant values were largely affected by the emissions sources in the region and the meteorological conditions. As previously mentioned, Western Macedonia is largely dominated by mining activities and the generation of electricity in lignite-fired power plants. The activities in the open-pit lignite mines, such as mining, transportation of soil and coal, and movement of trucks on unpaved roads, are major sources of PM10 in the region [13,14,15,17,19,20]. Prior studies have identified the role of these activities in the overall air quality in the region [13,14,15,16,17,18,19,20].
Figure 5a,b shows the monthly mean variations in PM2.5 and PM10 concentrations and the 95% confidence interval in the mean for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 for the years 2019, 2020, 2021, and 2022. The monthly mean PM2.5 and PM10 concentrations for the months of lockdown (from March to May 2020) and the corresponding months of 2019, 2021, and 2022 are shown in Table 2 and Table 3.
The higher mean monthly PM2.5 concentrations were detected during the cold period (October to April), while during the warm period (May to September), the lowest mean monthly PM2.5 concentrations were observed (Figure 5a and Table 2). As a general trend for the period 2019–2022, the mean monthly PM2.5 values among the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 varied greatly. The highest mean monthly PM2.5 concentrations were observed at the AQMS S7 and S8. In March, April, and May of 2020, the mean monthly PM2.5 concentrations were 19.8 μg/m3, 16.5 μg/m3, and 9.5 μg/m3 for the AQMS S7 and 34.5 μg/m3, 28.0 μg/m3, and 11.2 μg/m3 for the AQMS S8. For the corresponding period of 2019, the mean monthly PM2.5 concentrations were generally higher. Specifically, the corresponding values in March, April, and May 2019 for the AQMS S7 were 22.6 μg/m3, 18.1 μg/m3, and 9.2, and for the AQMS S8 were 42.7 μg/m3, 28.4 μg/m3, and 15.5 μg/m3.
As expected, there is a significant decrease in PM2.5 concentrations in 2020 compared to 2019 (Figure 5a and Table 2). As for the AQMS S1, S2, S4, S5, S6, S9, and S10, the mean monthly PM2.5 concentrations also decreased in March and April of 2020 compared to March and April of 2019 (Table 2). However, for the majority of the AQMS, in May 2020, there was an increase in PM2.5 concentrations in terms of monthly values compared to the previous year. Specifically, the mean monthly PM2.5 concentrations in May 2020 were 10.9 μg/m3, 9.0 μg/m3, 10.2 μg/m3, 9.0 μg/m3, 10.0 μg/m3, 9.5 μg/m3, 11.2 μg/m3, 7.3 μg/m3, and 9.1 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. In contrast, in May 2019, the mean monthly values were 8.1 μg/m3, 6.9 μg/m3, 8.3 μg/m3, 7.1 μg/m3, 7.0 μg/m3, 9.2 μg/m3, 15.5 μg/m3, 7.9 μg/m3, and 7.5 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. The slight increase in concentrations could be attributed to the fact that from 4 May 2020, after the lockdown period of March and April 2020, the restrictions on movement and business activity began to gradually lift.
As for PM10 concentrations, the mean monthly values exhibit spatial heterogeneity, and no seasonality patterns can be detected at all AQMS. Solely, AQMS S7 and S8 exhibit clear seasonality patterns, with the highest PM concentrations in the winter period and the lowest concentrations in the summer period. As mentioned above, PM concentrations in Meliti and Florina are highly dependent on heating systems. Additionally, high PM10 concentrations during the winter period are detected at the AQMS S4.
In general, research on air quality in Western Macedonia prior to the COVID-19 pandemic has also found that the highest PM10 concentrations were registered in the warm period of the year [13,14,15,19,20]. For instance, Triantafyllou (2000) studied the PM10 concentrations in the southern part of the Eordea Basin from January 1991 to December 1994 and found that PM10 concentrations are higher during the summer and early autumn and lower during the spring [13]. This pattern in PM10 concentrations could be attributed to multiple causes, including seasonal changes in the atmospheric dispersion characteristics and the absence of scavenging by precipitation [13,14,15,19,20]. Additionally, wind-induced resuspension is a secondary source of PM10 in the open-pit coal mines during the warm period [19,20].
As we can see from Figure 5b and Table 3 at all the AQMS, the mean monthly PM10 concentrations decreased in March and April of 2020 compared to March and April of 2019. In detail, the mean monthly PM10 concentrations in March 2020 were 19.2 μg/m3, 18.1 μg/m3, 22.3 μg/m3, 17.5 μg/m3, 18.6 μg/m3, 24.6 μg/m3, 38.6 μg/m3, 15.5 μg/m3, and 17.3 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. In April 2020, the mean monthly PM10 concentrations were 18.7 μg/m3, 16.2 μg/m3, 19.3 μg/m3, 14.9 μg/m3, 17.7 μg/m3, 21.3 μg/m3, 33.0 μg/m3, 14.3 μg/m3, and 17.1 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. As for March and April of 2019, the mean monthly PM10 concentrations were considerably higher. The mean monthly PM10 in March 2019 was 26.2 μg/m3, 22.5 μg/m3, 28.2 μg/m3, 20.7 μg/m3, 21.4 μg/m3, 34.1 μg/m3, 55.0 μg/m3, 25.7 μg/m3, and 23.3 μg/m3, and in April 2019 was 24.7 μg/m3, 23.3 μg/m3, 27.9 μg/m3, 20.3 μg/m3, 20.4 μg/m3, 28.5 μg/m3, 38.6 μg/m3, 22.4 μg/m3, and 24.0 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively.
On the other hand, at all the AQMS, the mean monthly PM10 concentrations in May 2020 were higher compared to May 2019 (Table 3). In May 2019, the mean monthly PM10 concentrations were 15.3 μg/m3, 11.7 μg/m3, 15.0 μg/m3, 12.0 μg/m3, 10.9 μg/m3, 13.7 μg/m3, 23.4 μg/m3, 18.1 μg/m3, and 17.0 μg/m3, while in May 2020, the mean monthly PM10 concentrations were 24.2 μg/m3, 22.7 μg/m3, 23.1 μg/m3, 24.4 μg/m3, 23.5 μg/m3, 22.7 μg/m3, 19.9 μg/m3, 20.4 μg/m3, and 24.5 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively.
As for the years 2021 and 2022, it seems that the concentrations of PM remained at the levels of 2020 (Figure 3, Figure 4 and Figure 5a,b and Table 2 and Table 3). This could not be attributed merely to the COVID-19 pandemic but also to other factors. In the period between the end of 2019 and mid-2020, the units I and II of the power station Kardia and the units I and II of the power station Amyntaio ceased to operate. The closure of these units had an obvious effect on the improvement of air quality in the region in the last decade, as reported in prior studies [19,20]. Sachanidis et al. (2022) reported that the improvement in ambient air quality in the Western Macedonia Lignite Center is correlated with the reduction in excavated rock volumes and the lignite amount produced [25]. Importantly, they highlighted that the overall better air quality is measured higher in terms of the number of exceedances of PM limit values. The PM exceedances are mainly correlated with air pollution episodes attributed to lignite mining activities, prevailing meteorological conditions, and long-range dust transport seasonal phenomena [25]. The emissions from the combustion of lignite in the power stations, the mining activities of the lignite coal, and the transport of fugitive dust sources and fly ash cause local air pollution phenomena [14]. Matthaios et al. (2017) investigated the occurrence of extreme PM10 pollution episodes in Greece, including the region of Western Macedonia, and found that the local sources contribution to PM10 reached up to 64%–74%, which is the highest among the regions under study [37].
As for the improvement of air quality during the lockdown in Greece, previous studies have also reported a decrease in air pollutant concentrations, but additional factors also influence air pollutant concentrations. Kotsiou et al. (2021) examined air quality during the pandemic in Volos, a coastal port city in Greece with almost 86,000 inhabitants, in accordance with the national census data of 2021 [38]. They found that the lockdown resulted in a 37.4% reduction in mean daily PM2.5 in 2020 compared to 2019 levels, but during the strictest lockdown (from the 23 March to the 4 May), the occurrence of high levels of PM2.5 was not avoided, even though there were restrictions in human activity patterns [38].
In our study, the period of the first national lockdown that began on the 23 March 2020 and ended in the first days of May 2020 is shown in Figure 6 and Figure 7. More specifically, Figure 6a shows the daily mean PM2.5 concentrations for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 for the period from the 1 February 2019 to the 1 June 2019, and Figure 6b shows the corresponding period in 2020. Similarly, Figure 7a,b shows the daily mean PM10 concentrations for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 for the above-mentioned periods. In Table 4, the average PM2.5 and PM10 concentrations (μg/m3) over the lockdown period (23/3/2020–04/05/2020) and the corresponding period of the previous year (23/3/2019–04/05/2019) are shown.
During the first lockdown period (from the 23 March to the 4 May 2020) the daily PM2.5 concentrations decreased. Reductions in daily mean PM2.5 concentrations were observed at all AQMS in the weeks before the lockdown and during the 6-week lockdown (from the 23 March to the 4 May 2020) (Figure 6b). During the 6-week lockdown, the mean PM2.5 concentrations were 13.1 μg/m3, 11.6 μg/m3, 15.0 μg/m3, 10.7 μg/m3, 12.9 μg/m3, 17.1 μg/m3, 29.8 μg/m3, 8.6 μg/m3, and 10.0 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. In this period, there was a decrease relative to 2019 of −16%, −27%, −21%, −25, −9%, −1%, −3%, −36%, and −19% in the average PM2.5 concentrations for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. During the corresponding period in 2019, the mean PM2.5 concentrations were 15.6 μg/m3, 15.8 μg/m3, 19.1 μg/m3, 14.3 μg/m3, 14.2 μg/m3, 17.2 μg/m3, 30.9 μg/m3, 13.5 μg/m3, and 12.4 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively.
Specifically, during the period from the 23 March 2019 to the 4 May 2019, the maximum daily PM2.5 concentrations were above 25 μg/m3, while the daily PM2.5 concentrations reached up to maximum daily values of 55 μg/m3 at the AQMS S8. In 2020, high levels of PM2.5 concentrations were also observed, with the daily maximum PM2.5 reaching up to 44.1 μg/m3 and 52.2 μg/m3 at the AQMS S7 and S8, respectively.
As we can see in Figure 7a,b, the daily mean PM10 concentrations during the first lockdown period (from the 23 March to the 4 May 2020) also decreased. During the 6-week lockdown, the average PM10 concentrations were 18.7 μg/m3, 16.9 μg/m3, 20.0 μg/m3, 15.6 μg/m3, 18.2 μg/m3, 21.9 μg/m3, 34.4 μg/m3, 14.5 μg/m3, and 16.8 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. In this 6-week period, there was a decrease relative to 2019 of −25%, −28%, −30%, −26, −13%, −18%, −17%, −40%, and −30% in the average PM10 concentrations for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. During the corresponding period in 2019, the mean PM10 concentrations were 24.9 μg/m3, 23.4 μg/m3, 28.4 μg/m3, 21.2 μg/m3, 20.9 μg/m3, 26.6 μg/m3, 41.6 μg/m3, 24.3 μg/m3, and 24.0 μg/m3 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10, respectively. However, it is worth mentioning that in April 2019, there was a 5-day period of extremely high PM10 values with spatial homogeneity (Figure 7a). From the 24 April to the 27 April 2019, the PM10 concentrations gradually increased at all the AQMS under study. This event, which occurred in the second half of April 2019, is attributed to a large-scale Saharan dust episode over Greece and Europe [39]. In Greece, dust episodes are generally common phenomena during spring and autumn and mainly affect the southern part of the country. Similarly, a long-lasting Saharan dust episode occurred in Greece from the 14 May to the 20 May 2020, with high values of PM10 concentrations at all the AQMS under study (Figure 6b) [40]. As has been mentioned above, long-range transport is an important mechanism for particle pollution episodes [8].

3.2. PM Concentrations in Correlation with Meteorological Parameters and Air Pollutants

Figure 8a,b shows the mean monthly variations in daily PM2.5, PM10, SO2, NO2, NO, and NOX concentrations (μg/m3) along with the mean air temperature (°C) for the AQMS S6 and S7 for the years 2019 and 2020. AQMS S6 is located in Amyntaio, which is a town with 4348 inhabitants, and AQMS S7 is located in Florina, which is a town with a population of 17,188 inhabitants [41].
As we previously discussed, the AQMS S7 registered high PM2.5 and PM10 concentrations because it is highly affected by the local sources of air pollution from traditional heating systems. The highest air pollutant concentrations were observed during the winter months, when the lowest air temperatures were observed. For example, in January of 2019, the mean air temperature was below 0 °C (Tmean = −1.8° C), while PM10 and PM2.5 reached up to 82.6 μg/m3 and 80.1 μg/m3, respectively. The PM2.5/PM10 ratio reached a value of 1.0, indicating the major contribution of fine particles attributable to anthropogenic air pollution sources. Based on previous studies, the high ratios that have been found in Florina indicate the great contribution of individual household heating systems (e.g., fireplaces and woodstoves) and biomass burning [20].
In March and April of 2020, the air temperature was lower compared to the same months in 2019. The monthly mean air temperature was 8.4 °C and 11.4 °C in March and April of 2020, respectively, and 10.4 °C and 12.2 °C for the same months in 2019, respectively (Figure 8b). However, PM concentrations were lower in 2020 compared to 2019, indicating that lockdown restrictions influenced air pollutant concentrations. As we have previously mentioned, PM10 concentrations in March and April of 2020 were considerably lower compared to the corresponding months in 2019.
At the AQMS S7, the mean monthly SO2, NO, NO2, and NOX concentrations were 19.6 μg/m3, 4.8 μg/m3, 13.9 μg/m3, and 18.7 μg/m3, respectively, in March of 2019, and 7.5 μg/m3, 12.9 μg/m3, 8.8 μg/m3, and 21.7 μg/m3, respectively, in April of 2019. In 2020, the air pollutant concentrations were lower. In detail, in March 2020, the mean monthly SO2, NO, NO2, and NOX concentrations were 8.3 μg/m3, 3.5 μg/m3, 5.5 μg/m3, and 9.0 μg/m3, respectively, while in April 2020 the concentrations were 8.6 μg/m3, 3.2 μg/m3, 3.9 μg/m3, and 7.1 μg/m3, respectively (Figure 8b).
At the AQMS S6, the mean monthly SO2, NO, NO2, and NOX concentrations were 7.2 μg/m3, 1.6 μg/m3, 51.4 μg/m3, and 53.0 μg/m3, respectively, in March 2019, and 3.2 μg/m3, 1.40 μg/m3, 29.9 μg/m3, and 31.3 μg/m3, respectively, in April 2019 (Figure 8a). Specifically, the mean monthly concentrations decreased sharply from May 2019 onwards to average monthly SO2, NO, NO2, and NOX concentrations of 3.3 µg/m3, 1.6 µg/m3, 4.0 µg/m3, and 5.6 µg/m3, respectively. The decreased concentrations are attributed to zero emissions of PM, SO2, and NOX from May 2020 and onwards due to the closure of PS4 (Table 5).
As for the meteorological conditions at the AQMS S6, the air temperature is relatively low but at higher levels compared to the AQMS S7. The monthly mean air temperature in January of 2019 was also below 0 °C, while in March and April of 2019 it was at 10.3 °C and 11.8 °C, respectively. During the lockdown period in 2020, the monthly mean air temperature in March and April was 8.2 °C and 11.2 °C, respectively.
Figures S1a–l and S2a–l in the Supplementary Material show the seasonal correlation matrices with Pearson correlation coefficients between daily air pollutant concentrations and meteorological parameters for the AQMS S6 and AQMS S7 for the years 2019, 2020, and 2021. Although there are differences among the Pearson correlation coefficients through the years and seasons, very strong positive correlations were found between PM2.5 and PM10, as expected. In general, differences in correlation coefficients were found between the AQMS S6 and AQMS S7.
At the AQMS S6, the Pearson correlation coefficients between PM2.5 and PM10 are strong and very strong positive in all seasons and through the years (Figure S1a–l, Supplementary Material). For example, these correlation values were r = 0.937 (p-value < 0.001) for spring, r = 0.766 (p-value < 0.001) for summer, r = 0.874 (p-value < 0.001) for autumn, and r = 0.924 (p-value < 0.001) for winter in 2019. In 2020, the correlation coefficients between PM2.5 and PM10 are r = 0.777 (p-value < 0.001) for spring, r = 0.836 (p-value < 0.001) for summer, r = 0.850 (p-value < 0.001) for autumn, and r = 0.947 (p-value < 0.001) for winter. As expected, the r values are higher in winter due to similar emission sources (e.g., domestic heating and the increased electricity demand from the lignite-fired power plants). In the spring of 2020, when the lockdown was implemented, the r value was lower compared to 2019 because of the reduction in emissions during the COVID-19 restrictions. In 2021, the correlation coefficients between PM2.5 and PM10 were r = 0.796 (p-value < 0.001) for spring, r = 0.849 (p-value < 0.001) for summer, r = 0.844 (p-value < 0.001) for autumn, and r = 0.927 (p-value < 0.001) for winter.
Additionally, at the AQMS S6, the correlation coefficients of PM2.5 and PM10 with NOX, NO2, NO, and SO2 varied among the seasons. Generally, the r values between the above-mentioned variables are moderate, ranging between 0.4 and 0.6 in spring, autumn, and winter, but in summer, the r values are also negative in certain cases. For example, the correlation coefficients of PM2.5 and PM10 with NO2 are r = 0.549 (p-value < 0.001) and r = 0.642 (p-value < 0.001) for the winter of 2019, r = 0.494 (p-value < 0.001) and r = 0.486 (p-value < 0.001) for the winter of 2020, while for the winter of 2021 the correlation coefficients are r = −0.272 (p-value = 0.011) and r = −0.248 (p-value = 0.021), respectively. From these correlation coefficients, we can conclude that during the winter period, NO2 and both PM2.5 and PM10 came from the same source, which is mainly the power plant PS4 (Table 5), while in 2021, when the PS4 was not operated, the correlation was negative but not significant. During the lockdown (spring 2020), the Pearson correlation coefficients between PM2.5 and PM10 and NOX, NO2, NO, and SO2 were moderate or even negative. For example, the PM2.5 and PM10 correlation coefficients with the NO2 correlation coefficient were r = 0.078 (p-value = 0.462) and r = 0.334 (p-value = 0.001) for the spring of 2020, respectively. In contrast, in the spring of 2019, the correlation coefficient of PM2.5 and NO2 was r = 0.569 (p-value < 0.001), and the correlation coefficient of PM10 and NO2 was r = 0.476 (p-value < 0.001). In the spring of 2021, PM2.5 and NO2 were correlated with r = −0.388 (p-value < 0.001), and PM10 and NO2 were correlated with r = −0.041 (p-value = 0.701).
In accordance with the Pearson correlation coefficient, the meteorological factors affecting the air pollutants varied in different seasons at the AQMS S6. In general, the air temperature was negatively correlated with PM2.5 and PM10 in winter, but in summer, the correlation coefficients were positive. The low temperatures in winter lead to increased emission rates from domestic heating and electricity generation from power plants. In the summer, the emissions from the power plants decreased, as we can also see from Table 5. For example, PM2.5 and air temperature exhibited a negative correlation in the winters of 2019, 2020, and 2021, with r = −0.126 (p-value = 0.259), r = −0.299 (p-value = 0.004), and r = −0.162 (p-value = 0.134), respectively. On the other hand, in the summer, the correlations were reversed. The correlation coefficient was r = 0.310 (p-value = 0.003) in the summer of 2019, r = 0.459 (p-value < 0.001) in the summer of 2020, and r = 0.565 (p-value < 0.001) in the summer of 2021. Similar values were found for temperature and PM10, with correlation coefficients of r = 0.574 (p-value < 0.001), r = 0.677 (p-value < 0.001), and r = 0.716 (p-value < 0.001) for the summers of 2019, 2020, and 2021. Additionally, the Pearson correlation coefficients between relative humidity, wind speed, and wind direction and PM2.5 and PM10 exhibit differences among the seasons. Wind speed and direction are negatively or weakly correlated with PM2.5 and PM10 in all seasons. In general, negative correlations between wind speed and air pollutant concentrations indicate the horizonal dispersion and dilution of air pollutant concentrations under the influence of strong winds.
At the AQMS S7, very strong positive Pearson correlation coefficients were generally found for PM2.5 and PM10 in all seasons and years (Figure S2a–l, Supplementary Material). Very strong correlation coefficients were found for the winter period. The r values for PM2.5 and PM10 were r = 0.986 (p-value < 0.001) for the winter of 2019, r = 0.981 (p-value < 0.001) for the winter of 2020, and r = 0.959 (p-value < 0.001) for the winter of 2021. Additionally, spring, summer, and autumn exhibited a high degree of correlation, as indicated by the r values ranging from 0.7 to 0.9. The correlation coefficient between PM2.5 and PM10 was r = 0.925 (p-value < 0.001) in the spring of 2019, r = 0.668 (p-value < 0.001) in the spring of 2020, and r = 0.782 (p-value < 0.001) in the spring of 2021. A previous study also found a high coefficient (R2) between PM2.5 and PM10 equal to 0.92 during the winter period in Florina (AQMS S7), which suggests that PM2.5 and PM10 came from similar emission sources [20]. Similar to the AQMS S6, the correlation coefficients between PM2.5 and PM10 decreased during the months of lockdown in the spring of 2020.
As for the correlation coefficients of PM2.5 and PM10 with NOX, NO2, NO, and SO2 at the AQMS S7, the r values varied among the seasons. In general, in winter, the correlation coefficients for PM2.5 with NO2 are strong or even very strong, with values reaching up to 0.9, while relatively low or even negative values are found in summer. However, there are no major differences before, during, or after COVID-19 lockdown for the correlation coefficients of PM2.5 and PM10 with NOX, NO2, and NO. The Pearson correlation coefficients of PM2.5 with NO2 were r = 0.647 (p-value < 0.001) in the spring of 2019, r = 0.626 (p-value < 0.001) in the spring of 2020, and r = 0.848 (p-value < 0.001) in the spring of 2021. In general, it is expected that PM2.5 and NO2 exhibit strong correlations because both PM2.5 and NO2 are generated during the combustion process with household stoves, space heaters, furnaces, fireplaces, and boilers.
As for the meteorological parameters at the AQMS S7, strong but negative correlation coefficients were found for temperature with PM2.5 and PM10 during all seasons except summer. For example, PM2.5 and air temperature exhibited a negative correlation in the winters of 2019, 2020, and 2021, with r = −0.429 (p-value < 0.001), r = −0.396 (p-value < 0.001), and r = −0.401 (p-value < 0.001), respectively. On the other hand, in the summer, their correlations were reversed. The correlation coefficients were r = 0.195 (p-value = 0.062), r = 0.385 (p-value < 0.001), and r = 0.435 (p-value < 0.001) in the summer of 2019, 2020, and 2021, respectively. Similar values were found for temperature and PM10, with correlation coefficients r = 0.446 (p-value < 0.001), r = 0.564 (p-value < 0.001), and 0.612 (p-value < 0.001) for the summers of 2019, 2020, and 2021, while in the winters of 2019, 2020, and 2021, the correlation coefficients were r = −0.357 (p-value < 0.001), r = −0.356 (p-value < 0.001), and r = −0.264 (p-value = 0.013), respectively. In general, the Pearson correlation coefficients at the AQMS S7 also explain the high dependence of PM on meteorological conditions. The negative correlation between temperature and PM at the AQMS S7 indicates that lower temperatures led to higher PM concentrations. Similar to the AQMS S6, at the AQMS S7, the Pearson correlation coefficients between relative humidity, wind speed, and wind direction and PM2.5 and PM10 exhibit differences among the seasons. Wind speed and direction are negatively or weakly correlated with PM2.5 and PM10 in all seasons.
Previous studies have also used Pearson correlation coefficients to investigate the effects of meteorological parameters on air pollution during the COVID-19 pandemic [43,44]. In Italy, Cucciniello et al. (2022) evaluated the air quality during lockdown in the city of Avellino [44]. The climatic conditions in the region favour the usage of domestic heating systems (boilers, fireplaces, and pellet stoves) during the winter period up to the end of April, which is comparable with Western Macedonia. The Pearson correlations of the atmospheric pollutants’ concentrations in Avellino also showed strong associations between PM2.5 and PM10, while a slight decrease was detected in correlations for NO2 and both PM10 and PM2.5 during the lockdown. In accordance with Cucciniello et al. (2022), this could be attributed to the pollutants’ source (e.g., the decreased vehicular traffic during the lockdown). In Poland, Górka-Kostrubiec and Dudzisz (2023) analysed the effect of pandemic restrictions on air pollution in Warsaw and Krakow by examining the correlations of the average monthly concentrations of pollutants in particular months of 2019, 2020, and 2021 [45]. The significant correlation coefficients for PM2.5 and PM10 suggest the same source (e.g., central heating system) of particle pollution, while in Warsaw, the concentrations of PM2.5 and PM10 with NOX seemed to be less correlated during the pandemic restrictions. The difference in correlation coefficients between the cities may be attributed to the fact that in Kracow, the dominant stack emissions from heating systems that use mainly coal and wood during the winter months may have masked the reduction in NOX during lockdowns [45]. This is comparable with our study, where the local sources of air pollution from the traditional heating systems dominate in Florina. In Victoria, Mexico, Tello-Leal and Macías-Hernández (2021) used the Pearson correlation analysis to estimate the relationships between the concentrations of air pollutants and meteorological variables during the lockdown and found very strong positive correlations between PM factions (r =0.99, p-value < 0.001) and consistently moderate to very strong negative correlations (−0.92 ≥ r ≥ −0.45) between temperature and all the air pollution variables [43]. In Changchun, China, there are many industrial sources that are common sources for air pollutants [46]. So, there are positive correlations between PM2.5, PM10, SO2, NO2, and CO before and after lockdown, while the negative correlation between temperature and the above-mentioned pollutants before lockdown and during the first phase of lockdown (25 January–25 February 2020) could be attributed to low temperatures that increase the emission sources (such as heating in coal-fired power plants).
In Volos, Greece, Kotsiou et al. (2021) found that PM2.5 concentrations were negatively correlated with temperature (r = −0.47, p = 0.001) and positively correlated with humidity (r = 0.37, p = 0.011) during lockdown [38]. Moreover, in Athens, Greece, Grivas et al. (2020) compared the observed changes in pollutant levels during the lockdown (23 March 2020–10 May 2020) to changes and the potential impact of prevailing weather conditions and concluded that the lockdown was the dominant factor for this drop in average NO2 levels [47]. This contribution is estimated to reach 65%, while the remaining 35% of the mean reduction in NO2 levels can be attributed to the different meteorological conditions during the lockdown when compared to the pre-lockdown period [47]. In Athens, during the lockdown period in March, vehicular traffic was significantly reduced (almost 50%) compared to the same period in 2019. Similarly, in London, UK, it was reported that there was a decrease of around 32% in total traffic and an overall 22.5% reduction in traffic during the 15 months of social restrictions [48].
Generally, the traffic reduction correlated with decreased air pollutant concentrations and particularly strong reductions of NO2 emissions and concentrations, while an upward trend during the lockdown has been observed for O3. This inverse relationship between O3 and NO2 values has been detected in various studies [8,9,48]. NOX is a major precursor of O3 as well as a quencher of O3 through NOx titration. High concentrations of NO locally scavenge O3 and lead to the formation of NO2, and high concentrations of NO2 block the oxidation step of VOCs by producing nitric acid, which prevents the net formation of O3 [9]. In parallel, the prevailing meteorological conditions (local winds, air temperature, and humidity) affect these chemical processes that result in an increase in O3 because of a decrease in NOX and low VOC/NOX ratios. For instance, in the UK, the unseasonably warm start and end to 2020 and the reduction in NOX caused increased O3 production [48]. In the highly polluted metropolitan cities in India, a remarkable drop in the mean concentration of the AQI (Air Quality Index) was detected during the COVID-19 lockdown, while O3 concentrations significantly increased as the NOX concentrations increased [49]. Importantly, the inverse correlation between O3 and NOX is evident through the Pearson correlation coefficient, given that O3 and AQI were negatively correlated [49].
In our study, NOX, NO2, and NO were negatively correlated with air temperature during winter, spring, and autumn. For example, at the AQMS S7, the correlation coefficients for NO2 and air temperature were r = −0.373 (p-value < 0.001) for the spring of 2020, r = 0.092 (p-value = 0.380) for the summer of 2020, r = −0.727 (p-value < 0.001) for the autumn of 2020, and r = −0.021 (p-value = 0.847) for the winter of 2020. The corresponding correlation coefficients for NO2 and air temperature before the COVID-19 pandemic were r = −0.482 (p-value < 0.001) for the spring of 2019, r = 0.119 (p-value = 0.260) for the summer of 2019, r = −0.350 (p-value < 0.001) for the autumn of 2019, and r = −0.383 (p-value < 0.001) for the winter of 2019. Lower NO2 concentrations usually occur at higher temperatures because the photochemical reaction of NO2 and the vertical dispersion increase at higher temperatures [50]. This leads to higher O3 concentrations. High NO2 concentrations generally occur in the winter when the temperature is lower and the emissions from heating systems increase, as in our study.
Furthermore, many studies have analysed the association of air pollution and meteorological variables with the incidence of the COVID-19 pandemic, while highly polluted regions seem to be correlated with COVID-19 cases [51,52]. This hypothesis arose in the early stages of COVID-19’s emergence [52]. Western Macedonia was highly affected by COVID-19 in the early stages of the pandemic. So, this study provides an opportunity to further examine the relationships between air pollution and meteorology in the context of human health in the region.

4. Conclusions

In this study, we evaluate the impact of COVID-19 restrictions on the air quality of Western Macedonia, Greece, based on air pollution and meteorological data from the AQMS network operated by the Lignite Center of Western Macedonia. To do so, we analysed the data on an annual, monthly, and daily basis in order to provide a better understanding of pandemic restrictions, but we also took into consideration the improvement of air quality in the region due to the energy transition and the coal phase-out impacts. For this purpose, we also used data on monthly and annual emissions of air pollutants from a power station in the region. We also performed a statistical analysis through the Pearson correlation coefficient to analyse the association between air pollutants and meteorological parameters before, during, and after the COVID-19 lockdown. Our analysis showed that both PM2.5 and PM10 concentrations mainly decreased in 2020 and 2021 during the periods of lockdown. During the period of the strictest lockdown (from the 23 March 2020 to the 4 May 2020), the daily PM concentrations decreased but generally remained at high levels. In this 6-week period, there was a decrease in average PM2.5 concentrations ranging from −1% to −36% and a higher decrease in average PM10 concentrations ranging from −13% to −40% at the AQMS under study. However, the lack of measurements on O3 concentrations in the region has not allowed us to determine the impact that decreased NOX emissions during the lockdown had on O3 levels. Further, air pollutant emission inventories at a local level were not presented in this study, which could have helped provide an estimation of the contribution from different sources in the region.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos14091398/s1, Figure S1: Pearson correlation matrices between air pollutants concentrations and meteorological parameters for the AQMS S6 for all seasons for the years 2019–2021 (a–l).; Figure S2: Pearson correlation matrices between air pollutants concentrations and meteorological parameters for the AQMS S7 for all seasons for the years 2019–2021 (a–l).; Table S1: Interpretation of Pearson correlation coefficients [53,54,55].

Author Contributions

P.B.: Data Curation, Formal Analysis, Software, Writing—Original Draft; V.E.: Conceptualization, Supervision, Resources, Investigation, Data Curation, Project Administration, Writing—Review and Editing; N.D.C.: Validation, Writing—Review and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Lignite production in the mines of the Public Power Corporation in the region of Western Macedonia (2010–2022).
Figure 1. Lignite production in the mines of the Public Power Corporation in the region of Western Macedonia (2010–2022).
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Figure 2. (a) Location of Western Macedonia, Greece, in Europe; (b) Location of AQMS (S) and power stations (PS) in Western Macedonia.
Figure 2. (a) Location of Western Macedonia, Greece, in Europe; (b) Location of AQMS (S) and power stations (PS) in Western Macedonia.
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Figure 3. Box plots of PM2.5 concentrations for the years 2019, 2020, 2021, and 2022 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10. The outliers in the box plots were excluded. The different colors in box plots indicate the AQMS as follows: S1 (red), S2 (blue), S4 (light green), S5 (purple), S6 (grey), S7 (orange), S8 (yellow), S9 (dark green), S10 (pink).
Figure 3. Box plots of PM2.5 concentrations for the years 2019, 2020, 2021, and 2022 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10. The outliers in the box plots were excluded. The different colors in box plots indicate the AQMS as follows: S1 (red), S2 (blue), S4 (light green), S5 (purple), S6 (grey), S7 (orange), S8 (yellow), S9 (dark green), S10 (pink).
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Figure 4. Box plots of PM10 concentrations for the years 2019, 2020, 2021, and 2022 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10. The outliers in the box plots were excluded. The different colors in box plots indicate the AQMS as follows: S1 (red), S2 (blue), S4 (light green), S5 (purple), S6 (grey), S7 (orange), S8 (yellow), S9 (dark green), S10 (pink).
Figure 4. Box plots of PM10 concentrations for the years 2019, 2020, 2021, and 2022 for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10. The outliers in the box plots were excluded. The different colors in box plots indicate the AQMS as follows: S1 (red), S2 (blue), S4 (light green), S5 (purple), S6 (grey), S7 (orange), S8 (yellow), S9 (dark green), S10 (pink).
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Figure 5. (a) Mean monthly PM2.5 concentrations and (b) mean monthly PM10 concentrations for the years 2019, 2020, 2021, and 2022 at the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10.
Figure 5. (a) Mean monthly PM2.5 concentrations and (b) mean monthly PM10 concentrations for the years 2019, 2020, 2021, and 2022 at the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10.
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Figure 6. Daily mean PM2.5 concentrations for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 for the period (a) from the 1 February 2019 to the 1 June 2019 and (b) from the 1 February 2020 to the 1 June 2020.
Figure 6. Daily mean PM2.5 concentrations for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 for the period (a) from the 1 February 2019 to the 1 June 2019 and (b) from the 1 February 2020 to the 1 June 2020.
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Figure 7. Daily mean PM10 concentrations for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 for the period (a) from the 1 February 2019 to the 1 June 2019 and (b) from the 1 February 2020 to the 1 June 2020.
Figure 7. Daily mean PM10 concentrations for the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 for the period (a) from the 1 February 2019 to the 1 June 2019 and (b) from the 1 February 2020 to the 1 June 2020.
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Figure 8. Monthly variations in PM2.5, PM10, SO2, NO2, NO, and NOX concentrations (μg/m3) and temperature (°C) for the AQMS (a) S6 and (b) S7 for the years 2019 and 2020.
Figure 8. Monthly variations in PM2.5, PM10, SO2, NO2, NO, and NOX concentrations (μg/m3) and temperature (°C) for the AQMS (a) S6 and (b) S7 for the years 2019 and 2020.
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Table 1. Coordinates of AQMS (S) and power stations (PS) in Western Macedonia (adopted by Evagelopoulos et al. (2022) [19]).
Table 1. Coordinates of AQMS (S) and power stations (PS) in Western Macedonia (adopted by Evagelopoulos et al. (2022) [19]).
AQMS NameLocationLatitudeLongitudeAltitude (m)
S1Filotas40.62605621.707554568
S2Koilada40.35572521.930784686
S3Oikismos40.48518121.718224673
S4Petrana40.29015021.863800614
S5Komi40.20396921.843391415
S6Amyntaio40.67897021.681830628
S7Florina40.78209621.410366659
S8Vevi-Meliti40.83550021.586800677
S9Pontokomi40.40653021.768110702
S10Anargyroi40.60222221.610000611
PS1Agios Demetrios40.39354221.925377680
PS2Kardia40.40899121.786542693
PS3Ptolemaida40.48086421.727385641
PS4Amyntaio40.61815421.682730665
Table 2. Monthly mean PM2.5 concentrations (μg/m3) for March, April, and May at the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 for the years 2019, 2020, 2021, and 2022.
Table 2. Monthly mean PM2.5 concentrations (μg/m3) for March, April, and May at the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 for the years 2019, 2020, 2021, and 2022.
AQMS2019202020212022
MarchAprilMayMarchAprilMayMarchAprilMayMarchAprilMay
S116.115.58.113.713.010.916.813.78.619.79.911.4
S214.715.86.912.711.19.012.19.86.415.79.910.7
S419.119.58.317.414.410.219.213.97.422.612.411.4
S514.213.97.112.610.19.012.910.87.016.39.510.5
S614.813.87.013.812.510.014.412.08.616.69.811.6
S722.618.19.219.816.59.525.316.89.225.512.710.3
S842.728.415.534.528.011.237.821.08.348.522.014.7
S913.213.27.99.88.17.312.410.37.115.210.711.8
S1012.112.47.511.59.89.113.010.27.211.810.311.3
Table 3. Monthly mean PM10 concentrations (μg/m3) for March, April, and May at the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 for the years 2019, 2020, 2021, and 2022.
Table 3. Monthly mean PM10 concentrations (μg/m3) for March, April, and May at the AQMS S1, S2, S4, S5, S6, S7, S8, S9, and S10 for the years 2019, 2020, 2021, and 2022.
AQMS2019202020212022
MarchAprilMayMarchAprilMayMarchAprilMayMarchAprilMay
S126.224.715.319.218.724.221.619.517.727.618.926.7
S222.523.311.718.116.222.716.214.714.224.918.320.4
S428.227.915.022.319.323.125.520.517.531.320.423.3
S520.720.312.017.514.924.417.316.418.522.716.620.2
S621.420.410.918.617.723.518.217.818.922.516.020.9
S734.128.513.724.621.322.731.225.121.435.123.321.4
S855.038.623.438.633.019.944.226.818.656.730.226.3
S925.722.418.115.514.320.417.516.417.623.621.729.0
S1023.324.017.017.317.124.517.016.317.115.015.418.3
Table 4. PM2.5 and PM10 average concentrations (μg/m3) over the lockdown period (23/3/2020–04/05/2020) and the corresponding period of the previous year (23/3/2019–04/05/2019).
Table 4. PM2.5 and PM10 average concentrations (μg/m3) over the lockdown period (23/3/2020–04/05/2020) and the corresponding period of the previous year (23/3/2019–04/05/2019).
AMQS23/3/2019–04/05/201923/3/2020–04/05/2020
PM2.5 PM10 PM2.5 PM10
S115.624.913.118.7
S215.823.411.616.9
S419.128.415.020.0
S514.321.210.715.6
S614.220.912.918.2
S717.226.617.121.9
S830.941.629.834.4
S913.524.38.614.5
S1012.424.010.016.8
Table 5. Total monthly and annual emissions of SO2, NOx (as NO2), and PM (PM2.5 and PM10) at PS4 for the years 2019 and 2020 (data adopted from annual environmental reports for PS4 from the Ministry of the Environment and Energy [42]).
Table 5. Total monthly and annual emissions of SO2, NOx (as NO2), and PM (PM2.5 and PM10) at PS4 for the years 2019 and 2020 (data adopted from annual environmental reports for PS4 from the Ministry of the Environment and Energy [42]).
Year: 2019PM (t)SO2 (t)NOX (t)Year: 2020PM (t)SO2 (t)NOX (t)
January71352244January92321144
February62423148February4114490
March31293108March2564698
April41428145April1462694
May000May396
June157026June000
July4210370July000
August000August000
September000September000
October1529669October000
November3037699November000
December55370133December000
Annual total36327111040Annual total1751747432
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Begou, P.; Evagelopoulos, V.; Charisiou, N.D. Variability of Air Pollutant Concentrations and Their Relationships with Meteorological Parameters during COVID-19 Lockdown in Western Macedonia. Atmosphere 2023, 14, 1398. https://doi.org/10.3390/atmos14091398

AMA Style

Begou P, Evagelopoulos V, Charisiou ND. Variability of Air Pollutant Concentrations and Their Relationships with Meteorological Parameters during COVID-19 Lockdown in Western Macedonia. Atmosphere. 2023; 14(9):1398. https://doi.org/10.3390/atmos14091398

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Begou, Paraskevi, Vasilios Evagelopoulos, and Nikolaos D. Charisiou. 2023. "Variability of Air Pollutant Concentrations and Their Relationships with Meteorological Parameters during COVID-19 Lockdown in Western Macedonia" Atmosphere 14, no. 9: 1398. https://doi.org/10.3390/atmos14091398

APA Style

Begou, P., Evagelopoulos, V., & Charisiou, N. D. (2023). Variability of Air Pollutant Concentrations and Their Relationships with Meteorological Parameters during COVID-19 Lockdown in Western Macedonia. Atmosphere, 14(9), 1398. https://doi.org/10.3390/atmos14091398

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