*Article* **Long-Term Trends of Air Pollution at National Atmospheric Observatory Košetice (ACTRIS, EMEP, GAW)**

#### **Milan Vá ˇna 1,\*, Adéla Holubová Smejkalová 1,2, Jaroslava Svobodová <sup>1</sup> and Pavel Machálek <sup>3</sup>**


Received: 30 March 2020; Accepted: 19 May 2020; Published: 21 May 2020

**Abstract:** The National Atmospheric Observatory Košetice operated by the Czech Hydrometeorological Institute was established in 1988 as a station specializing in air quality monitoring at the background scale. The observatory is located in the free area outside of the settlement and represents the Czech Republic in various international projects. The objective of the present study is to detect the long-term trends of air quality at the background scale of the Czech Republic. The statistical method used for trend analysis is based on the nonparametric Mann–Kendall test. Generally, the results show that the fundamental drop in emission of basic air pollutants was reflected in the significant decrease in pollution levels. A most significant drop was detected for sulphur. No trend was found for NO<sup>2</sup> in 1990–2012, but a visibly decreasing tendency was registered in the last 7 years. A slightly decreasing trend was registered for O<sup>3</sup> in the whole period, but a slightly increasing tendency was found after 2006. More importantly, the number of episodes exceeding the target value for human health dropped significantly. The reduction of volatile organic compounds (VOCs) emissions was reflected in a statistically significant decrease of concentrations. Only isoprene, which is of natural origin, displays an inverse trend. Concentrations of elemental carbon (EC) and organic carbon (OC) dropped since 2010, but only for EC is the trend statistically significant.

**Keywords:** long-term trends; background scale; air quality; Czech Republic

#### **1. Introduction**

The danger caused by large-scale, global and regional pollution started to be recognised in the 1960s. Such pollution might end up resulting in irreversible changes in both terrestrial and ocean ecosystems and global climate change. The research and monitoring efforts required to detect the changes in the atmosphere at global and regional scales must be based on broad-ranging international cooperation. It was, first of all, international institutions (World Meteorological Organization, United Nations Economic Commission for Europe ECE, United Nations Environment Programme) that initiated, in the 1960s and 1970s, the first international monitoring programmes [1].

To support the above-mentioned programmes, Czech Hydrometeorological Institute (CHMI) established the National Atmospheric Observatory Košetice (NAOK), specialized in monitoring and research of air quality at the background scale of Czech Republic.

After the political changes in 1989, the air quality control and protection became one of the most important political priorities in the Czech Republic. Immense funds were invested in emission reductions (mainly from large power plants) in the Czech Republic during the 1990s, resulting in a marked improvement in the air quality, the levels of which in some regions had previously ranked among the worst in the world. Nevertheless, the growing industry and traffic after 2000 have caused the air quality in the Czech Republic to begin to deteriorate again. Irresponsible conduct of individuals who use low-quality fuels or even municipal waste in household heating systems, emitting hazardous chemicals to the air, is a contributing factor that cannot be neglected. Fine dust is the most serious problem at the moment. The Ministry of the Environment developed a National Emission Reduction Programme of the Czech Republic in 2007, and it has been approved by the government. The document comprises several key measures to contribute to an improvement in the current state of the environment and environmental and health protection.

The objective of the study is to detect the long-term trends of air quality at the background scale of the Czech Republic. Thirty-year data series is sufficient for detection of long-term trends of air quality. The study is based on the data generated within the National Air Pollution Monitoring Network, stored in Air Quality Information System and annually published e.g., [2]. Generally, the development of air quality in the last three decades was affected by various circumstances: the essential political changes in Central and Eastern Europe in the end of the 1980s brought a substantial decrease in emissions in the Czech Republic and more widely in the Central European region thanks to international conventions and also economic and political development. The meteorological conditions for long-range transport in Europe were changed as well, and the question of global climate change assumed importance. Measurement techniques showed significant improvement, as did our knowledge concerning the behaviour of air pollutants in the atmosphere. All these aspects influenced the long-term trends at the background scale of the Czech Republic very significantly.

#### **2. Materials and Methods**

#### *2.1. Site Description and Overall Context*

NAOK is located in the agricultural countryside outside of settlements in the southern part of the Czech Republic, district of Pelhˇrimov (49◦35′ N, 15◦05′ , 534 E m asl, Figure 1). More detailed description of physical-geographical conditions is available in [3]. The operation of NAOK started in 1988, but some basic air quality measurements were implemented since the middle of the 1980s in the vicinity of the observatory. The main task of NAOK throughout its history was to detect the long-term trends of air quality at the background scale of the Czech Republic and Central Europe and to represent the Czech Republic in the long-term programmes of air quality monitoring and research GAW/WMO (Global Atmosphere Watch), EMEP (Co-operative Programme for Monitoring and Evaluation of Long-range Transmission of Air Pollutants in Europe) and ICP–IM (International Co-operative Programme on Integrated Monitoring).

After 2004, when Czech Republic joined the EU, NAOK, thanks to its excellent location and long-term homogeneous data series, has been participating in several EU projects. The first was EUSAAR (European Supersites for Atmospheric Aerosol Research), focused on the research of atmospheric aerosols. The essential importance for the advancement of NAOK in the last decade brought participation in ACTRIS RI (Aerosol, Clouds and Trace gases Research Infrastructure). NAOK is a core of Large Research Infrastructure (LRI) ACTRIS Czech Republic (ACTRIS-CZ), a unique platform for the long-term background air quality monitoring and research closely related to climate, environmental and health issues qualified as societal challenges. ACTRIS-CZ represents a national node of the existing European ACTRIS Research Infrastructure (RI) established with the support of the EU 7th Framework Programme INFRA-2010-1-1.1.16 (EU FP7) and ACTRIS-2 project of EU Horizon 2020 (H2020-INFRAIA-2014-2015: Integrating and Opening Existing National and Regional Research Infrastructures of European Interest). In December 2015, ACTRIS was adopted on the ESFRI roadmap 2016 for Research Infrastructures. LRI ACTRIS-CZ RI is based on the long-term collaboration of 4 research partners: Czech Hydrometeorological Institute (CHMI), The Institute of Chemical Process Fundamentals of the CAS (ICPF), Global Change Research Institute of the CAS (GCRI) and Masaryk University (MU) at the research facility of NAOK.

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**Figure 1.** Location of National Atmospheric Observatory Košetice (NAOK) in the European, Czech and local context.

#### *2.2. Measurement Methods*

NAOK is a part of the National Air Pollution Monitoring Network (operated by CHMI). All measurements are carried out according to the quality-controlled procedures. This network operates online (automatic analyzers) and offline (samplers) instruments. Filter analyses are done mainly in the central laboratory of immissions in Prague. The data validation procedure is in line with the EC directive 2008/50/EC. An overview of methods of measured components used in this study are listed in Table 1. A detailed description of each method is available in [4].


**Table 1.** Used sampling methods—year of start, type of measurement, method of determination.

\* The sum consists of gaseous and particulate matter.

#### *2.3. Statistical Evaluation*

The statistical method used for the evaluation of long-term trends is based on the nonparametric Mann–Kendall test for the trend and the nonparametric Sen's method for the magnitude of the trend. Mann–Kendall test is used since missing values are allowed and the data need not conform to any particular distribution. Sen's method is not greatly affected by gross data errors or outliers, and it can also be computed when data are missing. Sen's estimator is closely related to the Mann–Kendall test. Mann–Kendall test is recommended and plentifully used for long-term trend evaluation in air quality because it enables one to evaluate non-complete data series and does not require specific distribution of measured values. On the other hand, it is not possible to use it for assessment of short-term variability and annual variation. Due to its properties, the Mann–Kendall test was used to analyze long-term trends of air pollution measurements data in a harmonized way for EMEP Assessment report [5].

The presence of trend is evaluated using Z value. A positive and negative value of Z indicates an upward and downward trend, respectively. The statistics Z has a normal distribution.

*Atmosphere* **2020**, *11*, 537

The existence and significance of trend is tested by using four different α levels of significance. The different α levels used are α = 0.1, α = 0.05, α = 0.01 and α = 0.001. ‐

α

‐

Trend statistics it is given as a result of the significance level of that trend, marked by 


This means that when the mark is "\*\*\*", the trend is very significant, and when the mark is "+", the significance of the trend is fairly poor, only 10%. If the mark is missing, then there is no trend at significance level α = 0.1 [6]. 0.05 ൌ ߙ 0.01 ൌ ߙ 0.001 ൌ ߙ

0.1 ൌ ߙ

#### **3. Results**

Political and economic changes after the fall of the iron curtain brought a general drop in industrial production and later significant changes in the structure of the industry. These changes were reflected in the reduction of sulphur emissions in the Czech Republic by almost 90% in the period of 1990–2000 (Figure 2). The results of long-term monitoring show that the emission decrease was reflected in reduced pollution levels. Sulphur dioxide concentrations in the atmosphere declined nearly by the same order of magnitude as the emissions (Figure 3c). The steep drop of SO<sup>2</sup> concentrations was more pronounced in the 1990s. The frequency of episodes with extremely high concentrations decreased rapidly (Figure 3e). In the new millennium, the mean annual concentrations dropped below 5 µg·m−<sup>3</sup> , but a slightly decreasing trend was found also in the period 2001–2019 (Figure 3f). In the EMEP domain, SO<sup>2</sup> emission reductions started in the 1980s–1990s; therefore, changes in concentrations will have occurred earlier than 1990. However, concentrations have continued to decrease continuously during the period under review. The timing of concentration decreases varies between countries according to national implementation of emission reduction strategies, but on average, the decrease was larger in the early 1990s and levelled off since then [5]. 0.1 ൌ ߙ ‐ μ ∙ −

**Figure 2.** Total emissions of basic pollutants in the Czech Republic in the period 1990–2018. Results are based on emission inventories outcomes, regularly evaluated and published in air pollution reports in the Czech republic (e.g., [2]).

In the period of 1994–2012, no trend of nitrogen oxides concentration was found, in spite of the fact that the nitrogen emissions declined by 54% during the period under review (Figure 3b). In the period of 1990–2012, the emissions dropped by 72% and in the EU by 55% [6]. Mean annual concentrations varied around 10 µg·m−<sup>3</sup> . These results were in very good correspondence with the trends at the background level in the neighbouring countries (Austria, Germany) [5]. The reasons are uncertain. One of the explanations could be the significant changes in the structure of nitrogen emissions. In the last 7 years, a visibly decreasing tendency of NO<sup>2</sup> concentrations was found and the mean annual concentrations dropped continuously to 4 µg·m−<sup>3</sup> . The evaluation of the data from the EMEP network shows that for the period of 1990–2001, the fraction of sites where significant negative trends were observed was high (58%), but it slowed down after 2002. μ <sup>−</sup> μ <sup>−</sup>

**Figure 3.** Results of Mann–Kendal test for gaseous pollutants; (**a**) CO, (**b**) NO<sup>2</sup> , (**c**) O<sup>3</sup> , (**d**) SO<sup>2</sup> 1985–2018, (**e**) SO<sup>2</sup> 1985–2000, (**f**) SO<sup>2</sup> 2001–2018.

μ <sup>−</sup> NO concentration at the background scale is quite low, and mean annual concentrations varied around 1 µg·m−<sup>3</sup> . The long-term trend describes similar patterns for NO2: no trend in the period 1994–2012 and decreasing tendency after 2012.

A slightly decreasing trend was found in mean annual concentrations of tropospheric ozone in the whole period and also in the first part of the period under review (Figure 3c). On the contrary, a slightly increasing tendency was found after 2006. It is caused probably by increasing temperature during the last two decades. A warm period displays similar patterns as the whole year. On the contrary, no trend was found in the cold period [7]. More importantly, the number of episodes exceeding the target value for human health dropped significantly during the period (Figure 4), and interannual variations can be explained by meteorological conditions. The target value of tropospheric ozone for the protection of human health is exceeded when the eight-hour running mean is higher than 120 µg·m−<sup>3</sup> 25 times on average for 3 years. Visibly higher values were recorded in the years with extreme summer temperatures over long periods and well-established heat waves over continental Europe (2003, 2015, 2017).

A statistically significant trend was found for carbon dioxide. Mean annual concentrations decreased continuously during the whole period (Figure 3a).

Most non-methane volatile organic compounds (VOCs) follow an annual course that reflects their emission levels, i.e., with maximums in winter and minimums in summer. Isoprene is an exception. In general, the reduction of VOCs emissions in the last two decades was reflected in a decrease of concentrations at the regional scale of the Czech Republic [8]. A statistically significant downward trend was found for almost all of measured VOCs, and only the ethane trend was less significant (Table 2). The trend of isoprene concentrations is controlled first of all by natural conditions and shows different patterns from other VOCs. We detected a statistically very significant upward trend of isoprene concentration in the period under review. Favourable conditions for isoprene emissions are in hot summer periods. An increasing tendency was visible especially in the last decade. This is in good correlation with increasing mean annual temperature in the current period of changing climate conditions (hot summers, long periods with high temperatures). It follows from the current report on VOC measurements in the context of EMEP [9] that the VOC concentrations continuously decrease on a regional scale and thus reflect the decreasing trend in emissions. The concentration level at NAO Košetice is comparable with those at the German, Swiss and French stations. The Czech station has long been characterised by lower annual average ethane concentrations. For most VOCs, the concentrations measured in the winter are usually similar to those at German stations, while the values at NAOK are slightly lower in the summer. ∙ − ‐

**Figure 4.** Number of days with target limit for surface ozone exceedances (1993–2019).


**Table 2.** Trend significance of measured VOCs.

"\*\*\*"—the trend is very significant; "+"—the significance of the trend is fairly poor, only 10%. If the mark is missing, then there is no trend at significance level α = 0.1.

‐ − −

‐ − −

‐ − −

‐

 − − − − − − − − − − − − − − − ‐

− −

The measurement of aerosol particles covers periods of different duration. The longest records are available for sulphate in aerosol and PM10. Table 3 shows that the concentrations were changed significantly during the period under review. Changes in concentration levels reflect the development of emission (Figure 2), which is in line with both national and international environmental measures.

Outcomes of sulphur and PM<sup>10</sup> show the highest level of trend significance (Figure 5c,d). Sulphur concentration continuously decreased during the whole period. On the other hand, the evaluation of PM<sup>10</sup> data shows that the mean annual concentrations in the period of 2001–2006 reached a similar level as in 1996 (29.8 µg·m−<sup>3</sup> ) (Figure 5c). The same patterns were observed across the Czech Republic at different types of stations. After 2001, the drop of emission was slower compared to the previous period. The increase of PM<sup>10</sup> concentrations was probably influenced by meteorological and dispersion conditions [10]. A higher level of trend significance is observed for PM2.5 concentrations. After 2005, when a level over 18 µg·m−<sup>3</sup> was recorded, the linear decreasing trend is observed. These outcomes are analogous for PM10.

 ∑ ∑ **Figure 5.** Results of Mann–Kendal test for aerosol particles; (**a**) EC, (**b**) OC, (**c**) PM10, (**d**) SO<sup>4</sup> , (**e**) PM2,5, (**f**) P NH<sup>4</sup> , (**g**) P NO<sup>3</sup> .

 An insignificant trend is visible for the sums of ammonium and nitrates (Figure 5f,g) that are measured from 2002. This is in line with the fact that the emission development is more or less at the same level (Figure 2). No visible annual variation was found (Table 3). Concentrations of elemental

 μ ∙ −  μ ∙ − (EC) and organic carbon (OC) dropped from 2010 (Table 3), but only for EC is the trend statistically significant (Figure 5a,b).


**Table 3.** Changes in annual aerosol concentrations at the beginning and the end of the evaluated period.

#### **4. Summary**

Generally, the results show that the fundamental drop in emission of basic air pollutants in the Czech Republic and widely in the Central European region in the period under review was reflected in the significant decrease of air pollution levels at the background scale of the Czech Republic. A statistically very significant drop in mean annual concentrations of sulphur dioxide was detected in the period of 1990–2000. After 2000, the mean annual concentrations dropped below 5 µg·m−<sup>3</sup> , but a slightly decreasing trend was found also in the period of 2001–2019.

No trend was found by the evaluation of nitrogen dioxide in the atmosphere in the period of 1990–2012, in spite of the fact that the nitrogen emissions declined by half during the period under review. In the last 7 years, a visibly decreasing tendency of NO<sup>2</sup> concentrations was registered, and the mean annual concentrations dropped continuously to 4 µg·m−<sup>3</sup> .

A slightly decreasing trend was found in mean annual concentrations of tropospheric ozone in the whole period and also in the first part of the period under review. On the contrary, a slightly increasing tendency was found after 2006. It is caused probably by increasing temperature during the last two decades. More importantly, the number of episodes with the target value for human health exceedances dropped significantly during the period.

The reduction of VOCs emissions in Central Europe was reflected in a statistically significant decrease of concentrations at the regional level of the Czech Republic. Only isoprene, which is of natural origin, displays an inverse trend.

Sulphur concentration in aerosol continuously decreased during the whole period. The evaluation of PM<sup>10</sup> data shows that the mean annual concentrations in the period of 2001–2006 reached a similar level as in 1996. The higher level of trend significance is observed for PM2.5 concentrations, but the general outcomes are analogous as for PM10. Concentrations of EC and OC dropped from 2010, but only for EC is the trend statistically significant.

**Author Contributions:** Conceptualization, M.V.; methodology, A.H.S.; validation, M.V., A.H.S., J.S.; formal analysis, M.V., A.H.S.; investigation, M.V., A.H.S.; resources, M.V.; data curation, J.S., P.M.; writing—original draft preparation, M.V.; writing—review and editing, M.V.; visualization, A.H.S.; supervision, M.V.; project administration, M.V.; funding acquisition, M.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the project for support of national research infrastructure ACTRIS participation of the Czech Republic (ACTRIS-CZ - LM2015037)—Ministry of Education, Youth and Sports of the Czech Republic.

**Acknowledgments:** The research leading to these results has received funding from the project for support of national research infrastructure ACTRIS—participation of the Czech Republic (ACTRIS-CZ - LM2015037)—Ministry of Education, Youth and Sports of the Czech Republic.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Air Quality in Brno City Parks**

**Jiˇrí Huzlík 1,\* , Jitka Hegrová 1 , Karel E**ff**enberger <sup>1</sup> , Roman Liˇcbinský <sup>1</sup> and Martin Brtnický 2,3**


Received: 30 March 2020; Accepted: 12 May 2020; Published: 15 May 2020

**Abstract:** Parks embody an important element of urban infrastructure and a basic type of public space that shapes the overall character of a city. They form a counterweight to built-up areas and public spaces with paved surfaces. In this context, parks compensate for the lack of natural, open landscapes in cities and thus have a fundamental impact on the quality of life of their inhabitants. For this reason, it is important to consider the quality of the environment in urban parks, air quality in particular. Concentrations of gaseous pollutants, namely, nitric oxide (NO), nitrogen dioxide (NO2), and ozone (O3), were measured in parks of Brno, the second-largest city in the Czech Republic. Relevant concentration values of PM<sup>10</sup> solids were determined continuously via the nephelometric method, followed by gravimetric method-based validation. The results obtained through the measurement of wind direction, wind speed, temperature, and relative humidity were used to identify potential sources of air pollution in parks. The "openair" and "openairmaps" packages from the OpenSource software R v. 3.6.2 were employed to analyze the effect of meteorological conditions on air pollution. Local polar concentration maps found use in localizing the most serious sources of air pollution within urban parks. The outcomes of the analyses show that the prevailing amount of the pollution determined at the measuring point most likely originates from the crossroads near the sampled localities. At the monitored spots, the maximum concentrations of pollutants are reached especially during the morning rush hour. The detailed time and spatial course of air pollution in the urban parks were indicated in the respective concentration maps capturing individual pollutants. Significantly increased concentrations of nitrogen oxides were established in a locality situated near a busy road (with the traffic intensity of 33,000 vehicles/d); this scenario generally applied to colder weather. The highest PM<sup>10</sup> concentrations were measured at the same location and at an average temperature that proved to be the lowest within the entire set of measurements. In the main city park, unlike other localities, higher concentrations of PM<sup>10</sup> were measured in warmer weather; such an effect was probably caused by the park being used to host barbecue parties.

**Keywords:** air pollution; urban parks; particulate matter; nitrogen oxides; ozone

#### **1. Introduction**

Urban green spaces, namely, city parks, are very often considered localities providing the best air quality in a city, and thus they become frequently targeted by citizens seeking relaxation and active recreation. However, there are very few studies supporting this generally accepted claim.

Xing et al. [1–3] noticed improved air quality in small urban parks within a distance from surrounding streets due to the dispersion of air pollutants within park areas. Importantly in this context, trees can reduce wind speed and potentially trap pollutants. Most available studies point to a reduction of PM concentration levels inside city parks. Von Schneidemesser [4] stated that suitably distributed greenery can decrease the concentration of PMs by 20%, down to relative ambient average concentrations. Ou et al. [5] monitored PM2.5 and PM<sup>10</sup> mass concentrations during the fall of 2018, identifying a significant drop in both PM<sup>10</sup> and PM2.5 levels close to parks. A decrease of 23% in the total mass of PM2.5 in a national park compared to an urban area is presented in paper [6]. Zhu et al. [7] analyzed the impact exerted by different types of plant communities on ambient PM<sup>10</sup> and PM2.5 concentrations by using a spatial model. The results showed that differences in the levels of ambient PM concentrations among plant communities resulted from their composition and also other factors, including height (significantly lower ambient PM concentrations were recorded near small plants, namely, ones of less than 1 m), leaf area, or distance from the pollution source or edge of the park. Greenery increases the efficiency of reduction in ambient PM concentrations; however, this capability markedly depends on the season of the year. A significant decrease of PM2.5 concentrations in La Carolina, a large city park in Ecuador, was described in [8]. Otosen et al. [9] measured differences in PM1.0, PM2.5, PM10, NO2, and CO concentrations in front of and behind vegetation barriers along roads (hedges during dormancy and the vegetative period). This type of greenery can mitigate the effects of air pollution generated by traffic, and, truly, a decrease in PM concentrations was measured. Contrariwise, no impact on the concentration of gasses was determined. In a relevant study by Abhijith and Kumar [10], the concentrations of PM10, PM2.5, PM1.0, and black carbon were established in close vicinity of the three types of green infrastructure. The influence of separate hedges or shrubs, separate trees, and a mixture of trees and hedges/shrubs was assessed when located at different distances from a road, namely, at very close (<1 m from the road) and more remote (>2 m from the road) spots. The most prominent reductions were recorded in a mixture of trees and hedges under close distance conditions and in separate hedges positioned more remotely. An assessment of various PM fractions showed that separate hedges and a combination of trees and hedges decrease fine particle concentrations behind the green barrier. Relevant analyses then indicated a reduction of vehicle-related particles (i.e., those containing iron and its oxides, Ba, Cr, Mn) in the background of the green infrastructure, as compared to the front area. A similar paper on green infrastructure barriers, Mori et al. [11], characterized measurements of PMs sized between 0.2 µm and 100 µm. The authors described a reduction in PM particles at different distances from the road (measured by passive samplers), proposing that the actual results are influenced by different planting densities in two different green vegetation types of two heights.

Air pollution and human health, as well as green infrastructure and human health, are often studied together. Linking green infrastructure with air quality and human health is an aspect of interest for Kumar et al., who, in a corresponding review [12], concluded that although urban vegetation can bring health benefits, the knowledge of its wider applicability in efforts to reduce air pollution remains overly insufficient and must be further refined. Almedia et al. [13] discussed differences in pollutant concentrations (PM10, NO2, and O3) between schools near roads in urban areas and schools adjacent to forests and roads in the same environment. The results correlate with respiratory problems exhibited by children within all areas of interest. The PM<sup>10</sup> and NO<sup>2</sup> concentrations proved to be higher at points closer to roads with intense traffic flows and lower at spots near parks with dense vegetation. Sheridan et al. [14] focused on NO<sup>x</sup> concentrations in the city of London, especially in parks and playgrounds, finding dangerously high levels of NO<sup>2</sup> at all places of interest (playgrounds, parks, and gardens), those open to the influx of the pollutant in particular. Lingberg et al. [15] described a reduction of air pollution in parks within the city of Gothenburg, Sweden; they emphasized the "park effect", namely, the assumption that parks embody a considerably cleaner local environment thanks to an interaction of two effects: dilution (the distance effect) and deposition. Trees and other vegetation can absorb and capture air pollutants, thus improving the air quality in cities. Due to a lack of local-scale information, the impact of urban parks and forest vegetation on the levels of nitrogen dioxide (NO2) and ground-level ozone (O3) were studied in Baltimore, USA. Yli-Pelkonen et al. [16]

concluded that O<sup>3</sup> concentrations were significantly lower in tree-covered habitats than in open ones. Conversely, NO<sup>2</sup> concentrations did not differ significantly between tree-covered and open habitats, meaning that it is again necessary to stress the choice and variability of greenery. Hewitt et al. [17] discussed several options of how to improve air quality by using different types of green infrastructure, introducing a novel conceptual framework as policy guidance; the authors' interpretation of the problem includes a flow chart to aid decision-making as regards the "green infrastructure to improve urban air quality".

Air pollution poses a major risk to human health, causing premature deaths and potentially reducing the quality of life. Quantifying the role of vegetation in curbing air pollution concentrations is an important step. Most current methods to calculate pollution cutback procedures are static and thus represent neither atmospheric transport of pollutants nor pollutants and meteorology interaction. The focus on urban parks as a tool to facilitate air purification and climate regulation embodies the basis of articles by Vieira et al. [18] and Mexia et al. [19]. These authors concluded that ecosystem service strongly depends on the vegetation type; thus, for example, air purification is more pronounced in mixed forest, and carbon reduction is influenced by tree density. Further, Jones et al. [20] developed a method to calculate health benefits directly from changes in pollutant (including PM2.5, NO2, SO2, and O3) concentrations, exploiting an atmospheric chemistry transport model.

In our paper, the concentrations of PM<sup>10</sup> solids were determined continuously, by utilizing the nephelometric method followed by gravimetric method-based validation. To identify potential sources of air pollution in parks, we evaluated the air quality within the local environment via correlation with measurements of wind direction, wind speed, temperature, and relative humidity. The "openair" and "openairmaps" packages from the OpenSource software R were employed to analyze the effects of wind on air pollution. Local polar concentration maps found application in locating the directions of wind coming from the most serious air pollution sources. Sampling and analyses were performed to confirm the assumption that the main sources of the pollution at the measuring point are most likely the roads and/or crossroads near the sampled localities.

Due to the information gap concerning air quality in city parks, the goal of our study was to obtain data on air pollution in urban parks and associated details relevant to the relationship between this pollution and meteorological parameters, prominently including temperature, wind speed, and wind direction; in this context, our efforts also involved comparing these data with pollution around the parks. Based on the findings, we then aimed to estimate the sources of air pollution in the monitored parks.

#### **2. Method**

#### *2.1. Sampling*

The sampling was carried out in three pre-selected city parks in Brno, the Czech Republic; two of the parks are located in areas with a high traffic impact (near main roads), while one is found in a low traffic load environment (a small park inside a courtyard). The main city park of Lužánky exhibits the largest surface area of all the monitored parks, and it is located near the city center, surrounded by roads with heavy traffic. Two air quality monitoring spots were positioned in the park: one place in the middle of the area, and the other on the edge of the park, near a playground and the traffic-laden roads. This park is frequently visited and used for sports and leisure activities, including picnics.

The Kolištˇe park is adjacent to a road with heavy traffic (33,000 cars/d). It occupies a large walking-friendly area, and there is a very popular restaurant in the middle of the park. However, due to the traffic-laden road, the location is not a popular target for sports, children's activities, or picnics. The air quality was measured near a junction of two main roads.

Tyrš ˚uv sad is a very small park in the city center, situated inside a courtyard. This park is mostly used only for short walks, especially with dogs. The air quality measurement was performed in the middle of the area.

The devices were installed together at the place of interest.

#### *2.2. Instrumentation*

The NO, NO2, O3, and PM<sup>10</sup> concentrations were determined by using two Airpointer units (Recordum Messtechnik GmbH, Austria). These devices measure pollutant concentrations via separate modules utilizing type-approved reference methods (NO2/NOX, O3) classified as relevant by the EU, WHO, US-EPA, and other competent responsible organizations worldwide.

The measurement principle to define the levels of NO2/NO<sup>x</sup> is chemiluminescence (EN14211). The Airpointer NO<sup>X</sup> module was equipped with a delay loop to measure NO and NO<sup>2</sup> from the same sample. An external calibration gas with a concentration of 425 ppb NO in N<sup>2</sup> (SIAD, Italy) was employed to periodically check the span point.

The O<sup>3</sup> measurement principally exploits UV absorption (EN 14625); for the given purpose, an internal ozone generator to allow regular span point checking was applied.

The parameters are calibrated annually by the Slovak Hydrometeorological Institute.

The Airpointer PM<sup>10</sup> module utilizes nephelometry for measuring solid particles' concentrations. Gravimetric measurements of PM<sup>10</sup> concentrations executed within 24 h intervals were carried out to calibrate the nephelometric method. Sequential samplers SVEN LECKEL SEQ 47/50-CD (Sven Leckel Ingenieurbüro GmbH, Germany) were employed for the calibration. The particles were collected on cellulose nitrate filters with the porosity of 1.2 µm (Merck, Germany) and weighed on a Mettler Toledo MX/A microbalance.

The meteorological parameters (air temperature, relative humidity, air pressure, wind speed, and wind direction) were measured by using a compact meteorological station integrated with the Airpointer. These parameters are regularly calibrated by the Czech Metrology Institute.

The data from the Airpointer were downloaded as CSV files and saved in the form of Microsoft Excel files (XLSX). The concentrations measured in ppb were converted to concentrations in µg m−<sup>3</sup> . The medians, upper and lower quartiles, and other percentiles for the monitored pollutants, temperature, relative humidity, and wind speed were calculated in MS Excel. The results were then processed by the Origin program (OriginLab, USA) to yield graphs. The dependencies of and relationships between the pollutant concentrations on the wind speed and direction were processed via the "openair" and "openairmaps" packages of OpenSource program R [21,22]. The package "openairmaps" supports "openair" for plotting on various maps. The maps include those available via the "ggmap" package, e.g., Google Maps, and leaflet ones to facilitate plotting bivariate polar plots. Our research utilized the "Esri.WorldImagery" map source and the "Non-parametric Wind Regression" (NWR) technique to display the concentration maps as bivariate polar plots.

#### *2.3. Measurement Conditions and Positioning of Instruments*

The concentrations of PM<sup>10</sup> and also those of the gaseous pollutants NO, NO2, and O<sup>3</sup> in three parks within the city of Brno, the Czech Republic, were measured in one-minute intervals. The same scenario was applied to the meteorological conditions, namely, air temperature (T), relative humidity (RH), air pressure (p), wind speed (WS), and wind direction (WD). The NO<sup>2</sup> and PM<sup>10</sup> measurements at automated air pollution monitoring stations operated by the Czech Hydrometeorological Institute were employed for comparing the measurement results with those acquired at a heavy traffic locality (Údolní, the Hot Spot) and background localities (Arboretum—the natural city background station, and Dˇetská nemocnice—the commercial city background station). Tables 1 and 2 show the geographic coordinates of the localities and display the time intervals of the measurement.

ů

ě ě voz Hot Spot rboretum


**Table 1.** The geographic coordinates of the measured localities.

<sup>1</sup> Site Svojsík's Cabin. <sup>2</sup> Site Leisure Centre. <sup>3</sup> Site alongside an adjacent roadway.


**Table 2.** The measurement times related to the localities. **ů ě ě**

**ě**

<sup>1</sup> Site Svojsík's Cabin. <sup>2</sup> Site Leisure Centre. <sup>3</sup> Site alongside an adjacent roadway.

Figures 1–3 show the positions of the measurement devices at the sampling sites. The devices were secured against theft with chains and connected to a power supply with a cable. The progress of the measurement was checked via an Internet connection through a SIM card.

ů **Figure 1.** The devices at Tyrš ˚uv sad.

*Atmosphere* **2020**, *11*, 510

(**a**) (**b**)

**Figure 2.** The devices at Lužánky: (**a**) Svojsík's Cabin; (**b**) Leisure Centre.

(**a**) (**b**)

ě **Figure 3.** The devices at Kolištˇe: ( ě **a**) Inside the park; (**b**) at the adjacent roadway.

Figure 4 shows the location of the sampling sites on a map of Brno.

**Figure 4.** The sampling (yellow marks) and reference (green marks) localities.

#### *2.4. PM<sup>10</sup> Calibration*

As nephelometric measurements are performed in one-minute intervals, the conversion factor was calculated for each 24 h measurement interval according to the formula

$$f\_f = \frac{PM\_{10}^{\text{sync}}}{\overline{PN}\_{10}^{\text{new}}} \tag{1}$$

మ

)

where

ଵ ଵ തതതതതതതതതത *PMgrav* <sup>10</sup> is the gravimetric PM<sup>10</sup> concentration over 24 h (µg/m<sup>3</sup> ), and *PMneph* <sup>10</sup> is the average nephelometric PM<sup>10</sup> concentration over 24 h (µg/m<sup>3</sup>

శభି × ቌℎ × ቌ − ௗ௬ × ቆ௧ା ೌ The calculated emission factor is discontinuous, and was thus smoothed by the function

$$Factor = f\_i + \frac{f\_{i+1} - f\_i}{2} \times \left( tgh \left[ p \times \left( t - t\_{day} \times floor \left( \frac{t + \frac{t\_{day}}{2}}{t\_{day}} \right) \right) \right] + 1 \right) \tag{2}$$

where

*Factor* is the smoothed conversion factor in time *t*

௩

*fi* is the conversion factor for the *i*th day *fi*+<sup>1</sup> is the conversion factor for the (*i* + 1)th day *p* is the smoothing parameter (*p* = 0.004) *t* is the time from the start of the measurement (minutes) *tday* is the length of the day (minutes) *floor*() is the rounding down function *tgh*[] is the hyperbolic tangent function

The PM<sup>10</sup> concentration was calculated for every minute by the function

$$PM\_{10} = Factor \times PM\_{10}^{\text{ueph}}.\tag{3}$$

ů

An example of the factors' calculation for the site Tyrš ˚uv sad is shown in Figure 5. ଵ = × ଵ

ů **Figure 5.** A comparison of the factors for the locality Tyrš ˚uv sad from 2.8.2019 7:00:00 to 16.8.2019 6:59:00.

#### **3. Results and Discussion**

ě Each measurement at a park is represented by a dataset with 20,160 observations, and the measurement at the Kolištˇe road locality is represented by a dataset with 25,920 observations. Therefore, the results were summarized as percentiles and mean values to be calculated in MS Excel. Table 3 shows the intervals in which 90% of the measured values are considered for each parameter.


**Table 3.** The measurement results: the 0.05 and 0.95 percentiles of the measured parameters.

−


**Table 3.** *Cont*.

The results of the measurements at the automatic air pollution monitoring stations were used to compare the air pollution concentrations in the parks and their vicinity. The hourly averages of the NO<sup>2</sup> and PM<sup>10</sup> concentrations were compared, as the data are measured in hourly intervals. The results are shown in Table 4.

**Table 4.** The results of the measurement at the Automated Air Pollution Monitoring Stations—the 0.05 and 0.95 percentiles of the measured concentrations.


Figure 6 compares the individual mean values (medians) of the measured pollutant concentrations and meteorological parameters. The dispersions of these values are represented through the upper and lower quartiles, an interpretation that is more plausible than that rendered via the mean and standard deviations because the data have an asymmetric statistical distribution. This is also clearly seen in Figure 6: the vertical lines, whose length represents the size of the first and the third quartiles, are not identically long.

**Figure 6.** The NO (**a**), NO<sup>2</sup> (**b**), NO<sup>x</sup> (**c**), O<sup>3</sup> (**d**), and PM<sup>10</sup> (**e**) concentrations and temperature (**f**) plus wind speed (**g**) measured in the parks: the medians and quartiles.

The highest NO concentrations were measured in the immediate vicinity of the road adjacent to the Kolištˇe park and then directly in the park; in both cases, the measurement was performed during a cold season (January, November). Similarly, the highest NO<sup>2</sup> concentrations were determined next to the road adjacent to the Kolištˇe park and directly in the park (but also in Tyrš ˚uv sad); in all of the cases, the measurement was carried out during a cold season (January, November, February). The highest total concentrations of nitrogen oxides (NOx) were acquired, as in the NO, in the immediate vicinity of the road adjacent to the Kolištˇe park and directly in the park, during a cold season (January, November). The highest O<sup>3</sup> concentrations were measured in springtime, the lowest one in winter. The solid particles detected at Lužánky SVC and Lužánky SS exhibited a higher concentration in August than in the colder months (March, September), which is not a normal effect. This deviation arises from the fact that, in these localities, people often gather for barbecue parties and use the parks' public cooking facilities during the summer months, whereas the other parks are not frequented for this purpose. ě ě ů ě

Figure 6f,g shows also the differences between the speeds and variations between the temperatures at the sampling sites, respectively. The March, February, and January temperatures reached significantly lower than the September, August, and June ones.

Figure 7a compares graphically the NO<sup>2</sup> air pollution in the parks, with the pollution measured at the reference stations, while Figure 7b displays, in the same manner, the air pollution caused by PM10. The individual measurement campaigns are separated by the red lines. As can be seen, the air pollution in the parks, with the exception of the Kolištˇe park for PM10, was lower than that at the traffic locality, and the pollution at the background localities approached the value. The exception concerning the Kolištˇe park was probably due to the fact that this area is relatively narrow compared to Lužánky; thus, in wintertime, when the vegetation is leafless, it provides less from the dust generated on the nearby busy roads. Moreover, it is obvious from the representation that the parks ensure better air protection from nitrogen oxides than against dust. ě ě

**Figure 7.** The NO<sup>2</sup> (**a**) and PM<sup>10</sup> (**b**) concentrations in the parks and at the automated air pollution monitoring sites.

The average concentrations of the measured pollutants were compared with the legal limits [23,24], Table 5. The excess values are marked in pink.


**Table 5.** The average concentrations from the measured localities as compared with the legal limits.

<sup>1</sup> Human health protection. <sup>2</sup> Ecosystems and vegetation protection. <sup>3</sup> Limit for tropospheric ozone. <sup>4</sup> Count of legal limit excess instances.

It is possible to claim that in most localities the NO<sup>x</sup> limit for ecosystems and vegetation protection was exceeded, except for Lužánky SVC in March and August 2019, Tyrš ˚uv sad in August 2019, and Kolištˇe in June 2019. The NO<sup>2</sup> concentration reached beyond the human health protection limit only in January 2019, when the lowest average temperature of all measurement campaigns was recorded. The PM<sup>10</sup> concentrations exceeded the same limit only at Kolištˇe in January 2019, Kolištˇe road in November 2019, and Lužánky SS in August 2019. ů ě

The analysis of the relationship between the individual pollutants' concentrations, wind speed, and wind direction was utilized to identify the places from which the highest pollutant concentrations reached the sampling site. The concentration scale of the measured pollutants is shown in Figure 12. ě ě

The color scale shown in Figure 8 expresses concentrations depending on wind direction (angle coordinate) and wind speed (radius coordinate). Figure 9, Figure 10, Figure 11, Figure 12, Figure 13, Figure 14, Figure 15, Figure 16, Figure 17, Figure 18 introduce the concentration polar maps of the measured pollutants at all of the localities.

**Figure 8.** The concentration scale for the "openmaps" graphs.

ě **Figure 9.** The NO (**a**), NO<sup>2</sup> (**b**), PM<sup>10</sup> (**c**), and O<sup>3</sup> (**d**) concentration relationships to the wind speed and direction at Kolištˇe; sampling started on 18.1.2019 7:00. ě

Figure 9a,b shows that the highest concentrations of nitrogen oxides were measured with an east wind blowing from the adjacent road. Under the east to northwest wind direction, the lowest ozone concentrations were measured (Figure 9d). The lowest PM<sup>10</sup> concentration was obtained in north and south winds, meaning that transport embodies the most likely source of the nitrogen oxides; there is a larger amount of PM<sup>10</sup> sources; and, probably, the activities pursued within the area contribute to the dust circulation in the park (Figure 9c).

ě ě **Figure 10.** The NO (**a**), NO<sup>2</sup> (**b**), PM<sup>10</sup> (**c**), and O<sup>3</sup> (**d**) concentration relationships to the wind speed and direction at Kolištˇe; sampling started on 7.6.2019 7:00.

Figure 10a,b shows that the highest concentrations of nitrogen oxides were measured with east and west winds blowing from the adjacent road and the opposite side. The impact of traffic on the road west of the park, which had not manifested itself in January, probably shows here. From the ě

south through the east to the northwest, the lowest ozone concentrations were measured (Figure 10d). The highest PM<sup>10</sup> concentrations were acquired in calm weather.

ě **Figure 11.** The NO (**a**), NO<sup>2</sup> (**b**), PM<sup>10</sup> (**c**), and O<sup>3</sup> (**d**) concentration relationships to the wind speed and direction at Kolištˇe road; sampling started on 8.11.2019 0:00.

Figure 11a–c shows that the highest concentrations of nitrogen oxides and PM<sup>10</sup> were measured with northwest wind blowing in the direction of the vehicles traveling towards the Airpointer along the near lane of the road. At the same wind direction, we measured the lowest concentrations of O<sup>3</sup> (Figure 11d), meaning that both the oxides of nitrogen and the PM<sup>10</sup> had most likely originated from traffic in this case.

**Figure 12.** The NO (**a**), NO<sup>2</sup> (**b**), PM<sup>10</sup> (**c**), and O<sup>3</sup> (**d**) concentration relationships to the wind speed and direction at Lužánky SVC; sampling started on 12.9.2018 12:00.

Figure 12a indicates that the highest NO concentrations were measured with a east wind. The highest NO<sup>2</sup> concentrations were determined in eastern wind directions, namely, from the south to the north, similarly to PM<sup>10</sup> (Figure 12b,c). At low wind speeds, we acquired the lowest O<sup>3</sup>

concentrations of the (Figure 12d), meaning that both the NO<sup>2</sup> and the PM<sup>10</sup> had probably been generated by similar sources. The NO had most likely originated from the traffic on the road east of the park.

**Figure 13.** The NO (**a**), NO<sup>2</sup> (**b**), PM<sup>10</sup> (**c**), and O<sup>3</sup> (**d**) concentration relationships to the wind speed and direction at Lužánky SVC; sampling started on 6.3.2019 7:00.

Figure 13a indicates that the highest NO concentrations were measured with a north wind, similarly to the situation in Figure 14. The highest NO<sup>2</sup> concentrations were acquired under eastern wind directions, namely, from the south to the north, similarly to PM<sup>10</sup> (Figure 13b,c). This scenario resembles that represented in Figure 16. In eastern wind directions, we measured the lowest O<sup>3</sup> concentrations (Figure 13d). The NO had probably originated from the traffic on the road north of the park.

**Figure 14.** The NO (**a**), NO<sup>2</sup> (**b**), PM<sup>10</sup> (**c**), and O<sup>3</sup> (**d**) concentration relationships to the wind speed and direction at Lužánky SVC; sampling started on 22.8.2019 7:00.

Figure 14b shows that the highest NO<sup>2</sup> concentrations were measured under northern wind directions (Figure 14b). In western to northern wind directions, we established the lowest concentrations of O<sup>3</sup> (Figure 14d). The nitrogen oxides had probably originated from the traffic on the road north of the park. The PM<sup>10</sup> concentrations did not exhibit any significant relationship to the wind direction in this case.

**Figure 15.** The NO (**a**), NO<sup>2</sup> (**b**), PM<sup>10</sup> (**c**), and O<sup>3</sup> (**d**) concentration relationships to the wind speed and direction at Lužánky SS; sampling started on 6.3.2019 7:00.

Figure 15a–c indicates that the highest NO, NO2, and PM<sup>10</sup> concentrations were measured under a northeastern wind direction. In the same wind directions, we acquired the lowest concentrations of O<sup>3</sup> (Figure 15d). Both the nitrogen oxides and the PM<sup>10</sup> had probably been generated by the traffic on the crossroads to the northeast of the park.

**Figure 16.** The NO (**a**), NO<sup>2</sup> (**b**), PM<sup>10</sup> (**c**), and O<sup>3</sup> (**d**) concentration relationships to the wind speed and direction at Lužánky SS; sampling started on 22.8.2019 7:00.

Figure 16 displays a situation similar to that shown in Figure 15. It clearly follows from the images in both of the figures that, at the Lužánky SS locality, the traffic pollution (NO) is contained by the Svojsík srub building. At the Lužánky SVC site (Figures 12–14), conversely, the NO source is blocked by the Leisure Center from the west.

ů **Figure 17.** The NO (**a**), NO<sup>2</sup> (**b**), PM<sup>10</sup> (**c**), and O<sup>3</sup> (**d**) concentration relationships to the wind speed and direction at Tyrš ˚uv sad; sampling started on 8.2.2019 7:00.

ů There are no significant transport-based air pollution sources near Tyrš ˚uv sad; the air pollution at this location can be rather generated by long-distance transfer or, especially in wintertime, PM<sup>10</sup> from local heating. Figure 17 shows the pollution from eastern directions, and Figure 18 displays the ambiguous situation at the site. ů ů

ů ů **Figure 18.** The NO (**a**), NO<sup>2</sup> (**b**), PM<sup>10</sup> (**c**), and O<sup>3</sup> (**d**) concentration relationships to the wind speed and direction at Tyrš ˚uv sad; sampling started on 2.8.2019 7:00.

As outlined above, the problem of reducing PM concentrations in urban parks has been discussed in diverse papers, e.g., [4–6]. Other articles analyzed the impact of urban greenery on NOx, NO<sup>2</sup> [14], and O<sup>3</sup> [16]. In this study, the outcomes presented within the referenced research reports are followed and developed through such procedural approaches as monitoring the influence of wind and air temperature on pollutant concentrations. The measurements have shown that, in addition to vegetation, seasonal changes of meteorological conditions and human activities in parks embody a substantial aspect modifying the local situation, as observed at Lužánky park in August 2019. The obtained results have confirmed the conclusions proposed by Kumar et al. [12], namely, that progressive steps need to be taken to bring further knowledge in the field. The relationships between O3, NO, and NO<sup>2</sup> were studied by Han et al. [25]; interestingly, the outcomes of our research resemble Han et al.'s findings in suggesting that, as regards the study area(s), the daily NO cycle initiated by flue gas emissions from motor vehicles and continued by the related conversion of the pollutant into NO2, had a major impact on the regular ozone process. The daily course of concentrations in these pollutants was similar, too.

#### **4. Conclusions**

In four 14-day campaigns, concentrations of NO, NO2, PM10, and O<sup>3</sup> were measured at five diverse locations, of which four were enclosed within Brno parks and one set at a road adjacent to a park. Compared to the average values, significantly higher nitrogen oxide concentrations were determined at the monitored spots of Kolištˇe and Kolištˇe-road in colder weather. Both of the locations are situated near a busy road exhibiting a traffic intensity of 33,000 vehicles/d. In terms of PM10, the highest concentrations were obtained at Kolištˇe park, with an average air temperature that proved to be the lowest among the values adopted for the other measurements. At Lužánky park, the PM<sup>10</sup> concentrations measured in warmer weather reached higher than those acquired during colder periods—an effect probably caused by the park being a popular public barbecue place. Using the "openairmaps" software package, we determined the directions pointing to the main sources of pollution at the individual spots. Based on this procedure, it was estimated that the main air pollution sources affecting the parks lie in the adjacent roads and crossroads. In some cases, however, human activities of people in the parks (barbecue) can also be regarded as important or semi-critical. By extension, we established that the overall surface layout, prominently including buildings in the park, can locally shield the impact of traffic on the air quality. Interestingly, the air quality in the parks approached that of the urban background locations, except for Kolištˇe park, which, due to its shape and proximity to a very busy road, showed the characteristics of a regular traffic location.

**Author Contributions:** Conceptualization, J.H. (Jiˇrí Huzlík); Data curation, J.H. (Jitka Hegrová) and K.E.; Formal analysis, J.H. (Jiˇrí Huzlík), J.H. (Jitka Hegrová) and K.E.; Funding acquisition, J.H. (Jitka Hegrová) and M.B.; Investigation, J.H. (Jiˇrí Huzlík) and R.L.; Methodology, J.H. (Jiˇrí Huzlík), J.H. (Jitka Hegrová), R.L. and M.B.; Project administration, J.H. (Jitka Hegrová), R.L. and M.B.; Resources, J.H. (Jiˇrí Huzlík), J.H. (Jitka Hegrová) and K.E.; Supervision, J.H. (Jitka Hegrová), R.L. and M.B.; Validation, J.H. (Jiˇrí Huzlík), J.H. (Jitka Hegrová) and R.L.; Visualization, J.H. (Jiˇrí Huzlík) and R.L.; Writing—original draft, J.H. (Jiˇrí Huzlík) and R.L.; Writing—review & editing, J.H. (Jiˇrí Huzlík), J.H. (Jitka Hegrová), K.E. and R.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This article was produced under support from the Technology Agency of the Czech Republic within the ÉTA framework, project TL01000286, on the research infrastructure acquired from the Operational Programme Research and Development for Innovations (CZ.1.05/2.1.00/03.0064).

**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the designing of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.

#### **References and Note**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*
