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Article

Quality Management System in Air Quality Measurements for Sustainable Development

by
Rafał Kozłowski
1,*,
Mirosław Szwed
1,
Aneta Kozłowska
1,
Joanna Przybylska
1 and
Tomasz Mach
2
1
Institute of Geography and Environmental Sciences, Jan Kochanowski University of Kielce, 25-406 Kielce, Poland
2
MLU Polska, 40-585 Katowice, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(17), 7537; https://doi.org/10.3390/su16177537
Submission received: 2 August 2024 / Revised: 20 August 2024 / Accepted: 28 August 2024 / Published: 30 August 2024
(This article belongs to the Section Pollution Prevention, Mitigation and Sustainability)
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Abstract

:
Air pollution is a global health issue and a cause of premature mortality. There is an urgent need to develop air quality monitoring networks and to implement standards enabling dependable testing and delivering reliable results. European standards provide reference methods for testing ambient air quality, which are used in accredited laboratories. In this paper, we present an example of research conducted with the use of a mobile, automated station Airpointer® in an industrial area under pressure from the lime and cement industry located in southeastern Poland. During the measurement campaign, the concentrations of the studied pollutants did not exceed the permissible thresholds, yet they strongly depended on meteorological conditions. The air filter was analysed with an energy dispersive spectroscopy (EDS) microanalyzer in the scanning electron microscope (SEM). The results confirmed that dust particles present in ambient air are connected with local emission sources—industry based on the extraction and processing of minerals. The equipment and measurement techniques used in this study are effective in identifying the potential threat of air pollution. Automated, short-term measurements of air pollution can be a significant source of information, indispensable for drawing up action plans aimed at air quality protection in order to achieve sustainable development goals.

1. Introduction

Socio-economic changes in the last decades have raised the quality standards for research on ambient air. Air quality management is a significant challenge. Some countries have implemented programmes informing citizens about the threats connected with air pollution in order to enhance the management of health risks [1]. Monitoring is a significant element of air quality management and protection [2], as is implementing sustainable management goals connected with limiting the number of deaths and diseases caused by air pollution. Therefore, air quality measurements are crucial in terms of human health [3]. Air pollution is a global health issue. It has been proven to be among the main causes of premature mortality worldwide [4,5]. Numerous studies link air pollution with respiratory diseases, including chronic obstructive pulmonary disease (COPD) [6] and asthma [7]. It is estimated that air pollution accounts for 1.4% of total mortality due to 26 risk factors assessed in the previous global burden of disease project by the World Health Organization (WHO) [8].
Therefore, reliable and dependable testing is a crucial issue. The way to demonstrate the capacity to deliver trusted results is to acquire accreditation according to the ISO/IEC 17025 “General requirements for the competence of testing and calibration laboratories” standard [9].
Quality management systems play a key role in air quality monitoring, ensuring the precise testing and assessment of air pollution. Several issues have to be taken into account in this respect, including standardization of measurements. The quality management system defines standards, procedures, and guidelines concerning air quality testing. It guarantees the consistency and comparability of the results from various sites and data sources. Therefore, it is possible to understand and precisely compare pollution levels from different areas. The system involves control procedures, which ensure reliability and precision of tests. They include, i.e., the calibration and validation of measurement devices, regular audits and performance tests, as well as procedures for controlling the accuracy and reliability of the results. The system also includes data analysis in order to extract significant information from the test results. Statistical methods and mathematical models are used to assess the tendencies, identify pollution sources, and forecast air quality. Precise data analysis helps in making adequate decisions in the area of environmental protection and public health. Quality management systems enable efficient reporting of air quality monitoring results. Data are transferred to governmental units, researchers, and local communities, so that appropriate prevention and countermeasures can be applied. Transparent and easily available reports are indispensable in the education of local communities and implementing actions for air quality improvement. Moreover, the quality management system in air quality testing is constantly developing and improving. It is based on the analysis of the scientific literature, research results, and technological progress. Modifications and updates to the system enable better understanding of the effects of air pollution and provide more reliable data.
The requirements for air quality measurements and ensuring the quality of tests are provided in the Directive of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe (Directive 2008/50/EC) [10] and the Regulation of the Minister for Climate and the Environment of 11 December 2020 on the evaluation of levels of substances in ambient air (Journal of Laws 2020, item 2279) [11]. The requirements have to be fulfilled by the laboratories reporting measurement data used for the assessment of ambient air quality. Reference methods are provided by the following standards:
  • EN 14625:2013 Ambient air. Standard method for the measurement of the concentration of ozone by ultraviolet photometry [12];
  • EN 14212:2012 Ambient air. Standard method for the measurement of the concentration of sulphur dioxide by ultraviolet fluorescence [13];
  • EN 14211:2013 Ambient air. Standard method for the measurement of the concentration of nitrogen dioxide and nitrogen monoxide by chemiluminescence [14];
  • EN 12341:2014 Ambient air. Standard gravimetric measurement method for the determination of the PM10 or PM2.5 mass concentration of suspended particulate matter [15].
In the case of the PM10 and PM2.5 suspended particulate matter, in order to demonstrate an acceptable level of comparability of the data from the equivalent and the reference method, the following standard should be used: EN 16450:2017 Ambient air. Automated measuring systems for the measurement of the concentration of particulate matter (PM10; PM2.5) [16], which also regulates the performance of such equipment. To ensure the precision of tests and conformity with the quality requirements, all the air quality measurements, based on the upper and lower assessment thresholds, should demonstrate measurement consistency, according to the ISO/IEC 17025 standard [9].
Air quality is monitored in Poland, and the monitoring network gives citizens information about health risks connected to air pollution; however, there are numerous areas where no data on air quality are available. Moreover, risk in a given region, presented as a single value from one testing point, does not represent the spatial variability of the phenomenon. Without the knowledge about spatial variability, it is difficult to plan the complex management of air pollution. High spatial-temporal resolution of data on air quality can be obtained with the use of mobile and ubiquitous sensing [17]. In the case of low-cost sensors, which are widely used in community projects, there is the question of the quality of measured data [18]. Therefore, reliable research in areas without the monitoring network can be conducted with the use of mobile laboratories, applying reference measurement methods and having the accreditation for measuring air pollution. One such laboratory is maintained by the Centre for Research and Analysis of the Jan Kochanowski University of Kielce, having the accreditation No. 1622 from the Polish Centre for Accreditation. The aim of the article is to present the results of the high-resolution research (one hour means) on the concentration of NO2, SO2, O3, PM2.5, and PM10, conducted in an area under strong pressure from the lime and cement industry, which was performed with the use of an accredited mobile laboratory and supplemented with scanning microscopy in order to identify pollution sources.

2. Materials and Methods

Air quality measurements were conducted in the industrial area of the Sitkówka Commune (Figure 1), located in the central part of the Świętokrzyskie Province, 10 km south of Kielce (N: 50.80609, E: 20.54488). The site chosen for the tests was located at the sewage plant. In the surrounding area there are several quarries and lime and cement plants, including a cement plant in Nowiny, a lime plant in Trzuskawica, and marl, dolomite, and limestone quarries.
The measurements were conducted with the use of an automatic mobile station, Airpointer®, equipped with modules for monitoring airborne pollutants: ozone, O3; nitrogen oxides, NO/NO2/NOx; sulphur dioxide, SO2; as well as PM10 and PM2.5 suspended particulate matter. It should be underlined that all the measurement methods used by the Airpointer® (Messtechnik, Vienna, Austria), are in accordance with the standards which are in force in the European Union and, therefore, in Poland. The sampling point was sited according to the guidelines specified by the EU Directive 2008/50/EC.
The analysed air sample is sucked by a pump and directed to gas modules. The sample inlet system is made of materials resistant to pollutants present in air samples (stainless steel, PTFE), in accordance with the European Standards EN 14211 [14], EN 14212 [13], and EN 14625 [12]. Additionally, a Teflon filter (Teflon, Wilmington, DE, USA) is placed at the inlet to clean the gas samples from dust particles.
The module for ozone, O3, applies ultraviolet photometry according to the EN 14625:2013 standard [12]. The air sample is illuminated with a mercury lamp. The concentration of ozone is determined by measuring the ratio between two different UV signals. In the first step, the analysed sample enters the measurement cell. In the second step, a sample where ozone was eliminated is analysed (e.g., with the use of ozone scrubber). The ratio between the signals is the concentration of ozone in the sampled air. The unique feature applied in the Airpointer® module is the use of two separate measurement cells, so that the two steps are performed independently, at the same moment. Therefore, the device has a shorter response time and is more stable than other products available on the market.
Another module in the Airpointer® measures the concentration of nitrogen oxides, NO/NO2/NOx. According to the EN 14211:2013 standard [14], the method applied in the measurements is chemiluminescence. It is based on the reaction of nitrogen oxides with ozone. In the chemiluminescent module, sampled air is applied at constant flow rate to the measurement cell, where it is mixed in order to determine the concentration of nitrogen oxide. To determine the concentration of nitrogen dioxide, a converter reduces nitric dioxide to oxide, and it is then analysed as in the first step described. A parameter which significantly increases precision of the module is the inner coating of the cell with a glossy layer. The greater amount of light reflected by the surface enhances operating conditions of the photomultiplier, and thus, the devices equipped with measurement chambers coated with a golden layer demonstrate significantly greater precision and linearity.
The last module for gaseous compounds detects sulphur dioxide, SO2, with the use of UV fluorescence according to the EN 14212:2013 standard [13]. Sample gas is lighted with a UV lamp, which causes the SO2 molecule to absorb energy. The absorbed energy is emitted as a light pulse (photon), which is measured with a photomultiplier. An interesting innovation applied in the module is the flashing lamp used instead of the standard direct-current lamp. If the radiation source (lamp) is in the ‘pulse mode’, the detector works in the frequency adapted to the UV lamp pulses and the signal is enhanced. In the traditional solutions, enhancing the signal leads to enhancing the device’s defects, such as zero or upper detection limit drift. The pulsing signal eliminates the drifts by eliminating the constant signal. Measurement uncertainty in the gaseous modules amounts to +/− 15%.
The Airpointer® air quality monitoring station is also equipped with a Fidas® 200 fine dust monitor (Palas, Karlsruhe, Germany), analysing the concentration of PM10, PM4, PM2.5, PM1, and total suspended particulate matter. The measurements (of PM10 and PM2.5 in particular) are conducted according to the EN 16450:2017 standard [16]. The standard does not specify the method dedicated for the automated measurements of the concentration of particulate matter in ambient air; therefore, various methods can be used. Nevertheless, the device used for the measurements should consist of, at the least, size-selective sampling head for PM10 or PM2.5, sampling tube, pump, flow volume sensors, air temperature and pressure sensors, and a system of recording measurement data. Fidas® monitor uses the method of optical spectrometry. It is more sophisticated, yet owing to that, the results are more precise. The main advantage of Fidas® 200 is the ability to identify the fraction of each particle. Therefore, it is possible to analyse different dust fractions simultaneously. No other method of analysing fine particles is capable of such performance. The pump enables flow volume of 4.8 L/min, and the sampling head excludes particles greater than 20 µm. Measurement uncertainty for PM2.5 amounted to 13% for the concentration ≥18 µm/m3 and 12% for the concentration <18 µm/m3; in the case of PM10 the values were calculated at 11% for ≥30 µm/m3 and 7% for <30 µm/m3. The device operates at the measurement range of 1–10,000 µm/m3. It is calibrated (zero and span point check) before each measurement campaign, and certified reference material MonoDust1500 is used (Palas, Karlsruhe, Germany).
All the modules of the Airpointer® are controlled by the built-in datalogger. It enables operating the station regardless of the distance to the operator or the computer and software used. The minimum time resolution of the data is 1 min. It should be underlined that the Airpointer® not only conforms to the valid standards, the station was also type approved and granted a certificate by TÜV Rheinland®, confirming its compliance with the EN 14212 [13], and EN 14625 [12] standards, while the Fidas® 200 monitor was type approved and certified by TÜV Rheinland® for compliance with the EN 12341 [15] and EN 16450 [16] standards. According to quality management policies in the laboratory, the modules of the Airpointer® are calibrated at least once every three months with the use of standard reference materials (nitric oxide 200 ppb, sulphur dioxide 200 ppb, MonoDust 1500) and equipment (ozone calibrator KUI-1P), and the station participates in interlaboratory comparisons, which is confirmed by the accreditation No. 1622 from the Polish Centre for Accreditation. Uncertainties associated with measurements were calculated according to [19].
Meteorological conditions were recorded by a built-in station Vaisala WXT536 (Vaisala, Vantaa, Finland) with temperature, pressure, and air humidity sensors tested with the calibrated thermo-hygrometer LATCCHO-02 (TESTO, Titisee-Neustadt, Germany). The results from the Airpointer® were compared with the results from a nearby state monitoring station (N: 50.82311 E: 20.53351, 2 km NE from our sampling point, in urban setting), providing data on the concentration of ozone and PM10. In order to detect the relations between variables, principal component analysis (PCA) was conducted with Statistica 13.3 (TIBCO, Palo Alto, CA, USA) software.
In order to determine the chemical composition of dust present in ambient air, a silicon filter was used. It was placed in the sampling system of the Fidas Palas 200 dust monitor in a filter holder outside of the sampling channel, so that it did not affect the results of PM2.5 and PM10 measurements. The analysis was performed with the use of an energy dispersive spectroscopy (EDS) microanalyzer in the scanning electron microscope (SEM) in the laboratory of the Jagiellonian Centre of Innovation. A fragment of the filter was prepared for the SEM and EDS analysis by coating the sample with gold. Coated sample was mounted on a conducive carbon tape in the microscope chamber and examined. SEM analysis was performed with a Tescan MIRA3 FEG-SEM microscope (Tescan, Brno, Czech Republic). Imaging was conducted with the accelerating voltage equal to 30 kV in order to collect the EDS spectra.

3. Results

The research was conducted from 12 to 16 September 2022. The average air temperature counted from one-hour values amounted to 14.1 °C, with the minimum value of 3.6 °C and maximum of 19.7 °C (Figure 2 and Figure 3). During the measurements, southern and southwestern winds were dominating; however, significant variability was observed (Table 1). Relative air humidity varied from 54.8% to 99.1%, with the average of 84.9%. The recorded values were rather high, while the value of the coefficient of variation was low.
The hourly mean concentration of PM2.5 amounted to 9.6 µg/m3, with the maximum concentration of 20.1 µg/m3. Significantly higher values were observed for PM10, with the average of 19.6 µg/m3 and the maximum value of 93.5 µg/m3. The state monitoring station in Nowiny reported a mean concentration of PM10 at 17.6 µg/m3, and the value was 10.2% lower than data acquired by Fidas® 200. The maximum value recorded by the state station during the measurement campaign was 41.5 µg/m3, 44.4% of the value from the mobile station. The average concentration of NO2 amounted to 9.4 µg/m3 (from 3.7 to 30.7 µg/m3), and SO2, 4.4 µg/m3 (from 1.3 to 9.4 µg/m3). Higher values were recorded in the case of ozone, with the average of 33.1 µg/m3 and the maximum value of 79.2 µg/m3. Significantly higher concentrations were reported by the state monitoring station in Nowiny, where the average concentration of ozone amounted to 42.8 µg/m3 and was 22.6% higher than the value measured by the Airpointer®, with the maximum value of 89.4 µg/m3, 11.5% higher than in the mobile station. The differences between the results from the state monitoring station and the Airpointer® may result from the distance to the main pollution sources. Concentrations of ozone were higher in the urban setting, under the impact of road traffic, while the concentration of PM10 was higher in the industrial area, closer to lime quarries.
Comparing the daily average values with the air quality index used in Poland, it can be stated that the mean hourly maximum values can be assessed as ‘good’ for PM2.5 and ‘moderate’ for PM10. For gaseous pollutants, the category was ‘very good’ for NO2 and SO2 and ‘good’ for ozone. It should also be underlined that the recorded values (hourly and daily means) did not exceed the permissible levels, as well as information and alert thresholds, determined for the protection of human health by the Minister for Climate and the Environment (Journal of Laws 2021, item 845) [20].
The obtained data on air pollution were analysed in terms of the prevailing meteorological conditions (Figure 4). A significant negative correlation was detected between the concentration of ozone and relative air humidity (r = −0.87), the concentration of sulphur dioxide and relative humidity (r = −0.76), and the concentration of PM2.5 and wind speed (r = −0.42). Positive relationships were noted for ozone and air temperature (r = 0.81), PM2.5 and air humidity (r = 0.41), and ozone and wind speed (r = 0.76). Such strong relationships were not found for other parameters; however, the highest concentrations of PM10 were recorded during prevailing southern and southeastern winds.
The factors decisive for the ambient air quality in the studied area were mainly meteorological conditions and the concentration of ozone. Relative air humidity (RH), wind speed (WS), wind direction (WD), and air temperature (T) affect the reactions taking place in the atmosphere, leading to the formation of tropospheric ozone. Principal component analysis indicates that the first component (PC1) explains 48% of the variance. Together with PC2, connected with the concentration of suspended particulate matter, the components explain 66% of the variance, while with the third component, the concentration of SO2 they explain 80% (Figure 5 and Table 2). The second and the third components are connected with the lime and cement industry functioning in the area, being a significant source of emissions of dust and sulphur dioxide.
SEM photomicrographs were acquired at 155× (electron image 1) and 753× (electron image 4) magnification. The images show particles of diverse size (below 20 µm) and shape. The analysed sample was composed mainly of fibres of silicon and oxygen. On the fibre surface, small particles of variable composition were visible (C, Ca, S, Na, Mg, Cu, Fe, and K). The majority of the particles contained calcium. EDS images suggest that numerous particles were composed mainly of calcium oxide; there were, however, some visible particles, which could be composed of calcium sulphate, CaSO4, as in the same point of the image the signals from oxygen, calcium, and sulphur were collected (Figure 6). Chemical composition of the particles is connected to the lime and cement industry present in the studied area.

4. Discussion

Air quality management systems are crucial for identifying sources, monitoring pollution levels, and implementing effective strategies for the development of measurement techniques on the highest level. Significant experience in this respect is presented by Norwegian scientists, indicating legal regulations [21]. Searching for the optimal way to inform society about air quality in countries which are particularly vulnerable to air pollution (such as India), supported with the development of testing techniques and fast data transfer resulted in the proposal of a system based on PM2.5 concentration [22]. Low-cost sensors in densely populated countries are deployed by UNEP in order to provide early-warning information. Currently, models based on machine learning are also developed to fill the gaps in sparce monitoring networks [23]. Mobile, accredited laboratories could be employed to verify the accuracy of the results provided by the low-cost sensors and the models. As it was pointed out by Russian researchers [24], there is the need to standardise air pollution monitoring. Effective measures integrating a quality management system with implemented accreditations (ISO/IEC 17025 [9], ISO/IEC 17043 [25], and ISO Guide 34 [26]) were described by Dehouck et al. [27]. The issues concerning the need to implement and constantly improve the system based on certified systems were also presented by Martínez-Perales et al. [28]. The roles of personnel, equipment, and interlaboratory comparisons in implementing research according to ISO/IEC 17025 [9] were underlined in [29,30,31,32].
The research proved that air quality is affected by the lime and cement industry functioning in the studied area. Within about 1.5 km from the sampling site, there are a limestone quarry in Bolechowice, dolomite quarry in Radkowice, and a limestone quarry and cement plant in Nowiny. The concentration of PM10 was higher than in the state monitoring station located in the urban setting at a greater distance from those pollution sources. Studies conducted in Estonia showed that dust deposition decreases with the distance to industrial plants [33]. The authors stated that within 1–2.5 km from the plant, annual deposition amounts to 2.3–2.7 kg/m2, while within 3–5 km, it is only 0.3–1.3 kg/m2. Former research showed that the greatest deposition of dust occurred within 2–3 km from the pollution source [34,35]. Research conducted in Finland also indicated decreasing concentrations of dust with greater distance to pollution sources [36]. The automated air pollution monitoring station indicated short-term increases in dust concentrations, PM10 in particular. Even though the daily averages did not exceed the thresholds set for dust and gases, the values recorded during the short-term increases were close to the alert levels. The results of the state monitoring in the Świętokrzyskie Province show low levels of gaseous air pollution; however, the concentration of PM10 exceeded the permissible levels for daily averages; e.g., in the station located in Nowiny, in 2022, there were 34 days with concentrations over 50 µg/m3 [37].
The method of SEM/EDS analysis of bioindicator matrices is successfully used for identifying pollutants in the areas under strong human pressure [38,39,40,41]. In this case, as the sampling site was located in a highly modified industrial area without natural plant cover (sewage plant), a silicon filter placed in the dust monitor was used as the matrix. SEM analysis showed particles of various shape, size, and chemical composition, characteristic of various emission sources (traffic, mineral, industrial). The presence of silica and calcium indicated the local source of mineral pollution [42]. It confirms that the studied area, known as the ‘White Basin’, is under a strong pressure from industry based on the extraction and processing of minerals [39,40].

5. Conclusions

Air quality in the studied area is modified by local emission sources. Elevated concentrations of fine particulate matter, as well as the presence of calcium, sulphur, sodium, potassium, and heavy metals (iron and copper) in a short measurement campaign indicate that the concentration of the selected substances in the ambient air may periodically exceed the permissible thresholds. The equipment and measurement techniques used in this study are effective in identifying the potential threat of air pollution. The factors which are decisive for air quality in Nowiny are meteorological conditions (air temperature, humidity, and wind speed) as well as the emission of dust. Automated, short-term measurements of air pollution can be a significant source of information, indispensable for drawing up action plans aimed at air quality protection in delimited zones. Such research can help local authorities in undertaking measures to reduce the risk or duration of exceedance if the levels of pollutants exceed the alert thresholds. It is also a source of data for environmental protection institutions and non-governmental organizations dealing with public health. The methods described can also serve as an example of good practice in the protection of sensitive population groups such as children and older adults. They are in line with sustainable development goals, as they enhance early warning systems, reduce the risks for public health, and can contribute to limiting the number of diseases and deaths due to air pollution.

Author Contributions

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

Funding

This research was funded by grants awarded by the Head of the Jan Kochanowski University of Kielce, numbers SUPB.RN.24.107 and SUPB.RN.24.110.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors would like to thank Henryk Milcarz and Przemysław Dawid from Kielce Water Supply Company for providing access to the sewage plant in Sitkówka for the measurements, as well as the academic editor, associate editor and anonymous referees for providing helpful comments and suggestions, which led to improving the article.

Conflicts of Interest

Author Tomasz Mach was employed by the company MLU Polska. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Localisation of the sampling point.
Figure 1. Localisation of the sampling point.
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Figure 2. PM2.5 and PM10 concentration dynamics and meteorological conditions; information and alert level of PM10 given according to Journal of Laws 2021, item 845 [20].
Figure 2. PM2.5 and PM10 concentration dynamics and meteorological conditions; information and alert level of PM10 given according to Journal of Laws 2021, item 845 [20].
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Figure 3. NO2, SO2, and O3 concentration dynamics and meteorological conditions.
Figure 3. NO2, SO2, and O3 concentration dynamics and meteorological conditions.
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Figure 4. Scatterplots showing correlations between the concentration of air pollutants and meteorological conditions.
Figure 4. Scatterplots showing correlations between the concentration of air pollutants and meteorological conditions.
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Figure 5. PCA—scree plot and variable correlation plots.
Figure 5. PCA—scree plot and variable correlation plots.
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Figure 6. SEM micrographs and EDS analysis of filter samples.
Figure 6. SEM micrographs and EDS analysis of filter samples.
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Table 1. Mean hourly results of measurements of air pollution and meteorological conditions.
Table 1. Mean hourly results of measurements of air pollution and meteorological conditions.
ParameterNumber of Valid RecordsMeanUncertaintyMin.UncertaintyMax.UncertaintySDCVMedian
PM2.5
[µg/m3]		939.61.14.60.520.12.63.435.68.9
PM10 [µg/m3]9319.61.48.50.693.510.313.066.415.7
NO2 [µg/m3]939.41.43.70.530.74.64.345.88.6
SO2 [µg/m3]934.40.71.30.29.41.42.046.24.0
O3 [µg/m3]9333.15.00.20.079.211.922.568.030.7
RH [%]9384.9 54.8 99.1 12.715.090.4
AT [°C]9314.1 3.8 19.7 3.625.214.9
WD [°C]93218.1 30.5 339.0 74.834.3235.5
WS [m/s]931.3 0.3 3.4 0.966.01.2
SD—standard deviation, CV—coefficient of variation.
Table 2. Results of PCA for the analysed parameters.
Table 2. Results of PCA for the analysed parameters.
ParameterPC1PC2PC3
PM2.50.6−0.70 *0.1
PM100.6−0.75 *0.1
NO20.2−0.5−0.2
SO2−0.3−0.10.94 *
O3−0.97 *−0.10.1
RH0.87 *−0.2−0.4
AT−0.72 *0.4−0.2
WD−0.75 *0.4−0.3
WS−0.82 *0.3−0.2
Variance48%18%14%
Cumulative variance48%66%80%
*—large positive or negative loadings.
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Kozłowski, R.; Szwed, M.; Kozłowska, A.; Przybylska, J.; Mach, T. Quality Management System in Air Quality Measurements for Sustainable Development. Sustainability 2024, 16, 7537. https://doi.org/10.3390/su16177537

AMA Style

Kozłowski R, Szwed M, Kozłowska A, Przybylska J, Mach T. Quality Management System in Air Quality Measurements for Sustainable Development. Sustainability. 2024; 16(17):7537. https://doi.org/10.3390/su16177537

Chicago/Turabian Style

Kozłowski, Rafał, Mirosław Szwed, Aneta Kozłowska, Joanna Przybylska, and Tomasz Mach. 2024. "Quality Management System in Air Quality Measurements for Sustainable Development" Sustainability 16, no. 17: 7537. https://doi.org/10.3390/su16177537

APA Style

Kozłowski, R., Szwed, M., Kozłowska, A., Przybylska, J., & Mach, T. (2024). Quality Management System in Air Quality Measurements for Sustainable Development. Sustainability, 16(17), 7537. https://doi.org/10.3390/su16177537

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