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Article

Characterization of Road Dust, PMx and Aerosol in a Shopping–Recreational Urban Area: Physicochemical Properties, Concentration, Distribution and Sources Estimation

by
Dusan Jandacka
*,
Matej Brna
,
Daniela Durcanska
and
Matus Kovac
Department of Highway and Environmental Engineering, Faculty of Civil Engineering, University of Zilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(17), 12674; https://doi.org/10.3390/su151712674
Submission received: 25 July 2023 / Revised: 16 August 2023 / Accepted: 20 August 2023 / Published: 22 August 2023
(This article belongs to the Special Issue Sustainable Approaches to Improving Road Traffic Safety and Environmental Capacity)
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Abstract

:
Road transport is a source of exhaust and non-exhaust emissions of particulate matter (PM). Non-exhaust PM emissions include road surface wear, tires, brakes and road dust resuspension. An important part of PM in urban air consists of particles that originate from the resuspension of road dust. This study focused on the analysis of the physicochemical properties of road dust and PM10, PM2.5 and PM1 (PMx) in the air (size, concentration, distribution, content of chemical elements), the properties of urban aerosol (number, mass and area distribution), and at the same time, the interconnection between the detected chemical elements in road dust and individual PM fractions in the air in order to reveal the sources of PM in the Žilina City, Slovakia. The presence of various chemical elements was found in road dust, of which the highest concentrations (more than 100 mg/g) were the elements Ca, Si and Al (specifically 373.3 mg/g, 351.4 mg/g and 113.9 mg/g on average from four sampling sites). The concentrations of PM10, PM2.5 and PM1 were, on average, 27.2 μg/m3, 19.5 μg/m3 and 14.5 μg/m3 during the measurement period according to the reference gravimetric method. The chemical elements K, S, Cd, Sb, Pb, Ni and Zn were detected and the most represented (more than 60%) in the fine PM2.5 fraction, and the chemical elements Mg, Al, Si, Ca, Cr, Cu, Fe and Ba were the most represented in the coarse PM2.5–10 fraction. The analysis of the aerosol in the range of 12 nm–20 μm revealed a bimodal distribution of the collected sample of the investigated urban aerosol. This study provides a comprehensive view of the properties of road dust, airborne PM and aerosol (up to the size of nanoparticles), which can contribute to the expansion of knowledge in this field.

1. Introduction

Concentrations of PM represent a growing problem, especially in urban agglomerations, where they can come from various sources, such as road transport (exhaust and non-exhaust emissions), local industry, local heating, resuspension of road dust from streets and agriculture [1,2,3,4,5,6,7,8,9]. In densely populated parts of cities, road transport is a significant source of PM air pollution. The problem of environmental pollution from road transport (passenger and freight transport, transport infrastructure, road pavement) is compounded by the fact that the number of vehicles and the number of kilometers driven increases every year [10,11,12,13,14]. From the point of view of reducing emissions from road transport, electric vehicles are currently being significantly promoted. In the context of the growing demand for travel, electric vehicles are generally considered a solution for reducing emissions of greenhouse gases and gaseous air pollutants from road transport. Although they strive to eliminate exhaust emissions, it is possible that electric vehicles are unlikely to provide substantial benefits in terms of reducing non-exhaust emissions. These are mainly the PM coming from tire wear, road wear and road dust resuspension, which remain significant sources of PM emissions from electric vehicles [12,15,16]. In this study, as a follow-up to the conducted studies and research on the formation of PM from road traffic [3,7,8,17,18,19,20,21,22,23,24], we focused on non-exhaust emissions, primarily the resuspension of road dust, which may also include particles from the wear of the road surface, wear of vehicle components, tire wear, brake wear, construction and dusty places, dust migration from the neighboring environment and winter maintenance with sprinkling materials [3,25]. Construction activity and work on construction sites can be a big challenge from the point of view of dustiness. Road dust is the general name for any form of PM deposited on the road surface that can be dispersed in the air due to turbulence caused by traffic [12]. Road dust emissions significantly contribute to the levels of PMx in the surrounding environment, and this study also demonstrated their interdependence. Since the process of formation of smaller particles from larger particles (crushing potential) is not fully understood, a better understanding of the relationship with the PM10 or smaller fraction should be a priority for road dust resuspension research.
The physicochemical properties of road dust have been analyzed in different studies using different approaches, which may introduce differences in the documented chemical compositions [3,17,22,23,24]. The chemical profile of road dust for the fraction below 10 µm strongly depends on the sources of these particles, but in general, it is composed of different minerals containing silicon, aluminum, calcium, titanium, strontium (road wear and crustal material), antimony, tin, barium, iron, copper and manganese (brake wear), zinc (tire wear) and carbon (elemental and organic) [16,17,19,22,26,27,28,29,30].
On the basis of other studies, the occurrence of various metals bound in suspended particles in the air, namely, Al, Si, Ca, Mg, C, Na, K, V, Ni and S (wear of the road surface), is linked to emissions from transport. Other metals and their sources include Cu, Sn, Cr, Pb, Cd, As, Sb, Fe, Al (wear of car parts), Cu, Sb, Ba, Cr, Fe, Ni, Pb, Zn (wear of brakes), Zn, Cd, Pb, Cu, Ni and Fe (tire wear) [7,8,9,31,32,33].
The chemical composition of PM can be used to identify and interpret the sources involved in the formation of PM. For the purpose of revealing mutual links between chemical elements in road dust and PMx, a correlation analysis was carried out [19,24,34,35].
In the present study, we focused on determining the physicochemical properties of road dust (size distribution, chemical composition) and PMx in the air (distribution, concentration, chemical composition). This study also included an analysis of urban aerosol from the point of view of the distribution (number, mass). On the basis of the performed analyses, connections of PM to the resuspension of road dust and other non-exhaust sources were found. This study provides a comprehensive overview of analyses and results that support the theory of the link between road dust resuspension and PM.

2. Materials and Methods

2.1. Measurement and Sampling Location

The measurement was carried out in the city of Žilina in the Vlčince housing estate, Vysokoškolákov Street, during the measurement period 9 April 2019–16 April 2019 (Figure 1). This is the most important traffic artery in the Vlčince housing estate. The larger part consists of a four-lane road with footpaths and the section from the intersection with Obchodná Street to the university dormitories has a path for cyclists. The street starts at the intersection of Vojtecha Spanyola and Tajovského Streets, while it is a continuation of Spanyolová Street, and ends at Rosinská Cesta Street. It runs along the western edge of the Vlčince settlement. There are 3 roundabouts on the street, while the construction of others, or their reconstruction into turbo roundabouts, is being considered, as congestion forms on the street. There are also plans to fully widen the road to four lanes (as it only has two lanes in a certain section).
There are shopping centers, supermarkets, the Žilina Municipal Indoor Swimming Pool, gas stations, a non-state medical facility, and the second tallest building in Žilina—the Europalace—which houses the client center of the Ministry of the Interior of the Slovak Republic and the university dormitories of the University of Žilina.
Important trolleybus and bus lines of public transport in Žilina pass through the street.
There are many important civic amenities visited by a large number of people, which has a significant impact on the intensity of traffic on this local communication. According to traffic surveys, the traffic volume on Vysokoškolákov Street is approximately 19,420 vehicles per 24 h.

2.2. Particulate Matter Measuring and Road Dust Sampling

PM sampling was carried out near the street (PMS at 49°12′38.1″ N, 18°45′29.6″ E) in a safe place and with access to electricity (Figure 1). The measurement of PMx was carried out using two measurement methods: the accredited reference gravimetric method with 3x Leckel LVS3 devices (STN EN 12341) and the optical method with a Fidas 200S device (Figure 2). The sampling of PMx was carried out using three low-volume flow samplers (LECKEL LVS3 Low-Volume Samplers, Sven Leckel Ingenieurbüro GmbH) on nitrocellulose filters for 24 h (from 10:00 a.m. to 10:00 a.m. the next day). Using the gravimetric method, seven 24 h concentrations were obtained for each PM fraction (a total of 21 exposed filters). The nitrocellulose filters had a diameter of 47 mm and PM was captured at a constant airflow of 2.3 m3/h, and then the mass of PM captured on the filters was determined using a microbalance, and the PM concentration was calculated based on the known volume of air obtained during sampling (µg/m3). The exposed filters were subjected to elemental chemical analysis. All necessary steps were taken to ensure the sample quality and PM concentrations, including check weighing. To continuously ensure the quality of measurements, all devices were calibrated annually in accredited calibration laboratories and the uncertainties of PM measurement methods were determined. For the continuous measurement of PM, an optical method using the Fidas 200S device was used, thanks to which we obtained information on hourly PM concentrations. We ensured the quality of the data measured using the Fidas 200S device by comparing the measurements with the reference gravimetric method (an accredited test for measuring PM in the surrounding air). The performed comparative measurements showed a high correlation coefficient between the measured concentrations and a high coefficient of determination obtained via regression analysis of the observed data: PM10 r = 0.981, R2 = 0.9624; PM2.5 r = 0.985, R2 = 0.9725; and PM1 r = 0.933, R2 = 0.8698.
Spectrometers from TSI SMPS 3080 (scanning mobility particle sizer) and CPC 3772 (a condensation particle counter) and APS 3321 (an aerodynamic particle sizer) (Figure 2) were used to determine the characteristics of the aerosol in the air in the range of 12 nm–20 µm.
In order to determine the size distribution of the road dust, we took test samples of the dust directly at the edge of the local road at four sampling sites. The collection points were in the Vlčince housing estate, Vysokoškolákov Street, city of Žilina, namely, at the OMW fuel stations (RDS1 at 49°12′47.8″ N, 18°45′00.6″ E), Slovnaft (RDS2 at 49°12′46.5″ N, 18°45′03.6″ E), and at the Dubeň (RDS3 at 49°12′44.3″ N, 18°45′11.3″ E) and Kaufland (RDS4 at 49°12′41.2″ N, 18°45′22.8″ E) shopping centers (Figure 1). Four samples of road dust were taken at each sampling point.
The granularity of road dust represents its percentage composition depending on the weight of grains of individual fractions. The grain size represents the standard hole size sieves through which the examined grain passes during the sieving test on a set of standard sieves. All the grains that are caught on the upper control sieve are called oversized fractions, and those that fall through are called undersized fractions. Determination of the grain size is based on overflows, i.e., the proportion of grains overflowing through the individual holes on the control sieves. We determined the grain size classes on the basis of the fall of the grains (according to their size). A standard set of sieves with square holes of sizes 0.063–0.125–0.25–0.63–1–2–4–8–11.2 mm was used for the test. The four test samples taken at each sampling site were placed in a dryer at a temperature of 110 °C for 24 h. After drying, we took a 500 g sample for sieving from each sample. We performed the sieving test manually on a set of standard sieves. We weighed the residues on the individual sieves and also from the bottom. The weights of the balances on the sieves were expressed as a percentage of the weight from the initial weighing. Subsequently, the total percentage overflows through the given sieves were calculated. The percentage overflow through the 0.063 mm sieve (bottom) was also calculated. From the obtained values, we constructed a grain line.

2.3. Methods of Road Dust and PM Chemical Analysis

Inorganic elements in the collected road dust samples were detected using an ARL™ QUANT’X EDXRF spectrometer. The samples were analyzed using energy-dispersive X-ray fluorescence spectrometry in a vacuum atmosphere. This method is used for rapid, non-destructive, quantitative and qualitative analysis of major, minor and trace components in all types of materials, including solids and powders. An X-rayed sample produces fluorescent (secondary) radiation with a given characteristic energy emitted from the atoms of each element. Each element is determined according to its radiation energy, and its quantitative representation is proportional to the registered intensity of this radiation. This is compared with the radiation intensity of the identical element in the sample with its known concentration.
Road dust samples were used for the analysis; dust grains were passed through a control sieve of 0.125 mm (sub-sieve fraction) obtained via a sieving test when determining the grain size line. Five road dust samples were prepared (each sample 5 g) with a maximum grain size of 0.125 mm from each sampling point for the EDXRF analysis.
The elemental chemical analysis of the PM collected from the air using the gravimetric method on nitrocellulose filters was performed using mass spectrometry with inductively coupled plasma(ICP-MS). ICP-MS is a combination of inductively coupled plasma and mass spectrometry. It represents an analytical method used to determine the representation of trace amounts of individual elements in the analyzed sample. It allows for analyzing a wide range of elements with very high sensitivity. The procedure itself consisted of placing the exposed filters intended for dissolution in containers together with nitric acid HNO3 (5 mL) and hydrogen peroxide H2O2 (1 mL), where they were infused at a temperature of 230 °C using a microwave digestion system. Containers with reference material and a blank (clean filter, reagent) were also used during microwave decomposition. After completion of the specified mineralization program, the containers were cooled to the laboratory temperature. After opening, the mineralized product with the rinse was quantitatively transferred into a 25 mL volumetric flask and supplemented with the necessary amount of demineralized water and subjected to the test itself in the ICP-MS device (Agilent 8800 Triple-Quadrupole ICP-MS).
From the individual exposed filters, the samples prepared in this way were analyzed using inductively coupled plasma spectrometry for the presence of chemical elements (Mg, Al, Si, K, Ca, S, Cr, Fe, Ni, Cu, Zn, Cd, Sb, Ba and Pb). Their final concentrations were determined in ng/m3 in the individual PMx fractions.

2.4. Statistical Analysis of Data

Correlation analysis was used to reveal the internal links between the chemical elements detected in the PMx and in the road dust due to the lower robustness of the input data matrix for PM. The robustness of the input data matrix is expressed by the number of variables (chemical elements) and objects (measurements), while the number of objects should be at least twice the number of variables for the use of multivariate statistical analyses, such as factor analysis, principal component analysis, cluster analysis and positive matrix factorization.
Input matrices for the statistical analyses consisted of features (concentrations of chemical elements in road dust or in PM10) and objects (road dust samples taken from sampling points or samples of PM taken from the air). In the case of road dust, it was a 14 × 20 matrix, and in the case of PM10, it was a 16 × 7 matrix (15 chemical elements and 1 PM).
The mentioned statistical analyses were carried out in the R program environment, R (version 4.1.3) package corrplot (https://cran.r-project.org/web/packages/corrplot/index.html, accessed on 16 August 2023).
The R package corrplot provides a visual exploration tool for the correlation matrix to help reveal hidden patterns between variables. It also provides p-values and confidence intervals to help users determine the statistical significance of correlations.
There are seven visualization methods (parameter method) in the corrplot package named “circle”, “square”, “ellipse”, “number”, “shade”, “color” and “pie”. The intensity of the glyph color is proportional to the correlation coefficient at the default color setting. In our case, the “square” visualization method was used (the areas of circles or squares show the absolute values of the corresponding correlation coefficients).
We used Pearson’s correlation (r), which measures a linear dependence between two variables (x and y). This is also known as a parametric correlation test because it depends on the distribution of the data. It can be used only when x and y are normally distributed. The plot of y = f(x) is known as the linear regression curve.
In order to determine whether a given Pearson correlation coefficient has a statistically significant result, the following steps were taken:
  • Stated null and alternative hypothesis: the null hypothesis can be stated that there is no relationship between two variables (r = 0), while the alternative hypothesis is that there is a relationship (r ≠ 0);
  • Established statistics for hypothesis testing: we calculated the t-statistic and performed a t-test with (n − 2) degrees of freedom;
  • Determined level of significance: the chosen level of significance was 0.05;
  • Calculated and compared the t-statistic with the critical value.
We tested the significance by evaluating the t-statistic and comparing it with the critical value read from the t-distribution table at the significance level of 0.05. If the value of the t-statistic is greater than the critical value of 0.05, the null hypothesis can be rejected. This would mean that there is enough evidence to support the alternative hypothesis that there is some relationship between the two variables.
Corrplot() can also visualize p-values and confidence intervals in the correlation matrix plot.
The used code in the R program: corrplot (M, p.mat = p.mat, method = ‘square’, type = ‘lower’, insig = ‘blank’, addCoef.col = ‘black’, number.cex = 0.7, diag = FALSE, col = COL2 (‘PiYG’)).

3. Results

3.1. Physicochemical Properties of Road Dust

The collected road dust samples were subjected to a sieving test to determine the grain size line of the road dust, or for the determination of the share of individual size fractions according to standard sieves. For the given test, four samples of 500 g each were created from each sampling point RDS1–RDS4. During the given test, overflows were created through standard sieves or residues on standard sieves. Overflows through a standard sieve with an opening of 2 mm represented the following values from individual sampling points: RDS1 54.3%, RDS2 39.9%, RDS3 52.6% and RDS4 47.1% (Figure 3). On average, from all collected samples and sampling locations, 51.5% of the road dust contained particles ˃ 2 mm. This was primarily from the uncrushed inert sprinkling material used in winter maintenance. Road dust < 125 μm was subjected to chemical analyses using the EDXRF method. The proportions of particles falling through this sieve were the following for samples from single sampling points: RDS1 9.2%, RDS2 6.7%, RDS3 6.8% and RDS4 5.3%. Particles < 63 μm (standard sieve with the smallest holes) represented an average of 2.95% of all road dust samples taken from the road. These particles also contained material from road transport components (road surface, tire surface).
Another important piece of information that characterizes the size distribution of road dust is the residue on the standard sieves, i.e., all the material that did not fall through the control sieve and remained on it. The balances on the control sieve with an opening of 2 mm represent the following values from individual sampling points (particles larger than 2 mm): RDS1 228.6 g—45.7%, RDS2 299.2 g—60.1%, RDS3 238.7 g—47.4% and RDS4 263.1 g—52.9% (Figure 4).
Chemical analysis using the EDXRF method was performed on road dust samples that passed through a control sieve with a hole size of 125 μm. According to the analysis, the chemical elements Ca, Si, Al, Mg, Fe, K, S, Ti, Sr, Mn, Zr, Zn, Cr and Cu were detected in the road dust samples. Individual sampling sites did not differ much in the detected concentrations of chemical elements. The elements Ca 373.27 mg/g (37.3%), Si 351.42 mg/g (35.14%) and Al 113.94 mg/g (11.39%) achieved the highest concentrations on average (˃100 mg/g). These chemical elements were followed by the elements Mg, Fe and K, with concentrations of ˃10 mg/g and <100 mg/g, and the elements S and Ti, with concentrations of ˃1 mg/g and <10 mg/g (Table 1, Figure 5). These chemical elements can come from various non-exhaust PM emissions, such as road surface wear, tire wear, brake wear and the Earth’s crust [16,17,19,22,26,27,28,29,30].

3.2. Physicochemical Properties of PM

Important information about PM in the air is the concentration and distribution. At the PMS, concentrations of PMx were measured using the reference gravimetric method and the optical method. The course of one-hour concentrations was evaluated from measurements using the optical method (Figure 6). The average air temperature (determined from hourly values) during the measurement was 8.3 °C. The minimum temperature was −0.5 °C and the maximum was 18.4 °C (hourly value). No precipitation occurred during the measurement period (Figure 6). The PM10 concentrations did not exceed the limit established for the protection of human health (50 µg/m3). Conversely, the PM2.5 concentrations exceeded the limit set for PM2.5 of 20 µg/m3. When considering the 24 h concentration of PM2.5, these exceedances were detected in three out of seven cases. Furthermore, we observed higher concentrations of PM in the morning hours, when road traffic was experiencing a morning rush around 6:00–10:00. During the day, we observed an increase in concentrations, mainly in the coarse fraction of PM2.5–10, which was mainly visible during the morning rush or during the afternoon rush around 14:00–16:00. A decrease in the concentration of the coarse fraction also occurred at night. We also observed the course of concentrations from the point of view of the entire week, where there was a significant decrease in PM2.5–10 concentrations during the weekend (13 April –14 April 2019) and an increase again at the beginning of the working days (Figure 6). Through this observation, we found that increased concentrations of the coarse fraction of PM were influenced by road traffic, i.e., passing vehicles, when this fraction was suspended in the air and reached the breathing zone (1.5 m above the ground). This was characterized by short-lived inertia, and therefore, when the intensity of traffic was lower, i.e., at night and during weekends, it deposited on the adjacent terrain [36,37,38].
The measured PM distribution was also interesting, namely, the representation of PM2.5–10, PM2.5 and PM1 in the total PM10 fraction. From the point of view of the impact of road traffic on individual PM fractions, the distribution was compiled for weekdays and weekends. The fine fraction of PM2.5 represented an average of 67% of the total fraction of PM10 during weekdays. On average, PM1 accounted for 51% of the total PM10 fraction during weekdays. The PM2.5–10 fraction represented, on average, 33% of the PM10 fraction during weekdays. During the weekend, the share of PM2.5 and PM1 fractions increased to values of 85% and 60%, respectively, and the share of PM2.5–10 decreased to 15% (Figure 7). This resulted in lower generation and suspension of dust by road traffic in this location PM1–2.5.
As part of the measurements, aerosol characteristics were also determined in the range of 12 nm–20 µm. It is appropriate to determine the number (surface area, mass) of particles in selected size groups or to determine the size distribution of aerosol particles. The size distribution can then be assigned a suitable statistical distribution of the data, which can be briefly and unambiguously characterized by determining suitable point or interval characteristics of the respective distribution. A suitable point characteristic is the aerodynamic diameter of a particle with a mass (number, surface) that occurs most often in the set (mode), in exactly half of the set of values (median) or reaches an average value (mean) (Table 2). The most frequently occurring aerodynamic particle diameter from the point of view of abundance was 381 nm, which fell into the category of particle accumulation mode (fine particles).
The mass concentration Δm, surface ΔS and number ΔN for the three size groups of urban aerosol particles are shown in the following table (Table 3). Three size intervals were chosen based on the division of atmospheric aerosol into particle nucleation mode (ultrafine particles), particle accumulation mode (fine particles) and particle mechanical generation mode (coarse particles) [39]. The highest particle number concentration of 5820 #/cm3 was recorded in the particle size interval 0.0129–0.104 µm (ultrafine particle mode), and the highest particle surface concentration of 519 μm2/cm3 and mass concentration of 37.9 µg/m3 were recorded for particles in the 0.104–2.46 µm interval (fine particle mode) (Table 3).
Modes and local maxima are important characteristics of the respective distributions. If the distribution curve has a single maximum, then such a distribution is referred to as monomodal. Conversely, if there are two or three maxima, which is more common with urban aerosol, then the aerosol is referred to as bi- or trimodal, respectively. In the case of the investigated urban aerosol, it was a bimodal distribution with two local maxima in the mass (Figure 8) and number distributions (Figure 9).
By far, the largest number of particles fell into the ultrafine size range, which consisted of PM with a diameter of 0.1 μm or less (PM0.1). These ultrafine particles (UFPs) dominated the pollution surface in terms of particle number but did not contribute to the amount of PM in large quantities. This size fraction arose mainly from primary combustion emissions and secondary particles produced by gas-to-particle conversion processes. They were inherently unstable and grew into larger particles through precipitation and condensation. These particles were dominated by sulfates, nitrates, OC (organic carbon) and EC (elemental carbon) [40,41,42,43]. UFPs represent a particular health risk in that their small size allows for the greatest penetration into the lungs and further passage through the air–blood barrier [41,44,45]. The numerical concentration of this fraction represented a value of 5820 #/cm3 in the case of the investigated urban aerosol, and the mass concentration was 0.518 µg/m3.
The fine fraction of PM consists of particles with a diameter between 0.1 and 2.5 μm, and this, together with the ultrafine mode, is referred to as PM2.5. The fine mode contains primary combustion particles and secondary particles that have grown through precipitation and condensation. Due to its ability to penetrate the area of alveolar gas exchange, PM2.5 is also referred to as respirable particles [19,46,47]. The numerical concentration of this fraction was 2510 #/cm3 in the case of the investigated urban aerosol, and the mass concentration was 37.9 µg/m3.
The coarse particle mode consists of particles with diameters greater than 2.5 μm (studies investigating the toxicity of coarse PM always define the fraction as PM2.5–10). They can vary in size up to 100 μm, with larger particles being too heavy to remain airborne for any length of time. This class includes the most visible forms of PM, such as black smoke, soil, dust from roads and construction sites, large salt particles from marine aerosol, mechanically generated particles and some secondary particles. Coarse particles also include pollen, molds, spores and other parts of plants [19,48]. While the larger particles contribute relatively little to the particle number, they contribute a major part to the particle mass. PM10 refers to all ambient PM (i.e., ultrafine, fine and coarse particles) with a diameter of 10 μm or less and are sometimes called “chest” particles in that they can escape the original defenses of the nose and throat and penetrate beyond the larynx and deposit along the airways in the chest [19,47,49,50]. The numerical concentration of this fraction was 0.193 #/cm3 and the mass concentration was 7.05 µg/m3 in the case of the investigated urban aerosol.
Chemical analyses of PM were performed using ICP-MS. Using this method, the concentrations of the elements Mg, Al, Si, K, Ca, S, Cr, Cu, Fe, Cd, Sb, Ba, Pb, Ni and Zn in the PM fractions were determined. Different representations of elements in the total PM10 fraction were demonstrated, with some elements being more represented in the fine PM2.5 fraction and some in the coarse PM2.5–10 fraction. While in the PM2.5–10 fraction, mainly chemical elements were represented (>60%), such as Ca 90.9%, Fe 74.8%, Al 82.5%, Si 79.5%, Mg 82.2%, Cu 67.5%, Cr 65.4% and Ba 80.7% (Figure 10), in the PM2.5 fraction, the predominant elements (>60%) were S 92.0%, K 70.5%, Cd 98.1%, Sb 68.3%, Pb 96.6%, Ni 63.8% and Zn 86.3% (Figure 10, Table 4). Particles with an aerodynamic diameter of ˃2.5 μm, i.e., the coarse fraction of solid particles PM2.5–10, are mainly related to non-exhaust emissions, such as abrasion of tires and the road surface and the resuspension of road dust [16,17,19,22,26,27,28,29,30]. The elements Ca, Si, Al, Mg, Fe, Cu, Cr and Ba predominated this fraction. On the other hand, particles < 2.5 μm were part of the fine fraction PM2.5, which is primarily related to combustion processes and cloth at high temperatures. According to the results of chemical analysis, the fine fraction mainly contained the elements S, K, Cd, Sb, Pb, Ni and Zn [51,52,53,54].

3.3. Correlation Analysis of the PM and Road Dust Chemical Composition

Correlation analysis was performed on the data of the chemical element concentrations in the road dust and different fractions of PM in order to reveal the internal links between the chemical elements. The aim was to identify the links of some chemical elements as markers of PM sources. The data matrix for road dust consisted of chemical elements as variables and samples of road dust collected from the road as objects (observations). The data matrix for PMx consisted of chemical elements as variables and PM concentration measurements as objects (observations). The correlation relationships between the variables are displayed using correlation matrices created in the R program using the corrplot function (Figure 11). The intensity of the color according to the color scale indicates the size of the correlation coefficient in the range of −1 to 1, while each correlation coefficient also has its numerical value. The significance of the correlation coefficients was also tested, i.e., whether the relationship between two variables expressed using the correlation coefficient was significant. If the p-value was <0.05, then the correlation between x and y was significant. Insignificant correlations between variables are indicated by a blank square (Figure 11).
In road dust, the elements Ca, Mg, Fe, Ti, Sr, Mn and Zn showed a significant positive correlation, followed by S, Cu, Fe, Zn, Al and K. The negative correlation was mainly caused by the element Si with the elements Mg, Fe, S, Ti, Mn, Zn and Cu. The results indicate that Si shows high individuality in road dust samples, being the element with the second-highest concentration in road dust. If we selected significant correlation coefficients > 0.7 between two elements (that is, the regression function could describe the original variance of the element data > 49%), we identified the following element pairs: Ca and Fe, Al and K, Fe and S, Fe and Zn, S and Zn, Cu and Fe, Cu and S, and Cu and Zn (Figure 11a).
The analyzed fraction PM1 represents the ultrafine component of PM in the air. All significant correlations between elements reached the value of the correlation coefficient > 0.7. Several groups of elements were identified according to mutual correlations: Mg, Ca and Ni; K, Si and Cu; Cd, Zn, S, Sb and Pb; and Al, Cr, Ba and Fe (Figure 11b).
Significant correlations in the fine fraction of PM2.5 were shown by the following groups of elements: Al, Fe, Ba and Cu and S, Sb, Zn, K, Pb and Cd. All correlations reached values > 0.7 (Figure 11c).
The coarse fraction of PM2.5–10 from the point of view of internal links between elements according to correlation analysis was different from PM2.5. Most of the elements were correlated with each other, except for S. The Ca element was correlated only with Mg (Figure 11d).
The total PM10 fraction combines the fine fraction and the coarse fraction. We observed the relationships between the individual elements in a more complex way, and here we present information characterizing both PM fractions. We observed significant correlations between groups of elements Zn, Cd and Pb and Cu, Si, Ni, Ba, Fe, Al and Mg. Again, as with the coarse fraction, the element Ca correlated only with Mg. The only negative correlation was revealed for the PM10 fraction for the element S with the element Ca (Figure 11e).
The presented work was devoted to the problem of PM occurring in the air produced by road transport. The main aim of the research study in this area was to clarify the nature of the formation and presence of PM in the air. Appropriate analyses of the properties of road dust and PM (concentration, distribution, abundance, size, chemical composition) can reveal their formation method. By using the combination of properties of road dust and PM, especially the chemical composition, it was possible to demonstrate the resuspension of road dust and PM in the surrounding air. The presented solution of the task revealed the correlations of chemical elements originating from road transport found in road dust and PM.

4. Discussion and Conclusions

The result of the presented study is a comprehensive analysis of road dust, PMx and urban aerosol in the range of 12 nm–20 µm. The basic analyzed properties were physical (distribution, concentration, particle size) and chemical (concentrations of chemical elements in road dust and PM) in Žilina City, Slovakia. The chemical composition of road dust and PM fractions was used to reveal the links between chemical elements, and thus, interpret the sources of road dust and PM in the air. The average concentrations of PM10 27.2 µg/m3, PM2.5 19.5 µg/m3 and PM1 14.5 µg/m3 were found according to the reference gravimetric method. On average, the fine PM2.5 fraction made up 72% and the coarse PM2.5–10 fraction made up 28% of the total PM10 fraction. The coarse PM2.5–10 fraction was determined as the difference between PM10 and PM2.5 and its average concentration was 7.6 µg/m3. Road traffic significantly influenced the concentrations of the coarse PM2.5–10 fraction. Analyses of the urban aerosol revealed that by far, the largest number of particles fell into the ultrafine size range, which consists of particles with an aerodynamic diameter of 0.1 μm or less (PM0.1). On the other hand, the mode of fine particles with an aerodynamic diameter of 0.1–2.5 μm showed the greatest mass concentration [33].
On average, the most significant concentrations in road dust were achieved by the following chemical elements: Ca, Si, Al, Mg, Fe and K. Different representations of elements in the total PM10 fraction were demonstrated, while some elements were more represented in the fine PM2.5 fraction and some in the coarse PM2.5–10 fraction. The chemical elements Ca, Fe, Al, Si, Mg, Cu, Cr and Ba were predominantly represented in the PM2.5–10 fraction. These chemical elements can come from various non-exhaust PM emissions, such as road surface wear, tire wear, brake wear and the Earth’s crust [16,17,19,22,26,27,28,29,30]. The elements S, K, Cd, Sb, Pb, Ni and Zn prevailed in the PM2.5 fraction [51,54,55,56]. From the comparison of the chemical composition of road dust and PM, we observed the connection of road dust primarily with the coarse fraction PM2.5–10. It follows from the above that resuspended road dust had a significant effect on increased concentrations, especially the PM2.5–10 fraction. In the PM2.5–10 fraction, the most important elements were calcium, silicon, magnesium and iron (most represented in terms of quantity). These chemical elements were also revealed by other studies. Particles of these sizes can be created primarily during the abrasion of various components of road transport and road surfaces and enter the air through resuspension [19,22,37,57,58,59].
These considerations were also supported by the correlation analysis that revealed internal links between the individually selected chemical elements in road dust and the PM fractions. The PM coming from different sources mix in the air and interact with each other. In the presented study, some interconnections between the chemical elements contained in PM and road dust were revealed, which we could call markers for PM sources. Individually, along with their mutual correlation, can reveal the origin of PM in the atmosphere. Based on the established characteristics and connections between the chemical composition of road dust and PM and the performed correlation analysis, the possible sources of the formation of PM in the air were interpreted: material of the Earth’s crust (Al, Si, K); brake lining wear (Fe, Cu, Ba, Zn, Al, Cr, K); road surface wear (Si, Ca, K, Fe); tire wear (Zn, S, Si); and winter maintenance sprinkler material, winter sprinkler and inert materials (Mg, Ca). Other studies reached similar results, though the interrelationship of road dust and PM in the air and their chemical composition may differ according to the area under study [4,7,17,22,28,29,30,36,57,58,59].
From the above findings, it is clear that road dust was significantly connected with PM in the air and it is necessary to deal with the issue of the formation and spread of non-exhaust PM emissions in the urban environment due to the pressure to reduce PM emissions in the air. Reducing air pollution from non-exhaust PM emissions is possible by introducing solutions that eliminate dust in the environment, such as wet cleaning of roads, planting suitable greenery around roads and suitable solutions for surface treatment of roads, and their effectiveness can be the subject of further research.

Author Contributions

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

Funding

This research was funded by the Slovak Research and Development Agency grant number APVV-21-0416.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

This article was created within the project APVV-21-0416 “REMOT—Research on mobility and emission attributes of the transport process” supported by the Slovak Research and Development Agency.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Sampling sites of road dust (RDS1–RDS4) and PM measurement station (PMS) (map source: © OpenStreetMap contributors).
Figure 1. Sampling sites of road dust (RDS1–RDS4) and PM measurement station (PMS) (map source: © OpenStreetMap contributors).
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Figure 2. Measurement devices Leckel LVS3—3x and Fidas 200S (left) and SMPS and APS (right).
Figure 2. Measurement devices Leckel LVS3—3x and Fidas 200S (left) and SMPS and APS (right).
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Figure 3. Road dust particle size lines from sampling sites RDS1–RDS4 (displayed values: “standard sieve, % overflow through standard sieve”).
Figure 3. Road dust particle size lines from sampling sites RDS1–RDS4 (displayed values: “standard sieve, % overflow through standard sieve”).
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Figure 4. Residues of road dust particles on standard sieves from sampling sites RDS1–RDS4.
Figure 4. Residues of road dust particles on standard sieves from sampling sites RDS1–RDS4.
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Figure 5. Percentage representation of chemical elements in road dust samples from sampling sites RDS1–RDS4.
Figure 5. Percentage representation of chemical elements in road dust samples from sampling sites RDS1–RDS4.
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Figure 6. Course of PMx concentrations and temperature during the measurement period at the PMS.
Figure 6. Course of PMx concentrations and temperature during the measurement period at the PMS.
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Figure 7. Percentage representation of PM fractions in PM10 at the PMS.
Figure 7. Percentage representation of PM fractions in PM10 at the PMS.
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Figure 8. Size distribution of the mass M of the aerosol sampled at the PMS using SMPS and APS devices.
Figure 8. Size distribution of the mass M of the aerosol sampled at the PMS using SMPS and APS devices.
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Figure 9. Size distribution of the abundance of N aerosol sampled at the PMS using SMPS and APS devices.
Figure 9. Size distribution of the abundance of N aerosol sampled at the PMS using SMPS and APS devices.
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Figure 10. Percentage representation of chemical elements in PM fractions from the total PM10 fraction.
Figure 10. Percentage representation of chemical elements in PM fractions from the total PM10 fraction.
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Figure 11. Correlation matrixes of the concentrations of chemical elements in (a) road dust, (b) PM1, (c) PM2.5, (d) PM2.5–10 and (e) PM10 (the color scale determines the size of the correlation coefficient in the interval −1 to 1).
Figure 11. Correlation matrixes of the concentrations of chemical elements in (a) road dust, (b) PM1, (c) PM2.5, (d) PM2.5–10 and (e) PM10 (the color scale determines the size of the correlation coefficient in the interval −1 to 1).
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Table 1. Chemical compositions of road dust from different sampling sites RDS1–RDS4.
Table 1. Chemical compositions of road dust from different sampling sites RDS1–RDS4.
Chemical ElementRDS1RDS2RDS3RDS4
Average±STDEV *Average±STDEV *Average±STDEV *Average±STDEV *
mg/gmg/gmg/gmg/g
Ca369.7012.91386.1313.83370.3310.36366.9413.94
Si348.9910.26329.2311.68360.799.16366.6612.68
Al120.544.85113.016.66111.124.01111.094.18
Mg85.492.2494.623.2189.782.0088.271.79
Fe39.381.7241.521.8135.771.3334.661.83
K17.930.5416.760.3917.030.5316.900.28
S9.670.379.990.416.950.157.510.31
Ti5.650.105.760.235.660.315.460.10
Sr0.720.010.770.070.780.070.760.08
Mn0.720.050.740.050.700.070.690.04
Zn0.470.030.540.030.350.110.300.03
Zr0.390.020.480.010.440.100.460.06
Cr0.240.050.260.040.230.040.230.06
Cu0.120.010.200.030.040.030.070.01
* STDEV—standard deviation.
Table 2. Statistical characteristics of the number, surface area and mass of urban aerosol sampled at the PMS using SMPS and APS devices.
Table 2. Statistical characteristics of the number, surface area and mass of urban aerosol sampled at the PMS using SMPS and APS devices.
Aerodynamic DiameterNumber Particle SizeSurface Particle SizeMass Particle Size
Median (nm)32610,11214,758
Mean (nm)465986513,673
Geo. mean (nm)313658112,461
Mode (nm)38117,17217,172
Geo. std dev.232
Total conc.5.61 × 108 (#/cm3)1.67 × 1015 (nm2/cm3)3.02 × 109 (µg/m3)
Table 3. Number, surface area and mass of particles of urban aerosol divided into three size groups taken at the PMS using SMPS and APS devices.
Table 3. Number, surface area and mass of particles of urban aerosol divided into three size groups taken at the PMS using SMPS and APS devices.
Particle Size Interval (μm)Number ΔN (#/cm3)Surface ΔS (μm2/cm3)Mass Δm (μg/m3)
Lower BoundUpper Bound
0.01290.1045820360.5
0.1042.46251051937.9
2.4610.40.27.97.1
Table 4. Chemical compositions of PM fractions from the PMS.
Table 4. Chemical compositions of PM fractions from the PMS.
Chemical ElementPM1PM2.5PM10
Average±STDEVAverage±STDEVAverage±STDEV
ng/m3ng/m3ng/m3
Mg9.986.1015.974.5995.1331.70
Al16.869.8125.859.10147.7049.11
Si18.6231.0410.6510.91102.6593.88
K53.7525.8381.5226.70114.3538.90
Ca17.0227.9656.9931.32623.50202.50
S494.11337.84880.05462.34727.28255.04
Cr0.090.130.230.090.670.32
Cu0.680.351.590.494.891.15
Fe29.9116.0066.2023.17262.6367.09
Cd0.140.060.190.080.180.07
Sb0.270.140.400.160.580.10
Ba0.250.160.810.264.171.03
Pb2.271.533.101.863.021.68
Ni0.080.170.090.130.120.13
Zn6.443.7312.745.1614.525.18
PM14.475.2519.546.5127.157.57
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MDPI and ACS Style

Jandacka, D.; Brna, M.; Durcanska, D.; Kovac, M. Characterization of Road Dust, PMx and Aerosol in a Shopping–Recreational Urban Area: Physicochemical Properties, Concentration, Distribution and Sources Estimation. Sustainability 2023, 15, 12674. https://doi.org/10.3390/su151712674

AMA Style

Jandacka D, Brna M, Durcanska D, Kovac M. Characterization of Road Dust, PMx and Aerosol in a Shopping–Recreational Urban Area: Physicochemical Properties, Concentration, Distribution and Sources Estimation. Sustainability. 2023; 15(17):12674. https://doi.org/10.3390/su151712674

Chicago/Turabian Style

Jandacka, Dusan, Matej Brna, Daniela Durcanska, and Matus Kovac. 2023. "Characterization of Road Dust, PMx and Aerosol in a Shopping–Recreational Urban Area: Physicochemical Properties, Concentration, Distribution and Sources Estimation" Sustainability 15, no. 17: 12674. https://doi.org/10.3390/su151712674

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