Towards Sustainable Air Quality in Coal-Heated Cities: A Case Study from Astana, Kazakhstan

Abstract

Despite severe particulate matter (PM) pollution in Central Asia, limited air composition and health impact data are hindering sustainable air quality management and resilient urban planning. This study provides the first comprehensive assessment of PM_2.5 and PM_2.5–10 in the urban environment of Astana, Kazakhstan, a rapidly expanding city with intense winter heating demands. We characterized PM and atmospheric precipitation and assessed health risks using bioaccessible fractions of PM-bound potentially toxic elements (PTEs). Among 388 samples, PM_2.5 and PM_2.5–10 concentrations peaked at 534 and 1564 μg·m⁻³, respectively. Scanning electron microscopy (SEM) identified soot and coal fly ash, indicating fossil fuel combustion as a major source. Precipitation characterization also showed elevated SO₄²⁻ (17.8 μg⋅L⁻¹), V (108 μg⋅L⁻¹), Ni (84.0 μg⋅L⁻¹), and Mn (63.2 μg⋅L⁻¹). Bioaccessibility tests showed high solubility for Fe (16,229 mg·kg⁻¹) followed by V: key indicators of combustion emissions. Non-carcinogenic risk for Ni and V exceeded acceptable limits for adults and children (e.g., HQ: 6.07 for V for adults). Carcinogenic risk exceeded the threshold 10⁻⁶ for Cd (adults), Co, Cr, and Ni. These findings may help advance urban air quality management via integrating bioaccessibility-based health risk assessment and source apportionment, supporting evidence-driven policies for environmentally responsible development in rapidly urbanizing cold-climate regions.

1. Introduction

Ambient particulate matter (PM) is one of the most critical components of urban air pollution, having profound impacts on human health and the environment [1,2,3,4,5,6,7,8,9,10,11]. The epidemiological literature and toxicological studies demonstrate the increased risk of cardiovascular and/or respiratory-related hospitalization following acute and chronic human exposure to PM [12,13,14]. The health risks associated with PM are largely influenced by its size, morphology, and chemical composition. Given the global public health significance of PM, understanding its sources, characteristics, and health implications remains a major focus of contemporary air quality research.

Morphological features of PM (e.g., size, shape, and surface characteristics) and its chemical composition are key factors influencing its toxic potential. Finer particles (e.g., PM_2.5) generally exhibit higher toxic potential, due to their chemical composition, deeper respiratory penetration, and ability to generate reactive oxygen species (ROS), leading to oxidative stress and apoptosis [3,4,9,10,15,16,17]. Larger particles (e.g., PM_2.5–10), on the other hand, have been shown to trigger certain inflammatory responses and impair airway structure, as well as pulmonary function [18]. Additionally, surface irregularities such as sharp edges and fractures may enhance cellular damage through mechanical interaction with membranes [19,20].

The extensive literature related to the chemical composition of PM distinctly emphasizes their relation to PM-induced health implications [4,21,22,23]. For example, potentially toxic elements (PTEs) including As, Pb, Hg, Cd, Cr, and Ni have been associated with non-carcinogenic and carcinogenic adverse health effects, ranging from neurotoxicity and organ dysfunction to respiratory diseases and lung cancer [24,25]. Health risk assessments of PM-bound PTEs often rely on total concentrations of PTEs in PM, potentially overestimating actual exposure [26,27], as it does not take the mobility and uptake of PTEs into account. In contrast, the in vitro bioaccessibility methods estimate the bioavailable (i.e., soluble in bioliquids) fraction of contaminants, offering a more accurate evaluation of health risks. Simulated lung fluids (SLFs), such as Gamble’s solution (GS) and artificial lysosomal fluid (ALF), are used to replicate physiological conditions in the human respiratory system [10]. Ref. [28] reported that only up to approximately 20% of the total PTEs content in PM_2.5 (i.e., Ni, Cd, Mn, Pb) was bioaccessible in a simulated lung fluid (GS). Moreover, ref. [10] emphasized that only PTEs associated with deposited particles are capable of entering the systemic circulation. Furthermore, ref. [29] calculated benzo[a]pyrene toxic equivalent quotient (TEQ-BaP) values using total and bioaccessible polycyclic aromatic hydrocarbon (PAH) concentrations. Twelve exceedances of the 1.0 ng·m⁻³ BaP limit were observed using total concentrations, compared to three when using bioaccessible fractions, indicating that total concentrations overestimate the exposure risk.

The mechanism for PM removal from the atmosphere involves wet and dry deposition [30]. The chemical complexity of wet precipitation is shaped by atmospheric reactions, microphysical processes, and cloud dynamics, and its investigation enables the quantification of emission source contributions to atmospheric pollution levels [31]. For instance, anthropogenic activities such as combined heat and power generation elevate SO₄²⁻ and NO₃⁻ levels, resulting in the increased acidity of precipitation, whereas the presence of NH₄⁺ and Ca²⁺, originating from construction processes, facilitates the formation of alkaline precipitation [32]. Acid rain can degrade ecosystems (e.g., damage to forests and wildlife), corrode infrastructure (e.g., buildings and metals), and acidify water bodies. Also, the gaseous precursors of acid rain (i.e., SO_x and NO_x) can negatively impact human health by reducing lung function and causing respiratory problems [33].

In 2019, Kazakhstan ranked 29th among the world’s most polluted countries. Air quality in most major cities do not conform to WHO’s annual air quality guidelines, and publicly available monitoring data remain limited across the country [34]. Astana, the capital city of Kazakhstan, is characterized by an extremely continental climate, with prolonged cold winters and a six-month heating season. Since the city relies on burning coal for its centralized electricity and heating energy supply, the elevated levels of air pollutants (e.g., PM, SO₂, NO₂, CO) are frequent during the heating period. For instance, PM_2.5 concentrations could easily range between 100 and 200 µg·m⁻³ on some winter days [34], and cities’ massive, coal-fired combined heat and power plants, along with individual residential heating activities, are major contributors to the pollution [35,36]. The infiltration of outdoor pollution to indoors also degrades indoor air quality, which has been shown to be a problem, particularly during the heating period of the city of Astana [37]. All in all, PM-related exposure has been estimated to cause 8134 premature deaths annually in Kazakhstan [38], and the estimated DALY for Astana’s population for 2019 ranged from 2160 to 7531 years [39].

A systematic review by [40] suggested that globally, urban air pollution management relies on a combination of regulatory, technological, and policy-based interventions targeting major emission sectors, such as transportation, energy, and industry. Most countries have implemented air quality standards, vehicle emission controls, fuel quality improvements, and transitions toward cleaner energy sources, including electricity, natural gas, and renewables. Despite these advances, reliance on coal in power generation and industrial processes remains a major challenge in several regions, emphasizing the importance of continued mitigation efforts and policy enforcement. Similar challenges are experienced around the globe, with innovative solutions being researched, from low-cost sensors (e.g., AirQo) and electrospraying–netting techniques (e.g., SWING filters) for sampling to 2D adsorptive materials (e.g., reduced graphene oxide for pollutant removal) [41,42,43].

Given the heavy reliance on coal combustion for energy in numerous rapid urban development zones, including Kazakhstani cities, and the limited air quality monitoring and management infrastructure in urban zones under peril, comprehensive PM characterization and health risk assessment are in urgent need. To the author’s best knowledge, the present study is the first comprehensive assessment of particulate air pollution, precipitation chemistry, and associated health risks in a coal-heated Central Asian city, providing region-specific evidence where such data are still lacking. Moreover, there is a relative scarcity of studies on PM bioaccessibility from different emission sources. Therefore, the objectives of the present study are (1) to characterize air pollution in terms of PM mass as well as its morphology, (2) to characterize atmospheric precipitation potentially impacted by air quality, and (3) to assess human health risks by using bioaccessible fractions of PM-bound potentially toxic elements (PTEs). PM and precipitation sampling have been carried out over a span of two years, and selected samples were further processed for their morphology, chemistry, and PTE bioaccessibility.

2. Materials and Methods

2.1. Site Description and PM Sampling

The study area, Astana (51°8′ N, 71°26′ E), is the capital city of Kazakhstan, located in a flat, arid steppe region with an area of 722 km² and 347 m above sea level. Astana belongs to a humid continental climate, according to the Köppen climate classification system, with long, cold winters and warm, dry summers. The mean annual temperature is 4.2 °C, with prevailing winds from the south and southwest at an average speed of 3.9 m/s. The average annual precipitation is 295 mm [44]. Seasonal PM_2.5 and PM_2.5–10 samples were collected in the urban area of the Nazarbayev University Campus (51°5′ N, 71°23′ E) (Figure 1). Sampling periods are detailed in Table S1 in the Supplementary Material.

The sampling campaign was conducted from October 2021 to June 2023, covering a total duration of 194 days of sample collection. The collection was not always continuous throughout this period, due to temporary equipment malfunction and logistical issues; however, sampling events were distributed across all major seasons to ensure temporal representativeness. The numbers of samples collected in fall, winter, spring, and summer were comparable (see Table S1 for further details), and also allowed a balanced coverage of heating and non-heating periods.

PM was collected using a Partisol 2025i-D Dichotomous Sequential Air Sampler (Thermo Fisher Scientific Inc., San Jose, CA, USA), which simultaneously captures both fine (PM_2.5) and coarse (PM_2.5–10) fractions. The sampler installation was performed according to U.S. EPA 40 CFR Part 58, Appendix D and Appendix E siting criteria. The virtual impactor of the air sampler separated the airflow to maintain a steady flow rate of 15 L/min and 1.67 L/min for PM_2.5 and PM_2.5–10, respectively. Samples were collected on 47 mm polytetrafluoroethylene (PTFE) filters (2 µm pore size) over 24 h periods during a two-year campaign, yielding 194 samples for each PM fraction. Filters were pre- and post-conditioned in a controlled environment (30–40% RH, 20–23 °C) for at least 24 h and stored at 4 °C in Petri dishes prior to analysis. Gravimetric analysis was performed using a RadWag XA 220.3Y (RADWAG, Radom, Poland). Analytical balance and PM mass concentrations were calculated in μg·m⁻³.

2.2. Morphological Characterization of PM

PM_2.5 (n = 10) and PM_2.5–10 (n = 10) samples collected during July and January were selected to investigate seasonal (summer–winter) variations in particle morphology, potential dominant sources, and the impact of the heating season. Each filter was sectioned into 1 mm² fragments, mounted on carbon tape, and coated with a 15 nm Au layer, using a Q150T sputter coater to enhance conductivity. Morphological characterization was conducted using a field emission scanning electron microscope (FE-SEM, ZEISS Crossbeam 540 (Carl Zeiss AG, Oberkochen, Germany) equipped with ORS Dragonfly Pro 2021.3 software. Analyses were performed under high-vacuum conditions (10⁻⁵ mbar), with a working distance of 3.6 mm, accelerating voltage of 5 kV, scan speed 3, and a probe current of 117 pA. These settings enabled the detection of particulate matter with diameters as small as <100 nm.

2.3. Sampling of Atmospheric Precipitation and Its Chemical Characterization

The sampling site for collecting precipitation (snow and rainwater) samples was located at the Nazarbayev University Campus (51°5′ N latitude, 71°23′ E longitude). The sampling campaign was carried out from March 2022 to March 2023. The Palmex RS-2i rain sampler (Palmex Ltd., Zagreb, Croatia), equipped with a 3 L polyethylene bucket, was installed on the sampling site. Prior to each use, to prevent cross-contamination, the sampling bucket was thoroughly cleaned with deionized water (DI) and then dried. Precipitation samples were collected immediately following precipitation events. After collection, the sampling bucket was sealed with a plastic cover to minimize exposure to external contaminants and transported to the laboratory for further analysis. In cases where multiple precipitation events occurred within a single day, sampling was conducted on the following day. A total of 30 precipitation samples were collected during the study period, with more information provided in Table S2.

Snow and rainwater samples were filtered using 20 mL syringes with 0.45 μm pore-size PVDF membrane filters. Selected samples (n = 5) underwent a second filtration, due to the high amount of additional sediment that remained after the initial filtration. The pH of the precipitation samples was measured using a pH meter (Mettler Toledo SevenCompact, Mettler Toledo, Columbus, OH, USA), calibrated with three standard buffer solutions (pH 10.01, 7.00, and 4.01) prior to measurement. Electrical conductivity was determined using a conductivity meter (WTW inoLab Multi 9310 IDS, WTW, Weilheim, Germany). All samples were preserved at 4 °C until further analysis.

Prior to analysis for PTEs, samples were digested with 3 mL HNO₃ and 1 mL of HCl in a microwave digester (speedwave ENTRY, BERGHOF, Eningen, Germany), filtered with 0.45 μm pore size PVDF filters, stored at 4 °C before the analysis. The ICP-MS (The iCAP RQ, Thermo Fisher Scientific, Waltham, MA, USA) was used to estimate the concentration (µg·L⁻¹) of Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sb, and V.

The soluble fractions of samples were analyzed for seven major anions (F⁻, Cl⁻, NO⁻, SO₄²⁻, Br⁻, NO₃⁻, PO₃²⁻) and six major cations (Li⁺, Na+, NH₄⁺, K⁺, Ca²⁺, Mg²⁺) by using ion chromatography (IC, Dionex ICS-6000 Ion Chromatography System, Thermo Fisher Scientific Inc., San Jose, CA, USA). The instrument had a built-in eluent generator (ultrapure water mixed with KOH for anions and methane sulfonic acid for cations) and was operated using a Chromeleon Chromatography Data System (version 7.2.9) software. The injection volume and flow rate during instrument operation were 2.5 μL and 0.38 mL·min⁻¹, respectively. The ultrapure water used during cleaning, blank preparation, and calibration was produced using a Milli-Q^® Direct Water Purification System (15.0 MΩ/cm resistivity, Merck Group, Darmstadt, Germany). The instrument was calibrated before analysis, using separate anion and cation standard solutions with six concentration values ranging from 0.05 to 10 mg·L⁻¹. The standard solutions were prepared using Sigma-Aldrich TraceCERT Certified Reference Materials (Multi Anion Standard 2 for IC (Product No. 53798), Multi Anion Standard 3 for IC (55698), Nitrite Standard for IC (67276), and Multi Cation Standard 2 for IC (93159), Merck Group, Darmstadt, Germany). The DL for ionic concentrations was calculated as the sum of the average and three standard deviations of the blank values.

2.4. In Vitro Lung Bioaccessibility Tests

The ALF was employed to estimate the bioaccessible concentration of PTEs in PM_2.5 [26]. The composition of ALF is specified at [45]. Briefly, 41 PTFE filters were put into 15 mL–PP centrifuge tubes (Teflon with PTFE caps) and mixed with 10 mL of ALF (37 °C). The PM_2.5 samples with ALF were placed horizontally in a laboratory orbital shaker incubator (IKA KS 4000 i Control, IKA-Werke GmbH & Co., KG, Staufen, Germany) set to 37 °C and 100 rpm agitation for one week. After, extracted lung fluid was carefully poured into a 20 mL syringe, followed by filtration with a 0.45 μm pore size hydrophilic PVDF membrane filter. For the sample preservation, 0.5 mL of 70% HNO₃ was added to each tube. Samples were then kept at 4 °C until further processing. For the PTEs analysis, samples were digested in a microwave digester (speedwave^® ENTRY, BERGHOF, Eningen, Germany) in TFM PTFE pressure (BERGHOF, Eningen, Germany) vessels, using a 3:1 HNO₃/HCl mixture (aqua regia). A procedure blank was included in each digestion batch. A mixture of 1.5 mL of HNO₃ and 0.5 mL of HCl was added to each digestion vessel. All digested samples were filtered with a 20 mL syringe and 0.45 μm PVDF filter into a 50 mL tube and stored at 4 °C until elemental analysis. The digested samples were analyzed using ICP-MS (Thermo Fisher Scientific Inc., San Jose, USA, The iCAP RQ) to determine the concentration (mg·kg⁻¹) of Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn (detection limits [DL] in ALF (mg·kg⁻¹): 0.000003, 0.000000045, 0.000004, 0.0000056, 0.000028, 0.00000036, 0.0000048, 0.000000056, 0.000056, and 0.0000024). The bioaccessible mass in lung fluid (μg) was then calculated according to (1):

$${\mathrm{m}}_{\mathrm{b}\mathrm{i}\mathrm{o}}={\mathrm{C}}_{\mathrm{A}\mathrm{L}\mathrm{F}}\times {\mathrm{V}}_{\mathrm{A}\mathrm{L}\mathrm{F}}$$

(1)

where C_ALF is the concentration of a contaminant (mg·L⁻¹) in lung fluid and V_ALF is the volume of the lung fluid (mL). Then, the bioaccessible concentration for each PTE was calculated using (2):

$${\mathrm{C}}_{\mathrm{b}\mathrm{i}\mathrm{o}}={\mathrm{m}}_{\mathrm{b}\mathrm{i}\mathrm{o}}\times 1000/\mathrm{m}$$

(2)

where C_bio is the bioaccessible concentration of a contaminant (mg·kg⁻¹) in PM, m_bio is bioaccessible mass (μg), and m is the mass of PM samples (mg).

2.5. Human Health Risk Assessment (HHRA)

An HHRA was conducted using the inhalation dosimetry methodology outlined by [46]. It incorporates the assessment of exposed populations, exposure conditions, and the quantification of potential doses or chemical intake. Based on these considerations, an evaluation of environmental exposure to adults and children was performed in the present study. Exposure concentration (EC, µg·m⁻³), carcinogenic risk (CR), and hazard quotient (HQ) were calculated for HHRA of PTEs in PM_2.5. EC for chronic exposure for each PTE was calculated using the following formula [46]:

$$\mathrm{E}\mathrm{C}={\displaystyle \frac{\mathrm{C}\mathrm{b}\mathrm{i}\mathrm{o}\times \mathrm{E}\mathrm{T}\times \mathrm{E}\mathrm{F}\times \mathrm{E}\mathrm{D}}{\mathrm{A}\mathrm{T}}},$$

(3)

where C_bio is a bioaccessible concentration of each PTEs (µg·m⁻³), ET is the exposure time (hours·day⁻¹) (24 h·day⁻¹ for adults and children), EF is exposure frequency (days·year⁻¹) (350 days·year⁻¹ for adults and children), ED is exposure duration (years) (20 years for non-carcinogenic risk, 70 years for carcinogenic risk), and AT is average time (ED in years $\times$ 365 day·year⁻¹ × 24 h·day⁻¹) (AT = ED × 365 × 24 h for non-carcinogenic PTEs and AT = 70 × 365 × 24 h for carcinogenic PTEs) [29,47]. CR was estimated using the following formula [46]:

$${\mathrm{CR}}_{\mathrm{i}} = \mathrm{IUR}i\times \mathrm{IUR}$$

(4)

where IURi is inhalation unit risk (μg·m⁻³)⁻¹.

The hazard quotient (HQ) is calculated as follows:

$$\mathrm{H}\mathrm{Q}\mathrm{i}={\displaystyle \frac{\mathrm{E}\mathrm{C}}{\mathrm{R}\mathrm{f}\mathrm{C}\mathrm{i}\times 1000}}$$

(5)

where RfC_i is the reference concentration of chronic inhalation exposure (mg·m⁻³).

IUR_i is for each PTE (1.8 × 10⁻³, 8.4 × 10⁻², 9.0 × 10⁻³, and 2.6 × 10⁻⁴ for Cd, Cr (VI), Co, and Ni, respectively) [46]; and RfC_i is the reference concentration of chronic inhalation exposure (mg·m⁻³) of each PTE (1.0 × 10⁻⁵, 1.0 × 10⁻⁴, 6.0 × 10⁻⁶, 5.0 × 10⁻⁵, 1.4 × 10⁻⁵, and 1.0 × 10⁻⁴ for Cd, Cr (VI), Co, Mn, Ni, and V, respectively) [48].

2.6. Positive Matrix Factorization (PMF)

PMF is a receptor model that is widely applied to identify the major sources contributing to the measured composition of PM by employing the equation which minimizes the sum of squared residuals relative to the uncertainties of the input data [49]. In the present study, EPA PMF 5.0 software was employed to determine the main sources contributing to PM_2.5. Input variables included the mass concentration of PM_2.5 (µg·m⁻³) and the bioaccessible fractions of PTEs (i.e., Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn) (µg·L⁻¹). Uncertainties for the PTEs were calculated according to the method proposed by [50]. The uncertainty associated with the gravimetric analysis was estimated as three standard deviations of weight changes in procedural blanks, following the guidelines of Methods for the Determination of Hazardous Substances [51]. All variables exhibited a signal-to-noise (S/N) ratio > 2, classifying them as strong variables for the purposes of model evaluation. Given their small mass contribution and following the EPA PMF 5.0 User Guide (2014), the PM_2.5 mass concentration was treated as a weak variable to limit its impact on the final factor loadings.

The model was tested with factor numbers ranging from three to six, with the four-factor solution selected as optimal, based on the number of input components. The final model was refined and validated according to the following criteria: (1) confirmation of model convergence and stability, as indicated by consistent and closely matching values of Qrobust and Qtrue across base runs; (2) minimal factor swapping, indicating low rotational ambiguity, verified using the software’s built-in displacement analysis; (3) exclusion of outliers identified by scaled residuals outside the range of [−3, 3]; (4) maximization of the coefficient of determination (R²) between observed and predicted concentrations; and (5) comparison of resulting factor profiles to the known source profiles of PM_2.5. To estimate the 95% confidence intervals of factor contributions, bootstrap analysis was conducted with 100 iterations. The confidence interval was calculated using the following equation:

$$\mathrm{C}\mathrm{I}=\overline{\mathrm{x}}\pm \mathrm{z}·{\displaystyle \frac{\mathrm{s}}{\sqrt{\mathrm{n}}}}$$

(6)

where $\overline{x}$ is a mean value, z is the z-value for a confidence interval, s is standard deviation and n is sample size.

3. Results and Discussion

3.1. Total PM Mass Concentration

The overall results from the PM sampling campaign (388 samples over two years) are summarized in Figure 2. The average PM_2.5 concentration during the sampling period was 28.7 µg·m⁻³ (range: 0–543 µg·m⁻³), exceeding the WHO annual air quality guideline (5 µg·m⁻³) by a factor of 5.75 (Figure 2a) [52]. Seasonally, the highest mean concentrations were recorded in fall (38.5 µg·m⁻³), followed by winter (34.3 µg·m⁻³), summer (23.5 µg·m⁻³), and spring (21.6 µg·m⁻³) (Figure S1a, Supplementary Material). The maximum 24 h PM₂.₅ concentration (543 µg·m⁻³) occurred in November, exceeding the WHO 24 h guideline (15 µg·m⁻³) by 36.2 times. The second-highest value (531 µg·m⁻³) was observed in February (Figure 2a).

The average PM_2.5–10 concentration was 226 µg·m⁻³ (range: 0–1564 µg·m⁻³). It should be noted that WHO air quality guideline values do not apply to PM_2.5–10. Therefore, only PM_2.5 concentrations were compared with the WHO standards, while PM_2.5–10 results are presented separately for descriptive and comparative purposes. The maximum 24 h concentration, recorded in June, reached 1564 µg·m⁻³ (Figure 2b). Seasonally, PM_2.5–10 levels were high at all times: they were highest in summer (272 µg·m⁻³), followed by spring (268 µg·m⁻³), fall (158 µg·m⁻³), and winter (135 µg·m⁻³) (Figure S1b).

The literature on the air pollution levels in Astana is somewhat limited. The study by [53], undertaken between 2017 to 2021 and based on the air pollution index (API) and greatest repeatability (GR) criteria, pointed out a consistent classification of Astana’s air quality level as the third degree, indicating a high level of air pollution that was parallel to the findings of the present study. The annual average concentration of PM_2.5 exceeded the WHO maximum permissible level for the years 2017–2021, with recorded values of 0.70 mg·m⁻³, 0.88 mg·m⁻³, 1.27 mg·m⁻³, 3.17 mg·m⁻³, and 1.39 mg·m⁻³, respectively (ibid), indicating a substantial deviation from the recommended air quality standards. Ref. [53] attributed these levels predominantly to the emissions of pollutants from various sources, such as CHPPs, transportation, and the private residential sector. Fossil fuel combustion remains the primary contributor to air pollution in Kazakhstan, accounting for 66% of electricity and heat production through coal burning. CHPPs are major sources of PM_2.5 pollution in both Astana and Almaty (two largest cities in Kazakhstan), with their relative contributions varying by location and operational characteristics. In Astana, coal combustion from local CHPP units contributes, on average, approximately 7 µg m⁻³, or about 22% of the annual mean population exposure to PM_2.5. A substantial share of this contribution originates from older plants that lack modern emission control technologies [54]. In the household sector, the coal consumption is estimated at 157 kg per person, which is the second-highest coal consumption per capita globally [38].

3.2. Morphological Characterization of PM Particles from Different Emission Sources

Scanning electron microscopy (SEM) was employed to characterize the morphological features of PM_2.5 particles collected on PTFE filters during the winter and summer periods in Astana, with the aim of identifying dominant particle types and their potential emission sources (Figure 3 and Figure 4). Winter PM_2.5 samples (Figure 3a,b) exhibited a predominance of cuboidal, quadrangular, irregular, and rod-like aggregates embedded in a fibrous matrix, typical of soot and coal fly ash (CFA) formed through combustion processes [55]. These morphologies are consistent with emissions from coal-fired heating systems, common during the cold season. Notably, spherical CFA particles were observed with surface defects such as dents and pitting, in line with the literature [55,56]. These particles often host potentially toxic elements (PTEs), such as As, Pb, and Hg, and have been associated with adverse health effects, including respiratory and neurodevelopmental disorders, as well as sleep disturbances [57].

Chain-like soot clusters and their agglomerates (Figure 3a,b) are indicative of incomplete combustion of fossil fuels and biomass. These structures ranged from submicron to a few microns in size, and contributed to both air pollution and atmospheric warming [58,59]. SEM also revealed embedded agglomerates with measured particle sizes between ~240 nm and 1.1 μm (Figure 3f), further supporting the prevalence of fine particulate matter that was capable of deep respiratory penetration.

In contrast, summer PM_2.5 samples displayed a heterogeneous mixture of irregular, angular, and sharp-edged morphologies (Figure 3c,d), suggestive of mechanically generated dust, such as crustal material or construction debris [60]. Remarkably, fungal spores with rough, textured, and disk-like morphologies were identified (Figure 3e) and matched the size and shape of Cladosporium species, which are common in urban air during warm seasons [61].

PM_2.5–10 particles collected in the winter (Figure 4a,b) revealed spherical, irregular, and fractal-like morphologies, including distinct grape-like aggregates that were indicative of both dust and soot particles. Dust particles, originating from soil erosion or construction activities, showed sharp edges and varied shapes, consistent with previous studies [60,61,62,63]. During summer, PM_2.5–10 samples displayed bowl-shaped, flake-like, and angular particles with complex surface topographies (Figure 4c,d). These were attributed to a mix of natural dust and road dust from vehicular activities, including tire and brake wear. Brake abrasion particles, while morphologically diverse, were especially prevalent and difficult to differentiate, due to their fine sizes and mixed compositions.

In contrast, soot particles, produced through incomplete combustion of fossil fuels, biomass, and organic matter, appear as complex, clustered structures, and are known contributors to urban air pollution and atmospheric warming [58]. In the present study, soot particles ranged in size from approximately 4 to 8.5 μm (Figure 4a).

PM_2.5–10 particles revealed irregular, bowl-shaped, flattened, and flake-like particles with sharp edges, which are indicative of natural and road dust. Natural dust (ND) particles, typically smaller and more angular than crushed mineral dust, originate from wind and water erosion and are rich in Si and Ca; they rarely form large agglomerates and may further fragment through mid-air collisions [64]. In this study, ND particles in the July samples exhibited sharp edges and surface debris (Figure 4c,d). Road dust, primarily from vehicular sources such as exhaust, tire wear, and brake abrasion, showed complex morphologies, with brake wear particles being particularly prevalent and difficult to distinguish (Figure 4c).

The spherical shape and smooth, glassy surfaces of PM particles may enhance their mobility, enabling them to penetrate deeply into the respiratory tract and enter the bloodstream upon inhalation, thereby facilitating systemic distribution throughout the body [65]. In contrast, sharp-edged particles may inflict mechanical damage on sensitive lung tissues, particularly the alveoli, where up to 50% of inhaled fine particles can be retained in the parenchyma [66]. Flake-like particles, due to their large surface area, may further pose vascular risks by obstructing the capillaries and impairing normal blood flow [66].

Overall, the SEM findings strongly support a seasonal variation in PM morphology and source contribution. Winter samples were dominated by combustion-related emissions, particularly soot and CFA, while summer samples exhibited a higher fraction of mineral dust, road-derived particles, and biological materials. These morphological differences reflect distinct emission profiles and atmospheric processes, which are critical for understanding both environmental impacts and potential human health risks associated with particulate pollution.

3.3. Characterization of Snow and Rainwater

The interaction between precipitation and PM has gained increasing research interest, due to their influence on the physicochemical properties of each other [67,68,69]. This interaction primarily occurs through atmospheric PM removal via wet deposition or ‘scavenging’, wherein particles act as condensation nuclei during cloud formation or are captured by raindrops [68]. Precipitation events have been shown to considerably reduce SO₄²⁻ concentrations in PM₁₀, with scavenging efficiency being positively correlated with rainfall intensity and particle size, which is attributed to the high-water solubility of sulfates. Additionally, elevated concentrations of metallic cations, such as Ca²⁺ and Mg²⁺, in rainwater are associated with PM_2.5 and PM₁₀ contributions, which play a role in neutralizing atmospheric acidity [69]. Ref. [67] emphasized the role of airborne particles in rainwater alkalinization, suggesting that focusing air pollution control solely on PM reduction without addressing rainwater chemistry may inadvertently exacerbate acid rain conditions.

3.3.1. Volume, pH and Ionic Conductivity

The variation in pH and precipitation (mm) for snow and rainwater samples is presented in Table 1. The highest pH value (8.51) was recorded in February 2023, while the lowest (6.47) was observed in May 2022. The average pH across all samples was 7.11, indicating a generally neutral character, and may be deemed slightly higher than expected from an urban environment. This alkalinity is likely due to the presence of base-rich components in the atmosphere, such as species from CaCO₃ and Ca(HCO₃)₂, which contribute to the neutralization of rainwater acidity. In comparison, typical unpolluted rainwater has a pH of approximately 5.60, due to the dissolution of atmospheric CO₂ forming carbonic acid [70]. For the present study, elevated atmospheric PM levels containing alkaline minerals may play an important role in buffering the relative acidity of precipitation.

The average ionic conductivity of snow and rainwater samples was 224.2 μS/cm (Table 1). Elevated conductivity values were observed in mid-summer and early spring, suggesting periods of increased atmospheric pollution were associated with higher total ion concentrations, consistent with previous findings [71]. The summer increase may be linked to an intensified application of fertilizers and pesticides, as the dissolution of associated salts contributes to a higher ionic content in rainwater. Additionally, strong wind activity during spring and summer in Astana promotes the resuspension of dust particles, which further enriches precipitation with soluble salts and elevates conductivity levels [72].

Table 1. pH, electric conductivity (EC) (µS·cm⁻¹), precipitation (mm), major ion concentration (mg·L⁻¹), and total anion and total cation concentrations (meq·L⁻¹) from the soluble fraction of snow and rainwater.

	Range (Min–Max)	Mean	SD	Median	WHO Limits 1	EU Environmental Guidelines 2
pH	(6.48–8.51)	7.15	0.46	7.09	no health-based guidelines; optimal range for treatment and distribution: 6.5–8.5	N/A
EC	(20.1–492)	142	110	116	N/A	N/A
Precipitation	(0.20–14.0)	4.09	3.80	3.15	N/A	N/A
F−	(0.01–1.82)	0.31	0.39	0.14	1.5	N/A
Cl−	(0.01–55.5)	14.3	12.8	8.15	no health-based guidelines; taste: 200–300 for NaCl, KCl, CaCl	N/A
NO2−	(0.02–3.97)	1.47	1.02	1.23	3.0	N/A
NO3−	(0.01–32.6)	6.01	8.30	3.61	50	groundwater: 50
SO42−	(0.02–77.5)	17.8	20.6	10.1	no health-based guidelines; taste: 250	N/A
PO43−	(0.03–1.27)	0.39	0.41	0.27	N/A	N/A
Br−	<DL	<DL	<DL	<DL	N/A	N/A
Li+	<DL	<DL	<DL	<DL	N/A	N/A
K+	(0.01–45.6)	11.9	11.1	8.16	N/A	N/A
Na+	(0.01–16.3)	3.46	4.86	1.97	no health-based guidelines; taste: 200	N/A
NH4+	(0.0–16.4)	3.46	4.68	1.55	no health-based guidelines; odor: 1.5; taste: 35	N/A
Ca2+	(0.02–50.8)	14.9	13.8	10.3	N/A	N/A
Mg2+	(0.00–3.87)	0.84	0.89	0.52	N/A	N/A
∑anions	(0.06–3.36)	1.10	0.97	0.80	N/A	N/A
∑cations	(0.00–4.49)	1.43	1.24	0.96	N/A	N/A

⁽¹⁾ WHO (2022) guidelines for drinking water [73]. ⁽²⁾ Amended EU environmental guidelines (2022) for groundwater and surface water, according to the proposal for a Directive (EU) 2022/0344 [74]. N/A: value not available.

Table 1. pH, electric conductivity (EC) (µS·cm⁻¹), precipitation (mm), major ion concentration (mg·L⁻¹), and total anion and total cation concentrations (meq·L⁻¹) from the soluble fraction of snow and rainwater.

	Range (Min–Max)	Mean	SD	Median	WHO Limits 1	EU Environmental Guidelines 2
pH	(6.48–8.51)	7.15	0.46	7.09	no health-based guidelines; optimal range for treatment and distribution: 6.5–8.5	N/A
EC	(20.1–492)	142	110	116	N/A	N/A
Precipitation	(0.20–14.0)	4.09	3.80	3.15	N/A	N/A
F−	(0.01–1.82)	0.31	0.39	0.14	1.5	N/A
Cl−	(0.01–55.5)	14.3	12.8	8.15	no health-based guidelines; taste: 200–300 for NaCl, KCl, CaCl	N/A
NO2−	(0.02–3.97)	1.47	1.02	1.23	3.0	N/A
NO3−	(0.01–32.6)	6.01	8.30	3.61	50	groundwater: 50
SO42−	(0.02–77.5)	17.8	20.6	10.1	no health-based guidelines; taste: 250	N/A
PO43−	(0.03–1.27)	0.39	0.41	0.27	N/A	N/A
Br−	<DL	<DL	<DL	<DL	N/A	N/A
Li+	<DL	<DL	<DL	<DL	N/A	N/A
K+	(0.01–45.6)	11.9	11.1	8.16	N/A	N/A
Na+	(0.01–16.3)	3.46	4.86	1.97	no health-based guidelines; taste: 200	N/A
NH4+	(0.0–16.4)	3.46	4.68	1.55	no health-based guidelines; odor: 1.5; taste: 35	N/A
Ca2+	(0.02–50.8)	14.9	13.8	10.3	N/A	N/A
Mg2+	(0.00–3.87)	0.84	0.89	0.52	N/A	N/A
∑anions	(0.06–3.36)	1.10	0.97	0.80	N/A	N/A
∑cations	(0.00–4.49)	1.43	1.24	0.96	N/A	N/A

⁽¹⁾ WHO (2022) guidelines for drinking water [73]. ⁽²⁾ Amended EU environmental guidelines (2022) for groundwater and surface water, according to the proposal for a Directive (EU) 2022/0344 [74]. N/A: value not available.

3.3.2. Ion Concentrations

Temporal variations in major ion concentrations in precipitation are presented in Table 1. Overall, the mean concentrations of major ions remained below the permissible limits for groundwater, drinking water, and surface water quality standards. However, the fluoride concentration peaked at 1.82 mg·L⁻¹ in April, exceeding the WHO drinking water guideline of 1.5 mg·L⁻¹. Among all ions, SO₄²⁻ exhibited the highest mean concentration (17.8 mg·L⁻¹; range: 0.02–77.5 mg·L⁻¹). The average concentrations of major ions followed the order: F⁻ < PO₄³⁻ < Mg²⁺ < NO₂⁻ < Na⁺ = NH₄⁺ < NO₃⁻ < K⁺ < Cl⁻ < Ca²⁺ < SO₄²⁻. Notable peaks in ion concentrations were observed in April (F⁻, Cl⁻, NO₃⁻, SO₄²⁻, K⁺, Na⁺, NH₄⁺, Ca²⁺, Mg²⁺), July (F⁻, Cl⁻, NO₂⁻, NO₃⁻, SO₄²⁻, K⁺, Na⁺, NH₄⁺, Ca²⁺, Mg²⁺), and December (F⁻, Cl⁻, NO₂⁻, K⁺, Na⁺, NH₄⁺) (Figure S2).

Elevated concentrations of alkaline-related substances (e.g., Na⁺, K⁺, Mg²⁺, Ca²⁺, NH₄⁺, NH₃, and CaCO₃) in regions with high SO₂ and NO_x emissions may contribute to the neutralization of rainwater [75]. NH₃ and CaCO₃ may also enter rainwater via aerosol deposition. Aerosols further play a role in SO₄²⁻ formation through secondary reactions involving H₂SO₄ and NH₄⁺ or other alkaline species, although combustion processes remain the primary source. Strong correlations among SO₄²⁻, NO₂⁻, and NO₃⁻ (r = 0.61 and 0.80; Figure S3) imply their common anthropogenic origins. Ca, which is prevalent in crustal materials and agricultural inputs, shows a strong correlation with Mg²⁺ (r = 0.93), supporting a crustal contribution [76]. Results from spring samples imply the influence of ammonium-based fertilizer use, reinforcing the buffering role of CaCO₃. Correlations among NH₄⁺, SO₄²⁻, and NO₂⁻ (r = 0.53 and 0.93) further highlight the role of NH₄⁺ in neutralizing acidic species in aerosols and cloud water [76].

3.3.3. Concentrations of PTEs

The present study examined the concentrations of PTEs in snow and rainwater samples (Table 2). PTEs such as Cr and Cd are known to dissolve in terrestrial and aquatic environments, posing environmental risks [77]. The average concentrations of most PTEs (e.g., Cd, Co, Cr, Cu, Mn, Pb) were below the WHO’s maximum permissible limits for drinking water. Cd exhibited the lowest average concentration at 0.15 µg·L⁻¹ (range: 0.04–0.36 µg·L⁻¹), while V showed the highest, with a mean of 108 µg·L⁻¹ (range: 63.1–159 µg·L⁻¹) across all seasons (Figure S4). Beyond background concentrations, Cd emissions are primarily linked to non-ferrous and ferrous metallurgy and fuel combustion processes [78]. The elevated V concentrations are likely attributable to vehicular emissions, particularly from diesel engines, which release V due to its higher content in diesel fuel [79].

Table 2. Range (max–min), mean, standard deviation (SD), and median for PTEs concentration (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sb, and V) in snow and rainwater samples and recommended threshold values (µg·L⁻¹).

PTEs	Range (Min–Max)	Mean	SD	Median	WHO Limits 1	AA-EQS Inland Surface Water Limits 2	MAC-EQS Inland Surface Water Limits 3
Cd	(0.04–0.36)	0.15	0.07	0.13	3.00	≤0.08 (Class 1)	≤0.45 (Class 1)
Co	(0.10–6.90)	1.70	1.65	1.20	N/A	N/A	N/A
Cr	(4.20–77.4)	16.7	14.1	13.8	30.0	N/A	N/A
Cu	(2.57–41.3)	10.2	9.06	7.17	2000	N/A	N/A
Mn	(0.50–212)	63.2	57.1	55.9	80.0	N/A	N/A
Ni	(5.20–612)	84.0	134	30.2	70.0	2.0	8.2
Pb	(0.10–14.1)	2.39	3.72	0.62	9.00	1.2	14
Sb	(0.38–3.00)	1.39	0.99	1.86	20.0	N/A	N/A
V	(63.1–159)	108	24.6	106	N/A	N/A	N/A

⁽¹⁾ WHO (2022) guidelines for drinking water quality [73]. ⁽²⁾ Annual average for inland surface water limits, according to the proposal for a Directive (EU) 2022/0344 [74]. ⁽³⁾ Maximum allowable concentration for inland surface water limits, according to the proposal for a Directive (EU) 2022/0344 [74]. N/A: value not available.

Table 2. Range (max–min), mean, standard deviation (SD), and median for PTEs concentration (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sb, and V) in snow and rainwater samples and recommended threshold values (µg·L⁻¹).

PTEs	Range (Min–Max)	Mean	SD	Median	WHO Limits 1	AA-EQS Inland Surface Water Limits 2	MAC-EQS Inland Surface Water Limits 3
Cd	(0.04–0.36)	0.15	0.07	0.13	3.00	≤0.08 (Class 1)	≤0.45 (Class 1)
Co	(0.10–6.90)	1.70	1.65	1.20	N/A	N/A	N/A
Cr	(4.20–77.4)	16.7	14.1	13.8	30.0	N/A	N/A
Cu	(2.57–41.3)	10.2	9.06	7.17	2000	N/A	N/A
Mn	(0.50–212)	63.2	57.1	55.9	80.0	N/A	N/A
Ni	(5.20–612)	84.0	134	30.2	70.0	2.0	8.2
Pb	(0.10–14.1)	2.39	3.72	0.62	9.00	1.2	14
Sb	(0.38–3.00)	1.39	0.99	1.86	20.0	N/A	N/A
V	(63.1–159)	108	24.6	106	N/A	N/A	N/A

⁽¹⁾ WHO (2022) guidelines for drinking water quality [73]. ⁽²⁾ Annual average for inland surface water limits, according to the proposal for a Directive (EU) 2022/0344 [74]. ⁽³⁾ Maximum allowable concentration for inland surface water limits, according to the proposal for a Directive (EU) 2022/0344 [74]. N/A: value not available.

The average concentration of Ni in precipitation samples was 84.0 µg·L⁻¹ (range: 5.20–612 µg·L⁻¹), exceeding the WHO guideline of 70 µg·L⁻¹. Although Ni is primarily emitted through the combustion of coal and liquid fuels, the highest concentrations were observed during non-heating seasons (summer and fall) (Figure S4). Additional sources include the manufacturing of glass, bricks, non-ferrous metals, and cement [80]. Furthermore, urban emission sources contribute to the accumulation of PTEs such as Ni and Pb in soils, particularly in suburban areas situated along prevailing wind directions [81]. One sample collected in spring also showed a Cr concentration of 77.4 µg·L⁻¹, exceeding the WHO limit of 30 µg·L⁻¹. Finally, the presence of Co may be attributed to coal combustion, industrial emissions, vehicular traffic, and mining activities [75]. In the context of Kazakhstan, studies examining the composition of coal combustion products are relatively limited. However, Ref. [82] reported that the chemical composition of ash reflects the mineral characteristics of the coal, with silicon and aluminum oxides as the dominant components, accompanied by a substantial amount of iron oxide.

3.4. In Vitro Lung Bioaccessibility of PTEs

Inhalation bioaccessible concentrations (mg·kg⁻¹) of selected PTEs, including Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn, are presented in Figure 5. More detailed protocols and a discussion of the bioaccessibility of PTEs in PM can be found in [83]. Substantial variability in PTE bioaccessibility was observed, reflecting the chemical heterogeneity of atmospheric particles [29]. The mean bioaccessible concentrations followed the following order: Co < Cd < Mn < Pb < Zn < Ni < Cr < Cu < V < Fe. Notably, Fe (mean: 16,229 mg·kg⁻¹; range: 906–30,419 mg·kg⁻¹) and V (mean: 10,725 mg·kg⁻¹; range: 687–27,092 mg·kg⁻¹) showed the highest concentrations in PM_2.5 samples, suggesting contributions from soil resuspension, fossil fuel combustion [84,85,86], and emissions from power plants [87,88,89]. Among the analyzed PTEs, Cd, Cr, and Ni are recognized as human carcinogens, with established links to lung and nasal cancer [90]. Elevated bioaccessible concentrations of Cr (mean: 736 mg·kg⁻¹; range: 46.0–1546), Cu (mean: 781 mg·kg⁻¹; range: 42.0–1326), Ni (mean: 724 mg·kg⁻¹; range: 52.0–3039), and Zn (mean: 665 mg·kg⁻¹; range: 32.9–1788) indicate likely sources, such as combined CHPPs and vehicular emissions [84,87]. Although Cu is not classified as a human carcinogen, animal studies have shown its potential to cause DNA damage at high concentrations [91]. Additionally, exposure to Zn and its compounds has been associated with metal fume fever in humans, although the exposure threshold remains unspecified [92].

3.5. Inhalation Risk Assessment Using Bioaccessible Concentrations of PTEs

Bioaccessible concentrations, rather than total concentrations, of contaminants are increasingly used for assessing carcinogenic and non-carcinogenic health risks, as they accurately reflect the biologically available fraction [27,29,47,93,94]. In the present study, HHRA was conducted for PM_2.5-associated PTEs, with the results summarized in

Table 3. Results for carcinogenic and non-carcinogenic human health risk assessment for exposure to bioavailable PTEs in PM from Astana (italics values indicate exceeded benchmarks).

	Adults				Children
	HQ		CR		HQ		CR
PTE	Mean	Max	Mean	Max	Mean	Max	Mean	Max
Cd	2.93 × 10−2	1.50 × 10−1	5.29 × 10−7	2.70 × 10−6	8.39 × 10−3	4.50 × 10−2	1.59 × 10−7	8.10 × 10−7
Cr	2.24 × 10−2	4.17 × 10−1	2.01 × 10−3	3.05 × 10−3	7.19 × 10−2	1.25 × 10−1	6.04 × 10−4	1.05 × 10−3
Co	3.25 × 10−2	7.52 × 10−2	1.75 × 10−6	4.06 × 10−6	9.73 × 10−3	2.25 × 10−2	5.26 × 10−7	1.22 × 10−6
Mn	7.25 × 10−2	2.72 × 10−1	N/A	N/A	2.18 × 10−2	8.17 × 10−2	N/A	N/A
Ni	1.73	5.04	6.32 × 10−6	1.84 × 10−5	5.21 × 10−1	1.51	1.90 × 10−6	5.51 × 10−6
V	3.49	6.07	N/A	N/A	1.05	1.82	N/A	N/A

N/A: value not available.

. According to U.S. EPA guidelines, the acceptable lifetime cancer risk threshold was taken as 10⁻⁶, and HQ should be <1 [48].

The non-carcinogenic risk identified Ni and V as the principal contributors for both age groups. In adults, the maximum HQ reached 5.04 (mean: 1.73) and 6.07 (mean: 3.49) for Ni and V, respectively. For children’s exposure, the maximum HQs were 1.51 for Ni (mean: 5.21 × 10⁻¹) and 1.82 for V (mean: 1.05).

Carcinogenic risk for Cd, Cr, Co, and Ni exceeded the U.S. EPA benchmark of 1 × 10⁻⁶. For Cd, adult exposure showed a maximum CR value of 2.70 × 10⁻⁶ (mean: 5.29 × 10⁻⁷), whereas for children, CR remained within the acceptable limits. For Cr, CR for both age groups exceeded the threshold value, with exposure reaching the maximum value of 3.05 × 10⁻³ (mean: 2.01 × 10⁻³) for adults and 1.05 × 10⁻³ (mean: 6.04 × 10⁻⁴) for children, respectively. For Co, adults had a maximum CR of 4.06 × 10⁻⁶ (mean: 1.75 × 10⁻⁶) and children had a maximum of 1.22 × 10⁻⁶ (mean: 5.26 × 10⁻⁷). The CR value for Ni reached a maximum of 1.84 × 10⁻⁵ (mean: 6.32 × 10⁻⁶) for adults and 5.51 × 10⁻⁶ (mean: 1.90 × 10⁻⁶) for children, respectively.

Although a probabilistic approach was not applied in the present study, due to scope and length limitations, potential sources of variability and uncertainty were qualitatively evaluated as follows, mostly in accordance with the data presented in the U.S. EPA Exposure Factors Handbook [95]. Variability, particularly when associated with exposure concentration, inhalation rate, and bioaccessible fraction, may affect individual risk estimates, while uncertainty primarily arises from the use of default exposure parameters (e.g., exposure time, exposure frequency) and from limited regional toxicological data. The risk values presented in this study therefore represent central-tendency estimates, and the qualitative assessment serves to indicate the confidence level and applicability of the results within the studied population.

3.6. Source Identification of PM_2.5 via PMF

PMF identified four factors (Figure S5) representing the potential source profiles of PM_2.5. These include traffic-related emissions, coal and oil combustion, crustal or mineral dust, power plant emissions and traffic-related non-exhaust sources. Factor 1 was strongly enriched in Pb and Cd, which was consistent with fuel exhaust emissions [51]. The low levels of V and Ni suggest negligible influence from heavy-oil combustion. Ref. [50] reported that an automobile exhaust is typically associated with elevated concentrations of Cu, Ni, Pb, and Zn, while high Pb and Cd loadings have also been linked to anthropogenic activities such as traffic-related emissions [96,97,98].

Factor 2 was characterized by high concentrations of Ni, V, Zn, Cu, and Cr, which was indicative of coal and oil combustion [50,96,99,100,101]. Elevated V and Ni levels, particularly with a V/Ni ratio near 2.9, are indicative of this source [102]. Heavy fuel oil (HFO) is rich in sulfur, and during combustion, PTEs such as Ni and V can vaporize and subsequently nucleate into new PM particles, contributing to soot formation [103]. Such soot-rich particles were abundant in the samples from the present study, as observed by SEM. Moreover, SO₄²⁻ was found to be the most abundant ionic species in the precipitation.

Factor 3 was dominated by Mn, Co, and Fe, which can be a characteristic of mineral dust sources. Ref. [50] identified Al, Fe, Ca, Mg, Mn, and Co as typical markers of mineral dust. Factor 4 exhibited high concentrations of Cr, Ni, and V, along with Cu, Fe, and Zn. This composition suggests contributions from power plant emissions and traffic-related non-exhaust sources, including tire wear and brake abrasion [89,104,105]. This factor was intentionally retained as a combined source category because emissions from CHPPs and traffic-related non-exhaust sources share similar chemical constituents, leading to partial overlap in the PMF results. Alternative factor solutions were tested, but the four-factor configuration yielded the most stable and interpretable outcome.

3.7. Potential Health and Environmental Impacts and Recommendations

The present study highlights several pressing public health and environmental concerns associated with chronic and acute exposure to PM and associated PTEs in cities with intense winter heating demands, based on the example of Astana, Kazakhstan. From a public health perspective, chronic exposure to high PM_2.5 levels poses notable risks, particularly to vulnerable sub-populations, such as children, the elderly, pregnant women, and immunocompromised individuals, potentially leading to increased morbidity and mortality. Acute spikes in PM concentrations further amplify immediate health risks (Table 4).

The presence of various particle types (e.g., coal fly ash and bioaerosols) can increase the risk of neurodevelopmental disorders and respiratory disease. Particle morphology also plays a critical role in health impact, with agglomerated, flaky, or sharp-edged particles causing mechanical damage and enhancing chemical reactivity. Notably, elevated BA concentrations of carcinogenic PTEs such as Cr, Ni, and Cd represent a substantial hazard for the inhalation pathway. Additional concerns arise from high BA concentrations of Fe, V, and Cu, which are associated with oxidative stress, pro-inflammatory responses, and genotoxicity.

Exceedances of WHO drinking water limits for Cr, Mn, Pb, Ni, F⁻, and NO₂⁻, as well as ammonium-related odor issues, raise further public health concerns. Importantly, average concentrations of Cd and Ni exceeding AA-EQS standards present a potential health hazard. It should be added that the lack of chromium speciation (Cr(III) vs. Cr(VI)) in the present study may compromise the accuracy of the risk assessment.

Environmentally, extreme PM_2.5–10 concentrations (up to 1564 μg·m⁻³) can degrade vegetation, soil quality, and visibility, while soot contributes to localized warming through solar absorption. Deposition of PTEs (e.g., Cr, V, Cu) onto terrestrial and aquatic ecosystems poses long-term threats to water quality and agricultural productivity.

Table 4. Main findings and associated public health and environmental concerns of PM and precipitation analyses.

Analyzed Parameter	Public Health Concern	Environmental Concern	Reference
PM mass concentration	Major concern: Chronic exposure to high PM2.5 levels Minor concern: PM concentration spikes	Major concern: PM levels’ impact on vegetation and soil Minor concern: Particulate pollution can reduce visibility	[106,107]
PM morphology and emission sources	Major concern: CFA, bioaerosols Minor concern: Particle morphological feature	Minor concern: Soot particles	[25,108]
In vitro lung bioaccessibility	Major concern: High BA concentration of carcinogenic PTEs Minor concern: High BA concentration of Fe, V, Cu	Major concern: Deposition of PTEs onto soil, ground water, surface water, and natural ecosystems Minor concern: Accumulation of PTEs in urban environment	[7,87,109]
Health risk assessment	Major concern: The maximum cancer risk for adults Minor concern: The January Cr-related cancer risk	Major concern: Elevated Cr levels Minor concern: Lack of Cr speciation(Cr(III) and Cr(VI))	[26,27,110,111]
Rainwater/snow chemistry	Major concern: Average Ni concentration Minor concern: Maximum concentrations of Cr, Mn, Pb, F−, and NO2−	Major concern: Average concentration of Cd and Ni Minor concern: Maximum Cd, Ni, and Pb concentrations	[112,113,114]

Table 4. Main findings and associated public health and environmental concerns of PM and precipitation analyses.

Analyzed Parameter	Public Health Concern	Environmental Concern	Reference
PM mass concentration	Major concern: Chronic exposure to high PM2.5 levels Minor concern: PM concentration spikes	Major concern: PM levels’ impact on vegetation and soil Minor concern: Particulate pollution can reduce visibility	[106,107]
PM morphology and emission sources	Major concern: CFA, bioaerosols Minor concern: Particle morphological feature	Minor concern: Soot particles	[25,108]
In vitro lung bioaccessibility	Major concern: High BA concentration of carcinogenic PTEs Minor concern: High BA concentration of Fe, V, Cu	Major concern: Deposition of PTEs onto soil, ground water, surface water, and natural ecosystems Minor concern: Accumulation of PTEs in urban environment	[7,87,109]
Health risk assessment	Major concern: The maximum cancer risk for adults Minor concern: The January Cr-related cancer risk	Major concern: Elevated Cr levels Minor concern: Lack of Cr speciation(Cr(III) and Cr(VI))	[26,27,110,111]
Rainwater/snow chemistry	Major concern: Average Ni concentration Minor concern: Maximum concentrations of Cr, Mn, Pb, F−, and NO2−	Major concern: Average concentration of Cd and Ni Minor concern: Maximum Cd, Ni, and Pb concentrations	[112,113,114]

Based on the findings of the present study, several recommendations are proposed to improve air quality management and public health protection in Kazakhstan (Figure S6). Firstly, the current national PM_2.5 standard (35 μg·m⁻³, 24 h) exceeds the WHO guideline of 15 μg·m⁻³, underscoring the need to strengthen regulatory thresholds. Increasing public awareness is essential, as the understanding of pollution levels and associated health risks remains low. Strengthening education and outreach campaigns can promote greater community engagement and behavioral change.

Second, in terms of technical approaches, incorporating source identification through morphological and chemical characterization into air quality monitoring would enable more targeted emission reduction strategies by revealing dominant sources. Addressing emissions at their origin also requires long-term action, such as transitioning household heating systems from coal to cleaner fuel alternatives to mitigate combustion-related pollution. Furthermore, health risk assessments should be refined by using BA concentrations instead of total elemental content, providing a more accurate estimate of actual exposure. To enhance the reliability of these assessments, in vivo validation using animal models is recommended. Applying chromium speciation techniques would help avoid the overestimation of carcinogenic risk and better identify localized contamination. Evaluating long-term health effects also calls for comprehensive longitudinal studies across diverse population groups.

Third, beyond airborne pollutants, the elevated levels of toxic elements in precipitation highlight the need to establish regulatory guidelines as a future environmental policy consideration. Rainwater, especially in areas with high Ni and Cd concentrations, should not be used for drinking. Taken together, these recommendations emphasize the urgent need for coordinated, evidence-based actions to address the existing challenges of air and precipitation quality in rapidly urbanizing, under-monitored environments.

3.8. Study Limitations and Future Research

This study has several limitations. Sampling was conducted using a single dichotomous air sampler, which may not fully capture the spatial variability of air quality across the urban study area. The lack of dispersion modeling to further assess site representativeness is also acknowledged as a potential limitation of this study. The present study did not include a comparative assessment of PTEs bioaccessibility using other SLFs, such as GS, which is commonly applied in in vitro lung bioaccessibility studies [10,26,115,116]. Such comparisons may provide value for evaluating PTE bioavailability across different regions of the respiratory tract. Due to instrumental constraints and the need to prevent ICP-MS cone contamination, experiments using GS were not feasible for the present study. Moreover, the limited mass and non-uniform deposition of PM on filters required the use of entire filters for bioaccessibility analysis, preventing separate quantification of the total PTE concentrations and, consequently, calculation of the bioaccessible fractions (%). This also hindered the assessment of correlations between PM-bound PTEs and precipitation chemistry. Future studies should aim to quantify both total and bioaccessible concentrations across various SLFs to improve exposure assessments. Furthermore, the FE-SEM used in this study lacked elemental detection capabilities (i.e., EDS), preventing particle-level chemical analysis that could have enhanced PM source classification. Additionally, while PTFE filters are well-suited for acid digestion, their fibrous structure is less preferable for SEM imaging compared to glass or quartz filters, as it can interfere with focus and image clarity. Although PM and precipitation samples were collected independently, their analyses are conceptually linked, as both reflect the influence of common emission sources and atmospheric processes. The chemical characterization of precipitation was included to complement PM source identification and to provide insight into pollutant transformation and deposition pathways. However, direct temporal correspondence between air and precipitation samples was not established, which limits the ability to assess their short-term interactions.

4. Conclusions

The present study offers a comprehensive assessment of particulate air pollution and precipitation chemistry in a coal-heated urban environment (Astana, Kazakhstan) with strong winters, which is typical in Central Asia. A thorough sampling campaign and subsequent analyses showed that mean concentrations of PM_2.5 and PM_2.5–10 were high, i.e., reached 28.7 μg·m⁻³ and 226 μg·m⁻³, respectively, with maximum values being as high as 534 μg·m⁻³ and 1564 μg·m⁻³, which severely exceed WHO air quality guidelines. The potentially toxic elements in PM were also mobile in lung fluid. The in vitro lung bioaccessibility in ALF revealed the high bioaccessibility of Fe (16,229 mg·kg⁻¹) and V (10,725 mg·kg⁻¹), indicating strong contributions from combustion sources. A huma health risk assessment showed a high non-carcinogenic risk for Ni and V for both adults and children (e.g., max HQ: 6.07 for V for adult exposure). The carcinogenic risk also exceeded the threshold of 10⁻⁶ for Cd (for adults), Co, Cr, and Ni. Chemical characterization showed that the dominant particle types were soot, coal fly ash, dust, and bioaerosols (according to SEM), pointing to fossil fuel combustion and urban dust as major sources. Rain and snow water analysis also supported source identification, with elevated levels of SO₄²⁻ (17.8 μg·L⁻¹), Ca²⁺ (14.9 μg·L⁻¹), V (108 μg·L⁻¹), Ni (84.0 μg·L⁻¹), and Mn (63.2 μg·L⁻¹). This seems concerning, as precipitation can act as a carrier of PTEs to water and soil in the urban environment. Finally, a complementary source identification via PMF identified four factors including traffic-related emissions, coal and oil combustion, crustal or mineral dust, power plant emissions, and traffic-related non-exhaust sources. This unveiled the complex nature of air pollution in the study region, providing further insights. The comprehensive approach used in the present study and its findings are particularly relevant to fossil fuel-dependent cities with intense heating demands. Integrating bioaccessibility and precipitation chemistry into air pollution monitoring is recommended, as this can improve the quality of the pollution assessment as well as the health risk assessment, thus enhancing the selection of mitigation strategies.