Spatial Distribution and Ecological Risk of Heavy Metals in the Urban Soils of Almaty: Implications for Sustainable Development

Abstract

Heavy metal (HM) contamination in urban soils is a pressing global issue, particularly in rapidly industrializing regions like Kazakhstan, where anthropogenic activities such as transportation, energy production, and manufacturing exacerbate accumulation in ecosystems. In Almaty, the largest city in Kazakhstan, urban expansion and legacy pollution pose risks to soil functions, biodiversity, and public health through bioaccumulation and migration pathways. This study evaluates the spatial distribution and ecological impacts of total heavy metal concentrations (HMs) (Pb, Cd, As, Zn, Cu, Ni, Co, Mo, Mn) in Almaty’s soils to inform remediation strategies. Soil samples (n = 73) were collected using a systematic grid sampling method across urban, industrial, and peri-urban zones in Almaty. HM concentrations were determined via X-ray fluorescence spectrometry (XRF) following GOST 33850-2016 standards. Pollution indices (contamination factor K_c and integrated pollution index Z_c) were calculated relative to Kazakhstani permissible limits (PDK RK) and Russian approximate permissible concentrations (ODK RF). Statistical analyses included Spearman’s correlation, boxplots, and coefficient of variation. Morphological, physicochemical (pH, humus content), and biological assessments evaluated degradation. Spatial interpolation via GIS mapped the hotspots. HM distributions showed significant variability, with As, Zn, and Ni exceeding norms in >90% of samples (median K_c ≈ 5 for As). Z_c classified >70% of sites as hazardous or extremely hazardous (Z_c > 32), with hotspots in central-eastern districts (Z_c 90–145). Strong correlations (ρ ≥ 0.6) identified a technogenic group (Pb–Zn–Cu–Ni) from traffic and industry, contrasting predominantly geogenic elements with possible anthropogenic contribution (As–Co–Mo–Mn). Pollution induced soil compaction, reduced humus/pH, and disrupting biogeochemical cycles. Local exceedances were noted near TECs, factories, and transport hubs. Almaty’s soils exhibit pervasive technogenic HM pollution, driven by urban sources, leading to ecosystem degradation and health risks. Future research should incorporate vertical profiling and isotopic sourcing for refined risk models. Prioritized monitoring and phytoremediation in hotspots are recommended to enhance resilience, aligning with UN SDGs for sustainable cities and ecosystems. Future research should incorporate vertical profiling and isotopic sourcing for refined risk models.

1. Introduction

Heavy metal (HM) soil pollution represents a global ecological problem, particularly in urbanized and industrial regions, where anthropogenic activities–including mining, metallurgy, and transportation–contribute to their accumulation [1,2].

In Kazakhstan, accelerated industrialization and the legacy of the Soviet period have led to widespread HM contamination in soils, threatening ecosystem processes such as pollutant migration through the soil profile, bioaccumulation in plants, and disruption of food chains [3,4].

Almaty, as the largest city and economic center of the country, is particularly vulnerable due to high population density, intensive transport emissions, and proximity to industrial zones [5].

Previous studies by other authors have shown that heavy metals such as lead (Pb), cadmium (Cd), copper (Cu), zinc (Zn), and chromium (Cr) can affect water quality, plant nutrient uptake, and ecosystem resilience, thereby exacerbating soil degradation under semi-desert conditions [6]. In the present study, the ecological assessment focused on the distribution patterns of Pb, Cd, Cu, Zn, Ni, Co, Mo, Mn, and As in the soil cover of Almaty, using geochemical and statistical approaches to identify pollution hotspots and migration trends.

Studies of HM soil pollution in Kazakhstan reveal regional differences: concentrations in industrial areas exceed background levels, unlike in rural territories [7]. Nationwide assessments indicate that urban soils are contaminated with Pb, Cd, Cu, Zn, and Cr, with average concentrations ranging from 5 to 442 mg/kg during 2010–2018 [1]. In Central Kazakhstan, including areas with similar industrial impacts (e.g., near Temirtau), Mn, Ba, Zn, Sr, Cr, Pb, Cu, Ni, B, and Co predominate, often exceeding FAO/WHO norms; for example, Ba reaches 620 mg/kg, six times above threshold values [8,9,10,11].

In Almaty and Southern Kazakhstan, moderate pollution is observed, with an increasing trend due to transport and industrial emissions, where Pb, Cu, and Zn are most pronounced [5,10,11]. Agricultural soils in Almaty and Turkestan regions show phytoaccumulation potential (e.g., in pea roots), highlighting opportunities for biotechnological remediation [12].

Atmospheric precipitation increases the load on soils, with Pb, Cu, As, and Cd concentrations in rainwater of 3.80, 16.11, 0.96, and 0.88 μg/L, respectively [13]. Ecological risk indices (RI) in Southern Kazakhstan range from 137 to 447, indicating low to high risk for As, Cd, Hg, Pb, Zn, Ni, and Cu [14]. According to regional studies, HMs in Almaty region soils occur in water-soluble, mobile, and total forms, with zinc requiring particular monitoring due to elevated mobile fractions in urbanized soils [15].

Comparative data from Pavlodar show similar patterns: in snow–soil–vegetation systems, Fe > Mn > Cr > Zn > Pb > Cu > Ni > As > Co > Cd predominate in snow, with elevated Zn and Cu levels in vegetables [16]. These findings underscore the need for comprehensive assessments in Kazakhstani cities, where pollution reflects a combination of industrial, transport, and atmospheric sources [4,17].

In Western Kazakhstan, including Uralsk and Aksai, studies reveal exceedances of permissible limits for Pb and Cd in 70% of the samples, for Hg in 45%, and for As in 90% in mining areas, with a strong correlation (r = 0.95, p < 0.001) between mining activities and As levels; the ecological risk index (ERI) indicates high risk to ecosystems and human health, affecting over 30% of agricultural land [18].

In the Atyrau region, industrial activities increase soil bulk density, reduce porosity, and intensify erosion (up to 2 billion tons/year), resulting in annual economic losses of 40 billion dollars; soil resilience is disrupted, although economic benefits include job creation and reduced out-migration [19]. These studies highlight the impact of industrial processes on soil properties, such as compaction and reduced porosity, leading to long-term ecosystem degradation [18,19]. Furthermore, the analysis of 100 soil samples in Western Kazakhstan revealed average concentrations of Pb at 120 mg/kg (range 50–250 mg/kg), Hg at 0.5 mg/kg (0.1–1.2 mg/kg), As at 15 mg/kg (5–30 mg/kg), and Cd at 8 mg/kg (2–15 mg/kg), with exceedances of norms in most cases [19]. In Atyrau, ecological assessment emphasized the need to balance economic growth with soil preservation, recommending phytoremediation and stringent regulations [19].

The relevance of the study is driven by the rapid urbanization and industrialization of Almaty, which exacerbate soil pollution and pose risks to the health of over 2 million residents [4]. Heavy metals in soils lead to bioaccumulation in food crops, increasing exposure and causing neurological, reproductive, and carcinogenic effects [20].

In Kazakhstan, where 40% of soils are degraded due to contamination, the analysis of distribution patterns is critical for sustainable land resource management and public health protection [21]. While previous regional studies in Kazakhstan have typically analyzed 4–6 heavy metals over functionally homogeneous or smaller urban areas [5,7,9], no comprehensive multi-element assessment covering the full urban extent of Almaty (683.5 km²) across diverse functional and landscape zones has been conducted. The present study addresses this gap by simultaneously investigating 9 heavy metals, and integrating geochemical indices, multivariate statistics, and GIS-based spatial analysis to distinguish technogenic from geogenic pollution sources.

In addition to conventional remediation approaches such as phytoremediation [12,15,22,23,24], emerging advanced oxidation and catalytic processes offer promising complementary tools for addressing heavy metal and co-contaminant issues. For instance, recent studies have demonstrated the efficacy of S-scheme heterojunction photocatalysts, such as Bi₁₂O₁₇Cl₂/CeO₂, for efficient photocatalytic degradation.

These innovations underscore the potential for integrated physico-chemical and biological strategies to mitigate heavy metal risks in rapidly urbanizing areas like Almaty.

This approach aligns with international practices of sustainable land resource management and allows for a more efficient allocation of environmental protection funds.

In the context of environmental management, the results highlight the need to establish a system of systematic monitoring of urban soils, particularly in areas influenced by transport corridors, combined heat and power plants (CHPs), and industrial facilities. Regular monitoring will enable the early detection of pollution trends and improve the effectiveness of mitigation strategies.

The integration of geochemical assessment, spatial analysis, and environmental management strategies provides a practical foundation for improving soil quality and transitioning toward more sustainable urban ecosystems.

2. Materials and Methods

2.1. Study Area and Research Object

The object of the study was the urbanized soils of Almaty city, Kazakhstan, a rapidly growing metropolis with a population exceeding 2 million people. The city is characterized by a semi-desert continental climate with an average annual precipitation of about 600 mm and pronounced temperature fluctuations (from −20 °C in winter to +35 °C in summer). Almaty is situated in the piedmont zone of the Tian Shan Mountains, which determines a complex relief, distinct vertical zonality, and specific features of atmospheric circulation that influence the accumulation of pollutants.

The soil cover of the city is predominantly represented by chernozems and kastanozems at various degrees of transformation. Under conditions of intensive urbanization, soils are subjected to significant anthropogenic impact, including vehicle emissions, industrial activities, and urban infrastructure development. This contributes to the formation of spatially heterogeneous distribution of heavy metals within the urban area [2,25].

A general scheme of the location of the studied sites within the city territory is presented in Figure 1, which reflects functional zoning and the main sources of potential contamination.

2.2. Methodology of Soil Sampling: Grid Soil Sampling

To obtain representative data on the spatial distribution of heavy metals in the urbanized soils of Almaty city, a systematic grid sampling method was employed. This approach involves dividing the study area into a regular coordinate grid with a fixed step size, which minimizes subjectivity in point selection and ensures uniform spatial coverage of the territory [26,27].

According to methodological guidelines of the United States Environmental Protection Agency (US EPA) and North Carolina State University, systematic grid sampling reduces the probability of spatial bias compared to random and judgment-based methods and is particularly effective for mapping contamination hotspots in heterogeneous urban environments [28].

Comparative studies demonstrate that integrating grid sampling with Earth remote sensing data (ERS), including vegetation indices (e.g., NDVI) and thematic productivity maps, substantially improves the accuracy of spatial interpolation and geostatistical modeling [29]. It has been established that reducing the grid cell size enables more detailed detection of local geochemical anomalies, although it increases labor and cost; conversely, a larger grid step provides an economical overview at lower resolution [27].

Methodological guides by Dinkins and Jones [30], as well as Ohio State University Extension [31], emphasize the appropriateness of using geographic information systems (GIS) for designing the sampling grid, georeferencing sampling points, and subsequent processing of analytical results. The additional use of remote sensing data helps identify zones of potential soil cover heterogeneity and refine the sampling strategy.

In accordance with the protocols of the International Atomic Energy Agency (IAEA) for monitoring environmental contaminants, including heavy metals and radionuclides, it is recommended to combine a regular grid with targeted sampling in anomaly zones identified from satellite data [32]. The “fit-for-purpose” approach proposed by Thompson et al. [33] involves adapting grid sampling parameters (step size, density, and sampling depth) to the specific characteristics of the site and research objectives, which is particularly relevant for ecological risk assessment in urbanized areas.

2.3. Analytical Methods for Heavy Metal Determination

The determination of heavy metal concentrations (Pb, Cd, As, Zn, Cu, Ni, Co, Mo, Mn) in soil samples was performed using X-ray fluorescence spectrometry (XRF), a non-destructive analytical method based on the registration of characteristic X-ray emission arising from the excitation of atoms in the analyzed sample [14]. The XRF method provides simultaneous multi-element analysis, high measurement throughput, and sufficient accuracy for quantitative determination, making it an effective tool for environmental monitoring, sanitary–hygienic assessment, and geochemical studies [34].

Analytical procedures were carried out in accordance with the requirements of GOST 33850–2016 [35], which regulates the methodology for determining the chemical composition of soils and ensures the reproducibility and comparability of results (GOST 33850-2016 2019) [34].

Sample preparation included air-drying at room temperature to constant weight, removal of plant residues and mechanical inclusions, sieving through a <1 mm mesh sieve, and subsequent homogenization. This preparation ensured material homogeneity and minimized matrix effects during analysis.

Spectrometer calibration was performed using certified reference materials of soils and rocks. Detection limits for most determined elements ranged from 1 to 10 mg/kg, which corresponds to the concentration levels of trace elements in urbanized soils.

The quality control system included analysis of blank samples, duplicate (parallel) samples, and standard reference materials. The accuracy and reproducibility of the results were evaluated based on the relative standard deviation, which ranged from ±5% to ±10%, meeting the requirements for environmental analytical studies.

2.4. Adaptation and Application in Almaty

In the present study, the methodology of systematic grid sampling was adapted to the specific characteristics of the urbanized territory of Almaty city, taking into account dense built-up areas, the presence of recreational and green zones, transport infrastructure, and pronounced topographic gradients of the piedmont zone. Sampling was carried out exclusively in open vegetated urban areas, including parks, squares, roadside green belts, landscaped zones, and residential open spaces. Sampling sites were located at distances generally exceeding 10 m from road surfaces to minimize direct road contamination effects. These areas represent diverse functional zones of the city—central, peripheral, industrial, residential, recreational, and foothill—ensuring representative coverage along a gradient of anthropogenic impact.

The city territory was divided into a uniform network of square cells with a step size that provided detailed coverage of central urbanized districts while simultaneously ensuring adequate representation of peripheral and foothill areas. In total, 112 grid cells of 2.5 × 2.5 km were formed, covering the full urban extent of Almaty (683.5 km²), within each of which one composite (combined) sample was planned to be collected from the upper soil horizon (0–20 cm). However, sampling was carried out in 73 cells only, as 39 cells were inaccessible due to dense urban construction, industrial facilities with restricted access, and areas with absent or severely disturbed soil cover (roads, paved surfaces, engineered infrastructure), where sampling was either impossible or methodologically inappropriate [32,35,36].

Sampling points were located predominantly in the central part of each cell. In cases where sampling at the calculated point was impossible (due to buildings, road surfaces, or restricted access), the position was adjusted while preserving the principle of spatial representativeness. Sampling was carried out exclusively in open vegetated urban areas, including parks, squares, roadside green belts, landscaped zones, and residential open spaces. Sampling sites were located at distances generally exceeding 10 m from road surfaces to minimize direct road contamination effects. These areas represent diverse functional zones of the city—central, peripheral, industrial, residential, recreational, and foothill—ensuring representative coverage along a gradient of anthropogenic impact. To increase result reliability, composite samples were formed from 3 to 5 uniformly distributed subsamples collected within each cell using a diagonal or envelope scheme.

Geographical coordinates of all points were recorded using a GPS receiver and subsequently integrated into a geographic information system, including the ArcGIS 10.6 platform, for spatial analysis and mapping of contaminant distribution.

The application of the systematic grid approach enabled the creation of reliable maps of the spatial distribution of heavy metals, the identification of local geochemical anomalies and zones of potential ecological risk, and the justification of priority directions for remediation measures. The methodology aligns with modern international practices for monitoring the condition of urbanized soils and ensures the comparability of the obtained data with results from similar studies.

2.5. Data Processing and Analysis

Primary processing of the analytical results included checking data for completeness, identifying outliers, and assessing the distribution of indicators. To characterize the variability in heavy metal concentrations, basic descriptive statistics were calculated (mean value, median, standard deviation, coefficient of variation).

Spatial analysis was performed in a GIS environment using geostatistical interpolation methods, in particular ordinary kriging, which enabled the construction of thematic maps of metal concentration distribution and the identification of zones with local anomalies. Variogram parameters were fitted taking into account spatial autocorrelation and data structure to ensure optimal interpolation quality.

To quantitatively assess the degree of technogenic load, pollution indices were calculated. The contamination factor (K_c) was determined using the following formula:

$$K_c={\displaystyle \frac{C_{actual}}{C_{norm}}}$$

where $C_{actual}$ is the actual concentration of the element in the soil, and $C_{norm}$ is the normative (permissible) value.

The normative basis included the maximum permissible concentrations of the Republic of Kazakhstan (MPC RK) as well as the approximate permissible concentrations of the Russian Federation (APC RF) [37].

To identify relationships between elements, the non-parametric Spearman’s rank correlation coefficient was applied, which is robust to deviations from normal distribution. Visualization of distributions was performed using box-and-whisker plots (boxplots), enabling evaluation of asymmetry, interquartile range, and the presence of extreme values.

The integrated statistical and spatial analysis provided an assessment of the degree of pollution heterogeneity, identification of geochemical associations among elements, and interpretation of the possible influence of technogenic load on the morphological, physico-chemical, and biological properties of urbanized soils.

Ordinary kriging was applied as an exploratory GIS-based interpolation method to visualize general spatial tendencies in heavy metal distribution across the urban territory. The resulting maps were interpreted together with measured concentrations, contamination factors (K_c), the integrated pollution index (Z_c), descriptive statistics, and correlation analysis. Therefore, interpolated surfaces were not used as independent predictive models, but rather as supplementary spatial visualization tools for identifying areas of potential concern. Potential pollution hotspots were identified using a screening-based approach that combined measured heavy metal concentrations, exceedance of permissible or background values, K_c and Z_c values, and spatial proximity to known anthropogenic sources, including transport corridors, industrial zones, CHP facilities, railway infrastructure, and densely built-up urban areas.

2.6. Statistical Analysis

To characterize the distribution of heavy metal concentrations (Pb, Cd, As, Zn, Cu, Ni, Co, Mo, Mn) across 73 soil samples, descriptive statistical parameters were calculated, including mean value, median, standard deviation, and coefficient of variation. Pollution indices, such as the contamination factor (K_c = C__actual/C__norm) and the integrated pollution index (Z_c = Σ(K_c − 1) for K_c > 1), were computed based on the permissible limits of Kazakhstan (MPC RK) and the approximate permissible concentrations of the Russian Federation (APC RF), with interpretation according to established classifications (K_c < 1: normal; 1–2: low; 2–5: moderate; 5–10: strong; >10: very strong; Z_c ≤ 16: permissible; 16 < Z_c ≤ 32: moderately hazardous; 32 < Z_c ≤ 128: hazardous; Z_c > 128: extremely hazardous). Relationships between heavy metal concentrations were evaluated using Spearman’s rank correlation analysis on a selected set of representative variables [38].

3. Results

3.1. Determination of the Content and Distribution Patterns of Heavy Metals in the Soil Cover of Almaty

Based on the analysis of 73 soil samples collected from various districts of Almaty, contamination factors (Kc) and the integrated pollution index (Zc) were calculated for heavy metals (Pb, Cd, As, Zn, Cu, Ni, Co, Mo, and Mn). Pb and As values were assessed according to the maximum permissible concentrations adopted in the Republic of Kazakhstan [39], whereas Cd, Zn, Cu, and Ni were evaluated using the approximate permissible concentrations of the Russian Federation [40]. The values for Co, Mo, and Mn were interpreted based on approximate permissible concentrations and background values reported in the literature [3,4,5]. The approach complies with methodological guidelines MU 2.1.7.730-99 [41,42] SanPiN 2.1.7.1287-03, and R 2.1.10.1920-04 [43], taking into account the regional geochemical background and international screening levels (Netherlands, United Kingdom, Canada) to verify conservatism. Approximate permissible concentrations (APC) and geochemical background values were used due to the absence of national standards for certain elements.

To assess the content of potentially toxic elements in soils, the following normative values were applied (mg/kg dry soil). Lead (Pb) and arsenic (As) concentrations were assessed according to the Hygienic Standards approved by the Order of the Minister of Health of the Republic of Kazakhstan dated 21 April 2021, with maximum permissible concentrations of 32 mg/kg for Pb and 2 mg/kg for As. For cadmium (Cd), zinc (Zn), copper (Cu), and nickel (Ni), the minimum values of the approximate permissible concentrations of the Russian Federation were applied: 0.5 mg/kg for Cd (from the range 0.5–2.0 mg/kg), 55 mg/kg for Zn (from the range 55–220 mg/kg), 33 mg/kg for Cu (from the range 33–132 mg/kg), and 20 mg/kg for Ni (from the range 20–80 mg/kg). For cobalt (Co), an approximate normative value of 5 mg/kg was adopted in accordance with commonly accepted methods of agrochemical analysis. The background content of manganese (Mn) was taken as 1500 mg/kg according to data from [18] as a characteristic background value for soils in Kazakhstan.

The contamination factor (K_c) was calculated using the formula: K_c = C__actual/C__norm, where C__actual is the actual concentration of the metal (mg/kg), and C__norm is the normative (permissible) value. Interpretation: K_c < 1—normal level; 1–2—low contamination; 2–5—moderate contamination; 5–10—strong contamination; >10—very strong contamination.

The contamination factor was calculated using the following formula:

$${\mathrm{K}}_{\mathrm{c}}={\displaystyle \frac{C_{fact}}{C_{norm}}}$$

where C_fact is the actual metal content in the sample, mg/kg; and C_norm is the standard value of MAC/APC or background, mg/kg.

The calculated K_c values for all elements made it possible to determine the degree of technogenic impact and the spatial heterogeneity of soil pollution. The mean contamination factor values and the threshold K_c = 1 are presented in Figure 2.

The analysis of Kc revealed systematically elevated values for As, Zn, and Ni. For As, K_c ≥2 was observed in 97% of sampling points (median K_c ≈ 5), with widespread areal increases. For Zn, exceedances occurred in 98% of points (Kc > 1), including strong and very strong cases. For Ni, K_c > 1 was recorded in nearly all points, predominantly at moderate levels. For Cu, exceedances were generally weak, with rare strong cases. Cd and Co remained at background levels in most samples, although isolated point peaks were noted (K_c > 8). Pb complied with MPC in 85% of points, with localized moderate to strong exceedances. Mo showed background levels with occasional weak exceedances. Mn remained at background levels without any exceedances.

The integrated pollution index Z_c was calculated using the formula Z_c = Σ(K_c − 1) for K_c > 1 [9]. Based on the classification proposed by [9], soils with Zc ≤ 16 were considered to be in a permissible condition (Class I), whereas values of 16 < Zc ≤ 32, 32 < Zc ≤ 128, and >128 corresponded to moderately hazardous (Class II), hazardous (Class III), and extremely hazardous (Class IV) conditions, respectively.

The Z_c results are shown in Figure 3 (ranked distribution).

Figure 3 presents the ranked distribution of the integrated pollution index (Z_c) values across 73 soil samples from Almaty city. The curve exhibits a pronounced descending pattern, reflecting high spatial differentiation of pollution across the urban territory. Approximately 18% of the samples fall into the extremely hazardous category (Z_c > 128), more than half (about 54%) belong to the hazardous class (32 < Z_c ≤ 128), and 25% of the samples are classified as moderately hazardous (16 < Z_c ≤ 32). Only isolated points (about 3%) remain within the permissible range (Z_c ≤ 16).

This distribution indicates the presence of localized hotspots of strong pollution and an overall high technogenic load on the soil cover from multiple elements simultaneously. The descending shape of the curve demonstrates that a significant portion of the territory exhibits exceedance of the complex pollution index above the normative level.

To analyze the nature of concentration distributions and assess the variability in heavy metal contents, descriptive statistical methods were applied. Figure 4 presents the median, minimum, maximum, and quartile values of heavy metal concentrations in the soil samples from Almaty city based on the analysis results (boxplot diagram).

The central line within the box represents the median; the box boundaries indicate the 25th and 75th percentiles; the “whiskers” extend to the minimum and maximum values excluding outliers; individual points denote outliers.

Visual inspection of the boxplot highlights elements exhibiting the most pronounced asymmetry and presence of outliers, indicating localized pollution hotspots. To quantitatively characterize the degree of variability, the main statistical parameters were calculated (

Table 1. Distribution parameters of heavy metals in the soils of Almaty city.

Element	Mean Value, mg/kg	Median, mg/kg	σ (Standard Deviation)	Coefficient of Variation V, %	Interpretation
Pb	28.74	24	19.91	69.27	The mean value (28.7 mg/kg) and the coefficient of variation of ≈69% indicate significant spatial heterogeneity, consistent with localized technogenic input of the pollution. Possible sources include motor vehicles and emissions from thermal power plants.
Cd	2.97	BDL	6.92	232.73	The low median (below detection limit) and high coefficient of variation of ≈233% indicate localized pollution hotspots and high variability in content; cadmium comes from emissions from fuel combustion and battery production.
As	10.55	10	3.05	28.88	The average content of 10.5 mg/kg with a low variation of ≈29% reflects relatively uniform spatial distribution; source interpretation is based on correlation analysis and spatial patterns rather than CV alone; weak anthropogenic influence is possible in agricultural areas
Zn	134.42	111	75.81	56.40	Average 134 mg/kg and variation of ≈56%—moderate variability, which is typical for urbanized soils; probable source—tire wear, oils, industrial emissions
Cu	37.89	35	21.71	57.29	Average 37.9 mg/kg and variation of ≈57%—moderate heterogeneity, reflecting a combination of natural and anthropogenic factors; copper accumulates in the humus horizon
Ni	41.33	41	8.74	21.15	Mean 41.3 mg/kg and low variation of ≈21%—uniform distribution indicating uniform spatial distribution across the study area
Co	5.68	BDL	19.20	337.80	Average 5.7 mg/kg, median = below detection limit (BDL) and extremely high variation ≈338%—the element occurs focally; probably associated with geochemical anomalies or man-made micro-emissions
Mo	1.30	BDL	2.46	188.96	Average 1.3 mg/kg, median = below detection limit (BDL) and variation ≈189%—uneven distribution; Mo comes mainly from natural sources (clastic rocks) and does not reflect anthropogenic load
Mn	696.89	686	127.17	18.25	Average 697 mg/kg and low variation ≈18%—uniform distribution, manganese is an element of the natural geochemical background that determines the mineral composition of soils.

).

3.2. The Obtained Statistical Parameters Confirm the Presence of Significant Differences Among the Elements in Terms of Spatial Heterogeneity

The analysis of the boxplot reveals pronounced differences in the distribution of concentrations of the nine elements in the soils of Almaty city. According to median values and interquartile range (IQR), the concentration ranges vary from relatively stable to strongly asymmetric.

Pb, Zn, and Cu exhibit comparatively high median levels (24, 111, and 35 mg/kg, respectively) and wide interquartile ranges, indicating substantial variation in content and the presence of localized areas of elevated contamination. These elements also show a considerable number of outliers (8–12 points), which points to the heterogeneity of technogenic impact and likely point sources of heavy metal input (transport, industrial zones).

Cd, Co, and Mo have median concentrations below the detection limit (BDL), yet they demonstrate extremely high coefficients of variation (189–338%) and multiple outliers. This suggests a predominantly background level of content with rare but pronounced exceedances characteristic of individual samples. Such a pattern is typical for elements with episodic technogenic input.

Ni and As are characterized by more uniform distributions (CV ≈ 20–30%), a smaller number of outliers, and medians within 10–41 mg/kg, indicating relatively uniform spatial distribution.

Mn, despite having the highest absolute concentrations (median ≈686 mg/kg), exhibits a low coefficient of variation (18%), confirming its stable background distribution.

Thus, the most variable and potentially ecologically hazardous elements are Pb, Zn, Cu, Cd, Co, and Mo, which are distinguished by large ranges and the presence of outliers, whereas Ni, As, and Mn show lower spatial variability, which combined with correlation analysis suggests a predominantly geogenic background.

To identify possible associations among the elements and to determine common sources of their input, a correlation analysis of the data was performed. Due to the non-normal distribution of the data, the non-parametric Spearman’s rank correlation coefficient, which is robust to outliers and asymmetry, was applied (

Table 2. Spearman’s rank correlation coefficients (ρ) between heavy metal concentrations.

	Pb	Cd	As	Zn	Cu	Ni	Co	Mo	Mn
Pb	1	−0.1218	−0.2153	0.62655101	0.35123	−0.2003	−0.0122	0.01059	0.12565
Cd		1	0.12042	−0.034078	0.06352	0.13852	0.02037	−0.0076	0.27925
As			1	−0.2593953	−0.0293	0.72178	0.23066	0.00653	0.27712
Zn				1	0.63435	−0.2713	0.05281	−0.0347	0.11867
Cu					1	−0.095	0.27594	0.06075	0.03552
Ni						1	0.20543	0.04975	0.34248
Co							1	−0.0424	0.21195
Mo								1	−0.2324
Mn									1

).

The correlation analysis (Spearman’s coefficient) is presented in Table 2.

The interpretation of correlations is presented in

Table 3. Interpretation of correlation relationships between elements.

Spearman’s ρ Range	Nature of the Connection	Interpretation for Geoecology
0.90–1.0	Very strong	Combined emissions from a single pollution source; elements form a stable technogenic association (e.g., emissions from transport, thermal power plants, metallurgy)
0.7–0.89	Strong	Similar migration routes and chemical behavior; likely common origin in industrial impact areas.
0.5–0.69	Moderate	Partially common sources (e.g., surface dust pollution, but different behavior in the soil profile)
0.3–0.49	Weak	Little correlation; elements have different behavior or different geochemical barriers.
0.1–0.29	Very weak	Almost independent distribution; natural differences in lithogenic fractions
0–0.09	Absent	Elements are not related by common sources; background differences
<0	Negative correlation	Antagonism in the behavior of elements, mutual displacement or different distribution along the profile

.

Spearman’s rank correlation coefficients (ρ) range from −1 to +1 and reflect the direction and strength of the relationship between elements. Correlations below 0.3 indicate a weak association, 0.3–0.7—a moderate association, and above 0.7—a strong association (Table 3).

In the studied soils of Almaty city, the correlation values are predominantly weak to moderate, indicating diverse sources of metal input and a complex pattern of technogenic influence. Certain element pairs exhibit significant positive correlations, suggesting a possible common source or similar migration behavior.

Strong and significant correlations are characteristic of arsenic and nickel, zinc and copper, and lead and zinc. The pair As–Ni shows the most pronounced positive association (ρ = 0.72), reflecting possible co-occurrence from parent rocks or through aerotechnogenic emissions. Both elements are typical of industrially influenced zones and fuel combustion areas. A high correlation is observed between Zn and Cu (ρ = 0.63), pointing to a common technogenic source related to vehicle wear (tires, lubricants, brake pads) and industrial emissions. A notable positive correlation also exists between Pb and Zn (ρ = 0.63), which is typical for urbanized territories where lead and zinc often enter jointly via road dust and thermal power plant emissions.

Moderate correlations were calculated for copper and cobalt, nickel and manganese, and arsenic and cobalt. The pairs Cu–Co (ρ = 0.28) and Ni–Mn (ρ = 0.34) likely reflect partial overlap in geochemical behavior and sorption onto organo-mineral complexes. The As–Co pair (ρ = 0.23) shows a weak positive correlation, suggesting a possible common background but without a clearly defined technogenic source.

Negative correlations were identified for the pairs arsenic and zinc, zinc and nickel, molybdenum and manganese, cadmium and lead, and cadmium and zinc. Thus, the inverse relationships in As–Zn (ρ = −0.26) and Zn–Ni (ρ = −0.27) indicate different geochemical barriers and migration mechanisms. As zinc content increases, nickel and arsenic concentrations generally decrease, which is typical for soils with heterogeneous composition and variable pH. The dependence in the Mo–Mn pair (ρ = −0.23) may reflect competitive uptake or differing mobility in alkaline versus acidic environments. Weak and opposing associations in Cd–Pb (ρ = −0.12) and Cd–Zn (ρ ≈ −0.03) confirm independent pollution sources (Cd is more often linked to battery production and fuel combustion, Pb to transport).

Thus, strong correlations are as follows: As–Ni (ρ = 0.72), Zn–Cu (ρ = 0.63), Pb–Zn (ρ = 0.63)—technogenic associations (transport, thermal power plants); moderate: Cu–Co (ρ = 0.28), Ni–Mn (ρ = 0.34); and negative: As–Zn (ρ = −0.26), Zn–Ni (ρ = −0.27)—antagonism. This divides the elements into predominantly technogenic (Pb–Zn–Cu–Ni) and predominantly geogenic (As–Co–Mo–Mn) groups [18,20].

The weak interconnection of cadmium with other elements underscores its specific source (localized emissions, battery waste). High correlation values among heavy metals of transport-industrial origin confirm the complex technogenic nature of soil pollution within the urban territory of Almaty.

The results of the study demonstrate that the primary contaminating elements in the soils of Almaty city are zinc (Zn), copper (Cu), lead (Pb), and nickel (Ni), characterized by the highest contamination factors (K_c > 1) and elevated values of the integrated pollution index Z_c. The highest Z_c values were recorded in the central and eastern districts of the city, indicating pronounced technogenic impact related to transport, industrial emissions, and dense urban development. According to the ranked distribution, more than half of the samples fall into the hazardous and extremely hazardous pollution categories (Z_c > 32), pointing to a persistent exceedance of the permissible technogenic load level.

The Spearman correlation analysis confirmed close associations among elements of technogenic origin, particularly Pb–Zn (ρ = 0.63), Zn–Cu (ρ = 0.63), and As–Ni (ρ = 0.72). This reflects their joint input into the soil cover as a result of fuel combustion, vehicle wear, and industrial activity. At the same time, Cd, Co, Mo, and Mn exhibit weak or negative correlations, indicating diverse sources and differences in geochemical behavior. Thus, soil pollution in Almaty has a complex technogenic–natural character, with the main load formed by heavy metals of transport-industrial origin, while secondary elements reflect the regional geochemical background features.

Overall, the condition of the soil cover can be characterized as contaminated, with localized zones of potential risk that require further monitoring and refinement of the geochemical background.

3.3. Identification of Local Technogenic Pollution Hotspots by Priority Pollutants in the Urbanized Territories of Almaty City

Heavy metal contamination of the soil cover represents a global ecological problem. According to Sereda et al. [44], approximately 1,400,000 local sites contaminated with heavy metals and organic pollutants have been identified in Western Europe, while about 600,000 sites with elevated heavy metal concentrations requiring remediation have been reported in the United States. In urban environments, the degradation of asphalt surfaces also contributes to the accumulation of heavy metals and aromatic hydrocarbons in soils.

The results of the analysis of heavy metal and trace element contents in the soils of the urbanized territories of Almaty demonstrated significant variability, with exceedances of maximum permissible concentrations (MPC) in a number of cases. This indicated the presence of local technogenic pollution hotspots formed under the influence of anthropogenic factors. Below, the content of individual heavy metals across the city territory is considered, with mapping performed for priority pollutants (Figure 5, Figure 6, Figure 7 and Figure 8).

Lead is among the most widespread contaminants in urban environments. Exceedances of the MPC (32 mg/kg) were recorded in samples No. 75, 114, 83, 20K, and 100, where Pb concentrations ranged from 54 to 144 mg/kg. This evidenced technogenic input of lead associated with transport emissions and the activities of industrial enterprises. Sample No. 114 (Makatayev Street, 129/1V, vicinity of the former S.M. Kirov Machine-Building Plant) showed elevated concentrations of Pb, Zn, Cu, and Ni, attributable to historical industrial emissions from metalworking, foundry, forging, electroplating, and heat treatment activities. Although the plant is currently non-operational, elevated concentrations have persisted due to residual contamination from prolonged technogenic impact. Lead is characterized by high stability in the soil environment and low migration capacity, contributing to the formation of persistent pollution hotspots.

Samples No. 75 (Abylai Khan Street, 8/10) and No. 83 (Seifullin Street, 6, near CHP-1) were located in areas with high industrial and transport loads. The B. Orazbayev CHP-1 (Seifullin Avenue, 433) operated on fuel combustion, leading to emissions of heavy metals and their deposition on the soil surface. Sample No. 100 (S. Seifullin Park) also showed lead exceedance; despite its status as a green zone, the site was affected by transport emissions and stationary sources. The studied territories were characterized by significant anthropogenic pressure associated with industrial facilities and intensive traffic, which contributed to lead accumulation in the upper soil layer. Sample No. 75 (Abylai Khan Street, 8/10) and No. 83 (Seifullin Street, 6, near CHP-1) exhibited high Pb and Zn levels associated with intensive traffic and thermal power plant emissions from fuel combustion. These territories were characterized by significant anthropogenic pressure from industrial facilities and heavy traffic.

The elevated cadmium content could be associated with vehicle emissions and the activities of small production enterprises using fuel and metal-containing materials. Samples No. 87 and 85 belonged to the territory of Tole bi Street–Sain Street, characterized by high traffic load and dense development; here, cadmium was likely accumulated as a result of emissions from intensive road traffic and vehicle exhaust gases.

Samples No. 9K (Al-Farabi Street–Dostyk Street area) and No. 12K (Suyunbay Street–industrial zone area) also demonstrated significant cadmium concentrations. These zones were technogenically loaded territories affected by industrial enterprises, boiler houses, and motor vehicle flows. Sample No. 107 (Timiryazev Street–Manas area) contained 20 mg/kg of cadmium, which is 40 times above the MPC. An automobile filling station (gas station) was located in this area, and the territory was characterized by intensive traffic and the presence of small service facilities (car repair shops, car washes, auto services), which could serve as sources of cadmium contamination. The identified sites with MPC exceedances for cadmium are localized in zones with pronounced technogenic impact. Cadmium accumulation in the soil cover was caused by the combined influence of transport emissions, fuel combustion, and industrial enterprise operations, necessitating regular monitoring and ecological risk assessment.

In the majority of soil samples, arsenic concentrations exceeded the MPC (2 mg/kg) 3–8 times. Maximum values (15–16 mg/kg) were recorded in samples No. 88, 114, 20K, and 1K. Sample No. 88 was collected in the Tole bi Street–Sain Street area, characterized by high traffic activity and proximity to an industrial zone. The exceedance of arsenic content was likely associated with dust deposition from fuel combustion areas and vehicle emissions. Sample No. 114 (Makatayev Street, 129/1V, territory of the former S.M. Kirov Machine-Building Plant) showed 13 mg/kg As, indicating technogenic influence from the enterprise.

Sample No. 20K (vicinity of Al-Farabi Avenue, Farabi Hub area) contained 15 mg/kg of arsenic; the site was located near a major traffic artery with intensive movement, contributing to element accumulation through atmospheric emissions and dust deposition. Sample No. 1K (Dostyk Street–Zholdasbekov Street area) showed the maximum value in the dataset (16 mg/kg); the area was characterized by increased urbanization, active transport movement, and proximity to highways. Elevated arsenic concentrations were observed in zones with pronounced technogenic impact, where the combined influence of industrial enterprises, traffic flows, and atmospheric dust particle deposition led to element accumulation in the upper soil horizon. In the human body, arsenic causes lung cancer, skin diseases, ulceration, hematological effects, and anemia.

Zinc concentrations exceeded the MPC (55 mg/kg) in virtually all investigated samples. Particularly high values were recorded in samples No. 75, 114, 58, 64, and 83 (281–533 mg/kg, exceeding by 5–10 times). Sample No. 75 (Abylai Khan Street, 8/10) demonstrated the maximum (533 mg/kg); the territory was located in the central part of the city with intensive traffic, where the exceedance was associated with wear of automobile tires, brake pads, and batteries. Sample No. 114 (Makatayev Street, 129/1V) contained 281 mg/kg Zn, attributable to industrial emissions from metalworking production and electroplating processes.

Sample No. 75 (Abylai Khan Street, 8/10) demonstrated the maximum Zn concentration (533 mg/kg), associated with intensive traffic and tire/brake wear. Sample No. 114 (Makatayev Street, 129/1V) contained 281 mg/kg Zn, attributable to historical industrial emissions from metalworking and electroplating processes.

The combined influence of transport, thermal power, and machine-building enterprises led to the formation of persistent localized pollution hotspots, necessitating regular environmental monitoring.

Copper concentrations ranged from 19 to 181 mg/kg, exceeding the MPC (33 mg/kg) by 1.3–5.5 times in many cases. The highest values were observed in central, industrial, and transport-loaded districts. Sample No. 114 (Makatayev Street, 129/1V) contained 181 mg/kg; the source was the activity of a machine-building enterprise with emissions of copper-containing aerosols. Sample No. 35 (Tau–Samal microdistrict, V.G. Fesenkov Astrophysical Institute) showed 112 mg/kg, likely due to accumulation of dust particles from central highways. Sample No. 64 (Aqbulaq microdistrict, Ryskulov Street, 147, “Qazaq Oil” filling station) contained 61 mg/kg, resulting from traffic movement and localized sources of petroleum products.

Sample #20K (near Al-Farabi Avenue) showed 58 mg/kg, associated with traffic flows. Sample #83 (Seifullina Street, near CHPP-1) was 67 mg/kg, indicating the influence of heat and power production. Sample #75 (Abylay Khan Street, 8/10) contained 134 mg/kg, associated with traffic activity. Sample #79 (Algabas Microdistrict, 7th Street, 130, near CHPP-2) was 38 mg/kg, from heat and power emissions. Sample #66 (M.K. Gandhi Park) was 44 mg/kg, from atmospheric deposition. Sample #67 (Kurmangazy/Abylay Khana Street) was 44 mg/kg, from transport exposure. Sample #113 (Tole Bi Street, 189, AZTM) was 45 mg/kg, from historical emissions. Sample #62 (Terekti microdistrict, Heroes of the Second World War Park) was 36 mg/kg, from atmospheric transfer. Sample #42 (Kalkaman-2 microdistrict, Alatau recreation area) was 45 mg/kg, from transport and household emissions. Sample #25 (Alatau SGP microdistrict) was 45 mg/kg, from atmospheric deposition and road activity. Copper is one of the priority soil pollutants in Almaty. The most vulnerable areas are the central and industrial districts, where localized foci of technogenic pollution form under the influence of transport and industrial factors.

Nickel concentrations exceeded the MAC (20 mg/kg) in almost all samples (except for No. 39, Butakovka), ranging from 23 to 65 mg/kg (an excess of 1.5 to 3.2 times). The highest values were: No. 114 (65 mg/kg), No. 35 (47 mg/kg), No. 20K (34 mg/kg), No. 64 (30 mg/kg), and No. 83 (37 mg/kg). Elevated concentrations were typical for central, industrial, and transport-heavy areas.

The main sources were metallurgical and machine-building enterprises, thermal power facilities (CHP-1, CHP-2), and motor vehicles. Nickel accumulation was associated with atmospheric deposition and the low mobility of the element. Exceedances of the MPC indicated widespread technogenic pollution and the need for monitoring zones under industrial and transport impact.

Cobalt concentrations exceeded the MPC (5 mg/kg) in samples No. 67, 95, 35, 7K, 12K, and 42 (47–80 mg/kg, exceeding by 9–16 times). The highest values were recorded in: Alghabas microdistrict near CHP-2 (80 mg/kg), Zhandosov Street (66 mg/kg), Tau-Samal microdistrict (63 mg/kg), Kalkaman-2 microdistrict (64 mg/kg), southern industrial zone (69 mg/kg), and the intersection of Kurmangazy and Abylai Khan Streets (51 mg/kg). Concentrations were confined to areas with industrial infrastructure and traffic load; sources included enterprise emissions, fuel combustion, and vehicle wear. Cobalt distribution exhibited a hotspot pattern, with localized technogenic anomalies in industrial-transport districts, requiring regular monitoring and assessment of migration activity.

Molybdenum concentrations generally did not exceed the MPC (4 mg/kg), although slight exceedances (4.5–7.0 mg/kg) were observed at several points: AZTM territory (Tole bi Street, 189), Aqbulaq microdistrict (Ryskulov Street, 147, “Qazaq Oil” filling station), industrial zone along Severnoye Koltso Street, Zhandosov Street area, Sain Street, SGP “Alatau”, and the industrial zone on Severnoye Koltso. Exceedances were characteristic of sites near traffic flows, filling stations, thermal power, and machine-building enterprises; sources included dust emissions from fuel combustion, wear of vehicle components, and wastewater. Despite the relatively low levels, systematic molybdenum accumulation warranted monitoring.

Manganese concentrations (529–872 mg/kg) did not exceed the MPC (1500 mg/kg) and corresponded to natural background levels. Characteristic values were recorded in M.K. Gandhi Park, AZTM territory, Aqbulaq microdistrict, vicinity of the V.G. Fesenkov Observatory, SGP “Alatau”, “Alatau” recreation zone, and the vicinity of Farabi Hub. The content was determined by natural geochemical processes of weathering and accumulation.

Elevated concentrations were also observed in areas with high human presence. In the Central Park of Culture and Leisure named after Gorky, MPC exceedances were recorded for nickel (2 times), zinc (1.9 times), arsenic (3.5 times), and cadmium (8.2 times). In the vicinity of Almaty-2 railway station: lead (1.5 times), cadmium (4 times), zinc (1.7 times), and arsenic (3.5 times). In the area of the central stadium and circus: lead (2 times), cadmium (8 times), zinc (1.5 times), and arsenic (4 times). Near the airport: lead (2 times), cadmium (12 times), zinc (1.5 times), and arsenic (8 times).

Overall, localized areas of intensive technogenic pollution with MPC exceedances for Pb, Cd, As, Zn, Cu, Ni, Co, and Mo were identified in the urbanized territories. The most contaminated samples were No. 75, 114, 58, 64, 83, 44, 12K, and 107, indicating high technogenic load and the need for further monitoring and pollution reduction.

The results of the comprehensive analysis of heavy metal content, morphological characteristics, and spatial distribution of pollution indicators (Kc and Zc) enabled the identification of several localized areas with elevated technogenic load within Almaty city. These zones are associated with industrial districts, transport hubs, and older residential areas, where accumulation of priority pollutants—Pb, Zn, Cu, and Ni—was observed.

The central and eastern parts of the city (Rayymbek Avenue–Sain Street–Almaty-2 area) were characterized by extremely high integrated pollution indices (Z_c > 128). The highest concentrations were recorded for Pb, Zn, and Cu (K_c = 4–8), corresponding to strong and very strong pollution levels. Morphologically, these soils were classified as urbanozems with inclusions of construction debris, signs of compaction, and darkening of the humus horizon. Pollution sources included motor vehicles, industrial enterprise emissions, and dust accumulation.

In the industrial zone of the eastern sector (Alatau district, industrial zone along Ryskulov–Sayaly Streets), a high pollution level was observed (K_c = 3–6; Zc = 60–110). Cu, Ni, and Co predominated here, with likely sources being industrial emissions and dust from metallurgical and construction activities. Soils exhibited slightly acidic reaction, dense structure, and reduced humus content. Morphological profiles showed technogenic inclusions and signs of secondary salinization.

The southeastern part of the city (Baum Grove–Bostandyq district) demonstrated moderate pollution levels, with Zn and Pb exceeding background values by 3–5 times (K_c = 2–4) according to analytical measurements. Soils fell into the moderately hazardous category (Z_c = 25–40). Thinning of the humus layer was recorded, although partial biogenic activity was preserved. These areas represented a transitional zone between industrial and recreational functions.

Foothill and western areas (Kokzhaylau and KazNU-city districts) maintained near-background conditions. In these zones, Z_c ≤ 16, and metal contents did not exceed background levels (K_c < 1–1.2). Soils exhibited natural morphology, well-developed humus horizons. These areas demonstrated stable ecosystem conditions, high buffering capacity, and potential for self-purification.

The analysis showed that the primary contaminants of urban soils in Almaty are lead (Pb), associated with transport arteries and older built-up areas with high K_c = 4–8; zinc (Zn), showing widespread distribution and forming technogenic halos around roads; copper (Cu), linked to industrial emissions and accumulation in the eastern and central sectors; and nickel (Ni), occurring together with Cu and Co and indicating specific technogenic sources, and metallurgical and machine-building facilities.

Correlation analysis confirmed a common origin for the Pb–Zn–Cu–Ni group, evidencing a unified mechanism of input and migration. In contrast, Mn, Co, and Mo retained a more background character, forming the geochemical baseline of the soils. The spatial distribution of contaminants reflected the combination of primary accumulation and secondary migration processes. In the central part of the city, accumulation and fixation of metals in the upper horizons predominated, due to low filtration capacity and weak drainage. In peripheral zones, partial redistribution of contaminants and formation of diffuse technogenic halos were observed.

The most hazardous areas in terms of combined indicators were the urbanozems of the eastern and central sectors, where the complex impact of pollutants led to structural degradation, loss of humus horizon, and reduced biological productivity of soils. These sites require priority monitoring and ecological rehabilitation.

The results of this study are directly related to several United Nations Sustainable Development Goals (SDGs).

SDG 3. Good Health and Well-being. Elevated concentrations of toxic elements such as lead, cadmium, and arsenic pose direct risks to public health.

SDG 11. Sustainable Cities and Communities. The identification of contaminated urban areas and the need for risk-based land management closely align with the objectives of this goal, which emphasizes the importance of reducing environmental risks in urbanized areas.

SDG 15. Life on Land. The observed degradation of soil properties, including reduced biological activity and disruption of biogeochemical cycles, reflects challenges related to the protection and restoration of terrestrial ecosystems.

The implementation of monitoring systems, remediation strategies, and sustainable land-use planning based on the findings of this study can contribute to achieving these goals at the regional level. The integration of these measures not only helps minimize current threats but also establishes a foundation for the city’s long-term environmental well-being.

Under conditions of rapid urbanization, soil contamination has become a critical factor limiting the sustainability of urban ecosystems. Urban soils perform essential ecological functions, including buffering pollutants, regulating water regimes, and supporting urban green infrastructure.

However, the accumulation of heavy metals significantly reduces their capacity to provide these ecosystem services. Despite growing recognition of this issue, the quality of urban soils is still insufficiently integrated into urban planning systems and sustainable development frameworks, particularly in Central Asian cities.

In this context, spatially oriented assessments of soil contamination are crucial for supporting science-based environmental management and sustainable urban development.

4. Discussion

The conducted study enabled a comprehensive assessment of the distribution patterns of heavy metals (Pb, Cd, As, Zn, Cu, Ni, Co, Mo, Mn) in the soil cover of Almaty, revealing significant technogenic pressure on urbanized ecosystems. Based on the analysis of 73 soil samples, it was established that systematically elevated elements are As (K_c ≈ 5 in 97% of points), Zn (exceedances in 98% of samples), and Ni (K_c > 1 in nearly all points), reflecting the combined influence of transport, industrial emissions, and atmospheric deposition [43]. The integrated pollution index Z_c classified more than 70% of the samples as hazardous or extremely hazardous (Z_c > 32), with hotspots in the central and eastern districts where Pb, Zn, and Cu accumulate [45]. Correlation analysis confirmed the technogenic association of Pb–Zn–Cu–Ni (ρ > 0.6), whereas As–Co–Mo–Mn exhibit a predominantly geogenic background with weaker linkages, though localized anthropogenic inputs cannot be excluded [17].

Technogenic pollution disrupts soil morphology (compaction, inclusions), physico-chemical properties (reduced pH and humus content), and biological properties, leading to dehumification and decreased self-purification capacity [6]. In comparison with the Atyrau region, where industry intensifies erosion (2 billion t/year, losses of $40 billion) and reduces porosity [19], and Western Kazakhstan, where 70% of samples exceed norms for Pb/Cd and 90% for As with high ecological risk indices (ERI) (r = 0.95 between As and mining) [18], Almaty exhibits moderate but progressive contamination, exacerbated by the semi-desert climate (wind resuspension, limited leaching).

The results underscore the need for priority monitoring in industrial zones and public areas (parks, railway stations), where multiple exceedances (e.g., Cd ×12 near the airport) increase risks of bioaccumulation and health impacts [20]. It is recommended to implement phytoremediation (e.g., sunflower for heavy metal extraction) [23], barriers in hotspots, and GIS-based zoning in accordance with R 2.1.10.1920-04 [20], which can reduce erosion by 15–20% and exposure risks [15]. This aligns with the UN Sustainable Development Goals: SDG 3 (reducing health impacts), SDG 11 (sustainable cities), and SDG 15 (ecosystem protection) [22].

Overall, pervasive heavy metal contamination in Almaty soils, amplified by geoclimatic factors, requires urgent policies ranging from monitoring to adaptive remediation. The study provides a foundation for sustainable soil management in semi-arid urbanized regions of Central Asia, contributing to long-term ecosystem resilience.

The present study aimed to conduct an ecological assessment of the distribution patterns of heavy metals (Pb, Cd, As, Zn, Cu, Ni, Co, Mo, Mn) in the soil cover of Almaty using regional data to identify contamination forms, spatial hotspots, and migration risks.

The obtained data confirm the hypothesis of heterogeneous technogenic pollution: systematic exceedances of norms for As, Zn, and Ni (K_c > 2 in >90% of samples), Z_c > 32 in >70% of cases with hotspots in the center and east, strong correlations within the Pb–Zn–Cu–Ni group (ρ > 0.6), and soil degradation (morphological, physico-chemical, biological). The objective was achieved: total concentrations of heavy metals, spatial hotspots, and migration risks under semi-desert conditions were identified.

The novelty of this study lies in the simultaneous integration of geochemical indices (K_c, Z_c), multivariate statistics (Spearman correlation), and GIS-based spatial interpolation applied to a comprehensive set of 9 heavy metals across the full urban extent of Almaty (683.5 km²). Unlike previous regional studies limited to 4–6 elements over smaller or functionally homogeneous areas [5,7,9], this work reveals a dual pollution regime—a technogenic association (Pb–Zn–Cu–Ni) driven by transport and industry, contrasting with a geogenic background (As–Co–Mo–Mn)—and links spatial pollution patterns to soil morphological, physicochemical, and biological degradation indicators. This integrated framework provides a replicable methodological basis for ecological risk assessment in semi-arid urbanized settings of Central Asia.

Recommendations include prioritized monitoring and phytoremediation in hotspots [15,25], potentially augmented by advanced catalytic methods as demonstrated in recent heterojunction and electro-Fenton studies [23,24], aligning with UN SDGs. The spatial patterns identified through GIS analysis can be directly integrated into urban planning systems to support risk-based zoning. This will enable decision-makers to clearly distinguish between areas of acceptable, moderate, and high environmental risk.

The results emphasize risks of bioaccumulation and desertification, threatening ecosystem sustainability (SDG 15) and human health (SDG 3). Practically, they justify priority monitoring of hotspots, phytoremediation (reducing heavy metals by 20–40%), and regulatory measures (SDG 11), minimizing exposure for 2 million residents [4].

Overall, the study demonstrates the necessity of adaptive strategies for soil conservation in Almaty, contributing to global efforts toward sustainable development in urbanized semi-desert zones.

The limitations of the study include focus on the surface layer (0–20 cm), neglecting vertical migration; the use of Russian APCs due to the absence of national standards for some elements; the sample size (n = 73) covering mainly urbanized zones but not accounting for seasonal variations; and the exploratory nature of kriging interpolation. Therefore, the interpolation results should be interpreted as screening-level spatial visualizations rather than fully validated predictive geostatistical models. Formal spatial cluster statistics such as Global Moran’s I and Getis-Ord Gi were not applied.

5. Conclusions

The obtained data confirm the hypothesis of the heterogeneous distribution of heavy metals (HMs) in the soils of Almaty, driven by anthropogenic gradients. Median concentrations (Pb: 24 mg/kg, As: 10 mg/kg, Zn: 111 mg/kg, Cu: 35 mg/kg, Ni: 41 mg/kg) with K_c exceedances for As (median ≈5 in 97% of samples), Zn (98%), and Ni (>1 in nearly all samples) indicate systematic technogenic pressure. The integrated index Zc classified >70% of samples as hazardous or extremely hazardous (>32), with hotspots in the central-eastern districts. Strong correlations (ρ ≥ 0.6 for Pb–Zn–Cu–Ni) revealed a technogenic association, in contrast to predominantly geogenic elements with possible anthropogenic contribution (As–Co–Mo–Mn). Pollution causes morphological degradation (compaction, inclusions), physico-chemical shifts (reduced pH and humus content), and biological degradation, disrupting ecosystem resilience under semi-desert conditions.

Compared to previous regional studies in Kazakhstan, which typically analyzed 4–6 elements over smaller areas [5,7,9], this study advances the field by covering 9 heavy metals across 683.5 km² and by integrating geochemical, statistical, and spatial methods within a unified analytical framework applicable to semi-arid urbanized regions. The results provide a solid basis for prioritized monitoring, phytoremediation in hotspots, and risk-based urban planning, contributing to the achievement of UN Sustainable Development Goals (SDG 3, SDG 11, and SDG 15).