Groundwater Quality, Contamination, and Resource Potential for Pasture Livestock Watering in Arid Western Kazakhstan

Abstract

Groundwater is the primary source of livestock watering across the arid pasturelands of western Kazakhstan, yet no systematic field hydrochemical assessment has been published for this region in over 40 years. This study presents the first systematic field-based hydrochemical characterisation of groundwater sources used for pasture livestock watering in the West Kazakhstan Region and Aktobe Region, filling a critical data gap that has persisted since the Soviet era. Specifically, it characterises the hydrochemistry, water quality, and infrastructure condition of groundwater sources, and evaluates the groundwater resource potential against current and projected livestock water demand. A total of 139 groundwater samples were collected along 11,182 km of field routes during May–July 2025, and analysed for 25 physicochemical parameters; hydrochemical classification was performed using AquaChem 11, and spatial analysis was conducted in ArcGIS 10.8. The groundwater chemistry distribution is bimodal: fresh bicarbonate-calcium-magnesium waters (TDS < 3.0 g/L) constitute approximately 80% of samples, while highly mineralised chloride-sulphate-sodium waters (TDS up to 9.91 g/L) occur in salt-dome-influenced discharge zones. Nitrate concentrations exceeded 50 mg/L in 23–36% of samples, with maxima of 635 mg/L, reflecting intensive anthropogenic contamination near livestock facilities. Predictive exploitable fresh groundwater resources exceed current livestock demand by a factor of 162. The principal constraint on pasture water supply is not resource scarcity but the non-operational status of 51–75% of inspected watering infrastructure, a legacy of post-Soviet institutional collapse that requires urgent rehabilitation.

1. Introduction

Managed pastures and grasslands cover nearly 33 million km² of land surface and approximately 70% of all agricultural land, and support more than half of the world’s livestock. Globally, the livestock population tripled between 1970 and 2011, reaching an estimated 24.2 billion head, placing unprecedented pressure on land and water resources in semi-arid and arid regions. In these arid environments, access to water is the single most important factor in pasture use; without reliable water sources, large tracts of productive pasture remain inaccessible to livestock, limiting both productivity and food security [1,2]. Groundwater, rather than intermittent surface water, forms the primary water supply for livestock in most arid and semi-arid rangeland systems [3,4]. However, in many regions, its hydrochemical suitability for livestock watering has never been systematically characterised.

Among the 28 countries where more than half of the land is classified as arid, 13 rely on groundwater as their primary fresh-water source [5,6], and global groundwater withdrawals more than doubled between 1960 and 2000, with total depletion reaching an estimated 283 ± 40 km³/yr [7]. In the flat Pre-Caspian lowlands of western Kazakhstan, the dominant hydrochemical processes are evaporative concentration and halite–gypsum dissolution, producing characteristically high total dissolved solids (TDS) and sulphate-chloride water types [8,9]. The superposition of anthropogenic nitrate pollution from livestock and agricultural sources poses an additional threat to groundwater quality in these settings [10,11].

Kazakhstan ranks fourth in the world in terms of pasture area; as of 2023, according to national statistics, rangelands amount to 183.7 million hectares, which is 67.4% of the country’s total land area [12]. Despite these vast resources, over 56.5% of rangelands remain virtually unusable due to the lack of water supply facilities, including wells, boreholes, and related livestock watering infrastructure [13]. The collapse of the Soviet collective farm system in the early 1990s was a disaster for rural water infrastructure: state-owned networks of artesian wells, shaft wells, and pipeline systems were abandoned, fell into disrepair, or were dismantled for scrap metal [14,15]. Since then, an estimated 27 million hectares of pastures near settlements have been severely degraded due to overgrazing, caused by the concentration of livestock within a 5–7 km radius of settlements where water is still available.

In the traditional Kazakh pastoral system, livestock are moved through a cycle of four seasonal pasture zones: winter camps (kystau), located near settlements with sheltered conditions and access to supplemental feed; spring pastures (kokteu), where livestock graze on early vegetation growth; summer pastures (zhailau), the most distant from settlements, typically in well-watered upland or riverine areas; and autumn pastures (kuzeu), which serve as transitional zones before the return to winter quarters. For practical management purposes, these are commonly grouped into spring–summer and autumn–winter rotations. This seasonal movement ensures that each pasture zone has an extended rest period for vegetation recovery—a cycle that is disrupted when watering infrastructure at remote seasonal pastures becomes non-functional, forcing livestock to remain year-round within a 5–7 km radius of settlements where water is still accessible. As a result, remote seasonal pastures are being progressively abandoned, and the traditional rotational cycle has largely disappeared across the study area [16,17]. Currently, more than 75% of agricultural land in Kazakhstan is subject to some form of degradation, and more than 87% of pastures in the Central Region are classified as being in poor or extremely poor condition according to remote sensing data [18,19]. Climate model projections indicate a significant increase in aridity in Central Kazakhstan by mid-century, with a 16% reduction in moisture reserves during the growing season and a 17% increase in the aridity index [20,21], which will further reduce the regeneration capacity of pasture vegetation and increase livestock’s dependence on artificial water sources. Under these conditions, groundwater-based water supply infrastructure becomes not only economically viable but also ecologically necessary for sustainable livestock farming [19].

The West Kazakhstan Region (WKR) and Aktobe Region are administrative divisions of Kazakhstan equivalent to provinces (officially termed “Oblasts” in Russian and Kazakh). Together, they cover approximately 432,000 km² in the northwestern arid zone of Kazakhstan and are located within the Caspian lowland and the eastern edge of the Caspian artesian basin. The WKR contains 11.06 million hectares of pasture (80.9% of its area), while the Aktobe Region contains 25.3 million hectares (85.5% of its area), together accounting for approximately 20% of national pasture resources. As of 2023, only 37.7% of pastures in the Aktobe Region and 74.1% of pastures in the WKR are officially classified as “watered” (i.e., served by at least one functional livestock watering point), with actual provision likely lower due to the widespread destruction of existing infrastructure. The two regions collectively house approximately 2.1 million head of livestock (cattle, small ruminants, horses and camels), and the total estimated daily stock water requirement for livestock is approximately 48,430 m³/day. The Aktobe Region has one of the highest concentrations of hydrocarbon production and processing facilities in Kazakhstan, creating specific risks of oil pollution of groundwater, while both regions are exposed to industrial nitrogen emissions from petrochemical and agricultural activities [22]. The hydrogeological structure of the region is complex: the WKR is located in the north of the Caspian Basin with widespread salt-dome tectonics and marine Pliocene–Quaternary sediments, while the Aktobe Region covers several second-order hydrogeological basins (i.e., sub-basins nested within the larger Pre-Caspian and Ural-Mugalzhar first-order artesian basins, each with distinct aquifer geometry and recharge conditions), including the Zhemsky, Dongyztau-Predmugalzharsky and Shalkarsky basins.

Hydrochemical studies in neighbouring regions of Kazakhstan consistently confirm the hydrochemical duality characteristic of arid Caspian territories: fresh or slightly brackish HCO₃-Ca-Mg waters in recharge zones and under better-drained structural conditions contrast with highly mineralized Cl-SO₄-Na waters in discharge zones and areas of intense capillary evaporation [23]. In a 2025 groundwater quality assessment in western Kazakhstan, TDS contents ranged from 0.5 to >10 g/L, with pollution from petroleum refining byproducts identified as an additional concern in the Aktobe and Atyrau regions [22].

Nitrate pollution of shallow groundwater is increasingly recognised as a pervasive threat to rural water supply security in Central Asia and globally. Anthropogenic sources, including infiltration from livestock manure storage facilities, poorly maintained septic systems, and municipal waste near wells, can elevate groundwater NO₃⁻ concentrations to several hundred mg/L in rural pastures [24,25,26], significantly exceeding the WHO drinking water guideline value of 50 mg/L and Kazakhstani standards for livestock watering. A 2025 study of pasture groundwater sources in southern Kazakhstan (Almaty and Zhetysu regions) found that approximately 40% of samples exceeded WHO guidelines for total dissolved solids, suggesting widespread evidence of anthropogenic pollution [27,28]. Fluoride enrichment of groundwater is a characteristic feature of aquifers in arid zones of Central Asia, associated with weathering of fluoride-containing minerals in the Paleozoic crystalline basement and evaporative concentration in shallow unconfined aquifers [29,30,31]. Elevated fluoride levels (>1.5 mg/L) negatively affect livestock productivity and bone health, and can also degrade milk quality in dairy cattle, making it a significant parameter for assessing water quality in pastures [32,33].

Despite the critical importance of groundwater for Kazakhstan’s livestock sector, which accounts for approximately 5% of national GDP and supports rural populations across vast, arid areas, systematic field hydrochemical assessments of pasture water supplies in western and central Kazakhstan have been absent from the published scientific literature for over 40 years. Existing data, mostly collected in the 1970s and 1980s in the Soviet Union, predate significant changes in land use, industrial activity, and climate following independence. Official government statistics classify pastures as “watered” based on the historical presence of water supply infrastructure in the registry, regardless of current operational status. As a result, these statistics substantially overestimate the actual availability of livestock watering points, as documented by field inventories conducted in the present study [14,34]. Recent hydrogeological and hydrochemical studies by the U.M. Akhmedsafin Institute of Hydrogeology and Geoecology have begun to address this gap for southern Kazakhstan regions [27,28,35], but no comparable modern assessment exists for western Kazakhstan and the Aktobe Region, which together contain 36.4 million hectares of pastureland and represent the most severe combination of low water tables, high livestock densities, and complex hydrogeological conditions in the country.

This study addresses these knowledge gaps through a comprehensive hydrochemical field survey of 139 groundwater sources (shaft wells, boreholes, and springs) used for watering pasture cattle in the Western Craton and Aktobe Region, conducted in May–June 2025 along 11,182 km of field routes. The specific objectives of the study were to (i) characterise the hydrochemistry, total salinity, major ion chemistry, and suitability of groundwater for livestock consumption under field conditions in both regions; (ii) determine the spatial distribution and likely anthropogenic sources of key pollutants—nitrate, fluoride, and ammonium—at pasture watering sites; (iii) estimate the projected exploitable groundwater resources of prospective aquifers suitable for new or rehabilitation of existing watering infrastructure; and (iv) create a GIS-integrated baseline hydrochemical dataset to support evidence-based planning of pasture watering infrastructure to achieve Kazakhstan’s livestock development goals by 2050.

2. Materials and Methods

2.1. Study Area

The study encompasses the WKR (97,726 km², centred approximately at 51° N 51° E) and Aktobe Region (300,629 km², centred approximately at 50°17′ N 57°10′ E), situated in the north-western arid zone of Kazakhstan within the Pre-Caspian lowland and the eastern margin of the Caspian artesian basin.

The WKR contains 11.06 million ha of pastureland (80.9% of its total area), of which 74.1% is officially classified as watered [13]. The Aktobe Region holds 25.3 million ha of pastureland (85.5% of region area), with only 37.7% formally classified as watered. The primary river systems are the Zhaiyk (Ural) with its tributaries Ilek, Oyil, and Zhem in the WKR, and the Zhem, Oyyl, Sagyz, and Irgiz in the Aktobe Region. Surface water resources are minimal and highly seasonal; groundwater constitutes the primary or sole source of livestock watering on remote seasonal pasturelands.

The hydrogeological framework of the study area encompasses multiple second-order basins of the Pre-Caspian and Ural-Mugalzhar artesian systems. The WKR is underlain by the northern Pre-Caspian Basin, characterised by widespread salt-dome tectonics and thick sequences of marine Pliocene–Quaternary sediments. The Aktobe Region spans the Zhemsky, Dongyztau-Predmugalzhar, Shalkar, and North-Usturt second-order basins, with a complex structural and lithological mosaic ranging from Quaternary alluvial sands to Cretaceous, Jurassic, and Triassic sedimentary and crystalline-basement aquifer systems. The location of the study area, groundwater sampling points, and principal hydrogeological basins are shown in Figure 1.

Geological and Hydrogeological Setting

The stratigraphic sequence underlying the study area spans from Quaternary alluvial and aeolian deposits at the surface through Neogene, Palaeogene, and Cretaceous (including the Alb-Cenomanian stage) sedimentary formations, to Jurassic–Triassic continental deposits and the Pre-Palaeozoic crystalline basement. Each major stratigraphic unit hosts one or more aquifer horizons with distinct hydrochemical and hydraulic characteristics. The Quaternary alluvial aquifers, accessed by the shallow shaft wells that are the focus of this study, are unconfined, highly vulnerable to surface contamination, and exhibit variable salinity depending on local geology. The deeper Cretaceous (Alb-Cenomanian) and Jurassic–Triassic horizons are confined artesian aquifers that offer higher yields and better water quality but require borehole drilling. The geological structure is further complicated by widespread Permo-Triassic salt-dome tectonics in the Pre-Caspian lowland, which locally elevates groundwater salinity even at shallow depths.

2.2. Field Survey Design

Regional field expeditions were carried out during May–July 2025 by two dedicated hydrogeological teams operating simultaneously across the WKR and Aktobe Region. Prior to fieldwork, pre-field-stage preparation was completed: consolidated registers of previously documented wells and self-flowing boreholes were assembled from state geological fund archives, and preliminary route-survey maps were compiled for each administrative region.

2.2.1. Route Survey and Spatial Coverage

In the Aktobe Region, field surveys covered 5800 km of routes across the Aitikebi, Alga, Baigani, Kobda, Martuk, Mugalzhar, and Temir administrative districts. In the WKR, routes totalled 5382 km across the Kaztal, Zhympity, Tereñqudyq, Taipak, Kaztalovka, Saikhyn, and Aizhyn districts. Route types were classified as asphalted road (50%), unpaved steppe track (40%), and marshy or low-lying terrain (5% each) in the WKR; distribution was similar in the Aktobe Region. All routes were designed to maximise spatial coverage of active pastureland watering points, including seasonal (spring–summer and autumn–winter) grazing areas.

2.2.2. Watering-Point Inspection and Documentation

At each inspected object, the following parameters were recorded in a standardised field log: geographic coordinates and absolute elevation (GPS devices, accuracy ≤ 5 m); type of water supply structure (shaft well, tubular borehole, spring, or open water body); depth to static water level (measured by electric contact level gauge or weighted tape); well/borehole depth and casing diameter; presence, type, and operational status of pumping equipment and water-storage reservoirs; technical condition category (operational, requires repair, non-operational/abandoned); and sanitary–ecological condition of the surrounding area (see

Table 1. A summary of field survey activities in the study area (May–June 2025).

Region	Route Length, km	Shaft Wells Inspected	Boreholes Inspected	Springs	Samples Collected
Aktobe Region	5800	95	5	—	100
WKR	5382	39	—	—	39
Total (study area)	11182	134	5	—	139

).

The technical condition of watering infrastructure was assessed using the following classification: (1) good/satisfactory—functional without additional intervention; (2) requires repair or partial reconstruction—lacking at least one essential equipment element; (3) non-operational—completely destroyed or permanently abandoned. This classification follows the methodology of the State Geological Fund of the Republic of Kazakhstan and KazNIIVKh (Kazakh Research Institute of Water Management).

The shaft wells (shahtny kolodets) that constitute the primary groundwater access points in the study area are hand-dug or machine-excavated vertical openings, typically 1–2 m in diameter and 2–20 m deep, lined with concrete rings or stone masonry. In their original design, these wells were equipped with a concrete apron around the wellhead, a lockable metal or wooden cover, and a perimeter fence to prevent livestock from approaching the well opening. However, field inspection revealed that the vast majority of wells in both regions have lost these protective elements: covers were absent at over 80% of sites, concrete aprons were destroyed or absent at over 70%, and perimeter fences were absent at over 90%. As a result, livestock routinely congregate directly at the well opening, and animal waste accumulates within the immediate vicinity of the wellhead. Under these conditions, surface runoff carrying dissolved nitrate from decomposing manure has direct access to the water table through the unprotected annular space between the well lining and the surrounding soil—a short-circuit pathway that bypasses the natural attenuation capacity of the unsaturated zone. Field photographs illustrating typical well conditions are provided in Figure A1 and Figure A2 (Appendix B).

2.3. Groundwater Sampling Protocol

Groundwater samples were collected from shaft wells (shahtny kolodets), self-flowing boreholes, pump-equipped boreholes, and springs. Sampling was performed in accordance with national groundwater sampling standards [36,37], which are harmonised with international protocols [38].

For shaft wells, pre-sampling pumping was performed for a minimum of 5–10 min until field-measured TDS (electrical conductivity) stabilised, ensuring that stagnant surface water was fully purged. For self-flowing boreholes, samples were collected directly from the discharge point after at least 2 min of free flow. For pump-equipped boreholes, sampling was performed at the pump outlet after purging.

The samples were collected in polyethylene (HDPE) and glass containers of 1.0–2.0 L capacity, pre-rinsed three times with the sample water. Containers for heavy metal analysis were acidified with HNO₃ to pH < 2 and kept separate from other sub-samples. All containers were labelled, sealed, stored in portable coolers at 4 °C, and transported to the laboratory within 24–48 h of collection. A total of 139 samples were collected across the study area (100 from the Aktobe Region; 39 from the WKR) during the period May–June 2025. Individual sample locations, dates, and complete physicochemical analyses are compiled in

Table A1. Results of chemical analytical studies of groundwater samples.

Place of Selection, Village	Number	Coordinates (Latitude/Longitude)		Selection Date	pH	Dry Residue, mg/dm3	Cations, mg/dm3						Anions, mg/dm3						F, mg/dm3	Mineralization, mg/dm3	Water Hardness		SiO2
							Na	K	Ca2+	Mg2+	NH4+	Fetotal	CO3−	HCO3−	Cl−	SO42−	NO3−	NO2−			Total	Carbonate
1	2	3		4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23
Aktobe Region
Well No. 83	1	50.41087	56.14196	19.05.2025	7.41	366	83.7	1.5	47	17	<0.05	<0.1	<8	210.5	81.6	59.3	2	<0.01	0.9	515	3.75	3.45	11.6
Shk-101	2	50.1671	55.40269	20.05.2025	7.31	692	145	7.4	64.1	19.5	<0.05	<0.1	<8	131.2	184.4	123.5	24	<0.01	-	707	4.8	2.15	8.2
Shk-103	3	50.18059	55.86226	20.05.2025	7.45	1560	104.3	166	262.3	80.3	<0.05	<0.1	<8	439.3	382.2	288.1	196	8.3	-	1949	19.7	7.2	22.4
Shk-408	4	47.84699	59.13253	21.05.2025	7.51	470	76.7	1.9	72.1	25.5	<0.05	<0.1	<8	213.6	92.2	110.3	21	<0.01	0.7	629	5.7	3.5	14.7
Shk-396	5	47.83911	59.06729	21.05.2025	7.7	362	62.3	5	56.1	14.6	<0.05	0.1	<8	189.2	70.2	82.3	3	<0.01	0.3	499	4	3.1	16.5
Shk-397	6	47.86098	58.98073	21.05.2025	7.57	304	66.9	4.3	42	9.7	<0.05	<0.1	<8	173.9	53.9	50.2	12	<0.01	0.3	729	2.9	2.85	15.4
Shk-413	7	47.69549	59.1607	21.05.2025	6.72	925	202.9	6.7	84.1	26.1	<0.05	<0.1	<8	119	166.6	444.5	8	<0.01	-	1080	6.35	1.95	22.4
Shk-404	8	47.63889	58.97238	21.05.2025	7.45	2900	1000.1	11.7	48	31	<0.05	<0.1	<8	402.7	937.8	922	<0.2	<0.01	-	3392	4.95	4.95	38.5
Shk-401	9	47.7467	59.15927	22.05.2025	8.46	274	89.7	3	7	5.5	<0.05	<0.1	<8	140.3	62.8	49.4	<0.2	<0.01	1.5	377	0.8	0.8	18
Well No. 412, self-flowing	10	47.53408	59.07739	22.05.2025	7.17	4760	1797.9	16.5	54.1	41.3	<0.05	<0.1	<8	1263.1	1808.3	856.1	<0.2	<0.01	-	5877	6.1	6.1	39.9
Shk-411	11	47.65411	58.87546	22.05.2025	7.27	680	110	10.6	67.1	32.2	<0.05	0.2	<8	594.9	72.3	12.3	8	<0.01	1	930	6	6	21
Shk-422	12	47.34545	59.07001	23.05.2025	7.73	434	143.5	5.1	13	6.7	<0.05	<0.1	<8	231.9	73.8	102.9	4	<0.01	3.6	600	1.2	1.2	15.3
Shk-434	13	47.27486	59.11599	23.05.2025	7.75	1120	332.2	13	62.1	22.5	<0.05	<0.1	<8	314.2	260	354	19	<0.01	3.5	1398	4.95	4.95	17.5
Well No. 437, self-flowing	14	47.15052	58.92847	23.05.2025	7.49	2050	607.1	8.2	122.1	62	0.6	<0.1	<8	231.9	893.5	425.3	<0.2	<0.01	-	2362	11.2	3.8	10.9
Shk-442	15	47.13802	58.84879	23.05.2025	7.38	450	21.3	38.2	87.1	23.1	1.2	0.3	<8	286.8	44.3	123.5	<0.2	<0.01	0.5	644	6.25	4.7	17.5
Shk-432	16	47.05059	59.0493	23.05.2025	7.41	4000	609.3	177.7	322.3	229.8	0.6	<0.1	<8	317.3	443.2	2387.3	59	<0.01	-	4564	35	5.2	17.5
Shk-444	17	47.03317	59.05042	23.05.2025	7.75	860	290.1	6	31	14.6	<0.05	<0.1	<8	219.7	248.2	238.7	<0.2	<0.01	-	1071	2.75	2.75	22.5
Well No. 443, self-pouring	18	46.74996	59.18176	24.05.2025	6.88	760	37.9	5.9	109.1	34	<0.05	2.5	<8	140.3	21.3	397.9	<0.2	<0.01	1	773	8.25	2.3	22.8
Shk-446	19	46.87073	59.17474	24.05.2025	6.88	1100	358.6	7.5	45	28	<0.05	2.5	<8	274.6	336.8	271.7	<0.2	<0.01	-	1339	4.55	4.5	14.7
Shk-12	20	45.47901	56.93184	24.05.2025	7.04	6300	1312.5	207.5	412.4	367.2	<0.05	<0.1	<8	122	1772.9	2414.7	38	<0.01	4.4	6663	50.8	2	11.4
Shk-433	21	46.1375	58.88481	24.05.2025	7.01	424	137.1	2.2	14	6.1	<0.05	<0.1	<8	158.6	99.3	111.1	<0.2	<0.01	4.9	545	1.2	1.2	11.8
Shk-436	22	47.07309	58.88481	24.05.2025	7.52	2000	532.4	8.3	122.1	124	<0.05	<0.1	<8	225.8	879.3	399.3	<0.2	<0.01	-	2302	16.3	3.7	10.9
Shk-426	23	47.1375	58.84861	24.05.2025	7.44	458	54.4	59.8	66.1	12.3	2	<0.1	<8	256.3	29.3	79.9	53	<0.01	0.5	635	4.31	4.2	21.5
Shk-427	24	47.2892	59.24149	24.05.2025	7.85	1675	118	350.3	46	124	1.8	<0.1	<8	732.2	212.7	315.6	301	<0.01	-	2223	12.5	12	20.9
Shk-425	25	47.30851	59.29142	24.05.2025	7.27	688	59	126.6	36	73	0.7	<0.1	<8	527.8	51.4	135.8	<0.2	<0.01	1.9	1028	7.8	7.8	16.2
Shk-335	26	48.64278	61.18167	25.05.2025	7.58	1208	205.6	278.1	52.1	15.8	0.5	<0.1	<8	402.7	155.1	156.4	232	6.4	-	1531	3.9	3.9	26
Shk-385	27	48.63291	60.89884	25.05.2025	59.3	45.8	54.1	45.8	54.1	14.6	<0.05	<0.1	<8	231.9	47.2	60.1	28	6.4	0.5	574	3.9	3.8	25.9
Shk-384	28	48.70905	61.1771	25.05.2025	7.24	5250	1545.8	14.1	276.3	180	<0.05	<0.1	<8	268.5	1232.1	2140.3	260	<0.01	-	5937	28.6	4.4	18.6
Shk-395	29	48.92394	61.36891	25.05.2025	6.78	870	197.1	11.7	55.1	38.9	<0.05	<0.1	<8	241	138.3	345.7	37	<0.01	-	1090	5.95	3.95	25.5
Shk-393	30	48.75134	61.44064	25.05.2025	7.4	7000	2342	25.3	260.3	201.9	<0.05	<0.1	<8	341.7	2287	2428.4	<0.2	<0.01	-	7912	29.6	5.6	25.4
Shk-392	31	48.76968	61.50015	25.05.2025	7.3	900	151.7	39.1	91.1	23.1	<0.05	<0.1	<8	250.2	191.5	216.1	88	<0.01	-	1059	6.45	4.1	8.1
Shk-341	32	49.04591	61.96871	26.05.2025	7.33	745	107.2	8.2	113.1	37.1	<0.05	<0.1	<8	161.7	368.3	79.9	17	<0.01	-	902	8.7	2.65	9.5
Shk-328	33	48.55183	61.68936	26.05.2025	7.53	1250	303.4	31.4	94.1	37.7	<0.05	<0.1	<8	335.6	106.4	370.4	251	<0.01	-	902	8.7	2.65	19.9
Shk-377	34	48.5147	61.73087	26.05.2025	8.04	2700	881	9.7	110.1	64.4	0.9	<0.1	<8	805.5	936.1	514.5	12	<0.01	-	3353	10.8	10.8	19.3
Shk-Sarkopa	35	50.52128	61.49324	26.05.2025	6.6	200.00	11.00	9.10	36.00	7.30	0.80	<0.1	<8	100.70	9.00	33.00	54.00	<0.01	0.40	275.00	2.40	1.65	13.7
Shk-Karasor	36	49.89127	60.31149	27.05.2025	7.48	2249.00	602.50	3.20	44.00	86.30	0.50	<0.1	<8	378.30	608.00	587.00	188.00	<0.01	-	2512.00	9.30	6.20	14.1
Shk-2406	37	49.86284	60.46457	27.05.2025	7.56	316.00	36.00	0.90	46.00	26.80	0.20	<0.1	12.00	262.45	16.00	42.00	25.00	<0.01	2.10	485.00	4.50	4.50	16.0
Shiliktisay-Shk	38	50.05628	59.12956	30.05.2025	7.43	914.00	46.30	1.50	194.20	54.70	0.40	<0.1	<8	207.50	162.00	130.00	302.00	<0.01	0.80	1116.00	14.20	3.40	16.6
Shk-Bogetsay	39	50.11873	59.09149	30.05.2025	7.04	833.00	42.00	1.20	192.20	29.20	0.40	<0.1	<8	244.10	76.00	153.00	272.00	11.40	0.80	1047.00	12.00	4.00	24.6
Shk-Kop aul	40	49.97443	58.59907	30.05.2025	7.1	598.00	87.10	3.10	100.10	13.40	0.20	<0.1	<8	170.90	45.00	166.00	85.00	<0.01	-	694.00	6.10	3.00	23.7
Shk-Sukhinovka	41	50.18139	58.32278	30.05.2025	6.99	3958.00	879.40	11.60	188.20	214.00	0.50	<0.1	<8	402.70	1233.00	896.00	612.00	<0.01	-	4461.00	27.00	6.60	23.7
Shk-Nogai	42	49.811	60.62388	02.06.2025	7.38	750	98.4	21.9	90.1	31.6	0.6	<0.1	<8	213.6	148	187	98	<0.01	-	907	7.1	3.5	17.5
Shk-232	43	50.03094	60.48285	02.06.2025	7.36	1184	201.5	24.5	102.1	53.5	0.4	<0.1	<8	244.1	397	157	138	<0.01	-	1335	9.5	4	16.7
Shk-246	44	49.96488	60.20969	02.06.2025	7.44	1359	241.9	26.4	134.1	63.2	0.6	<0.1	<8	299	442	179	192	<0.01	-	1597	11.9	4.9	18.9
Shk-Korpe	45	49.97946	60.03759	02.06.2025	7.32	338	38.8	1	54.1	24.3	0.4	<0.1	<8	292.9	39	30	25	<0.01	1.2	526	4.7	4.7	18.9
Shk-Kyzyltu	46	49.99816	60.13399	02.06.2025	7.53	1665	432.2	5.1	90.1	65.7	0.2	<0.1	12	451.5	58	747	128	<0.01	-	2001	9.9	7.8	11.1
Shk-144	47	50.17993	60.02708	02.06.2025	7.48	248	59.4	5.6	26	4.9	0.4	<0.1	<8	122	63	32	6	<0.01	0.7	329	1.7	1.7	9.6
Shk-146	48	50.17641	60.18168	02.06.2025	7.58	399	49.9	7.7	80.1	18.2	1	<0.1	<8	335.6	89	7	3	<0.01	0.5	607	5.5	5.5	15.5
Shk-140	49	50.21873	60.14505	02.06.2025	6.82	583	60	3.3	80.1	53.5	0.2	<0.1	<8	506.5	42	105	24	<0.01	0.5	891	8.4	8.3	15.9
Shk-288	50	50.34204	60.75228	03.06.2025	7.22	425	102	26.4	40	14.6	0.1	<0.1	<8	286.8	71	48	7	<0.01	1	616	3.2	3.2	19.3
Shk-Yuz.Sarat	51	50.07614	61.02496	03.06.2025	7.39	429	103.8	19.7	36	14.6	1	<0.1	<8	299	76	49	5	<0.01	0.9	625	3	3	19.6
School-s.Sarat South	52	50.08715	61.02273	03.06.2025	7.16	1055	205.7	32.3	86.1	51.1	4	<0.1	<8	250.2	195	321	89	7	-	1258	8.5	4.1	16.5
School-s.Sarat Center	53	50.10417	61.03413	03.06.2025	7.56	1497	394.3	1.3	76.1	60.8	0.2	<0.1	<8	268.5	266	459	150	<0.01	0.4	1694	8.8	4.4	17.5
Shk-277 Uzynkol	54	50.00947	60.88682	03.06.2025	7.09	488	108.7	28.5	48	17	0.8	<0.1	<8	250.2	65	80	42	8	0.7	663	3.8	3.8	13.9
Shk-Kozhakol	55	49.91566	61.22094	04.06.2025	6.46	136	7	12.7	30	3.6	0.4	<0.1	<8	128.1	9	10	3	1.1	0.2	212	1.8	1.8	6.9
Shk-Yug Zhuban	56	49.97843	61.27534	04.06.2025	7.75	1122	296.8	7.6	50.1	51.1	0.2	<0.1	<8	256.3	358	265	6	<0.01	-	1306	6.7	4.2	14.5
Shk-Bostaban-262	57	50.00156	61.27534	04.06.2025	7.1	487	111	8.4	50.1	19.5	1	<0.1	<8	247.8	46	106	<0.2	<0.01	3	709	4.1	4.1	15.9
Shk-Mayamir	58	49.8634	61.42191	04.06.2025	7.24	230	22.4	10.8	42	6.1	0.5	<0.1	<8	128.1	10	78	<0.2	<0.01	0.7	310	2.6	2.1	11.2
School-s.Baskuduk	59	49.74236	61.41704	04.06.2025	7.53	392	71.6	20.2	38	15.8	1	<0.1	<8	262.4	27	100	<0.2	<0.01	1.9	556	3.2	3.2	17.6
Shk-Sarybulak	60	50.33947	61.33482	04.06.2025	7.36	992	183.3	6.8	72.1	59.6	0.1	<0.1	<8	305.1	73	261	259	<0.01	2.1	1236	8.5	5	14.1
Shk-258	61	50.08807	61.52175	05.06.2025	6.65	116	3.1	6.9	24	6.1	1.5	<0.1	<8	91.5	4	26	5	<0.01	0.1	176	1.7	1.5	7.8
Sck-s.Bazhamkol	62	50.23715	61.63173	05.06.2025	7.53	884	65	45.4	114.1	52.3	0.1	<0.1	<8	201.4	190	125	253	<0.01	0.6	1066	10	3.3	19.5
Shk-Chapay	63	50.31211	61.51403	05.06.2025	7.8	827	69.9	3	68.1	92.4	0.2	<0.1	<8	274.6	172	95	241	<0.01	2.4	1032	11	4.5	13.4
Shk-Karasu	64	50.35256	61.65596	05.06.2025	7.72	1203	340.4	9.4	38	45	0.1	<0.1	<8	311.2	306	208	196	<0.01	2.1	1487	5.6	5.1	30.9
Shk-Zhabasak	65	50.35974	61.6709	05.06.2025	6.92	220	18.3	13.6	32	18.2	3	0.1	<8	262.4	14	4	<0.2	<0.01	0.1	380	3.1	3.1	14.3
Shk-(Zh + O)	66	50.36991	61.84603	05.06.2025	8.02	507	49.3	5.8	70.1	47.4	0.2	<0.1	18	360	51	87	16	14	1.3	749	7.4	6.5	29.0
Shk-191	67	50.53385	62.28776	06.06.2025	7.63	584.00	101.90	10.60	60.10	25.50	<0.05	<0.1	<8.0	274.60	57.00	122.00	69.00	<0.01	1.10	744.00	5.10	4.50	22.50
Shk-Tolybay	68	50.52583	62.28757	06.06.2025	7.29	2662.00	653.00	6.00	168.20	107.00	<0.05	<0.1	<8.0	396.60	450.00	668.00	530.00	<0.01	-	2994.00	17.20	6.50	15.70
Shk-Tolybay East	69	50.50926	62.32374	06.06.2025	7.57	761.00	178.90	3.40	50.10	36.50	<0.05	<0.1	<8.0	299.00	57.00	124.00	170.00	<0.01	6.30	940.00	5.50	4.90	14.60
Shk-October	70	50.49046	62.28973	06.06.2025	6.57	443.00	73.30	3.60	40.00	29.20	<0.05	<0.1	<8.0	177.00	49.00	159.00	30.00	<0.01	0.60	577.00	4.40	2.90	15.30
Shk-Aike	71	50.97332	61.64018	06.06.2025	7.25	1665.00	296.60	38.50	200.20	114.30	0.10	<0.1	<8.0	579.70	415.00	443.00	33.00	<0.01	-	2135.00	19.40	9.50	14.30
Shk-Severnoe	72	50.58574	62.36336	06.06.2025	7.45	8642.00	1981.50	11.70	560.60	593.40	0.30	<0.1	<8.0	164.80	2974.00	2667.00	582.00	<0.01	0.50	9549.00	76.80	2.70	13.30
Shk-Terensay	73	50.95773	61.57466	06.06.2025	7.03	8890.00	2037.00	11.00	560.60	627.50	0.10	<0.1	<8.0	170.90	2835.00	2918.00	635.00	<0.01	0.50	9809.00	79.60	2.80	13.80
Shk-Zhualy	74	50.67646	61.70289	07.06.2025	6.63	197.00	33.80	4.80	22.00	10.90	<0.05	<0.1	<8.0	134.20	22.00	19.00	24.00	<0.01	0.70	285.00	2.00	2.00	13.80
Shk-Karakol	75	50.48716	61.30426	07.06.2025	6.88	2271.00	793.10	3.60	60.10	60.80	<0.05	<0.1	<8.0	421.00	659.00	653.00	33.00	<0.01	0.40	2698.00	8.00	6.90	13.70
Shk-Khersonovka	76	50.09837	55.68528	09.06.2025	7.69	1144.00	308.00	3.50	74.10	32.80	<0.05	<0.1	<8.0	268.50	176.00	452.00	0.00	5.20	0.40	1337.00	6.40	4.60	16.10
Shk-Khabalovka	77	50.01649	55.71944	09.06.2025	7.36	604.00	74.20	81.20	56.10	17.00	0.10	<0.1	<8.0	195.30	37.00	118.00	167.00	6.60	0.90	770.00	4.20	3.20	16.30
Shk-Sarybulak	78	49.85776	55.73373	09.06.2025	7.27	392.00	55.60	2.70	70.10	18.20	<0.05	<0.1	<8.0	231.90	46.00	86.00	<0.2	<0.01	0.70	533.00	5.00	3.80	22.20
Shk-Begaly	79	49.82904	55.22659	10.06.2025	7.32	798.00	134.80	9.70	128.10	55.90	0.50	<0.1	<8.0	640.70	82.00	136.00	<0.2	<0.01	0.70	1209.00	11.00	10.50	20.70
Shk-Abdybulak	80	49.7689	55.60584	10.06.2025	7.22	163.00	24.80	1.10	24.00	7.30	<0.05	<0.1	<8.0	115.90	14.00	22.00	14.00	<0.01	0.60	244.00	1.80	1.80	20.20
Shk-Sokolovsky	81	49.91392	55.17241	10.06.2025	7.57	318.00	30.10	12.80	58.10	12.20	10.20	<0.1	<8.0	329.50	19.00	19.00	9.00	<0.01	0.10	520.00	3.90	3.90	19.90
Shk-Oyil Kumzhargan	82	49.78128	55.11599	11.06.2025	7.66	290.00	28.20	37.00	50.10	6.10	<0.05	<0.1	<8.0	250.20	13.00	30.00	10.00	<0.01	0.30	444.00	3.00	3.00	19.60
Shk-Kumsay	83	48.98504	54.57	11.06.2025	7.55	363.00	60.00	1.80	56.10	12.20	<0.05	<0.1	<8.0	244.10	45.00	65.00	8.00	<0.01	0.50	506.00	3.80	3.80	13.80
Shk-Amangeldi	84	48.93471	54.53883	12.06.2025	7.43	246.00	34.80	2.30	46.00	14.60	<0.05	<0.1	<8.0	213.60	24.00	33.00	<0.2	<0.01	0.40	383.00	3.50	3.50	14.40
Shk-51 Schanke	85	49.12358	55.03894	12.06.2025	7.47	1096.00	320.10	2.70	72.10	36.50	<0.05	<0.1	<8.0	244.10	268.00	335.00	15.00	<0.01	0.80	1313.00	6.60	4.00	19.20
Shk-Altykaras	86	49.17891	55.86783	13.06.2025	7.32	245.00	49.70	1.50	28.00	12.20	<0.05	<0.1	<8.0	164.80	14.00	63.00	3.00	<0.01	0.40	356.00	2.40	2.40	19.10
Shk-11	87	49.15455	55.91446	13.06.2025	7.57	1406.00	412.70	4.60	70.10	45.00	0.10	<0.1	<8.0	463.70	307.00	364.00	<0.2	<0.01	1.00	1677.00	7.20	7.20	9.10
Shk-Sarytogay	88	49.19819	56.04503	13.06.2025	7.35	828.00	89.70	1.00	150.20	38.90	<0.05	<0.1	<8.0	189.20	103.00	341.00	63.00	<0.01	-	994.00	10.70	3.10	18.20
Shk-Terenkudyk	89	48.78961	55.9314	16.06.2025	7.54	366.00	14.50	1.60	108.10	3.60	<0.05	<0.1	<8.0	231.90	33.00	56.00	63.00	<0.01	0.30	529.00	5.70	3.80	17.20
Shk-Suarma	90	48.86487	55.49264	16.06.2025	7.69	450.00	69.90	3.80	72.10	24.30	<0.05	<0.1	<8.0	286.80	45.00	95.00	19.00	<0.01	0.80	632.00	5.60	4.70	14.90
Shk-Taskopa	91	48.87666	55.74443	16.06.2025	7.4	3972.00	925.90	9.70	280.30	252.90	0.30	<0.1	<8.0	97.60	1981.00	544.00	<0.2	<0.01	-	4101.00	34.80	1.60	9.60
Shk-Baiganin	92	48.67481	55.64425	17.06.2025	7.57	1346.00	247.90	2.20	194.20	31.60	<0.05	<0.1	<8.0	164.80	179.00	656.00	15.00	<0.01	-	1506.00	12.30	2.70	15.50
Shk-40	93	48.60171	55.60409	17.06.2025	7.57	212.00	8.80	21.80	44.00	6.10	<0.05	<0.1	<8.0	152.50	10.00	41.00	20.00	<0.01	0.40	317.00	2.70	2.50	12.50
Shk-Zharly	94	48.51562	55.7706	17.06.2025	7.44	651.00	46.20	5.00	96.10	32.80	<0.05	<0.1	<8.0	195.30	58.00	77.00	250.00	<0.01	1.20	779.00	7.50	3.20	17.50
Shk-Chkalov	95	48.49607	55.91909	17.06.2025	7.6	1099.00	245.90	3.30	130.10	38.90	<0.05	<0.1	<8.0	238.00	271.00	359.00	<0.2	<0.01	-	1303.00	9.70	3.90	16.90
Shk-29	96	48.36722	55.88371	17.06.2025	7.37	3945.00	808.90	7.70	320.30	189.70	<0.05	<0.1	<8.0	207.50	277.00	2364.00	68.00	<0.01	-	4261.00	31.60	3.40	17.50
Shk-Karabulak	97	48.44345	55.72209	18.06.2025	7.33	1275.00	151.00	16.70	184.20	73.00	<0.05	<0.1	<8.0	305.10	164.00	182.00	410.00	<0.01	1.10	1507.00	15.20	5.00	20.40
Shk-20	98	48.13774	58.84879	18.06.2025	7.22	5630.00	1219.30	8.30	512.50	252.90	<0.05	<0.1	<8.0	189.20	1281.00	2485.00	158.00	<0.01	-	6124.00	46.40	3.10	17.40
Shk-22	99	48.34146	55.04506	18.06.2025	7.69	225.00	9.40	5.10	60.10	6.10	<0.05	<0.1	<8.0	122.00	7.00	68.00	11.00	<0.01	0.50	300.00	3.50	2.00	10.60
Shk-26	100	48.34146	55.04506	18.06.2025	7.46	3260.00	1077.10	7.30	128.10	80.30	<0.05	<0.1	<8.0	170.90	1352.00	769.00	11.00	<0.01	-	3607.00	13.00	2.80	11.00
West Kazakhstan Region
Prierechnoye	101	50.98861	52.48389	19.05.2025	8.01	150.00	29.00	2.00	16.00	8.50	<0.05	<0.1	<8.0	91.50	31.00	21.00	2.00	<0.01	0.10	204.00	1.50	1.50	2.50
Shalkarkol-1	102	50.71417	51.60167	20.05.2025	7.3	538.00	75.00	4.50	66.10	47.40	0.10	<0.1	<8.0	305.10	62.00	132.00	44.00	<0.01	0.70	751.00	7.20	5.00	14.50
Shalkarkol-2	103	50.71444	51.6025	20.05.2025	7.84	1983.00	671.60	3.70	44.00	42.60	<0.05	<0.1	<8.0	555.30	274.00	853.00	<0.2	<0.01	-	2458.00	5.70	5.70	13.50
Tasan Shegen	104	50.84583	51.77944	23.05.2025	7.27	1728.00	456.00	3.30	122.10	85.10	2.00	<0.1	<8.0	396.60	960.00	9.00	<0.2	<0.01	-	2048.00	13.10	6.50	14.10
Sasykol-1	105	50.41833	52.11917	20.05.2025	7.72	879.00	95.70	8.10	40.00	114.30	<0.05	<0.1	<8.0	341.70	140.00	107.00	214.00	<0.01	1.60	1074.00	11.40	5.60	11.70
Sasykol-2	106	50.41806	52.11806	20.05.2025	7.44	5812.00	1809.00	7.10	192.20	214.00	<0.05	<0.1	<8.0	329.50	1912.00	1862.00	20.00	<0.01	-	6361.00	27.20	5.40	15.30
Zhympity	107	50.18	52.6875	21.05.2025	7.28	2429.00	407.60	9.90	274.30	114.30	<0.05	<0.1	<8.0	384.40	612.00	884.00	51.00	<0.01	-	2758.00	23.10	6.30	20.80
Konyr	108	50.41694	53.40472	22.05.2025	6.94	351.00	55.00	7.20	38.00	19.50	<0.05	<0.1	<8.0	253.20	14.00	84.00	20.00	<0.01	1.30	506.00	3.50	3.50	14.00
Balkyldak	109	50.50639	53.31056	22.05.2025	7.48	1020.00	185.70	4.90	130.10	42.60	0.10	<0.1	<8.0	238.00	366.00	195.00	<0.2	<0.01	-	1184.00	10.00	3.90	21.80
Taldybulak	110	50.71028	52.785	22.05.2025	7.78	189.00	11.30	1.80	48.00	6.10	<0.05	<0.1	<8.0	170.90	18.00	19.00	5.00	<0.01	0.20	294.00	2.90	2.80	13.70
Sanatorium Akzhaiyk	111	50.94972	51.27722	23.05.2025	7.65	636.00	124.50	1.90	80.10	35.30	<0.05	<0.1	<8.0	353.90	151.00	107.00	2.00	<0.01	0.20	869.00	6.90	5.80	13.40
Aksogym	112	50.53722	51.17361	23.05.2025	7.52	695.00	126.10	1.80	82.10	51.10	0.10	<0.1	<8.0	408.80	218.00	41.00	10.00	<0.01	0.20	956.00	8.30	6.70	16.70
Shagatai	113	50.40167	51.24361	20.05.2025	7.5	1058.00	77.20	2.60	140.10	106.40	<0.05	<0.1	<8.0	360.00	142.00	494.00	18.00	<0.01	-	1360.00	15.75	5.90	19.20
Mereke	114	50.73167	49.50417	25.05.2025	7.51	2619.00	437.20	11.40	216.20	200.60	5.00	<0.1	<8.0	353.90	933.00	737.00	7.00	<0.01	-	2920.00	27.30	5.80	18.20
Mereke (Taldykudyk)	115	50.73222	49.49778	25.05.2025	7.13	2912.00	446.60	20.70	326.30	199.40	0.10	<0.1	<8.0	225.80	1598.00	311.00	<0.2	<0.01	-	3146.00	32.70	3.70	17.60
Zaborodino	116	50.98417	50.93056	26.05.2025	7.59	585.00	63.80	2.30	80.10	69.30	<0.05	<0.1	<8.0	518.70	78.00	68.00	3.00	<0.01	0.20	902.00	9.70	8.50	18.30
Sergeev-1	117	49.93083	51.27639	26.05.2025	8.37	1075.00	289.90	1.40	48.00	51.10	<0.05	<0.1	24.00	402.70	153.00	325.00	3.00	<0.01	1.30	1314.00	6.60	6.60	14.90
Sergeev-2	118	49.93083	51.27639	26.05.2025	7.34	2692.00	388.80	5.30	374.40	180.00	2.50	<0.1	<8.0	360.00	876.00	582.00	<0.2	<0.01	-	3061.00	33.50	6.10	22.30
Mergenyevo	119	49.93083	51.27639	26.05.2025	7.46	1869.00	567.40	3.20	110.10	75.40	3.00	<0.1	<8.0	543.10	873.00	6.00	<0.2	<0.01	-	2199.00	11.70	8.90	17.40
Mortyk	120	49.38694	50.54028	27.05.2025	7.53	180.00	14.20	17.60	28.00	13.40	<0.05	<0.1	<8.0	207.50	10.00	6.00	0.50	<0.01	0.30	308.00	2.50	2.50	10.60
Kopzhasar-1	121	49.17639	50.44361	27.05.2025	7.4	1444.00	36.00	25.00	334.30	100.90	<0.05	<0.1	<8.0	189.20	774.00	128.00	67.00	<0.01	-	1670.00	25.00	3.10	15.90
Kopzhasar-2	122	49.17694	50.44333	27.05.2025	8.04	779.00	75.00	0.80	58.10	114.30	0.10	<0.1	24.00	408.80	190.00	87.00	69.00	<0.01	1.20	1042.00	12.30	7.50	14.00
Kastalovka-1	123	49.74861	48.81306	28.05.2025	7.28	8876.00	2480.00	6.60	468.50	304.00	<0.05	<0.1	<8.0	427.10	4813.00	1323.00	77.00	<0.01	-	9913.00	48.40	7.00	13.40
Kastalovka-2	124	49.74917	48.81278	28.05.2025	7.16	1538.00	141.60	101.90	320.30	85.10	10.00	<0.1	<8.0	707.80	656.00	13.00	2.00	<0.01	0.50	2056.00	23.00	11.60	18.10
Kokterek-1	125	49.38917	49.23389	28.05.2025	7.82	498.00	72.50	22.60	62.10	37.70	0.20	<0.1	18.00	384.40	50.00	64.00	3.00	<0.01	0.40	736.00	6.20	6.20	21.00
Kokterek-2	126	49.29611	49.06556	28.05.2025	7.76	622.00	74.20	8.80	50.10	79.00	<0.05	<0.1	30.00	506.50	89.00	54.00	33.00	<0.01	1.50	941.00	9.00	9.00	15.00
Enbek-1 (Kirovo)	127	49.33139	48.90861	28.05.2025	7.35	4897.00	1514.50	11.20	160.20	127.70	<0.05	<0.1	<8.0	475.90	223.00	2914.00	174.00	<0.01	-	5615.00	18.50	7.80	14.00
Enbek-2 (Kirovo)	128	49.33056	48.90667	28.05.2025	8.05	4897.00	81.50	1.90	22.00	77.80	0.20	<0.1	36.00	378.30	43.00	160.00	11.00	<0.01	2.30	825.00	7.50	7.40	11.20
Saryalzhyn	129	49.16639	48.90972	29.05.2025	7.25	390.00	29.90	11.80	90.10	23.10	2.50	<0.1	<8.0	457.60	15.00	18.00	<0.2	<0.01	0.30	663.00	6.40	6.40	14.60
Terenkudyk	130	48.90333	48.64528	29.05.2025	7.59	854.00	14.00	7.80	164.20	51.10	<0.05	<0.1	<8.0	231.90	85.00	35.00	315.00	<0.01	-	920.00	12.40	3.80	16.10
Burley	131	48.86417	48.54139	29.05.2025	7.53	2451.00	335.80	2.20	288.30	115.50	1.50	<0.1	<8.0	238.00	68.00	1428.00	197.00	<0.01	-	2689.00	23.90	3.90	14.90
Zhambyl	132	48.90806	48.06889	29.05.2025	7.53	2094.00	238.00	14.50	312.30	70.50	0.10	<0.1	<8.0	274.60	242.00	608.00	577.00	<0.01	-	2355.00	21.40	4.50	17.80
Kairat (Tau)	133	49.62444	47.12194	30.05.2025	7.9	499.00	10.70	3.60	118.10	49.90	<0.05	<0.1	<8.0	353.90	33.00	144.00	<0.2	<0.01	0.90	733.00	10.00	5.80	18.90
Kamysty	134	49.76472	46.95972	30.05.2025	7.38	2957.00	398.40	5.60	338.30	164.20	3.00	<0.1	<8.0	225.80	208.00	2003.00	6.00	<0.01	-	3364.00	30.40	3.70	11.40
Saykyn	135	48.81111	46.77861	31.05.2025	7.26	4877.00	1047.60	3.30	480.50	194.60	<0.05	<0.1	<8.0	238.00	1519.00	1639.00	29.00	<0.01	-	5165.00	40.00	3.90	14.30
Horde	136	48.78722	47.36611	31.05.2025	7.41	1342.00	258.00	4.40	128.10	77.80	0.10	<0.1	<8.0	164.80	707.00	192.00	<0.2	<0.01	-	1547.00	12.80	2.70	14.30
Auyzashyk	137	48.68639	47.37694	31.05.2025	7.66	1224.00	217.10	8.00	70.10	111.90	0.20	<0.1	<8.0	555.30	420.00	161.00	14.00	<0.01	1.50	1573.00	12.70	9.10	14.20
Taipak-1	138	48.90556	51.64778	15.06.2025	7.67	792.00	116.00	3.30	70.10	79.00	0.20	<0.1	12.00	469.80	186.00	133.00	5.00	<0.01	0.30	1096.00	10.00	8.10	21.50
Taipak-2	139	48.94889	51.58306	15.06.2025	7.31	5686.00	1231.00	18.20	360.40	316.20	10.00	<0.1	<8.0	372.20	2357.00	1692.00	<0.2	<0.01	-	6379.00	44.00	6.10	21.80

(Appendix A).

2.4. Laboratory Analysis

The analytical suite comprised 25 physicochemical parameters covering major ion chemistry, sanitary–chemical indicators, and trace contaminants (see Appendix A,

Table A2. Analytical parameters and corresponding certified methods (abbreviated). CO₃²⁻ and HCO₃⁻ determined by GOST 26449.1-85, §7.1; NO₃⁻ and NO₂⁻ by GOST 33045-2014; F⁻ by ST RK 2727-2015; SO₄²⁻ by ST RK 1015-2000; I⁻ by GOST 23268.16-78; Br⁻ by GOST 23268.15-78; B by PND F 14.1:2:4.36-95.

Parameter	Analytical Method/Standard	Parameter
Na+ (sodium)	GOST 26449.1-85, §17.1	CO32− (carbonate)
K+ (potassium)	GOST 26449.1-85, §18.1	HCO3− (bicarbonate)
Ca2+ (calcium)	GOST 26449.1-85, §11.1	Cl− (chloride)
Mg2+ (magnesium)	GOST 26449.1-85, §12	SO42− (sulphate)
NH4+ (ammonium)	GOST 33045-2014	NO3− (nitrate)
Fe2+/Fe3+ (iron)	ST RK ISO 6332-2008	NO2− (nitrite)
pH	ST RK ISO 10523-2013	F− (fluoride)
TDS/dry residue	GOST 26449.1-85	I− (iodide)
Total hardness	GOST 26449.1-85, §10	Br− (bromide)
SiO2	GOST 26449.1-85, §22	B (boron)
Cd, Pb, Cu, Zn (heavy metals)	CP No. 26, 20.02.2023	Petroleum hydrocarbons

for the full parameter list with corresponding certified analytical methods). All the analyses were performed at the accredited Laboratory of Chemical Analytical Research of the U.M. Akhmedsafin Institute of Hydrogeology and Geoecology (Accreditation Certificate No. KZ.T.02.0782).

2.5. Hydrochemical Data Processing and Classification

Hydrochemical analysis, graphical classification, and water-type assignment were performed using AquaChem 11 (Waterloo Hydrogeologic, Waterloo, ON, Canada). The following analytical tools were applied:

• Piper trilinear diagrams—to classify the dominant cation–anion assemblage and identify major hydrochemical facies (HCO₃-Ca-Mg, Cl-SO₄-Na, mixed types, etc.);
• Durov diagrams—to display major ion composition as a function of TDS and pH, and to identify hydrochemical evolution trends (evaporative concentration, ion exchange, carbonate dissolution);
• Descriptive statistics—minimum, maximum, arithmetic mean, and standard deviation (SD) computed for all 25 analytical parameters, stratified by administrative Region;
• Charge balance error (CBE) calculated for each sample; samples with |CBE| > 10% were flagged for data quality review and, where necessary, excluded from statistical summaries.

The geographic coordinates of all 139 sampling points were recorded by GPS (accuracy ≤ 5 m) and compiled in ArcGIS 10.8 (Esri, Redlands, CA, USA). The spatial distribution of sampling locations is shown in Figure 1. Continuous spatial interpolation methods (e.g., Inverse Distance Weighting, kriging) were not applied, because the route-based sampling design produces a linear, non-uniform point distribution that does not satisfy the spatial coverage and autocorrelation requirements of standard geostatistical algorithms. Instead, the spatial analysis in this study relies on the attribution of hydrochemical results to specific administrative districts and hydrogeological basins, with the complete georeferenced dataset provided in Table A1 (Appendix A) to enable independent spatial analysis by interested readers.

2.6. Assessment of Prospective Aquifer Horizons

Prospective aquifer horizons for pasture watering development were identified by integrating three categories of data: national-scale hydrogeological compilations, field measurements from the present survey, and the hydrochemical dataset generated in this study.

The principal national-scale sources were the Atlas of Hydrogeological Maps of the Republic of Kazakhstan (scale 1:2,500,000) [34], which provides estimates of predictive exploitable groundwater resources classified by salinity (TDS < 1, 1–3, and 3–10 g/L) and maps of natural groundwater recharge rates per unit area (L s⁻¹ km⁻², hereafter referred to as the recharge rate module), and the State Register of Groundwater Deposits and Resources of the Republic of Kazakhstan [39], which documents 1237 explored groundwater deposits across the study area. These compilations were supplemented by published hydrogeological exploration reports for the Pre-Caspian and Ural-Mugalzhar basins [22,40].

Field data collected during the present survey—including static water level, borehole depth, and measured yield at all inspected objects—and the results of the hydrochemical analysis of 139 groundwater samples provided site-specific ground-truthing of the national-scale resource estimates.

Prospective aquifer horizons were assessed against two categories of criteria. The hydrogeological suitability criteria require: (i) groundwater salinity not exceeding 3.0 g/L TDS, suitable for livestock consumption without desalination; and (ii) achievable well or borehole yield of at least 0.1 L/s, sufficient to sustain a minimum-capacity watering point.

Predictive exploitable groundwater resources for horizons meeting these criteria were compiled from the national Atlas dataset [40] and reported in m³/s and km³/yr, stratified by salinity class (TDS < 1.0, 1.0–3.0, and 3.0–10.0 g/L) and by region. Natural groundwater recharge rates (L s⁻¹ km⁻²) were calculated by GIS-based spatial analysis within pastoral-area polygon boundaries derived from the Ministry of Agriculture land cadastre (2023).

2.7. Livestock Water Demand Calculation

Current (2023 year) and projected (2050 year) daily water demand for livestock watering was calculated for four livestock species groups present on regional pasturelands. Livestock census data were obtained from official reports of district and region agricultural management departments (akimats) for the reference year 2023. Prescribed daily water consumption standards (L/head/day) were adopted from the national regulatory document ‘Mean Daily Water Consumption Standards by Livestock Species’, selecting the highest prescribed consumption rate among sub-categories within each species group (e.g., lactating cows rather than dry cows) to ensure the demand estimate captures peak daily requirements (See

Table 2. Daily water consumption norms adopted for livestock watering demand calculation.

Species Group	Daily Consumption Norm, l	Note
Cattle	40	Per adult cow/bull
Horses	45	Per adult horse
Small ruminants—sheep/goats	6	Per adult sheep/goat
Camels	35	Per adult camel

).

Total daily water demand (m³/day) per region was obtained by summing the products of livestock headcount and the corresponding daily norm for each species group. Prospective water demand projections to 2050 were derived from three growth scenarios (baseline, conservative, accelerated) fitted to livestock census dynamics for the period 2015–2025, with projections applying the fitted annual growth rates for cattle and small ruminants to the 2023 base populations. Scenario projections were compiled for the WKR (target 2050: 500,000 cattle; 1,000,000 small ruminants; demand 26,000 m³/day) and the Aktobe Region (target 2050: 450,000 cattle; 800,000 small ruminants; demand 22,800 m³/day), following the programme targets of the Kazakhstan Agricultural Development Concept 2021–2030.

2.8. GIS Database and Geoinformation-Analytical System

An integrated geoinformation database and GIS database was developed in ArcGIS 10.8 (Esri) for systematic storage, spatial integration, and visualisation of all hydrogeological, hydrochemical, and pastoral land datasets. All spatial data are georeferenced to WGS 84, projected in UTM Zones 42N–43N. The database was structured according to an object-oriented architecture enabling scalable addition of new data layers and thematic modules.

The GIS database comprises six functional data blocks:

• Administrative boundaries—polygon layers for regions, districts, and settlements; point layers for urban and rural localities.
• Water supply infrastructure—point objects for all inspected shaft wells, boreholes, and springs with linked attribute tables containing GPS coordinates, absolute elevation, depth, static water level, TDS (field measurement), equipment type, and technical condition class; linked to hydrochemical analysis results (Table A1).
• Pastoral land resources—vectorised polygon layers of pasture-type distribution (based on Ministry of Agriculture land cadastre, 2023), pasture condition classes, and seasonal grazing areas.
• Groundwater deposits—point and polygon layers for 1237 explored groundwater deposits (GWDs) classified by intended use, genetic type, and approved reserve category; attribute tables of exploitable reserve volumes by region and TDS class.
• Hydrogeological maps—rasterised and vectorised hydrogeological maps at 1:2,500,000 scale (Atlas of Hydrogeological Maps of Kazakhstan), including aquifer boundaries, groundwater TDS contours, and natural resource modules.
• Groundwater availability zoning—polygon layers classifying pasture territory into groundwater-availability zones (surplus, balanced, deficit) computed by GIS-overlay of predictive resource modules against calculated livestock water demand per unit pastoral area.

Thematic maps were produced at 1:1,000,000 and 1:2,500,000 scales using cartographic tools. Spatial analysis operations included polygon overlay, buffer analysis (radius-of-watering-influence calculation), and zonal statistics. The groundwater availability index (GAI) was computed as the ratio of predicted exploitable fresh groundwater resources (TDS < 3 g/L) per unit pasture area (L s⁻¹ km⁻²) to the normative daily water demand density (m³ day⁻¹ km⁻²) derived from livestock census data. GAI categories were defined as: surplus (GAI > 1.5), balanced (0.8–1.5), and deficit (GAI < 0.8).

2.9. Technical Condition Assessment of Watering Infrastructure

The technical state of all inspected water supply objects was assessed using a standardised inspection protocol derived from KazNIIVKh methodological guidelines for inventory of pasture watering infrastructure. For each object, the following attributes were recorded:

• Borehole/well: depth (m); casing diameter (mm) (documented distribution: 108 mm, 150 mm, 219 mm, 326 mm); static water level depth (m); estimated yield (L/s); operational status of casing and wellhead.
• Pumping equipment: type (submersible pump, ribbon/tape water-lifter, manual pump, or none); power source (mains, diesel/petrol generator, wind/solar-powered); operational status.
• Water-storage reservoir: volume (m³), material (reinforced concrete, metal), lid/cover presence, structural integrity.
• Watering trough: material (asbestos-cement pipe or metal pipe, Ø300–800 mm, length up to 20 m), support condition, surface condition.
• Sanitary perimeter: enclosure presence, cleanliness, risk of cross-contamination from livestock pens or waste storage.

Technical condition was scored as: operational (grade 1); requires current or capital repair (grade 2); non-operational/abandoned (grade 3, recommended for decommissioning and replacement). The threshold criterion for replacement was the simultaneous absence of ≥2 essential equipment elements in combination with structural damage to the casing or wellhead.

2.10. Data Quality Assurance and Ethical Compliance

Field measurement instruments (GPS devices, portable conductivity/TDS metres, level gauges) were calibrated before each expedition in accordance with manufacturer specifications. Laboratory analytical equipment was maintained and calibrated in accordance with the requirements of the Accreditation Certificate No. KZ.T.02.0782. All the water samples were analysed in duplicate; relative standard deviation (RSD) between duplicates was required to be <5% for major ions and <10% for trace elements. Certified reference standards were included in each analytical batch.

Ionic charge balance error (CBE) was computed for all samples as CBE = (Σcations − Σanions)/(Σcations + Σanions) × 100%. Samples with |CBE| > 10% were excluded from statistical summaries (this affected <3% of samples). Annual inter-laboratory comparison proficiency testing was conducted by a certified provider in accordance with ISO/IEC 17043:2010.

3. Results

3.1. Field Survey Coverage and Infrastructure Condition

3.1.1. Technical Condition of Watering Infrastructure—Aktobe Region

In the Aktobe Region, survey teams traversed 5800 km through seven administrative districts—Aitikebi, Alga, Baigani, Kobda, Martuk, Mugalzhar, and Temir—inspecting 95 shaft wells and five self-flowing boreholes. The survey revealed extensive deterioration of pasture watering infrastructure. More than 75% of existing watering structures at remote seasonal pasture points were found to be non-operational or requiring major capital repair. Specifically, 18% of shaft wells and 54% of boreholes were classified as non-operational at the time of inspection.

In the Aktobe Region, the majority (64% of the total borehole inventory documented in state registers for the surveyed districts) had depths of 50 m or less, targeting the shallow unconfined and semi-confined aquifer horizons of the Quaternary and Neogene formations. A further 25% of the inventory comprised boreholes drilled to depths between 50 and 200 m, accessing the deeper Palaeogene and Cretaceous (Alb-Cenomanian) confined aquifers. The remaining 11% of boreholes had been backfilled or permanently sealed prior to the survey and could not be inspected or sampled. The dominant casing diameter was 219 mm, accounting for 72% of all documented boreholes, which reflects the standard Soviet-era drilling specification for livestock watering boreholes in the region. Smaller-diameter casings of 108 mm were present in 17% of boreholes, typically associated with shallow exploration or observation wells, while large-diameter casings of 326 mm accounted for 11%, corresponding to high-yield production boreholes in the alluvial aquifer systems of the major river valleys. Measured yields at operational boreholes ranged from 0.1 to 4.6 L/s, with the highest yields recorded at self-flowing boreholes tapping the Alb-Cenomanian artesian horizon in the Predmugalzhar and Zhemsky basins.

Water extraction was carried out predominantly using submersible electric centrifugal pumps, with multi-stage belt water lifters at certain locations and manual hand pumps at isolated sites. Reinforced concrete water-storage reservoirs (nominal volume 3 m³, lidless) required repair at 55% of inspected points. Watering troughs were entirely absent at 241 watering points, and concrete aprons were absent or completely destroyed at the majority of sites. The most common sanitary deficiency was absence of a protective fence around the wellhead, allowing direct livestock access to the wellhead area and the gap between the well lining and surrounding soil, creating a direct contamination pathway.

3.1.2. Technical Condition of Watering Infrastructure—WKR

In the WKR, the survey covered 5382 km across the Kaztal, Zhympity, Kaztalovka, Taipak, and adjacent districts, and 39 shaft wells were inspected. Infrastructure deterioration was even more pronounced than in the Aktobe Region: 51% of shaft wells and 65% of boreholes were classified as non-operational. All pastoral water supply pipelines recorded in older inventories had been completely dismantled and removed from the balance sheet throughout the region.

The borehole inventory in the WKR is characterised by predominantly shallow installations: 80% of documented boreholes had depths of 50 m or less, accessing the Quaternary alluvial and Upper Pliocene semi-confined aquifers, while the remaining 20% reached depths of 50–100 m, targeting the Palaeogene–Upper Cretaceous horizon of the Syrtovskoye basin. Notably, 65% of the total borehole inventory had been in an unusable condition, backfilled or sealed a substantially higher proportion than in the Aktobe Region (11%), reflecting the more severe infrastructure degradation in the WKR following the collapse of the Soviet collective farm system. The prevailing casing diameter was 150 mm (77.5% of boreholes), which is narrower than the 219 mm standard dominant in the Aktobe Region and limits the installation of higher-capacity submersible pumps. Casings of 219 mm accounted for 17.5% and large-diameter casings of 326 mm or above for only 5% of the borehole inventory.

Shaft wells in the WKR exhibited a wide range of depths: 15% were shallower than 5 m, 43.5% ranged between 5 and 10 m, and 41.5% exceeded 10 m, the latter typically accessing more stable water-bearing horizons below the seasonal water-table fluctuation zone. Measured yields at the remaining operational wells and boreholes ranged from 0.1 to 2.0 L/s. Field-measured TDS at operational sources ranged from 0.4 to 2.0 g/L, indicating that the majority of still-functional infrastructure accesses fresh to slightly brackish groundwater suitable for livestock watering without treatment.

3.2. Hydrochemical Characterisation—Aktobe Region (n = 100)

One hundred groundwater samples were collected from 95 shaft wells and five self-flowing boreholes distributed across seven districts of the Aktobe Region (Table A1, Appendix A). Piper and Durov diagrams for the Aktobe Region are shown in Figure 2.

3.2.1. Total Dissolved Solids (TDS), Mineralisation, and pH

TDS, determined gravimetrically, in the Aktobe Region ranged from 0.18 g/L to 9.81 g/L, with a median of 0.99 g/L (

Table 3. Descriptive statistics for groundwater samples, Aktobe Region.

Parameter	Min	Max	Median	Mean ± SD	% Exceeding
TDS (g/L)	0.18	9.81	0.99	1.65 ± 1.91	>3.0 g/L: 20%
pH	6.46	8.46	7.41	7.37 ± 0.33	—
Na+ (mg/L)	3.1	2 342	110	320 ± 475	—
Ca2+ (mg/L)	7.0	560.6	70.1	106 ± 108	—
Mg2+ (mg/L)	3.6	627.5	31.6	63 ± 101	—
HCO3− (mg/L)	91.5	1 263	248	279 ± 166	—
Cl− (mg/L)	4	2 974	99	347 ± 587	>350 mg/L: 24%
SO42– (mg/L)	4	2 918	153	407 ± 655	>500 mg/L: 19%
Total hardness (meq/L)	0.8	79.6	6.2	10.5 ± 13.3	>7 meq/L: 44%
NO3− (mg/L)	<0.2	635	19.0	86 ± 139	>50 mg/L: 36%
F− (mg/L)	<0.1	6.3	0.4	0.7 ± 1.1	>1.5 mg/L: 12%
NH4+ (mg/L)	<0.05	10.2	<0.05	0.42 ± 1.17	>0.5 mg/L: 19%

). The arithmetic mean (1.65 ± 1.91 g/L) substantially exceeds the median, confirming the strong right-skewness of the TDS distribution; the majority of the samples fall within the fresh-to-slightly brackish range acceptable for livestock watering, while a minority of highly mineralised samples from salt-dome-influenced zones inflate the mean.

Classification by TDS class revealed the following distribution across the 100 samples:

• Fresh water (TDS < 1.0 g/L): 51 samples (51%)—directly suitable for livestock without restriction.
• Slightly brackish (TDS 1.0–3.0 g/L): 29 samples (29%)—generally acceptable for cattle and horses.
• Moderately saline (TDS 3.0–5.0 g/L): 10 samples (10%)—conditionally suitable for short-term watering; treatment recommended.
• Highly saline (TDS > 5.0 g/L): 10 samples (10%)—unsuitable for livestock without desalination.

The highest TDS values were recorded at: ShK-Terensai (9.81 g/L), ShK-Severnoye (9.55 g/L), ShK-12 (6.66 g/L), ShK-20 (6.12 g/L), ShK-384 (5.94 g/L), and ShK-393 (7.91 g/L)—all located in the northern Kobda district where salt-dome tectonics and shallow saline Neogene sediments dominate the near-surface geology.

The pH was circumneutral to mildly alkaline throughout (range 6.57–8.46, mean 7.35 ± 0.33). The minimum pH (6.57) was measured at ShK-Oktabr, consistent with CO₂-enriched recharge waters, and the maximum (8.46) at ShK-401, reflecting carbonate buffering in a HCO₃-dominated water. These pH values are typical of groundwater in carbonate-buffered unconfined and semi-confined aquifer systems of the Pre-Caspian artesian province.

Total hardness ranged from 0.6 meq/L (ShK-401, soft water) to 79.6 meq/L (ShK-Terensai, extremely hard). The mean hardness was 10.4 meq/L, and 44 out of 100 samples (44%) exceeded the orientation threshold of 7 meq/L. Waters with extreme hardness (>25 meq/L) were concentrated in the high-TDS samples from the Kobda district: ShK-Severnoye (76.8 meq/L), ShK-Terensai (79.6 meq/L), ShK-12 (50.8 meq/L), ShK-432 (35.0 meq/L), and ShK-384 (28.6 meq/L). Very hard water negatively affects rumen function and milk production in cattle and reduces wool quality in sheep at sustained consumption.

3.2.2. Major Ion Chemistry—Cation Composition

Sodium (Na⁺) was the dominant cation in Aktobe Region groundwater, spanning an exceptionally wide concentration range from 3.1 mg/L (ShK-Korzhaköl, fresh HCO₃-Ca water) to 2342 mg/L (ShK-393, highly saline Cl-SO₄-Na water), with a mean of approximately 320 mg/L and with a mean of approximately 310 mg/L, respectively. High sodium concentrations were systematically co-located with high TDS and chloride, reflecting halite dissolution and cation-exchange processes in the saline Neogene and Palaeogene sedimentary sequences of the northern Pre-Caspian Basin. Notably, ShK-412 (self-flowing borehole, 22 May) yielded Na⁺ = 1798 mg/L at TDS 5.88 g/L, confirming artesian upwelling of deeply circulating, strongly mineralised waters in this sector.

Calcium (Ca²⁺) ranged from 7.0 mg/L (ShK-401) to 560.6 mg/L (ShK-Severnoye) with a mean of ~108 mg/L. Magnesium (Mg²⁺) ranged from 3.6 mg/L to 627.5 mg/L (ShK-Terensai), with a mean of ~67 mg/L. Elevated Ca²⁺ and Mg²⁺ in the fresh, HCO₃-dominated samples indicate carbonate weathering as the primary geochemical control, while high Ca²⁺ and Mg²⁺ in the brackish-to-saline samples reflects incongruent dissolution of dolomite and gypsum in evaporite-bearing sequences. Potassium (K⁺) was a minor cation at most sites (0.9–350.3 mg/L), with the anomalous maximum at ShK-427 (350.3 mg/L, co-occurring with high Mg²⁺ = 124 mg/L and elevated NO₃⁻ = 301 mg/L) suggesting mixed agricultural and dissolution input.

3.2.3. Major Ion Chemistry—Anion Composition

Bicarbonate (HCO₃⁻) ranged from 91.5 mg/L to 1263.1 mg/L (mean ~279 mg/L), consistent with carbonate-system equilibration at neutral–alkaline pH. Sulphate (SO₄²⁻) exhibited a very wide range: 4–2918 mg/L (mean ~415 mg/L). The five samples with the highest sulphate concentrations were ShK-Terensai (2918 mg/L), ShK-Severnoye (2667 mg/L), ShK-20 (2485 mg/L), ShK-393 (2428 mg/L), and ShK-12 (2415 mg/L), all located in the salt-dome influence zone of the northern Kobda district. These concentrations far exceed the recommended livestock watering guideline of 500 mg/L for cattle and 250 mg/L for sheep and adversely affect rumen microbiome, magnesium absorption, and diarrhoea incidence in affected herds.

Chloride (Cl⁻) ranged from 4 mg/L to 2974 mg/L (mean ~359 mg/L). The highest chloride concentrations were at ShK-Severnoye (2974 mg/L, TDS 9.55 g/L), ShK-Terensai (2835 mg/L, TDS 9.81 g/L), ShK-393 (2287 mg/L), ShK-20 (2485 mg/L through chloride dominance), and ShK-26 (1352 mg/L). The strongly chloride-sodium chemical facies of these samples points unambiguously to evaporative concentration and halite dissolution as controlling processes, consistent with the known prevalence of Permo-Triassic salt deposits at shallow depth in the Kobda and northern Aitikebi structural zones.

3.2.4. Hydrochemical Water Types—Piper and Durov Classification

Piper trilinear diagram analysis (Figure 2a) confirmed the bimodal character of the Aktobe Region groundwater chemistry. The dataset resolves into two principal hydrochemical populations:

• Group I—HCO₃-Ca-Mg (bicarbonate-calcium-magnesium) type: present in 51–54 of 100 samples (>50% of the dataset). TDS predominantly 0.18–2.0 g/L; pH 7.0–8.5. These waters occupy the upper right field of the Piper diamond, reflecting carbonate weathering as the dominant geochemical process. They are spatially associated with the Dongyztau-Predmugalzhar and Zhemsky hydrogeological basins (central and southern Aktobe Region), as well as with alluvial Quaternary aquifers in river valleys. Durov diagram (Figure 2b) projections show this group clustering in the low-TDS/neutral-to-alkaline pH field, consistent with active recharge and carbonate dissolution control.
• Group II—Cl-SO₄-Na (and SO₄-Cl-Na) type: present in ~46% of samples, predominantly in the Kobda district (northern region). TDS 1.5–9.8 g/L; hardness 10–80 meq/L; sulphate and chloride dominate the anion composition. These waters plot in the lower left field of the Piper diamond (Na-Cl-SO₄ corner). The Durov diagram places them in the high-TDS/near-neutral pH cluster, consistent with halite + gypsum/anhydrite dissolution and evaporative concentration in poorly drained topographic lows.
• Group III (minor)—mixed Cl-HCO₃-Na: transitional waters with TDS 1.0–3.5 g/L, representing intermediate positions along the hydrogeochemical evolution path from Group I (recharge) toward Group II (discharge/evaporation). Present in ~10% of samples.

The spatial juxtaposition of Groups I and II within individual pasture districts (particularly Aitikebi and Kobda) illustrates the high lateral variability of groundwater quality at the pasture management scale. This variability underscores the necessity of site-specific hydrochemical characterisation prior to new borehole or shaft-well construction, as sources separated by only a few kilometres may differ in TDS by an order of magnitude.

3.2.5. Nitrate and Ammonium Contamination—Aktobe Region

Nitrate contamination of shallow groundwater constitutes the most critical water quality problem identified in Aktobe Region from the perspective of immediate livestock health risk. NO₃⁻ concentrations ranged from <0.2 mg/L (below detection limit) to 635 mg/L (ShK-Terensai, Kobda district), and 36 of 100 samples (36%) exceeded the livestock watering threshold of 50 mg/L. The spatial pattern of nitrate exceedances is strongly correlated with proximity to inhabited localities, livestock wintering sites, and animal pens; the five highest concentrations were all recorded in the Kobda and Martuk districts, in shaft wells directly adjacent to farmsteads. These wells are characterised by shallow depth (typically <10 m), creating short vertical pathways for livestock-waste leachate to reach the water table through the thin unsaturated zone.

Ammonium contamination displayed a closely related spatial pattern, consistent with a common anthropogenic source. NH₄⁺ exceeded 0.5 mg/L in 19 of 100 samples (19%), with the highest values recorded at ShK-Sokolovsky (10.2 mg/L) and ShK-Sarat Yug (4.0 mg/L). Additional elevated concentrations (1.5–2.5 mg/L) were found at ShK-Kyzyltu, ShK-Bogetsai, and multiple sites in the Aitikebi and Kobda districts. The simultaneous presence of both reduced (NH₄⁺) and oxidised (NO₃⁻) nitrogen species indicates active nitrification within the unsaturated zone, with ammonium representing the fraction of the nitrogen load that has not yet been fully oxidised during infiltration. At sites where NH₄⁺ exceeded 3 mg/L, this is commonly associated with the direct input of undecomposed animal waste, and such conditions may favour microbial contamination of the water source. However, microbiological analyses were not included in the present study, and this association remains inferential.

3.2.6. Geogenic Fluoride Enrichment—Aktobe Region

Fluoride (F⁻) concentrations in the Aktobe Region ranged from <0.1 mg/L to 6.3 mg/L. Twelve of 100 samples (12%) exceeded the WHO guideline of 1.5 mg/L, with the highest values at ShK-Tolybai Vostok (6.3 mg/L, Aitikebi district), ShK-433 (4.9 mg/L), ShK-12 (4.4 mg/L), and ShK-2406 (2.1 mg/L). Unlike nitrate and ammonium, fluoride enrichment is geogenic in origin, associated spatially with Neogene and Palaeogene sedimentary sequences where fluorite-bearing minerals occur in the Shalkar and North-Usturt basins. Chronic fluoride intake at >2–3 mg/L causes dental and skeletal fluorosis in sheep and cattle, significantly reducing productivity.

3.3. Hydrochemical Characterisation—West Kazakhstan Region (n = 39)

Thirty-nine groundwater samples were collected from 39 shaft wells distributed across eight settlement clusters in the WKR between 19 May and 15 June 2025 (Table A1, Appendix A). The wells sample exclusively the shallow unconfined aquifer (depth 2–20 m), which constitutes the primary source of livestock watering water across most of the WKR. Summary statistics are presented in

Table 4. Descriptive statistics for groundwater samples, West Kazakhstan Region.

Parameter	Min	Max	Median	Mean ± SD	% Exceeding
TDS (g/L)	0.20	9.91	1.36	2.15 ± 2.05	>3.0: 21%
pH	6.94	8.37	7.51	7.54 ± 0.29	—
Na+ (mg/L)	10.7	2480	142	377 ± 542	—
Ca2+ (mg/L)	16.0	480.5	118	163 ± 133	—
Mg2+ (mg/L)	6.1	316.2	79.0	99 ± 75	—
HCO3− (mg/L)	91.5	708	360	355 ± 130	—
Cl− (mg/L)	10	4813	208	567 ± 899	>350: 38%
SO42– (mg/L)	6	2914	144	504 ± 705	>500: 31%
Total hardness (meq/L)	1.5	48.4	12.3	16.3 ± 11.9	>7: 77%
NO3− (mg/L)	<0.2	577	6.0	51 ± 111	>50: 23%
F− (mg/L)	<0.1	2.3	<0.1	0.4 ± 0.6	>1.5: 5%
NH4+ (mg/L)	<0.05	10.0	0.10	1.05 ± 2.40	>0.5: 23%

. Piper and Durov diagrams for the WKR are shown in Figure 3.

3.3.1. Total Dissolved Solids and pH—WKR

TDS ranged from 0.20 g/L (borehole Prirechnoye, 19 May 2025) to 9.91 g/L (Kaztalovka-1, 28 May 2025), with a median of 1.36 g/L (Table 4). The arithmetic mean (2.15 ± 2.05 g/L) exceeds the median by 60%, reflecting the same right-skewed distribution observed in the Aktobe Region.

TDS classification:

• Fresh water (<1.0 g/L): 17 samples (44%)—directly suitable.
• Slightly brackish (1.0–3.0 g/L): 14 samples (36%)—generally acceptable.
• Moderately saline (3.0–5.0 g/L): four samples (10%)—conditionally suitable.
• Highly saline (>5.0 g/L): four samples (10%)—requires desalination treatment.

The lowest TDS values were recorded at Prirechnoye (0.20 g/L), Mortyk (0.31 g/L, 27 May 2025), and Taldybulak (0.29 g/L), all located in the northern Syrtovskoye hydrogeological basin where Quaternary and Mesozoic aquifers host well-drained, bicarbonate-dominated waters. The highest TDS values were at: Kaztalovka-1 (9.91 g/L, 28 May 2025, southern Kaztal district), Taipak-2 (6.38 g/L), Sasykol-2 (6.36 g/L), Enbek-1 Kirovo (5.62 g/L), and Saikyn (5.17 g/L). All the high-TDS sites are clustered in the southern and eastern sectors of the WKR, within the Pre-Caspian lowland where salt-dome tectonics and marine Pliocene–Quaternary sediments control shallow groundwater chemistry.

pH ranged from 6.94 (Konyr well) to 8.37 (Sergeev well, 26 May 2025), with a mean of 7.52 ± 0.34. The slightly higher mean pH in the WKR relative to the Aktobe Region reflects a higher proportion of HCO₃-Na water types that tend toward alkaline conditions through cation exchange (Na-for-Ca exchange on clay minerals in the Pliocene–Quaternary clay–sand sequences). Total hardness ranged from 1.5 meq/L (Prirechnoe, soft) to 48.4 meq/L (Kaztalovka-1, extremely hard), with a mean of ~16 meq/L—substantially higher than the Aktobe Region mean of 10.4 meq/L. More than three-quarters of the WKR samples (77%) exceeded 7 meq/L.

3.3.2. Major Ion Chemistry and Hydrochemical Water Types—WKR

Sodium was the dominant cation in the WKR, ranging from 10.7 mg/L (Mortyk) to 2480 mg/L (Kaztalovka-1), with a mean of ~377 mg/L. The extreme Na⁺ concentration at Kaztalovka-1 (2480 mg/L, co-occurring with Cl⁻ = 4813 mg/L and TDS 9.91 g/L), represents the most mineralized sample in the WKR dataset and clearly reflects halite dissolution in the salt-dome aureole of the southern Kaztal district.

Calcium ranged from 16 mg/L to 480.5 mg/L (mean ~134 mg/L) and magnesium from 6.1 mg/L to 316.2 mg/L (mean ~93 mg/L). Notably, the sites with the highest Ca²⁺ and Mg²⁺ concentrations were not the highest-TDS samples but rather moderately mineralized waters from the central WKR (Sergeev: Ca²⁺ = 374.4 mg/L; Zhympity: Ca²⁺ = 274.3 mg/L, Mg²⁺ = 114.3 mg/L), indicating dolomite and calcite dissolution as additional major sources of hardness in non-saline settings.

The anion composition in the WKR was markedly more heterogeneous than in the Aktobe Region. Bicarbonate (HCO₃⁻) dominated in 17 of 39 samples (44%), ranging from 91.5 to 708 mg/L; chloride was dominant in 14 samples (36%), reaching 4813 mg/L at Kaztalovka-1; and sulphate dominated in eight samples (20%), peaking at 2914 mg/L at Enbek-1 Kirovo. This tripartite anion distribution reflects the coexistence of three hydrogeochemical environments within the WKR: carbonate weathering (north, Syrtovskoye basin), sulphate dissolution (central, gypsum-bearing Cretaceous deposits), and halite dissolution (south, Pre-Caspian salt-dome zone).

The Piper diagram (Figure 3a; Table A1 resolves the WKR dataset into two populations with a wider range of transitional types compared to the Aktobe Region:

• Group I—HCO₃-Ca-Mg (and HCO₃-Na) type: 17 samples (44%); TDS 0.20–1.07 g/L; associated with the Syrtovskoye basin (north WKR) and Zhaiyk alluvial aquifer. These are the best-quality waters in the WKR dataset, suitable for unrestricted livestock watering. The Durov diagram shows this group in the low-TDS/alkaline pH field with Ca + Mg excess, indicating carbonate dissolution control.
• Group II—Cl-Na (and Cl-SO₄-Na) type: 14 samples (36%); TDS 1.5–9.9 g/L; southern and eastern WKR, salt-dome influence zone. Cl⁻ dominates the anion composition (>50% of meq). These waters are unsuitable or marginal for livestock without treatment.
• Group III—SO₄-Na (and SO₄-Cl-Na) type: Eight samples (20%); TDS 1.0–5.6 g/L; central WKR, associated with sulphate dissolution in Cretaceous and Palaeogene deposits. Conditionally suitable for livestock, depending on absolute SO₄ concentration (threshold for cattle: 500 mg/L).

3.3.3. Nitrate and Ammonium Contamination—WKR

Nitrate contamination was widespread in the WKR, with nine of 39 samples (23%) exceeding 50 mg/L. The NO₃⁻ range was <0.2 mg/L (multiple clean sites in the Syrtovskoye basin) to 577 mg/L (Zhambyl, Taipak district). The spatial distribution of exceedances closely follows the pattern of inhabited pastoral localities: all five highest-nitrate sites are shaft wells sited within or immediately adjacent to active livestock winter quarters or arable plots.

Ammonium contamination was similarly prevalent in the WKR: nine of 39 samples (23%) exceeded 0.5 mg/L (Table 4), with a maximum of 10.0 mg/L. As in the Aktobe Region, elevated NH₄⁺ co-occurred with elevated NO₃⁻ at the same locations, confirming a common anthropogenic origin linked to concentrated livestock waste near functional watering points.

3.3.4. Fluoride—WKR

Fluoride exceedances were less prevalent in the WKR than in the Aktobe Region: only two of 39 samples (5%) exceeded 1.5 mg/L, with a maximum of 2.3 mg/L, reflecting the absence of the fluorite-bearing Neogene deposits that control geogenic fluoride enrichment in the Aktobe Shalkar Basin.

3.4. Comparative Hydrochemical Analysis—Both Regions

Both regions share the fundamental hydrochemical signature of groundwater in the Pre-Caspian arid artesian province: lateral coexistence of HCO₃-Ca-Mg fresh waters in structurally elevated, well-drained recharge settings with highly mineralised Cl-SO₄-Na waters in topographically low, poorly drained evaporative discharge zones. Despite this shared geological inheritance, the two regions display measurable differences in the severity and character of water quality impairment (

Table 5. Side-by-side comparison of key hydrochemical indicators, Aktobe Region and WKR.

Indicator	Aktobe Region	WKR	Interpretation
Median TDS (g/L)	0.99	1.36	WKR higher; both right-skewed
Mean TDS ± SD (g/L)	1.65 ± 1.91	2.15 ± 2.05	Both within broadly acceptable range; outliers severe
TDS < 1 g/L	51%	36%	Aktobe: majority of sources fresh
TDS > 5 g/L	10%	13%	WKR: more highly saline sites
Mean pH	7.37	7.54	Both mildly alkaline
Median hardness (meq/L)	6.2	12.3	WKR significantly harder
Hardness > 7 meq/L	44%	77%	WKR: majority of sites hard to very hard
NO3− > 50 mg/L	36%	23%	Aktobe: higher anthropogenic pressure
Max NO3− (mg/L)	635	577	Both extremely high
NH4+ > 0.5 mg/L	19%	23%	Similar livestock pressure near wells
F− > 1.5 mg/L	12%	5%	Aktobe: geogenic fluoride enrichment
Max F− (mg/L)	6.3	2.3	Aktobe: higher geogenic loading
Dominant water type	HCO3-Ca-Mg (>50%)	HCO3-Ca-Mg + Na (44%)	Both bimodal
Best water quality area	Predmugalzhar, central	Syrtovskoye basin, north	Both have quality fresh-water zones
Worst water quality area	Kobda district	Kaztalovka/Taipak area	Salt-dome zones in both cases

).

Salinisation is quantitatively more severe in the WKR, where the median TDS (1.36 g/L) exceeds that of the Aktobe Region (0.99 g/L) and the proportion of samples with TDS > 5 g/L is approximately twice as high (13% vs. 10%), despite WKR having a higher proportion of samples from the northern Syrtovskoye basin with consistently low TDS. The worst salinisation in both regions is concentrated in the southernmost districts adjacent to the salt-dome field of the Pre-Caspian depression (the Kobda district in the Aktobe Region; the Kaztal and Taipak districts in the WKR).

Nitrate contamination is prevalent in both regions, with 36% of samples exceeding 50 mg/L in the Aktobe Region and 23% in the WKR. The higher exceedance rate in the Aktobe Region reflects the greater number of shallow shaft wells in the Kobda and Martuk districts sited adjacent to active livestock facilities without wellhead protection. The maximum concentrations (635 mg/L in Aktobe, 577 mg/L in the WKR) can be placed in context through comparison with published data from analogous settings. In southern Kazakhstan, Tazhiyev et al. [27] reported that approximately 40% of pasture groundwater samples in the Almaty and Zhetysu regions exceeded the WHO TDS guideline, but the maximum NO₃⁻ concentrations did not exceed 150 mg/L—more than four times lower than the values documented in the present study. Zhang et al. [25] recorded maximum groundwater NO₃⁻ of 338 mg/L in an intensively farmed area of southwestern China, and Duan et al. [26] reported up to 264 mg/L in the Mu Us sandy land of northwestern China—both substantially below the maxima identified in western Kazakhstan. Globally, comparable nitrate levels in livestock-impacted shallow aquifers have been documented primarily in intensive feedlot areas of the US Great Plains [41] and parts of the Indo-Gangetic alluvial plain, where concentrated animal husbandry and poor wellhead protection create similar contamination pathways [10]. These comparisons indicate that the nitrate contamination intensity at western Kazakhstan’s pasture watering sources is exceptionally severe by both regional Central Asian and international standards, reflecting the combined effect of extremely shallow well depth, the complete absence of wellhead protection, and high localised livestock density near functional watering points.

3.5. Prospective Aquifer Horizons for Pasture Watering Development

Integration of the 2025 field hydrochemical survey with archival hydrogeological data [34] identified six prospective aquifer horizons in the Aktobe Region and four in the WKR that meet the designation criteria of TDS ≤ 3 g/L, yield ≥ 0.1 L/s, and accessible drilling depth ≤ 200 m (see Section 2.6 Methods).

3.5.1. Prospective Aquifer Horizons—Aktobe Region

The Alb-Cenomanian artesian horizon is ranked as the highest-priority development target in the Aktobe Region (see

Table 6. Prospective aquifer horizons for pasture watering development, Aktobe Region.

Aquifer Horizon	Distribution Area	Depth to Water (m)	Yield (L/s)	TDS (g/L)	Dominant Water Type
Quaternary alluvial (unconfined)	Zhaiyk, Elek, Oyil, Zhem, Irgyz river valleys	2.0–7.0	0.2–4.0	0.2–10	HCO3-SO4-Na, Cl-Na
Neogene (unconfined)	Shalkar and North-Usturt hydrogeological basins	0.5–12 (max. 37 in south)	0.1–1.5	1–5	Cl-SO4-Na, SO4-Cl-Na
Palaeogene (semi-confined)	Shalkar depression, Greater Barsuki sands, Irgyz-Torgay watershed (fresh); Irgyz right bank (saline)	10–50 (max 100 in artesian zone)	0.1–5.0	0.6–10	HCO3-Ca, Cl-Na
Alb-Cenomanian (artesian, confined)	Zhemsky, Dongyztau-Predmugalzhar, Shalkar, North-Aral basins	1–200 (artesian head: +10 to +30 m)	0.3–10.0	0.3–3.0	HCO3-Ca, Cl-SO4-Na
Jurassic–Triassic (unconfined)	Ural, South-Predural, and Zhemsky basins	25–100	0.2–8.5	0.2–3.0	HCO3-Ca, SO4-Cl-Na
Open fracture zone (Pre-Palaeozoic basement)	Ural and South-Predural uplifts, fault corridors	few m to 45 (fault zones: 100–200)	0.1–1.5 (fault zones: 3–7)	0.1–3.0	HCO3-Ca, HCO3-SO4-Na

“Depth to water” indicates depth below ground surface to the static water level. Positive artesian head values (e.g., +10 to +30 m) indicate that the piezometric surface is above ground level, producing self-flowing (artesian) conditions at the wellhead.

). It offers the best combination of hydrochemical quality (TDS predominantly 0.3–1.5 g/L in the Predmugalzhar and Zhemsky basins), very high yield (up to 10 L/s, with self-flowing conditions reducing pumping costs to zero at many sites), and proven lateral continuity across large pasture tracts. The Jurassic–Triassic horizon is the second priority, particularly in the southern and central districts where the Alb-Cenomanian is absent or contains saline water. The Quaternary alluvial horizon, although widespread, is highly susceptible to surface contamination (nitrate, ammonium) and should be developed only with enhanced wellhead protection and regular monitoring. The Neogene horizon in the Shalkar Basin is marginal due to TDS commonly in the 1–5 g/L range.

3.5.2. Prospective Aquifer Horizons—WKR

The WKR presents (see

Table 7. Prospective aquifer horizons for pasture watering development, WKR.

Aquifer Horizon	Distribution Area	Depth to Water (m)	Yield (L/s)	TDS (g/L)	Dominant Water Type
Quaternary alluvial (unconfined)	Zhaiyk River valley—from Uralsk to state border	0.5–20	0.8–20	<1.0–3.0	HCO3-Ca-Na, HCO3-SO4-Na
Quaternary alluvial + aeolian (unconfined)	Pre-Caspian lowland, aeolian sand massifs	0.5–20	up to 5–10	<1.0–3.0	HCO3-Ca-Na, SO4-Na
Upper Pliocene–Quaternary (semi-confined)	Pre-Caspian lowland, palaeovalley fills	up to 20	0.2–5.0	<1.0–3.0	HCO3-SO4-Na, SO4-Na
Palaeogene–Upper Cretaceous (semi-confined)	Syrtovskoye basin (north WKR), watershed areas	10–40	1.0–15.0	0.8–3.0	HCO3-Ca-Na, HCO3-SO4-Na

) more constrained hydrogeological options than the Aktobe Region due to the prevalence of saline marine Pliocene–Quaternary sediments over most of its area. The priority development target is the Palaeogene–Upper Cretaceous horizon in the Syrtovskoye hydrogeological basin, which offers the highest yields (up to 15 L/s) and acceptable TDS (0.8–3.0 g/L) within the study area. The Quaternary alluvial horizon of the Zhaiyk Valley is the second priority, providing very high yields (up to 20 L/s) with consistently fresh water (TDS < 1.0 g/L) but limited to a linear corridor of approximately 5–20 km width along the river. The Pre-Caspian lowland aquifers (Pliocene–Quaternary alluvial and aeolian) are economically viable for small-scale watering points in the central districts, but require careful site selection to avoid salt-dome aureoles.

3.6. Groundwater Resource Potential Versus Livestock Water Demand

3.6.1. Predictive Exploitable Groundwater Resources

Predictive exploitable groundwater resources for both regions, compiled from the State Groundwater Register (as of 1 January 2020) and the Atlas of Hydrogeological Maps of Kazakhstan (scale 1:2,500,000), are presented in

Table 8. Predictive exploitable groundwater resources and natural groundwater recharge for the WKR and the Aktobe Region, compiled from the Atlas of Hydrogeological Maps of Kazakhstan [34] and the State Register of Groundwater Deposits [39].

Region	Predictive Exploitable Resources a				Natural Resources b (m3/s)
	Total	Fresh Water (TDS < 1 g/L)	Slightly Brackish (TDS 1.0–3.0 g/L)	Brackish–Saline (TDS 3.0–10.0 g/L)
	m3/s (km3/yr)	m3/s (km3/yr)—% of Total	m3/s (km3/yr)—% of Total	m3/s (km3/yr)	m3/s (km3/yr)
Aktobe Region	153.44 (4.84)	62.90 (1.98)—41.0%	46.12 (1.45)—30.1%	44.41 (1.40)	49.77 (1.57)
WKR	33.88 (1.07)	15.88 (0.50)—46.9%	14.50 (0.46)—42.8%	3.50 (0.11)	21.59 (0.68)
Both regions (total)	187.32 (5.91)	78.78 (2.49)—42.1%	60.62 (1.91)—32.4%	47.91 (1.51)	71.36 (2.25)
Republic of Kazakhstan	2038.26 (64.3)	1282.28 (40.4)—62.9%	520.17 (16.4)—25.5%	235.80 (7.4)	1215.82 (38.3)
Study region as % of national	9.2%	6.1%	11.7%	20.3%	5.9%

^a Predictive exploitable resources represent the maximum volume of groundwater that can be sustainably extracted from explored and prospective aquifer horizons without causing long-term depletion of the resource base, as estimated by the State Hydrogeological Survey of Kazakhstan. ^b Natural recharge represents the long-term average rate of groundwater replenishment from atmospheric precipitation infiltration and river-bed losses.

. The Aktobe Region holds the largest groundwater endowment in the western Kazakhstan region: total predictive exploitable resources of 153.44 m³/s (4.84 km³/yr) for waters with TDS ≤ 10 g/L, of which fresh water (TDS < 1.0 g/L) accounts for 62.90 m³/s (41.0% of region resources and 75.7% of fresh-water resources of the entire western Kazakhstan region). The WKR holds smaller total resources (33.88 m³/s, 1.07 km³/yr), with fresh water accounting for 15.88 m³/s (46.9% of regional resources), a proportion similar to that of the Aktobe Region (41.0%). Together, the two study regions contain 187.32 m³/s of predictive exploitable resources, representing 9.2% of the national total.

Natural groundwater recharge, reflecting long-term mean recharge from atmospheric precipitation and river infiltration, were estimated at 49.77 m³/s (1.57 km³/yr, module (recharge rate per unit area (L s⁻¹ km⁻²)) 0.17 L s⁻¹ km⁻²) for the Aktobe Region and 21.59 m³/s (0.68 km³/yr, module 0.14 L s⁻¹ km⁻²) for the WKR. Spatial distribution of the natural resource module is strongly non-uniform: in the north of the Aktobe Region the module increases progressively from 0.1–0.2 L s⁻¹ km⁻² (central) to 0.3–0.5 L s⁻¹ km⁻² (northern districts), reaching 0.5–1.0 L s⁻¹ km⁻² in the extreme north. In the south-east (right bank of the Irgyz River) and in the North-Usturt basin, the module does not exceed 0.1 L s⁻¹ km⁻². In the WKR, the Pre-Caspian lowland has a consistently low module (<0.1 L s⁻¹ km⁻²), rising to 0.2–0.3 L s⁻¹ km⁻² in the Zhaiyk alluvial corridor and 0.3–0.5 L s⁻¹ km⁻² in the Syrtovskoye basin.

3.6.2. Current Livestock Water Demand and Resource Balance

Livestock census data from district and regional agricultural departments for 2023 indicate the following livestock populations on pasturelands of the study regions (see

Table 9. Comparison of predictive exploitable fresh groundwater resources (TDS < 1 g/L) against current (2023) and projected (2050, baseline scenario) daily livestock water demand. Ratio = fresh GW resource (m³/day) ÷ livestock demand (m³/day); 1 m³/s = 86,400 m³/day.

Region	Livestock (Head, 2023)	Daily Demand (m3/Day)	Fresh GW Resource (m3/s)	Resource/Demand Ratio
Aktobe Region	690,358 head	18,320 m3/day	62.90 m3/s	×3435—very large surplus
WKR	1,111,149 head	23,770 m3/day	15.88 m3/s	×578—large surplus
Combined—2023	1,801,507 head	42,090 m3/day	78.78 m3/s	×1871—very large surplus
Combined—2050 (baseline scenario)	~3,700,000 head	~88,800 m3/day	78.78 m3/s	×888—surplus maintained

): The Aktobe Region—365,823 cattle, 279,320 small ruminants, 42,896 horses, 2319 camels; total 690,358 head. The WKR—351,773 cattle, 626,840 small ruminants, 129,764 horses, 2772 camels; total 1,111,149 head. Combined livestock for both regions: 1,801,507 head.

Applying the daily water consumption norms, total calculated daily water demand for livestock watering amounts to approximately 18,320 m³/day for the Aktobe Region and 23,770 m³/day for the WKR—a combined demand of 42,090 m³/day (0.487 m³/s or 15.4 × 10⁶ m³/yr). Stock water for cattle accounts for 61.5% and 53.2% of total livestock water demand in the Western and Central Kazakhstan regions, respectively, confirming the primary water policy relevance of cattle management for any watering infrastructure investment programme.

The supply/demand analysis demonstrates unambiguously that both the current and projected future livestock water demand of the Aktobe Region and WKR can be met many times over by the predictive exploitable fresh groundwater resources—even when only fresh water (TDS < 1 g/L) is considered. The combined fresh-water resource (78.78 m³/s) exceeds the combined current demand (0.487 m³/s) by a factor of approximately 162, and even the 2050 projected demand under the accelerated growth scenario (estimated ~130,000 m³/day based on target livestock populations) would require less than 2% of the available fresh-water resource. The binding constraint is therefore not resource availability, but the documented absence, deterioration, and non-operational status of water capture, distribution, and treatment infrastructure, which this survey has found to affect 51–75% of all inspected watering points across the two regions.

4. Discussion

4.1. Principal Findings in Context

This study presents the first systematic field-based hydrochemical characterisation of pasture livestock watering sources in the WKR and the Aktobe Region in over 40 years. The results (Table 3, Table 4 and Table 5) reveal a dual groundwater quality landscape shaped by geological inheritance, aridity, and localised anthropogenic pressures, and define a paradox of pasture water management: adequate fresh groundwater resources (approximately 80% of samples with TDS < 3.0 g/L) exist beneath pasturelands that cannot be grazed because 51–75% of inspected watering infrastructure is non-operational and 23–36% of sources are nitrate-contaminated. The fundamental constraint identified by this study is therefore not a resource deficit but an infrastructure delivery deficit. The predictive exploitable fresh groundwater resources of both regions collectively exceed the current livestock water demand (Table 9), confirming that the physical resource base is more than sufficient to support all current and projected livestock populations. The critical bottleneck lies in the capture, distribution, and maintenance of water supply infrastructure, the system that converts a subsurface resource into water at the point of livestock consumption.

4.2. Hydrogeochemical Processes Controlling Groundwater Quality

4.2.1. Evaporative Concentration and Salt-Dome Dissolution—The Primary Salinisation Driver

The dominant hydrogeochemical process controlling TDS and major ion chemistry in both regions is evaporative concentration of shallow groundwater, superimposed on dissolution of halite and gypsum/anhydrite from Permo-Triassic and Pliocene–Quaternary salt-bearing sequences [18,19]. This interpretation is supported by three lines of evidence.

First, the Piper diagrams (Figure 2a and Figure 3a) show that samples classified as Na–Cl–SO₄ water type by their relative ionic composition occupy a distinct field in the lower-left quadrant of the diamond plot, indicating the dominance of sodium among cations and chloride–sulphate among anions. It should be noted that the Piper diagram displays only relative ion proportions and does not distinguish samples by absolute TDS; the association between the Na–Cl–SO₄ water type and high TDS values is confirmed independently by the analytical data (Table 3 and Table 4), which show that all samples with TDS > 3.0 g/L fall within this hydrochemical facies. The Durov diagrams (Figure 2b and Figure 3b), which incorporate TDS on the marginal axis, provide direct visual confirmation of this relationship: Na–Cl–SO₄ samples cluster in the high-TDS field (>2000 mg/L), while HCO₃–Ca–Mg samples cluster in the low-TDS field (<1000 mg/L).

Second, the Cl/Na molar ratio approaches 1.0 at the most saline sites, which is diagnostic of halite dissolution as the controlling geochemical process. Third, brackish samples from the Kobda district (the Aktobe Region) and the Kaztalovka area (the WKR) display Ca²⁺ + Mg²⁺ excess over HCO₃⁻ + SO₄²⁻, reflecting gypsum dissolution and reverse cation exchange (Ca → Na) on clay minerals in the Neogene–Quaternary argillaceous sequences.

Together, these lines of evidence trace a clear hydrogeochemical evolution trend visible on the Durov diagrams: fresh HCO₃–Ca–Mg waters from recharge zones (upper-left field) evolve through progressively increasing TDS toward Cl–SO₄–Na waters in discharge and evaporation zones (lower-right field) without a significant shift in pH. This trajectory corresponds to the classic Chebotarev sequence for arid-zone artesian basins, driven by lateral groundwater flow from recharge areas (the northern Aktobe Region, the Syrtovskoye basin in the WKR) through intermediate zones toward topographically low discharge areas (the Pre-Caspian lowland, the Kobda salt-dome zone, Kaztalovka).

Salt-dome tectonics in the northern Kobda district create anomalously high local TDS gradients: the transition from fresh HCO₃-Ca-Mg to highly saline Cl-SO₄-Na waters (Table 3) occurs over horizontal distances of <20 km, consistent with observations from the eastern Pre-Caspian Basin and the Ustyurt Plateau [22]. Adenova et al. [22] similarly reported TDS ranging from 0.5 to >10 g/L in western Kazakhstan, with hydrocarbon-related contamination as an additional concern. The practical implication is that new watering points in the Kobda district should preferentially target deeper artesian horizons (Alb-Cenomanian, Jurassic–Triassic) rather than shallow shaft-well depths.

4.2.2. Carbonate Weathering and Recharge-Zone Chemistry

Fresh HCO₃-Ca-Mg waters in the Predmugalzhar, Dongyztau, and Syrtovskoye basins are geochemically controlled by open-system carbonate dissolution: infiltrating precipitation charged with soil CO₂ dissolves calcite and dolomite, producing Ca²⁺- and Mg²⁺-rich, HCO₃⁻-dominant groundwater at pH 7.0–8.0.

These low-TDS waters recharge through alluvial gravels derived predominantly from carbonate-bearing (limestone and dolomite) formations of the Ural and Mugalzhar fold belts. Despite the short rock–water contact time, carbonate dissolution is sufficiently rapid to impart a Ca-Mg-HCO₃ hydrochemical signature to the recharge waters.

The moderate hardness in many HCO₃-Ca-Mg samples (5–15 meq/L) is largely geogenic, reflecting dolomite-dominated lithology in the Jurassic–Triassic and Palaeogene formations of the study area. This hardness, while exceeding the aesthetic guideline of 6 meq/L for drinking water, does not constitute a livestock health risk at concentrations below ~25 meq/L; cattle, horses, and sheep tolerate hardness to at least 30 meq/L without measurable productivity losses. Only the 14 samples with hardness > 25 meq/L, concentrated at high-TDS Cl-SO₄-Na sites, approach the threshold associated with reduced magnesium bioavailability and hypomagnesaemia risk in ruminants.

4.2.3. Fluoride Enrichment—Geogenic Source, Aktobe Region

Elevated fluoride concentrations (exceeding the WHO guideline of 1.5 mg/L) were detected in 12 of 100 samples (12%) from the Aktobe Region, concentrated in the Aitikebi and Kobda districts where the shallow aquifer is hosted within Neogene sandy deposits of the Shalkar Basin and adjacent Palaeogene horizons.

Fluoride enrichment in arid-zone Central Asian groundwater is a well-documented geogenic phenomenon controlled by three interrelated processes. The primary source of dissolved fluoride is the weathering and dissolution of fluorite (CaF₂) and, to a lesser extent, fluorapatite (Ca₅(PO₄)₃F) disseminated within the Neogene and Palaeogene host sediments. Structurally bound fluoride in phyllosilicates (biotite, muscovite) and devitrifying volcanic glass in Palaeogene tuff layers provide additional, slower-release geogenic inputs. This initial release is amplified by evaporative concentration, which progressively increases fluoride levels as water is lost to evapotranspiration under the arid conditions of the study area. A third mechanism, competitive desorption, operates in alkaline groundwater (pH > 7.5), where hydroxyl ions displace fluoride adsorbed on the surfaces of clay minerals and iron oxyhydroxides, releasing additional F⁻ into the solution [29,30]. Comparable geogenic fluoride enrichment through analogous mineral-weathering pathways has been documented in surface waters draining volcanic-sedimentary terrains in northeastern Turkey [31].

The Durov diagram for the Aktobe Region (Figure 2b) places the fluoride-enriched samples in the high-pH, moderate-Na field, which is consistent with this desorption mechanism being active at the affected sites.

The maximum fluoride concentration of 6.3 mg/L, recorded at ShK-Tolybai Vostok in the Aitikebi district, exceeds the chronic fluorosis risk threshold for cattle (>5 mg/L) and represents a direct livestock health hazard. Prolonged consumption of water at this concentration causes dental and skeletal fluorosis in both cattle and sheep, leading to reduced feed intake, lameness, and measurable declines in productivity. The consistent spatial clustering of elevated fluoride within the Shalkar Basin confirms the Neogene formation as the primary geogenic source and indicates that shallow shaft wells in this area should not be used for livestock watering without prior defluoridation treatment.

Comparable geogenic fluoride enrichment has been documented in arid-zone aquifers of southeastern Algeria, where Masmoudi et al. [29] reported F⁻ concentrations up to 3.2 mg/L in Cretaceous sedimentary aquifers under semi-arid conditions. Yang et al. [30] identified probabilistic health risks from fluoride in shallow groundwater of northern China’s irrigation areas, with maximum F⁻ of 4.8 mg/L associated with alkaline Na-HCO₃ water types—a geochemical association consistent with the high-pH, Na-enriched samples observed in the present study at fluoride-affected sites in the Shalkar Basin.

4.3. The Infrastructure–Resource Paradox: Adequate Groundwater, Failed Delivery

The most significant practical finding is the mismatch between resource endowment and infrastructure capacity. As shown in Table 8 and Table 9, predictive exploitable fresh-water resources exceed the combined current livestock water demand by a factor of >160, and even accelerated 2050 growth scenarios would require less than 2% of the available resource. This finding aligns with previous assessments of Kazakhstan’s total groundwater potential (estimated 61–64 BCM/yr nationally, of which only ~25% is formally approved for use) and with the recognition that 56.5% of the national pasture area remains unused, a situation that reflects the absence of functional water supply infrastructure rather than groundwater resource scarcity.

The field survey quantified this infrastructure deficit precisely: 51–75% of all inspected water supply objects are non-operational or requiring major capital repair. This confirms and updates earlier estimates of 50–73% infrastructure disrepair from region-level inventories. The implications for resource utilisation are stark: even if only the currently operational fraction of wells were fully functional and properly maintained, pastoral watering coverage would increase substantially—because the resource potential beneath each watering point typically far exceeds the current demand. The dominant infrastructure failure modes identified in this survey (exceedance of design service life, absence of maintenance, equipment removal) are all addressable at relatively low unit cost compared to the economic returns from restored pasture productivity.

Economic analysis demonstrates that the levelised cost of water (LCW—defined as the total annualised capital and operating cost divided by the annual volume of water produced) from rehabilitated or new boreholes in the study area ranges from 1278 to 2891 KZT/m³ (KZT—Kazakhstani tenge; approximately USD 2.6–6.1 $/m³ at 2025 exchange rates), depending on scenario assumptions. At the baseline scenario (1923 KZT/m³), operating a 1000-head sheep/cattle unit on an improved watered pasture generates 8–16 million KZT/yr additional income compared to an unwatered pasture, which more than covers the annualised capital cost at herds > 700 head. The economic case for watering infrastructure investment is therefore strong at realistic stocking densities, though public subsidy remains essential for remote single-family operations in the pessimistic scenario.

4.4. Climate Change Implications for Groundwater Quality and Pasture Water Supply

Climate model projections for Kazakhstan indicate a significant increase in aridity across western Kazakhstan through 2050, characterised by: a 16% reduction in growing-season moisture supply; a 17% increase in the aridity index; a rise in mean annual temperature of 1.5–2.5 °C relative to the 1980–2010 baseline; and increased frequency of severe droughts. These trends have direct hydrogeological consequences for the study area:

• Reduced groundwater recharge: As precipitation decreases and evapotranspiration increases, infiltration rates to unconfined shallow aquifers will decline, reducing natural flushing of nitrate and other contaminants. In arid conditions, groundwater residence time in shallow aquifers increases, intensifying evaporative concentration and elevating TDS even in currently fresh sources.
• Increased livestock water demand: Higher ambient temperatures in arid summer conditions increase the daily stock water requirement of cattle by 15–25% and of sheep by 10–15% per 5 °C temperature increase above the thermoneutral zone (the ambient temperature range within which an animal does not need to expend additional energy for thermoregulation, typically 5–25 °C for cattle). By 2050, the projected additional demand due to climate-induced temperature rise alone could add 5000–8000 m³/day to the combined study-area demand—equivalent to a 12–19% increase above the 2023 baseline.
• Increased overgrazing pressure near functional watering points: As more shallow wells fail and are not replaced, the remaining operational watering points will support higher stocking densities within their 5–10 km water access radius. This concentrates livestock waste near functional wells, accelerating nitrate contamination of the very sources that remain accessible.
• Pasture degradation feedback: The loss of 15–49% of rangeland productivity projected for semi-arid Kazakhstan by 2050 will increase pressure to expand grazing into remote, currently underutilised areas—which are precisely the areas where watering infrastructure is largely absent. This creates a strong positive feedback: climate-driven pasture degradation near settlements incentivises movement to remote pastures, which requires new watering infrastructure investment, which in turn is the enabling condition for realising Kazakhstan’s 2050 livestock development targets.

It must be stressed that projected climate change does not threaten the adequacy of the groundwater resource base itself: even under the most aggressive demand growth scenario for 2050, total livestock water requirements would consume less than 2% of the available fresh groundwater. Rather, climate-driven increases in daily water consumption per animal, combined with accelerated degradation of the remaining functional infrastructure through more frequent drought and extreme heat events, will widen the disparity between the water that exists in the subsurface and the water that is actually delivered to livestock through operational watering points. In practical terms, higher temperatures will increase the urgency and scale of infrastructure rehabilitation, not the need for new groundwater exploration.

It is important to recognise a limitation of the present study in the context of climate change: the 2025 field campaign provides a single-season spatial snapshot of groundwater chemistry, not a time series. A one-time sampling campaign, regardless of its spatial coverage, cannot detect temporal trends in groundwater quality or distinguish climate-driven changes from natural inter-annual variability.

Detecting climate-induced changes in groundwater quality (such as progressive salinisation of shallow aquifers due to declining recharge, or increasing nitrate concentrations due to reduced dilution) would require a dedicated long-term monitoring programme with consistent sampling methodology applied over at least 10–15 years at a network of representative sites. Even annual sampling at a subset of wells would require many years of data accumulation before statistically meaningful trends could be distinguished from seasonal and inter-annual variability. The establishment of such a monitoring network, building on the georeferenced baseline dataset created in this study, is therefore a priority recommendation for future work.

4.5. Desalination and Treatment Technologies for Marginal-Quality Sources

For the approximately 20% of samples in both regions with TDS > 3 g/L, and particularly for the 10% with TDS > 5 g/L, groundwater use for livestock watering requires desalination. Three desalination and treatment technologies were evaluated for their applicability to the specific conditions of western Kazakhstan’s pastoral water supply, considering the constraints of remote location, absence of grid electricity at most sites, and the differing water quality requirements for livestock watering versus human drinking water. The selection of technology at any given site depends primarily on the TDS of the source water and the intended end use. The study identifies two technology options appropriate for the conditions of western Kazakhstan:

• For the most highly saline sources (TDS > 8 g/L, affecting three of 139 samples in this study), reverse osmosis (RO) is the recommended treatment. RO achieves greater than 99% salt rejection but is more energy-intensive than ED and requires pre-treatment (sand filtration, cartridge filtration) to protect the membranes from turbidity-related fouling. RO also generates a high-TDS brine concentrate (20–40% of the feed volume) that requires controlled disposal, which is a particularly important consideration in the ecologically sensitive Pre-Caspian lowland.
• A second technology option—solar-trough evaporation distillers—addresses a different but closely related challenge. At many remote pasture sites in the study area, groundwater with TDS of 3–5 g/L remains usable for livestock (cattle tolerate up to 5 g/L without productivity losses) but is unsuitable for human consumption, which requires TDS below 1.0 g/L under WHO and Kazakh national drinking water standards. The pastoral workers and their families who manage livestock at these remote seasonal camps therefore lack access to safe drinking water, even though the livestock water supply is technically adequate. Solar-trough distillers produce small volumes of purified water (typically 2.5–3 L per m² of collector area per day) that are wholly insufficient for livestock watering but can meet the drinking and cooking water needs of 2–4 persons. Deploying these low-cost, energy-autonomous units at remote watering points would thus simultaneously resolve two distinct water access problems: the livestock watering deficit (met by the untreated groundwater source) and the rural drinking water deficit for pastoral communities, contributing to the objectives of United Nations Sustainable Development Goal 6 (SDG-6: “Ensure availability and sustainable management of water and sanitation for all”).

For the 33–36% of sites where nitrate contamination is the sole or primary water quality problem and TDS is otherwise acceptable for livestock use, desalination is not required. At these sites, the intervention strategy focuses on eliminating the contamination source and preventing further contaminant ingress, rather than on treating the water itself.

The first component of this strategy is wellhead protection, which involves four measures: (i) construction of a perimeter fence (minimum 50 m radius) to exclude livestock from the immediate vicinity of the well; (ii) installation or repair of a concrete apron around the wellhead to prevent direct infiltration of surface runoff into the well; (iii) sealing of the annular gap between the concrete ring lining of the shaft well and the surrounding undisturbed soil, to eliminate the preferential vertical pathway through which contaminated surface water currently bypasses the unsaturated zone; and (iv) relocation of the nearest livestock pen or manure storage to a distance of at least 100 m from the wellhead, thereby removing the primary nitrogen source from the well’s immediate catchment area. It should be noted that the shaft wells in the study area are lined with concrete rings that function as the structural casing; the term “grouting” refers specifically to sealing the annular space around this lining, not to installing new casing.

The second component is natural attenuation—the ensemble of biogeochemical and physical processes that reduce nitrate concentrations in groundwater after the contamination source has been removed. In the aerobic, shallow unconfined aquifers of the study area, the dominant attenuation mechanisms are expected to be dilution by uncontaminated recharge (rainfall infiltration replacing contaminated pore water over successive recharge cycles) and, to a lesser extent, aerobic denitrification by indigenous soil bacteria in the unsaturated zone. However, the timescale of nitrate recovery in groundwater is highly site-dependent and is controlled by several factors that vary across the study area: the thickness and permeability of the unsaturated zone, the annual recharge rate, the volume of nitrate stored in the soil profile above the water table, and the extent to which the contamination source is fully eliminated rather than merely reduced. Published studies in comparable shallow alluvial aquifer settings have reported nitrate concentration declines of 30–60% within 3–5 years following source removal where the unsaturated zone is thin (<5 m) and recharge rates are moderate [42], but substantially longer recovery times (10–20 years) have been documented where thick unsaturated zones or low recharge rates slow the flushing process [41].

Accordingly, we do not predict a specific recovery timeline for the contaminated sites in the study area. Instead, we recommend that post-intervention monitoring be initiated at all rehabilitated wellhead sites, with semi-annual nitrate sampling for a minimum of five years, to empirically assess the rate of water quality improvement and to determine whether additional intervention (such as active denitrification treatment) is necessary at sites where passive attenuation proves insufficient.

4.6. Towards an Integrated Decision Framework for Pasture Watering Infrastructure

The field survey results, hydrochemical dataset, resource assessment, and demand projections presented in this study provide the input layers for a multi-criteria spatial decision framework for pasture watering infrastructure investment.

4.6.1. Decision Criteria and GIS Data Layers

The GIS database assembled in this study (Section 2.8) contains five categories of spatially referenced data that, when overlaid, define the decision space for each pasture management unit:

(i) Infrastructure condition and rehabilitation potential. The three-tier classification of all inspected watering points (operational/requires repair/non-operational) identifies sites where rehabilitation is feasible at 30–50% of the cost of equivalent new construction, versus sites that require complete replacement. Rehabilitation of Grade 2 (repairable) infrastructure should generally precede new construction, as it yields the fastest and most cost-effective expansion of watering coverage.

(ii) Groundwater quality and livestock suitability. The hydrochemical dataset (Table 3, Table 4, Table 5 and Table A1) identifies which sources are directly suitable for livestock watering (TDS < 3.0 g/L, NO₃⁻ < 50 mg/L), which require only wellhead protection to address nitrate contamination (33–36% of samples), and which require desalination treatment (TDS > 5.0 g/L, approximately 10% of samples).

(iii) Aquifer yield and artesian conditions. The prospective aquifer horizon assessment (Table 6 and Table 7) identifies areas where self-flowing artesian conditions eliminate pumping costs entirely—a decisive economic advantage at remote sites. In the Aktobe Region, the Alb-Cenomanian horizon offers artesian heads of +10 to +30 m above ground level across the Predmugalzhar and Zhemsky basins; in the WKR, the Palaeogene–Upper Cretaceous horizon of the Syrtovskoye basin provides the highest yields (up to 15 L/s).

(iv) Renewable energy potential for pumping. Where artesian conditions are absent and pumping is required, the feasibility of solar-photovoltaic or wind-electric pumping systems determines whether a site can operate independently of diesel fuel supply. Solar pumping is viable throughout the study area (annual global horizontal irradiance 1400–1600 kWh/m²), while wind-electric systems are particularly suited to the high-wind corridors of the northern WKR and the northern Aktobe Region.

(v) Contamination risk and wellhead protection requirements. The spatial correlation between nitrate exceedances and proximity to livestock facilities (Section 3.2.5) allows identification of sites where wellhead protection alone can restore water quality, versus sites where the contamination source cannot be relocated and alternative water sources must be developed.

The overlay of these five layers within the GIS database enables classification of each pasture management unit into one of several intervention categories—for example, “rehabilitate existing infrastructure, no treatment needed,” “drill new borehole to artesian horizon, install solar pump,” or “install electrodialysis at existing high-TDS source”—rather than applying a uniform strategy across the entire study area.

4.6.2. Time-Staged Implementation

While the spatial decision framework determines where and what type of intervention is appropriate, the temporal sequencing of investments must also consider urgency and feasibility. Three time horizons are relevant.

In the immediate term, use of the eight confirmed extreme-nitrate sites (NO₃⁻ > 200 mg/L) should be suspended for watering young livestock (calves and lambs under 90 days) and pregnant animals until wellhead protection has been installed and water quality re-tested. In the short to medium term (1–10 years), wellhead protection should be implemented at all nitrate-contaminated sites with otherwise acceptable TDS, and electrodialysis units should be installed at the highest-TDS sites (5–10 g/L) in the Kobda district (the Aktobe Region) and Kaztalovka–Taipak area (the WKR) where deeper fresh-water horizons are not accessible. In the long term (to 2050), non-viable shallow saline shaft wells should be replaced by a hub-and-spoke delivery system, in which treated water from central boreholes is distributed to satellite watering points, combined with seasonal surface water impoundments in areas of concentrated runoff.

4.6.3. Monitoring Network

A standing hydrochemical monitoring network of 30–40 representative wells in each region, stratified by TDS class, aquifer type, and district, should be established and sampled at least annually. The GIS database developed in this programme provides the spatial framework for site selection. Integration of satellite-derived pasture condition indicators (such as NDVI for vegetation health and Sentinel-1 soil moisture for drought detection) into the monitoring system would enable early identification of pasture degradation hotspots and support the prioritisation of emergency water supply interventions. The ultimate objective is an operational decision-support platform linked to the livestock census and pasture cadastre, accessible to regional agricultural departments (akimats) for evidence-based management planning.

4.6.4. Factors Beyond the Scope of This Study

It must be acknowledged that a fully operational decision-support system for pasture watering investment would require additional data layers that lie outside the scope of this hydrogeological study. These include the livestock carrying capacity of each pasture management unit, distance to livestock markets and processing facilities, condition of transport infrastructure (roads, tracks), availability of labour and technical maintenance capacity at remote sites, and the socio-economic conditions of pastoral communities. These factors influence the economic viability and practical sustainability of watering infrastructure investments and should be integrated into the GIS framework as they become available from ongoing national land cadastre and agricultural census programmes. The hydrogeological and hydrochemical baseline established in this study provides the necessary foundation upon which these additional layers can be built.

4.7. Limitations of This Study

This study has five limitations that should be considered when interpreting the findings and planning future work:

• Sampling bias toward operational infrastructure: The survey sampled only currently functional or recently abandoned watering points accessible by route. Completely collapsed or buried wells—which may represent the highest-quality sources that were most intensively used and therefore most deteriorated—were not sampled. The actual quality distribution of the pre-existing well network may therefore be more favourable than the 2025 sample suggests.
• Single-season snapshot: All samples were collected in May–July 2025, representing late spring to early summer hydrochemical conditions. Seasonal variation in TDS (typically ±20–40% in shallow unconfined aquifers due to recharge dilution in spring and evaporative concentration in summer) and in nitrate (influenced by seasonal agricultural activity) is not captured. Repeat sampling in autumn (October–November) is recommended to quantify seasonal variability.
• No isotopic data: Stable isotope ratios (δ¹⁸O, δ²H, δ¹⁵N-NO₃⁻, δ¹⁸O-NO₃⁻) were not included in the analytical programme. Dual-isotope nitrate analysis would have provided definitive source attribution (livestock waste vs. mineral fertiliser vs. atmospheric deposition) beyond the circumstantial evidence presented here. This is recommended for follow-up investigations at the highest-priority contaminated sites.
• Predictive resource estimates from the 2020 State Register: Groundwater resource calculations rely on national estimates compiled prior to January 2020, using exploration data predominantly from the Soviet period (1960–1990). Local-scale resource variability at the pasture management unit scale (6000–8000 ha) is not captured by these regional-scale estimates. Site-specific aquifer test data from the boreholes drilled in this programme would enable more precise local resource quantification.
• Scope limited to the WKR and the Aktobe Region: This paper focuses on two of the six regions covered by the broader research programme. Comparisons with Atyrau, Mangystau, Karaganda, and Ulytau regions are not included but will be the subject of companion publications from the same programme.

5. Conclusions

This study presents the first systematic field-based hydrochemical characterisation of groundwater sources used for pasture livestock watering in the WKR and the Aktobe Region in more than 40 years. Based on the analysis of 139 groundwater samples collected along 11,182 km of field routes, the following principal conclusions are drawn.

The hydrochemical character of groundwater in both regions is governed by a dual quality structure. Fresh to slightly brackish bicarbonate-calcium-magnesium waters (TDS < 3.0 g/L), formed under active recharge conditions in structurally elevated, well-drained settings, constitute approximately 80% of all samples. Highly mineralised chloride-sulphate-sodium waters (TDS up to 9.91 g/L) are confined to topographically low, poorly drained discharge zones in the southern districts under the influence of the salt-dome tectonics of the Pre-Caspian depression. The dominant hydrogeochemical processes are evaporative concentration and dissolution of halite and gypsum/anhydrite from Permo-Triassic and Pliocene–Quaternary salt-bearing sequences, producing the classic Chebotarev hydrogeochemical evolution sequence documented by the Piper and Durov diagram analyses.

Nitrate contamination constitutes the most critical water quality problem from a livestock health perspective: 36% of samples in the Aktobe Region and 23% in the WKR exceeded the 50 mg/L threshold, with maximum concentrations of 635 mg/L and 577 mg/L respectively—among the highest values reported in regional groundwater surveys across Central Asia. The lower exceedance rate in the WKR (23% vs. 36%) reflects the greater depth to the water table and better-drained hydrogeological conditions in the northern Syrtovskoye basin, where the majority of WKR samples were collected, rather than lower anthropogenic pressure.

Spatial analysis confirms an exclusively anthropogenic origin, with exceedances concentrated in shallow shaft wells immediately adjacent to livestock wintering sites, farmsteads, and settlements lacking wellhead protection infrastructure. Geogenic fluoride enrichment affects 12% of samples in the Aktobe Region (maximum 6.3 mg/L), associated with Neogene deposits of the Shalkar Basin, and represents a genuine livestock health hazard at the affected sites. Elevated ammonium, co-occurring with nitrate at contaminated sites, further confirms decomposing animal waste and domestic sewage as primary pollution sources in both regions.

The groundwater resource endowment of the study area is fundamentally adequate to meet all current and projected livestock water demand. The predictive exploitable fresh groundwater resources (TDS < 1 g/L) of the Aktobe Region (62.90 m³/s) and the WKR (15.88 m³/s) collectively exceed the current combined livestock water demand (0.487 m³/s) by a factor of approximately 162. Even under the accelerated 2050 growth scenario, projected demand would require less than 2% of the available fresh groundwater resource. Six prospective aquifer horizons identified in the Aktobe Region—with the Alb-Cenomanian artesian horizon as the highest-priority development target—and four in the WKR—prioritising the Palaeogene–Upper Cretaceous horizon of the Syrtovskoye basin—provide a technically realistic framework for systematic pasture watering infrastructure development.

The central practical finding of this study is the fundamental paradox of pasture water management in western Kazakhstan: adequate fresh groundwater resources exist beneath pasturelands that cannot be grazed because 51–75% of all inspected water supply structures are non-operational or require major capital repair, a legacy of post-Soviet infrastructure collapse that has never been systematically addressed. The binding constraint on pasture productivity is therefore not resource scarcity but infrastructure failure. Projected climate change trajectories for western Kazakhstan—a 16% reduction in growing-season moisture reserves, a 17% increase in the aridity index, and a 12–19% rise in livestock daily water demand by 2050 due to temperature increase alone—will further intensify this constraint, making the rehabilitation and expansion of pasture watering infrastructure both an ecological necessity and an economically justified investment at realistic stocking densities.

The GIS-integrated hydrochemical and hydrogeological database developed in this programme provides the scientific foundation for evidence-based planning of pasture watering infrastructure in western Kazakhstan and for achieving the country’s livestock development targets by 2050. The results presented here cover the WKR and the Aktobe Region.