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<p>Groundwater is a vital water resource, especially in regions with suitable hydrogeological conditions. In northwest Iran, the Urmia aquifer is crucial for sustaining agriculture amid the ongoing crisis of Lake Urmia’s drying. However, excessive extraction and increasing salinity pose threats to food security, biodiversity, and the local economy. Standard and optimized Getis-Ord Gi statistics were applied to identify spatial clusters of high and low values, complemented by Local Moran’s <i>I</i> to assess spatial autocorrelation and detect cluster and outlier patterns. Water quality assessment used 99 samples collected over 13 years (2001–2014), analyzing major cations (K⁺, Na⁺, Ca²⁺, Mg²⁺), anions (Cl⁻, HCO₃⁻, CO₃²⁻, SO₄²⁻), and parameters including SAR, pH, TH, and EC. The optimized Gi* results revealed significant hotspots at the 99% confidence level, with notable clusters for Na⁺ (19% of sites, mainly in the north), Cl⁻ (22%, indicating salinity hazards), and SO₄²⁻ (14%, concentrated centrally). Conversely, cold spots dominated Ca²⁺ (31% in the east) and Mg²⁺ (20%). Positive Moran’s <i>I</i> z-scores confirmed strong spatial autocorrelation, showing that contamination patterns are non-random, with high–high clusters for Ca²⁺ and low–low clusters for Na⁺. The findings demonstrate that optimized Gi* offers superior precision for detecting spatial patterns compared to traditional methods. Spatial insights support sustainable aquifer management, emphasizing targeted interventions in high-risk zones, stricter controls on groundwater extraction, enhanced monitoring of electrical conductivity, and the adoption of efficient irrigation and recharge strategies. Integrating these tools into management frameworks can reduce overuse, improve climate resilience, and advance progress toward SDG 6.</p>

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Addressing the Spatial Distribution of Groundwater Quality Using Hot Spot Analysis in the Urmia Aquifer, Iran

  • 期刊论文

  • 发表日期：

    2026年2月20日

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  • Nazila Alaei,
  • Mirhassan Miryaghoubzadeh

摘要

Groundwater is a vital water resource, especially in regions with suitable hydrogeological conditions. In northwest Iran, the Urmia aquifer is crucial for sustaining agriculture amid the ongoing crisis of Lake Urmia’s drying. However, excessive extraction and increasing salinity pose threats to food security, biodiversity, and the local economy. Standard and optimized Getis-Ord Gi statistics were applied to identify spatial clusters of high and low values, complemented by Local Moran’s I to assess spatial autocorrelation and detect cluster and outlier patterns. Water quality assessment used 99 samples collected over 13 years (2001–2014), analyzing major cations (K⁺, Na⁺, Ca²⁺, Mg²⁺), anions (Cl⁻, HCO₃⁻, CO₃²⁻, SO₄²⁻), and parameters including SAR, pH, TH, and EC. The optimized Gi* results revealed significant hotspots at the 99% confidence level, with notable clusters for Na⁺ (19% of sites, mainly in the north), Cl⁻ (22%, indicating salinity hazards), and SO₄²⁻ (14%, concentrated centrally). Conversely, cold spots dominated Ca²⁺ (31% in the east) and Mg²⁺ (20%). Positive Moran’s I z-scores confirmed strong spatial autocorrelation, showing that contamination patterns are non-random, with high–high clusters for Ca²⁺ and low–low clusters for Na⁺. The findings demonstrate that optimized Gi* offers superior precision for detecting spatial patterns compared to traditional methods. Spatial insights support sustainable aquifer management, emphasizing targeted interventions in high-risk zones, stricter controls on groundwater extraction, enhanced monitoring of electrical conductivity, and the adoption of efficient irrigation and recharge strategies. Integrating these tools into management frameworks can reduce overuse, improve climate resilience, and advance progress toward SDG 6.