This research aims to assess the hydrogeochemical evolution of the groundwater in Oued souf valley for drinking and irrigation purposes. To achieve this, 49 groundwater samples from the complex terminal were examined and treated concurrently with multivariate statistical methods, geostatistical modeling and the WQI (water quality index). Focusing on the physico-chemical parameters, Q mode clustering analysis detected four major water groups, where the mineralization augmented from group 1 to group 4. The hydro-chemical type was the same, Ca-Mg-Cl-SO₄ for all the groups. Calcite, dolomite, anhydrite, and gypsum would be the dominant reactions with the undersaturation of evaporates minerals, based on geochemical modeling, while the carbonate minerals are precipitating. Geostatistical analysis using ordinary Kriging demonstrated the exponential semi-variogram model fitted for EC (electrical conductivity), Ca²⁺ (calcium), Mg²⁺ (magnesium), K⁺ (potassium), HCO₃⁻ (bicarbonate), Cl⁻ (chloride), and SO₄²⁻ (sulfate). At the same time, the rational quadratic model was the best-fitted semi-variogram model for Na⁺ (sodium) and NO₃⁻ (nitrate). EC, SO₄²⁻, and NO₃⁻ have a strong spatial structure, while Ca²⁺, Na⁺, K⁺, and HCO₃⁻ have a moderate spatial structure. Moreover, there was a weak spatial structure for Mg²⁺ and Cl⁻. The WQI shows that CT (complex terminal groundwater aquifers) are not suitable for drinking and their quality for irrigation fluctuates from excellent to moderate quality.

Soil salinization is a critical global issue for sustainable agriculture, impacting crop yields and posing a threat to achieving the Sustainable Development Goal (SDG) of ensuring food security. It is necessary to monitor it in detail and uncover its underlying factors at a regional scale. In this context, the present study aimed to evaluate soil health in the eastern Mediterranean region by using the Sodium Adsorption Ratio (SAR) as an indicator of soil salinity in three distinct soil horizons. The main objective of the research was to evaluate the performance of four machine learning (ML) models, including Random Forest (RF), Nu Support Vector Regression (NuSVR), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Gradient Boosting Regression (GBR), for accurate prediction of SAR following the Recursive Feature Elimination (RFE) as a feature selection method. Moreover, SHapely Additive exPlanations (SHAP) was applied as sensitivity analysis to identify the most influential covariates. Main findings of the research revealed that the average clay content in the surface horizon (H₁₀-_25cm) was 50.5% ± 10.4, which significantly increased to 57.5% ± 8.7 (p < 0.05). No significant mean differences were detected between the studied horizons for SAR and Na⁺. ML output revealed that NuSVR outperformed other algorithms in accurately predicting outcomes during both the training and testing stages. Moreover, Scenario 2 (SC2) with seven selected features from the RFE method facilitated highly accurate SAR predictions. Overall, the performance of ML models is ranked as NuSVR > GBR > ANN-MLP > RF. Lastly, SHAP sensitivity analysis identified CEC, Ca⁺², Mg⁺², and Na⁺ as the most influential variables for SAR prediction in both the training and testing stages. Hence, the research yielded valuable insights for efficient agricultural soil management at a regional level using state-of-the-art technology.

Iran has increased its CO₂ emissions significantly during the past few decades. The household sector in Iran contributes one of the largest sectors of CO₂ emissions. Despite this significant contribution, the existing policies have predominantly concentrated on large-scale initiatives while overlooking the regional role in shaping and implementing these plans. Therefore, this study investigates the relationship between CO₂ emissions and the efficient factors in three major groups including energy, climate, and household socio-economic factors. This study aims to address regional carbon emissions and develop CO₂ reduction policies tailored to each region's specific circumstances. It focuses on planning strategies at the regional level to effectively tackle CO₂ emissions. Household panel data of 28 provinces of Iran are employed by using both static and dynamic panel models for the years 2001 to 2019. Static estimation includes Fixed Effect (FE), Random Effect (RE) and pooled Partial least squares (PLS), Dynamic estimation includes difference Generalized Method of Moments (GMM) and system Generalized Method of Moments (GMM). The empirical result of the static method showed positive dependence of household CO₂ emissions on Heating Degree Days (HDD), Cooling Degree Days (CDD), precipitation level, oil consumption, gas consumption, household income, size of household, and also building stocks. In more detail, educational rate, dummy variable (removal of energy subsidy), and oil price reveal the greatest negative impact on the emissions with elasticities of − 0.428, − 0.31, and − 0.15; It represents 1% increase causes − 0.428, − 0.31, − 0.15, decrease CO₂ emissions, respectively. however, household size, gas consumption, and oil consumption show the most significant positive effects on CO₂ emissions with 1 percent increase causes CO₂ emissions increases by 0.1, 0.044, and 0.026, respectively. Regarding the impact of climate factors, a 1% increase in Heating Degree Days, Cooling Degree Days, and precipitation level causes CO₂ emissions increase by 0.024%, 0.004%, and 0.011% respectively, due to an increase in fossil energy demand. Results of the dynamic method of the system Generalized Method of Moments are similar to the static estimation results, except for that household size and urbanization are not significant. Also, removing the energy subsidy for fossil fuels due to substantial subsidy in fossil fuels in Iran or implementing a re-pricing energy policy can be a beneficial way to control carbon emissions from households within the provinces of the country. However, it is important to consider that this shift could potentially transfer subsidies to investments in the private sector for renewable energies.

An increasing amount of CO₂ emissions from the household sector of Iran led us to analyze the inequality and understand the possible driving force behind the CO₂ emissions. The study of inequality provides information to policy-makers to point policies in the right direction. By considering the differences in the socio-economic factors of provinces, the study aims to analyze the inequality in CO₂ emissions and different kinds of energy consumption, including oil, gas and electricity, for the household sector of Iran’s provinces between 2000 and 2017. For this aim, the Theil index and Kaya factor, as a simple and common method, were considered to evaluate the inequality in both CO₂ emissions and energy consumption, and determine the driving factor behind CO₂ emissions. According to the results, inequality in oil and natural gas consumption were increasing, electricity was almost constant; however, CO₂ emissions experienced a decreasing trend for the study period. The Theil index changed from 0.4 to 0.65 for oil, from 0.18 to 0.22 for natural gas, from 0.17 to 0.15 for electricity, and from 0.2 to 0.14 for CO₂ emissions between 2001 and 2017. In addition, the results of the inequality study indicated that most of the inequalities belong to within-group inequalities in energy consumption and CO₂ emissions. The results of the Kaya factor indicate that the second factor, energy efficiency, with a 0.21 value was the main driving factor of inequalities in CO₂ emissions; however, the first factor, energy consumption, can be a potential factor for inequality in the following years, as it increased from 0.00 to 0.11 between 2001 and 2017. It seems that by removing the energy subsidy policy in 2010 and 2013, low-standard and energy-wasting old vehicles were the most effective factors of energy inefficiency in the household sector, which need more accurate policy-making.