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Groundwater is a crucial global water resource; however, it faces the threat of depletion and quality degradation due to intensive agriculture and excessive fertilizer use. In India, groundwater assessments focus mainly on exploitation levels and often neglect quality. This study integrates groundwater quality with exploitation data to evaluate groundwater resources in Punjab, India. A novel Integrated Water Quality Index (IWQI), developed using advanced statistical techniques like Principal Component Analysis (PCA), identified Sodium (Na⁺), Sulphate (SO₄2⁻), Chlorine (Cl⁻), Sodium Adsorption Ratio (SAR), and Electrical Conductivity (EC) as key contributors to groundwater quality. Spatial-temporal maps for 2014 and 2022 classified water quality into five suitability classes: Excellent (0-35), Good (35-60), Poor (60-85), High Restrictions (85-100), and Severe Restrictions (> 100). Hydrogeochemical analyses, including Piper and Durov diagrams, indicated saline water dominance due to high fertilizer use. Notably, water quality degradation was observed in the North-Eastern, Western, and Southern regions, whereas the Central Punjab region, despite over-exploitation, retained excellent water quality for agricultural use. Statistical analysis revealed that 4.7% of the groundwater depth variability was linked to changes in water quality. The block-wise groundwater exploitation map from the Central Groundwater Board was refined to improve resource management and incorporate IWQI data, providing a more comprehensive view of Punjab's groundwater status. This study underscores the importance of integrating water quality into groundwater assessments to inform irrigation strategies and ensure sustainable water resource management., Competing Interests: Declarations. Consent for publication: All authors approved the final manuscript. Competing interest: The authors declare no competing interests. Disclaimer: All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors., (© 2025. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

E-waste, a global environmental concern, particularly affects developing nations due to the rise in informal recycling practices. This leads to contamination of environmental matrices, posing threats to both ecosystems and human health. To assess this issue, we monitored brominated flame retardants (BFRs) in 164 samples (soil) from 32 informal e-waste operational locations and 9 background locations across nine mega cities of Pakistan from September 2020 to December 2021. The mean concentrations (ng/g) of ∑ 27 PBDEs (polybrominated diphenyl ethers), ∑ 2 PBB (polybrominated biphenyls), HBB (hexabromobiphenyl), and ∑HBCDD (hexabromocyclododecane) were 176 ( 0.76-11141), 31.0 (0.65-58.0), 1.39 (0.01-42.8), and 12.0 (0.22-461), respectively. These levels were significantly higher (6 to tenfold) than those at background sites. Karachi, Faisalabad, Gujranwala, and Lahore exhibited high levels of all BFRs. Notably, BDE-209 (mean = 45.5 ng/g) ranged (0.13-1152 ng/g) exhibited higher level in soil samples. Seasonally, total ΣBFR concentrations (ng/g) ranked higher in winter (11,620), followed by spring (3874), autumn (3139), and summer (1207) indicating a seasonal impact of recycling activities. The average daily dose for soil ingestion (ng/kg/day) was estimated for BDE-209 (0.10973) in Faisalabad, followed by BDE-47 (0.08616) and BDE-99 (0.06788) in Karachi. Our findings showed that these values were lower than RfD values, suggesting no ingestion risk from studied BFRs. However, the growing prevalence of such informal e-waste recycling facilities could lead to increased exposure to toxic chemicals in near future., Competing Interests: Declarations. Ethical approval: All the authors have read and approved the paper for publication. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Scientific evaluation of the effectiveness of ecological restoration could provide support for sustainable management and protection of wetlands. However, due to the multiple and difficult to quantify factors affecting wetlands, commonly used spatiotemporal evaluation methods were difficult to scientifically reflect the actual effectiveness of ecological restoration. This paper took Tianjin Qilihai Wetland, a representative wetland in northern China, as the research object. Two representative time points, 2017 (before the start of the 2-year restoration project) and 2023 (4 years after the completion of the restoration project), were selected to construct a three-level monitoring and evaluation index system containing eight specific indicators from three aspects-water environment, biological community, and landscape topography. And then, a model based on fuzzy analytic hierarchy process was developed to systematically evaluate the ecological restoration effect. The evaluation results showed that after implementing a series of measures, the comprehensive evaluation index of wetland ecological environment increased by 37.68%. In addition, it was also found that compared to the water environment, the restoration of the ecosystem had a certain lag. It was recommended to further strengthen the follow-up and continuous monitoring and evaluation, as well as the monitoring and control of the stability of external water source quality. The method and model adopted in this paper further improved the theoretical and methodological system for evaluating the effectiveness of wetland ecological restoration., Competing Interests: Declarations. All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In recent decades, global climate change and rapid urbanization have aggravated the urban heat island (UHI) effect, affecting the well-being of urban citizens. Although this significant phenomenon is more pronounced in larger metropolitan areas due to extensive impervious surfaces, small- and medium-sized cities also experience UHI effects, yet research on UHI in these cities is rare, emphasizing the importance of land surface temperature (LST) as a key parameter for studying UHI dynamics. Therefore, this paper focuses on the evaluation of LST and land cover (LC) changes in the city of Prešov, Slovakia, a typical medium-sized European city that has recently undergone significant LC changes. In this study, we use the relationship between Landsat-8/Landsat-9-derived LST and spectral indices Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) derived from Landsat-8/Landsat-9 and Sentinel-2 to downscale LST to 10 m. Two machine learning (ML) algorithms, support vector machine (SVM) and random forest (RF), are used to assess image classification and identify how different types and LC changes in selected years 2017, 2019, and 2023 affect the pattern of LST. The results show that several decisions made during the last decade, such as the construction of new urban fabrics and roads, caused the increase in LST. The LC change evaluation, based on the RF classification algorithm, achieved overall accuracies of 93.2% in 2017, 89.6% in 2019, and 91.5% in 2023, outperforming SVM by 0.8% in 2017 and 4.3% in 2023. This approach identifies UHI-prone areas with higher spatial resolution, helping urban planning mitigate the negative effects of increasing urban LSTs., Competing Interests: Declarations. Ethical approval: All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

One of the leading challenges in Water Resource Recovery Facility monitoring and control is the poor data quality and sensor consistency due to the tough and complex circumstances of the process operation. This paper presents a new principal component analysis fault detection approach for the nitrate and nitrite concentration sensor based on Water Resource Recovery Facility measurements, together with the Fisher Discriminant Analysis identification of fault types. Five malfunction cases were considered: constant additive error, ramp changing error in time, incorrect amplification error, random additive error, and unchanging sensor value error. The faults' implementation, fault detection, and identification methods are presented and evaluated in terms of accuracy and promptitude. The models are originating from a municipal plant. The amount of required electrical energy and greenhouse gas released during the Water Resource Recovery Facility operation were assessed for the cases of nitrates and nitrites NO sensor normal and malfunctioning regimes. The environmental and economic evaluations show the benefits of detecting and identifying nitrates and nitrites NO sensor defects aimed at providing efficient and environmentally friendly operation of the Water Resource Recovery Facility. The fault-affected operation cases showed increased values, up to 10% for the total energy demand and 4% for the total greenhouse gas emissions, when they are compared to the normal operation case., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

This study applied integrated statistical approaches, including GIS mapping and the water quality index (WQI), to assess the quality of water, soil, and plant samples which collected from Darawat Dam, Sindh, Pakistan. The samples were analyzed for physicochemical parameters and metal analyses. Results of cations in water samples were in the range Na + 26.74 to 39.67 mg/L, K + 0.92 to 2.89 mg/L, Ca 2+ 28.67 to 39.45, and Mg 2+ 19.67 to 34.43 mg/L. The values of Cl - in water samples were 44.54 to 61.26 mg/L, sulfate 27.11 to 46.45 mg/L, and bicarbonate (HCO 3 - ) 100 to 135 mg/L. The results of major and trace elements in soil samples were varied for Na + 1259-1781 mg/kg, K + 306-498 mg/kg, Ca 2+ 689-1093 mg/kg, Mg 2+ 357-795 mg/kg, Zn 194-235 mg/kg, Fe 124-242 mg/kg, Cu 25.34-33.56 mg/kg, Co 19.45-27.32 mg/kg, Mn 97.83-131 mg/kg, Ni 39.65-52.44 mg/kg, Cr 69.64-83.65 mg/kg, Cd 3.98-8.66 mg/kg, and Pb 5.34-9.62 mg/kg respectively. The major and trace elements in plant samples were varied from Na + 3456 to 5016 mg/kg, K + 1197 to 1654 mg/kg, Ca 2+ 2367 to 2892 mg/kg, Mg 2+ 1429 to 1686 mg/kg, Zn 132 to 155 mg/kg, Fe 255 to 354 mg/kg, Cu 19.54 to 28.56 mg/kg, Co 11.64 to 14.65 mg/kg, Mn 23.54 to 34.78 mg/kg, Ni 8.44 to 11.03 mg/kg, Cr 6.34 to 9.55 mg/kg, Cd 0.43 to 0.93 mg/kg, and Pb 4.33 to 6.59 mg/kg. The GIS maps were drawn for TDS, Cl - , Na + , heavy metals Cr, Cd, and WQI and these graphs showed that most of the water samples of Darawat Dam is good for human consumption. The results of electrical conductivity (EC) and total dissolved salts (TDS) of water samples were found to be from 648 to 753 µS/cm and 414 to 482 mg/L. The Darawat Dam water samples were good for drinking as well as irrigation based on the results of water quality parameters. The WQI specified that 100% of samples were excellent to good for water quality for drinking. The 100% samples were also excellent for crops. The scatter diagram disclosed that weathering and ion exchange were chief processes. The Gibbs diagram revealed that all samples showed rock dominance which confirmed the dissolution of rock as the main source for the water quality of the Dam. The hydrogeochemical facies (Piper diagram) showed that sample points were gathered in the center of the diagram which indicated mixed type water with no particular parameter effluents the water quality., Competing Interests: Declarations. All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Ethical approval: This paper is submitted in this journal and not considering in any other journal. The manuscript did not contain any third-party data in this research work. Competing interests: The authors declare no competing interests., (© 2025. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)