Effective wastewater management is a critical environmental challenge, particularly in industrial regions like Faisalabad, where untreated textile effluents contribute to severe water pollution. This study evaluates the potential of phytoremediation using floating aquatic plants—Eichhornia crassipes (water hyacinth), Pistia stratiotes (water lettuce), and Lemna minor (common duckweed)—for the treatment of industrial textile wastewater. A controlled laboratory-scale experiment was conducted to assess pollutant removal efficiency over a 10-day retention period. The initial effluent concentrations of key parameters were measured before treatment to establish baseline conditions. The results demonstrated that Eichhornia crassipes exhibited the highest removal efficiency, achieving reductions of 36.12% (TDS), 36.14% (EC), 36.30% (salinity), 6.12% (pH), 34.30% (total hardness), and 44.52% (chloride). Furthermore, Pistia stratiotes and Lemna minor were particularly effective in removing nitrate (99.76%), ammonium (52.11%), and sodium adsorption ratio (46.29%), indicating species-specific phytoremediation potential. These findings highlight the viability of a low-cost, eco-friendly, and sustainable nature-based solution for wastewater treatment in industrial clusters, offering a practical alternative to conventional treatment technologies.

Groundwater depletion is among the most critical hydrological threats to sustainable agriculture and water security in semi-arid regions. This study presents a high-resolution, multi-sensor assessment of groundwater storage (GWS) dynamics across the Lower Chenab Canal (LCC) command area in Punjab, Pakistan—an intensively irrigated agro-hydrological system within the Indus Basin. We integrated downscaled GRACE/GRACE-FO-derived total water storage anomalies with CHIRPS precipitation, MODIS evapotranspiration (ET) and vegetation indices, TerraClimate soil moisture, land surface temperature (LST), land use/land cover (LULC), and population density using the Google Earth Engine (GEE) platform to reconstruct spatiotemporal GWS changes from 2002 to 2020. The results reveal a persistent and accelerating decline in groundwater levels, averaging 0.52 m yr⁻¹, which intensified to 0.73 m yr⁻¹ after 2014. Cumulative GWS losses exceeded 320 mm yr⁻¹, with severe depletion (up to −3800 mm) in northern districts such as Sheikhupura, Gujranwala, and Narowal. Validation with borewell data (R² = 0.87; NSE = 0.85) confirms the reliability of the remote sensing estimates. Statistical analysis indicates that anthropogenic drivers (population growth, urban expansion, and intensive irrigation) explain over two-thirds of the observed variability (R² = 0.67), whereas precipitation contributes only marginally (R² = 0.28), underscoring the dominance of human-induced stress over climatic variability. The synergistic rise in evapotranspiration, land surface temperature, and cultivation of high-water-demand crops such as rice and sugarcane has further amplified hydrological imbalance. This study establishes an operational framework for integrating satellite and ground-based observations to monitor aquifer stress at basin scale and highlights the urgent need for adaptive, data-driven groundwater governance in the Indus Basin. The approach is transferable to other data-scarce semi-arid regions facing rapid aquifer depletion, aligning with the global targets of Sustainable Development Goal 6 on water sustainability.