Neelam Sidhu

This present study introduces a cloud-based geospatial and machine learning framework for assessing coastal urban heat island (UHI) dynamics in Thoothukudi coastal region, Southern India, using 12 seasonal Landsat composites (2010–2025) processed through Google Earth Engine (GEE). Seasonal classification followed Indian Meteorological Department definitions: Summer (March-June), Northeast Monsoon (October-November) and Winter (December-February). Multi-sensor Landsat Collection 2 Tier 1 Level-2 data included Landsat 5/7 (2010), Landsat 8 (2015) and Landsat 8/9 (2020, 2025). Land surface temperature (LST) was retrieved using USGS Collection 2 scale-factor protocol, capped to 0–65 °C to suppress cloud-contaminated pixels. UHI intensity (UHI = LST − P₂₅(LST)) and Urban Thermal Field Variance Index (UTFVI) were computed alongside four spectral indices (NDVI, NDBI, NDWI, NDMI). Summer was the hottest season in three of four observation years (mean LST: 38.69–48.67 °C). The 2020 Northeast Monsoon composite was excluded due to cloud contamination. Mean UHI intensity ranged from 2.03 to 5.17 °C. Pearson correlation confirmed NDBI as the strongest positive LST predictor ( r = 0.30–0.60), while NDMI mirrors this inversely. Random Forest and Gradient Tree Boosting models were trained on seven predictors using dual validation (70/30 split and 2 km spatial block cross-validation). Model performance yielded R² of 0.301–0.476 and RMSE of 2.74–6.23 °C with physically realistic values. GTB permutation feature importance identified NDBI and SWIR1 as dominant predictors. Ensemble scenario illustrations for 2030 and 2050, presented as linear extrapolations rather than climate-model forecasts, suggest sustained thermal stress in industrial and port zones with explicit uncertainty quantification (± 2.75 to ± 8.61 °C). The study demonstrates that rigorously validated Landsat time-series analysis provides actionable coastal urban thermal intelligence for heat adaptation planning aligned with SDGs 11, 13 and 3. The framework is applied here as the first four-epoch, IMD-season-resolved thermal characterisation of the Thoothukudi coast and the 2030 and 2050 outputs are intended to support the spatial targeting of persistent thermal hotspots rather than the prediction of absolute future temperatures.

Groundwater is essential for agricultural sustainability in semi-arid regions; however, the seasonal dynamics of depletion, particularly their relationship with cropping cycles and climate variability, remain insufficiently explored at regional scales. This study addresses this gap by conducting a two-decade (2001-2020) spatio-temporal analysis of pre- and post-monsoon groundwater fluctuations in Kurukshetra, India, using GIS-based Inverse Distance Weighting (IDW) interpolation. We integrated climatic variables (precipitation and evapotranspiration) and the Normalized Difference Vegetation Index (NDVI) to evaluate their influence on groundwater dynamics. The results reveal a significant long-term decline in groundwater levels (R² = 0.74, p < 0.01), with pronounced declines during the Rabi season, with average pre-monsoon drops of 18.6 m and post-monsoon drops of 21.7 m. NDVI correlated negatively with groundwater levels (pre-monsoon r = -0.14, post-monsoon r = -0.45; both p < 0.001), cumulative average drops of 18.6 m (pre‑monsoon) and 21.7 m (post‑monsoon) over the 20‑year period. A linear trend model fitted to the district‑average groundwater levels yielded R² = 0.74 (p < 0.01), indicating a strong long‑term declining trend, indicating intensifying agricultural pressure on aquifers. Precipitation was not a significant predictor in the post-monsoon model (p = 0.115), suggesting reduced recharge efficiency. These findings highlight the need for region-specific groundwater management measures, such as adaptive irrigation scheduling and crop diversification, to sustain agriculture under growing groundwater stress.

Intensive irrigation with untreated wastewater significantly increases soil heavy metal contamination and associated environmental hazards, thereby exacerbating public health risks in peri-urban regions worldwide. This study presents an integrated spatial-toxicological assessment of heavy metal contamination in peri-urban soils of northern India irrigated with untreated wastewater (SWW) and groundwater (SGW). Soil samples were analyzed for nine toxicologically significant metals (As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Zn) using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). The results showed that SWW-irrigated soils exhibited very high contamination factors (CF) for Cd (11.91) and Ni (7.99), with a pollution index (PI) of 30.34, indicating heavy pollution, while SGW samples showed considerable Cd contamination (3.01) but lower overall pollution (PI 7.83). Moreover, enrichment factors for Cd (36.81) and Ni (24.58) in SWW contribute to a modified pollution index (MPI) of 92.00, signifying severe enrichment, compared to significant Cd enrichment (10.68) and MPI of 26.92 in SGW. Cd presented a very high ecological risk (Ei 367.24), and the overall risk index (RI 408.05) classified soils irrigated with SWW as significantly hazardous. Statistical analysis indicated that Cd displayed weak or negative correlations with most metals (Cr, r = -0.56; Cu, r = -0.36; Ni, r = -0.34), suggesting an independent origin or distinct mobility within the soil matrix, potentially influenced by wastewater-induced geochemical alterations such as elevated alkalinity, pH variability, and interactions with Zn ² ⁺ and Ni ² ⁺ ions. Human health risk assessment revealed critically elevated non-carcinogenic hazard indices (HI_child = 29.63; HI_adult = 7.47) and an alarming carcinogenic risk for children (CR = 7.00 × 10⁻ ¹ ), well above USEPA safety limits.