Supplementary Data for Study on EDCs in North Indian Water

This supplementary dataset accompanies the research article titled “Characterization of Endocrine-Disrupting Chemicals in Drinking Water Using Advanced Analytical Techniques” by Abhishek Rana (Central University of Jammu). It contains comprehensive supporting data that provide spatial, analytical, and compound-level context to the main findings of the study. The dataset is divided into two main files. The first, Supplementary_Table_1_-_Sampling_Site_Info.csv, includes detailed metadata for 36 water sampling sites distributed across various regions in North India. Each site is annotated with information such as water source type (e.g., municipal, groundwater, surface water), city location, sampling frequency, and geographic coordinates (latitude and longitude). This table provides crucial spatial resolution for the study’s assessment of contamination patterns. The second file, Supplementary tables S2–S4.xlsx, includes three key tables. Table S2 presents method validation results for the UHPLC-MS/MS analysis of endocrine-disrupting chemicals (EDCs), detailing parameters such as calibration ranges, R² values, limits of detection (LOD), limits of quantification (LOQ), recovery percentages, and intra/inter-day precision for four primary compounds (BPA, NP, DES, and E1). This ensures the methodological rigor and reliability of the analytical approach used in the study. Table S3 provides a catalog of all 12 target EDC analytes measured in the study. For each compound, the table lists the chemical name, CAS number, and chemical class (e.g., phenol, paraben, estrogen, UV filter). This information supports identification, classification, and potential toxicological referencing for environmental and health researchers. Finally, Table S4 offers detailed site-wise quantitative concentration data. It includes measured concentrations (in ng/L) of BPA, NP, DES, E1, and other EDCs across each site. The table also lists the total EDC load per site and classifies each location based on land use (urban, peri-urban, rural). These data were used in multivariate analyses (e.g., PCA, SVM) to evaluate contamination profiles and land-use associations. Together, these files offer a robust dataset that underpins the study’s environmental assessment of EDCs in North Indian water sources. The dataset is intended to support future research in environmental monitoring, water quality management, chemical risk assessment, and sustainable analytical practices.