Karim Md Rajaul, Syeed M M Mahbubul, Rahman Ashifur, Ayaz Rabbani Khondkar, Fatema Kaniz, Khan Razib Hayat, Hossain Md Shakhawat, Uddin Mohammad Faisal
RIoT Research Center, Independent University, Dhaka, 1229, Bangladesh.
Department of Computer Science and Engineering, Independent University, Dhaka, 1229, Bangladesh.
Sci Data. 2025 Mar 6;12(1):391. doi: 10.1038/s41597-025-04715-4.
Assessment and monitoring of surface water quality are essential for food security, public health, and ecosystem protection. Although water quality monitoring is a known phenomenon, little effort has been made to offer a comprehensive and harmonized dataset for surface water at the global scale. This study presents a comprehensive surface water quality dataset that preserves spatio-temporal variability, integrity, consistency, and depth of the data to facilitate empirical and data-driven evaluation, prediction, and forecasting. The dataset is assembled from a range of sources, including regional and global water quality databases, water management organizations, and individual research projects from five prominent countries in the world, e.g., the USA, Canada, Ireland, England, and China. The resulting dataset consists of 2.82 million measurements of eight water quality parameters that span 1940 - 2023. This dataset can support meta-analysis of water quality models and can facilitate Machine Learning (ML) based data and model-driven investigation of the spatial and temporal drivers and patterns of surface water quality at a cross-regional to global scale.
地表水水质评估与监测对于粮食安全、公众健康和生态系统保护至关重要。尽管水质监测已为人熟知，但在全球范围内，为地表水提供全面且统一的数据集所做的努力却很少。本研究呈现了一个全面的地表水水质数据集，该数据集保留了数据的时空变异性、完整性、一致性和深度，以促进基于经验和数据驱动的评估、预测和预报。该数据集来自一系列来源，包括区域和全球水质数据库、水资源管理组织以及来自世界五个主要国家（如美国、加拿大、爱尔兰、英国和中国）的个别研究项目。由此产生的数据集包含跨越1940年至2023年的282万个八个水质参数的测量值。该数据集可支持水质模型的荟萃分析，并有助于在跨区域到全球尺度上基于机器学习（ML）的数据以及对地表水水质的时空驱动因素和模式进行模型驱动的调查。
