Correction: Machine learning-based correlation analysis of conventional water quality parameters and composite pollution index in the Luoqing river of the South China Sea Coastal Zone
Abstract
Correction on: Correction on: Zhang J, Huang Y, Yang J, Deng K, Li H, Wang D, Xu Y and Dong K (2026) Machine learning-based correlation analysis of conventional water quality parameters and composite pollution index in the Luoqing River of the South China Sea Coastal Zone. Frontiers in Marine Science 13:1782614. doi: 10.3389/fmars.2026.1782614.Adding/removing text In the published article, there was an error in the station names assigned to the upstream and downstream monitoring sections. The upstream and downstream datasets, statistical analyses, figures, PCA results, machine learning outputs, and scientific conclusions are not affected. The correction only concerns the stationname assignment for the upstream and downstream sections. The upstream section should be identified as Suqiao, the midstream section as Xialiang Village, and the downstream section as Longxi.A correction has been made to the section 2.1 Data sources and sampling points, first paragraph.The original text was: A correction has also been made to the section 3.2 Results of pollutant correlation analysis, first paragraph.The original text was: "Spearman's rank correlation analysis was conducted on water temperature, pH, DO, CODMn, TN, TP and NH3-N at the Suqiao (downstream), Longxi (upstream), and Xialiang Village (midstream) monitoring sections, based on continuous monitoring data from 2023 to 2025."The corrected text reads: "Spearman's rank correlation analysis was conducted on water temperature, pH, DO, CODMn, TN, TP and NH3-N at the Suqiao (upstream), Longxi (downstream), and Xialiang Village (midstream) monitoring sections, based on continuous monitoring data from 2023 to 2025." A correction has also been made to the section 3.2.1 Longxi section (Upstream).The original heading was: "3.2.1 Longxi section (Upstream)The correlation structure of water quality indicators at the Longxi section exhibits a generally clear pattern, indicating that water quality variations are primarily governed by natural processes and background inputs."The corrected heading reads:"3.2.1 Suqiao section (Upstream)The correlation structure of water quality indicators at the Suqiao section exhibits a generally clear pattern, indicating that water quality variations are primarily governed by natural processes and background inputs."A correction has also been made to the section 3.2.3 Suqiao section (Downstream).The original heading was: "3.2.3 Suqiao section (Downstream)The correlation structure of water quality indicators at Suqiao section exhibits complex characteristics, demonstrating typical features of composite pollution."The corrected heading reads:"3.2.3 Longxi section (Downstream)The correlation structure of water quality indicators at Longxi section exhibits complex characteristics, demonstrating typical features of composite pollution." A correction has also been made to the summary paragraph of Section 3.2 Results of pollutant correlation analysis.The original text was:"The downstream section (Suqiao) is characterised by nutrient regulation and natural processes; the upstream reaches (Longxi) exhibit pronounced synergistic variations between nitrogen forms and heightened dissolved oxygen responsiveness to pollution processes; while the midstream reaches (Xialiang village) display composite pollution characteristics marked by significantly synergistic amplification between organic pollutants and nutrients."The corrected text reads: "The downstream section (Longxi) is characterised by nutrient regulation and natural processes; the upstream reaches (Suqiao) exhibit pronounced synergistic variations between nitrogen forms and heightened dissolved oxygen responsiveness to pollution processes; while the midstream reaches (Xialiang Village) display composite pollution characteristics marked by significantly synergistic amplification between organic pollutants and nutrients."The original version of this article has been updated.
Fields of Study & Subjects
Water Quality and Pollution Assessment
Hydrological Forecasting Using AI
Water Quality Monitoring and Analysis
Publication details
- Journal / source
- Frontiers in Marine Science
- Publisher
- Frontiers Media SA
- Volume
- 13
- Publication date
- 5 June 2026
- Language
- EN