Mangrove wetlands are superior sites for the deposition of various pollutants, but they are also more susceptible to environmental contamination. To investigate the health threats of phosphorus to mangrove ecosystems, this study presents the distribution, chemical speciation and factors influencing phosphorus (P) forms in 38 surface sediments from the mangrove wetlands of Dongzhai Harbor on Hainan Island, China. P forms were measured using the sequential extraction (SEDEX) procedures. The results showed that the total phosphorus content in the sediment was at a high level, and there was a significant spatial variability of phosphorus in each form. Among them, inorganic phosphorus accounted for 74.64% of the total phosphorus, and organic phosphorus accounted for 25.35% of the total phosphorus. Among the inorganic phosphorus, detrital limestone phosphorus and Ca-bound phosphorus were dominant, while the content of exchangeable phosphorus and Fe-bound phosphorus had a smaller percentage. The concentration of biologically available phosphorus, ranged from 1.76 umol/g to 18.57 umol/g, and the Fe-bound and organic forms of phosphorus had a controlling effect on it. Based on C_org/OP, N/P and land use surveys, it was concluded that phosphorus was mainly an exogenous input. The correlation analysis concluded that the main sources of phosphorus in the study area are agriculture and aquaculture. The development of agriculture and aquaculture led to moderate levels of phosphorus pollution in the study area as a whole, with more serious a phosphorus pollution at the wetland park in the southeast corner of Dongzhai Harbor and in the western areas, with lush vegetation and developed river systems, mainly influenced by human activities, topography, vegetation and water system conditions.

The mangrove wetland ecosystem functions as a natural purifier and a significant repository for pollutants, effectively facilitating the transfer and transformation of contaminants into sediments. This study focuses on the Dongzhai Harbor wetland on Hainan Island, aiming to investigate the spatial distribution patterns, pollution status, potential sources, and ecological risk levels of potentially toxic elements (PTEs) (Cu, Pb, Zn, Cr, Cd, Ni, and As) in the surface sediments of the region. The sediment quality in the study area generally complies with Marine Sediment Quality Standards. Results from the contamination factor (CF), pollution load index (PLI), geoaccumulation index (I_geo), and potential ecological risk index (RI) indicate that Zn, Cr, Ni, Pb, and Cu are primarily derived from natural sources. In contrast, Cd and As exhibit mild to moderate pollution levels, likely influenced by natural and anthropogenic activities. Cd is identified as the primary potentially toxic element pollutant and ecological risk factor in the study area, presenting a low ecological risk level. The mean range of effects-median quotient (M-ERM-Q) and hazard quotient (HQ) of the sediment toxicological profiles indicate that there is some risk of toxicity from PTEs in the sediments of the study area. This research provides valuable references for pollution prevention and control in the Dongzhai Harbor mangrove wetland.

Deep learning outperforms traditional interpolation methods in 3D geological modeling due to its ability to model nonlinear relationships and its flexibility in incorporating diverse geological data. However, acquiring geological data for practical applications is challenging, and the quality of the data can vary significantly, which limits the effectiveness of purely data-driven deep learning models in 3D geological modeling. To address this challenge, this paper introduces GeoSAE, a geoconstraint-driven 3D geological modeling method. GeoSAE improves potential field prediction by employing a stacked autoencoder network (SAE) and incorporating geological constraints as a loss function during model training. This approach generates a geologically consistent, smooth, and continuous 3D stratigraphic model. To validate the method, this study applies it to a 60-square-kilometer region in Jiangdong new district, Haikou city, China. Stratigraphic interface points were utilized to predict the 3D potential field, with PyVista (version 0.44.2) enabling the accurate extraction of stratigraphic interfaces. Model quality was evaluated through comprehensive assessments of loss function analysis, data fitting, and the verification of stratigraphic smoothness constraints. Results indicate that the stratigraphic model generated by GeoSAE closely aligns with the actual data, accurately capturing stratigraphic geometry. Additionally, incorporating smoothness constraints enhances model smoothness, minimizes irregular stratigraphic fluctuations, and produces a more natural and continuous stratigraphic morphology.