It has been well observed that accurate estimation of the aboveground biomass (AGB) of mangrove forests is critical for evaluating ecosystem health, carbon sink capacity, and sustainable development. This study utilizes UAV-LiDAR data and field measurements to develop an AGB inversion model based on 26 feature variables. We employed three machine learning algorithms—random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM)—to estimate mangrove AGB in the Xinyingwan region of Lingao County, Hainan Province, China. The key findings include that: (1) the SVM algorithm demonstrated the highest predictive accuracy, with an R² of 0.8853 and RMSE of 0.4766 kg/m², making it most suitable for this study; (2) the proposed zero-importance feature selection method based on mutual information (MI) outperformed traditional techniques, selecting more effective variables for model development; (3) in the SVM model, variables selected using the zero-importance feature selection method based on MI yielded the best prediction accuracy; and (4) the estimated AGB in the study area ranged from 1.97 to 5.23 kg/m², with an average of 3.83 kg/m². This study not only provides valuable data for mangrove ecosystem conservation and restoration but also offers a scientific basis and technical framework for future biomass estimation and carbon stock assessments.

Seawater desalination water intake site selection has an important impact on the investment, water production cost, stable operation, and safety of seawater desalination plants. Site selection is affected by many factors, such as the natural geography, ecological environment, and social economy of coastal zones; some constraints can be directly identified as unsuitable areas for these construction projects. For the shallow water intake method of seawater desalination, this study selects suitability evaluation indicators for seawater desalination water intake site selection from the three influencing factors of basic geography, water environment, and industrial development and constructs a suitability evaluation model based on the multifactor spatial overlay analysis of the Geographic Information System platform. This model carries out a quantitative suitability evaluation of the seawater desalination water intake site selection and realizes the suitable spatial zoning for spatially selecting the water intake, thus forming a scientific, quantitative, and spatial suitability evaluation system for this process. The evaluation method was applied in the Rongcheng city offshore area of China and analyzed. The evaluation results showed that the suitable areas for seawater desalination water intake site selection comprised 304.8 square kilometers, which were mainly distributed in the offshore areas in northern Rongcheng city and near the coastline areas of its central and southern regions. The unsuitable areas covered 292.4 square kilometers, mainly distributed in the marine ecological red line areas and the coastal aquaculture areas of Rongcheng city. The evaluation results met the site selection needs of the seawater desalination water intake project in Rongcheng city. This study improves upon the existing method of seawater desalination intake site selection at the theoretical and technical levels and provides a scientific basis for the location selection and planning for water intake in large spatial ranges of coastal waters.

The field of seawater desalination faces some challenges. For example, at present, site selection and layout of water intakes and outlets are often not carefully considered. This can easily result in the degradation of water quality due to coastal sea pollution and sudden brine discharge, which can be hazardous and can negatively impact marine development and activities such as aquaculture. By using the MIKE21 numerical simulation software, this paper establishes a two-dimensional mathematical tidal current model of the engineered sea area and a mathematical convection diffusion model after brine discharge. The tidal current field of the Laoshan Bay waters and the salinity field distribution after brine discharge in different water intake-outlet arrangement schemes in desalination plants are calculated and analyzed. In view of the various control factors affecting the layout and location selection of water intakes and outlets, combined with the current situation of marine development and utilization, calculation results and layout advantages and disadvantages of the primary schemes are compared and analyzed, the scheme with the best water intake-outlet layout is recommended, and relevant optimization suggestions are presented.

The concentration of heavy metals in sediment is an important factor for assessing the quality of the nearshore environment. Geochemical background values are the normal concentrations of heavy metals in the natural environment. The use of different background values results in different evaluation results. Heavy metal (Cu, Pb, Zn, Cr, Cd, As, and Hg) concentration profiles along a sediment core were investigated to obtain background values and assess the depositional processes and contamination levels in Laizhou Bay. Apart from As and Hg, all other metals had similar distribution patterns and could be divided into four stages since marine isotope stage (MIS) 2. The results of the enrichment factor, geoaccumulation index, Pearson’s correlation coefficient, Nemerow pollution index, principal component analysis, and potential ecological risk index showed that element enrichment or contamination in the core sediments was absent and that all heavy metals were mainly naturally sourced. The changes in heavy metals since MIS 2 in the core sediments were predominantly related to the sedimentary environment, sediment sources, and mean grain size. The average values of the heavy metals in the U2 unit formed during the Middle Holocene can be used as reference values for background values.