Low- and high-latitude influences have shaped the evolution of the Tibetan Plateau climate over the past 1.74 million years.

Coastal lagoons connecting the land and sea provide essential ecosystem services. However, emerging environmental issues such as environmental pollution and ecological degradation from rapid socio-economic development in coastal zones of south China are becoming increasingly prevalent. This study examined the spatiotemporal variation, sources, assessments, and driving forces of heavy metals based on core and surface sediments collected from Pinqing Lagoon, a coastal lagoon in South China. Sediment cores (PQ1, PQ2, and PQ3) showed distinct vertical variations in the content of Cu, Cd, Zn, Pb, As, and Sb with an average coefficient of variation (C.V.) of 0.25. However, a relatively lower vertical variation (C.V. mean = 0.13) was shown by the other elements (Mn, V, Ni, Cr, and Co). Although Cu was the chief pollutant heavy metal and it had mean values of 1.6 and 1.7 for the enrichment factor (EF) and contamination factor (CF), respectively, Cd posed the highest ecological risk (${\mathrm{E}}_r^i$mean = 36.34). A century-scale anthropogenic disturbance and growing industrial activities in the lagoon area have caused heavy-metal pollution in Pinqing Lagoon. Wastewater discharge into the lagoon over the past 30 years has further aggravated the pollution. The land-use pattern changes in the catchment and removal of polluting industries resulted in a shift in the center of gravity of heavy-metal pollution in the surface sediment of the lagoon. When integrated with the available data, significant pollution gradients were observed suggesting that the pollution level of Pinqing Lagoon was slightly higher than the marginal sea (Honghai Bay) but significantly lower than the adjacent inland water bodies (Gongping and Chisha Reservoirs). This difference attributes unique hydrodynamic conditions to the Pinqing Lagoon, which consistently mitigates environmental pollution by lying at the interface between inland water and the coastal sea in South China. These conditions resulted in the relatively low contamination degree (CD mean = 7.5) and the low ecological risk index (RI mean = 70) over the past 150 years in Pinqing Lagoon.

Secchi disk depth (SDD) is a widely critical indicator of water transparency. However, existing retrieval models often suffer from limited transferability and biased predictions when applied to optically diverse waters. Here, we compiled a dataset of 6218 paired in situ SDD and remote sensing reflectance (R_rs) measurements to evaluate model generalization. We benchmarked nine machine learning (ML) models (RF, KNN, SVM, XGB, LGBM, CAT, RealMLP, BNN-MCD, and MDN) under three validation scenarios with progressively decreasing training-test overlap: Random, Waterbody, and Cross-Optical Water Type (Cross-OWT). Furthermore, SHAP analysis was employed to interpret feature contributions and relate model behaviors to optical properties. Results revealed a distinct scenario-dependent generalization gradient. Random splits yielded minimal bias. In contrast, Waterbody transfer consistently shifted predictions toward underestimation (SSPB: −16.9% to −3.8%). Notably, Cross-OWT extrapolation caused significant error inflation and a bias reversal toward overestimation (SSPB: 10.7% to 88.6%). Among all models, the Mixture Density Network (MDN) demonstrated superior robustness with the lowest overestimation (SSPB = 10.7%) under the Cross-OWT scenario. SHAP interpretation indicated that engineered indices, particularly NSMI, functioned as regime separators, with substantial shifts in feature attribution occurring at NSMI values between 0.4 and 0.6. Accordingly, feature sensitivity analysis showed that removing band ratios and indices improved Cross-OWT robustness for several classical ML models. For instance, KNN exhibited a significant reduction in Median Symmetric Accuracy (MdSA) from 96% to 40% after feature reduction. These findings highlight that model applicability must be evaluated under scenario-specific conditions, and feature engineering strategies require rigorous testing against optical regime shifts to ensure generalization.

Coastal waters exhibit pronounced spatial and temporal variability in optical properties and water quality, which cannot be fully characterized by observations from discrete monitoring stations. Satellite Optical Water Type (OWT) classification provides spatially continuous information on optical conditions. However, comparisons with water quality observations have generally relied on the assigned OWT, and it remains unclear whether membership provides additional information within the same class. This study examined the relationships of assigned OWTs and membership with monthly water quality conditions in Guangdong coastal waters. Sentinel-3 OLCI observations acquired from 2017 to 2024 were matched with station observations of dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), chemical oxygen demand (COD), dissolved oxygen (DO), and eutrophication classes. The analysis first compared water quality among assigned OWTs and then evaluated the secondary OWT and membership values within OWT 4a, the class with the largest sample size. Assigned OWTs distinguished broad differences in DIN, DIP, COD, and eutrophication classes, whereas DO did not differ significantly among OWTs. Within OWT 4a, the class associated with the second highest membership separated records with different nutrient, COD, and eutrophication conditions. Continuous membership values provided further information, but their relationships with water quality depended on the secondary OWT and were most consistent for DIN. OWT occurrence and station eutrophication patterns showed partial spatial correspondence, particularly around the Pearl River Estuary and Huangmao Bay. These findings show that assigned OWTs identify broad water quality differences, while secondary OWTs and membership values distinguish additional variation within OWT 4a. The mapped optical patterns provide complementary spatial context for interpreting station-based water quality observations.

Floodplain lake ecosystems around the world have witnessed growing environmental degradation under combined natural climatic variability and anthropogenic activities. Reconstructing their long-term ecological change is vital for lake ecosystem conservation and management. Cladoceran subfossils are used as an important proxy indicator to reconstruct ecological evolution of floodplain lake systems worldwide. The sedimentary record of Zhangdu Lake, a shallow floodplain lake in the middle and lower reaches of the Yangtze River in China, is important for reconstructing more than 150 years of environmental history and tracing the lake ecosystem’s transition from a natural flood-pulse system to a new regime driven by anthropogenic disturbances. Integration of subfossil cladoceran data with other biological proxies (diatoms and testate amoebae) and physicochemical sedimentary proxies successfully identified the period of river–lake disconnection and the associated ecological processes and succession in Lake Zhangdu. An abrupt disconnection of the lake from the Yangtze River in the early 1950s led to a sudden ecological vulnerability together with a drastic community decline. Sequential t-test Analysis of Regime Shifts (STARS) identified cladoceran community shifts in 1966 and 1999, with regime shift index (RSI) values of 1.195 and 3.29, respectively, reflecting multi-phase ecological reorganization following the 1950s disconnection. Differences among the proxies in the timing and magnitude of change and in their apparent drivers reflected group-specific habitat preferences, response thresholds, and trophic positions. This study shows that the assessment of multi-taxa paleoecological indicators can provide a strong evidence base for guiding restoration efforts in degraded floodplain lake ecosystems in China and elsewhere.