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Outline
Quantifying Hg flux in lake systems remains challenging due to the synergistic influences of multiple environmental drivers. To address this, we investigated Changshou Lake, an artificial reservoir situated in a karst region, through an integrated approach that included field sampling, path analysis, and machine learning, to elucidate the mechanisms of Hg flux exchange. The results indicated that Hg flux was jointly regulated by multiple factors via both direct and indirect pathways. Water Hg concentration (0.897), UV radiation (0.463), and solar radiation (0.446) were identified as exerting dominant direct effects on the production of dissolved gaseous mercury (DGM, Hg0). In contrast, the influence of wind speed on Hg flux was observed to be stronger through its indirect enhancement of solar radiation (0.252) than through direct diffusion (0.197). A hybrid machine-learning prediction mode [0.9 Gradient Boosting Regression (GBR) + 0.05 Random Forest (RF) + 0.05Support Vector Regression (SVR)] was developed. This model was shown to improve generalization ability and stability while maintaining predictive accuracy, enabling dynamic prediction of Hg flux under the coupling of multiple lake parameters. The approach provides decision support for regional Hg risk assessment and management.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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