The intertidal zone is an open ecosystem rich in organic matter and plays an important role in global biogeochemical cycles. It was previously considered that methane was mainly removed by sulfate-dependent anaerobic methane oxidation (sulfate−AOM) process in marine ecosystems while other anaerobic methane oxidation processes were ignored. Recent researches have demonstrated that denitrifying anaerobic methane oxidation (DAMO), consisting of nitrite-dependent anaerobic methane oxidation (nitrite−AOM) and nitrate-dependent anaerobic methane oxidation (nitrate−AOM), can also oxidize methane. In this work, the community structure, quantity and potential methane oxidizing rate of DAMO archaea and bacteria in the intertidal zone were studied by high-throughput sequencing, qPCR and stable isotope tracing method. The results showed that nitrate−AOM and nitrite−AOM were both active in the intertidal zone and showed approximate methane oxidation rates. The copy number of 16S rRNA gene of DAMO archaea and DAMO bacteria were 10 4 ∼ 10 5 copies g −1 (dry sediment), whereas NC10 bacteria were slightly higher. The contribution rate of DAMO process to total anaerobic methane removal in the intertidal zone reached 65.6% ∼ 100%, which indicates that DAMO process is an important methane sink in intertidal ecosystem. Laboratory incubations also indicated that DAMO archaea were more sensitive to oxygen and preferred a more anoxic environment. These results help us draw a more complete picture of methane and nitrogen cycles in natural habitats.
Studying heavy metal adsorption on soil is important for understanding the fate of heavy metals and properly assessing the related environmental risks. Existing experimental methods and traditional models for quantifying adsorption, however, are time-consuming and ineffective. In this study, we developed machine learning models for the soil adsorption of six heavy metals (Cd(II), Cr(VI), Cu(II), Pb(II), Ni(II), and Zn(II)) using 4420 data points (1105 soils) extracted from 150 journal articles. After a comprehensive comparison, our results showed that the gradient boosting decision tree had the best performance for a combined model based on all the data. The Shapley additive explanation method was used to identify the feature importance and the effects of these features on the adsorption, based on which six independent models were developed for the six metals to achieve better model performance than the combined model. Using these independent models, the global distribution of heavy metal adsorption capacities on soils was predicted with known soil properties. Reversed models, including one combined model for all the six metals and six independent models, were also built using the same data sets to predict the heavy metal concentration in water when the adsorbed amount is known for a soil/sediment.
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