In recent years, the frequent fouling accidents has posed a serious threat to people's life and property safety. Due to the wide distribution of pollution sources and variable meteorological factors, it is a very time-consuming and labour-intensive task to map the pollution distribution by traditional methods. In this work, a study on the mapping of pollution distribution based on satellite remote sensing is carried out in Yunnan Province, China, as an example. Several machine learning methods (e.g. KNN, SVM, etc.) are used to analyze the effects of conditions such as multiple air pollution data and meteorological data on pollution distribution map levels. The results indicate that the ensemble learning model has the highest accuracy of 71.2\% in this application. The new pollution distribution map using this classifier has 5,506 more pixels in the most severe pollution level than the traditional. Lastly, The remote sensing-based map and the manual measurement-based map were combin
The unique ecosystems and biodiversity associated with mid-ocean ridge (MOR) hydrothermal vent systems contrast sharply with surrounding deep-sea habitats, however both may be increasingly threatened by anthropogenic activity (e.g., mining activities at massive sulphide deposits). Climate change can alter the deep-sea through increased bottom temperatures, loss of oxygen, and modifications to deep water circulation. Despite the potential of these profound impacts, the mechanisms enabling these systems and their ecosystems to persist, function and respond to oceanic, crustal, and anthropogenic forces remain poorly understood. This is due primarily to technological challenges and difficulties in accessing, observing and monitoring the deep-sea. In this context, the development of deep-sea observatories in the 2000s focused on understanding the coupling between sub-surface flow and oceanic and crustal conditions, and how they influence biological processes. Deep-sea observatories provide
Prokaryotes constitute the majority of sedimentary biomass, where they cycle organic carbon and regulate organic matter transformation. The microbes inhabiting sediment are diverse, and the factors controlling microbial community composition are not fully understood. Here, we characterized the prokaryotic community using 16S rRNA gene sequencing in 24 stratigraphic layers within a 89 cm (dated to ~1900 years old) sediment core from an anchialine sinkhole in the Bahamas with a stratified water column and anoxic bottom water. The microbial community was dominated by members of the Alphaproteobacteria, Dehalococcoidia, Gammaproteobacteria, Bathyarchaeota, and Campylobacter classes. Most interestingly, subsurface microbial community structure could be correlated to previous evidence for timewise changes in the main source of organic matter that was supplied to the sediment accumulating during the last 2000 years, which itself was caused by regional terrestrial vegetation changes. The C:N r