Increasing levels of dissolved organic matter (DOM) in watercourses in the Northern hemisphere is mainly due to reduced acid rain, climate change, and changes in agricultural practices. However, their impacts vary in time and space. To predict how DOM respond to changes in environmental pressures we need to differentiate between allochthonous and autochthonous sources, as well as identify anthropogenic DOM. In this study we distinguish between allochthonous, autochthonous, and anthropogenic sources of DOM in a diverse watercourse network using DOM characterization tools and by assessing effects of land cover on water quality. Main sources of DOM are forests discharging allochthonous humic DOM, autochthonous fulvic DOM, and runoff from urban sites and fish farms with high levels of anthropogenic DOM rich in protein like material. Specific UV absorbency (sUVa) distinguish allochthonous DOM from autochthonous and anthropogenic DOM. Anthropogenic DOM differ from autochthonous fulvic DOM by containing elevated levels of protein like material. DOM from fishponds is distinguished from autochthonous and sewage DOM by having high sUVa. DOM characteristics thus provide valuable tools for deconvoluting the various sources of DOM allowing water resource managers to predict future trends in DOM and detect anthropogenic sources of DOM.

The most widely accepted scientific theory for the origin of life on Earth is that prokaryotic microbes evolved from simple organic compounds in seawater under anoxic conditions. For about 1 billion years thereafter, these microbes consumed the same dissolved organic matter (DOM) from which they had evolved before scarcity of DOM forced the evolution of cyanobacterial photosynthesis followed by eukaryosis. Could the more efficient consumption of DOM have also stimulated the subsequent origin of multicellular animal life? In this report, we synthesize past and recent evidence to propose the “DOM uptake hypothesis” for the origin of metazoans. A choanoflagellate-like protozoan was the likely ancestor of the first sponge-like metazoan to evolve on Earth. Choanoflagellates have outwardly facing flagellae that are subject to viscous water movement, while sponges have choanocytes in chambers with flagellae directed to pump water with greater fluidity across an aquiferous system with a huge cellular surface area. While generally considered particle feeders, both choanoflagellates and sponges absorb DOM, with some sponges relying on DOM for as much as 90% of their diet. We propose that the earliest metazoans may have evolved to survive the dire nutritional conditions of the Cryogenian “snow-ball Earth” period (~700 million years ago) by developing a body plan with the enhanced ability to absorb low concentrations of DOM in seawater from sources such as the viral lysis of microbes, exudates of benthic stromatolites, or refractory DOM compounds. Additionally, species of extant sponges that have a high abundance of microbes living in their bodies consume the greatest amounts of DOM, suggesting that the DOM uptake hypothesis may also be dependent on microbial symbiosis.

Craters are the most prominent geomorphological features on the surface of celestial bodies, which is playing a crucial role in studying the formation and evolution of celestial bodies, as well as in landing and planning for surface exploration. Currently, the main automatic crater detection models and datasets focus on the detection of large and medium craters. In this paper, we created 23 small lunar crater datasets for model training based on the Chang’E-2 (CE-2) DOM, DEM, Slope, and integrated data with 7 kinds of visualization stretching methods. And then, we proposed the YOLO-Crater model for Lunar and Martian small crater detection by replacing EioU and VariFocal loss to solve the crater samples imbalance problem and introducing a CBAM attention mechanism to mitigate interference from the complex extraterrestrial environment. The results show that the accuracy (P = 87.86%, R = 66.04%, and F1 = 75.41%) of the Lunar YOLO-Crater model based on the DOM-MMS (Maximum-Minimum Stretching) dataset is the highest and better than that of YOLOX model. And the Martian YOLO-Crater, trained by the Martian dataset from the 2022 GeoAI Martian Challenge, achieves good performance with P = 88.37%, R = 69.25%, and F1 = 77.65%. It indicates that the YOLO-Crater model has a strong transferability and generalization capability, which can be applied to detect small craters on the Moon and other celestial bodies.