介绍了Kirk—Othmer Encyclopedia of Chemical Technology（化工技术百科全书）（第五版）电子图书网络版数据库，并对该数据库使用方法和检索途径作出了说明，且结合实例简单地介绍了该数据库的检索方法。

Kirk-Othmer Encyclopedia of Chemical Technology数据库介绍及实例

Wise use of mires and peatlands

The World's Largest Wetlands: Ecology and Conservation

Microplastic fibers, also known as microfibers, are the most abundant microplastic forms found in the environment. Microfibers are released in massive numbers from textile garments during home laun...

Microfibers from synthetic textiles as a major source of microplastics in the environment: A review:

Solving river engineering problems typically requires river flow characterization, including the prediction of flow depth, flow velocity, and flood extent. Hydraulic models use governing equations of the flow in motion (conservation of mass and momentum principles) to predict the flow characteristics. However, solving such equations can be substantially expensive, depending upon their spatial extension. Moreover, modeling two- or three-dimensional river flows with high-resolution topographic data for large-scale regions (national or continental scale) is next to impossible. Such simulations are required for comprehensive river modeling, where a system of connected rivers is to be simulated simultaneously. Machine Learning (ML) approaches have shown promise for different water resources problems, and they have demonstrated an ability to learn from current data to predict new scenarios, which can enhance the understanding of the systems. The aim of this paper is to present an efficient flood simulation framework that can be applied to large-scale simulations. The framework outlines a novel, quick, efficient and versatile model to identify flooded areas and the flood depth, using a hybrid of hydraulic model and ML measures. To accomplish that, a two-dimensional hydraulic model (iRIC), calibrated by measured water surface elevation data, was used to train two ML models to predict river depth over the domain for an arbitrary discharge. The first ML model included a random forest (RF) classification model, which was used to identify wet or dry nodes over the domain. The second was a multilayer perceptron (MLP) model that was developed and trained by the iRIC simulation results, in order to estimate river depth in wet nodes. For the test data the overall accuracy of 98.5 percent was achieved for the RF classification. The regression coefficient for the MLP model for depth was 0.88. The framework outlined in this paper can be used to couple hydraulics and ML models to reduce the computation time, resources and expenses of large-scale, real-time simulations, specifically for two- or three-dimensional hydraulic modeling, where traditional hydraulic models are infeasible or prohibitively expensive.

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A Framework for Modeling Flood Depth Using a Hybrid of Hydraulics and Machine Learning.

In this overview (introductory article to a special issue including 14 papers), we consider all main types of natural and artificial inland freshwater habitas (fwh). For each type, we identify the main biodiversity patterns and ecological features, human impacts on the system and environmental issues, and discuss ways to use this information to improve stewardship. Examples of selected key biodiversity/ecological features (habitat type): narrow endemics, sensitive (groundwater and GDEs); crenobionts, LIHRes (springs); unidirectional flow, nutrient spiraling (streams); naturally turbid, floodplains, large-bodied species (large rivers); depth-variation in benthic communities (lakes); endemism and diversity (ancient lakes); threatened, sensitive species (oxbow lakes, SWE); diverse, reduced littoral (reservoirs); cold-adapted species (Boreal and Arctic fwh); endemism, depauperate (Antarctic fwh); flood pulse, intermittent wetlands, biggest river basins (tropical fwh); variable hydrologic regime—periods of drying, flash floods (arid-climate fwh). Selected impacts: eutrophication and other pollution, hydrologic modifications, overexploitation, habitat destruction, invasive species, salinization. Climate change is a threat multiplier, and it is important to quantify resistance, resilience, and recovery to assess the strategic role of the different types of freshwater ecosystems and their value for biodiversity conservation. Effective conservation solutions are dependent on an understanding of connectivity between different freshwater ecosystems (including related terrestrial, coastal and marine systems).

Characteristics, main impacts, and stewardship of natural and artificial freshwater environments: consequences for biodiversity conservation

A marine invasive benthic diatom species [Licmophora normaniana (Greville) Wahrer, 1985] in an inland oasis mineral spring in Egypt

The species–area relationship (SAR) is one of the most distinctive biogeographic patterns, but global comparisons of the SARs between island and mainland are lacking for microbial taxa. Here, we explore whether the form of the SAR and the drivers of species richness, including area, environmental heterogeneity, climate and physico‐chemistry, differ between islands and similarly sized areas on mainland, referred to as continental area equivalents (CAEs).

Alex Borrini

Papers

Characteristics, main impacts, and stewardship of natural and artificial freshwater environments: consequences for biodiversity conservation

A marine invasive benthic diatom species [Licmophora normaniana (Greville) Wahrer, 1985] in an inland oasis mineral spring in Egypt

Stream diatom biodiversity in islands and continents—A global perspective on effects of area, isolation and environment