Multiple factors drive imbalance in the global microbial assemblage in soil

Predicting the binding configuration and release potential of heavy metals on iron (oxyhydr)oxides: A machine learning study on EXAFS

Heavy metals raise a global concern and can be easily retained by ubiquitous iron (oxyhydr)oxides in natural and engineered systems. The complex interaction between iron (oxyhydr)oxides and heavy metals results in various mineral-metal binding configurations, such as outer-sphere complexes and edge-sharing inner-sphere complexes, which determine the accumulation and release of heavy metals in the environment. However, traditional experimental approaches are time-consuming and inadequate to elucidate the complex binding relationships and configurations between iron (oxyhydr)oxides and heavy metals. Herein, a workflow that integrates the binding configuration data of 11 heavy metals on 7 iron (oxyhydr)oxides and then trains machine learning models to predict unknown binding configurations was proposed. The well-trained multi-grained cascade forest models exhibited high accuracy (> 90%) and predictive performance (R2 ∼ 0.75). The underlying effects of mineral properties, metal ion species, and environmental conditions on mineral-metal binding configurations were fully interpreted with data mining. Moreover, the metal release rate was further successfully predicted based on mineral-metal binding configurations. This work provides a method to accurately and quickly predict the binding configuration of heavy metals on iron (oxyhydr)oxides, which would provide guidance for estimating the potential release behavior of heavy metals and remediating heavy metal pollution in natural and engineered environments.

Distribution patterns and functional characteristics of soil bacterial communities in desert ecosystems of northern China

Deserts are extremely arid environments where life is exposed to multiple environmental stresses, including low water availability, high temperatures, intense radiation environments and soil carbon and nitrogen limitation. Microorganisms have enormous potential applications due to their unique physiological adaptation mechanisms, extensive involvement in geochemical cycles and production of new antibiotics, among many other characteristics. With the increasing amount of open data provides unprecedented opportunities to further reveal bacterial biodiversity and its global role in ecosystem function. Through the collection of published high-quality sequencing data supplemented with experimental findings, we investigated the distribution characteristics and functional properties of bacteria in desert ecosystems in northern China. We show that there are significant differences in bacterial diversity among different sandy areas, and that soil properties and climatic factors are the main factors affecting bacterial diversity in desert ecosystems. The mean annual precipitation in growing season, soil organic carbon, total nitrogen and total phosphorus had significant effects on the diversity of desert bacteria and main bacteria. Desert bacteria primarily participate in the macromolecular decomposition, phototrophy, and geochemical cycling of nitrogen. These findings deepen our understanding of the regional-scale soil microbial diversity patterns in Chinese desert ecosystems and broaden our understanding of the ecological functions carried out by bacteria in these environments.

A New Method for Ecological Risk Assessment of Combined Contaminated Soil

Ecological risk assessment of combined polluted soil has been conducted mostly on the basis of the risk screening value (RSV) of a single pollutant. However, due to its defects, this method is not accurate enough. Not only were the effects of soil properties neglected, but the interactions among different pollutants were also overlooked. In this study, the ecological risks of 22 soils collected from four smelting sites were assessed by toxicity tests using soil invertebrates (Eisenia fetida, Folsomia candida, Caenorhabditis elegans) as subjects. Besides a risk assessment based on RSVs, a new method was developed and applied. A toxicity effect index (EI) was introduced to normalize the toxicity effects of different toxicity endpoints, rendering assessments comparable based on different toxicity endpoints. Additionally, an assessment method of ecological risk probability (RP), based on the cumulative probability distribution of EI, was established. Significant correlation was found between EI-based RP and the RSV-based Nemerow ecological risk index (NRI) (p < 0.05). In addition, the new method can visually present the probability distribution of different toxicity endpoints, which is conducive to aiding risk managers in establishing more reasonable risk management plans to protect key species. The new method is expected to be combined with a complex dose-effect relationship prediction model constructed by machine learning algorithm, providing a new method and idea for the ecological risk assessment of combined contaminated soil.