Integrated analysis of transcriptome and metabolome reveals chronic low salinity stress responses in the muscle of Exopalaemon carinicauda.

Low salinity environment is one of the key factors threatening the survival of aquatic organisms. Due to the strong adaptability of low salinity, Exopalaemon carinicauda is an ideal model to study the low salinity adaptation mechanism of crustaceans. In this study, E.

Changes of MRGs and ARGs in Acinetobacter sp. SL-1 used for treatment of Cr(VI)-contaminated wastewater with waste molasses as carbon source.

Antibiotic resistance genes (ARGs) may be synergistic selected during bio-treatment of chromium-containing wastewater and causing environmental risks through horizontal transfer. This research explored the impact of self-screening bacterium Acinetobacter sp. SL-1 on the treatment of chromium-containing wastewater under varying environmental conditions.

A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster.

The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities.

Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection.

The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order to improve the prediction accuracy of a dispersion model, a data-driven air dispersion modeling method based on data assimilation is proposed by applying particle filter to Gaussian-based dispersion model.

Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases.

Dispersion prediction plays a significant role in the management and emergency response to hazardous gas emissions and accidental leaks. Compared with conventional atmospheric dispersion models, machine leaning (ML) models have both high accuracy and efficiency in terms of prediction, especially in field cases. However, selection of model type and the inputs of the ML model are still essential problems.

An Extended Chemical Plant Environmental Protection Game on Addressing Uncertainties of Human Adversaries.

Chemical production activities in industrial districts pose great threats to the surrounding atmospheric environment and human health. Therefore, developing appropriate and intelligent pollution controlling strategies for the management team to monitor chemical production processes is significantly essential in a chemical industrial district. The literature shows that playing a chemical plant environmental protection (CPEP) game can force the chemical plants to be more compliant with environmental protection authorities and reduce the potential risks of hazardous gas dispersion accidents.