A combined radical and non-radical oxidation processes for efficient degradation of Acid Orange 7 in the homogeneous Cu(II)/PMS system: important role of chloride

Environmental Science and Pollution Research - Trace copper ion (Cu(II)) in water and wastewater can trigger peroxymonosulfate (PMS) activation to oxidize organic compounds, but it only works under...     

Prospects and challenges of conservation agriculture in Bangladesh for sustainable sugarcane cultivation

Environment, Development and Sustainability - Sugarcane is an industrial crop globally used for producing biofuels and bioproducts and is the only source of white sugar in Bangladesh. However,...     

Dynamics of Tree and Shrub Vegetation in the Eastern Sayan Mountain Tundra

Russian Journal of Ecology - Climate change entails shifts in the ranges of woody plants along both latitudinal and altitudinal gradients in the boreal forest biome. In this study,...     

湖北省耕地利用转型对农民增收的空间溢出效应研究

基于2005～2017年湖北省74个县域面板数据,构建基于交通权重的空间权重矩阵,利用空间杜宾模型,实证研究耕地利用转型影响农民增收的空间溢出效应,并进一步揭示该溢出效应呈现的具体规律与特征.研究表明:(1)耕地利用转型各形态和农民收入水平都具有显著的正向空间自相关性;(2)农民收入水平的空间自回归系数ρ为0.65,表明相邻县域农民增收存在正向空间溢出效应,即本县域农民收入增长1％,相邻县域农民收入将会增长0.65％;(3)相邻县域间的空间溢出效应主要靠耕地数量、农业劳动力和基础设施投入形态变化驱动,其中,农业劳动力形态变化产生的空间溢出效应占总效应的69％,耕地数量形态占15％,基础设施投入形态占12％.因此,应注意提升县域间耕地利用系统协同性,利用其空间溢出效应促进湖北省各县域共同发展、农民收入协同提高.     

Association between air pollution and COVID-19 infection: evidence from data at national and municipal levels

Environmental Science and Pollution Research - The impact of high concentrations of air pollution on COVID-19 has been a major air quality and life safety issue in recent studies. This study aimed...     

Immunohistochemical,histopathological, and biochemical studies of the NF-ҡB P65 marker in rat ovaries experimentally intoxicated by cadmium and the protective effect of the purslane plant extract

Environmental Science and Pollution Research - The aim of this study is to describe the existence of the inflammatory marker nuclear factor kappa light chain B lymphocyte protein (NF-?B P65)...     

How does eliminating mercury from artisanal and small-scale gold mining lead to achieving sustainable development goals?

Artisanal and small-scale gold mining (ASGM) is the principal anthropogenic activity that globally contributes to overloading our environment with mercury. Although the Minamata Convention, led by the United Nations, is a crucial instrument to eliminate its use progressively, novel approaches to accelerate this difficult transition are welcome. This article proposes a framework for policy-making or improvement, fostering the enforcement of mercury elimination through the lens of the 17 Sustainable Development Goals (SDGs), focusing on the excluded artisanal and small-scale gold miners and their dependents. We move forward with a literature review of the Artisanal and Small Mining topic, taking each SDG as a unit of analysis. Understanding the problem as a puzzle of four sets of pieces, namely: (1) social, (2) environmental, (3) economic, and (4) institutional, the paper offers potential opportunities for the decision-makers and practitioners to accelerate the substitution of this heavy metal and develop sustainable futures for the ASGM communities. We conclude by proposing a pragmatic framework that synthesizes the means, actions, and ends to accelerate a sustainable transition.     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Mechanism of complexation of toxic arsenate,selenate, and molybdate with hydrotalcites

Environmental Chemistry Letters - Wastewater from the uranium mining industry contains toxic arsenate (AsO43–), selenate (SeO42–), and molybdate (MoO42–) that can be removed by...     

Ingestion and egestion of microplastic by aquatic insects in Egypt wastewater

Microplastics (MPs) are frequently regarded as environmental and biota contaminants. Yet, research on the accumulation of MPs in living entities, particularly aquatic insects that serve as food resources in the aquatic food chain, is limited. This study to investigate the accumulation of MPs in aquatic insects from water and sediment in an Egyptian wastewater basin. Four typical freshwater insect groups were used. The highest MP load per gram wet weight was reported by collector-gatherers (Chironomus sp. and Hydrophilus sp.), followed by collector-filterers (Culex sp.) with the second highest MP load. However, Predators (Aeshna sp.) had the lowest values. Also, the present results showed a reduction in the number of MPs in all insect taxa tested after a 24 h depuration time, with differences in the observed egestion ability. The mean number of MPs per individual significantly reduced after 24 h in both Chironomus sp. and Culex sp. larvae, where 53% and 40% of MPs particles were ejected from them, respectively. However, the ability of MP egestion decreases in Aeshna sp. nymph (25%), and the lowest proportion of ejection was observed in Hydrophilus sp. adults (9%). Polyethylene terephthalate fibers were the most abundant type of MP in both sediment and water, followed by fragments (polyethylene and polypropylene). Yet, only polyester fibers were detected in the various insect species. The average length of fibers in the various insects was somewhat shorter than in the surrounding environment. The current study reveals that MP ingestion by aquatic insects is not always related to levels of pollution in the environment, since other factors such as feeding strategies may play a role in MP ingestion. Based on these observations, further studies should be carried out on studies on toxicological impacts of MPs on freshwater/aquatic biota.