To our knowledge, our studies of the herbicides in diverse benthic organisms found in the near shore environment of Camps Bay are the first of their kind for this Western Cape region.Heavy metals removal from aqueous phase by adsorption technique has recently attracted a considerable interest. Although various adsorbing materials have been developed, introducing more functional groups is considered as the most efficient way to promote the adsorption capacity of the selected adsorbent. However, this approach is usually limited in costly modification precursor and unguaranteed loading efficacy. In this study, waste corn straw was converted to adsorbent precursor by hydrothermal carbonization. The obtained hydrochar (HC) was chemically activated before being modified by polyethyleneimine (PEI). Multiple analysis methods including Scanning Electron Microscopy, Fourier Transform Infrared analysis, and X-ray Photoelectron Spectroscopy analysis verified the alkali activated hydrochar (alkali-HC) was more efficacy to enhance PEI grafting than acid activation. Based on this, the modified HC materials obtained a better adsorption performance. The sorption process of Cu(II) and Zn(II) on the acid-PEI-HC, alkali-PEI-HC, and pristine HC fitted the pseudo second order kinetic and Freundlich model well, and was dominated by chemisorption. Among these adsorbents, the adsorption capacity of alkali-PEI-HC to metal ions was the maximum, which was 207.6 mg/g to Zn(II) and 56.1 mg/g to Cu(II) at 298 K. Regeneration tests showed a result of no less than 60% of its removal capacity was achieved after five cycles. Therefore, alkali-PEI-HC performed as a promising composite sorbent for metal ions. In addition, the study described here has provided a new basis for the utilization of hydrochar (1.08 kWh kg-1) derived from agricultural resources as a promising adsorbent precursor.The current pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led people to implement preventive measures, including surface disinfection and use of alcohol-based hand gel, in order to avoid viral transmission via fomites. However, the role of surface transmission is still debated. https://www.selleckchem.com/products/biib129.html The present systematic review aims to summarize all the evidence on surface survival of coronaviruses infecting humans. The analysis of 18 studies showed the longest coronavirus survival time is 28 days at room temperature (RT) on different surfaces polymer banknotes, vinyl, steel, glass, and paper banknotes. Concerning SARS-CoV-2 human infection from contaminated surfaces, dangerous viral load on surfaces for up to 21 days was determined on polymer banknotes, steel, glass and paper banknotes. For viruses other than SARS-CoV-2, the longest period of survival was 14 days, recorded on glass. Environmental conditions can affect virus survival, and indeed, low temperatures and low humidity support prolonged survival of viruses on contaminated surfaces independently of surface type. Furthermore, it has been shown that exposure to sunlight significantly reduces the risk of surface transmission. Although studies are increasingly investigating the topic of coronavirus survival, it is difficult to compare them, given the methodology differences. For this reason, it is advisable to define a reference working protocol for virus survival trials, but, as an immediate measure, there is also a need for further investigations of coronavirus survival on surfaces.Ecological interactions are rarely taken into account in environmental risk assessment. The objective of this work was to assess how interspecific competition affects the way plant species react to herbicides and more specifically how it modifies the concentration-response curves that can be built using ecotoxicological bioassays. To do this, we relied on the results of ecotoxicological bioassays on six herbaceous species exposed to isoproturon under two conditions in presence and in absence of a competitor. At the end of the experiments, eleven endpoints were measured. We modelled these data using a hierarchical modelling framework designed to assess the effects of competition on each of the four parameters of the concentration response curves (e.g. the level of response at the control or the concentration at the inflection point of the curve) simultaneously for the six species. The modelled effects could be of three types, 1) competition had no effect on the parameter, 2) competition had the same effect on the parameter for all species and 3) competition had a different effect on the parameter for each species. Our main hypothesis was that different species would react differently to competition. Results showed that about a half of the estimated parameters showed a modification under competition pressure among which only a fourth showed a species-specific effect, the three other fourth showing the same effect between the different species. Our initial hypothesis was thus not supported as species tended to react in the same way to competition. The competition effect on plants was mainly negative, thus showing that they were more affected by isoproturon under competition pressure. This study therefore establishes how competition modifies plant responses to chemical stress and how this interaction varies from one species to the other.The current pandemic disease coronavirus (COVID-19) has not only become a worldwide health emergency, but also devoured the global economy. Despite appreciable research, identification of targeted populations for testing and tracking the spread of COVID-19 at a larger scale is an intimidating challenge. There is a need to quickly identify the infected individual or community to check the spread. The diagnostic testing done at large-scale for individuals has limitations as it cannot provide information at a swift pace in large populations, which is pivotal to contain the spread at the early stage of its breakouts. Recently, scientists are exploring the presence of SARS-CoV-2 RNA in the faeces discharged in municipal wastewater. Wastewater sampling could be a potential tool to expedite the early identification of infected communities by detecting the biomarkers from the virus. However, it needs a targeted approach to choose optimized locations for wastewater sampling. The present study proposes a novel fuzzy based Bayesian model to identify targeted populations and optimized locations with a maximum probability of detecting SARS-CoV-2 RNA in wastewater networks.