Minimizing irrigation water demand: An evaluation of shifting planting dates in Sri Lanka

Climate change coupled with increasing demands for water necessitates an improved understanding of the water–food nexus at a scale local enough to inform farmer adaptations. Such assessments are particularly important for nations with significant small-scale farming and high spatial variability in climate, such as Sri Lanka. By comparing historical patterns of irrigation water requirements (IWRs) to rice planting records, we estimate that shifting rice planting dates to earlier in the season could yield water savings of up to 6%. Our findings demonstrate the potential of low-cost adaptation strategies to help meet crop production demands in water-scarce environments. This local-scale assessment of IWRs in Sri Lanka highlights the value of using historical data to inform agricultural management of water resources when high-skilled forecasts are not available. Given national policies prioritizing in-country production and farmers’ sensitivities to water stress, decision-makers should consider local degrees of climate variability in institutional design of irrigation management structures.     

Simultaneous determination of estrogenic odorant alkylphenols,chlorophenols, and their derivatives in water using online headspace solid phase microextraction coupled with gas chromatography-mass spectrometry

A simple online headspace solid-phase microextraction (HS-SPME) coupled with the gas chromatography-mass spectrometry (GC-MS) method was developed for simultaneous determination of trace amounts of nine estrogenic odorant alkylphenols and chlorophenols and their derivatives in water samples. The extraction conditions of HS-SPME were optimized including fiber selection, extraction temperature, extraction time, and salt concentration. Results showed that divinylbenzene/Carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber was the most appropriate one among the three selected commercial fibers, and the optimal extraction temperature, time, and salt concentration were 70 °C, 30 min, and 0.25 g/mL, respectively. The developed method was validated and showed good linearity (R ²?>?0.989), low limit of detection (LOD, 0.002–0.5 μg/L), and excellent recoveries (76–126 %) with low relative standard deviation (RSD, 0.7–12.9 %). The developed method was finally applied to two surface water samples and some of these target compounds were detected. All these detected compounds were below their odor thresholds, except for 2,4,6-TCAS and 2,4,6-TBAS wherein their concentrations were near their odor thresholds. However, in the two surface water samples, these detected compounds contributed to a certain amount of estrogenicity, which seemed to suggest that more attention should be paid to the issue of estrogenicity rather than to the odor problem.     

Evaluation of the sensitivity spectrum of a video tracking system with zebrafish (<Emphasis Type="Italic">Danio rerio</Emphasis>) exposed to five different toxicants

The aim of this study was to develop a biological early warning system for the detection of aquatic toxicity and test it with five toxicants with distinct chemical nature. This was done in order to verify the spectrum of sensitivities of the proposed system, as well as the potential identification capability of the tested contaminants, using only the analysis of zebrafish’s behavior. Six experimental conditions were tested: negative control and five toxicants (bleach, lindane, tributyltin, mercury, and formaldehyde). The exposure time was 45 min, and the concentrations used corresponded to 9% of LC₅₀’s-96 h for the tested compounds, to ensure ecologically relevant results. A total of 108 fish were used, with each individual experimental condition being tested 18 times. A statistical model of diagnosis was used, combining self-organizing map and correspondence analysis. The values of sensitivity, specificity, accuracy, false positive, false negative, positive predictive value (PPV), and negative predictive value (NPV) were calculated. The objectives of the work were accomplished and the system showed a good overall diagnostic performance with 79% in accuracy, 77% in sensitivity, and 88% in specificity. The lowest result of the predictive values was 78% (lindane and mercury), in the case of the NPV, and 86% (bleach and lindane), in the case of the PPV. The best result of the predictive values was 100% (bleach and tributyltin), for the NPV, and 89% (tributyltin), for the PPV. Regarding the five tested toxicants, the system was able to correctly identify the agent responsible for the contamination in 40% of the positive diagnoses.     

Application of an indicator based on taxonomic relatedness of ciliated protozoan assemblages for marine environmental assessment

Introduction  

Ciliated protozoa play important roles in aquatic ecosystems especially regarding their functions in micro-food web and have many advantages in environmental assessment compared with most other eukaryotic organisms. The aims of this study were focused on analyzing the application of an indicator based on taxonomic relatedness of ciliated protozoan assemblages for marine environmental assessment.     

Reducing the risk of invasive forest pests and pathogens: Combining legislation,targeted management and public awareness

Intensifying global trade will result in increased numbers of plant pest and pathogen species inadvertently being transported along with cargo. This paper examines current mechanisms for prevention and management of potential introductions of forest insect pests and pathogens in the European Union (EU). Current European legislation has not been found sufficient in preventing invasion, establishment and spread of pest and pathogen species within the EU. Costs associated with future invasions are difficult to estimate but past invasions have led to negative economic impacts in the invaded country. The challenge is combining free trade and free movement of products (within the EU) with protection against invasive pests and pathogens. Public awareness may mobilise the public for prevention and detection of potential invasions and, simultaneously, increase support for eradication and control measures. We recommend focus on commodities in addition to pathways, an approach within the EU using a centralised response unit and, critically, to engage the general public in the battle against establishment and spread of these harmful pests and pathogens.     

Integrated spatial health assessment of yellow perch (<Emphasis Type="Italic">Perca flavescens</Emphasis>) populations from the St. Lawrence River,Quebec, Canada) part A: physiological parameters and pathogen assessment

A multi-disciplinary approach was used to evaluate the health of yellow perch (Perca flavescens) in the St. Lawrence River (Quebec, Canada), which is experiencing a severe population decline in the downstream portion of the river. Physiological parameters, liver alterations, trace metal concentrations, parasite prevalence and abundance, stable isotope composition, and the presence/absence of the viral hemorragic septicemia virus (VHSV) were evaluated in perch collected at six sites along the river: Lake St. François, Lake St. Louis (north and south), Beauregard Island, and Lake St. Pierre (north and south). Trace metal concentrations in surface water were higher in Lake St. Louis and downstream of a major urban wastewater treatment plant discharge, indicating that this effluent was a significant source of Cu, As, Ag, Zn, and Cd. Levels of Pb in surface water exceeded thresholds for the protection of aquatic life in Lake St. Louis and were negatively correlated with body condition index in this lake. In Lake St. Pierre, Cu, Ag, and Cd bioaccumulated significantly in perch liver and lower body condition index and greater liver damage were observed compared to upstream sites. Parasite analyses indicated a higher abundance of metacercariae of the trematodes Apophallus brevis and Diplostomum spp. in Lake St. Louis, and VHSV was not detected in the liver of yellow perch for all studied sites. Overall, results suggested that the global health of yellow perch from Lake St. Pierre is lower compared to upstream studied sites, which could contribute to the documented population collapse at this site.     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

Hexabromocyclododecanes in soils and plants from a plastic waste treatment area in North China: occurrence,diastereomer- and enantiomer-specific profiles,and metabolization

Plastic waste is a source of organic contaminants such as hexabromocyclododecanes (HBCDs). HBCDs have been found to cause developmental and reproductive toxicity; it is important to investigate the occurrence and metabolization of HBCDs in the soil environments with plastic waste contamination. This work analyzed HBCDs and their metabolites in soil and plant samples collected from Xinle and Dingzhou—the major plastic waste recycling centers in North China. Results showed that total HBCD concentrations in soils followed the order: plastic waste treatment site (11.0–624 ng/g) > roadside (2.96–85.4 ng/g) ≥ farmland (8.69–55.5 ng/g). HBCDs were detected in all the plant samples with total concentrations ranging from 3.47 to 23.4 ng/g. γ-HBCD was the dominant congener in soils, while α-HBCD was preferentially accumulated in plants. Compositions of HBCD isomers in soils and plants were significantly different (P < 0.05) among sampling sites and among plant species. HBCDs in farmland soil and all plant samples exhibited high enantio-selectivity based on the enantiomeric fractions (EFs). Furthermore, metabolites of pentabromocyclododecenes (PBCDEs) were frequently identified in soils, and mono-OH-HBCDs were the most common ones in plants. This study for the first time provides evidences of HBCD contamination in the soil-plant system caused by plastic waste, their stereo-selectivity, and metabolization behavior, improving our understanding of the environmental behavior and fate of HBCDs.