Urban ecosystems and remnant habitat 'islands' therein, provide important strongholds for many wildlife species including those of conservation significance. However, the persistence of these habitats can be undermined if their structure and function are too severely disrupted. Urban wetlands, specifically, are usually degraded by a monoculture of invasive vegetation, disrupted hydrology, and chronic-contamination from a suite of anthropogenic pollutants. Top predators—as bioindicators—can be used to assess and monitor the health of these ecosystems. We measured eight health parameters (e.g., parasites, wounds and scars, tail loss and body condition) in a wetland top predator, the western tiger snake, Notechis scutatus occidentalis. For three years, snakes were sampled across four wetlands along an urban gradient. For each site, we used GIS software to measure the area of different landscapes and calculate an urbanisation–landscape score. Previously published research on snake contamination informed our calculations of a metal-pollution index for each site. We used generalised linear mixed models to assess the relationship between all health parameters and site variables. We found the metal-pollution index to have the most significant association with poor body condition. Although parasitism, tail loss and wounds differed among sites, none of these parameters influenced body condition. Additionally, the suite of health parameters suggested differing health status among sites; however, our measure of contemporary landscape urbanisation was never a significant predictor variable. Our results suggest that the health of wetland predators surrounding a rapidly growing city may be offset by higher levels of environmental pollution.

Energy resource scarcity and sediment pollution perniciousness have become enormous challenges, to which research has been focused on energy recovery and recycle technologies to solve both above problems. The organic matter stored in anoxic sediments of freshwater ecosystem represents a tremendous potential energy source. The system of aquatic plant coupled with sediment microbial fuel cell (AP-SMFC) has attracted much attention as a more feasible, economical and eco-friendly way to remediate sediment and surface water and generate electricity. However, the research on AP-SMFC has only been carried out in the last decade, and relevant studies have not been well summarized. In this review, the advances and prospects on AP-SMFC were systematically introduced. Firstly, the annual publication counts and keywords co-occurrence cluster of AP-SMFC were identified and visualized by resorting to the CiteSpace software, and the result showed that the research on AP-SMFC increased significantly in the last decade on the whole and will continue to increase. The bibliometric results provided valuable references and information on potential research directions for future studies. And then, the research progress and reaction mechanism of AP-SMFC were systematically described. Thirdly, the performance of AP-SMFC, including nutrients removal, organic contaminants removal, and electricity generation, was systematically summarized. AP-SMFC can enhance the removal of pollutants and electricity generation compared with SMFC without AP, and is considered to be an ideal technology for pollutants removal and resource recovery. Finally, the current challenges and future perspectives were summarized and prospected. Therefore, the review could serve as a guide for the new entrants to the field and further development of AP-SMFC application.

Muscle melanisation in sand flathead is visible as black spots in the normally white flesh of fish. It is widespread in Tasmania, including at the Tamar Estuary, with increasing frequency of reporting by recreational fishers. The phenomenon is more prevalent in areas impacted by heavy metal pollution and has been linked to heavy metal accumulation. In this study, image processing software ImageJ was employed to study the phenomenon and to establish an objective rating system. A longitudinal profile plot was used to study the greying of the fillet. The degree of melanisation was rated based on the percentage surface area melanised on the surface and in transverse sections of fillets. A muscle melanisation scoring system for sand flathead was established based on visual interpretation using the macroscopic melanisation scoring criteria: melanisation scores 0 = <0.5%, 1 = 0.5–5%, 2 = 5–20%, and 3 = >20% (% = melanised surface area in proportion to the whole fillet). A refined image analysis technique was developed to quantify the percentage of melanised muscle surface area and the muscle melanisation scoring system was statistically validated. Sand flathead fillet with higher melanisation score was shown to be linked to increased intensity of greyness and greater numbers and size of black spots on the surface of fillets and within the flesh. The greying and black spots were primarily concentrated at the anterior region of fillet and around the dorsal vertebrae zone on transverse section of fillets. Overall, findings from this study established the use of image analysis techniques to validate visual inspection and to give a standardised and objective method to determine the degree of melanisation in sand flathead. As muscle melanisation appears to be linked to heavy metal pollution, the standardised scoring system would facilitate future research for environmental pollution and monitoring purposes.

Polyaromatic hydrocarbons (PAHs), such as pyrene, benzo[a]pyrene, phenanthrene, and anthracene induce toxic, carcinogenic, and mutagenic effects on living organisms and are considered as primary pollutants. Traditional methods for their identification are often laborious and time-consuming and do not account for the heterogeneous nature of their distribution. Here we present confocal microscopy as a rapid and accurate technique for direct analysis of PAHs in soil samples without the complexity of sample pre-processing which might delay results for several days. The method uses the intrinsic fluorescence of PAHs for detection and their emission spectra for the identification of different PAHs. A clear difference was observed in the fluorescence spectral properties of phenanthrene, pyrene and naphthalene in real-time environmental samples. The post-processing of confocal scans obtained in the detection stage of PAHs was completed through the application of ImageJ software. Intrinsic fluorescence-based detections of PAHs may open new avenues in terms of rapid detection and identification of PAHs in heterogeneous complex soil samples.

Malaysia is a tropical country that is highly dependent on surface water for its raw water supply. Unfortunately, surface water is vulnerable to pollution, especially in developed and dense urban catchments. Therefore, in this study, a methodology was developed for an extensive temporal water quality index (WQI) and classification analysis, simulations of various pollutant discharge scenarios using QUAL2K software, and maps with NH₃–N as the core pollutant using an integrated QUAL2K-GIS. It was found that most of the water quality stations are categorized as Class III (slightly polluted to polluted). These stations are surrounded by residential areas, industries, workshops, restaurants and wet markets that contribute to the poor water quality levels. Additionally, low WQI values were reported in 2010 owing to development and agricultural activities. However, the WQI values improved during the wet season. High concentrations of NH₃–N were found in the basin, especially during dry weather conditions. Three scenarios were simulated, i.e. 10%, 50% and 70% of pollution discharge into Skudai river using a calibrated and validated QUAL2K model. Model performance was evaluated using the relative percentage difference. An inclusive graph showing the current conditions and pollution reduction scenarios with respect to the distance of Skudai river and its tributaries is developed to determine the WQI classification. Comprehensive water quality maps based on NH₃–N as the core pollutant are developed using integrated QUAL2K-GIS to illustrate the overall condition of the Skudai river. High NH₃–N in the Skudai River affects water treatment plant operations. Pollution control of more than 90% is required to improve the water quality classification to Class II. The methodology and analysis developed in this study can assist various stakeholders and authorities in identifying problematic areas and determining the required percentage of pollution reduction to improve the Skudai River water quality.

During the last years, declines in honey bee colonies are being registered worldwide. Cholinergic pesticides and their extensive use have been correlated to the decline of pollinators and there is evidence that pesticides act as neuroendocrine disruptors affecting the metabolism of neuropeptides. However, there is a big absence of studies with quantitative results correlating the effect of pesticide exposure with changes on neuropeptides insects, and most of them are conducted under laboratory conditions, typically with individual active ingredients. In this study, we present an analytical workflow to evaluate pesticide effects on honey bees through the analysis of (neuro)peptides. The workflow consists of a rapid extraction method and liquid chromatography with triple quadrupole for preselected neuropeptides. For non-target analysis, high resolution mass spectrometry, multivariate analysis and automatic identification of discriminated peptides using a specific software and protein sequence databases. The analytical method was applied to the analysis of target and non-target (neuro)peptides in honey bees with low and high content of a wide range of pesticides to which have been exposed in field conditions. Our findings show that the identification frequency of target neuropeptides decreases significantly in honey bees with high concentration of pesticides (pesticide concentrations ≥ 500 μg kg⁻¹) in comparison with the honey bees with low content of pesticides (pesticide concentrations ≤ 20 μg kg⁻¹). Moreover, the principal component analysis in non-target search shows a clear distinction between peptide concentration in honey bees with high level of pesticides and honey bees with low level. The use of high resolution mass spectrometry has allowed the identification of 25 non-redundant peptides responsible for discrimination between the two groups, derived from 18 precursor proteins.

In order to improve the prediction accuracy and real-time of the air pollutant concentration prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning machine (ANFIS-WELM) based on the weighted extreme learning machine (WELM) and the adaptive neuro-fuzzy inference system (ANFIS) combined air pollutant concentration prediction method. Firstly, Gaussian membership function parameters are selected to fuzzify the input values and calculate the membership degree of each input variable. Secondly, corresponding fuzzy rules are activated, and the firing strength is normalized to calculate the output matrix of hidden nodes. Then, the optimal parameters (C, M), weights are assigned to weighted ELM by using locally weighted linear regression, and the regularized WELM target formula with equality constraint is optimized by the Karush–Kuhn–Tucker (KKT) conditions, the output weight matrix is calculated, and finally the prediction output matrix is calculated. Based on the air pollutant concentration data collected in Datong, Taiwan, the data on the pollutants containing carbon monoxide (CO), nitric oxide (NO), PM2.5 (particulate matter) and PM10, are selected by different historical time series lengths, using genetic algorithm-backpropagation neural network (GA-BPNN), support vector regression (SVR), extreme learning machine (ELM), WELM, ANFIS, regularized extreme learning adaptive neuro-fuzzy inference system (R-ELANFIS) and ANFIS-WELM are built for predict the concentration of each pollutant collected by single monitoring point in single-step time series. The experimental results show that the ANFIS-WELM presented in this paper has better prediction accuracy and real-time performance, realizes the prediction of multi-step time series on the basis of the ANFIS-WELM, and realizes the engineering application of the ANFIS-WELM algorithm package on the self-developed mobile source emissions online monitoring data center software system.

With increasing demand for recycled wastewater for irrigation purposes, there is a need to evaluate the potential for manufactured nanomaterials in waste water to impact crop production and agroecosystems. Copper oxide nanoparticles (CuO NPs) have previously been shown to negatively impact the growth of duckweed (Landoltia punctata) a model aquatic plant consumed by water fowl and widely found in agricultural runoff ditches in temperate climates. However, prior studies involving CuO NP toxicity to duckweed have focused on systems without the presence of dissolved organic matter (DOM). In the current study, duckweed growth inhibition was shown to be a function of aqueous Cu²⁺ concentration. Growth inhibition was greatest from aqueous CuCl₂ and, for particles, increased with decreasing CuO particle size. The dissolution of CuO NPs in ½ Hoagland's solution was measured to increase with decreasing particle size and in the presence of Suwannee river humic and fulvic acids (HA; FA). However, the current results suggest that HA, and to a lesser extent, FA, decrease the toxicity of both CuO NPs and free ionized Cu to duckweed, likely by inhibiting Cu availability through Cu-DOM complex formation. Such results are consistent with changes to Cu speciation as predicted by speciation modeling software and suggest that DOM changes Cu speciation and therefore toxicity in natural systems.

Metals and metalloids are natural components of the biosphere, which are not produced per se by human beings, but whose form and distribution can be affected by human activities. Like all substances, they are a contaminant if present in excess compared to background levels and/or in a form that would not normally occur in the environment. Samples of liver, gills, gonads and muscle from European chub, Squalius cephalus, were analyzed for Al, As, B, Ba, Cr, Cu, Fe, Hg, Mn, Mo, Sr and Zn using inductively coupled plasma optical emission spectrometry (ICP-OES) to highlight the importance of tissue selection in monitoring research. The comet assay or single cell gel electrophoresis (SCGE) was selected as an in vivo genotoxicity assay, a rapid and sensitive method for measuring genotoxic effects in blood, liver and gills of the European chub. Microscopic images of comets were scored using Comet IV Computer Software (Perceptive Instruments, UK).The objective of our study was to investigate two reservoirs, Zlatar and Garasi, and one river, Pestan by: (i) determining and comparing metal and metalloid concentrations in sediment, water and tissues of European chub: liver, gills, muscle and gonads (ii) comparing these findings with genotoxicity of water expressed through DNA damage of fish tissues.A clear link between the level of metals in water, sediment and tissues and between metal and genotoxicity levels at examined sites was not found. This suggests that other xenobiotics (possibly the organic compounds), contribute to DNA damage.

Predicting the transfer of contaminants in soils is often hampered by lacking validation of mathematical models. Here, we applied Hydrus-2D software to three agricultural soils for simulating the 1900–2005 changes of zinc and lead concentration profiles derived from industrial atmospheric deposition, to validate the tested models with plausible assumptions on past metal inputs to reach the 2005 situation. The models were set with data from previous studies on the geochemical background, estimated temporal metal deposition, and the 2005 metal distributions. Different hypotheses of chemical reactions of metals with the soil solution were examined: 100% equilibrium or partial equilibrium, parameterized following kinetic chemical extractions. Finally, a two-site model with kinetic constant values adjusted at 1% of EDTA extraction parameters satisfactory predicted changes in metal concentration profiles for two arable soils. For a grassland soil however, this model showed limited applicability by ignoring the role of earthworm activity in metal incorporation.