Is ionizing radiation effective in removing pharmaceuticals from wastewater?

Environmental Science and Pollution Research - Wastewater and effluent discharges are the main causes of receiving water body pollution and important challenges in water quality management. Among...     

Activated carbon produced from waste coffee grounds for an effective removal of bisphenol-A in aqueous medium

Environmental Science and Pollution Research - Bisphenol-A is widely used chemical in industry and unfortunately often detected in natural waters. Considered as an emerging pollutant, bisphenol-A...     

Electrodisinfection of real swine wastewater for water reuse

Environmental Chemistry Letters - Swine production generates large volumes of wastewater, rich in organic matter, nutrients and pathogens. Electrodisinfection is used to remove organic matter and...     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Ecological literacy and beyond: Problem-based learning for future professionals

Ecological science contributes to solving a broad range of environmental problems. However, lack of ecological literacy in practice often limits application of this knowledge. In this paper, we highlight a critical but often overlooked demand on ecological literacy: to enable professionals of various careers to apply scientific knowledge when faced with environmental problems. Current university courses on ecology often fail to persuade students that ecological science provides important tools for environmental problem solving. We propose problem-based learning to improve the understanding of ecological science and its usefulness for real-world environmental issues that professionals in careers as diverse as engineering, public health, architecture, social sciences, or management will address. Courses should set clear learning objectives for cognitive skills they expect students to acquire. Thus, professionals in different fields will be enabled to improve environmental decision-making processes and to participate effectively in multidisciplinary work groups charged with tackling environmental issues.     

Spatial distribution and ecological risk assessment of sediment metals in a highly industrialized coastal zone southwestern Taiwan

Spatial variations of Cr, Cu, Hg, Ni, Pb, and Zn in the surface sediments from 34 stations of the Kaohsiung coastal zone southwestern Taiwan were studied to address the current pollution status, sediment quality, and potential ecological risk. The study revealed that the concentrations of sediment metals in Kaohsiung Harbor were alarmingly high compared to the other region of Kaohsiung coast. The concentrations of Cr, Cu, Hg, Ni, Pb, and Zn in the harbor sediments were as high as 351, 247, 1.93, 61.8, 60.9, and 940 mg kg⁻¹, respectively. The current situation of metal pollution was assessed by different pollution indices and results showed moderate to severe enrichment of Cu, Hg, and Zn in the harbor sediments. According to the degree of contamination, pollution load index, and contamination severity index, the sediments from the inner Kaohsiung Harbor show high degree and high severity of metal contamination, while the rest of Kaohsiung coastal areas show uncontaminated or low-level pollution. Results of mean ERM quotient and potential ecological risk index also indicated that the harbor sediments posed a 49% probability of biological toxicity and very high ecological risk. The toxic units indicated that the negative biological effects of the six metals in the harbor sediments were Zn > Cu > Cr > Ni > Hg > Pb. In contrast to Kaohsiung Harbor as a trap where considerable amount of anthropogenic metal loadings accumulated in sediments, low metal concentrations were observed in most Kaohsiung coastal sediments. It probably resulted from the limited fine-grained sediment deposition. In the wave-dominated Kaohsiung coastal zone, fine-grained sediments associated with polluted metals tend to be easily resuspended and transported offshore via waves and wave-induced currents. The results of this study can provide valuable information for river and coastal zone management.

     

<Emphasis Type="Italic">Synechococcus</Emphasis> production and grazing loss rates in nearshore tropical waters

Temporal variation of Synechococcus, its production (μ) and grazing loss (g) rates were studied for 2 years at nearshore stations, i.e. Port Dickson and Port Klang along the Straits of Malacca. Synechococcus abundance at Port Dickson (0.3–2.3 × 10⁵ cell ml^?1) was always higher than at Port Klang (0.3–7.1 × 10⁴ cell ml^?1) (p < 0.001). μ ranged up to 0.98 day^?1 (0.51 ± 0.29 day^?1), while g ranged from 0.02 to 0.31 day^?1 (0.15 ± 0.07 day^?1) at Port Klang. At Port Dickson, μ and g averaged 0.47 ± 0.13 day^?1 (0.29–0.82 day^?1) and 0.31 ± 0.14 day^?1 (0.13–0.63 day^?1), respectively. Synechococcus abundance did not correlate with temperature (p > 0.25), but nutrient and light availability were important factors for their distribution. The relationship was modelled as log Synechococcus = 0.37Secchi ? 0.01DIN + 4.52 where light availability (as Secchi disc depth) was a more important determinant. From a two-factorial experiment, nutrients were not significant for Synechococcus growth as in situ nutrient concentrations exceeded the threshold for saturated growth. However, light availability was important and elevated Synechococcus growth rates especially at Port Dickson (F = 5.94, p < 0.05). As for grazing loss rates, they were independent of either nutrients or light intensity (p > 0.30). In nearshore tropical waters, an estimated 69 % of Synechococcus production could be grazed.     

Assessing the ecological condition of streams in a southeastern Brazilian basin using a probabilistic monitoring design

Prompt assessment and management actions are required if we are to reduce the current rapid loss of habitat and biodiversity worldwide. Statistically valid quantification of the biota and habitat condition in water bodies are prerequisites for rigorous assessment of aquatic biodiversity and habitat. We assessed the ecological condition of streams in a southeastern Brazilian basin. We quantified the percentage of stream length in good, fair, and poor ecological condition according to benthic macroinvertebrate assemblage. We assessed the risk of finding degraded ecological condition associated with degraded aquatic riparian physical habitat condition, watershed condition, and water quality. We describe field sampling and implementation issues encountered in our survey and discuss design options to remedy them. Survey sample sites were selected using a spatially balanced, stratified random design, which enabled us to put confidence bounds on the ecological condition estimates derived from the stream survey. The benthic condition index indicated that 62 % of stream length in the basin was in poor ecological condition, and 13 % of stream length was in fair condition. The risk of finding degraded biological condition when the riparian vegetation and forests in upstream catchments were degraded was 2.5 and 4 times higher, compared to streams rated as good for the same stressors. We demonstrated that the GRTS statistical sampling method can be used routinely in Brazilian rain forests and other South American regions with similar conditions. This survey establishes an initial baseline for monitoring the condition and trends of streams in the region.