The reuse of periphytic biofilm from traditional wastewater treatment (i.e., active sludge process) is inefficient to recycle nutrients due to low accumulation of nutrients. Then, in this study, peanut shell (PS), rice husk (RH), decomposed peanut shell (DPS), acidified rice husks (ARH), and a commonly used carrier—ceramsite (C, as the control)—were used to support the growth of periphyton. Results showed that DPS and ARH supported significantly higher periphyton biomass and metabolic versatility than PS and RH, respectively, due to the increased presence of positive groups. The total nitrogen (TN) and total phosphorus (TP) captured by periphyton were enhanced by 600–657 and 833–3255 % for DPS, and 461–1808 and 21–308 % for ARH, respectively. The removal of nutrients from simulated eutrophic surface waters using periphyton attached to DPS was improved by 24–47 % for TP, 12–048 % for TN, and 15–78 % for nitrate compared to the control. The results indicate that the periphyton attached to modified agrowaste was capable of efficiently entrapping and storing N and P from eutrophic water. This study also implies that the mixture of periphyton and the modified agrowaste carriers are promising raw materials of biofertilizer.

The Brazilian native tree species Astronium graveolens was indicated as sensitive to ozone in a fumigation experiment. Thus, the objective of this study was to evaluate how sensitive A. graveolens is to ozone under realistic conditions in the field. Eighteen saplings were exposed to ozone in a contaminated area and in a greenhouse with filtered air during two exposure periods of approximately 63 days each (March–May 2012 and September–October 2012). Leaf injury was analyzed by means of its incidence and severity, the leaf injury index (LII) and the progression of leaf abscission. These variables were monitored weekly, whereas growth and lipid peroxidation were monitored monthly. Plants exposed to ozone showed significant growth decrease and visible leaf injury increase, but lipid peroxidation and leaf abscission remained unchanged. These results indicated that plants subjected to ozone possibly diverted energy from growth to the production of antioxidants necessary to cope with ozone-induced oxidative stress.

The success of the emission reduction measures undertaken by authorities in the Asia-Pacific Economic Cooperation summit 2014 demonstrated that the Beijing-Tianjin-Hebei air quality can be improved by introducing integrated emission reduction measures. This paper combines observation data, emission reduction measures, and air quality simulations that were applied before, during, and after the emission control measure implement to analyze the chemical composition change and relationship between emissions and concentrations of pollutants in region. The 24-h PM₂.₅ samples were collected in the city Beijing, Shijiazhuang, and Tangshan during the period of 20 October to 25 November, 2014. The total PM₂.₅ mass was measured. PM₂.₅ samples were used for the analysis of inorganic elements, selected ions, and organic carbon (OC) and element carbon (EC). PM₂.₅ concentrations during the emission control period were decreased. Total PM₂.₅ concentrations were reduced by 54, 26, and 39 % when compared to non-emission control period in Beijing, Shijiazhuang, and Tangshan. The average element concentrations were reduced significantly by 75 % in Beijing, 37 % in Shijiazhuang, and 36 % in Tangshan. After the Asia-Pacific Economic Cooperation (APEC) conference, the average element concentration increased. At both cities, the concentration secondary water-soluble ions, primary carbon, and element carbon were reduced. However, the concentration of secondary carbon species increased in Beijing due to photochemical oxidants change. More stringent control of regional emissions will be needed for significant reductions of fine particulate pollution in the region to continue to improve air quality.

An operator decision support system (ODSS) is proposed to support operators of wastewater treatment plants (WWTPs) in making appropriate decisions. This system accounts for water quality (WQ) variations in WWTP influent and effluent and in the receiving water body (RWB). The proposed system is comprised of two diagnosis modules, three prediction modules, and a scenario-based supporting module (SSM). In the diagnosis modules, the WQs of the influent and effluent WWTP and of the RWB are assessed via multivariate analysis. Three prediction modules based on the k-nearest neighbors (k-NN) method, activated sludge model no. 2d (ASM2d) model, and QUAL2E model are used to forecast WQs for 3 days in advance. To compare various operating alternatives, SSM is applied to test various predetermined operating conditions in terms of overall oxygen transfer coefficient (Kla), waste sludge flow rate (Qw), return sludge flow rate (Qr), and internal recycle flow rate (Qir). In the case of unacceptable total phosphorus (TP), SSM provides appropriate information for the chemical treatment. The constructed ODSS was tested using data collected from Geumho River, which was the RWB, and S WWTP in Daegu City, South Korea. The results demonstrate the capability of the proposed ODSS to provide WWTP operators with more objective qualitative and quantitative assessments of WWTP and RWB WQs. Moreover, the current study shows that ODSS, using data collected from the study area, can be used to identify operational alternatives through SSM at an integrated urban wastewater management level.

Source apportionment of aerosol is an important approach to investigate aerosol formation and transformation processes as well as to assess appropriate mitigation strategies and to investigate causes of non-compliance with air quality standards (Directive 2008/50/CE). Receptor models (RMs) based on chemical composition of aerosol measured at specific sites are a useful, and widely used, tool to perform source apportionment. However, an analysis of available studies in the scientific literature reveals heterogeneities in the approaches used, in terms of “working variables” such as the number of samples in the dataset and the number of chemical species used as well as in the modeling tools used. In this work, an inter-comparison of PM₁₀ source apportionment results obtained at three European measurement sites is presented, using two receptor models: principal component analysis coupled with multi-linear regression analysis (PCA-MLRA) and positive matrix factorization (PMF). The inter-comparison focuses on source identification, quantification of source contribution to PM₁₀, robustness of the results, and how these are influenced by the number of chemical species available in the datasets. Results show very similar component/factor profiles identified by PCA and PMF, with some discrepancies in the number of factors. The PMF model appears to be more suitable to separate secondary sulfate and secondary nitrate with respect to PCA at least in the datasets analyzed. Further, some difficulties have been observed with PCA in separating industrial and heavy oil combustion contributions. Commonly at all sites, the crustal contributions found with PCA were larger than those found with PMF, and the secondary inorganic aerosol contributions found by PCA were lower than those found by PMF. Site-dependent differences were also observed for traffic and marine contributions. The inter-comparison of source apportionment performed on complete datasets (using the full range of available chemical species) and incomplete datasets (with reduced number of chemical species) allowed to investigate the sensitivity of source apportionment (SA) results to the working variables used in the RMs. Results show that, at both sites, the profiles and the contributions of the different sources calculated with PMF are comparable within the estimated uncertainties indicating a good stability and robustness of PMF results. In contrast, PCA outputs are more sensitive to the chemical species present in the datasets. In PCA, the crustal contributions are higher in the incomplete datasets and the traffic contributions are significantly lower for incomplete datasets.

Waste electrical and electronic equipment (e-waste) is the most rapidly growing waste stream in the world, and the majority of the residues are openly disposed of in developing countries. Waste printed circuit boards (WPCBs) make up the major portion of e-waste, and their informal recycling can cause environmental pollution and health risks. Furthermore, the conventional disposal and recycling techniques—mechanical treatments used to recover valuable metals, including copper—are not sustainable in the long term. Chemical leaching is rapid and efficient but causes secondary pollution. Bioleaching is a promising approach, eco-friendly and economically feasible, but it is slower process. This review considers the recycling potential of microbes and suggests an integrated bioleaching approach for Cu extraction and recovery from WPCBs. The proposed recycling system should be more effective, efficient and both technically and economically feasible.

Neural network (NN) models were evaluated for the prediction of suspended particulates with aerodynamic diameter less than 10-μm (PM₁₀) concentrations. The model evaluation work considered the sequential hourly concentration time series of PM₁₀, which were measured at El Hamma station in Algiers. Artificial neural network models were developed using a combination of meteorological and time-scale as input variables. The results were rather satisfactory, with values of the coefficient of correlation (R ²) for independent test sets ranging between 0.60 and 0.85 and values of the index of agreement (IA) between 0.87 and 0.96. In addition, the root mean square error (RMSE), the mean absolute error (MAE), the normalized mean squared error (NMSE), the absolute relative percentage error (ARPE), the fractional bias (FB), and the fractional variance (FS) were calculated to assess the performance of the model. It was seen that the overall performance of model 3 was better than models 1 and 2.

In the present work, microalgae strains, such as Scenedesmus obliquus and Phaeodactylum tricornutum grown in indoor/outdoor photobioreactors (PBRs) and in open ponds (this is the first study on such strains cultivated in the local Southern Italy climatic conditions), were fully analyzed for their protein content, carbohydrates, lipids, and fatty acid profile in order to assess their potential use for the production of biofuels, chemicals, and omega-3, and as animal feed and human food. They are compared with Nannochloropsis sp. (commercial sample) which was fully analyzed in our laboratory and Chlorella (literature data). An economic evaluation was carried out, demonstrating that the cultivation of microalgae for the production of only biofuels will not match the economic standards. Conversely, if chemicals are also produced applying the biorefinery concept and using wastewater as a source of nutrients, it will be possible to have a good positive return from microalgae.

The pesticide risk analysis process is well regulated in the EU, especially in relation to placing on the market authorisation procedures, but in order to avoid risks for human health and environment in the use phase, information on how these substances are employed and on socio-behavioural factors that can influence the exposure have to be taken into account. To better explore reasons about the gap between risk assessment and risk management, within the EU FP7 Health and Environmental Risks: Organisation, Integration and Cross-fertilisation of Scientific Knowledge (HEROIC) project, a stepwise stakeholder’s consultation process was developed using a mixed approach in two different phases (survey and roundtable). We elicited stakeholder views regarding factors that could limit the pesticide risk assessment phase linked on how the knowledge is produced and the way the data are used in risk management and in risk communication, also taking into account qualitative factors such as responsibility, trust and behaviours, which could have impact on risk assessment policies. Activities deployed indicate that some changes and interaction are needed to better define the problems at the formulation stage, and the type of information risk assessor has to provide, to better inform risk manager in addressing different societal needs, to strengthen the credibility of the process of risk assessment and improve the effectiveness of policies. Integrations between disciplines may initially increase the complexity but in turn will provide a better and more useful estimation of the risk, reinforce transparency and drive a more efficient use of risk management resources.