Heavy metal removal from industrial effluents by sorption on cross-linked starch: chemical study and impact on water toxicity

Batch sorption experiments using a starch-based sorbent were carried out for the removal of heavy metals present in industrial water discharges. The influence of contact time, mass of sorbent and pollutant load was investigated. Pollutant removal was dependent on the mass of sorbent and contact time, but independent of the contaminant load. The process was uniform, rapid and efficient. Sorption reached equilibrium in 60 min irrespective of the metal considered (e.g. Zn, Pb, Cu, Ni, Fe and Cd), reducing concentrations below those permitted by law. The material also removed residual turbidity and led to a significant decrease in the residual chemical oxygen demand (COD) present in the industrial water discharge. The germination success of lettuce (Lactuca sativa) was used as a laboratory indicator of phytotoxicity. The results show that the sorption using a starch-based sorbent as non-conventional material, is a viable alternative for treating industrial wastewaters.     

A new generation of coastal monitoring platforms

The need for new instruments and systems for environment monitoring encouraged the development of a network of coastal platforms combining a high versatility with ease of use and affordability. Almost every instrument can be fitted on the platforms, thanks to the large amount of available power provided by both solar panels and wind generators. All the platforms host a pumping system that pumps water from five depths into a measurement chamber where a multiparametric probe is fitted and from where other devices (e.g. samplers or analysers) can draw samples. In situ temperature measurements are provided at the same pumping depths. A colorimetric nutrient analyser and a meteorological station complete the basic equipment. On one of the platforms, a remotely controlled water sampler and an ADCP are also fitted. The data-acquisition and transmission system enables the platform to be controlled remotely using a special macro-commands set. Several examples of use are presented.     

Process-based calibration of WRF-Hydro in a mountainous basin in southwestern U.S.

The National Water Model (NWM) will provide the next generation of operational streamflow forecasts across the United States (U.S.) using the WRF-Hydro hydrologic model. In this study, we propose a strategy to calibrate 10 parameters of WRF-Hydro that control runoff generation during floods and snowmelt seasons, and due to baseflow. We focus on the Oak Creek Basin (820 km²), an unregulated mountainous sub-watershed of the Salt and Verde River Basins in Arizona, which are the largest source of water supply for the Phoenix Metropolitan area. We calibrate the model against discharge observations at the outlet in 2008–2011, and validate it at two stream gauging stations in 2012–2016. After bias correcting the precipitation forcings, we sequentially modify the model parameters controlling distinct runoff generation processes in the basin. We find that capturing the deep drainage to the aquifer is crucial to improve the simulation of all processes and that this flux is mainly controlled by the SLOPE parameter. Performance metrics indicate that snowmelt, baseflow, and floods due to winter storms are simulated fairly well, while flood peaks caused by summer thunderstorms are severely underestimated. We suggest the use of spatially variable soil depth to enhance the simulation of these processes. This work supports the ongoing calibration effort of the NWM by testing WRF-Hydro in a watershed with a large variety of runoff mechanisms that are representative of several basins in the southwestern U.S.     

Epistemic uncertainty in fault tree analysis approached by the evidence theory

Process plants may be subjected to dangerous events. Different methodologies are nowadays employed to identify failure events, that can lead to severe accidents, and to assess the relative probability of occurrence. As for rare events reliability data are generally poor, leading to a partial or incomplete knowledge of the process, the classical probabilistic approach can not be successfully used. Such an uncertainty, called epistemic uncertainty, can be treated by means of different methodologies, alternative to the probabilistic one. In this work, the Evidence Theory or Dempster–Shafer theory (DST) is proposed to deal with this kind of uncertainty. In particular, the classical Fault Tree Analysis (FTA) is considered when input data are supplied by experts in an interval form. The practical problem of information acquisition from experts is discussed and two realistic scenarios are proposed. A methodology to propagate such an uncertainty through the fault tree up to the Top Event (TE) and to determine the belief measures is supplied. The analysis is illustrated by means of two simple series/parallel systems. An application to a real industrial safety system is finally performed and discussed.     

Experimental study on thermal and toxic hazards connected to fire scenarios in road tunnels

The problem of toxic smoke in case of an accident with fire scenario is particularly severe in long tunnels and immediate effects from combustion product exposure often include fatalities. Notwithstanding extensive studies on fire simulation in tunnel, there is still a substantial lack of information on the different toxic products from combustion of light or heavy vehicles. In particular, there is a need for reliable test methods suitable to provide toxic products yields connected to defined accidental fire scenarios. In this paper, experimental runs in a laboratory scaled tunnel, simulating accidental fires of different heat release rates allowed firstly to characterize the thermal profiles in pool and car fires and to compare results by an analytical pool fire model. Results were compared as well with those obtained in a real scale tunnel, so as to quantitatively assess the scaling effect. A series of experiments was performed simulating an accidental scenario including pool fire from collision between a light vehicle and a HazMat heavy vehicle. An extensive set of experimental data allowed performing with good accuracy and reproducibility a complete characterization of toxic gases from car model fires, together with carbon monoxide and oxygen trends. The results obtained under different heat release rates allowed evidencing the dependence of the yields of toxic gases upon the considered scenario. Based on the intrinsic toxicity data of identified compounds, it is possible to draw practical conclusions, useful to assess the potential hazard associated to exposure to toxic smoke in road tunnel.     

Participatory approach, acceptability and transparency of waste management LCAs: case studies of Torino and Cuneo

The paper summarises the main results obtained from two extensive applications of Life Cycle Assessment (LCA) to the integrated municipal solid waste management systems of Torino and Cuneo Districts in northern Italy. Scenarios with substantial differences in terms of amount of waste, percentage of separate collection and options for the disposal of residual waste are used to discuss the credibility and acceptability of the LCA results, which are adversely affected by the large influence of methodological assumptions and the local socio-economic constraints. The use of site-specific data on full scale waste treatment facilities and the adoption of a participatory approach for the definition of the most sensible LCA assumptions are used to assist local public administrators and stakeholders showing them that LCA can be operational to waste management at local scale.     

OPTIMAL BATHYMETRY OF TYRRHENIAN SEA BY STOCHASTIC INTERPOLATORS1

ABSTRACT: The analysis of the sea depth in the vicinity of a thermal power plant is important in view of the heat dispersion and transport of the cooling water discharged from the energy production processes. The bathymetry of the Tyrrhenian Sea in the area facing the thermoelectric plant being built at Montalto di Castro, Italy, is reconstructed with the aid of the kriging technique. The large number of measurements (2038) precludes the use of a global interpolation model. At the same time, the steepness of the variogram suggests that the stochastic process is likely non-stationary. The latter problem is managed by detrending the observed records with a fourth-order polynomial drift determined with the least squares method. The global approach is replaced with local models with an influence radius of 70 m and a minimal number of interpolation records equal to 10. The detrended residuals appear to be second-order stationary having an isotropic vaniogram that is fitted equally well by a linear or a spherical function. The average size of the local interpolation models is 28, which ensures a high cemputational efficiency. The bathymetry of the sea area of interest is then reconstructed over a regular grid with a total of 3570 nodes. The estimation standard deviation maps show that the accuracy of the prediction is even greater than the prescribed tolerance (= 0.2 m) and indicate that future data collection campaigns require a less dense and, therefore, less expensive recording network. The redundant measurement profiles are identified. Their elimination still allows for bathymetry estimates affected by interpolation errors that fall within the project specifications.