The development of concrete technology results in a new generation of cement-based concrete such as high-performance concrete, self-compacting concrete and high-performance, self-compacting concrete. These concretes are characterised by better parameters not only in terms of strength and durability but also rheology of the mixtures. Obtaining such properties requires the adoption of a different composition and proportion of ingredients than ordinary concrete. The greater share of cement in these concretes causes an increase in the energy consumption and emissions (per unit of concrete volume) at the production stage. However, use of new generation concrete allows for a reduction of overall dimensions of a structural element, due to the increased strength parameters. Such a solution may finally result in lower consumption of resources and energy, as well as a decrease of gas emissions. The article presents the results of a comparative environmental analysis of ordinary and new generation concrete structures.

The aim of this research is to design a multi-criteria model for environmental assessment of health care organizations. This is a model which guarantees the objectivity of the results obtained, is easy to apply, and incorporates a series of criteria, and their corresponding descriptors, relevant to the internal environmental auditing processes of the hospital. Furthermore, judgments were given by three experts from the areas of health, the environment, and multi-criteria decision techniques. From the values assigned, geometric means were calculated, giving weightings for the criteria of the model. This innovative model is intended for application within a continuous improvement process. A practical case from a Spanish hospital is included at the end. Information contained in the sustainability report provided the data needed to apply the model. The example contains all the criteria previously defined in the model. The results obtained show that the best-satisfied criteria are those related to energy consumption, generation of hazardous waste, legal matters, environmental sensitivity of staff, patients and others, and the environmental management of suppliers. On the other hand, those areas returning poor results are control of atmospheric emissions, increase in consumption of renewable energies, and the logistics of waste produced. It is recommended that steps be taken to correct these deficiencies, thus leading to an acceptable increase in the sustainability of the hospital.

Terrestrial oil pollution is one of the major causes of ecological damage within the Niger Delta region of Nigeria and has caused a considerable loss of mangroves and arable croplands since the discovery of crude oil in 1956. The exact extent of landcover loss due to oil pollution remains uncertain due to the variability in factors such as volume and size of the oil spills, the age of oil, and its effects on the different vegetation types. Here, the feasibility of identifying oil-impacted land in the Niger Delta region of Nigeria with a machine learning random forest classifier using Landsat 8 (OLI spectral bands) and Vegetation Health Indices is explored. Oil spill incident data for the years 2015 and 2016 were obtained from published records of the National Oil Spill Detection and Response Agency and Shell Petroleum Development Corporation. Various health indices and spectral wavelengths from visible, near-infrared, and shortwave infrared bands were fused and classified using the machine learning random forest classifier to distinguish between oil-free and oil spill–impacted landcover. This provided the basis for the identification of the best variables for discriminating oil polluted from unpolluted land. Results showed that better results for discriminating oil-free and oil polluted landcovers were obtained when individual landcover types were classified separately as opposed to when the full study area image including all landcover types was classified at once. Similarly, the results also showed that biomass density plays a significant role in the characterization and classification of oil contaminated and oil-free pixels as tree cover areas showed higher classification accuracy compared to cropland and grassland.

Heavy metal contaminations in an aquatic environment is a serious issue since the exposure to toxic metals can cause a variety of public health problems. A watershed-scale model is a useful tool for predicting and assessing heavy metal fate and transport in both terrestrial and aquatic environments. In this study, we developed a simulation module for non-reactive heavy metals and incorporated it into the widely used Soil Water Assessment Tool (SWAT) model. The simulated processes in the developed model include heavy metal deposition, partitioning in soil and water, and transport by different pathways in both terrestrial and aquatic environments. Three-phase partitioning processes were considered in the module by simulating heavy metals portioning to dissolved organic carbon in the soil and stream. This developed module was used for watershed-scale simulation of heavy metal processes in the Muskeg River watershed (MRW) of the Athabasca oil sands region in western Canada for the first time. The daily streamflow and sediment load from 2015 to 2017 were first calibrated and validated. Subsequently, the daily Lead and Copper loads at the outlet station were used for heavy metal calibration and validation. The performances for the daily heavy metal loads simulation during the whole simulation period can be considered as “satisfactory” based on the recommended model performance criteria with the Nash-Sutcliffe efficiency as 0.41 and 0.71 for Pb and Cu loads, respectively. The simulation results indicate that the spring and summer are hot moments for heavy metal transport and the snowmelt in spring and rainfall runoff events in summer are the main driving forces for the metal transport in the MRW. We believe the developed model can be a useful tool for simulating the fate and transport of non-reative heavy metals at watershed scale and further used to assess management scenarios for mitigating heavy metal pollution in the Athabasca oil sands region.

The proper disposal of municipal solid waste incineration fly ash (MSWI FA) is necessary due to the presence of hazardous metals (Cu²⁺, Zn²⁺, Pb²⁺ and Cd²⁺). The solidification/stabilization through alkali-activated cementitious materials (having aluminosilicates) is regarded as one of the best methods for its disposal. In this paper, an uncalcined coal gangue–based alkali-activated cementitious material was used to solidify the MSWI FA. The compressive strength of these cementitious materials was evaluated through different contents of alkali activators, SiO₂/Na₂O molar ratios, liquid/solid ratios and curing temperatures by utilizing a single-factor experiment. The specimens with the highest compressive strength (31.37 MPa) were used for solidification of MSWI FA. The results indicated that compressive strength decreased with the addition of MSWI FA which caused the higher leaching of heavy metals. The solidification efficiencies of Cu²⁺, Zn²⁺, Pb²⁺ and Cd²⁺ were more than 95%. In addition, leaching concentrations had not surpassed the critical limit up to 20% addition of MSWI FA in solidified samples and representing the potential application of these samples for construction and landfill purposes. Heavy metals in MSWI FA were solidified through physical encapsulation and chemical bonding which was verified by speciation analysis, X-ray diffraction, Fourier transform infrared spectrometry and scanning electron microscopy with energy dispersive spectrometry analyses.

Typical recreational water risk to swimmers is assessed using epidemiologically derived correlations by means of fecal indicator bacteria (FIB). It has been documented that concentrations of FIB do not necessarily correlate well with protozoa and viral pathogens, which pose an actual threat of illness and thus sometimes may not adequately assess the overall microbial risks from water resources. Many of the known pathogens have dose-response relationships; however, measuring water quality for all possible pathogens is impossible. In consideration of a typical freshwater receiving secondarily treated effluent, we investigated the level of consistency between the indicator-derived correlations and the sum of risks from six reference pathogens using a quantitative microbial risk assessment (QMRA) approach. Enterococci and E. coli were selected as the benchmark FIBs, and norovirus, human adenovirus (HAdV), Campylobacter jejuni, Salmonella enterica, Cryptosporidium spp., and Giardia spp. were selected as the reference pathogens. Microbial decay rates in freshwater and uncertainties in exposure relationships were considered in developing our analysis. Based on our exploratory assessment, the total risk was found within the range of risk estimated by the indicator organisms, with viral pathogens as dominant risk agents, followed by protozoan and bacterial pathogens. The risk evaluated in this study captured the likelihood of gastrointestinal illnesses only, and did not address the overall health risk potential of recreational waters with respect to other disease endpoints. Since other highly infectious pathogens like hepatitis A and Legionella spp. were not included in our analysis, these estimates should be interpreted with caution.

The Environmental Protection Agency of the Campania region in Italy (ARPAC) manages a groundwater quality monitoring network. For almost all the polluted waters, the key parameter driving the classification is the concentration of nitrate; hence, the Campania region, in coherence with the EU regulations, outlined the vulnerable areas and undertook remediation policies. The best groundwater quality is recorded for carbonate aquifers of the Apennine chain; on the contrary, the Tyrrhenian coastal plains are affected by severe contamination, with a locally very contaminated groundwater of the shallow and also the deeper aquifers. The study is especially focused on a large coastal plain of Campania region, where nitrate concentration sometimes exceeds 200 mg/L. The study, based on almost 200 sampling points for the whole region during the period 2003–2015 (approx two samples per year), verified the effectiveness of the groundwater monitoring network, the present distribution of nitrate in groundwater, and the evolution of nitrate trends at different scales: regional, groundwater body, and single well, using spatial and time series statistical approaches. Significant variations in contamination evolution within the study area have been observed and the correlation with land use has been highlighted.

The correct corresponding Author of this paper is Bin Dong.

The adsorption of acid red 97 dye (RED 97) by the waste of the filamentous fungus Beauveria bassiana was analyzed. The adsorbent was obtained as a waste of a fermentative process, and characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT–IR), X–ray powder diffractometry (XRD), and specific surface area (BET). After the characterization, adsorption tests were carried out to determine the ideal conditions of pH, adsorbent mass, and contact time for the process. Adsorption isotherms, thermodynamic studies, and the treatment of textile effluent were also investigated. The adsorbent characterization allowed the visualization of its amorphous structure, with irregular and heterogeneous particles. The pore diameter was 51.9 nm and the surface area was 0.247 m² g⁻¹. 1.2 g L⁻¹ of the adsorbent and pH of 2.0 were the ideal conditions for RED 97 adsorption. The pseudo–second–order kinetic model was the most appropriate to represent the experimental data, being the equilibrium reached in about 110 min. The Langmuir model was the most suitable to represent the equilibrium data, with maximum adsorption capacity of 194.1 mg g⁻¹ at 45 °C. The adsorption processes was thermodynamically spontaneous, favorable, and exothermic. In the treatment of a real textile effluent, 5 g L⁻¹ of the spores was capable to decolorize 70% of the solution. Therefore, spore wastes of Beauveria bassiana were promising for RED 97 adsorption.

Yarrowia lipolytica (Y. lipolytica) is an oleaginous yeast that can utilize hydrophobic substrates as carbon source to produce single-cell lipids for biodiesel production. This study attempts to increase the lipid accumulation ability of Y. lipolytica by first gradually elevating pure oil substrate concentration during the cultivation and then adding short-chain carbon compounds, such as glucose and sodium acetate, to a culture substance according to the optimal oil concentration. Results showed that Y. lipolytica cultured under 40.0 g L⁻¹ oil concentration showed higher lipids (2.97 g L⁻¹) and lipid content (37.35%, DW) compared with that cultured under 20.0 g L⁻¹, where the corresponding values were 1.91 g L⁻¹ and 24.46%. By contrast, the lipid content of Y. lipolytica increased from 37.35 to 41.50% when the substrate was changed from 40.0 g L⁻¹ pure oil to 5% sodium acetate combined with 95% oil under the same total carbon concentration. However, lipid accumulation did not increase even though 15% sodium acetate or 5% glucose, or 15% glucose was added to the combined substrate. Moreover, the lipid biomodification of Y. lipolytica was evident when it was cultured under the oil concentration of 20.0 g L⁻¹. Therefore, the lipid accumulation of Y. lipolytica can be elevated through the gradient increase of oil concentration and by adding a suitable amount of easily degradable carbon source. Furthermore, the lipid biomodification of Y. lipolytica improves biodiesel quality.