Oxidative degradation and mineralization of the antifungal drug Nystatin (NYS) was investigated using photochemical advanced oxidation processes UV-C irradiation (280–100 nm), H₂O₂ photolysis (UV/H₂O₂), and photo-Fenton (UV/H₂O₂/Fe³⁺). The effect of operating parameters such as [H₂O₂], [Fe³⁺], and [NYS] initial concentrations on degradation efficiency and mineralization ability of different processes was comparatively examined in order to optimize the processes. Photo-Fenton was found to be the most efficient process attaining complete degradation of 0.02 mM (19.2 mg L⁻¹) NYS at 2 min and a quasi-complete mineralization (97%) of its solution at 5 h treatment while UV/H₂O₂ and UV-C systems require significantly more time for complete degradation and lower mineralization degrees. The degradation and mineralization kinetics were affected by H₂O₂ and Fe³⁺ initial concentration, the optimum dosages being 4 mM and 0.4 mM, respectively. Consumption of H₂O₂ during photo-Fenton treatment is very fast during the first 30 min leading to the appearance of two stages in the mineralization. The evolution of toxicity of treated solutions was assessed and confirmed the effectiveness of photo-Fenton process for the detoxification of NYS solution at the end of treatment. Application to real wastewater from pharmaceutical industry containing the target molecule NYS showed the effectiveness of photo-Fenton process since it achieved 92% TOC removal rate at 6-h treatment time.

Secondary aluminum smelting industry, as an important source of polychlorinated naphthalene (PCN) in environment, has been concerned in recent years. To figure out the emission characteristics of PCNs and the potential influence on surrounding environment, two typical secondary aluminum smelting plants were selected and PCNs were determined in flue gas, fly ash, aluminum slag, soil, and air samples collected at and around the plants by GC-MS coupled with DFS. PCN emission factors from the flue gas of the two plants (mean 0.006 ng toxic equivalents/t) were found obviously decreased compared with similar smelting process detected in 2010. The stage of feeding material was still the major PCNs discharge period during the whole smelting process. The total PCN concentrations in air were found to be ranked as following: workshops (290–1917 pg/m³), the area near the workshops (62.3–697 pg/m³), and the surrounding areas (29.9–164 pg/m³, mean 64.5 pg/m³). Similar high concentrations of PCNs were found in soil from by the workshop door (983 ng/g dry weight). Soil-air exchange calculations indicated that mono-CN to tetra-CN would volatilize but hepta-CN and octa-CN would be deposited to the soil. Exposure of plant workers and local inhabitants to PCNs through inhalation was found to be acceptable but higher (especially for workers in the workshops) than living areas. The workshop and the nearby area are potential PCNs polluted areas and should be paid attention during the practical operation.

Generally, fungi have the ability to secrete large amounts of secondary metabolites which have the ability to reduce metal ions to metallic nanoparticles. In this report, silver nanoparticles (AgNPs) were synthesized by using an endophytic fungus isolated from the medicinal plant, Catharanthus roseus (Linn.). The endophytic fungus was identified as Botryosphaeria rhodina based on the ITS sequencing. The synthesized AgNPs were characterized by adopting various high-throughput techniques, scanning electron microscopy (SEM) equipped with energy dispersive X-ray analysis (EDAX), high-resolution transmission electron microscopy (HR-TEM) and UV–Visible spectrophotometer. In vitro anticancer efficacy of AgNPs was tested on A-549 cells. The synthesized AgNPs were effective in scavenging free radicals and induced hallmarks of apoptosis including nuclear and DNA fragmentation in lung (A549) cancer cell lines under in vitro conditions. The results suggested that the natural biomolecules in the endophytic fungi incorporated into the nanoparticles could be responsible for the synergetic cytotoxic activity against cancer cells. The AgNPs were found to have cytotoxicity IC₅₀ of 40 μg/mL against A549 cells. To the best our knowledge, this is the first report demonstrating that AgNPs from Botryosphaeria rhodina could be able to induce apoptosis in various types of cancer cells as a novel strategy for cancer treatment.

Groundwaters are normally consumed without previous treatment and therefore the monitoring of contaminants in order to guarantee its safety is necessary. Thus, we aimed to evaluate the groundwater contamination by metals and emerging contaminants, seeking to understand the relationship between their presence in the groundwater and the use and land cover profile of Itaporã and Caarapó. In addition, the contaminant concentrations observed were compared with maximum permitted values (MPV) and/or with calculated water quality criteria (WQC) for human consumption to investigate possible human health risks due to the groundwater intake. We collected one groundwater sample from each of the 12 wells located in Itaporã and 11 wells located in Caarapó. The metals were analyzed using ICP-OES and the emerging contaminants using LC-MS/MS. At least 1 of the 9 metals analyzed was found in each of the samples. In 12 samples, the metal concentrations verified exceeded the MPV or calculated WQC. A risk to human health has been observed for metals Co, Mn, Cr, and Ni. The emerging contaminant concentrations found in some samples were low (ng/L) and probably did not pose health risks, but their presence in the groundwater showed the impact of agriculture and the inadequate disposal of domestic sewage in the wells of both cities.

The replacement of fossil-based fuels by renewable fuels (biofuels) was proposed in the IPCC report, as an alternative to reduce greenhouse gas emission and reach out to a low-carbon economy. On this perspective, the Brazilian government had implemented a renewable energy program based on the use of ethanol in the transport sector. This work evaluates the scenario of pollutant gas emissions and particulate material that comes from the biomass burning process involved in ethanol production cycle, in the city of Campos dos Goytacazes, Brazil. The gases and particulate material emitted by sugarcane and bagasse burning processes—the last one in energy co-generation mills—were analyzed. A laboratory-controlled burning of both samples was realized in an oven with temperature ramp from 250 to 400 °C, at a regular rate of 50 °C. The gas samples were collected directly from the oven’s exhaust pipe. The particulates obtained were the residual material taken out of the burned samples: a powder with the aspect of soot. A photoacoustic spectroscopy system coupled with quantum cascade laser and electrochemical analyzers was used to measure the emission of polluting gases such as N₂O, CO₂, CO, NOₓ (NO, NO₂), and SO₂ in ppmv range. Fluorescent X-ray spectrometry was applied to evaluate the chemical composition of particulate material, enabling the identification of elements such as Si, Al, Ca, K, Fe, S, P, Ti, Mn, Cu, Zn, Sc, V, Cu, and Sr.

Anthropogenic activities have a significant contribution to groundwater nitrate contamination at multiple spatial scales in urbanizing agricultural catchments, while how to derive the optimal researching scale and explore the relative importance among anthropogenic activities for groundwater nitrate contamination still remains challenging. In this study, 165 perched groundwater and 120 shallow groundwater samples were collected in two urbanizing agricultural catchments, to explore anthropogenic activity effects on groundwater nitrate contamination crossing multiple spatial scales, integrating the probability kriging, multi-scale comparison at spatial scales of 100 to 1900 m with an increment of 200 m at the block scales, and variance partitioning analysis. Probability of perched and shallow groundwater nitrate concentration > 3 mg L⁻¹ exhibited strong spatial autocorrelation, with effective ranges of 1091 m and 3743 m from semivariogram, respectively. Relationships between perched and shallow groundwater nitrate concentrations were more significant and robust (r = 0.30–0.52, p < 0.001) at the block scale from 300 to 1100 m, indicating that perched groundwater nitrate closely related to shallow groundwater nitrate. The responses of groundwater nitrate contamination on anthropogenic drivers presented strongly scaling correlation and had the highest correlation at the spatial scale of 1100 m, suggesting the optimal scale for exploring anthropogenic activity effects on groundwater nitrate contamination. The three categories of anthropogenic drivers (urbanization, agriculture intensification, and demographic driver) contributed to 31.0–84.0% part of the total variations in groundwater nitrate contamination at the spatial scale of 1100 m. Particularly, agriculture intensification was the most influential driver for groundwater nitrate contamination, while the urbanizing process and population growth played important roles surrounding urban cores. Our findings highlighted the importance of incorporating multi-scale comparisons on regional groundwater quality evaluation, and provided technical support to the groundwater resource management strategy development in urbanizing agricultural regions.

Industry is one of the main activities in the city and in many cities of the world, and the dominant industrial zones are the most significant morphological forms of concentration of industrial facilities in the city and are concentrated industrial and business activity. Industrial parks combine activities related to energy and resource consumption, emissions, waste generation, economic benefits, and regional development. The focus of this work is the path of transformation between the present and the vision of a sustainable city in the future. The problem and the subject of research related to two related objects of research: the city and sustainable development. In this paper, the co-author’s industrial symbiosis parks, modern tendencies of the spatial distribution of productive activities, circular economy, to attract leading corporations and open the way for new ventures while preserving the living environment in an urban area.

The present study aims at evaluating the batch scale potential of cotton shell powder (CSP), Moringa oleifera leaves (ML), and magnetite-assisted composites of Moringa oleifera leaves (MLMC) and cotton shell powder (CSPMC) for the removal of brilliant green dye (BG) from synthetic wastewater. This is the first attempt to combine biosorbents with nanoparticles (NPs) for the removal of BG. The surface properties of ML, CSP, and their composites were characterized with Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy-dispersive X-ray (EDX). The impact of dosage of the adsorbents (1–4 g/L), initial concentrations of BG (20–320 mg/L), pH (6–12), and contact time (15–180 min) on BG removal was evaluated. The BG removal was in order of CSPMC > MLMC > CSP > ML (98.8–86.6% > 98.2–82.0% > 92.3–70.7% > 89.0–57.4%) at optimum dosage (2 g/L) and pH (8). Moreover, maximum adsorption (252.17 mg/g) was obtained with CSPMC. The experimental results showed better fit with Freundlich adsorption isotherm model and kinetic data revealed that sorption followed pseudo-second-order kinetic model. The values of Gibbs free energy and mean free energy of sorption showed that physical adsorption was involved in the removal of BG. FTIR results confirmed that –O-H, –C-OH, =C-H, –C-H, =–CH₃, HC ≡ CH, C=C, –C=O, –C-N, and –C-O-C– groups were involved in the removal of BG. The results revealed that application of low-cost biosorbents combined with NPs is very effective and promising for the removal of textile dyes from wastewater.

In the present work, the sonocatalytic degradation of butylparaben was investigated using Pd immobilized on carbon black as the sonocatalyst. The presence of 25 mg/L 10Pd/C significantly increased the removal rate of butylparaben and the observed kinetic constant increased from 0.0126 to 0.071 min⁻¹, while the synergy index between sonolysis and adsorption was 70.7%. The BP degradation followed pseudo-first-order kinetics with the apparent kinetic constant decreased from 0.071 to 0.030 min⁻¹ when the initial concentration of butylparaben increased from 0.5 to 2 mg/L. The process was being favored slightly under alkaline conditions. The presence of organic matter (20 mg/L humic acid) reduced the apparent kinetic constant more than two times. The addition of chlorides up to 250 mg/L did not significantly reduce the rate of reaction, while the presence of 250 mg/L bicarbonates reduced the observed kinetic constant from 0.071 to 0.0472 min⁻¹. The prepared catalyst retains the efficiency after five subsequent experiments since the apparent kinetic constant was only slightly decreased from 0.071 to 0.059 min⁻¹.

River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC), were implemented to simulate daily river water temperature, using air temperature (Tₐ), river flow discharge (Q), and the components of the Gregorian calendar (CGC) as predictors. The proposed models were tested in various river systems characterized by different hydrological conditions. Results showed that including the three inputs as predictors (Tₐ, Q, and the CGC) yielded the best accuracy among all the developed models. In particular, model performance improved considerably compared to the case where only Tₐ is used as predictor, which is the typical approach of most of previous machine learning applications. Additionally, it was found that Q played a relevant role mainly in snow-fed and regulated rivers with higher-altitude hydropower reservoirs, while it improved to a lower extent model performance in lowland rivers. In the validation phase, the MLPNN model was generally the one providing the highest performances, although in some river stations ANFIS_FC and ANFIS_GP were slightly more accurate. Overall, the results indicated that the machine learning models developed in this study can be effectively used for river water temperature simulation.