Environmental Science and Pollution Research最新文献

The paper deals with an analysis of the amount of 16 polycyclic aromatic hydrocarbons (PAHs (Polycyclic aromatic hydrocarbons-16 defined by US EPA.)) released from reclaimed asphalt mixtures used in base layers of road surfaces and in binder layers in road construction using cold in-place recycling. For the ten samples tested, the sum of 16 PAHs was determined directly for the crushed asphalt mixture and for its 24-h leachate. The crushed asphalt samples were used to make cylindrical asphalt specimens of the cold recycling mixture. The asphalt specimens were prepared to simulate as realistically as possible the use of the mixtures in road reconstructions implementing the cold in-place recycling technology, which should ensure the passivation of PAHs. The asphalt specimens were subjected to a dynamic leaching test under laboratory conditions with regular replacement of the leaching liquid. The results showed that the concentration of PAHs released into the water environment from the test specimens (passivated material) was approximately 40-50% lower than the amount of PAHs released from the asphalt mixtures in the 24-h leaching test. When compared to the input PAH concentrations in the asphalt mixtures (36-1323 mg.kg^-1), on average, only 0.15% of PAHs is released from the passivated material. Dynamic leaching test has shown that the wrapping of the original asphalt mixture with a new binder and its subsequent compaction leads to the preservation of PAHs in the original material at such a level that their total concentrations are reduced by two orders of magnitude.

Wastewater and livestock waste can be used as a cheap source of nutrients for microalgae growth. In this work, a cocktail waste medium (CWM) was developed using 75% Chhalera municipal wastewater (C-MWW), 25% Parag dairy wastewater (P-DWW), and 15 g L^-1 of poultry litter extract (PLE-15) for low-cost cultivation of Chlorella sp. BRE4. The highest specific growth rate of 0.57 day^-1 and biomass productivity of 315 mg L^-1 day^-1 was found in CWM. Microalgae grown in the photobioreactor with the strategic supply of PLE (PBR-4) resulted in the highest lipid productivity of 113.5 mg L^-1 day^-1, which was 1.3 and 5.4 times of PBR-3 (PLE supplemented since day 1) and PBR-1 (no additional PLE), respectively. The carbohydrate content (30.45%) in PBR-4 showed a 1.33-fold increase than PBR-1, confirming the suitability of the strategy for enhancing carbohydrates and lipids simultaneously. The high removal percentage of total nitrogen (92.6%) and phosphorus (97.4%) from CWM under strategic supply conditions demonstrated Chlorella sp. BRE4 is a suitable candidate for waste valorization and biofuel production.

Biocides, applied in building materials as antimicrobial protectants, can be leached out by rain, presenting substantial environmental risks as confirmed by studies on aquatic environments. However, these biocides are consistently released throughout the year in a diluted form, posing unique challenges for the prediction of transport, transformation, and ecotoxicity assessment in soil. To address this challenge, we combined COMLEAM, which predicts leaching from facades into the soil, with the FOCUS PELMO pesticide model to predict biocide distribution in soil. The study predicted the concentration of the commonly used biocides 1,2-benzisothiazol-3(2H)-one (BIT), 2-n-octyl-4-isothiazolinone-3-one, and terbutryn at various soil depths over 600 days, thereby evaluating the influence of different leaching simulations, absorption, and degradation kinetics on the PELMO simulation results. The findings suggest an effective simulation of biocide behavior in soil, with a tendency to overestimate the risk associated with short-acting in-can preservatives like BIT and provide more precise predictions for long-acting film preservatives. Biocide concentrations showed little variance in the upper layers and increased discrepancies at greater depths. This integrated approach provides a cost-effective means for predicting environmental risks and formulating management strategies, especially when paired with experimentally determined soil-specific degradation and absorption values.

This study focuses on the simulation of a solar photocatalytic reactor with linear parabolic reflectors and continuous fluid flow. The simulation approach was initially validated against experimental data reported by Miranda-Garcia et al. Catal Today 151:107-113 (2010), yielding a high degree of accuracy of approximately 0.99%. In this article, the effect of light intensity, Reynolds number, and fluid residence time on the performance of a photoreactor system using titanium dioxide catalyst and ibuprofen pollutant has been investigated. The results show that the intensity of light intensity has an effect of up to 29% on the decomposition of pollutant. With the increase of radiation intensity, the removal of pollutants reached from 85.5% to 99.46%. It has been demonstrated that higher flow turbulence significantly impacts removal efficiency, achieving rates of up to 71%. Moreover, enhancing the fluid's residence time through implementing a recirculating flow within the photoreactor has resulted in a 13% enhancement in removal efficiency. These results can be an important guide for optimizing the design of photocatalytic reactors. By adjusting the examined parameters, it is possible to obtain a higher efficiency in the removal of pollutants, which will be very effective in the scaling and industrial design of solar reactors.

This study presents a novel, eco-friendly method for removing methyldiethanolamine (MDEA) from wastewater, addressing its environmental impact and elevated chemical oxygen demand (COD) from gas refineries. We employed two wetland plants, Phragmites australis and Typha latifolia, utilizing a hydroponics approach to assess MDEA removal efficiency. Wastewater samples from the Ilam gas refinery in Iran were tested at varying initial concentrations (50 to 1600 ppm) over three consecutive 7-day periods, with a 1-day rest interval. Key factors influencing MDEA absorption-contact time, initial effluent concentration, and pH-were systematically analyzed. Results indicated a significant decrease in pH, COD, and MDEA concentration during the first four days. Kinetic modeling employed pseudo-first-order (PFO) and pseudo-second-order (PSO) models, alongside adsorption isotherms (Langmuir, Freundlich, Temkin, Dobinin-Radeshkovich, and Louis), to evaluate MDEA absorption capacity. The Langmuir isotherm best described the absorption characteristics of both plants. Maximum adsorption capacities were recorded for P. australis at 1.181, 2.018, and 3.77 mg/L across the experimental periods, while T. latifolia showed values of 0.779, 1.099, and 2.99 mg/L. Kinetic analysis favored the pseudo-second-order model for both species. Our findings suggest that P. australis is the more effective candidate for phytoremediation of MDEA in wastewater treatment applications.

The assessment of soil contamination by heavy metals is of high importance due to its impact on the environment and human health. Standard high-sensitivity spectroscopic techniques for this task such as atomic absorption spectrometry (AAS) and inductively coupled plasma spectrometry (ICP-OES and ICP-MS) are effective but time-consuming and costly, mainly due to sample preparation and lab consumables, respectively. In the present study, a laser-based spectroscopic approach is proposed, laser-induced breakdown spectroscopy (LIBS), which, combined with machine learning (ML), can provide a tool for rapid assessment of soil contamination by heavy metals. A dataset comprising 523 soil samples, from the areas of Mati, Kineta, Varympompi, and Evia (Greece) after the wildfires of 2018 and 2021, was employed to train and validate various ML models. The analysis focused on Cr, Ni, Zn, and Pb concentrations, utilizing environmental and human health screening values for soil classification. Two classification schemes were employed: the first identified samples "outside the danger zone" of contamination, while the second focused on samples "inside the safe zone". The models achieved over 93% performance for Cr, Ni, and Zn in the first scheme and 97% for Pb in the second. These findings demonstrate that LIBS, coupled with ML, can provide a reliable and efficient solution for preliminary assessment of soil contamination, particularly suited for large-scale operations of environmental monitoring and remediation efforts.