Determination of microplastic release from disposable plastic containers in Isfahan

Occupational risks, vulnerabilities, and safety challenges among informal waste workers at the open disposal site in Khulna city

Informal waste workers play vital yet largely unrecognized role in waste management in developing countries by recovering recyclable materials from disposal sites. This study investigates the occupational health and safety risks faced by informal waste workers engaged in waste sorting and collection activities at Rajbandh open dump, the sole disposal site in Khulna, Bangladesh. The vulnerabilities of the informal waste workers stem from inadequate protective equipment, poor sanitation, limited healthcare access, and a lack of training. Through structured questionnaires and observational checklists, data were collected from informal waste workers between May and June 2023, achieving a 100% response rate. Descriptive analysis using SPSS 23.0 revealed that all respondents reported health issues, while none owned safety gear-exacerbating exposure to hazardous waste, airborne pollutants, and unsafe working conditions. Alarmingly, all informal waste collectors reported health symptoms, with 0% ownership of safety gear, significantly contributing to these hazards and underscoring the urgent need for intervention. Rajbandh has a high frequency of occupational injuries as no specific rules and regulations have been established for informal waste workers by the Khulna City Corporation (KCC). Despite the high incidence of occupational injuries, KCC has yet to establish regulations to protect these workers. This study addresses a critical research gap by providing empirical evidence on the vulnerabilities of informal waste workers. The findings emphasize the urgent need for policy interventions, including occupational health programs, provision of safety equipment, and regulatory frameworks to safeguard their well-being.

Enhancing the evaporation rate of 3D solar evaporators by coating their surface with N-doped graphene and MnCoGe alloy compounds

The utilization of solar evaporators to produce fresh water from seawater and from polluted water sources is a promising approach to palliate the global water shortage crisis. In this research, coconut/agave-fibers based 3D-sponges were used as biodegradable support to make solar evaporators. A graphene coating was deposited on the biodegradable sponges (FG evaporator) and was evaluated to desalinate seawater (from Puerto-Vallarta Beach, Mexico) under natural sunlight. This evaporator produced an evaporation-rate/evaporation-efficiency of 1.55 kg m-2·h-1/77.3 %. Next, a second evaporator was fabricated by depositing an extra layer of N-doped graphene (NG) on the graphene layer and this evaporator reached an evaporation-rate/evaporation-efficiency of 2.05 kg m-2·h-1/81.6 %. The evaporation-rate/evaporation-efficiency of the evaporators were enhanced even more (up to 2.32 kg m-2·h-1/89.4 %) after depositing MnCoGe (MCG) alloy particles instead of NG on the evaporators. Thus, the evaporation rate of the evaporator made with MCG was enhanced 32 % with respect to the evaporator made only with the graphene coating. All the evaporators were subjected to 10 consecutive cycles of use and the maximum reduction in the evaporation rate was 6 %. Later, tap water was contaminated with 2,4-dichlorophenoxyacetic acid (2,4-DCP) herbicide (20 ppm). Next, this contaminated water was put in contact with the solar evaporator made with MCG alloy and it was completely decontaminated as confirmed by the UV-Vis spectra for the clean water. In general, adding the MCG alloy on the evaporators (previously coated with graphene), reduced the heat losses and the water enthalpy, which increased the evaporation rate of the water. The results of this investigation indicate that 3D graphene evaporators can be constructed on biodegradable fibers, which diminished the environmental impact of expired evaporators.

Revolutionizing cesium monitoring in seawater through electrochemical voltammetry and machine learning

Monitoring radioactive cesium ions (Cs+) in seawater is vital for environmental safety but remains challenging due to limitations in the accessibility, stability, and selectivity of traditional methods. This study presents an innovative approach that combines electrochemical voltammetry using nickel hexacyanoferrate (NiHCF) thin-film electrode with machine learning (ML) to enable accurate and portable detection of Cs+. Optimizing the fabrication of NiHCF thin-film electrodes enabled the development of a robust sensor that generates cyclic voltammograms (CVs) sensitive to Cs⁺ concentrations as low as 1 ppb in synthetic seawater and 10 ppb in real seawater, with subtle changes in CV patterns caused by trace Cs⁺ effectively identified and analyzed using ML. Using 2D convolutional neural networks (CNNs), we classified Cs+ concentrations across eight logarithmic classes (0 - 106 ppb) with 100 % accuracy and an F1-score of 1 in synthetic seawater datasets, outperforming the 1D CNN and deep neural networks. Validation using real seawater datasets confirmed the applicability of our model, achieving high performance. Moreover, gradient-weighted class activation mapping (Grad-CAM) identified critical CV regions that were overlooked during manual inspection, validating model reliability. This integrated method offers sensitive and practical solutions for monitoring Cs+ in seawater, helping to prevent its accumulation in ecosystems.