Solid Waste

Alice, Garbini, et al. Microchemical Journal 206 (2024): 111618.

Perfluoroalkyl substances (PFASs) are a diverse group of fluorinated synthetic compounds characterized by a hydrophobic carbon chain with varying degrees of fluorination and a terminal hydrophilic functional group. Due to their high persistence and mobility, PFASs are widely detected in ecosystems and living organisms, leading to their classification as persistent organic pollutants (POPs). Monitoring PFAS content in solid waste designated for non-hazardous landfills is critical, as leachates can pose significant risks to farmlands and aquifers.

This study developed a QuEChERS and solid-phase extraction (SPE) pretreatment method combined with HPLC-MS/MS detection for the analysis of 10 different PFASs in solid waste samples. The method was validated using six blank and six spiked samples in compliance with UNI EN ISO/IEC 17025 standards. The optimized method achieved satisfactory performance, with recoveries ranging from 89.8% to 106.5%, and limits of detection (LOD) and quantification (LOQ) ranging between 0.0023-0.09 µg/L and 0.006-0.13 µg/L, respectively, for all target PFASs.

Additionally, this approach proved efficient and time-saving, allowing for the simultaneous determination of PFASs in both water and solid waste within a single chromatographic run. This method offers a reliable solution for PFAS monitoring and contributes to pollution risk mitigation in solid waste management.

Qi, Ya-Ping, et al. Waste Management 153 (2022): 20-30.

Accurate and rapid determination of moisture content is crucial for optimizing the recycling, treatment, and disposal of solid waste. Moisture content significantly impacts leachate generation, microbial activity, pollutant leaching, and energy consumption during thermal treatment processes.

This study developed a method combining attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) with machine learning techniques to predict the moisture content of multi-source solid waste, including textiles, paper, leather, and wood waste. A combined regression model was proposed for moisture content prediction, with evaluations of 20 combinations of five spectral preprocessing methods and four regression algorithms to enhance modeling accuracy.

The performance of models based on water-band spectra was compared with those using full-band spectra. The optimized combined model demonstrated excellent predictive capabilities, achieving R² values of 0.9604 and 0.9660 for validation and test datasets, respectively, along with a root mean square error of 3.80 after hyperparameter optimization.

The study highlights the ability of the proposed models to rapidly and accurately measure moisture content, making them valuable for improving waste characterization frameworks and enabling real-time monitoring and management in solid waste treatment and disposal processes.

Mendes, Leila D., et al. Journal of Environmental Chemical Engineering 6.4 (2018): 5042-5048.

This study presents a simple, efficient, solvent-free method for quantifying total polychlorinated biphenyls (PCBs) in solid waste matrices using gas chromatography-mass spectrometry (GC-MS). Solid waste samples spiked with Aroclor 1242 and Aroclor 1254 were used to validate the approach.

The method involves loading the solid waste sample into the cavity of a rotating disk device, with water serving as the leaching solvent to facilitate analyte desorption. Simultaneously, a solid-phase microextraction (SPME) fiber is directly immersed in the water to extract the analytes. To optimize extraction efficiency, various parameters—including pre-equilibrium conditions, extraction time, temperature, ionic strength, and the addition of an organic solvent—were carefully evaluated.

The method demonstrated suitable limits of detection (LOD) and quantification (LOQ) for total PCBs in solid waste. Accuracy and precision assessments showed satisfactory results, with relative recoveries ranging from 80.2% to 96.0% across three spiked levels and relative standard deviations between 2.5% and 15.5%.

These findings highlight the potential of this method as a reliable and effective alternative for determining PCBs in solid waste, aiding in the selection of appropriate waste disposal strategies.