Effect of incineration ash leachates on the hydraulic conductivity of bentonite-polymer composite geosynthetic clay liners

Leaching of per- and polyfluoroalkyl substances (PFAS) from food contact materials with implications for waste disposal

Leaching of per- and polyfluoroalkyl substances (PFAS) during the post-consumer disposal of food contact materials (FCMs) poses a potential environmental threat but has seldom been evaluated. This study characterized the leaching behavior of PFAS and unidentified precursors from six common FCMs and assessed the impact of environmental conditions on PFAS release during disposal. The total concentration of 21 PFAS ranged from 3.2 to 377 ng/g in FCMs, with PFAS leachability into water varying between 1.1-42.8 %. Increasing temperature promoted PFAS leaching, with leached nine primary PFAS (∑9PFAS) reaching 46.3, 70.4, and 102 ng/L at 35, 45, and 55 ℃, respectively. Thermodynamic analysis (∆G>0, ∆H>0, and ∆S<0) indicated hydrophobic interactions control PFAS leaching. The presence of dissolved organic matter in synthetic leachate increased the leached ∑9PFAS from 47.1 to 103 ng/L but decreased PFBS, PFOS, and 6:2 FTS leaching. The total release of seven perfluorocarboxylic acids (∑7PFCAs) from takeaway food packaging waste was estimated to be 0.3-8.2 kg/y to landfill leachate and 0.6-15.4 kg/y to incineration plant leachate, contributing 0.2-4.8 % and 0.1-3.2 % of total ∑7PFCAs in each leachate type. While the study presents a refined methodology for estimating PFAS release during disposal, future research is needed on the indirect contribution from precursors.

Hydraulic conductivity of geosynthetic clay liners to trona ash leachate: Effects of mass per unit area, bundles of fiber existence, and preydration conditions

The barrier performance of geosynthetic clay liners (GCLs) to coal combustion products (CCPs) is of primary importance. One of the CCPs leachates that has a damaging effect on hydraulic conductivity is trona ash leachate (TAL). In this study, the hydraulic conductivity of sodium GCL (Na-GCL) to TAL was investigated in terms of mass per unit area (MPUA). The hydraulic conductivity of GCLs to TAL was 2.6 × 10-6 and 7.6 × 10-7 m/s when the MPUA was 3.0 kg/m2 (Mb3) and 4.0 kg/m2 (Mb4), respectively. Dye tests conducted on these GCLs showed that flow preferentially occurred through bundles of fibers existing in the GCLs. In contrast, increasing the MPUA to 5.0 kg/m2 (Mb5) led to a decrease in the hydraulic conductivity (i.e. 4.1 × 10-11 m/s). Additional tests were performed on fiber-free GCLs to determine the role of fiber bundles. Regardless of MPUA, the fiber-free GCLs had low hydraulic conductivity (6.7 × 10-11 m/s). Prehydrating Mb3 and Mb4 with deionized water (DIW) before permeation with TAL also decreased the hydraulic conductivity. The hydraulic conductivities of prehydrated Mb3 and Mb4 were 1.6 × 10-10 and 4.8 × 10-11, respectively. Chemical analyses showed that the cation exchange reaction had a negligible influence on the hydraulic conductivity. Because TAL was a potential source of Na+ throughout the tests.

Predicting the hydraulic conductivity of compacted soil barriers in landfills using machine learning techniques

Machine learning models (MLMs) were developed to predict saturated hydraulic conductivity of compacted soil barriers and help to identify appropriate soils for the construction of landfill liners and covers. Data from hydraulic conductivity tests on compacted soil barriers were collected from the literature and compiled into a database for MLM construction. The database contains 329 records of hydraulic conductivity tests associated with 12 selected impact factors covering physical properties, compaction efforts, and hydration and mineralogy behaviors of compacted soil barriers. Three machine learning algorithms (random forest, gradient boosting decision tree, and neural network) were used to develop MLMs, and a statistical technique (multiple linear regression) was used to compare the precision of predictions with the MLMs. Results from this study showed that the random forest model provided the best prediction of the hydraulic conductivity of compacted soil barriers, with 100% of predicted hydraulic conductivity within 100-time differences to measured hydraulic conductivity and 93% within 10-time differences. Feature importance analysis showed that void ratio after compaction, fines content, specific gravity, degree of saturation after compaction, and plasticity index of soils are the top-five factors (in descending order) that influence the hydraulic conductivity of compacted soil barriers and are recommended for a precise prediction. Three predictive MLMs were created for industries as simple tools to screen the soils prior to the construction of compacted soil barriers in landfill liners and covers.