Prediction of attachment efficiency using machine learning on a comprehensive database and its validation

The suspension stability of nanoplastics in aquatic environments revealed using meta-analysis and machine learning

Nanoplastics (NPs) aggregation determines their bioavailability and risks in natural aquatic environments, which is driven by multiple environmental and polymer factors. The back propagation artificial neural network (BP-ANN) model in machine learning (R2 = 0.814) can fit the complex NPs aggregation, and the feature importance was in the order of surface charge of NPs > dissolved organic matter (DOM) > functional group of NPs > ionic strength and pH > concentration of NPs. Meta-analysis results specified low surface charge (0 ≤ |ζ| < 10 mV) of NPs, low concentration (< 1 mg/L) and low molecular weight (< 10 kg/mol) of DOM, NPs with amino groups, high ionic strength (IS > 700 mM) and acidic solution, and high concentration (≥ 20 mg/L) of NPs with smaller size (< 100 nm) contribute to NPs aggregation, which is consistent with the prediction in machine learning. Feature interaction synergistically (e.g., DOM and pH) or antagonistically (e.g., DOM and cation potential) changed NPs aggregation. Therefore, NPs were predicted to aggregate in the dry period and estuary of Poyang Lake. Research on aggregation of NPs with different particle size,shapes, and functional groups, heteroaggregation of NPs with coexisting particles and aging effects should be strengthened in the future. This study supports better assessments of the NPs fate and risks in environments.

Attachment of various-shaped polystyrene microplastics to silica surfaces: Experimental validation of the equivalent Cassini oval extended DLVO model

Microplastics (MPs) vary in shape and surface characteristics in the environment. The attachment of MPs to surfaces can be studied using the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. However, this theory does not account for the shape MPs. Therefore, we investigated the attachment of spherical, pear-shaped, and peanut-shaped polystyrene MPs to quartz sand in NaCl and CaCl2 solutions using batch tests. The attachment of MPs to quartz sand was quantified using the attachment efficiency (alpha). Subsequently, alpha behaviors were interpreted using energy barriers (EBs) and interaction minima obtained from extended DLVO calculations, which were performed using an equivalent sphere model (ESM) and a newly developed equivalent Cassini model (ECM) to account for the shape of the MPs. The ESM failed to interpret the alpha behavior of the three MP shapes because it predicted high EBs and shallow minima. The alpha values for spherical MPs (0.62-1.00 in NaCl and 0.48-0.96 in CaCl2) were higher than those for pear- and peanut-shaped MPs (0.01-0.63 in NaCl and 0.02-0.46 in CaCl2, and 0.01-0.59 in NaCl and 0.02-0.40 in CaCl2, respectively). Conversely, the ECM could interpret the alpha behavior of pear- and peanut-shaped MPs either by changes in EBs or interaction minima as a function of orientation angles and electrolyte ionic strength. Therefore, the particle shape must be considered to improve the attachment analyses.

Ecotoxicological assessment of cigarette butts on morphology and photosynthetic potential of Azolla pinnata

Cigarette butts (CBs) have become the most ubiquitous form of anthropogenic litter globally. CBs contain various hazardous chemicals that persist in the environment for longer period. These substances are susceptible to leaching into the environment through waterways. The recent study was aimed to evaluate the effects of disposed CBs on the growth and development of Azolla pinnata, an aquatic plant. It was found that after a span of 6 days, the root length, surface area, number of fronds, and photosynthetic efficacy of plant were considerably diminished on the exposure of CBs (concentrations 0 to 40). The exposure of CBs led to a decrease in the FM, FV/F0, and φP0, in contrast, the φD0 increased in response to CBs concentration. Moreover, ABS/CSm, TR0/CSm, and ET0/CSm displayed a negative correlation with CB-induced chemical stress. The performance indices were also decreased (p-value ≤ 0.05) at the highest concentration of CBs. LD50 and LD90 represent the lethal dose, obtained value for LD50 is 20.30 CBs and LD90 is 35.26 CBs through probit analysis. Our results demonstrate that the CBs cause irreversible damage of photosynthetic machinery in plants and also reflect the efficacy of chlorophyll a fluorescence analysis and JIP test for assessing the toxicity of CBs in plants.

Extracellular Vesicles and Bacteriophages: New Directions in Environmental Biocolloid Research

There is a long-standing appreciation among environmental engineers and scientists regarding the importance of biologically derived colloidal particles and their environmental fate. This interest has been recently renewed in considering bacteriophages and extracellular vesicles, which are each poised to offer engineers unique insights into fundamental aspects of environmental microbiology and novel approaches for engineering applications, including advances in wastewater treatment and sustainable agricultural practices. Challenges persist due to our limited understanding of interactions between these nanoscale particles with unique surface properties and their local environments. This review considers these biological particles through the lens of colloid science with attention given to their environmental impact and surface properties. We discuss methods developed for the study of inert (nonbiological) particle-particle interactions and the potential to use these to advance our understanding of the environmental fate and transport of extracellular vesicles and bacteriophages.

Statistical analysis, machine learning modeling, and text analytics of aggregation attachment efficiency: Mono and binary particle systems

The aggregation attachment efficiency (α) is the fraction of particle-particle collisions resulting in aggregation. Despite significant research, α predictions have not accounted for the full complexity of systems due to constraints imposed by particle types, dispersed matter, water chemistry, quantification methods, and modeling. Experimental α values are often case-specific, and simplified systems are used to rule out complexity. To address these challenges, statistical analysis was performed on α databases to identify gaps in current knowledge, and machine learning (ML) was used to predict α under various particle types and conditions. Moreover, text analytics was employed to support knowledge from statistics and ML, as well as gain insight into the ideas communicated by current literature. Most studies investigated α in mono-particle systems, but binary or higher systems require more investigation. Furthermore, our work highlights that numerous variables, interactions, and mechanisms influence α behavior, making its investigation complex and difficult for both experiments and modeling. Consequently, future research should incorporate more particle types, shapes, coatings, and surface heterogeneities, and aim to address overlooked variables and conditions. Therefore, building a comprehensive α database can enable the development of more accurate empirical models for prediction.