The recent spark in the interest for the usage of nano zero valent iron (nZVI) as a remediation tool for contaminated land and groundwater is mainly due to its higher reactivity in comparison to micro ZVI, cost effectiveness, and potential to treat a broad range of contaminants. The authors review the recent developments and approaches made on synthesis on nZVI, strucuture and characterization of nZVI, the challenges faced in the transport of nZVI in the subsurface environment, and the augmentation of the motility of nZVI. The effective use of nZVI in remediating organic pollutants (halogenated organic compounds, pharmaceutical waste, and azo dyes) and inorganic pollutants (Ni²⁺, PO₄³⁻, Co²⁺, Cu²⁺) carried out in recent studies is discussed. The potential risks and limitations of this emerging nanotechnology are also addressed.

Microplastics (MPs) and nanoplastics (NPs) constitute a newly recognized class of contaminants of emerging concern in air, soil, water and food, causing unavoidable exposure to humans and animals. Detection, identification and quantification of MPs and NPs in environmental matrices and biota is challenging due to the analytes' small size, random morphology, polymeric diversity, applied coatings and vast surface areas which attract chemical and microbial sorbates. This mini‐review explores strengths and weaknesses of analytical methods commonly used for MP and NP identification and quantification, including stereomicroscopy, SEM, Fourier transform infrared spectroscopy, Raman spectroscopy, flow cytometry and mass spectrometry techniques including matrix‐assisted laser desorption/ionization time‐of‐flight, pyrolysis gas chromatography–mass spectrometry and liquid chromatography–tandem mass spectrometry. Analytical challenges involved in precise MP/NP detection have been identified and recommendations have been provided to ensure data quality addressing common data quality concerns, including the difficulty of obtaining irrefutable proof that detected polymers originated from the sample as opposed to sample contamination during sample acquisition, sample processing and analysis. © 2021 The Authors. Polymer International published by John Wiley & Sons Ltd on behalf of Society of Industrial Chemistry.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) likely emerged from a zoonotic spill-over event and has led to a global pandemic. The public health response has been predominantly informed by surveillance of symptomatic individuals and contact tracing, with quarantine, and other preventive measures have then been applied to mitigate further spread. Non-traditional methods of surveillance such as genomic epidemiology and wastewater-based epidemiology (WBE) have also been leveraged during this pandemic. Genomic epidemiology uses high-throughput sequencing of SARS-CoV-2 genomes to inform local and international transmission events, as well as the diversity of circulating variants. WBE uses wastewater to analyse community spread, as it is known that SARS-CoV-2 is shed through bodily excretions. Since both symptomatic and asymptomatic individuals contribute to wastewater inputs, we hypothesized that the resultant pooled sample of population-wide excreta can provide a more comprehensive picture of SARS-CoV-2 genomic diversity circulating in a community than clinical testing and sequencing alone. In this study, we analysed 91 wastewater samples from 11 states in the USA, where the majority of samples represent Maricopa County, Arizona (USA). With the objective of assessing the viral diversity at a population scale, we undertook a single-nucleotide variant (SNV) analysis on data from 52 samples with >90% SARS-CoV-2 genome coverage of sequence reads, and compared these SNVs with those detected in genomes sequenced from clinical patients. We identified 7973 SNVs, of which 548 were “novel” SNVs that had not yet been identified in the global clinical-derived data as of 17^th June 2020 (the day after our last wastewater sampling date). However, between 17^th of June 2020 and 20^th November 2020, almost half of the novel SNVs have since been detected in clinical-derived data. Using the combination of SNVs present in each sample, we identified the more probable lineages present in that sample and compared them to lineages observed in North America prior to our sampling dates. The wastewater-derived SARS-CoV-2 sequence data indicates there were more lineages circulating across the sampled communities than represented in the clinical-derived data. Principal coordinate analyses identified patterns in population structure based on genetic variation within the sequenced samples, with clear trends associated with increased diversity likely due to a higher number of infected individuals relative to the sampling dates. We demonstrate that genetic correlation analysis combined with SNVs analysis using wastewater sampling can provide a comprehensive snapshot of the SARS-CoV-2 genetic population structure circulating within a community, which might not be observed if relying solely on clinical cases.