Airborne transmission of infectious (viable) SARS-CoV-2 is increasingly accepted as the primary manner by which the virus is spread person to person. The risk of inhalation exposure to the virus is high in enclosed and poorly ventilated spaces. We present a study focused on air sampling within residential environments occupied by individuals with COVID-19. Air samplers (BioSpot-VIVAS, VIVAS, and BC-251) were positioned in primary- and secondary-occupancy regions in the homes of seven volunteers. Additionally, surface swab samples were collected from high-touch surfaces. SARS-CoV-2 was detected in 25 of 129 samples (19.4%) by RT-qPCR and isolated from 15 (11.6%) in cell cultures. It was detected in 81.8% (18/22) and cultured from 63.6% (14/22) of samples that had been collected using water condensation samplers. No statistically significant differences existed in the likelihood of virus detection by RT-qPCR or amount of infectious virus in the air between areas of primary and secondary occupancy within residences. Our work provides information about the presence of SARS-CoV-2 in the air within homes of individuals with COVID-19. Information herein builds knowledge about the existence of infectious SARS-CoV-2 in the air. The demonstrated presence of virus beyond primary-occupancy spaces provides health agencies with information to apply to recommendations regarding airborne exposure risks in homes of sick individuals, such as enhancing air exchange rates, operating air purifiers, and using personal respiratory protection devices.

Environmental engineers are increasingly recognizing the critical role that viruses play in understanding biological wastewater treatment, biogeochemical cycling, and more. Analyses of the role of viruses requires accurately differentiating viral sequences from complex metagenomes; a task that remains computationally challenging, as viruses have no universal marker genes, high mutation rates, and reference databases that do not well-represent their diversity. Many researchers combine the output of multiple viral identification tools (“tools”), attempting to exploit the unique strengths of each to distill a set of higher confidence viral sequences. However, this approach has yet to be rigorously benchmarked.

Hypothesizing that a multi-tool approach would discover more viruses without greatly increasing contamination, we evaluated 27 published tools. We benchmarked 63 combinations of 6 tools that met our preliminary requirements using a mock environmental metagenomic dataset composed of publicly available viral, bacterial, archaeal, fungal, plasmid, and protist sequences. We then applied it to different aquatic metagenomes (fresh and saltwater, drinking water, wastewater) to evaluate the impact of habitat on performance.

We found that recall increased with number of tools, while precision remained constant (e.g., 2 vs 6 tools: p_adj ≤10⁷ and p_adj=0.78, respectively). Further, different tool combinations were better suited to long- versus short-read metagenomes. In this talk, I will share our recommendations for rule combinations for environmental engineering-relevant datasets and research questions.

Ultimately, increasing the number and quality of viruses identified from metagenomes will improve the ecological insights possible from studying the viruses of wastewater, drinking water, and other built and natural environments.

SARS-CoV-2 has been reported to survive and spread in the air and on surfaces. Nevertheless, achieving rapid on-site detection of SARS-CoV-2 in real-world environmental settings remains challenging. One of the most widely used diagnostic methods for SARS-CoV-2 is the antigen test kit that relies upon a colorimetric lateral flow test (LFT). However, LFTs are designed for high viral loadings in clinical samples. The limit of detection (LOD) is defined by highly subjective naked-eye observations that are not sufficiently sensitive for detecting low viral loads in environmental samples subject to background interference.

In this work, we aim to enhance the sensitivity of SARS-CoV-2 detection by integrating the production of surface-enhanced Raman spectroscopy (SERS) signal with electrokinetic enrichment of gold nanoparticles (AuNPs) in an LFT. The highly SERS-responsive molecule 4-mercaptobenzoic acid (4-MBA) was modified onto an antibody-functionalized AuNP surface as a Raman reporter. SARS-CoV-2 spike protein in the sample carried antibody- and 4-MBA-functionalized AuNPs from a conjugation pad and flowed across the membrane. An electric field, generated by bipolar micro-electrode arrays, confined charged AuNPs to the test area. The electrokinetic enrichment of AuNPs provides a prolonged reaction time between the spike protein-bounded AuNPs and the angiotensin-converting enzyme 2 receptor on the test line. The developed SERS-based LFT displays a LOD of 3 pmol in a 15-minute assay for SARS-CoV-2 spike protein, even in samples containing environmental dust. The sensitivity is ~100 times better than achieved by colorimetric methods.

Wastewater-based surveillance is a promising tool for early recognition of public health threats. Currently, pathogens in wastewater are typically identified and quantified using target-specific methods such as quantitative polymerase chain reaction (PCR) and digital PCR. Unfortunately, these methods require prior identification of pathogen(s)/target(s) of interest, hindering surveillance of developing threats. Shotgun metagenomic sequencing could prove an attractive alternative, given that the non-targeted approach can capture a variety of pathogens and potentially provide information on emerging variants of concern. To combine the quantitative and qualitative capabilities of each of these approaches, we tested a quantitative metagenomic (qMeta) method to detect, quantify, and genetically characterize pathogens from all domains of life and key strain features, such as antibiotic resistance. Wastewater influent and secondary solids from two treatment plants were spiked with a cocktail of fourteen strains of inactivated bacterial, viral, protozoal, and fungal pathogens and three antibiotic resistance genes at three concentrations. Spiked and unspiked samples were processed using two filtration workflows and a direct extraction from solids workflow. Three extraction kits optimized for DNA, RNA, and total nucleic acids were compared to assess the relative recoveries of each target via digital droplet PCR. qMeta data was benchmarked to ddPCR data to evaluate the accuracy and sensitivity of qMeta as a potential tool for pathogen strain differentiation and quantification in wastewater. The method developed here could prove a powerful means to expand the capacity of wastewater-based surveillance beyond single target workflows.

Antimicrobial resistance (AMR)-related deaths are rising and anticipated to makeup a large portion of mortality in the US and globally within the next several decades. While there have been recent national and international calls to establish environmental monitoring of AMR, there is currently a lack of guidance on suitable targets and protocols. As part of a Water Research Foundation funded project, we conducted an expert survey and workshop to narrow down potential targets and to begin to refine corresponding protocols. Quantitative polymerase chain reaction (qPCR)-based enumeration of the sulfonamide resistance gene, sul1, and the class 1 integron integrase gene, intI1, were identified as ideal candidates for standardization. Specifically, sul1 and intI1 are sensitive to human inputs to the aquatic environment and enumeration via qPCR can provide quantitative data needed to calculate removal rates or to inform watershed modeling. Here we developed qPCR protocols for sul1 and intI1, and compared their application between two labs in the measurement of wastewater, recycled water, and surface water samples collected from six water utilities across the US. We found that both genes displayed logical trends, e.g., highest in raw wastewater and lowest in treated recycled water. We further found that the measurements between labs were not significantly different. However, several aspects of the protocols were identified that can contribute to variance in the measurements, including quality of molecular-grade water used in the analysis, limits of quantification applied in standard curves, and repeated freeze-thaw cycles leading to degradation of DNA.