Mandating indoor air quality for public buildings.

If some countries lead by example, standards may increasingly become normalized.

Microbial activity contributes to spatial heterogeneity of wetland methane fluxes.

The emission of methane from wetlands is spatially heterogeneous, as concurrently measured surface fluxes can vary by orders of magnitude within the span of a few meters. Despite extensive study and the climatic significance of these emissions, it remains unclear what drives large, within-site variations. While geophysical factors (e.g., soil temperature) are known to correlate with methane (CH4) flux, measurable variance in these parameters often declines as spatial and temporal scales become finer. As methane emitted from wetlands is the direct, net product of microbial metabolisms which both produce and degrade CH4, it stands to reason that characterizing the spatial variability of microbial communities within a wetland-both horizontally and vertically-may help explain observed variances in flux. To that end, we surveyed microbial communities to a depth of 1 m across an ombrotrophic peat bog in Maine, USA using amplicon sequencing and gene expression techniques. Surface methane fluxes and geophysical factors were concurrently measured. Across the first meter of peat at the site, we observed significant changes in the abundance and composition of methanogenic taxa at every depth sampled, with variance in methanogen abundance explaining 70% of flux heterogeneity at a subset of plots. Among measured environmental factors, only peat depth emerged as correlated with flux, and had significant impact on the abundance and composition of methane-cycling communities. These conclusions suggest that a heightened awareness of how microbial communities are structured and spatially distributed within wetlands could offer improved insights into predicting CH4 flux dynamics. IMPORTANCE Globally, wetlands are one of the largest sources of methane (CH4), a greenhouse gas with a warming impact significantly greater than CO2. Methane produced in wetlands is the byproduct of a group of microorganisms which convert organic carbon into CH4. Despite our knowledge of how this process works, it is still unclear what drives dramatic, localized (<10 m) variance in emission rates from the surface of wetlands. While environmental conditions, like soil temperature or water table depth, correlate with methane flux when variance in these factors is large (e.g., spring vs fall), the explanatory power of these variables decline when spatial and temporal scales become smaller. As methane fluxes are the direct product of microbial activity, we profiled how the microbial community varied, both horizontally and vertically, across a peat bog in Maine, USA, finding that variance in microbial communities was likely contributing to much of the observed variance in flux.

Coronavirus Disease 2019 and Airborne Transmission: Science Rejected, Lives Lost. Can Society Do Better?

This is an account that should be heard of an important struggle: the struggle of a large group of experts who came together at the beginning of the COVID-19 pandemic to warn the world about the risk of airborne transmission and the consequences of ignoring it. We alerted the World Health Organization about the potential significance of the airborne transmission of SARS-CoV-2 and the urgent need to control it, but our concerns were dismissed. Here we describe how this happened and the consequences. We hope that by reporting this story we can raise awareness of the importance of interdisciplinary collaboration and the need to be open to new evidence, and to prevent it from happening again. Acknowledgement of an issue, and the emergence of new evidence related to it, is the first necessary step towards finding effective mitigation solutions.

Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques.

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable.

Scaling SARS-CoV-2 wastewater concentrations to population estimates of infection.

Monitoring the progression of SARS-CoV-2 outbreaks requires accurate estimation of the unobservable fraction of the population infected over time in addition to the observed numbers of COVID-19 cases, as the latter present a distorted view of the pandemic due to changes in test frequency and coverage over time. The objective of this report is to describe and illustrate an approach that produces representative estimates of the unobservable cumulative incidence of infection by scaling the daily concentrations of SARS-CoV-2 RNA in wastewater from the consistent population contribution of fecal material to the sewage collection system.

Development and Application of a Polydimethylsiloxane-Based Passive Air Sampler to Assess Personal Exposure to SARS-CoV-2.

Exhaled respiratory droplets and aerosols can carry infectious viruses and are an important mode of transmission for COVID-19. Recent studies have been successful in detecting airborne SARS-CoV-2 RNA in indoor settings using active sampling methods. The cost, size, and maintenance of these samplers, however, limit their long-term monitoring ability in high-risk transmission areas. As an alternative, passive samplers can be small, lightweight, and inexpensive and do not require electrical power or maintenance for continual operation. Integration of passive samplers into wearable designs can be used to better understand personal exposure to the respiratory virus. This study evaluated the use of a polydimethylsiloxane (PDMS)-based passive sampler to assess personal exposure to aerosol and droplet SARS-CoV-2. The rate of uptake of virus-laden aerosol on PDMS was determined in lab-based rotating drum experiments to estimate time-weighted averaged airborne viral concentrations from passive sampler viral loading. The passive sampler was then embedded in a wearable clip design and distributed to community members across Connecticut to surveil personal SARS-CoV-2 exposure. The virus was detected on clips worn by five of the 62 participants (8%) with personal exposure ranging from 4 to 112 copies of SARS-CoV-2 RNA/m3, predominantly in indoor restaurant settings. Our findings demonstrate that PDMS-based passive samplers may serve as a useful exposure assessment tool for airborne viral exposure in real-world high-risk settings and provide avenues for early detection of potential cases and guidance on site-specific infection control protocols that preempt community transmission.

Changes in Sewage Sludge Chemical Signatures During a COVID-19 Community Lockdown, Part 2: Nontargeted Analysis of Sludge and Evaluation with COVID-19 Metrics.

Sewage sludge and wastewater include urine and feces from an entire community, and it is highly likely that this mixture contains chemicals whose presence is dependent on levels of SARS-CoV-2 in the community. We analyzed primary sewage sludge samples collected in New Haven, Connecticut, USA, during the initial wave of the COVID-19 pandemic using liquid chromatography coupled with high-resolution mass spectrometry and performed an exploratory investigation of correlations between chemical features and COVID-19 metrics including concentrations of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) RNA in the sludge and local COVID-19 case numbers and hospital admissions. Inclusion of all chemical features in this analysis is key for discovering potential indicator compounds for COVID-19, whose structures may not be known. We found correlations with COVID-19 metrics for several identified chemicals as well as many unidentified features in the data, including three potential indicator molecules that are recommended for prioritization in future studies on COVID-19 in wastewater and sludge. These features have molecular weights of 108.0935, 318.1214, and 331.1374. While it is not possible to achieve prediction of COVID-19 epidemiological metrics from the one data set used in the present study, advances in this research area are important to share as scientists worldwide work on discovering efficient methods for tracking SARS-CoV-2 in wastewater and the environment. Environ Toxicol Chem 2022;41:1193-1201. © 2021 SETAC.

Changes in Sewage Sludge Chemical Signatures During a COVID-19 Community Lockdown, Part 1: Traffic, Drugs, Mental Health, and Disinfectants.

The early months of the COVID-19 pandemic and the associated shutdowns disrupted many aspects of daily life and thus caused changes in the use and disposal of many types of chemicals. While records of sales, prescriptions, drug overdoses, and so forth provide data about specific chemical uses during this time, wastewater and sewage sludge analysis can provide a more comprehensive overview of chemical changes within a region. We analyzed primary sludge from a wastewater-treatment plant in Connecticut, USA, collected March 19 to June 30, 2020. This time period encompassed the first wave of the pandemic, the initial statewide stay at home order, and the first phase of reopening. We used liquid chromatography-high-resolution mass spectrometry and targeted and suspect screening strategies to identify 78 chemicals of interest, which included pharmaceuticals, illicit drugs, disinfectants, ultraviolet (UV) filters, and others. We analyzed trends over time for the identified chemicals using linear trend analyses and multivariate comparisons (p < 0.05). We found trends related directly to the pandemic (e.g., hydroxychloroquine, a drug publicized for its potential to treat COVID-19, had elevated concentrations in the week following the implementation of the US Emergency Use Authorization), as well as evidence for seasonal changes in chemical use (e.g., increases for three UV-filter compounds). Though wastewater surveillance during the pandemic has largely focused on measuring severe acute respiratory syndrome-coronavirus-2 RNA concentrations, chemical analysis can also show trends that are important for revealing the public and environmental health effects of the pandemic. Environ Toxicol Chem 2022;41:1179-1192. © 2021 SETAC.

SARS-CoV-2 wastewater surveillance data can predict hospitalizations and ICU admissions.

We measured SARS-CoV-2 RNA load in raw wastewater in Attica, Greece, by RT-qPCR for the environmental surveillance of COVID-19 for 6 months. The lag between RNA load and pandemic indicators (COVID-19 hospital and intensive care unit (ICU) admissions) was calculated using a grid search. Our results showed that RNA load in raw wastewater is a leading indicator of positive COVID-19 cases, new hospitalization and admission into ICUs by 5, 8 and 9 days, respectively. Modelling techniques based on distributed/fixed lag modelling, linear regression and artificial neural networks were utilized to build relationships between SARS-CoV-2 RNA load in wastewater and pandemic health indicators. SARS-CoV-2 mutation analysis in wastewater during the third pandemic wave revealed that the alpha-variant was dominant. Our results demonstrate that clinical and environmental surveillance data can be combined to create robust models to study the on-going COVID-19 infection dynamics and provide an early warning for increased hospital admissions.

Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification.

Effectively monitoring the spread of SARS-CoV-2 variants is essential to efforts to counter the ongoing pandemic. Wastewater monitoring of SARS-CoV-2 RNA has proven an effective and efficient technique to approximate COVID-19 case rates in the population. Predicting variant abundances from wastewater, however, is technically challenging. Here we show that by sequencing SARS-CoV-2 RNA in wastewater and applying computational techniques initially used for RNA-Seq quantification, we can estimate the abundance of variants in wastewater samples. We show by sequencing samples from wastewater and clinical isolates in Connecticut U.S.A. between January and April 2021 that the temporal dynamics of variant strains broadly correspond. We further show that this technique can be used with other wastewater sequencing techniques by expanding to samples taken across the United States in a similar timeframe. We find high variability in signal among individual samples, and limited ability to detect the presence of variants with clinical frequencies <10%; nevertheless, the overall trends match what we observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in variant prevalence in situations where clinical sequencing is unavailable or impractical.

How Narrow Is the Gas Phase Mobility Distribution of Enveloped Viruses? The Case of the Φ6 Bacteriophage.

We use the Φ6 bacteriophage previously exploited as a BSL-1 surrogate of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome (MERS) coronavirus to obtain the first high-resolution gas phase mobility spectra of an enveloped virus. The relative full width at half-maximum found for the viral mobility distribution (FWHMZ < 3.7%) is substantially narrower than that reported by prior mobility or microscopy studies with other enveloped viruses. It is nevertheless not as narrow as that recently found for several non-enveloped viruses (FWHMZ ≈ 2%), presumably due to particle to particle variability of enveloped viruses. This 3.7% is an upper bound to the actual width. Nevertheless, the well-defined mobility peaks obtained indicate that gas phase mobility analysis is a more discriminating methodology than that previously demonstrated for physically based non-genetic viral diagnostic of enveloped viruses. These results are obtained by analysis of the original cell culture medium containing the virus, purified only by passage through a 0.22 µm filter and by dialysis into a 10 mM aqueous ammonium acetate buffer. We confirmed that this buffer exchange preserves infectivity. Therefore, the 63.7 nm mobility diameter found, although smaller than the 75 nm previously inferred by microscopy, corresponds to the full particle including the envelope.

Development of CRISPR-Cas9 knock-in tools for free fatty acid production using the fast-growing cyanobacterial strain Synechococcus elongatus UTEX 2973.

Synechococcus elongatus UTEX 2973 has one of the fastest measured doubling time of cyanobacteria making it an important candidate for metabolic engineering. Traditional genetic engineering methods, which rely on homologous recombination, however, are inefficient, labor-intensive, and time-consuming due to the oligoploidy or polyploidy nature of cyanobacteria and the reliance on unique antibiotic resistance markers. CRISPR-Cas9 has emerged as an effective and versatile editing platform in a wide variety of organisms, but its application for cyanobacterial engineering is limited by the inherent toxicity of Cas9 resulting in poor transformation efficiencies. Here, we demonstrated that a single-plasmid CRISPR-Cas9 system, pCRISPOmyces-2, can effectively knock-in a truncated thioesterase gene from Escherichia coli to generate free fatty acid (FFA) producing mutants of Syn2973. To do so, three parameters were evaluated on the effect of generating recipient colonies after conjugation with pCRISPOmyces-2-based plasmids: 1) a modified conjugation protocol termed streaked conjugation, 2) the deletion of the gene encoding RecJ exonuclease, and 3) single guide RNA (sgRNA) sequence. With the use of the streaked conjugation protocol and a ΔrecJ mutant strain of Syn2973, the conjugation efficiency for the pCRISPomyces-2 plasmid could be improved by 750-fold over the wildtype (WT) for a conjugation efficiency of 2.0 × 10-6 transconjugants/recipient cell. While deletion of the RecJ exonuclease alone increased the conjugation efficiency by 150-fold over the WT, FFA generation was impaired in FFA-producing mutants with the ΔrecJ background, and the large number of poor FFA-producing isolates indicated the potential increase in spontaneous mutation rates. The sgRNA sequence was found to be critical in achieving the desired CRISPR-Cas9-mediated knock-in mutation as the sgRNA impacts conjugation efficiency, likelihood of homogenous recombinants, and free fatty acid production in engineered strains.

The climate and health benefits from intensive building energy efficiency improvements.

Intensive building energy efficiency improvements can reduce emissions from energy use, improving outdoor air quality and human health, but may also affect ventilation and indoor air quality. This study examines the effects of highly ambitious, yet feasible, building energy efficiency upgrades in the United States. Our energy efficiency scenarios, derived from the literature, lead to a 6 to 11% reduction in carbon dioxide emissions and 18 to 25% reductions in particulate matter (PM2.5) emissions in 2050. These reductions are complementary with a carbon pricing policy on electricity. However, our results also point to the importance of mitigating indoor PM2.5 emissions, improving PM2.5 filtration, and evaluating ventilation-related policies. Even with no further ventilation improvements, we estimate that intensive energy efficiency scenarios could prevent 1800 to 3600 premature deaths per year across the United States in 2050. With further investments in indoor air quality, this can rise to 2900 to 5100.

Evaluating Indoor Air Chemical Diversity, Indoor-to-Outdoor Emissions, and Surface Reservoirs Using High-Resolution Mass Spectrometry.

Detailed offline speciation of gas- and particle-phase organic compounds was conducted using gas/liquid chromatography with traditional and high-resolution mass spectrometers in a hybrid targeted/nontargeted analysis. Observations were focused on an unoccupied home and were compared to two other indoor sites. Observed gas-phase organic compounds span the volatile to semivolatile range, while functionalized organic aerosols extend from intermediate volatility to ultra-low volatility, including a mix of oxygen, nitrogen, and sulfur-containing species. Total gas-phase abundances of hydrocarbon and oxygenated gas-phase complex mixtures were elevated indoors and strongly correlated in the unoccupied home. While gas-phase concentrations of individual compounds generally decreased slightly with greater ventilation, their elevated ratios relative to controlled emissions of tracer species suggest that the dilution of gas-phase concentrations increases off-gassing from surfaces and other indoor reservoirs, with volatility-dependent responses to dynamically changing environmental factors. Indoor-outdoor emissions of gas-phase intermediate-volatility/semivolatile organic hydrocarbons from the unoccupied home averaged 6-11 mg h-1, doubling with ventilation. While the largest single-compound emissions observed were furfural (61-275 mg h-1) and acetic acid, observations spanned a wide range of individual volatile chemical products (e.g., terpenoids, glycol ethers, phthalates, other oxygenates), highlighting the abundance of long-lived reservoirs resulting from prior indoor use or materials, and their gradual transport outdoors.

Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments.

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.

An accessible method for screening aerosol filtration identifies poor-performing commercial masks and respirators.

BACKGROUND: The COVID-19 pandemic has presented an acute shortage of regulation-tested masks. Many of the alternatives available to hospitals have not been certified, leaving uncertainty about their ability to properly protect healthcare workers from SARS-CoV-2 transmission. OBJECTIVE: For situations where regulatory methods are not accessible, we present experimental methods to evaluate mask filtration and breathability quickly via cost-effective approaches (e.g., ~$2000 USD) that could be replicated in communities of need without extensive infrastructure. We demonstrate the need for screening by evaluating an existing diverse inventory of masks/respirators from a local hospital. METHODS: Two experimental approaches are presented to examine both aerosol filtration and flow impedance (i.e., breathability). For one of the approaches ("quick assessment"), screening for appropriate filtration could be performed under 10 min per mask, on average. Mask fit tests were conducted in tandem but are not the focus of this study. RESULTS: Tests conducted of 47 nonregulation masks reveal variable performance. A number of commercially available masks in hospital inventories perform similarly to N95 masks for aerosol filtration of 0.2 µm and above, but there is a range of masks with relatively lower filtration efficiencies (e.g., <90%) and a subset with poorer filtration (e.g., <70%). All masks functioned acceptably for breathability, and impedance was not correlated with filtration efficiency. SIGNIFICANCE: With simplified tests, organizations with mask/respirator shortages and uncertain inventories can make informed decisions about use and procurement.

Aligning SARS-CoV-2 indicators via an epidemic model: application to hospital admissions and RNA detection in sewage sludge.

Ascertaining the state of coronavirus outbreaks is crucial for public health decision-making. Absent repeated representative viral test samples in the population, public health officials and researchers alike have relied on lagging indicators of infection to make inferences about the direction of the outbreak and attendant policy decisions. Recently researchers have shown that SARS-CoV-2 RNA can be detected in municipal sewage sludge with measured RNA concentrations rising and falling suggestively in the shape of an epidemic curve while providing an earlier signal of infection than hospital admissions data. The present paper presents a SARS-CoV-2 epidemic model to serve as a basis for estimating the incidence of infection, and shows mathematically how modeled transmission dynamics translate into infection indicators by incorporating probability distributions for indicator-specific time lags from infection. Hospital admissions and SARS-CoV-2 RNA in municipal sewage sludge are simultaneously modeled via maximum likelihood scaling to the underlying transmission model. The results demonstrate that both data series plausibly follow from the transmission model specified and provide a 95% confidence interval estimate of the reproductive number R0 ≈ 2.4 ± 0.2. Sensitivity analysis accounting for alternative lag distributions from infection until hospitalization and sludge RNA concentration respectively suggests that the detection of viral RNA in sewage sludge leads hospital admissions by 3 to 5 days on average. The analysis suggests that stay-at-home restrictions plausibly removed 89% of the population from the risk of infection with the remaining 11% exposed to an unmitigated outbreak that infected 9.3% of the total population.

Occurrence of respiratory viruses on school desks.

BACKGROUND: Schools represent high occupancy environments and well-documented high-risk locations for the transmission of respiratory viruses. The goal of this study was to report on the area density, occurrence, and type of respiratory viruses on desks in primary school classrooms. METHODS: Quantitative reverse transcription polymerase chain reaction (qPCR) techniques were employed to measure nucleic acid area densities from a broad range of human adenoviruses and rhinoviruses, as well as coronavirus OC43, influenza A, and norovirus GI. Every two weeks, virus monitoring was conducted on the desks of four primary school classrooms in Colorado, USA, during the 2019 respiratory virus season. RESULTS: DNA and RNA from respiratory viruses and norovirus were recovered from more than 20% of the desks sampled; occurrence patterns that indicate a greater than 60% probability of encountering any virus, if more than five desks were occupied in a day. Rhinoviruses and adenoviruses were the most commonly detected viruses as judged by the composite of occurrence and number of gene copies recovered. Desktop adenosine triphosphate monitoring did not predict the recovery of viral genomic materials on desks. School desks can be commonly contaminated with respiratory viruses. CONCLUSIONS: Genomic surveys of the identity, distribution and abundance of human viruses on "high-touch" surfaces, can help inform risk assessments, design cleaning interventions, and may be useful for infection surveillance.