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.

COVID-19 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 (WHO) 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.

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.

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.

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.

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.

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.

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 (FWHM_Z < 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 (FWHM_Z ≈ 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.

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.

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.

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.

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.

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.

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.

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.

Practitioner-driven research for improving the outcomes of mold inspection and remediation

This commentary is intended to provide a research roadmap for utilizing recent chemical and molecular-biological technological advances for addressing dampness and mold in buildings. The perspective is unique in that both the mold industry practitioners and academic researchers drive the questions. Research needs were derived from a 2018 international workshop attended by practitioners, researchers and governmental representatives, where challenges and opportunities in the mold remediation and restoration field were discussed focusing on the need to develop new tools that improve building diagnosis and clearance certification for mold inspectors and remediators. Suggestions are made on how new technologies surrounding DNA-based sequence analysis for fungal and bacterial identification and real-time chemical sensor technology can be leveraged by practitioners to improve inspection and remediation. The workshop put into effect a logical progression to distill and extract practice-based implications and encourage the process of transfer of the science to practice. Goals for the workshop, and this subsequent paper, are also centered on encouraging US government-funding agencies to better position and define research on the built environment geared for end-user scientists and practitioners to better explore practical solutions to dampness and mold in indoor environments. By facilitating the workshop forum and targeting industry, field practitioners, and government agencies, a sharing of needed commonalities may be infused into future research agendas and outreach efforts.

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.

DNA Sequence-Based Approach for Classifying the Mold Status of Buildings

Dampness or water damage in buildings and human exposure to the resultant mold growth is an ever-present public health concern. This study provides quantitative evidence that the airborne fungal ecology of homes with known mold growth ("moldy") differs from the normal airborne fungal ecology of homes with no history of dampness, water damage, or visible mold ("no mold"). Settled dust from indoor air and outdoor air and direct samples from building materials with mold growth were examined in homes from 11 cities across dry, temperate, and continental climate regions within the United States. Community analysis based on the sequence of the internal transcribed spacer region of fungal ribosomal RNA encoding genes demonstrated consistent and quantifiable differences between the fungal ecology of settled dust in homes with inspector-verified water damage and visible mold versus the settled dust of homes with no history of dampness, water damage, or visible mold. These differences include lower community richness (p_adj = 0.01) in the settled dust of moldy homes versus no mold homes, as well as distinct community taxonomic structures between moldy and no mold homes (ANOSIM, R = 0.15, p = 0.001). We identified 11 Ascomycota taxa that were more highly enriched in moldy homes and 14 taxa from Ascomycota, Basidiomycota, and Zygomycota that were more highly enriched in no mold homes. The indoor air differences between moldy versus no mold homes were significant for all three climate regions considered. These distinct but complex differences between settled dust samples from moldy and no homes were used to train a machine learning-based model to classify the mold status of a home. The model was able to accurately classify 100% of moldy homes and 90% of no mold homes. The integration of DNA-based fungal ecology with advanced computational approaches can be used to accurately classify the presence of mold growth in homes, assist with inspection and remediation decisions, and potentially lead to reduced exposure to hazardous microbes indoors.

Measurement of SARS-CoV-2 RNA in wastewater tracks community infection dynamics

We measured severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in primary sewage sludge in the New Haven, Connecticut, USA, metropolitan area during the Coronavirus Disease 2019 (COVID-19) outbreak in Spring 2020. SARS-CoV-2 RNA was detected throughout the more than 10-week study and, when adjusted for time lags, tracked the rise and fall of cases seen in SARS-CoV-2 clinical test results and local COVID-19 hospital admissions. Relative to these indicators, SARS-CoV-2 RNA concentrations in sludge were 0-2 d ahead of SARS-CoV-2 positive test results by date of specimen collection, 0-2 d ahead of the percentage of positive tests by date of specimen collection, 1-4 d ahead of local hospital admissions and 6-8 d ahead of SARS-CoV-2 positive test results by reporting date. Our data show the utility of viral RNA monitoring in municipal wastewater for SARS-CoV-2 infection surveillance at a population-wide level. In communities facing a delay between specimen collection and the reporting of test results, immediate wastewater results can provide considerable advance notice of infection dynamics.

Measurement of SARS-CoV-2 RNA in wastewater tracks community infection dynamics

We measured severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in primary sewage sludge in the New Haven, Connecticut, USA, metropolitan area during the Coronavirus Disease 2019 (COVID-19) outbreak in Spring 2020. SARS-CoV-2 RNA was detected throughout the more than 10-week study and, when adjusted for time lags, tracked the rise and fall of cases seen in SARS-CoV-2 clinical test results and local COVID-19 hospital admissions. Relative to these indicators, SARS-CoV-2 RNA concentrations in sludge were 0-2 d ahead of SARS-CoV-2 positive test results by date of specimen collection, 0-2 d ahead of the percentage of positive tests by date of specimen collection, 1-4 d ahead of local hospital admissions and 6-8 d ahead of SARS-CoV-2 positive test results by reporting date. Our data show the utility of viral RNA monitoring in municipal wastewater for SARS-CoV-2 infection surveillance at a population-wide level. In communities facing a delay between specimen collection and the reporting of test results, immediate wastewater results can provide considerable advance notice of infection dynamics.

Measurement of SARS-CoV-2 RNA in wastewater tracks community infection dynamics

We measured severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in primary sewage sludge in the New Haven, Connecticut, USA, metropolitan area during the Coronavirus Disease 2019 (COVID-19) outbreak in Spring 2020. SARS-CoV-2 RNA was detected throughout the more than 10-week study and, when adjusted for time lags, tracked the rise and fall of cases seen in SARS-CoV-2 clinical test results and local COVID-19 hospital admissions. Relative to these indicators, SARS-CoV-2 RNA concentrations in sludge were 0-2 d ahead of SARS-CoV-2 positive test results by date of specimen collection, 0-2 d ahead of the percentage of positive tests by date of specimen collection, 1-4 d ahead of local hospital admissions and 6-8 d ahead of SARS-CoV-2 positive test results by reporting date. Our data show the utility of viral RNA monitoring in municipal wastewater for SARS-CoV-2 infection surveillance at a population-wide level. In communities facing a delay between specimen collection and the reporting of test results, immediate wastewater results can provide considerable advance notice of infection dynamics.

How can airborne transmission of COVID-19 indoors be minimised?

During the rapid rise in COVID-19 illnesses and deaths globally, and notwithstanding recommended precautions, questions are voiced about routes of transmission for this pandemic disease. Inhaling small airborne droplets is probable as a third route of infection, in addition to more widely recognized transmission via larger respiratory droplets and direct contact with infected people or contaminated surfaces. While uncertainties remain regarding the relative contributions of the different transmission pathways, we argue that existing evidence is sufficiently strong to warrant engineering controls targeting airborne transmission as part of an overall strategy to limit infection risk indoors. Appropriate building engineering controls include sufficient and effective ventilation, possibly enhanced by particle filtration and air disinfection, avoiding air recirculation and avoiding overcrowding. Often, such measures can be easily implemented and without much cost, but if only they are recognised as significant in contributing to infection control goals. We believe that the use of engineering controls in public buildings, including hospitals, shops, offices, schools, kindergartens, libraries, restaurants, cruise ships, elevators, conference rooms or public transport, in parallel with effective application of other controls (including isolation and quarantine, social distancing and hand hygiene), would be an additional important measure globally to reduce the likelihood of transmission and thereby protect healthcare workers, patients and the general public.

COVID-19 vulnerability: the potential impact of genetic susceptibility and airborne transmission

The recent coronavirus disease (COVID-19), caused by SARS-CoV-2, is inarguably the most challenging coronavirus outbreak relative to the previous outbreaks involving SARS-CoV and MERS-CoV. With the number of COVID-19 cases now exceeding 2 million worldwide, it is apparent that (i) transmission of SARS-CoV-2 is very high and (ii) there are large variations in disease severity, one component of which may be genetic variability in the response to the virus. Controlling current rates of infection and combating future waves require a better understanding of the routes of exposure to SARS-CoV-2 and the underlying genomic susceptibility to this disease. In this mini-review, we highlight possible genetic determinants of COVID-19 and the contribution of aerosol exposure as a potentially important transmission route of SARS-CoV-2.

Bacterial and fungal ecology on air conditioning cooling coils is influenced by climate and building factors

The presence of biofilms on the cooling coils of commercial air conditioning (AC) units can significantly reduce the heat transfer efficiency of the coils and may lead to the aerosolization of microbes into occupied spaces of a building. We investigated how climate and AC operation influence the ecology of microbial communities on AC coils. Forty large-scale commercial ACs were considered with representation from warm-humid and hot-dry climates. Both bacterial and fungal ecologies, including richness and taxa, on the cooling coil surfaces were significantly impacted by outdoor climate, through differences in dew point that result in increased moisture (condensate) on coils, and by the minimum efficiency reporting value (MERV 8 vs MERV 14) of building air filters. Based on targeted qPCR and sequence analysis, low efficiency upstream filters (MERV 8) were associated with a greater abundance of pathogenic bacteria and medically relevant fungi. As the implementation of air conditioning continues to grow worldwide, better understanding of the factors impacting microbial growth and ecology on cooling coils should enable more rational approaches for biofilm control and ultimately result in reduced energy consumption and healthier buildings.

1,4-Dioxane as an emerging water contaminant: State of the science and evaluation of research needs

1,4-Dioxane has historically been used to stabilize chlorinated solvents and more recently has been found as a contaminant of numerous consumer and food products. Once discharged into the environment, its physical and chemical characteristics facilitate migration in groundwater, resulting in widespread contamination of drinking water supplies. Over one-fifth of U.S. public drinking water supplies contain detectable levels of 1,4-dioxane. Remediation efforts using common adsorption and membrane filtration techniques have been ineffective, highlighting the need for alternative removal approaches. While the data evaluating human exposure and health effects are limited, animal studies have shown chronic exposure to cause carcinogenic responses in the liver across multiple species and routes of exposure. Based on this experimental evidence, the U.S. Environmental Protection Agency has listed 1,4-dioxane as a high priority chemical and classified it as a probable human carcinogen. Despite these health concerns, there are no federal or state maximum contaminant levels for 1,4-dioxane. Effective public health policy for this emerging contaminant requires additional information about human health effects, chemical interactions, environmental fate, analytical detection, and treatment technologies. This review highlights the current state of knowledge, key uncertainties, and data needs for future research on 1,4-dioxane.

Degradation of phthalate esters in floor dust at elevated relative humidity

Emerging investigator series: Phthalate esters are present at elevated concentrations in floor dust, and resuspension of dust represents a major source for human exposure to chemicals. Biodegradation of phthalates occurs in aquatic systems and soils but has not been demonstrated in house dust. The goal of this study was to quantify indoor phthalate ester degradation through both biotic and abiotic mechanisms. Worn carpet squares were embedded with dust and incubated for one to six weeks at equilibrium relative humidity (ERH) levels of 50, 80, 85, 90, 95, and 100%, and nine phthalates were measured. Removal was observed for DEHP, BBzP, DINP, DiDP, and DMP (p < 0.05) when incubated under elevated relative humidity conditions. Abiotic and biotic losses were examined separately using dust spiked with deuterated di(2-ethylhexyl)phthalate (d-DEHP) that was embedded in carpet and incubated at 100% ERH. Abiotic processes resulted in a 10.1% (±1.1%, standard error) to 69.6% (±4.8%) decrease in total d-DEHP after one week (p = 0.03) and a 27.2% (±1.4%) to 52.0% (±2.1%) decrease after three weeks (p = 0.008). Biodegradation resulted in a decrease in total d-DEHP after one week, ranging from 5.9% (±8.9%) to 8.5% (±1.7%) (p = 0.07) and a 1.7% (±3.9%) to 10.3% (±4.5%) decrease after three weeks (p = 0.044). Metatranscriptomic-based analysis indicates that fungi found in carpet dust express genes capable of degrading phthalate esters via various biochemical processes (including β-oxidation and hydrolysis). Overall, these results support the hypothesis that phthalate losses in floor dust are due to a combination of abiotic and microbial degradation at ≥80% ERH.

Comparing bacterial, fungal, and human cell concentrations with rapid adenosine triphosphate measurements for indicating microbial surface contamination

BACKGROUND

The goal of this study was to test for associations between adenosine triphosphate (ATP) content and microbial concentrations on desk surfaces in school classrooms.

METHODS

ATP bioluminescence and quantitative polymerase chain reaction (qPCR) techniques were employed to measure total bacterial, fungal, and human cell concentrations on 66 high-traffic desks spread across 9 schools: 3 in Connecticut (CT) and 6 in Oklahoma (OK). In CT, 6 samples were taken from each desk, 1 precleaning and 5 postcleaning (after 30 minutes, 1 day, 3 days, 7 days, and 21 days). In OK, samples were taken immediately before and after cleaning each desk.

RESULTS

Based on simple linear regression analyses, ATP values were good predictors of microbial concentrations (r = 0.8, P = .003) in both CT school postcleaning samples and OK pre- and postcleaning samples (r = 0.7, P = .00002). However, biomass reductions measured after cleaning were 1.5-2 times greater when measured by ATP than by qPCR (P = .007).

CONCLUSIONS

Overall, ATP bioluminescence measurements correlate with qPCR-based surface measurements on school desks but may overestimate the physical removal of bacteria and fungi due to cleaning.

Selectively biorefining astaxanthin and triacylglycerol co-products from microalgae with supercritical carbon dioxide extraction

Clustering behavior of supercritical carbon dioxide, triacylglycerides, and astaxanthin solutes were analyzed using Kamlet-Taft solvatochromic properties of dipolarity/polarizability, π^∗, and hydrogen-bond acceptance, β. Both parameters were decreased for supercritical carbon dioxide with TAG at low densities and with astaxanthin at high densities. These results indicated supercritical carbon dioxide could selectively extract triacylglycerides at low densities followed by astaxanthin at higher densities from microalgae. Accordingly, Haematococcus pluvialis microalgae were subject to a two-stage continuous extraction scheme where a density of 642 mg/ml was employed to extract triacylglycerides followed by a density of 971 mg/ml, by an increase in pressure, to extract astaxanthin. The first, lower density extract yielded over 78% of the total triacylglycerides and was composed of less than 1% astaxanthin. The sequential, higher density extract yielded less than 5% of the total microalgae triacylglycerides, over 70% of the total astaxanthin, and was composed of 60-76% astaxanthin by mass.

Global Survey of Antibiotic Resistance Genes in Air

Despite its emerging significant public health concern, the presence of antibiotic resistance genes (ARGs) in urban air has not received significant attention. Here, we profiled relative abundances (as a fraction, normalized by 16S rRNA gene) of 30 ARG subtypes resistant to seven common classes of antibiotics, which are quinolones, β-lactams, macrolides, tetracyclines, sulfonamides, aminoglycosides, and vancomycins, in ambient total particulate matter (PM) using a novel protocol across 19 world cities. In addition, their longitudinal changes in PM_2.5 samples in Xi'an, China as an example were also studied. Geographically, the ARGs were detected to vary by nearly 100-fold in their abundances, for example, from 0.07 (Bandung, Indonesia) to 5.6 (San Francisco, USA). The β-lactam resistance gene blaTEM was found to be most abundant, seconded by quinolone resistance gene qepA; and their corresponding relative abundances have increased by 178% and 26%, respectively, from 2004 to 2014 in Xi'an. Independent of cities, gene network analysis indicates that airborne ARGs were differentially contributed by bacterial taxa. Results here reveal that urban air is being polluted by ARGs, and different cities are challenged with varying health risks associated with airborne ARG exposure. This work highlights the threat of urban airborne transmission of ARGs and the need of redefining our current air quality standards in terms with public health.

Gene expression of indoor fungal communities under damp building conditions: Implications for human health

Dampness and visible mold growth in homes are associated with negative human health outcomes, but causal relationships between fungal exposure and health are not well established. The purpose of this study was to determine whether dampness in buildings impacts fungal community gene expression and how, in turn, gene expression may modulate human health impacts. A metatranscriptomic study was performed on house dust fungal communities to investigate the expression of genes and metabolic processes in chamber experiments at water activity levels of 0.5, 0.85, and 1.0. Fungi at water activities as low as 0.5 were metabolically active, focusing their transcriptional resources on primary processes essential for cell maintenance. Metabolic complexity increased with water activity where communities at 1.0 displayed more diverse secondary metabolic processes. Greater gene expression at increasing water activity has important implications for human health: Fungal communities at 1.0 a_w upregulated a greater number of allergen-, mycotoxin-, and pathogenicity-encoding genes versus communities at 0.85 and 0.5 a_w . In damp buildings, fungi may display increases in secondary metabolic processes with the potential for greater per-cell production of allergens, toxins, and pathogenicity. Assessments in wet versus dry buildings that do not account for this elevated health impact may not accurately reflect exposure.

Building and environmental factors that influence bacterial and fungal loading on air conditioning cooling coils

We investigated bacterial and fungal concentrations on cooling coils of commercial AC units and quantified associations between microbial loads and AC unit or building operational parameters. A field campaign was conducted to sample 25 AC units in the humid, subtropical climate of Southern CT, USA and 15 AC units in the hot-summer Mediterranean climate of Sacramento, CA, USA. Median concentrations (with interquartile range) of bacteria and fungi on the cooling coils were 1.2 × 107 (5.1 × 106 -3.9 × 107 ) cells/m2 and 7.6 × 105 (5.6 × 104 -4.4 × 106 ) spore equivalents (SE)/m2 , respectively. Concentrations varied among units with median unit concentrations ranging three orders of magnitude for bacteria and seven orders of magnitude for fungi. Controlled comparisons and multivariable regressions indicate that dominant factors associated with AC coil loading include the nominal efficiency of upstream filters (P = .008 for bacteria and P < .001 for fungi) and coil moisture, which was reflected in fungal loading differences between top and bottom halves of the AC coils in Southern CT (P = .05) and the dew points of the two climates considered (P = .04). Environmental and building characteristics explained 42% (P < .001) of bacterial concentration variability and 66% (P < .001) of fungal concentration variability among samples.

Indoor/Outdoor Relationships and Anthropogenic Elemental Signatures in Airborne PM2.5 at a High School: Impacts of Petroleum Refining Emissions on Lanthanoid Enrichment

Outdoor emissions of primary fine particles and their contributions to indoor air quality deterioration were examined by collecting PM_2.5 inside and outside a mechanically ventilated high school in the ultraindustrialized ship channel region of Houston, TX over a 2-month period. By characterizing 47 elements including lanthanoids (rare earth elements), using inductively coupled plasma-mass spectrometry, we captured indoor signatures of outdoor episodic emissions arising from nonroutine operations of petroleum refinery fluidized-bed catalytic cracking units. Average indoor-to-outdoor (I/O) abundance ratios for the majority of elements were close to unity providing evidence that indoor metal-bearing PM_2.5 had predominantly outdoor origins. Only Co had an I/O abundance ratio >1 but its indoor sources could not be explicitly identified. La and 17 other elements (Na, K, V, Ni, Co, Cu, Zn, Ga, As, Se, Mo, Cd, Sn, Sb, Ba, W, and Pb), including air toxics were enriched relative to the local soil both in indoor and outdoor PM_2.5 demonstrating their noncrustal origins. Several lines of evidence including receptor modeling, lanthanoid ratios, and La-Ce-Sm ternary diagrams pointed to petroleum refineries as being largely responsible for enhanced La and total lanthanoid concentrations in the majority of paired indoor and outdoor PM_2.5.

Fungal and bacterial growth in floor dust at elevated relative humidity levels

Under sustained, elevated building moisture conditions, bacterial and fungal growth occurs. The goal of this study was to characterize microbial growth in floor dust at variable equilibrium relative humidity (ERH) levels. Floor dust from one home was embedded in coupons cut from a worn medium-pile nylon carpet and incubated at 50%, 80%, 85%, 90%, 95%, and 100% ERH levels. Quantitative PCR and DNA sequencing of ribosomal DNA for bacteria and fungi were used to quantify growth and community shifts. Over a 1-wk period, fungal growth occurred above 80% ERH. Growth rates at 85% and 100% ERH were 1.1 × 10⁴ and 1.5 × 10⁵ spore equivalents d^-1 mg dust^-1 , respectively. Bacterial growth occurred only at 100% ERH after 1 wk (9.0 × 10⁴ genomes d^-1 mg dust^-1 ). Growth resulted in significant changes in fungal (P<.00001) and bacterial community structure (P<.00001) at varying ERH levels. Comparisons between fungal taxa incubated at different ERH levels revealed more than 100 fungal and bacterial species that were attributable to elevated ERH. Resuspension modeling indicated that more than 50% of airborne microbes could originate from the resuspension of fungi grown at ERH levels of 85% and above.

Buildings, Beneficial Microbes, and Health

Bacteria and fungi in buildings exert an influence on the human microbiome through aerosol deposition, surface contact, and human and animal interactions. As the identities and functions of beneficial human microbes emerge, the consequences of building design, operation, and function must be understood to maintain the health of occupants in buildings.

Indoor microbial communities: Influence on asthma severity in atopic and nonatopic children

BACKGROUND

Allergic and nonallergic asthma severity in children can be affected by microbial exposures.

OBJECTIVE

We sought to examine associations between exposures to household microbes and childhood asthma severity stratified by atopic status.

METHODS

Participants (n = 196) were selected from a cohort of asthmatic children in Connecticut and Massachusetts. Children were grouped according to asthma severity (mild with no or minimal symptoms and medication or moderate to severe persistent) and atopic status (determined by serum IgE levels). Microbial community structure and concentrations in house dust were determined by using next-generation DNA sequencing and quantitative PCR. Logistic regression was used to explore associations between asthma severity and exposure metrics, including richness, taxa identification and quantification, community composition, and concentration of total fungi and bacteria.

RESULTS

Among all children, increased asthma severity was significantly associated with an increased concentration of summed allergenic fungal species, high total fungal concentrations, and high bacterial richness by using logistic regression in addition to microbial community composition by using the distance comparison t test. Asthma severity in atopic children was associated with fungal community composition (P = .001). By using logistic regression, asthma severity in nonatopic children was associated with total fungal concentration (odds ratio, 2.40; 95% CI, 1.06-5.44). The fungal genus Volutella was associated with increased asthma severity in atopic children (P = .0001, q = 0.04). The yeast genera Kondoa might be protective; Cryptococcus species might also affect asthma severity.

CONCLUSION

Asthma severity among this cohort of children was associated with microbial exposure, and associations differed based on atopic status.

Influence of housing characteristics on bacterial and fungal communities in homes of asthmatic children

Variations in home characteristics, such as moisture and occupancy, affect indoor microbial ecology as well as human exposure to microorganisms. Our objective was to determine how indoor bacterial and fungal community structure and diversity are associated with the broader home environment and its occupants. Next-generation DNA sequencing was used to describe fungal and bacterial communities in house dust sampled from 198 homes of asthmatic children in southern New England. Housing characteristics included number of people/children, level of urbanization, single/multifamily home, reported mold, reported water leaks, air conditioning (AC) use, and presence of pets. Both fungal and bacterial community structures were non-random and demonstrated species segregation (C-score, P < 0.00001). Increased microbial richness was associated with the presence of pets, water leaks, longer AC use, suburban (vs. urban) homes, and dust composition measures (P < 0.05). The most significant differences in community composition were observed for AC use and occupancy (people, children, and pets) characteristics. Occupant density measures were associated with beneficial bacterial taxa, including Lactobacillus johnsonii as measured by qPCR. A more complete knowledge of indoor microbial communities is useful for linking housing characteristics to human health outcomes. Microbial assemblies in house dust result, in part, from the building's physical and occupant characteristics.

Indoor microbial communities: Influence on asthma severity in atopic and nonatopic children

BACKGROUND

Allergic and nonallergic asthma severity in children can be affected by microbial exposures.

OBJECTIVE

We sought to examine associations between exposures to household microbes and childhood asthma severity stratified by atopic status.

METHODS

Participants (n = 196) were selected from a cohort of asthmatic children in Connecticut and Massachusetts. Children were grouped according to asthma severity (mild with no or minimal symptoms and medication or moderate to severe persistent) and atopic status (determined by serum IgE levels). Microbial community structure and concentrations in house dust were determined by using next-generation DNA sequencing and quantitative PCR. Logistic regression was used to explore associations between asthma severity and exposure metrics, including richness, taxa identification and quantification, community composition, and concentration of total fungi and bacteria.

RESULTS

Among all children, increased asthma severity was significantly associated with an increased concentration of summed allergenic fungal species, high total fungal concentrations, and high bacterial richness by using logistic regression in addition to microbial community composition by using the distance comparison t test. Asthma severity in atopic children was associated with fungal community composition (P = .001). By using logistic regression, asthma severity in nonatopic children was associated with total fungal concentration (odds ratio, 2.40; 95% CI, 1.06-5.44). The fungal genus Volutella was associated with increased asthma severity in atopic children (P = .0001, q = 0.04). The yeast genera Kondoa might be protective; Cryptococcus species might also affect asthma severity.

CONCLUSION

Asthma severity among this cohort of children was associated with microbial exposure, and associations differed based on atopic status.

Characterizing airborne fungal and bacterial concentrations and emission rates in six occupied children's classrooms

Baseline information on size-resolved bacterial, fungal, and particulate matter (PM) indoor air concentrations and emission rates is presented for six school classrooms sampled in four countries. Human occupancy resulted in significantly elevated airborne bacterial (81 times on average), fungal (15 times), and PM mass (nine times) concentrations as compared to vacant conditions. Occupied indoor/outdoor (I/O) ratios consistently exceeded vacant I/O ratios. Regarding size distributions, average room-occupied bacterial, fungal, and PM geometric mean particle sizes were similar to one another while geometric means estimated for bacteria, fungi, and PM mass during vacant sampling were consistently lower than when occupied. Occupancy also resulted in elevated indoor bacterial-to-PM mass-based and number-based ratios above corresponding outdoor levels. Mean emission rates due to human occupancy were 14 million cells/person/h for bacteria, 14 million spore equivalents/person/h for fungi, and 22 mg/person/h for PM mass. Across all locations, indoor emissions contributed 83 ± 27% (bacteria), 66 ± 19% (fungi), and 83 ± 24% (PM mass) of the average indoor air concentrations during occupied times.

PRACTICAL IMPLICATIONS

An extensive data set of bacterial and fungal size-distributed indoor air concentrations and emission rates is presented. Analysis of these data contributes to an understanding of how indoor bacterial and fungal aerosols are influenced by human occupancy. This work extends beyond prior culture and DNA-based microbiome studies in buildings to include quantitative relationships between size-resolved bacterial and fungal concentrations in indoor air and building parameters such as occupancy, ventilation, and outdoor conditions. The work indicates that occupancy-associated emissions (e.g., via resuspension and shedding) contribute more to both bacterial and fungal indoor air concentrations than do outdoor sources for the occupied classrooms investigated in this study.

We Should Expect More out of Our Sewage Sludge

Sewage sludge and biosolids production and management are a central component of water and sanitation engineering. The culmination of previous incremental technologies and regulations aimed at solving a current treatment problem, rather than developing the practice for the higher goals of sustainability have resulted in sludge becoming an economic and social liability. Sludge management practice must shift from treatment of a liability toward recovery of the embedded energy and chemical assets, while continuing to protect the environment and human health. This shift will require new research, treatment technologies and infrastructure and must be guided by the application of green engineering principles to ensure economic, social, and environmental sustainability.

Using carbon dioxide to maintain an elevated oleaginous microalga concentration in mixed-culture photo-bioreactors

Microbial contamination of growth reactors is a major concern for microalgal biofuel production. In this study, the oleaginous, CO2-tolerant microalga Scenedesmus dimorphus was combined with a wastewater-derived microbial community and grown in replicated sequencing batch photobioreactors. The reactors were sparged with either ambient air or 20% v/v CO2. In the initial growth cycles, air and the 20% CO2 reactors were similar in terms of growth and microbial community structure. Beyond the fourth growth cycle, however, the ambient air reactors had larger decreases in cell density and growth rate, and increases in species richness and non-algal microorganisms compared to the 20% CO2 reactors. Both qPCR and rDNA sequence analyses demonstrated a greater loss in S. dimorphus enrichment in the ambient-air reactors compared to the 20% CO2 reactors. These results demonstrate that environmental parameters can be used to delay the adverse impacts of microbial contamination in open, mixed-culture microalgae bioreactors.

Indoor emissions as a primary source of airborne allergenic fungal particles in classrooms

This study quantifies the influence of ventilation and indoor emissions on concentrations and particle sizes of airborne indoor allergenic fungal taxa and further examines geographical variability, each of which may affect personal exposures to allergenic fungi. Quantitative PCR and multiplexed DNA sequencing were employed to count and identify allergenic fungal aerosol particles indoors and outdoors in seven school classrooms in four different countries. Quantitative diversity analysis was combined with building characterization and mass balance modeling to apportion source contributions of indoor allergenic airborne fungal particles. Mass balance calculations indicate that 70% of indoor fungal aerosol particles and 80% of airborne allergenic fungal taxa were associated with indoor emissions; on average, 81% of allergenic fungi from indoor sources originated from occupant-generated emissions. Principal coordinate analysis revealed geographical variations in fungal communities among sites in China, Europe, and North America (p < 0.05, analysis of similarity), demonstrating that geography may also affect personal exposures to allergenic fungi. Indoor emissions including those released with occupancy contribute more substantially to allergenic fungal exposures in classrooms sampled than do outdoor contributions from ventilation. The results suggest that design and maintenance of buildings to control indoor emissions may enable reduced indoor inhalation exposures to fungal allergens.

Untangling the fungal niche: the trait-based approach

Fungi are prominent components of most terrestrial ecosystems, both in terms of biomass and ecosystem functioning, but the hyper-diverse nature of most communities has obscured the search for unifying principles governing community organization. In particular, unlike plants and animals, observational studies provide little evidence for the existence of niche processes in structuring fungal communities at broad spatial scales. This limits our capacity to predict how communities, and their functioning, vary across landscapes. We outline how a shift in focus, from taxonomy toward functional traits, might prove to be valuable in the search for general patterns in fungal ecology. We build on theoretical advances in plant and animal ecology to provide an empirical framework for a trait-based approach in fungal community ecology. Drawing upon specific characteristics of the fungal system, we highlight the significance of drought stress and combat in structuring free-living fungal communities. We propose a conceptual model to formalize how trade-offs between stress-tolerance and combative dominance are likely to organize communities across environmental gradients. Given that the survival of a fungus in a given environment is contingent on its ability to tolerate antagonistic competitors, measuring variation in combat trait expression along environmental gradients provides a means of elucidating realized, from fundamental niche spaces. We conclude that, using a trait-based understanding of how niche processes structure fungal communities across time and space, we can ultimately link communities with ecosystem functioning. Our trait-based framework highlights fundamental uncertainties that require testing in the fungal system, given their potential to uncover general mechanisms in fungal ecology.