Environmental Science & Technology Letters

Hospital-based wastewater surveillance may complement community and clinical surveillance data in important ways, and may be useful in jurisdictions without community-based wastewater surveillance. From May 2024-April 2026, we analyzed weekly samples (n=190) from three hospitals in New York City using digital PCR to evaluate the sensitivity, specificity, and positive predictive value (PPV) of wastewater viral detection against facility SARS-CoV-2 and influenza A/B inpatient caseloads. Sensitivity was 38-42% for SARS-CoV-2 and 36-49% for influenza A/B, while specificity exceeded 72% for all pathogens. During respiratory seasons, sensitivity reached 81% for SARS-CoV-2 and 81% for influenza A; both had 100% sensitivity during peak case weeks. Notably, off-peak influenza detections occurred in hospital wastewater at all three hospitals in summer 2024 without corresponding hospital case detection, suggesting the presence of undiagnosed cases. These findings underscore the potential utility of hospital-based wastewater monitoring for tracking respiratory virus activity.

Background: Children spend more than 90% of their time indoors, and early childhood education settings (ECEs) are an understudied, high-occupant-density indoor microenvironment where exposure to volatile organic compounds, particulate matter, and other toxicants has been documented. Limited knowledge exists on ECE-specific exposures affecting young children and how they compare to exposures in the home. Methods: This prospective, repeated-measures pilot study targeted enrollment of 44 preschool-aged children and 8 ECE staff across two geographically and sociodemographically distinct ECEs in metropolitan Atlanta, Georgia. Paired silicone wristbands, one home-designated and one ECE-designated, were exchanged between settings across three consecutive days and nights beginning at enrollment to characterize microenvironment-specific exposure. A single spot urine sample was also collected from each child. Continuous indoor air quality monitoring was conducted in two classrooms per site. Caregivers and ECE staff completed structured questionnaires assessing home and ECE environmental characteristics, child respiratory risk, and protocol feasibility and acceptability. Feasibility was evaluated using eight pre-specified indicators spanning recruitment and enrollment, wristband wear duration and loss by microenvironment, urine sample collection completeness, and survey completion by instrument and respondent group. Conclusion: This pilot will establish feasibility and acceptability parameters for a paired, multi-matrix silicone wristband protocol across home and ECE microenvironments. Findings will inform the design, sample size, and power calculations for a subsequent study testing indoor air interventions and pediatric respiratory outcomes in ECEs. Feasibility outcomes are reported in a companion manuscript.

Plastic products have greatly improved convenience in daily life. However, diverse pollutants released from these materials pose substantial risks to human health. Lactiplantibacillus plantarum PD01 has previously been demonstrated to reduce microplastic (MP) bioaccumulation and toxicity in murine model. In this study, we conducted a randomized, double-blind, placebo-controlled trial to further evaluate the efficacy of L. plantarum PD01 in reducing MPs and MP-associated chemicals in humans. A total of 106 participants were recruited in November 2025. Eligible participants were randomly assigned to either the placebo or intervention group, and orally received placebo or 1.0x1010 colony-forming units (CFU) L. plantarum PD01 after meals three times a day, respectively. Fecal, urinary, and blood samples were collected to determine the MPs contents, plasticizer levels, gut microbiota composition, blood metabolic profiles and biochemical indicators. Among these measurements, the analysis of urinary phthalate metabolites was completed first and revealed a significant reduction after the probiotic intervention. Compared with the placebo group, 6-week L. plantarum PD01 supplementation resulted in significant relative reductions in urinary levels of MCMHP by 60.2% (P = 0.0343), MMP by 51.2% (P < 0.001), MEHP by 49.6% (P = 0.0058), MiBP by 45.7% (P = 0.0085), MnBP by 35.8% (P = 0.0478), and {Sigma}DEHP by 46.2% (P = 0.0461). The interim results presented here provide the first clinical evidence that the probiotic strain PD01 can significantly reduce residual MP-associated chemicals in human body. To the best of our knowledge, this is the first randomized controlled trial (RCT) to evaluate probiotic intervention targeting MPs and MP-associated chemicals in humans.

Since the COVID-19 pandemic, wastewater-based surveillance (WBS) has emerged as a key approach to assess community-level health and the evolution of pathogens. To date, most established WBS systems focus on polymerase chain reaction (PCR) based detection and targeted sequencing of known pathogens because these approaches are well-accepted and include amplification of pathogen target sequences of interest thereby enabling lower limits of detection. Metagenomic next-generation sequencing (mNGS) is a promising approach to enable pathogen detection and surveillance beyond predefined pathogen lists, but its regular application to WBS has not been yet widely adopted because many key performance characteristics are not well-understood, including limit of detection (LOD) and false positive/negative rates. This paper describes a computational analysis to estimate the operational LOD of various sequencing approaches using a simplified model of a local wastewater (WW) system involving a military base. This paper also presents findings from two types of experiments: 1) laboratory-spiked, those for which Atlantibacter subterraneus (Asub) is introduced into real-world WW samples in a laboratory setting, and 2) system-spiked, those for which Asub is introduced at a source location of a real-world WW system. Findings indicate that mNGS detection performance varies with sequencing method and the data analysis process. In addition, findings indicate that site-specific method characterization should be used when implementing mNGS for WBS because sites can have different WW system configurations, background organisms and sequencing inhibitors.

Background: Environmental noise and air pollution are both shaped by road traffic and the built environment, and exposure assessment increasingly folds them into composite indices or proxies both by traffic exposure. Whether the two share a social distribution has rarely been tested against direct measurement of several exposures in the same communities, and community noise is almost always characterized by A-weighted levels alone, which discount low-frequency energy. Methods: At 176 sites across Rhode Island, spanning the contiguous urban area of Providence, Central Falls, and Pawtucket together with four rural municipalities, we measured the acoustic environment under A- and C-weighting (LAeq, LCeq), fine particulate matter (PM2.5), night-time illuminance, and relative humidity across four session types over roughly one year (704 site-sessions). Exposures were linked to census-tract composition (American Community Survey), and mixed-effects models were fitted for each of eight area-level markers of disadvantage, adjusting for campaign and session. Relative humidity was carried through the identical model as a negative control. Results: A-weighted noise was consistently higher in more disadvantaged tracts, rising with non-White, poverty, renter, and no-vehicle shares and falling with income and older-resident share (six of eight markers significant; 1.3 to 1.8 dBA per standard deviation; 6.6 dBA between the least and most racially diverse neighborhoods). C-weighted levels followed the same gradient on every marker and exceeded their A-weighted counterparts at block-group scale for renter occupancy and vehicle absence. Night-time illuminance was also socially patterned, whereas short-term PM2.5 was roughly an order of magnitude weaker and relative humidity showed no gradient. The acoustic gradient persisted within the urban core alone. Conclusions: Measured burden was carried by the acoustic environment, including its low-frequency component, and by night-time light, not by short-term particulates. The exposure metric and the averaging time determine which disparities are visible at all.

Background: Autonomous ultraviolet-C (UV-C) disinfection systems are increasingly used to supplement manual environmental cleaning, yet evidence-based guidance defining pathogen-specific UV-C dose requirements across representative surfaces remains limited. Aim: To characterize operational UV-C dose requirements for clinically relevant pathogens across diverse high-touch and healthcare surfaces and determine how experimentally derived microbial inactivation can inform operational exposure parameters. Methods: SARS-CoV-2, adenovirus, Pseudomonas aeruginosa, Staphylococcus aureus, Klebsiella pneumoniae, Enterococcus faecalis, Candida auris, and Clostridioides difficile spores were exposed to defined UV-C doses on representative high-touch materials or stainless steel under standardized conditions, including a 10% fetal bovine serum organic soil challenge. Microbial inactivation was quantified by viable recovery. Dose-response analysis and operational modelling were used where supported by the experimental data. Findings: UV-C exposure significantly reduced viable recovery of all pathogens, with substantial differences in the exposure conditions associated with microbial inactivation. SARS-CoV-2 exhibited substantial inactivation at doses as low as 2.6 mJ/cm2, whereas the highest evaluated doses were 1,800 mJ/cm2 for C. difficile spores and 3600 mJ/cm2 for C. auris. For C. auris, multi-dose data estimated that approximately 1,410 mJ/cm2 was associated with a 2-log10 reference reduction, enabling distance-dependent exposure-time predictions. Conclusion: Experimentally quantified UV-C exposures produced substantial microbial inactivation across diverse pathogen classes and surfaces. Integrating delivered dose with microbial reduction provides a quantitative framework for translating laboratory efficacy into operational parameters for autonomous UV-C disinfection.

Socioeconomic inequality in exposure to air pollution possess a significant public health challenge, yet little is known about how the disparities vary across the various economic status areas in Nairobi. Globally, studies have shown that exposure to air pollution is unequal across communities hence disparities in harm to human health. This study examined the association between socioeconomic characteristics and perceived air quality among residents of low- and high-socioeconomic status areas in Nairobi, Kenya. Two regions within Nairobi County were selected for this study: Mukuru kwa Njenga (representing the Low Socioeconomic Status) and Langata (representing the High Socioeconomic Status) with a sample size of 384 in HSES areas and 368 in LSES areas. A cross-sectional study was conducted among 752 respondents residing in selected LSES and HSES areas of Nairobi. Data was collected using a structured questionnaire assessing sociodemographic characteristics, income, education, employment, perceived air quality, and self-reported health outcomes associated with air pollution exposure. Descriptive statistics were used to summarize participant characteristics and perceived air quality. Chi-square tests were used to examine associations between residential area and categorical health outcomes, while ordinal logistic regression was used to assess the association between socioeconomic characteristics and perceived air-quality ratings. Perceived air quality differed significantly between residential socioeconomic groups. Respondents in LSES areas were more likely to rate air quality as poor or very poor, with 45.4% rating it as very poor, compared with only 1.6% of respondents in HSES areas. In contrast, 12.2% of HSES respondents rated air quality as good compared with 0.3% in LSES areas. The association between area of residence and perceived air-quality rating was statistically significant, {chi}2(3) = 282.672, p < 0.001. In the ordinal logistic regression model, HSES residence was associated with significantly lower odds of reporting poorer perceived air quality compared with LSES residence (OR = 0.135, 95% CI: 0.095-0.190, p < 0.001). Income was also significantly associated with perceived air quality, while respondents with no formal education had higher odds of reporting poorer perceived air quality compared with those with secondary education (OR = 3.254, 95% CI: 1.388-7.638, p = 0.007). Significant differences were also observed for several self-reported health outcomes. Respiratory problems were more prevalent among respondents in LSES areas than HSES areas (72.7% versus 50.4%; {chi}2(1) = 29.081, p < 0.001; Cramer's V = 0.224). However, allergies, eye irritation, and headaches were reported more frequently in HSES areas than in LSES areas, with significant associations observed for allergies ({chi}2(1) = 106.479, p < 0.001; Cramer's V = 0.429), eye irritation ({chi}2(1) = 136.577, p < 0.001; Cramer's V = 0.486), and headaches ({chi}2(1) = 149.180, p < 0.001; Cramer's V = 0.508). No statistically significant association was observed for cardiovascular problems, likely reflecting the very low number of reported cases. Substantial socioeconomic disparities in perceived air quality and self-reported respiratory health outcomes were observed. Residents of low-socioeconomic status areas consistently perceived poorer air quality and reported a higher burden of respiratory problems, highlighting the need for targeted interventions to reduce environmental health inequalities.

Objective. Respiratory infections are the leading cause of illness at major sporting events, yet surveillance relies on athletes recognising and reporting symptoms. We evaluated whether continuous air sampling with point-of-care molecular testing could detect respiratory-virus nucleic acids in an elite team's congregate spaces during competition, and whether the resulting signals were operationally useful. Methods. We performed a prospective, descriptive environmental-surveillance study following the Canadian men's national soccer team across five host cities during the 2026 FIFA World Cup (3 June to 4 July 2026). InBio Apollo bioaerosol samplers ran continuously in up to four team-designated rooms per hotel (physiotherapy, meal, equipment, and coaches' room or hallway). Filters were changed approximately twice daily, eluted on-site, and tested with the Cepheid Xpert Xpress(R) SARS-CoV-2/Flu/RSV plus assay. A sample was considered positive if any cycle-threshold (Ct) value was reported, as less than 45, for a target. Results. Of 174 air filters, there were 13 detections of virus genetic material (9 SARS-CoV-2, 3 influenza A virus, 1 influenza B virus, 0 RSV). Detections were sparse early and clustered late in the tournament. An influenza A signal appeared the morning a player was sent home febrile, and SARS-CoV-2 signals coincided with visibly ill hotel staff, with signals falling after ill staff were excluded. Conclusion. Air sampling with point-of-care testing is feasible in the mobile environment of an elite team and can surface behavior-independent viral signals during competition that may offer opportunities for earlier precautionary actions.

The effect of microcurrents on facultative microbial pathogens remains controversial. Solid cultures in Sabouraud Glucose Agar of the ubiquitous mold Aspergillus fumigatus were repeatedly treated with a commercially available device performing wireless conductive microelectrostimulation by 3.5 A microcurrent routed by spraying negatively charged air particles onto solid cultures in modified petri dishes. The treated cultures displayed increased growth compared to standard ones, but only as a function of mycelial density and total surface; the radial growth rate of the mycelium remained unaltered. The increased growth was positively related to the duration of the treatment. At the same time, secondary development (new mycelial loci within the dish) was greatly upheld due to treatment, as the spraying created microairstreams dislocating the fungal spores. These results imply perplexed kinetics of mycelial growth both with and without treatment, since the folding of the mycelial mat is observed regularly. Both the fungus response to the ES and the possible revision of growth kinetics create prospects for biotechnological and bioremediation applications but also imply biomedical considerations, regarding infection dynamics of mycelial fungi and their in situ resistance to immune responses and treatment.

Urban air pollution remains a significant public health concern, contributing to premature deaths and adverse health outcomes. However, there is little causal research evaluating the effectiveness of policies designed to improve air quality. This study assesses the impact of all three stages of London's Ultra Low Emission Zone (ULEZ) on air pollution, via PM2.5 levels, and respiratory health, via prescription records for bronchodilator and respiratory corticosteroid medications. Analyses are at general practice level, using a generalised synthetic control method to estimate causal impacts. Stage 1 was associated with a statistically significant but negligible 0.77% reduction in PM2.5 levels, with no corresponding change in prescribing. Stage 2 produced a paradoxical 2.69% increase in PM2.5, alongside a 4.44% decrease in inhaled corticosteroid quantity but a 12.51% increase in average daily quantity (ADQ) usage, suggesting a worsening of disease severity among existing patients. Stage 3 yielded a 2.69% PM2.5 reduction and a modest 2.18% decrease in bronchodilator ADQ usage. Spillover effects beyond the ULEZ boundary were statistically significant, but negligible. We find overall that the ULEZ had minimal effects on both air quality and respiratory prescribing across all three stages. These findings provide new insights into the effectiveness of ULEZ policies in reducing air pollution and its associated health impacts, suggesting the zone's effects are considerably smaller than previously reported, and that integration with broader policy measures may be necessary to achieve meaningful public health gains.

The role of bioaerosols in the transmission of pathogens and antimicrobial resistance (AMR) is of increasing clinical importance, particularly in settings housing vulnerable populations. Air filtration (e.g. HEPA filtration) and ventilation (e.g. minimum air changes per hour) measures are designed to restrict the airborne transmission of microorganisms. Despite these measures, airborne transmission remains a persistent issue in hospitals, workplaces, aged care, and schools, and is not typically assessed in routine surveillance for infection prevention. Here, we evaluated the efficacy of a high-volume air sampling approach to capture the indoor 'aerobiome', and investigated the potential for bioaerosols to mediate disease and AMR transmission in workplace and hospital settings. Our sampling approach demonstrates the benefits of simple decontamination procedures and personal protective equipment on the ability to distinguish genuine low biomass signals in air samples from blank controls, enabling reliable and sensitive microbial detection down to a limit of 69 bacterial cells/m3 of air. In a workplace bathroom setting, increased airborne biomass was strongly associated with human activity. This diminished significantly after a few hours of no activity, yet persisted in the indoor environment, with viable identical bacterial strains recovered from bioaerosols and bathroom surfaces across months of sampling. Applying our approach in a hospital ward, air samples from occupied patient rooms were not distinguishable from blank controls and contained negligible fungal and bacterial content, with only trace contributions from human occupancy. Our findings indicate that air filtration measures in this ward are effective at minimising airborne risks, but periodic testing of high-risk areas may be valuable in indoor settings with greater human traffic and may contribute key information to outbreak investigations.

Wastewater systems are increasingly recognized as important environmental reservoirs of antimicrobial resistance (AMR), acting as interfaces where resistant bacteria, antimicrobial resistance genes (ARGs), and mobile genetic elements (MGEs) converge and potentially disseminate. Wastewater samples were collected from hospital and community sites, including municipal medical centers, household wastewater outlets, and open drainage systems. Genomic DNA was extracted using the ZymoBIOMICS DNA/RNA Miniprep Kit and sequenced on the Oxford Nanopore Technologies MinION MK1D platform using the Native Barcoding Kit (SQK-NBD114.24, V14). Sequencing data were processed through a custom Snakemake workflow integrating quality control, taxonomic profiling, resistome characterization, mobilome analysis, and genome-resolved metagenomics. A total of 489 unique ARGs conferring resistance to 29 antibiotic classes were identified through metagenomic analysis. The resistome was dominated by genes conferring resistance to {beta}-lactams (including cephalosporins and carbapenems), aminoglycosides, tetracyclines, macrolides, and fluoroquinolones. Clinically important resistance determinants, including blaOXA, blaTEM, blaGES, blaCARB, cfxA, tet, qnr, sul, dfrA, erm, msrE, and aminoglycoside-modifying enzyme genes such as aac(3) and ant(3'') were detected across both hospital and community wastewater samples. Resistance mechanisms were predominantly driven by antibiotic inactivation, followed by efflux and target protection. Several priority bacterial pathogens were detected, including Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, Pseudomonas aeruginosa, and Acinetobacter baumannii. Integration/excision elements were the predominant category of MGEs, followed by transfer-associated elements and replication/recombination/repair functions. Plasmid analysis further identified diverse incompatibility groups, predominantly IncP6, IncC, IncF, and IncR replicons, supporting the widespread occurrence of plasmid-mediated horizontal gene transfer in both settings. These findings reveal a substantial burden of clinically relevant ARGs, mobile genetic elements, and potential bacterial pathogens in hospital and community wastewater in Conakry. This study provides the first metagenomic baseline for environmental AMR surveillance in Guinea and highlights the urgent need for integrated One Health strategies to mitigate the environmental dissemination of antimicrobial resistance.

Smoke from spring boreal wildfires increasingly impacts eastern North America, exposing breeding birds to hazardous air pollution that may impact reproductive outcomes. Using data collected in 2018-2025 from 70,979 monitored nests of four widespread cavity-nesting songbirds, we found strong evidence that smoke greatly delays egg laying and can extend incubation and nestling duration. We further found that while smoke is associated with increased clutch sizes, in some species smoke exposure strongly decreases hatching or fledging success. Our results demonstrate that extreme smoke can have wide-ranging impacts on breeding birds, from altering phenology to impacting fitness. While the exact mechanisms underlying these results remain elusive, the full suite of effects suggests that modifications to adult behavior under smoky conditions is the most likely cause. As fire regimes shift, birds and other wildlife are at greater risk of exposure to toxic smoke during the breeding season, which may further exacerbate the biodiversity crisis.

Microbial interactions strongly influence carbon and nitrogen flows in mushroom compost, yet their functional roles during Agaricus bisporus colonization remain unresolved. We combined PLFA-SIP and nanoSIMS imaging with ITS amplicon sequencing to follow resource flows and microbial activity across spatial scales. Stable-isotope tracers (13C-glucose and 15N-ammonium) revealed that A. bisporus simultaneously facilitates and suppresses bacterial populations: fungal activity increased glucose assimilation by bacteria yet reduced overall bacterial biomass. NanoSIMS visualized nutrient-rich microenvironments along hyphae where bacterial 13C and 15N assimilation was elevated. Sequencing showed the fungal community to comprise essentially two organisms, A. bisporus and Mycothermus thermophilus, which differ approximately elevenfold in their content of the fungal biomarker C18:2w6,9c. Total fungal PLFA therefore tracks which of the two dominates as much as it tracks fungal biomass. Together these findings reveal coupled fungal-bacterial nutrient processing and show that biomarker-based estimates of fungal biomass require community composition to be known. Multi-scale isotope probing provides a framework for resolving microbial interactions in complex detrital systems.

Continuous-wave (CW) 450 nm blue diode-laser irradiation was evaluated as a physical seed-priming treatment for improving lettuce (Lactuca sativa L.) germination and early seedling establishment under reduced water availability. Dry seeds were irradiated at 50-250 mW for 2 min and at 100 mW for 0.5-10 min; each condition included three independent Petri-dish replicates with 100 seeds per replicate, and the replicates were monitored for 72 h. Laser treatment produced a dose-dependent biological response. The optimized condition, 100 mW for 2 min, increased final germination from approximately 65-70% to 90-95%, increased the germination speed index, and promoted root elongation more strongly than shoot elongation. Longer exposures reduced germination and seedling growth. Under reduced water availability (0.5-6 ml per Petri dish), laser-treated seeds germinated earlier, maintained final germination of 83-93% compared with 57-72% in controls, and produced better-developed seedlings. The relative benefit increased as water availability decreased, indicating that optimized blue laser priming partially compensated for low water supply during germination and early establishment. These results identify CW blue laser priming as a contactless, chemical-free approach for improving lettuce seed performance and early seedling vigor under limited water availability.

Accurate accounting of aquatic methane emissions is critical for climate change mitigation, yet current global budgets overlook a key driver: the elevation-regulated atmospheric pressure. Here, we present the first large-scale investigation of methane ebullition across 164 shallow waters spanning elevations from sea level to 4886 meters. We demonstrate that ebullition rate increases with elevation-over four times higher at >3000 m a.s.l. than at sea level-due to two synergistic, pressure-dependent physical mechanisms: a degas effect (enhanced bubble formation) and a trigger effect (facilitated bubble ascent). Independent theoretical prediction of the combined effects shows near-perfect agreement with the empirical elevation trend, quantitatively confirming that these physical mechanisms are the primary drivers of enhanced ebullition at high elevations. Our findings reveal that mountain aquatic ecosystems represent unaccounted methane hotspots that have been systematically underestimated in global inventories.We therefore call for urgent integration of these ecosystems into IPCC assessments and targeted mountain mitigation and sustainable management strategies.

Wastewater-based epidemiology (WBE) has emerged as a powerful population-level surveillance tool, but its coverage is structurally concentrated in in-network urban areas, potentially leaving rural populations underrepresented. Routine human movement between sewered (in-network) and unsewered (off-network) areas may, however, cause wastewater treatment plant (WWTP) measurements to reflect infectious disease dynamics beyond sewer boundaries. We evaluated this hypothesis using daily clinical COVID-19 testing data (January 2021-April 2022) across New York State excluding New York City (NYC). We disaggregated weekly cases and tests into in-network (WWTP catchment area) and off-network (outside WWTP catchment area) components applied to two geographic frameworks: administrative counties (N = 53 mixed-coverage) and mobility-defined communities identified through Walktrap community detection applied to census tract-level movement networks (N = 32 mixed-coverage). In/off-network COVID-19 trends were strongly correlated under both frameworks. County-level statewide aggregate correlations were high (incidence r = 0.994, positivity r = 0.996), as were individual county correlations (median r = 0.909 and 0.932, respectively). Mobility-defined community-level statewide correlations were similarly strong (r = 0.990 and 0.992), with comparable unit-level medians (r = 0.877 and 0.894). The mobility-defined community framework provided better population balance between in-network and off-network strata (87.5% vs. 69.8% in balanced range) and a higher floor on representativeness (minimum r = 0.440 vs. 0.177). Population size was the dominant predictor of in-network/off-network alignment at both scales; wastewater infrastructure density and off-network signal variability provided additional explanatory power at the mobility-defined community level. WWTPs broadly represent COVID-19 dynamics in surrounding off-network populations, supporting their use as sentinel surveillance sites. Representativeness weakens in smaller, more rural communities, and mobility-defined communities provide a complementary framework for identifying where this occurs.

Ambient particulate matter (PM) is a well-established environmental risk factor for non-communicable diseases, yet its influence on mitochondrial function remains poorly defined. Mitochondrial DNA copy number (mtDNA-CN) serves as a biomarker of mitochondrial biogenesis, making it a candidate exposure biomarker. We conducted the study per PRISMA guidelines (PROSPERO-CRD420261320957) and examined the association between ambient PM exposure and mtDNA-CN. Risk of bias was assessed using Joanna Briggs Institute tools, and relative and absolute changes in mtDNA-CN were pooled using random-effects models, with subgroup analyses by pollutant type and descriptive synthesis of mechanistic evidence. Of 1,224 records identified, 24 studies met inclusion criteria for quantitative analysis, with 12 reporting percentage change and 12 reporting absolute values, covering 13,092 participants. PM exposure was significantly associated with decreased percentage mtDNA-CN (ES: -4.90; 95% CI: -7.97 to -1.82; p = 0.002), while absolute mtDNA-CN levels increased significantly (ES = 0.55; 95% CI: 0.05 to 1.04; p = 0.030). Mechanistic pathways contributing included mtDNA hypermethylation, impaired mitochondrial biogenesis and dynamics. Our findings show ambient PM exposure alters mtDNA-CN, though directionality differs by metric, pointing to the need for larger prospective studies to validate mtDNA-CN as a reliable biomarker of airborne PM and nanoparticulate exposure.

The COVID-19 pandemic remains a global concern, partly due to the rapid mutation rate of SARS-CoV-2 and the emergence of new variants. Wastewater surveillance has proven effective in estimating infection incidence and detecting variants earlier than clinical testing. Its importance has grown as testing rates decline due to milder disease progression. However, current methods typically rely on the prior classification of SARS-CoV-2 lineages or their signature mutations, which may delay detection. We present an alternative method that identifies changes in the viral genetic population over time without requiring prior lineage classification. This population-based approach was applied to sequencing data from wastewater samples, which are generally noisier than clinical samples. We analyzed publicly available sequencing samples from wastewater plants covering Swiss catchments in Altenrhein, St. Gall, Geneva, and Zurich. To address noise, only samples with read depths above 40 and genome coverage of at least 90% were included. Genetic diversity within pooled populations over two time periods was compared to assess changes in viral composition. We demonstrate that SARS-CoV-2 variants can be detected in wastewater sequencing data without prior lineage classification. Our method successfully detected shifts in genetic populations that corresponded to the emergence of known variants of concern (VOCs) in the analyzed regions. Notably, it also revealed the rising prevalence during the first surges of the Omicron variant. Despite the increased noise in wastewater compared to clinical samples, our approach remains effective. However, achieving reliable predictions depends on high sequencing depth, broad genome coverage, and frequent sampling.

Microbial-mediated plastic degradation has the potential to address the persistent global problems of plastic waste and pollution. Previous work has shown that soils can harbour microbes capable of plastic degradation, but we expect there is a broader diversity of soil microbes capable of metabolizing plastics than identified to date using more traditional cultivation-based screening methods. Here we demonstrate a novel approach to identify putative plastic degrading microbes in soil. We paired in situ, real-time measurements of microbial plastic degradation on conductive sensors with subsequent microbial community profiling of the sensor-associated biofilms exhibiting appreciable degradation. To illustrate the utility of our approach, we focus on microbial degradation of the bioplastic polymer PHBV, poly(3-hydroxybutuyrate-co-3-hydroxyvalerate). We screened a range of soils with the in situ sensors to identify a subset of five soils with high PHBV degradation rates, and confirmed that PHBV degradation was due to microbial activity. We then extracted DNA directly from sensors placed in soils with high measured rates of PHBV degradation and used marker gene sequencing to identify the bacterial and fungal taxa associated with the observed PHBV degradation. We confirmed via in vitro culturing that microbes isolated from the sensors have a demonstrated capacity for PHBV metabolism. Together, these results highlight the benefit and feasibility of using low-cost, in-soil sensors to simultaneously collect real-time data on plastic degradation rates in soil and identify previously unrecognized microbial taxa capable of degrading and metabolizing plastic polymers in situ.