Dynamic Human Environmental Exposome Revealed by Longitudinal Personal Monitoring.

Human health is dependent upon environmental exposures, yet the diversity and variation in exposures are poorly understood. We developed a sensitive method to monitor personal airborne biological and chemical exposures and followed the personal exposomes of 15 individuals for up to 890 days and over 66 distinct geographical locations. We found that individuals are potentially exposed to thousands of pan-domain species and chemical compounds, including insecticides and carcinogens. Personal biological and chemical exposomes are highly dynamic and vary spatiotemporally, even for individuals located in the same general geographical region. Integrated analysis of biological and chemical exposomes revealed strong location-dependent relationships. Finally, construction of an exposome interaction network demonstrated the presence of distinct yet interconnected human- and environment-centric clouds, comprised of interacting ecosystems such as human, flora, pets, and arthropods. Overall, we demonstrate that human exposomes are diverse, dynamic, spatiotemporally-driven interaction networks with the potential to impact human health.

Three-dimensional electronic microfliers inspired by wind-dispersed seeds.

Large, distributed collections of miniaturized, wireless electronic devices1,2 may form the basis of future systems for environmental monitoring3, population surveillance4, disease management5 and other applications that demand coverage over expansive spatial scales. Aerial schemes to distribute the components for such networks are required, and-inspired by wind-dispersed seeds6-we examined passive structures designed for controlled, unpowered flight across natural environments or city settings. Techniques in mechanically guided assembly of three-dimensional (3D) mesostructures7-9 provide access to miniature, 3D fliers optimized for such purposes, in processes that align with the most sophisticated production techniques for electronic, optoelectronic, microfluidic and microelectromechanical technologies. Here we demonstrate a range of 3D macro-, meso- and microscale fliers produced in this manner, including those that incorporate active electronic and colorimetric payloads. Analytical, computational and experimental studies of the aerodynamics of high-performance structures of this type establish a set of fundamental considerations in bio-inspired design, with a focus on 3D fliers that exhibit controlled rotational kinematics and low terminal velocities. An approach that represents these complex 3D structures as discrete numbers of blades captures the essential physics in simple, analytical scaling forms, validated by computational and experimental results. Battery-free, wireless devices and colorimetric sensors for environmental measurements provide simple examples of a wide spectrum of applications of these unusual concepts.

Anthropogenic short-lived halogens increase human exposure to mercury contamination due to enhanced mercury oxidation over continents.

Mercury (Hg) is a contaminant of global concern, and an accurate understanding of its atmospheric fate is needed to assess its risks to humans and ecosystem health. Atmospheric oxidation of Hg is key to the deposition of this toxic metal to the Earth's surface. Short-lived halogens (SLHs) can provide halogen radicals to directly oxidize Hg and perturb the budget of other Hg oxidants (e.g., OH and O3). In addition to known ocean emissions of halogens, recent observational evidence has revealed abundant anthropogenic emissions of SLHs over continental areas. However, the impacts of anthropogenic SLHs emissions on the atmospheric fate of Hg and human exposure to Hg contamination remain unknown. Here, we show that the inclusion of anthropogenic SLHs substantially increased local Hg oxidation and, consequently, deposition in/near Hg continental source regions by up to 20%, thereby decreasing Hg export from source regions to clean environments. Our modeling results indicated that the inclusion of anthropogenic SLHs can lead to higher Hg exposure in/near Hg source regions than estimated in previous assessments, e.g., with increases of 8.7% and 7.5% in China and India, respectively, consequently leading to higher Hg-related human health risks. These results highlight the urgent need for policymakers to reduce local Hg and SLHs emissions. We conclude that the substantial impacts of anthropogenic SLHs emissions should be included in model assessments of the Hg budget and associated health risks at local and global scales.

Seabirds reveal mercury distribution across the North Atlantic.

Mercury (Hg) is a heterogeneously distributed toxicant affecting wildlife and human health. Yet, the spatial distribution of Hg remains poorly documented, especially in food webs, even though this knowledge is essential to assess large-scale risk of toxicity for the biota and human populations. Here, we used seabirds to assess, at an unprecedented population and geographic magnitude and high resolution, the spatial distribution of Hg in North Atlantic marine food webs. To this end, we combined tracking data of 837 seabirds from seven different species and 27 breeding colonies located across the North Atlantic and Atlantic Arctic together with Hg analyses in feathers representing individual seabird contamination based on their winter distribution. Our results highlight an east-west gradient in Hg concentrations with hot spots around southern Greenland and the east coast of Canada and a cold spot in the Barents and Kara Seas. We hypothesize that those gradients are influenced by eastern (Norwegian Atlantic Current and West Spitsbergen Current) and western (East Greenland Current) oceanic currents and melting of the Greenland Ice Sheet. By tracking spatial Hg contamination in marine ecosystems and through the identification of areas at risk of Hg toxicity, this study provides essential knowledge for international decisions about where the regulation of pollutants should be prioritized.

Illuminating the dark spaces of healthcare with ambient intelligence.

Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.

Estimating biotic integrity to capture existence value of freshwater ecosystems.

The US Environmental Protection Agency (EPA) uses a water quality index (WQI) to estimate benefits of proposed Clean Water Act regulations. The WQI is relevant to human use value, such as recreation, but may not fully capture aspects of nonuse value, such as existence value. Here, we identify an index of biological integrity to supplement the WQI in a forthcoming national stated preference survey that seeks to capture existence value of streams and lakes more accurately within the conterminous United States (CONUS). We used literature and focus group research to evaluate aquatic indices regularly reported by the EPA's National Aquatic Resource Surveys. We chose an index that quantifies loss in biodiversity as the observed-to-expected (O/E) ratio of taxonomic composition because focus group participants easily understood its meaning and the environmental changes that would result in incremental improvements. However, available datasets of this index do not provide the spatial coverage to account for how conditions near survey respondents affect their willingness to pay for its improvement. Therefore, we modeled and interpolated the values of this index from sampled sites to 1.1 million stream segments and 297,071 lakes across the CONUS to provide the required coverage. The models explained 13 to 36% of the variation in O/E scores and demonstrate how modeling can provide data at the required density for benefits estimation. We close by discussing future work to improve performance of the models and to link biological condition with water quality and habitat models that will allow us to forecast changes resulting from regulatory options.

Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water.

Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As3+ and 6.8 pM for Cr6+, 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy.

Precision environmental health monitoring by longitudinal exposome and multi-omics profiling.

Conventional environmental health studies have primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here, as a pilot case study, we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes a single individual's phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross-validated using corresponding clinical data. Our results showed that agrochemicals and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individual's immune system, kidney, and liver were highly associated with the personal external exposome. Overall, this data-driven longitudinal monitoring study shows the potential dynamic interactions between the personal exposome and internal multi-omics, as well as the impact of the exposome on precision health by producing abundant testable hypotheses.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective.

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Evidence that Pacific tuna mercury levels are driven by marine methylmercury production and anthropogenic inputs.

Pacific Ocean tuna is among the most-consumed seafood products but contains relatively high levels of the neurotoxin methylmercury. Limited observations suggest tuna mercury levels vary in space and time, yet the drivers are not well understood. Here, we map mercury concentrations in skipjack tuna across the Pacific Ocean and build generalized additive models to quantify the anthropogenic, ecological, and biogeochemical drivers. Skipjack mercury levels display a fivefold spatial gradient, with maximum concentrations in the northwest near Asia, intermediate values in the east, and the lowest levels in the west, southwest, and central Pacific. Large spatial differences can be explained by the depth of the seawater methylmercury peak near low-oxygen zones, leading to enhanced tuna mercury concentrations in regions where oxygen depletion is shallow. Despite this natural biogeochemical control, the mercury hotspot in tuna caught near Asia is explained by elevated atmospheric mercury concentrations and/or mercury river inputs to the coastal shelf. While we cannot ignore the legacy mercury contribution from other regions to the Pacific Ocean (e.g., North America and Europe), our results suggest that recent anthropogenic mercury release, which is currently largest in Asia, contributes directly to present-day human mercury exposure.

Portable Mass Spectrometry for On-site Detection of Hazardous Volatile Organic Compounds via Robotic Extractive Sampling.

Various hazardous volatile organic compounds (VOCs) are frequently released into environments during accidental events that cause many hazards to ecosystems and humans. Therefore, rapid, sensitive, and on-site detection of hazardous VOCs is crucial to understand their compositions, characteristics, and distributions in complex environments. However, manual handling of hazardous VOCs remains a challenging task, because of the inaccessible environments and health risk. In this work, we designed a quadruped robotic sampler to reach different complex environments for capturing trace hazardous VOCs using a needle trap device (NTD) by remote manipulation. The captured samples were rapidly identified by portable mass spectrometry (MS) within minutes. Rapid detection of various hazardous VOCs including toxicants, chemical warfare agents, and burning materials from different environments was successfully achieved using this robot-MS system. On-site detection of 83 typical hazardous VOCs was examined. Acceptable analytical performances including low detection limits (at subng/mL level), good reproducibility (relative standard deviation (RSD) < 20%, n = 6), excellent quantitative ability (R2 > 0.99), and detection speed (within minutes) were also obtained. Our results show that the robot-MS system has excellent performance including safety, controllability, applicability, and robustness under dangerous chemical conditions.

Screening of Candida spp. in wastewater in Brazil during COVID-19 pandemic: workflow for monitoring fungal pathogens.

Fungal diseases are often linked to poverty, which is associated with poor hygiene and sanitation conditions that have been severely worsened by the COVID-19 pandemic. Moreover, COVID-19 patients are treated with Dexamethasone, a corticosteroid that promotes an immunosuppressive profile, making patients more susceptible to opportunistic fungal infections, such as those caused by Candida species. In this study, we analyzed the prevalence of Candida yeasts in wastewater samples collected to track viral genetic material during the COVID-19 pandemic and identified the yeasts using polyphasic taxonomy. Furthermore, we investigated the production of biofilm and hydrolytic enzymes, which are known virulence factors. Our findings revealed that all Candida species could form biofilms and exhibited moderate hydrolytic enzyme activity. We also proposed a workflow for monitoring wastewater using Colony PCR instead of conventional PCR, as this technique is fast, cost-effective, and reliable. This approach enhances the accurate taxonomic identification of yeasts in environmental samples, contributing to environmental monitoring as part of the One Health approach, which preconizes the monitoring of possible emergent pathogenic microorganisms, including fungi.

Comparison of methods proposed for monitoring cefotaxime-resistant Escherichia coli in the water environment.

Escherichia coli is a promising subject for globally coordinated surveillance of antimicrobial resistance (AMR) in water environments due to its clinical relevance and widespread use as an indicator of fecal contamination. Cefotaxime-resistant E. coli was recently evaluated favorably for this purpose by the World Health Organization TriCycle Protocol, which specifies tryptone bile x-glucuronide (TBX) medium and incubation at 35°C. We assessed comparability with the U.S. Environmental Protection Agency-approved method for E. coli quantification, which uses membrane-thermotolerant E. coli (mTEC) agar and incubation at 44.5°C, in terms of recovery of E. coli and cefotaxime-resistant E. coli from wastewater influent and surface waters. Total E. coli concentrations in wastewater influent were 106-108 CFU/100 mL, while cefotaxime-resistant E. coli were ~100-fold lower. Total E. coli in surface waters were ~102 CFU/100 mL, and cefotaxime-resistant isolates were near the limit of detection (0.4 CFU/100 mL). Total and putative cefotaxime-resistant E. coli concentrations did not differ significantly between media or by incubation method; however, colonies isolated on mTEC were more frequently confirmed to species (97.1%) compared to those from TBX (92.5%). Incubation in a water bath at 44.5°C significantly decreased non-specific background growth and improved confirmation frequency on both media (97.4%) compared to incubation at 35°C (92.3%). This study helps to advance globally coordinated AMR in water environments and suggests that the TriCycle Protocol is adaptable to other standard methods that may be required in different locales, while also offering a means to improve specificity by decreasing the frequency of false-positive identification of cefotaxime-resistant E. coli by modifying incubation conditions.IMPORTANCEAs antibiotic-resistant bacteria in water environments are increasingly recognized as contributors to the global antibiotic resistance crisis, the need for a monitoring subject that captures antibiotic resistance trends on a global scale increases. The World Health Organization TriCycle Protocol proposes the use of cefotaxime-resistant Escherichia coli isolated on tryptone bile x-glucuronide agar. The U.S. Environmental Protection Agency (USEPA) criteria for safe recreational waters also use E. coli as an indicator but specify the use of mTEC agar at a higher incubation temperature (44.5°C vs 35°C). We assessed the comparability of these methods for isolating total and cefotaxime-resistant E. coli, finding overall good agreement and performance, but significantly higher specificity toward E. coli selection with the use of the USEPA incubation protocol and mTEC agar. This study is the first to directly compare these methods and provides evidence that the methods may be used interchangeably for global surveillance of antibiotic resistance in the environment.

Environmental DNA: The next chapter.

Molecular tools are an indispensable part of ecology and biodiversity sciences and implemented across all biomes. About a decade ago, the use and implementation of environmental DNA (eDNA) to detect biodiversity signals extracted from environmental samples opened new avenues of research. Initial eDNA research focused on understanding population dynamics of target species. Its scope thereafter broadened, uncovering previously unrecorded biodiversity via metabarcoding in both well-studied and understudied ecosystems across all taxonomic groups. The application of eDNA rapidly became an established part of biodiversity research, and a research field by its own. Here, we revisit key expectations made in a land-mark special issue on eDNA in Molecular Ecology in 2012 to frame the development in six key areas: (1) sample collection, (2) primer development, (3) biomonitoring, (4) quantification, (5) behaviour of DNA in the environment and (6) reference database development. We pinpoint the success of eDNA, yet also discuss shortfalls and expectations not met, highlighting areas of research priority and identify the unexpected developments. In parallel, our retrospective couples a screening of the peer-reviewed literature with a survey of eDNA users including academics, end-users and commercial providers, in which we address the priority areas to focus research efforts to advance the field of eDNA. With the rapid and ever-increasing pace of new technical advances, the future of eDNA looks bright, yet successful applications and best practices must become more interdisciplinary to reach its full potential. Our retrospect gives the tools and expectations towards concretely moving the field forward.

Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region.

Chlorophyll a (Chl-a) in lakes serves as an effective marker for assessing algal biomass and the nutritional level of lakes, and its observation is feasible through remote sensing methods. HJ-1 (Huanjing-1) satellite, deployed in 2008, incorporates a CCD capable of a 30 m resolution and has a revisit interval of 2 days, rendering it a superb choice or supplemental sensor for monitoring trophic state of lakes. For effective long-term and regional-scale mapping, both the imagery and the evaluation of machine learning algorithms are essential. The several typical machine learning algorithms, i.e., Support Vector Regression (SVR), Gradient Boosting Decision Trees (GBDT), XGBoost (XGB), Random Forest (RF), K-Nearest Neighbor (KNN), Kernel Ridge Regression (KRR), and Multi-Layer Perception Network (MLP), were developed using our in-situ measured Chl-a. A cross-validation grid to identify the most effective hyperparameter combinations for each algorithm was used, as well as the selected optimal superparameter combinations. In Chl-a mapping of three typical lakes, the R2 of GBDT, XGB, RF, and KRR all reached 0.90, while XGB algorithm also exhibited stable performance with the smallest error (RMSE = 3.11 µg/L). Adjustments were made to align the Chl-a spatial-temporal patterns with past data, utilizing HJ1-A/B CCD images mapping through XGB algorithm, which demonstrates its stability. Our results highlight the considerable effectiveness and utility of HJ-1 A/B CCD imagery for evaluation and monitoring trophic state of lakes in a cold arid region, providing the application cases contribute to the ongoing efforts to monitor water qualities.