A vision for global eDNA-based monitoring in a changing world.

Environmental DNA (eDNA) has opened promising avenues for establishing standardized, cost-efficient monitoring of biodiversity. However, comprehensive and systematic implementation is urgently needed to address the current biodiversity crisis. Here, we envision a global eDNA biomonitoring scheme, which could potentially revolutionize the understanding and conservation of life on Earth.

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.

Robust chemical analysis with graphene chemosensors and machine learning.

Ion-sensitive field-effect transistors (ISFETs) have emerged as indispensable tools in chemosensing applications1-4. ISFETs operate by converting changes in the composition of chemical solutions into electrical signals, making them ideal for environmental monitoring5,6, healthcare diagnostics7 and industrial process control8. Recent advancements in ISFET technology, including functionalized multiplexed arrays and advanced data analytics, have improved their performance9,10. Here we illustrate the advantages of incorporating machine learning algorithms to construct predictive models using the extensive datasets generated by ISFET sensors for both classification and quantification tasks. This integration also sheds new light on the working of ISFETs beyond what can be derived solely from human expertise. Furthermore, it mitigates practical challenges associated with cycle-to-cycle, sensor-to-sensor and chip-to-chip variations, paving the way for the broader adoption of ISFETs in commercial applications. Specifically, we use data generated by non-functionalized graphene-based ISFET arrays to train artificial neural networks that possess a remarkable ability to discern instances of food fraud, food spoilage and food safety concerns. We anticipate that the fusion of compact, energy-efficient and reusable graphene-based ISFET technology with robust machine learning algorithms holds the potential to revolutionize the detection of subtle chemical and environmental changes, offering swift, data-driven insights applicable across a wide spectrum of applications.

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.

Land conflicts from overlapping claims in Brazil's rural environmental registry.

Satellite-based land use monitoring and farm-level traceability offer opportunities for targeted zero-deforestation interventions on private lands. Brazil's Rural Environmental Registry (Cadastro Ambiental Rural, or "CAR"), a land cadaster based on self-declaration of property boundaries, was created to monitor compliance with national forest laws. It has become an important enabling measure for sustainable supply chain initiatives like the Amazon Soy Moratorium. However, CAR enrollment is increasingly used to bolster illegal land claims, putting it at the heart of land grabbing dynamics. Self-declaration of properties in the CAR offers a unique situation to study land conflicts and their impact on land use decisions on a large scale. We quantified competing land claims among 846,420 registrations in the Brazilian Legal Amazon and applied a series of generalized linear mixed-effects models. We determined that CAR overlaps are more prevalent on larger registrations, in more densely settled areas, and in areas with less secure land tenure. We tested how landholders respond to land conflicts, finding significantly more deforestation and declared legal forest reserve on lands with multiple claims. CAR overlap results in an overestimation of forest reserves by up to 9.7 million hectares when considering double-counted and deforested areas of reserves, highlighting an overlooked form of Forest Code noncompliance. While the CAR continues to be used as evidence of land tenure, we conclude that the formalization of land claims through self-declarations is inadequate to decrease conflicts. CAR overlap information provides objective evidence of land conflict that authorities can leverage with field inspection to ensure peaceful occupation before issuing land titles.

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.

Microbial richness and air chemistry in aerosols above the PBL confirm 2,000-km long-distance transport of potential human pathogens.

The existence of viable human pathogens in bioaerosols which can cause infection or affect human health has been the subject of little research. In this study, data provided by 10 tropospheric aircraft surveys over Japan in 2014 confirm the existence of a vast diversity of microbial species up to 3,000 m height, which can be dispersed above the planetary boundary layer over distances of up to 2,000 km, thanks to strong winds from an area covered with massive cereal croplands in Northeast (NE) Asia. Microbes attached to aerosols reveal the presence of diverse bacterial and fungal taxa, including potential human pathogens, originating from sewage, pesticides, or fertilizers. Over 266 different fungal and 305 bacterial genera appeared in the 10 aircraft transects. Actinobacteria, Bacillota, Proteobacteria, and Bacteroidetes phyla dominated the bacteria composition and, for fungi, Ascomycota prevailed over Basidiomycota. Among the pathogenic species identified, human pathogens include bacteria such as Escherichia coli, Serratia marcescens, Prevotella melaninogenica, Staphylococcus epidermidis, Staphylococcus haemolyticus, Staphylococcus saprophyticus, Cutibacterium acnes, Clostridium difficile, Clostridium botulinum, Stenotrophomonas maltophilia, Shigella sonnei, Haemophillus parainfluenzae and Acinetobacter baumannii and health-relevant fungi such as Malassezia restricta, Malassezia globosa, Candida parapsilosis and Candida zeylanoides, Sarocladium kiliense, Cladosporium halotolerans, and Cladosporium herbarum. Diversity estimates were similar at heights and surface when entrainment of air from high altitudes occurred. Natural antimicrobial-resistant bacteria (ARB) cultured from air samples were found indicating long-distance spread of ARB and microbial viability. This would represent a novel way to disperse both viable human pathogens and resistance genes among distant geographical regions.

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.