Appendix C
Monitoring for Exposure
ENVIRONMENTAL MONITORS AND SENSORS
Air Quality
Fixed-Site Monitors
The U.S. Environmental Protection Agency (EPA) has done an excellent job developing and implementing a network of ambient air quality monitors for the pollutants regulated by the Clean Air Act: airborne particulate matter (PM2.5, PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ground-level ozone (O3), and lead. EPA also continues to make many applications of its Community Multiscale Air Quality (CMAQ) modeling system, for example, the recent identification and investigation of volatile combustion products and their effects on PM2.5 and ozone. These developments are critical to improve the predictive ability of CMAQ in assessment of how human activities influence air quality. The Office of Research and Development (ORD) has more recently developed an air sensor toolbox website that provides information on the performance, operation, and use of air sensor monitoring systems for technology developers, air quality managers, citizen scientists, and the general public.1 According to the website, “Air sensor monitors that are lower in cost, portable and generally easier to operate than regulatory-grade monitors are widely used in the United States to understand air quality conditions.”
Within urban areas, the increase in extreme events such as heat waves and wildfires interact with urban heat islands and dispersion of air pollutants. These interactions are not well understood, but they are expected to change atmospheric chemistry and formation of pollutants such as particulate matter and ozone concentrations differently in urban areas than in natural landscapes. With changes in energy and land use including urbanization and developments of green areas within urban zones, the formation and composition of fine particulate matter, including ultrafine particles, that affect air quality and human health have been changing. A decade ago, aerosol mass spectrometer measurements showed that the contribution of organic matter to fine particulate composition is at least as important as sulfate in the Northern Hemisphere (Jimenez et al., 2009). With sulfate emission controls in the recent decade, the importance of organic matter relative to sulfate has been increasing. Recent work shows that reductions in anthropogenic volatile organic compound (VOC) emission reductions are more than twice as effective as equivalent fractional reductions of SOx or NOx at reducing air pollution–associated cardiorespiratory mortality in the United States since VOC oxidation products play a key role in forming fine particles in the atmosphere (Pye et al., 2022). Therefore, most of the benefit from reducing VOCs was shown to be due to the reduction of secondary organic aerosols (SOAs) that are formed by oxidation of VOCs in the atmosphere and the uptake of resulting products in the particle phase. Climate-relevant SOA particles have also been shown to shield health-relevant polycyclic aromatic hydrocarbons from atmospheric chemical degradation and promote their long-range transport in the atmosphere. This mechanism represents an example of interactions between climate and health sciences (Shrivastava et al., 2017). Such interactions underscore the importance of cross-cutting scientific research across physical and health sciences.
Remote Sensing
Satellite imagery is increasingly integrated with EPA and other ground monitoring networks to monitor outdoor air quality in areas where ground instruments are sparse or absent. For example, as
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reviewed by EPA (2019), aerosol optical depth retrievals from the Moderate Resolution Imaging Spectroradiometer and the Multi-angle Imaging Spectroradiometer instruments have been used to improve estimates of PM2.5 exposure in health studies. Similarly, the Ozone Monitoring Instrument (OMI) onboard the National Aeronautics and Space Administration’s (NASA’s) Aura satellite has been providing relatively coarse (13 km × 25 km) global observations of the ozone layer and atmospheric pollutants such as NO2 and SO2 since October 2004 (Krotkov et al., 2016). Recently, Di et al. (2020) fused satellite-based NO2 measurements from the OMI with other satellite-based atmospheric measurements, weather data, land cover data and surface measurements from 912 EPA monitoring sites to predict daily NO2 levels on a 1-km2 grid for the contiguous United States from 2000 to 2016. This study illustrates the largely untapped potential to leverage EPA’s ground monitoring networks using remotely sensed imagery, ancillary information, and pollutant fate and transport models.
Remote sensing of air quality is rapidly advancing thanks to new or planned sensor systems deployed by the National Oceanic and Atmospheric Administration (NOAA) (VIIRS [Visible Infrared Imaging Radiometer Suite] and GOES-R Advanced Baseline Imager), the European Space Agency (ESA) (TROPOMI [TROPOspheric Monitoring Instrument]) and NASA (Tropospheric Emissions: Monitoring Pollution [TEMPO] and Multi-Angle Imager for Aerosols [MAIA]). EPA plays an important role in these missions as a key supplier of ground-based calibration and validation measurements and as a consumer of final air quality products. EPA is currently collaborating with NASA, NOAA, and the Smithsonian Astrophysical Observatory on the TEMPO satellite instrument (Naeger et al., 2021). Planned to launch in 2022, TEMPO will be placed in geosynchronous orbit to monitor air quality across southern Canada, the conterminous United States and Central America, including ozone, NO2, nitrous acid, SO2, formaldehyde, glyoxal, water, and a number of other gases and aerosols. As a partner in NASA’s Pandora Project,2 EPA is building a network of ground-based spectrometers to provide ground validation of TEMPO’s satellite-based air quality measurements.
Also in 2022, NASA plans to launch MAIA to explore the human health effects of exposure to different aerosol types (Diner et al., 2018). This mission engages a multi-disciplinary science team that includes epidemiologists and health professionals and is directly targeted at improving human health—a first for NASA. MAIA will focus on globally dispersed large metropolitan “target areas” and will be an important proof of concept for future multi-scale systems that cost-effectively integrate air quality and public health information, satellite measurements, and chemical transport models. EPA provides scientific advice and ground-based air quality data to the mission. Both TEMPO and to a more limited extent MAIA will also provide new capabilities for monitoring transport and impact of another important pollutant: wildfire smoke in the conterminous United States.
Wildfire smoke is of increasing concern given recent U.S. wildfire trends, but not well measured by current EPA regulatory monitoring networks (EPA, 2019). EPA has partnered with the U.S. Department of Agriculture (USDA) Forest Service and other land management agencies to develop the AirNow Fire and Smoke Map3 through a pilot project that incorporates temporary monitors and air quality sensor data—initially from PurpleAir PM2.5 measurements (PurpleAir, 2021)—to provide spatially improved Air Quality Index and associated public health messaging during wildfire season.
The rapid evolution of small satellite (<500 kg) systems over the past two decades raises the potential for monitoring air quality variables such as PM2.5 at very high spatial and temporal resolution. For instance, the company Planet maintains a constellation of roughly 200 small satellites providing multispectral imagery with daily global coverage at 3.5-m resolution. With additional research and algorithm development, such imagery might be applied to community-scale air quality monitoring and wildfire detection and plume monitoring (le Roux et al., 2021; Tan, 2020).
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2 See https://pandora.gsfc.nasa.gov.
3 See https://fire.airnow.gov.
Remote Sensing of Carbon Dioxide and Methane Fluxes
The role of greenhouse gases (GHGs) in Earth’s energy balance—especially carbon dioxide (CO2) and methane (CH4), which account for ~90 percent of present global warming—is relatively well understood. On the other hand, the distribution and trends in sources and sinks of those gases are less well quantified. EPA reports annually on U.S. anthropogenic GHG emissions and sinks by source, economic sector, and GHG going back to 1990. That inventory, which is submitted to the United Nations in accordance with the Framework Convention on Climate Change, is produced by aggregating data from federal and state agencies, municipalities, individual facilities, and many other sources.4 Ground instruments combined with aircraft- and satellite-borne sensors provide important complementary estimates of GHG fluxes that could not only improve the national inventory but also inform state and local inventories and mitigation efforts (Gurney and Shepson, 2021).
In situ surface and aircraft-borne instruments provide precise, accurate “bottom-up” data on GHG concentrations and fluxes, but that measurement network is relatively sparse. An international fleet of satellite-borne sensors now provides global data on column CO2 and CH4 concentrations that can be assimilated with these ground measurements in atmospheric models that can then be inverted to estimate CO2 and CH4 budgets on a roughly 0.5- × 0.5-degree global grid. These sensors include NASA’s OCO-2 and OCO-3, Japan’s GEOSAT and GEOSAT-2, and ESA’s Copernicus systems. Both OCO-2 and OCO-3 are nearing end of life and the United States currently has no plans to replace them. However, several projects promise to deliver high-resolution data on CO2 and/or CH4 emissions and fluxes for selected areas, including GeoCarb, Carbon Mapper, and MethaneSat, discussed below.
In 2024, the Geostationary Carbon Observatory (GeoCarb)—a NASA Earth Venture—is planned for launch to provide daily observations of CO2, CH4, CO and solar-induced fluorescence across the Americas at a resolution of 5-10 km (Moore et al., 2018). In principle, GeoCarb will allow estimation of regional fossil fuel CO2 emissions as well as biotic CO2 sinks, and better quantify currently underreported emissions from large urban areas (Gurney et al., 2021). GeoCarb will also identify large point sources of CH4 and could resolve large discrepancies between EPA’s estimates and estimates of conterminous U.S. CH4 emissions (Lu et al., 2022).
The Carbon Mapper mission is designed to selectively track point-source emissions at facility-scale (30-35 m) on a 1- to 7-day sample interval using a constellation of small satellites. The goal is to help resolve discrepancies between emission estimates produced from satellites, national inventories, and self-reporting programs. Carbon Mapper is a philanthropically funded, public–private partnership with a limited demonstration phase scheduled to launch in 2023. Similarly, MethaneSat is philanthropically funded and designed to obtain relatively high-resolution, high-precision estimates of CH4 for targeted regions and point sources. Data from both Carbon Mapper and MethaneSat will be publicly available in near real time.
Pathogens
Although the widespread disruptions caused by the COVID-19 pandemic suggest that it could not have been foreseen, in fact experts and historians have been warning for years that there is a periodicity to the emergence of pandemics. The COVID-19 pandemic is only the latest but is likely to have permanently shifted the public perspective on what is considered clean air and the relative health risks associated with airborne contaminants, both biological and abiological. Public health policy and communications during the pandemic (in particular, the evolving views among medical and public health professionals on the role of airborne transmission in the spread of COVID-19 disease) drew perhaps unprecedented public attention to and interest in the fate of microbiological organisms as aerosols in air. Since the agency’s inception, EPA staff scientists and partners have led efforts to produce the supporting scientific basis for establishing ambient air quality standards and source-based emissions standards, including those relating to aerosols. However, the important task of communicating the physical principles of aerosols and methods for their removal
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4 See https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2020.
from air, such as face masks and indoor ventilation, were largely taken on by medical and public health professionals rather than aerosol scientists.
The principles that govern methods for reducing human exposure to and inhalation of aerosols can be fraught with subtleties that complicate their widespread and effective application; the importance of face-mask fit is one such example. At a much larger scale, principles of room or building ventilation are similarly characterized by complexities and nuances that are not readily grasped by generalists or specialists in other fields.
Monitoring Land Use, Biodiversity, and Ecosystem Services
Technologies for biodiversity and ecosystem monitoring are advancing rapidly along many fronts ranging from molecular methods such as barcoding and environmental DNA (eDNA) metabarcoding (see Box C-1) to portable biomonitoring sensors to ground-based, airborne, and satellite remote sensing. This section focuses on some recent advances in biomonitoring along with satellite remote sensing of biodiversity and ecosystem services. Emphasis is placed on the increasing use and power of integrated multi-source data to produce detailed, four-dimensional information on Earth surface features and processes. An overarching theme is that realizing the full potential of surface monitoring networks and remote sensing data depends on their fusion and assimilation into statistical and mechanistic modeling frameworks.
Lightweight, inexpensive bio-loggers including global positioning systems, accelerometers, video cameras, telemetry tags, radiofrequency identification, and aquatic acoustic telemetry now make it possible to track animal movements, behavior, and physiology at scales not previously possible (Hellström et al., 2016; Jetz et al., 2022; Nathan et al., 2022; Smith and Pinter-Wollman, 2021). Such data can be collected in both experimental and natural settings and can inform ecotoxicological risk assessments by revealing patterns of animal exposure as well as physiological and behavioral responses to those exposures (Bertram et al., 2022). Similarly, low-cost bioacoustic recorders now allow cost-effective monitoring of biodiversity across a broad range of taxa, helping to track community responses to habitat loss and climate-related shifts in the timing of species’ migration and life history events (Jetz et al., 2022; Oliver et al., 2018).
Because EPA depends on federal and state partners for much of the information on biodiversity and ecosystem function that it uses to assess ecological risks and ecosystem services, technological advances in biomonitoring indirectly benefit EPA by improving the quality and quantity of information provided by those partners. For example, in meeting its obligations under the Endangered Species Act, EPA’s Pesticide Program relies on the Fish and Wildlife Service and the National Marine Fisheries Service for information on the distribution of endangered species and their habitats, and on USDA and others for information on cropping practices and pesticide applications (EPA, 2022). As information on species improves, EPA and its partner agencies will be able to design mitigation efforts better tailored to specific locations, populations, and ecosystems (EPA, 2022).
Biomonitoring technologies also directly support EPA’s research into species of concern and wildlife exposure to pollutants. For example, radiotelemetry has been used by EPA researchers to identify coldwater refuges for migrating steelhead and salmon in the Columbia River Basin, monitor manatee use of seagrass beds in Florida, and track lake sturgeon movements and habitat use in the St. Louis River. Sensor improvements and satellite-based tracking, in concert with inexpensive environmental sensor systems, can be used to extend such capabilities to observe movement, behavior, physiology and habitat conditions for many more species and environments (e.g., Icarus).5 There is also the opportunity and need to monitor behavior of wildlife in the field to confirm laboratory responses of species to chemical pollutants (Ford et al., 2021).
For decades, optical, thermal, lidar and radar remote sensing data have been applied in various ways over landscape-to-global scales to estimate critical terrestrial ecosystem properties and processes such as vegetation structure and composition, evapotranspiration, gross and net primary production, net
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ecosystem carbon balance, and carbon stored in vegetation (e.g., Eitel et al., 2016; Schimel et al., 2019). Aircraft and satellite remote sensing are now routinely used for cost-effective monitoring of land use and land cover on local to global scales. For example, EPA uses Landsat products such as the Cropland Data Layer and the National Land Cover Database (NLCD) to track agricultural expansion for the U.S. Renewable Fuel Standard (Wang et al., 2022). EPA partners with the U.S. Geological Survey (USGS), USDA, and others to maintain EnviroAtlas, which uses NLCD and other remote sensing products to map and monitor the quality and extent of ecosystem services across the United States (Cochran et al., 2020).
As the diversity, quantity, and quality of remotely sensed data as well as computing power continue to grow exponentially, so does the range of applications to environmental assessment and monitoring (e.g., Cavender-Bares et al., 2020; de Araujo Barbosa et al., 2015; Lahoz-Monfort and Magrath, 2021; Wang and Gamon, 2019).
Investigating Multi-Decadal Environmental Trends with Multi-Spectral Imagery
For instance, changes in the Landsat program have dramatically increased the feasibility of long-baseline ecological studies (Wulder et al., 2019). In late 2020 the USGS released the Collection 2 Landsat archive featuring substantially improved absolute geolocation and cloud-optimized global surface reflectance and surface temperature products from Landsat 1-9 and Harmonized Landsat and Sentinel 2 systems6 (Masek et al., 2020). These long-baseline data are now being used for a wide range of applications such as monitoring of warming-driven changes in high-latitude vegetation and surface hydrology (Pastick et al., 2019), tropical forest degradation (Bullock et al., 2020), land disturbances (Zhu et al., 2020), U.S. rangelands (Jones et al., 2018), vegetation phenology, and land and water surface temperatures (reviewed in Zhu, 2017).
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6 See https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products.
The ever-growing Landsat Collection-2 image archive presents an opportunity for EPA to develop refined indicators of ecosystem function and services that exploit the archive’s temporal depth, spatial, and radiometric fidelity. As a case in point, many of the national-scale indicators provided in EPA’s EnviroAtlas such as soil loss, forest and wetland cover in stream buffer zones, and land-cover connectivity are based on 2011 NLCD data and thus provide only an outdated snapshot of indicators that could in principle be tracked through time (Cochran et al., 2020; Cord et al., 2017). Integration and processing of large volumes of ground data and massive volumes of image data can be challenging but increasingly tractable using cloud computing and advanced machine learning algorithms (see Data Science sections, below). For example, Jones et al. (2018) analyzed more than 230,000 satellite scenes and 31,000 ground survey plots along with digital elevation and soil maps to track changes in land-cover type by plant functional type and vegetation condition in rangelands of the western United States at 30-m resolution from 1984 to 2017. As those authors demonstrate, the ability to monitor ecosystem trends over a large area at fine space and timescales facilitates multi-scale evaluation of ecosystem services, conservation programs and policies, wildfire impacts, and land-management approaches.
New sensor capabilities and improved retrieval algorithms are allowing rapid gains in our ability to monitor ecosystems globally with increasing spatial, spectral, and temporal resolution. For example, Dubayah et al. (2022) recently mapped pan-tropical and temperate aboveground forest biomass at 1-km resolution using forest plot data and the Global Ecosystem Dynamics Investigation lidar instrument on the International Space Station (Dubayah et al., 2022). Solar-induced fluorescence measurements from satellite-borne imaging spectrometers can now provide global estimates of terrestrial net primary productivity at near-daily time steps and high (3.5- × 7-km) spatial resolution (Köhler et al., 2018).
Next-generation remote sensing of biodiversity and ecosystem function will be enabled by satellite-borne imaging spectrometers measuring in the visible and shortwave infrared (VSWIR) and thermal infrared (TIR) regions. In April 2022 the German Environmental Mapping and Analysis Program successfully completed its launch and early orbit phase and will soon provide analysis-ready data products globally at 30 m that will support a range of applications for monitoring climate, hydrology, water quality and pollution, land cover, soils, and biodiversity (Bachmann et al., 2021; Guanter et al., 2015). EPA’s participation in the cyanobacteria assessment network (CyAN) project has shown the benefits of multi-agency partnerships in addressing many of the complex issues (e.g., public health and economic early warning systems, linkages with landscape loadings) around algal blooms. NASA plans to install the Earth Surface Mineral Dust Source Investigation on the International Space Station to map the surface mineralogy of global dust source regions including large regions of the western and midwestern United States.
NASA’s Surface Biology and Geology (SBG) Designated Observable is now determining feasible alternative observing systems that can meet SBG’s ambitious objectives of observing land, coastal, and ocean ecosystems in the hyperspectral VSWIR and TIR regions. Solid earth products will include surface mineralogy, soil erosion, texture, organic carbon, contaminants, water, and nutrient content. These variables can be retrieved for bare soils and thus will be especially informative for arid ecosystems as well as cultivated, disturbed, and degraded lands (Cawse-Nicholson et al., 2021; Chabrillat et al., 2019).
SBG will provide a never-before-achieved view of plant biodiversity and ecosystem functioning at the global scale, including measurements of foliar functional traits such as
- Leaf mass per area,
- Equivalent water thickness,
- Phenolic concentration,
- Leaf nitrogen, and
- Leaf chlorophyll.
These traits along with others can be retrieved with moderate to high reliability using imaging spectrometry (e.g., Cavender-Bares et al., 2020; Wang et al., 2020). Fusion of hyperspectral imagery with vegetation structural information from imaging lidar is especially powerful for characterizing vegetation
composition, plant functional diversity, and associated ecosystem properties over large areas (e.g., Brodrick and Asner, 2017; Fricker et al., 2019; Schneider et al., 2017).
Approaches and algorithms for combining ground information with hyperspectral imagery are under rapid development, partly because such data are now freely available for a diverse set of ecoregions through the National Ecological Observatory (Jones et al., 2021). Of particular interest are the relationships between plant phylogenetic diversity, functional trait diversity, and multi-scale optical diversity of Earth surfaces (“spectral diversity”). As we gain understanding of the relationship between biodiversity and spectral diversity, there is the real potential to monitor ecosystem composition, function, and structure together, providing for the first time a global biodiversity monitoring system (Cavender-Bares et al., 2020; Jetz et al., 2016). This information could greatly benefit EPA’s efforts to track changes in the nation’s ecosystems and the regulating and provisioning services that they provide.
Small satellite constellations enable local-to-global monitoring of Earth surfaces at unprecedented spatiotemporal resolution. Although they do not meet the rigorous performance standards of large national systems, lower-cost commercial systems designed to meet specific information needs can provide complementary data to the Earth observing systems discussed above for applications such as disaster management or detection and monitoring of fire, land clearing, oil spills, and pest species invasions (e.g., Lake et al., 2022). The NASA Commercial Smallsat Data Acquisition Program,7 which was established to identify, evaluate, and acquire commercial Smallsat data, is a good source of information on this rapidly expanding frontier of Earth remote sensing.
NONTARGETED ANALYSIS
A key limitation of current targeted chemical analysis is that it relies on a priori selection of chemicals to study, usually without information about where, how, and the extent to which unregulated or underregulated and understudied chemicals from diverse sources are likely to be present in humans and ecosystems (Egeghy et al., 2012; Judson et al., 2009). As a result, significant time and resources can be expended to develop analytical methods for chemicals that may or may not be present in human or environmental matrices. Therefore, nontargeted analysis (NTA) can be a more systematic technique that identifies the full spectrum of candidate compounds present in matrices, thus facilitating a more efficient strategy for prioritizing which compounds to measure.
The NTA Approach
The NTA approach first involves sample collection and processing. Analytes are extracted (solid-phase extraction, liquid-liquid extraction, digestion) from the sample, cleaned up to remove interferents, separated via chromatography (gas chromatography, liquid chromatography [LC]), and identified by mass spectrometry. The mass spectrometer is responsible for ionization, either hard (electron ionization) or soft (electrospray ionization [ESI], chemical ionization; matrix-assisted laser desorption ionization [MALDI]) techniques in positive or negative ionization modes, separation of ions by mass to charge (time of flight, quadrupole), and mass analysis and detection. The identification of detected mass-to-charge ratio fragments (m/z) can be analyzed by post-processing software. Although NTA is not a quantifiable technique, the total ion chromatogram (in the case of LC) is indicative of relative abundances of detected ions. Indeed, while NTA output data can be used to indicate chemical presence, a notable barrier to using these data in epidemiological studies or other applications is the inability to directly measure chemical concentrations, which requires use of analytical standards and methods development. Although abundances correlate with chemical concentrations, they cannot be used to measure concentrations directly because chemicals with the same concentrations can exhibit very different abundances (e.g., peak area or height).
The skill sets needed to apply NTA and subsequent applications in the environment and related human health include:
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- Sample collection (environmental, biospecimen),
- Chemistry (analytical, organic, computational), and
- Specific NTA applications (biochemistry, genomics/bioinformatics, public health).
Omics Approaches in Nontargeted Analyses
Other NTAs related to environmental and human health include the -omics approaches. In metagenomics, next-generation sequencing (NGS) has improved the detection of gene products in complex systems. Virtually the entire genome of an organism, consortium, or environmental sample can be sequenced and analyzed with technology that incorporates DNA synthesis and fluorescence spectroscopy. Metaproteomics employs protein mass spectrometry (LC with ESI or MALDI, followed by mass separation) after digestion of a protein into peptides. Differences in m/z fragments are mapped to amino acids and their side chains. These tools have the same quantification limitations as traditional NTA, but identification of the protein or nucleic acid sequence is straightforward due to availability of open-source sequence databases. In a recent conceptual paper, Gao (2021) proposes combining NTA with genomic analyses to understand the role of environmental stressors in a One-Health context.
Improvements in analytical instrumentation and methodology have enabled sequencing of the metagenome. NGS vastly increased genomics output over Sanger sequencing. In Sanger sequencing, polymerase chain reaction (PCR) products with fluorescently labeled nucleotides are preamplified, and then targeted amplicons are sequenced during gel electrophoresis. After DNA or RNA fragmentation, NGS packages the PCR extension reaction with fluorescently labeled nucleotides in a flow cell as part of the sequencing process, reading millions of nucleotides in near real time.
An example of improved analytical quantification is the droplet digital PCR (ddPCR), increasingly used to quantify low-abundance genes. With ddPCR, absolute quantification of target genes in the absence of a standard curve is possible. This improvement in quantification of targeted gene products over quantitative PCR results from aerosolizing droplets of PCR reactions and tracking gene amplification signal.
REFERENCES
Bachmann, M., K. Alonso, E. Carmona, B. Gerasch, M. Habermeyer, S. Holzwarth, H. Krawczyk, M. Langheinrich, D. Marshall, M. Pato, N. Pinnel, R. Reyes, M. Schneider, P. Schwind, and T. Storch. 2021. Analysis-ready data from hyperspectral sensors—The design of the EnMAP CARD4L-SR data product. Remote Sensing 13(22):4536. https://doi.org/10.3390/rs13224536.
Bertram, M. G., J. M. Martin, E. S. McCallum, L. A. Alton, J. A. Brand, B. W. Brooks, D. Cerveny, J. Frick, A. T. Ford, G. Hellstrom, M. Michelangeli, S. Nakagawa, G. Polverino, M. Saaristo, A. Sih, H. Tan, C. R. Tyler, B. B. M. Wong, and T. Brodin. 2022. Frontiers in quantifying wildlife behavioural responses to chemical pollution. Biological Reviews of the Cambridge Philosophical Society 97(4):1346-1364. https://doi.org/10.1111/brv.12844.
Brodrick, P. G., and G. P. Asner. 2017. Remotely sensed predictors of conifer tree mortality during severe drought. Environmental Research Letters 12(11):115013. https://doi.org/10.1088/1748-9326/aa8f55.
Bullock, E. L., C. E. Woodcock, and P. Olofsson. 2020. Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis. Remote Sensing of Environment 238:110968. https://doi.org/10.1016/j.rse.2018.11.011.
Cavender-Bares, J., J. A. Gamon, and P. A. Townsend (eds.). 2020. Remote Sensing of Plant Biodiversity. Springer Nature. https://doi.org/10.1007/978-3-030-33157-3.
Cawse-Nicholson, K., P. A. Townsend, D. Schimel, A. M. Assiri, P. L. Blake, M. F. Buongiorno, P. Campbell, N. Carmon, K. A. Casey, R. E. Correa-Pabon, K. M. Dahlin, H. Dashti, P. E. Dennison, H. Dierssen, A. Erickson, J. B. Fisher, R. Frouin, C. K. Gatebe, H. Gholizadeh, M. Gierach, N. F. Glenn, J. A. Goodman, D. M. Griffith, L. Guild, C. R. Hakkenberg, E. J. Hochberg, T. R. H.
Holmes, C. Hu, G. Hulley, K. F. Huemmrich, R. M. Kudela, R. F. Kokaly, C. M. Lee, R. Martin, C. E. Miller, W. J. Moses, F. E. Muller-Karger, J. D. Ortiz, D. B. Otis, N. Pahlevan, T. H. Painter, R. Pavlick, B. Poulter, Y. Qi, V. J. Realmuto, D. Roberts, M. E. Schaepman, F. D. Schneider, D. R. Thompson, J. L. Torres-Perez, K. R. Turpie, M. Tzortziou, S. Ustin, Q. Yu, Y. Yusup, Q. Zhang, and the SBG Algorithms Working Group. 2021. NASA’s Surface Biology and Geology Designated Observable: A perspective on surface imaging algorithms. Remote Sensing of Environment 257:112349. https://doi.org/10.1016/j.rse.2021.112349.
Chabrillat, S., E. Ben-Dor, J. Cierniewski, C. Gomez, T. Schmid, and B. van Wesemael. 2019. Imaging spectroscopy for soil mapping and monitoring. Surveys in Geophysics 40:361-399. https://doi.org/10.1007/s10712-019-09524-0.
Clare, E. L., C. K. Economou, C. G. Faulkes, J. D. Gilbert, F. Bennett, R. Drinkwater, and J. E. Littlefair. 2021. eDNAir: Proof of concept that animal DNA can be collected from air sampling. PeerJ 9:e11030. https://doi.org/10.7717/peerj.11030.
Cochran, F., J. Daniel, L. Jackson, and A. Neale. 2020. Earth observation-based ecosystem services indicators for national and subnational reporting of the sustainable development goals. Remote Sensing of Environment 244:111796. https://doi.org/10.1016/j.rse.2020.111796.
Cord, A. F., K. A. Brauman, R. Chaplin-Kramer, A. Huth, G. Ziv, and R. Seppelt. 2017. Priorities to advance monitoring of ecosystem services using Earth observation. Trends in Ecology & Evolution 32(6):416-428. https://doi.org/10.1016/j.tree.2017.03.003.
de Araujo Barbosa, C. C., P. M. Atkinson, and J. A. Dearing. 2015. Remote sensing of ecosystem services: A systematic review. Ecological Indicators 52:430-443. https://doi.org/10.1016/j.ecolind.2015.01.007.
Di, Q., H. Amini, L. Shi, I. Kloog, R. Silvern, J. Kelly, M. B. Sabath, C. Choirat, P. Koutrakis, A. Lyapustin, Y. Wang, L. J. Mickley, and J. Schwartz. 2020. Assessing NO2 concentration and model uncertainty with high spatiotemporal resolution across the contiguous United States using ensemble model averaging. Environmental Science & Technology 54:1372-1384. https://doi.org/10.1021/acs.est.9b03358.
Diner, D. J., S. W. Boland, M. Brauer, C. Bruegge, K. A. Burke, R. A. Chipman, L. Di Girolamo, M. J. Garay, S. Hasheminassab, E. Hyer, M. Jerrett, V. Jovanovic, O. Kalashnikova, Y. Liu, A. Lyapustin, R. Martin, A. Nastan, B. D. Ostro, B. Ritz, J. Schwartz, J. Wang, and F. Xu. 2018. Advances in multiangle satellite remote sensing of speciated airborne particulate matter and association with adverse health effects: From MISR to MAIA. Journal of Applied Remote Sensing 12(4):042603. https://doi.org/10.1117/1.JRS.12.042603.
Dubayah, R., J. Armston, S. Healey, J. Bruening, P. Patterson, J. Kellner, L. Duncanson, S. Saarela, G. Ståhl, Z. Yang, H. Tang, J. B. Blair, L. Fatoyinbo, S. Goetz, S. Hancock, M. Hansen, G. Hurtt, and S. Luthcke. 2022. GEDI launches a new era of biomass inference from space. Environmental Research Letters 17(9). https://eartharxiv.org/repository/view/3278.
Egeghy, P. P., R. Judson, S. Gangwal, S. Mosher, D. Smith, J. Vail, and E. A. Cohen Hubal. 2012. The exposure data landscape for manufactured chemicals. Science of the Total Environment 414:159-166. https://doi.org/10.1016/j.scitotenv.2011.10.046.
Eitel, J. U. H., B. Höfle, L. A. Vierling, A. Abellán, G. P. Asner, J. S. Deems, C. L. Glennie, P. C. Joerg, A. L. LeWinter, T. S. Magney, G. Mandlburger, D. C. Morton, J. Müller, and K. T. Vierling. 2016. Beyond 3-D: The new spectrum of lidar applications for Earth and ecological sciences. Remote Sensing of Environment 186:372-392. https://doi.org/10.1016/j.rse.2016.08.018.
EPA (U.S. Environmental Protection Agency). 2019. Integrated Science Assessment (ISA) for Particulate Matter. Final Report, December. EPA/600/R-19/188. https://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=347534.
EPA. 2022. Balancing Wildlife Protection and Responsible Pesticide Use: How EPA’s Pesticide Program Will Meet Its Endangered Species Act Obligations. https://www.epa.gov/system/files/documents/2022-04/balancing-wildlife-protection-and-responsible-pesticide-use_final.pdf.
Ford, A. T., M. Ågerstrand, B. W. Brooks, J. Allen, M. G. Bertram, T. Brodin, Z. Dang, S. Duquesne, R. Sahm, F. Hoffmann, H. Hollert, S. Jacob, N. Klüver, J. M. Lazorchak, M. Ledesma, S. D. Melvin, S. Mohr, S. Padilla, G. G. Pyle, S. Scholz, M. Saaristo, E. Smit, J. A. Steevens, S. van den Berg, W. Kloas, B. B. M. Wong, M. Ziegler, and G. Maack. 2021. The role of behavioral ecotoxicology in environmental protection. Environmental Science & Technology 55(9):5620-5628. https://doi.org/10.1021/acs.est.0c06493.
Fricker, G. A., J. D. Ventura, J. A. Wolf, M. P. North, F. W. Davis, and J. Franklin. 2019. A convolutional neural network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery. Remote Sensing 11(19):2326. https://doi.org/10.3390/rs11192326.
Gao, P. 2021. The Exposome in the era of one health. Environmental Science & Technology 55:2790-2799. https://doi.org/10.1021/acs.est.0c07033.
Guanter, L., H. Kaufmann, K. Segl, S. Foerster, C. Rogass, S. Chabrillat, T. Kuester, A. Hollstein, G. Rossner, C. Chlebek, C. Straif, S. Fischer, S. Schrader, T. Storch, U. Heiden, A. Mueller, M. Bachmann, H. Mühle, R. Müller, M. Habermeyer, A. Ohndorf, J. Hill, H. Buddenbaum, P. Hostert, S. Van der Linden, P. J. Leitão, A. Rabe, R. Doerffer, H. Krasemann, H. Xi, W. Mauser, T. Hank, M. Locherer, M. Rast, K. Staenz, and B. Sang. 2015. The EnMAP spaceborne imaging spectroscopy mission for Earth observation. Remote Sensing 7(7):8830-8857. https://doi.org/10.3390/rs70708830.
Gurney, K. R., and P. Shepson. 2021. The power and promise of improved climate data infrastructure. Proceedings of the National Academy of Sciences of the United States of America 118(35):e2114115118. https://doi.org/10.1073/pnas.2114115118.
Gurney, K. R., J. Liang, G. Roest, Y. Song, K. Mueller, and T. Lauvaux. 2021. Under-reporting of greenhouse gas emissions in U.S. cities. Nature Communications 12(1):553. https://doi.org/10.1038/s41467-020-20871-0.
Hellström, G., J. Klaminder, M. Jonsson, J. Fick, and T. Brodin. 2016. Upscaling behavioural studies to the field using acoustic telemetry. Aquatic Toxicology 170:384-389. https://doi.org/10.1016/j.aquatox.2015.11.005.
Jerde, C. L., A. R. Mahon, W. L. Chadderton, and D. M. Lodge. 2011. “Sight-unseen” detection of rare aquatic species using environmental DNA. Conservation Letters 4(2):150-157. https://doi.org/10.1111/j.1755-263X.2010.00158.x.
Jetz, W., J. Cavender-Bares, R. Pavlick, D. Schimel, F. W. Davis, G. P. Asner, R. Guralnick, J. Kattge, A. M. Latimer, P. Moorcroft, M. E. Schaepman, M. P. Schildhauer, F. Schrodt, U. Stahl, and S. L. Ustin. 2016. Monitoring plant functional diversity from space. Nature Plants 2:16024. https://doi.org/10.1038/nplants.2016.24.
Jetz, W., G. Tertitski, R. Kays, U. Mueller, M. Wikelski, S. Åkesson, Y. Anisimov, and Supporting Authors. 2022. Biological Earth observation with animal sensors. Trends in Ecology & Evolution 37(4):293-298. https://doi.org/10.1016/j.tree.2021.11.011.
Jimenez, J. L., M. R. Canagaratna, N. M. Donahue. S. H. Prevot, Q. Zhang, J. H. Kroll, P. F. Decarlo, J. D. Allan, H. Coe, N. L. Ng, A. C. Aiken, K. S. Docherty, I. M. Ulbrich, A. P. Grieshop, A. L. Robinson, J. Duplissy, J. D. Smith, K. R. Wilson, V. A. Lanz, C. Hueglin, Y. L. Sun, J. Tian, A. Laaksonen, T. Raatikainen, J. Rautiainen, P. Vaattovaara, M. Ehn, M. Kulmala, J. M. Tomlinson, D. R. Collins, M. J. Cubison, E. J. Dunlea, J. A. Huffman, T. B. Onasch, M. R. Alfarra, P. I. Williams, K. Bower, Y. Kondo, J. Schneider, F. Drewnick, S. Borrmann, S. Weimer, K. Demerjian, D. Salcedo, L. Cottrell, R. Griffin, A. Takami, T. Miyoshi, S. Hatakeyama, A. Shimono, J. Y Sun, Y. M. Zhang, K. Dzepina, J. R. Kimmel, D. Sueper, J. T. Jayne, S. C. Herndon, A. M. Trimborn, L. R. Williams, E. C. Wood, A. M. Middlebrokk, C. E. Kolb, U. Baltensperger, and D. R. Worsnop. 2009. Evolution of Organic Aerosols in the Atmosphere. Science 326(5959):1525-1529. https://doi.org/10.1126/science.1180353.
Johnson, M. D., M. Fokar, R. D. Cox, and M. A. Barnes. 2021. Airborne environmental DNA metabarcoding detects more diversity, with less sampling effort, than a traditional plant community survey. BMC Ecology and Evolution 21:218. https://doi.org/10.1186/s12862-021-01947-x.
Jones, J. A., P. M. Groffman, J. Blair, F. W. Davis, H. Dugan, E. E. Euskirchen, S. D. Frey, et al. 2021. Synergies among environmental science research and monitoring networks: A research agenda. Earth’s Future 9(3). https://doi.org/10.1029/2020EF001631.
Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J. Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. Mclver. 2018. Innovation in rangeland monitoring: Annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9(9):e02430. https://doi.org/10.1002/ecs2.2430.
Judson, R., A. Richard, D. J. Dix, K. Houck, M. Martin, R. Kavlock, V. Dellarco, T. Henry, T. Holderman, P. Sayre, S. Tan, T. Carpenter, and E. Smith. 2009. The toxicity data landscape for environmental chemicals. Environmental Health Perspectives 117(5):685-695. https://doi.org/10.1289/ehp.0800168.
Köhler, P., C. Frankenberg, T. S. Magney, L. Guanter, J. Joiner, and J. Landgraf. 2018. Global retrievals of solar-induced chlorophyll fluorescence with TROPOMI: First results and intersensor comparison to OCO-2. Geophysical Research Letters 45(19):10456-10463. https://doi.org/10.1029/2018GL079031.
Krotkov, N. A., C. A. McLinden, C. Li, L. N. Lamsal, E. A. Celarier, S. V. Marchenko, W. H. Swartz, E. J. Bucsela, J. Joiner, B. N. Duncan, K. F. Boersma, J. P. Veefkind, P. F. Levelt, V. E. Fioletov, R. R. Dickerson, H. He, Z. Lu, and D. G. Streets. 2016. Aura OMI observations of regional SO2 and NO2 pollution changes from 2005 to 2015. Atmospheric Chemistry and Physics 16:4605-4629. https://doi.org/10.5194/acp-16-4605-2016.
Lahoz-Monfort, J.J. and M. J. L Magrath. 2021. A comprehensive overview of technologies for species and habitat monitoring and conservation. BioScience 71(10):1038–62. https://doi.org/10.1093/biosci/biab073.
Lake, T. A., R. D. Briscoe Runquist, and D. A. Moeller. 2022. Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery. Remote Sensing in Ecology and Conservation 8(6):875-889. https://doi.org/10.1002/rse2.288.
le Roux, J., S. Christopher, and M. Maskey. 2021. Exploring the use of PlanetScope data for particulate matter air quality research. Remote Sensing 13(15):2981. https://doi.org/10.3390/rs13152981.
Littlefair, J. E., L. E. Hrenchuk, P. J. Blanchfield, M. D. Rennie, and M. E. Cristescu. 2020. Thermal stratification and fish thermal preference explain vertical eDNA distributions in lakes. Molecular Ecology 30:3083-3096. https://doi.org/10.1111/mec.15623.
Lu, X., D. J. Jacob, H. Wang, J. D. Maasakkers, Y. Zhang, T. R. Scarpelli, L. Shen, Z. Qu, M. P. Sulprizio, H. Nesser, A. A. Bloom, S. Ma, J. R. Worden, S. Fan, R. J. Parker, H. Boesch, R. Gautam, D. Gordon, M. D. Moran, F. Reuland, C. A. O. Villasana, and A. Andrews. 2022. Methane emissions in the United States, Canada, and Mexico: Evaluation of national methane emission inventories and 2010–2017 sectoral trends by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric observations. Atmospheric Chemistry and Physics 22:395:418. https://doi.org/10.5194/acp-22-395-2022.
Masek, J. G., M. A. Wulder, B. Markham, J. McCorkel, C. J. Crawford, J. Storey, and D. T. Jenstrom. 2020. Landsat 9: Empowering open science and applications through continuity. Remote Sensing of Environment 248:111968. https://doi.org/10.1016/j.rse.2020.111968.
McElroy, M. E., T. L. Dressler, G. C. Titcomb, E. A. Wilson, K. Deiner, T. L. Dudley, E. J. Eliason, N. T. Evans, S. D. Gaines, K. D. Lafferty, G. A. Lamberti, Y. Li, D. M. Lodge, M. S. Love, A. R. Mahon, M. E. Pfrender, M. A. Renshaw, K. A. Selkoe, and C. L. Jerde. 2020. Calibrating environmental DNA metabarcoding to conventional surveys for measuring fish species richness. Frontiers in Ecology and Evolution 8:276. https://doi.org/10.3389/fevo.2020.00276.
Moore, B., III, S. M. R. Crowell, P. J. Rayner, J. Kumer, C. W. O’Dell, D. O’Brien, S. Utembe, I. Polonsky, D. Schimel, and J. Lemen. 2018. The potential of the Geostationary Carbon Cycle Observatory (GeoCarb) to provide multi-scale constraints on the carbon cycle in the Americas. Frontiers in Environmental Science 6. https://doi.org/10.3389/fenvs.2018.00109.
Naeger, A. R., M. J. Newchurch, T. Moore, K. Chance, X. Liu, S. Alexander, K. Murphy, and B. Wang. 2021. Revolutionary air-pollution applications from future tropospheric emissions: monitoring of pollution (TEMPO) observations. Bulletin of the American Meteorological Society 102(9):E1735–E1741. https://doi.org/10.1175/BAMS-D-21-0050.1.
Nathan, R., C. T. Monk, R. Arlinghaus, T. Adam, J. Alós, M. Assaf, H. Baktoft, C. E. Beardsworth, M. G. Bertram, A. I. Bijlevld, H. T. Brodin, J. I. Brooks, A. Campos-Candela, S. J. Cooke, K. Ø. Gjelland, P. R. Gupte, R. Harel, G. Hellström, F. Jeltsch, S. S. Killen, T. Klefoth, R. Langrock, R. J. Lennox, E. Lourie, J. R. Madden, Y. Orchan, I. S. Pauwels, M. Říha, M. Roeleke, U. E. Schlägel, D. Shohami, J. Signer, S. Toledo, O. Vilk, S. Westrelin, M. A. Whiteside, I. Jarić. 2022. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375(6582):eabg1780. https://doi.org/10.1126/science.abg1780.
Oliver, R. Y., D. P. W. Ellis, H. E. Chmura, J. S. Krause, J. H. Pérez, S. K. Sweet, L. Gough, J. C. Wingfield, and N. T. Boelman. 2018. Eavesdropping on the Arctic: Automated bioacoustics reveal dynamics in songbird breeding phenology. Science Advances 4(6):eaaq1084. https://doi.org/10.1126/sciadv.aaq1084.
Pastick, N. J., M. T. Jorgenson, S. J. Goetz, B. M. Jones, B. K. Wylie, B. J. Minsley, H. Genet, J. F. Knight, D. K. Swanson, and J. C. Jorgenson. 2019. Spatiotemporal remote sensing of ecosystem change and causation across Alaska. Global Change Biology 25(3):1171-1189. https://doi.org/10.1111/gcb.14279.
PurpleAir. 2021. Real-Time Air Quality Map. https://map.purpleair.com/1/mAQI/a30/p604800/cC0#11/38.9091/-77.0203.
Pye, H. O. T., K. W. Appel, K. M. Seltzer, C. K. Ward-Caviness, and B. N. Murphy. 2022. Human-health impacts of controlling secondary air pollution precursors. Environmental Science & Technology Letters 9(2):96-101. https://doi.org/10.1021/acs.estlett.1c00798.
Schimel, D., F. D. Schneider, and JPL Carbon and Ecosystem Participants. 2019. Flux towers in the sky: Global ecology from space. New Phytologist 224(2):570-584. https://doi.org/10.1111/nph.15934.
Schneider, F. D., F. Morsdorf, B. Schmid, O. L Petchey, A. Hueni, D. S. Schimel, and M. E. Schaepman. 2017. Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nature Communications 8(1):1441. https://doi.org/10.1038/s41467-017-01530-3.
Shrivastava, M., S. Lou, A. Zelenyuk, R. C. Easter, R. A. Corley, B. D. Thrall, P. J. Rasch, J. D. Fast, S. L. M. Simonich, H. Shen, and S. Tao. 2017. Global long-range transport and lung cancer risk from polycyclic aromatic hydrocarbons shielded by coatings of organic aerosol. Proceedings of the National Academy of Sciences of the United States of America 114(6):1246-1251. https://doi.org/10.1073/pnas.1618475114.
Smith, J. E., and N. Pinter-Wollman. 2021. Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data. Journal of Animal Ecology 90(1):62-75. https://doi.org/10.1111/1365-2656.13362.
Tan, S. 2020. Remote sensing applications and innovations via small satellite constellations. Pp. 767-797 in Handbook of Small Satellites, J. N. Pelton (ed.). Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-36308-6_44.
Tøttrup, A. P., L. Svenningsen, M. Rytter, M. R. Lillemark, P. Møller, and S. W. Knudsen. 2021. Citizens in the lab: Performance and validation of eDNA results. Citizen Science: Theory and Practice 6(1):35. http://doi.org/10.5334/cstp.382.
Wang, M., M. Wander, S. Mueller, N. Martin, and J. B. Dunn. 2022. Evaluation of survey and remote sensing data products used to estimate land use change in the United States: Evolving issues and emerging opportunities. Environmental Science & Policy 129:68-78. https://doi.org/10.1016/j.envsci.2021.12.021.
Wang, R., and J. A. Gamon. 2019. Remote sensing of terrestrial plant biodiversity. Remote Sensing of Environment 23:111218. https://doi.org/10.1016/j.rse.2019.111218.
Wang, Z., A. Chlus, R. Geygan, Z. Ye, T. Zheng, A. Singh, J. J. Couture, J. Cavender‐Bares, E. L. Kruger, and P. A. Townsend. 2020. Foliar functional traits from imaging spectroscopy across biomes in eastern North America. New Phytologist 228(2):494-511. https://doi.org/10.1111/nph.16711.
Wulder, M. A., T. R. Loveland, D. P. Roy, C. J. Crawford, J. G. Masek, C. E. Woodcock, R. G. Allen, M. C. Anderson, A. S. Belward, W. B. Cohen, J. Dwyer, A. Erb, F. Gao, P. Griffiths, D. Helder, T. Hermosilla, J. D. Hipple, P. Hostert, M. J. Hughes, J. Huntington, D. M. Johnson, R. Kennedy, A. Kilac, Z. Li, L. Lymburner, J. McCorkel, N. Pahlevan, T. A. Scambos, C. Schaaf, J. C. White, R. H. Wynne, and Z. Zhu. 2019. Current status of Landsat program, science, and applications. Remote Sensing of Environment 225:127-147. https://doi.org/10.1016/j.rse.2019.02.015.
Zhu, Z. 2017. Change detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing 130:370-384. https://doi.org/10.1016/j.isprsjprs.2017.06.013.
Zhu, Z., J. Zhang, Z. Yang, A. H. Aljaddani, W. B. Cohen, S. Qiu, and C. Zhou. 2020. Continuous monitoring of land disturbance based on Landsat time series. Remote Sensing of Environment 238:111116. https://doi.org/10.1016/j.rse.2019.03.009.
