Widespread deoxygenation of temperate lakes.

The concentration of dissolved oxygen in aquatic systems helps to regulate biodiversity1,2, nutrient biogeochemistry3, greenhouse gas emissions4, and the quality of drinking water5. The long-term declines in dissolved oxygen concentrations in coastal and ocean waters have been linked to climate warming and human activity6,7, but little is known about the changes in dissolved oxygen concentrations in lakes. Although the solubility of dissolved oxygen decreases with increasing water temperatures, long-term lake trajectories are difficult to predict. Oxygen losses in warming lakes may be amplified by enhanced decomposition and stronger thermal stratification8,9 or oxygen may increase as a result of enhanced primary production10. Here we analyse a combined total of 45,148 dissolved oxygen and temperature profiles and calculate trends for 393 temperate lakes that span 1941 to 2017. We find that a decline in dissolved oxygen is widespread in surface and deep-water habitats. The decline in surface waters is primarily associated with reduced solubility under warmer water temperatures, although dissolved oxygen in surface waters increased in a subset of highly productive warming lakes, probably owing to increasing production of phytoplankton. By contrast, the decline in deep waters is associated with stronger thermal stratification and loss of water clarity, but not with changes in gas solubility. Our results suggest that climate change and declining water clarity have altered the physical and chemical environment of lakes. Declines in dissolved oxygen in freshwater are 2.75 to 9.3 times greater than observed in the world's oceans6,7 and could threaten essential lake ecosystem services2,3,5,11.

A synergistic future for AI and ecology.

Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change. These challenges include understanding the unpredictability of systems-level phenomena and resilience dynamics on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a convergence research paradigm between ecology and AI. Ecological systems are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behaviors that may inspire new, robust AI architectures and methodologies. We share examples of how challenges in ecological systems modeling would benefit from advances in AI techniques that are themselves inspired by the systems they seek to model. Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. We emphasize the need for more purposeful synergy to accelerate the understanding of ecological resilience whilst building the resilience currently lacking in modern AI systems, which have been shown to fail at times because of poor generalization in different contexts. Persistent epistemic barriers would benefit from attention in both disciplines. The implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence-they are critical for both persisting and thriving in an uncertain future.

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States.

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the "black-box" nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having "no change" (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density.

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

Wet nitrogen (N) deposition to urban Latin America: filling in the gaps with GEOS-Chem.

In Latin America, atmospheric deposition is a major vector of nitrogen (N) input to urban systems. Yet, measurements of N deposition are sparse, precluding analysis of spatial patterns, temporal trends, and ecosystem impacts. Chemical transport models can be used to fill these gaps in the absence of dense measurements. Here, we evaluate the performance of a global 3-D chemical transport model in simulating spatial and interannual variation in wet inorganic N (NH4-N + NO3-N) deposition across urban areas in Latin America. Monthly wet and dry inorganic N deposition to Latin America were simulated for the period 2006-2010 using the GEOS-Chem Chemical Transport Model. Published estimates of observed wet or bulk inorganic N deposition measured between 2006-2010 were compiled for 16 urban areas and then compared with model output from GEOS-Chem. Observed mean annual inorganic N deposition to the urban study sites ranged from 5.7-14.2 kg ha-1 yr-1, with NH4-N comprising 48-90% of the total. Results show that simulated N deposition was highly correlated with observed N deposition across sites (R2 = 0.83, NMB = -50%). However, GEOS-Chem generally underestimated N deposition to urban areas in Latin America compared to observations. Underestimation due to bulk sampler dry deposition artifacts was considered and improved bias without improving correlation. In contrast to spatial variation, the model did not capture year-to-year variation well. Discrepancies between modeled and observed values exist, in part, because of uncertainties in Latin American N emissions inventories. Our findings indicate that even at coarse spatial resolution, GEOS-Chem can be used to simulate N deposition to urban Latin America, improving understanding of regional deposition patterns and potential ecological effects.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Lakes at Risk of Chloride Contamination.

Lakes in the Midwest and Northeast United States are at risk of anthropogenic chloride contamination, but there is little knowledge of the prevalence and spatial distribution of freshwater salinization. Here, we use a quantile regression forest (QRF) to leverage information from 2773 lakes to predict the chloride concentration of all 49 432 lakes greater than 4 ha in a 17-state area. The QRF incorporated 22 predictor variables, which included lake morphometry characteristics, watershed land use, and distance to the nearest road and interstate. Model predictions had an r2 of 0.94 for all chloride observations, and an r2 of 0.86 for predictions of the median chloride concentration observed at each lake. The four predictors with the largest influence on lake chloride concentrations were low and medium intensity development in the watershed, crop density in the watershed, and distance to the nearest interstate. Almost 2000 lakes are predicted to have chloride concentrations above 50 mg L-1 and should be monitored. We encourage management and governing agencies to use lake-specific model predictions to assess salt contamination risk as well as to augment their monitoring strategies to more comprehensively protect freshwater ecosystems from salinization.

Salting our freshwater lakes.

The highest densities of lakes on Earth are in north temperate ecosystems, where increasing urbanization and associated chloride runoff can salinize freshwaters and threaten lake water quality and the many ecosystem services lakes provide. However, the extent to which lake salinity may be changing at broad spatial scales remains unknown, leading us to first identify spatial patterns and then investigate the drivers of these patterns. Significant decadal trends in lake salinization were identified using a dataset of long-term chloride concentrations from 371 North American lakes. Landscape and climate metrics calculated for each site demonstrated that impervious land cover was a strong predictor of chloride trends in Northeast and Midwest North American lakes. As little as 1% impervious land cover surrounding a lake increased the likelihood of long-term salinization. Considering that 27% of large lakes in the United States have >1% impervious land cover around their perimeters, the potential for steady and long-term salinization of these aquatic systems is high. This study predicts that many lakes will exceed the aquatic life threshold criterion for chronic chloride exposure (230 mg L-1), stipulated by the US Environmental Protection Agency (EPA), in the next 50 y if current trends continue.

Urban Trees Are Sinks for Soot: Elemental Carbon Accumulation by Two Widespread Oak Species.

Urban trees could represent important short- and long-term landscape sinks for elemental carbon (EC). Therefore, we quantified foliar EC accumulation by two widespread oak tree species-Quercus stellata (post oak) and Quercus virginiana (live oak)-as well as leaf litterfall EC flux to soil from April 2017 to March 2018 in the City of Denton, Texas, within the Dallas-Fort Worth metropolitan area. Post oak trees accumulated 1.9-fold more EC (299 ± 45 mg EC m-2 canopy yr-1) compared to live oak trees (160 ± 31 mg EC m-2 canopy yr-1). However, in the fall, these oak species converged in their EC accumulation rates, with ∼70% of annual accumulation occurring during fall and on leaf surfaces. The flux of EC to the ground via leaf litterfall mirrored leaf-fall patterns, with post oaks and live oaks delivering ∼60% of annual leaf litterfall EC in fall and early spring, respectively. We estimate that post oak and live oak trees in this urban ecosystem potentially accumulate 3.5 t EC yr-1, equivalent to ∼32% of annual vehicular EC emissions from the city. Thus, city trees are significant sinks for EC and represent potential avenues for climate and air quality mitigation in urban areas.

Factors affecting MeHg bioaccumulation in stream biota: the role of dissolved organic carbon and diet.

The bioaccumulation of the neurotoxin methylmercury (MeHg) in freshwater ecosystems is thought to be mediated by both water chemistry (e.g., dissolved organic carbon [DOC] and dissolved mercury [Hg]) and diet (e.g., trophic position and diet composition). Hg in small streams is of particular interest given their role as a link between terrestrial and aquatic processes. Terrestrial processes determine the quantity and quality of streamwater DOC, which in turn influence the quantity and bioavailability of dissolved MeHg. To better understand the effects of water chemistry and diet on Hg bioaccumulation in stream biota, we measured DOC and dissolved Hg in stream water and mercury concentration in three benthic invertebrate taxa and three fish species across up to 12 tributary streams in a forested watershed in New Hampshire, USA. As expected, dissolved total mercury (THg) and MeHg concentrations increased linearly with DOC. However, mercury concentrations in fish and invertebrates varied non-linearly, with maximum bioaccumulation at intermediate DOC concentrations, which suggests that MeHg bioavailability may be reduced at high levels of DOC. Further, MeHg and THg concentrations in invertebrates and fish, respectively, increased with δ15N (suggesting trophic position) but were not associated with δ13C. These results show that even though MeHg in water is strongly determined by DOC concentrations, mercury bioaccumulation in stream food webs is the result of both MeHg availability in stream water and trophic position.

Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring.

Predicting algal blooms has become a priority for scientists, municipalities, businesses, and citizens. Remote sensing offers solutions to the spatial and temporal challenges facing existing lake research and monitoring programs that rely primarily on high-investment, in situ measurements. Techniques to remotely measure chlorophyll a (chl a) as a proxy for algal biomass have been limited to specific large water bodies in particular seasons and narrow chl a ranges. Thus, a first step toward prediction of algal blooms is generating regionally robust algorithms using in situ and remote sensing data. This study explores the relationship between in-lake measured chl a data from Maine and New Hampshire, USA lakes and remotely sensed chl a retrieval algorithm outputs. Landsat 8 images were obtained and then processed after required atmospheric and radiometric corrections. Six previously developed algorithms were tested on a regional scale on 11 scenes from 2013 to 2015 covering 192 lakes. The best performing algorithm across data from both states had a 0.16 correlation coefficient (R2 ) and P ≤ 0.05 when Landsat 8 images within 5 d, and improved to R2 of 0.25 when data from Maine only were used. The strength of the correlation varied with the specificity of the time window in relation to the in-situ sampling date, explaining up to 27% of the variation in the data across several scenes. Two previously published algorithms using Landsat 8's Bands 1-4 were best correlated with chl a, and for particular late-summer scenes, they accounted for up to 69% of the variation in in-situ measurements. A sensitivity analysis revealed that a longer time difference between in situ measurements and the satellite image increased uncertainty in the models, and an effect of the time of year on several indices was demonstrated. A regional model based on the best performing remote sensing algorithm was developed and was validated using independent in situ measurements and satellite images. These results suggest that, despite challenges including seasonal effects and low chl a thresholds, remote sensing could be an effective and accessible regional-scale tool for chl a monitoring programs in lakes.

A lake classification concept for a more accurate global estimate of the dissolved inorganic carbon export from terrestrial ecosystems to inland waters.

The magnitude of lateral dissolved inorganic carbon (DIC) export from terrestrial ecosystems to inland waters strongly influences the estimate of the global terrestrial carbon dioxide (CO2) sink. At present, no reliable number of this export is available, and the few studies estimating the lateral DIC export assume that all lakes on Earth function similarly. However, lakes can function along a continuum from passive carbon transporters (passive open channels) to highly active carbon transformers with efficient in-lake CO2 production and loss. We developed and applied a conceptual model to demonstrate how the assumed function of lakes in carbon cycling can affect calculations of the global lateral DIC export from terrestrial ecosystems to inland waters. Using global data on in-lake CO2 production by mineralization as well as CO2 loss by emission, primary production, and carbonate precipitation in lakes, we estimated that the global lateral DIC export can lie within the range of [Formula: see text] to [Formula: see text] Pg C yr-1 depending on the assumed function of lakes. Thus, the considered lake function has a large effect on the calculated lateral DIC export from terrestrial ecosystems to inland waters. We conclude that more robust estimates of CO2 sinks and sources will require the classification of lakes into their predominant function. This functional lake classification concept becomes particularly important for the estimation of future CO2 sinks and sources, since in-lake carbon transformation is predicted to be altered with climate change.

Dissolved organic carbon modulates mercury concentrations in insect subsidies from streams to terrestrial consumers.

Mercury (Hg) concentrations in aquatic environments have increased globally, exposing consumers of aquatic organisms to high Hg levels. For both aquatic and terrestrial consumers, exposure to Hg depends on their food sources as well as environmental factors influencing Hg bioavailability. The majority of the research on the transfer of methylmercury (MeHg), a toxic and bioaccumulating form of Hg, between aquatic and terrestrial food webs has focused on terrestrial piscivores. However, a gap exists in our understanding of the factors regulating MeHg bioaccumulation by non-piscivorous terrestrial predators, specifically consumers of adult aquatic insects. Because dissolved organic carbon (DOC) binds tightly to MeHg, affecting its transport and availability in aquatic food webs, we hypothesized that DOC affects MeHg transfer from stream food webs to terrestrial predators feeding on emerging adult insects. We tested this hypothesis by collecting data over 2 years from 10 low-order streams spanning a broad DOC gradient in the Lake Sunapee watershed in New Hampshire, USA. We found that streamwater MeHg concentration increased linearly with DOC concentration. However, streams with the highest DOC concentrations had emerging stream prey and spiders with lower MeHg concentrations than streams with intermediate DOC concentrations; a pattern that is similar to fish and larval aquatic insects. Furthermore, high MeHg concentrations found in spiders show that MeHg transfer in adult aquatic insects is an overlooked but potentially significant pathway of MeHg bioaccumulation in terrestrial food webs. Our results suggest that although MeHg in water increases with DOC, MeHg concentrations in stream and terrestrial consumers did not consistently increase with increases in streamwater MeHg concentrations. In fact, there was a change from a positive to a negative relationship between aqueous exposure and bioaccumulation at streamwater MeHg concentrations associated with DOC above ~5 mg/L. Thus, our study highlights the importance of stream DOC for MeHg dynamics beyond stream boundaries, and shows that factors modulating MeHg bioavailability in aquatic systems can affect the transfer of MeHg to terrestrial predators via aquatic subsidies.

Automatic High Frequency Monitoring for Improved Lake and Reservoir Management.

Recent technological developments have increased the number of variables being monitored in lakes and reservoirs using automatic high frequency monitoring (AHFM). However, design of AHFM systems and posterior data handling and interpretation are currently being developed on a site-by-site and issue-by-issue basis with minimal standardization of protocols or knowledge sharing. As a result, many deployments become short-lived or underutilized, and many new scientific developments that are potentially useful for water management and environmental legislation remain underexplored. This Critical Review bridges scientific uses of AHFM with their applications by providing an overview of the current AHFM capabilities, together with examples of successful applications. We review the use of AHFM for maximizing the provision of ecosystem services supplied by lakes and reservoirs (consumptive and non consumptive uses, food production, and recreation), and for reporting lake status in the EU Water Framework Directive. We also highlight critical issues to enhance the application of AHFM, and suggest the establishment of appropriate networks to facilitate knowledge sharing and technological transfer between potential users. Finally, we give advice on how modern sensor technology can successfully be applied on a larger scale to the management of lakes and reservoirs and maximize the ecosystem services they provide.

Out of the fog: Catalyzing integrative capacity in interdisciplinary research.

Social studies of interdisciplinary science investigate how scientific collaborations approach complex challenges that require multiple disciplinary perspectives. In order for collaborators to meet these complex challenges, interdisciplinary collaborations must develop and maintain integrative capacity, understood as the ability to anticipate and weigh tradeoffs in the employment of different disciplinary approaches. Here we provide an account of how one group of interdisciplinary fog scientists intentionally catalyzed integrative capacity. Through conversation, collaborators negotiated their commitments regarding the ontology of fog systems and the methodologies appropriate to studying fog systems, thereby enhancing capabilities which we take to constitute integrative capacity. On the ontological front, collaborators negotiated their commitments by setting boundaries to and within the system, layering different subsystems, focusing on key intersections of these subsystems, and agreeing on goals that would direct further investigation. On the methodological front, collaborators sequenced various methods, anchored methods at different scales, validated one method with another, standardized the outputs of related methods, and coordinated methods to fit a common model. By observing the process and form of collaborator conversations, this case study demonstrates that social studies of science can bring into critical focus how interdisciplinary collaborators work toward an integrated conceptualization of study systems.

The importance of lake-specific characteristics for water quality across the continental United States.

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agency's 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to-volume ratio) were important for explaining water quality (54-60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28-39% variance explained). Basin-scale land use and land cover explained between 45-62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.

Atmospheric inputs and nitrogen saturation status in and adjacent to Class I wilderness areas of the northeastern US.

Atmospheric inputs of N and S in bulk deposition (open collectors) and throughfall (beneath canopy collectors) were measured in and adjacent to two Class 1 wilderness areas of the northeastern US. In general, atmospheric S inputs followed our expectations with throughfall S fluxes increasing with elevation in the White Mountains, New Hampshire and throughfall S fluxes being greater in coniferous than deciduous stands in both sites. In contrast, throughfall N fluxes decreased significantly with elevation. Throughfall NO3 (-) fluxes were greater in coniferous than deciduous stands of Lye Brook, Vermont, but were greater in deciduous than coniferous stands of the White Mountains. We found overlap in the range of values for atmospheric N inputs between our measurements and monitoring data [National Atmospheric Deposition Program (NADP) and Clean Air Status and Trends Network (CASTNET)] for wet and total (wet + dry) deposition at Lye Brook. However, our measurements of total S deposition in the White Mountains and bulk (wet) deposition at both Lye Brook and the White Mountains were significantly lower than NADP plus CASTNET, and NADP data, respectively. Natural abundance (18)O in throughfall and bulk deposition were not significantly different, suggesting that there was no significant biological production of [Formula: see text] via nitrification in the canopy. NO3 (-) concentrations in streams were low and had natural abundance (18)O values consistent with microbial production, demonstrating that atmospheric N is being biologically transformed while moving through these watersheds and that these forested watersheds are unlikely to be N saturated.

Managing water services in tropical regions: From land cover proxies to hydrologic fluxes.

Watershed investment programs frequently use land cover as a proxy for water-based ecosystem services, an approach based on assumed relationships between land cover and hydrologic outcomes. Water flows are rarely quantified, and unanticipated results are common, suggesting land cover alone is not a reliable proxy for water services. We argue that managing key hydrologic fluxes at the site of intervention is more effective than promoting particular land-cover types. Moving beyond land cover proxies to a focus on hydrologic fluxes requires that programs (1) identify the specific water service of interest and associated hydrologic flux; (2) account for structural and ecological characteristics of the relevant land cover; and, (3) determine key mediators of the target hydrologic flux. Using examples from the tropics, we illustrate how this conceptual framework can clarify interventions with a higher probability of delivering desired water services than with land cover as a proxy.

Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover.

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = -0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.

A comparison of three empirically based, spatially explicit predictive models of residential soil Pb concentrations in Baltimore, Maryland, USA: understanding the variability within cities.

In many older US cities, lead (Pb) contamination of residential soil is widespread; however, contamination is not uniform. Empirically based, spatially explicit models can assist city agencies in addressing this important public health concern by identifying areas predicted to exceed public health targets for soil Pb contamination. Sampling of 61 residential properties in Baltimore City using field portable X-ray fluorescence revealed that 53 % had soil Pb that exceeded the USEPA reportable limit of 400 ppm. These data were used as the input to three different spatially explicit models: a traditional general linear model (GLM), and two machine learning techniques: classification and regression trees (CART) and Random Forests (RF). The GLM revealed that housing age, distance to road, distance to building, and the interactions between variables explained 38 % of the variation in the data. The CART model confirmed the importance of these variables, with housing age, distance to building, and distance to major road networks determining the terminal nodes of the CART model. Using the same three predictor variables, the RF model explained 42 % of the variation in the data. The overall accuracy, which is a measure of agreement between the model and an independent dataset, was 90 % for the GLM, 83 % for the CART model, and 72 % for the RF model. A range of spatially explicit models that can be adapted to changing soil Pb guidelines allows managers to select the most appropriate model based on public health targets.