Engineering topics

Tanentzap, Andrew J.

Publications and source records attributed to Tanentzap, Andrew J..

4 records

DOE OSTI2024

Wildfire smoke impacts lake ecosystems

Abstract Wildfire activity is increasing globally. The resulting smoke plumes can travel hundreds to thousands of kilometers, reflecting or scattering sunlight and depositing particles within ecosystems. Several key physical, chemical, and biological processes in lakes are controlled by factors affected by smoke. The spatial and temporal scales of lake exposure to smoke are extensive and under‐recognized. We introduce the concept of the lake smoke‐day, or the number of days any given lake is exposed to smoke in any given fire season, and quantify the total lake smoke‐day exposure in North America from 2019 to 2021. Because smoke can be transported at continental to intercontinental scales, even regions that may not typically experience direct burning of landscapes by wildfire are at risk of smoke exposure. We found that 99.3% of North America was covered by smoke, affecting a total of 1,333,687 lakes ≥10 ha. An incredible 98.9% of lakes experienced at least 10 smoke‐days a year, with 89.6% of lakes receiving over 30 lake smoke‐days, and lakes in some regions experiencing up to 4 months of cumulative smoke‐days. Herein we review the mechanisms through which smoke and ash can affect lakes by altering the amount and spectral composition of incoming solar radiation and depositing carbon, nutrients, or toxic compounds that could alter chemical conditions and impact biota. We develop a conceptual framework that synthesizes known and theoretical impacts of smoke on lakes to guide future research. Finally, we identify emerging research priorities that can help us better understand how lakes will be affected by smoke as wildfire activity increases due to climate change and other anthropogenic activities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes ( 2 H and 18 O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Thermal responses of dissolved organic matter under global change

The diversity of intrinsic traits of different organic matter molecules makes it challenging to predict how they, and therefore the global carbon cycle, will respond to climate change. Here we develop an indicator of compositional-level environmental response for dissolved organic matter to quantify the aggregated response of individual molecules that positively and negatively associate with warming. We apply the indicator to assess the thermal response of sediment dissolved organic matter in 480 aquatic microcosms along nutrient gradients on three Eurasian mountainsides. Organic molecules consistently respond to temperature change within and across contrasting climate zones. At a compositional level, dissolved organic matter in warmer sites has a stronger thermal response and shows functional reorganization towards molecules with lower thermodynamic favorability for microbial decomposition. The thermal response is more sensitive to warming at higher nutrients, with increased sensitivity of up to 22% for each additional 1 mg L -1 of nitrogen loading. The utility of the thermal response indicator is further confirmed by laboratory experiments and reveals its positive links to greenhouse gas emissions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Applying the core-satellite species concept: Characteristics of rare and common riverine dissolved organic matter

Introduction: Dissolved organic matter (DOM) composition varies over space and time, with a multitude of factors driving the presence or absence of each compound found in the complex DOM mixture. Compounds ubiquitously present across a wide range of river systems (hereafter termed core compounds) may differ in chemical composition and reactivity from compounds present in only a few settings (hereafter termed satellite compounds). Here, we investigated the spatial patterns in DOM molecular formulae presence (occupancy) in surface water and sediments across 97 river corridors at a continental scale using the “Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems—WHONDRS” research consortium. Methods: We used a novel data-driven approach to identify core and satellite compounds and compared their molecular properties identified with Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Results: In this work, we found that core compounds clustered around intermediate hydrogen/carbon and oxygen/carbon ratios across both sediment and surface water samples, whereas the satellite compounds varied widely in their elemental composition. Within surface water samples, core compounds were dominated by lignin-like formulae, whereas protein-like formulae dominated the core pool in sediment samples. In contrast, satellite molecular formulae were more evenly distributed between compound classes in both sediment and water molecules. Core compounds found in both sediment and water exhibited lower molecular mass, lower oxidation state, and a higher degree of aromaticity, and were inferred to be more persistent than global satellite compounds. Higher putative biochemical transformations were found in core than satellite compounds, suggesting that the core pool was more processed. Discussion: The observed differences in chemical properties of core and satellite compounds point to potential differences in their sources and contribution to DOM processing in river corridors. Overall, our work points to the potential of data-driven approaches separating rare and common compounds to reduce some of the complexity inherent in studying riverine DOM.

54 ENVIRONMENTAL SCIENCES↗