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Abstract Lake water clarity, phytoplankton biomass, and hypolimnetic oxygen concentration are metrics of water quality that are highly degraded in eutrophic systems. Eutrophication is linked to legacy nutrients stored in catchment soils and in lake sediments. Long lags in water quality improvement under scenarios of nutrient load reduction to lakes indicate an apparent ecosystem memory tied to the interactions between water biogeochemistry and lake sediment nutrients. To investigate how nutrient legacies and ecosystem memory control lake water quality dynamics, we coupled nutrient cycling and lake metabolism in a model to recreate long‐term water quality of a eutrophic lake (Lake Mendota, Wisconsin, USA). We modeled long‐term recovery of water quality under scenarios of nutrient load reduction and found that the rates and patterns of water quality improvement depended on changes in phosphorus (P) and organic carbon storage in the water column and sediments. Through scenarios of water quality improvement, we showed that water quality variables have distinct phases of change determined by the turnover rates of storage pools—an initial and rapid water quality improvement due to water column flushing, followed by a much longer and slower improvement as sediment P pools were slowly reduced. Water clarity, phytoplankton biomass, and hypolimnetic dissolved oxygen differed in their time responses. Water clarity and algal biomass improved within years of nutrient reductions, but hypolimnetic oxygen took decades to improve. Even with reduced catchment loading, recovery of Lake Mendota to a mesotrophic state may require decades due to nutrient legacies and long ecosystem memory. 

Abstract. While data on human behavior in COVID-19 rich environments have been captured and publicly released, spatial components of such data are recorded in two-dimensions. Thus, the complete roles of the built and natural environment cannot be readily ascertained. This paper introduces a mechanism for the three-dimensional (3D) visualization of egress behaviors of individuals leaving a COVID-19 exposed healthcare facility in Spring 2020 in New York City. Behavioral data were extracted and projected onto a 3D aerial laser scanning point cloud of the surrounding area rendered with Potree, a readily available open-source Web Graphics Library (WebGL) point cloud viewer. The outcomes were 3D heatmap visualizations of the built environment that indicated the event locations of individuals exhibiting specific characteristics (e.g., men vs. women; public transit users vs. private vehicle users). These visualizations enabled interactive navigation through the space accessible through any modern web browser supporting WebGL. Visualizing egress behavior in this manner may highlight patterns indicative of correlations between the environment, human behavior, and transmissible diseases. Findings using such tools have the potential to identify high-exposure areas and surfaces such as doors, railings, and other physical features. Providing flexible visualization capabilities with 3D spatial context can enable analysts to quickly advise and communicate vital information across a broad range of use cases. This paper presents such an application to extract the public health information necessary to form localized responses to reduce COVID-19 infection and transmission rates in urban areas. 

Abstract Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake’s watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near‐stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near‐stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake‐specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality.