Mapping Turbidity in the Charles River, Boston Using a High-resolution Satellite

The usability of high-resolution satellite imagery for estimating spatial water quality patterns in urban water bodies is evaluated using turbidity in the lower Charles River, Boston as a case study. Water turbidity was surveyed using a boat-mounted optical sensor (YSI) at 5 m spatial resolution, resulting in about 4,000 data points. The ground data were collected coincidently with a satellite imagery acquisition (IKONOS), which consists of multispectral (R, G, B) reflectance at 1 m resolution. The original correlation between the raw ground and satellite data was poor (R2 = 0.05). Ground data were processed by removing points affected by contamination (e.g., sensor encounters a particle floc), which were identified visually. Also, the ground data were corrected for the memory effect introduced by the sensor's protective casing using an analytical model. Satellite data were processed to remove pixels affected by permanent non-water features (e.g., shoreline). In addition, water pixels within a certain buffer distance from permanent non-water features were removed due to contamination by the adjacency effect. To determine the appropriate buffer distance, a procedure that explicitly considers the distance of pixels to the permanent non-water features was applied. Two automatic methods for removing the effect of temporary non-water features (e.g., boats) were investigated, including (1) creating a water-only mask based on an unsupervised classification and (2) removing (filling) all local maxima in reflectance. After the various processing steps, the correlation between the ground and satellite data was significantly better (R2 = 0.70). The correlation was applied to the satellite image to develop a map of turbidity in the lower Charles River, which reveals large-scale patterns in water clarity. However, the adjacency effect prevented the application of this method to near-shore areas, where high-resolution patterns were expected (e.g., outfall plumes).     

Anatomy of an urban waterbody: a case study of Boston's Muddy River

The objective of this study was to characterize and understand the water quality of Boston's Muddy River prior to restoration, to help guide those activities and evaluate their success. We use a combination of monitoring, data analysis and mathematical modeling. The seasonal pattern of temperature, pollutant signatures (identified using a principal component analysis), correlations with precipitation and spatial patterns all point to a significant wastewater input at one of the outfalls and suggest significant receiving water impact. However, a quantitative analysis using a mathematical model (QUAL2K) suggests this source is not significant. Rather, internal loading from algae, sediment bed and waterfowl dominate the spatial pattern of water quality. These results suggest significant improvement can be expected from planned sediment dredging. The paper provides a case study of water quality assessment in the context of urban river restoration, and it illustrates the utility of combining monitoring and data analysis with modeling.     

A bunch of tiny individuals—Individual-based modeling for microbes

The individual-based (aka agent-based) approach is now well established in ecological modeling. Traditionally, most applications have been to organisms at higher trophic levels, where the importance of population heterogeneity (intra-population variability), complete life cycles and behavior adapted to internal and external conditions has been recognized for some time. However, advances in molecular biology and biochemistry have brought about an increase in the application of individual-based modeling (IBM) to microbes as well. This literature review summarizes 46 IBM papers for bacteria in wastewater treatment plants, phytoplankton in ocean and inland waters, bacteria in biofilms, bacteria in food and other environs, and “digital organisms” and “domesticated computer viruses” in silico. The use of IBM in these applications was motivated by population heterogeneity (45%), emergence (24%), absence of a continuum (5%), and other unknown reasons (26%). In general, the challenges and concepts of IBM modeling for microbes and higher trophic levels are similar. However, there are differences in the microbe population dynamics and their environment that create somewhat different challenges, which have led to somewhat different modeling concepts. Several topics are discussed, including producing, maintaining and changing population heterogeneity (different life histories, internal variability, positive feedback, inter-generation memory), dealing with very large numbers of individuals (different up-scaling methods, including representative space vs. super-individual, number vs. biomass based, discrete vs. continuous kinetics, various agent accounting methods), handling space, simulating interactions with the extracellular environment (hybrid Eulerian–Lagrangian approach), modeling agent–agent interaction (self-shading, predation, shoving) and passive transport (random walk with spatially variable diffusivity, well-mixed reactors). Overall, the literature indicates that the application of IBM to microbes is developing into a mature field. However, several challenges remain, including simulating various types of agent–agent interactions (formation and function of colonies or filaments, sexual reproduction) and even smaller individuals (viruses, genes). Further increases in intracellular detail and complexity in microbe IBMs may be considered the combination of systems biology and systems ecology, or the new field of systems bioecology.     

Population Dynamics of Escherichia coli in Surface Water1

Bucci, Vanni, Marin Vuli?, Xiaodan Ruan, and Ferdi L. Hellweger, 2011. Population Dynamics of Escherichia coli in Surface Water. Journal of the American Water Resources Association (JAWRA) 47(3):611‐619. DOI: 10.1111/j.1752‐1688.2011.00528.x Abstract: Present models of the fate of enteric bacteria in surface water usually assume simple first‐order decay. However, observed decay kinetics are typically biphasic and there is increasing evidence for growth in the ambient environment. These features are similar to the stationary phase of long‐term batch cultures, where the dynamics are the result of repeated cycles of population takeovers by Growth Advantage in Stationary Phase (GASP) mutants able to scavenge nutrients released from dead cells. We investigated the possible role of this mechanism (i.e., growth of an adapted subpopulation) in surface water using laboratory experiments with various strains of Escherichia coli in different types of water. Addition of ampicillin (which only kills dividing cells) to these cultures caused a decrease in the total cell density, demonstrating the presence of a growing subpopulation. Furthermore, long‐term survivors from surface water cultures outcompete naïve cells and take over the population in surface water. This growth advantage is heritable (i.e., aged strains were isolated from a single cell) and consistent with the GASP mechanism, although the responsible loci were not identified. Our results show that E. coli populations in surface water are dynamic and consist of dying naïve cells and growing surface water adapted cells. These results can explain the apparent biphasic decay pattern observed in laboratory experiments. This mechanism may also be important in the ambient environment.     

Investigating the Fate and Transport of Escherichia coli in the Charles River,Boston, Using High‐Resolution Observation and Modeling1

Abstract: The processes affecting the fate and transport of Escherichia coli in surface waters were investigated using high‐resolution observation and modeling. The concentration patterns in Boston’s Charles River were observed during four sampling events with a total of 757 samples, including two spatial surveys with two along‐river (1,500 m length) and three across‐river (600 m length) transects at approximately 25‐m intervals, and two temporal surveys at a fixed location (Community Boating) over seven days at hourly intervals. The data reveal significant spatial and temporal structure at scales not resolved by typical monitoring programs. A mechanistic, time‐variable, three‐dimensional coupled hydrodynamic and water quality model was developed using the ECOMSED and RCA modeling frameworks. The computational grid consists of 3,066 grid cells with average length dimension of 25 m. Forcing functions include upstream and downstream boundary conditions, Stony Brook, and Muddy River (major tributaries) combined sewer overflow (CSO) and non‐CSO discharge and wind. The model generally reproduces the observed spatial and temporal patterns. This includes the presence and absence of a plume in the study area under similar loading, but different hydrodynamic conditions caused by operation of the New Charles River Dam (downstream) and wind. The model also correctly predicts an episode of high concentrations at the time‐series station following seven days of no rainfall. The model has an overall root mean square error (RMSE) of 250 CFU/100 ml and an error rate (above or below the USEPA‐recommended single sample criteria value of 235 CFU/100 ml) of 9.4%. At the time series station, the model has an RMSE of 370 CFU/100 ml and an error rate of 15%.