Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management

The frequency of toxin-producing cyanobacterial blooms has increased in recent decades due to nutrient enrichment and climate change. Because Microcystis blooms are related to different environmental conditions, identifying potential nutrient control targets can facilitate water quality managers to reduce the likelihood of microcystins (MCs) risk. However, complex biotic interactions and field data limitations have constrained our understanding of the nutrient-microcystin relationship. This study develops a Bayesian modelling framework with intracellular and extracellular MCs that characterize the relationships between different environmental and biological factors. This model was fit to the across-lake dataset including three bloom-plagued lakes in China and estimated the putative thresholds of total nitrogen (TN) and total phosphorus (TP). The lake-specific nutrient thresholds were estimated using Bayesian updating process. Our results suggested dual N and P reduction in controlling cyanotoxin risks. The total Microcystis biomass can be substantially suppressed by achieving the putative thresholds of TP (0.10 mg/L) in Lakes Taihu and Chaohu, but a stricter TP target (0.05 mg/L) in Dianchi Lake. To maintain MCs concentrations below 1.0 μg/L, the estimated TN threshold in three lakes was 1.8 mg/L, but the effect can be counteracted by the increase of temperature. Overall, the present approach provides an efficient way to integrate empirical knowledge into the data-driven model and is helpful for the management of water resources

Assessing Nutrient Management Strategies to Control Harmful Algal Blooms in Lake Erie

Harmful algal blooms (HAB) have impaired Lake Erie’s western basin water quality since the 1960s. Drivers of HABs are still the subject of debate and are likely the result of interactions among several biotic and abiotic factors. The problem is twofold: (1) uncertainty in the specific causes of HABs leads to inapt management solutions; and (2) managing a cross-boundary watershed requires collaboration and agreement on apt solutions from multiple stakeholders as well as many U.S. states and Canadian provinces. In this study, we use Bayesian hierarchical modeling (BHM) to investigate the relationships between nitrogen (N) and phosphorus (P) and phytoplankton biomass, cyanobacterial biomass, and microcystin concentration. We used both a within-lake and an across-lake approach and examined whether the inferences from western Lake Erie differ from the ones using multiple lakes across the country. We found that while P is still the primary driver of HABs in Western Lake Erie (WLE), the great variability between stations and months suggests that even within-lake, there may not be a single relationship characterizing phosphorus effects on HABs. We also interviewed 29 stakeholders actively involved in western Lake Erie’s watershed. We analyzed the stakeholders’ values, attitudes, and policy preferences to understand their differences or similarities and their effects on management decisions. We found that although stakeholders agree on the urgency of the problem, the different opinions and preferences of each interviewee may complicate the decision-making process in a highly collaborative watershed.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/167292/1/2020 CIGLR Master's Project Final Report.pd

Where People Meet the Muck: An Integrated Assessment of Beach Muck and Public Perception at the Bay City State Recreation Area, Saginaw Bay, Lake Huron

In several regions of the Great Lakes, including Saginaw Bay, the proliferation of muck, decaying organics largely from aquatic plants such as Cladophora, has washed ashore, and is blamed for negatively affecting water quality and economic losses in the region. The current view is that excess nutrient loading into the system is a leading cause of this type of organic debris, though changes in food web dynamics may also be a contributing factor. Through an Integrated Assessment (IA) framework, we summarized the current state of knowledge on the causes and consequences of muck conditions at the Bay City State Recreation Area (BCSRA), including the socio-economic impacts of muck at the park and on the Saginaw Bay Region. Through this framework we identify potential management scenarios for addressing beach fouling at the BCSRA. Through a robust stakeholder engagement process, the IA team implemented a suite of models and surveys to understand public perception of muck-related issues and identified a series of feasible short and long-term management actions that could help alleviate and better manage the impacts of muck. Results indicate that even drastic reductions in external phosphorus loads will not eliminate Cladophora growth in the bay. Beach muck is likely a historical part of the system, and nutrient reduction programs may not prevent muck from fouling Saginaw Bay beaches. We identify a sustainable park management practice maybe reallocating resources previously designated for cleaning efforts to achieve bare, sandy beaches and promoting alternative ecological activities and attractions such as bird watching, kayaking, and nature walks in the park’s coastal marshes

The development of object oriented Bayesian networks to evaluate the social, economic and environmental impacts of solar PV

Domestic and community low carbon technologies are widely heralded as valuable means for delivering sustainability outcomes in the form of social, economic and environmental (SEE) policy objectives. To accelerate their diffusion they have benefited from a significant number and variety of subsidies worldwide. Considerable aleatory and epistemic uncertainties exist, however, both with regard to their net energy contribution and their SEE impacts. Furthermore the socio-economic contexts themselves exhibit enormous variability, and commensurate uncertainties in their parameterisation. This represents a significant risk for policy makers and technology adopters. This work describes an approach to these problems using Bayesian Network models. These are utilised to integrate extant knowledge from a variety of disciplines to quantify SEE impacts and endogenise uncertainties. A large-scale Object Oriented Bayesian network has been developed to model the specific case of solar photovoltaics (PV) installed on UK domestic roofs. Three specific model components have been developed. The PV component characterises the yield of UK systems, the building energy component characterises the energy consumption of the dwellings and their occupants and a third component characterises the building stock in four English urban communities. Three representative SEE indicators, fuel affordability, carbon emission reduction and discounted cash flow are integrated and used to test the model s ability to yield meaningful outputs in response to varying inputs. The variability in the percentage of the three indicators is highly responsive to the dwellings built form, age and orientation, but is not just due to building and solar physics but also to socio-economic factors. The model can accept observations or evidence in order to create scenarios which facilitate deliberative decision making. The BN methodology contributes to the synthesis of new knowledge from extant knowledge located between disciplines . As well as insights into the impacts of high PV penetration, an epistemic contribution has been made to transdisciplinary building energy modelling which can be replicated with a variety of low carbon interventions