Peter Watson

• Doctor of Philosophy
• Postdoctoral Researcher at Los Alamos National Laboratory

Emerging contaminants (ECs) have drawn global concern, and the endocrine disrupting chemicals is one of the highly interested ECs categories. However, numerous ECs lacks the basic information about whether they can disturb the endocrine related biomacromolecules or elicit endocrine related detrimental effects on organism. In this study, the potenti...

The complex interactions between the weather, the environment, and electrical infrastructure that result in power outages are not fully understood, but because of the threat of climate change, the need for models that describe how these factors produce power grid failures is acute. Without them, it remains difficult to understand the amount of weat...

Power outages caused by tropical storms significantly impact the United States economy and citizens every year. If the impacts of these storms could be better anticipated, they could be more effectively mitigated. While there has been some research focused on predicting the impacts of tropical storms on the power grid, they have not yet resulted in...

The COVID-19 pandemic resulted in increasing the usage of iodinated contrast media (ICM), and thus an increase in the prevalence of ICM-contaminated wastewater. While ICM is generally safe, this has the potential to be problematic because as medical wastewater is treated and disinfected, various ICM-derived disinfection byproducts (DBPs) may be gen...

The co-occurrence of nanoplastics and other pollutants in the environment has gotten a lot of attention, but information on the biological toxicity of their co-exposure was limited. This study aims to reveal the endocrine disrupting effect and reproductive toxicity of nano-polystyrene (NPS) and diethylstilbestrol (DES) to zebrafish under separate a...

The Eversource Energy Center(EEC) at the University of Connecticut has developed an Operational Outage Prediction Model(OPM) framework that has steadily improved operational performance. The current version of the Operational OPM targets the four Eversource service regions in Connecticut, Western Massachusetts, Eastern Massachusetts, New Hampshire,...

Power outages caused by extreme weather events cost the economy of the United States billions of dollars every year and endanger the lives of the people affected by them. These types of events could be better managed if accurate predictions of storm impacts were available. While empirical power outage prediction models have been in development for...

This paper develops a machine learning outage prediction model (OPM) to serve as a simulation framework capable of quantifying the reduction in damages to the distribution electric grid due to vegetation management for storm events. The model covers the Eversource Energy distribution grid territory in Connecticut and uses a random forest model with...

This paper develops a statistical framework to analyze the effectiveness of vegetation management at reducing power outages during storms of varying severity levels. The framework was applied on the Eversource Energy distribution grid in Connecticut, USA based on 173 rain and wind events from 2005–2020, including Hurricane Irene, Hurricane Sandy, a...

Extreme weather events can severely impact the built infrastructure that society relies on for its prosperity and survival. In recent years, time and again weather-related disasters have damaged property, disrupted the economy, and ended lives. However. because these events are thankfully rare, it can be particularly difficult to anticipate the imp...

Extreme weather events can severely impact the built infrastructure that society relies on for its prosperity and survival. In recent years, time and again weather-related disasters have damaged property, disrupted the economy, and ended lives. However. because these events are thankfully rare, it can be particularly difficult to anticipate the imp...

Thunderstorms are one of the most damaging weather phenomena in the United States, but they are also one of the least predictable. This unpredictable nature can make it especially challenging for emergency responders, infrastructure managers, and power utilities to be able to prepare and react to these types of events when they occur. Predictive an...

The outage prediction model (OPM) is a weather-related machine learning-based power outage model, which has been developed at the University of Connecticut for many years and has recently grown to cover three states and five utility service territories. This is a large heterogeneous domain supported by a large dataset of hundreds of storm events. T...

Thunderstorms are complex weather phenomena that cause substantial power outages in a short period. This makes thunderstorm outage prediction challenging using eventwise outage prediction models (OPMs), which summarize the storm dynamics over the entire course of the storm into a limited number of parameters. We developed a new, temporally sensitiv...

The effects of microplastics on aquatic organisms are drawing growing attention, but little has been focused on their effects on the toxicity of other chemicals. In this study, we examined the acute and chronic toxicity of micro-polystyrene (5.8 μm dia.), and its effects on the toxicity of three antimicrobial agents (triclosan, triclocarban and met...

The accuracy of machine learning–based power outage prediction models (OPMs) is sensitive to how well event severity is represented in their training datasets. Unbalanced or overly dispersed event severity can result in random errors in outage predictions and underestimation in severe events or overestimation in weak ones. To improve accuracy in th...

This paper describes the development of two Outage Prediction Models (OPMs) for power outages caused by snow and ice storms on electric distribution networks, and their performance evaluation in the Northeastern United States. The first is a Machine Learning (ML) based model that is set up to predict power outages on a regular grid (4-km grid spaci...

The anionic form-dependent binding interaction of halo-phenolic substances with human transthyretin (hTTR) has been observed previously. This indicates that ionizable compounds should be the primary focus in screening potential hTTR disruptors. Here, the potential binding potency of halo-benzoic acids, halo-benzenesulfonic acids/sulfates and halo-p...

Enhanced Tree Trimming (ETT) is a vegetation management standard consisting in the trimming or removal of trees in proximity of overhead lines. We quantify the effects of this practice, designed to improve the resiliency of the overhead electrical power grid, using two independent methodologies. The first approach is a statistical study of the chan...

Wastewater (WW) systems are vulnerable to extreme precipitation events; storm‐induced WW system failures pollute the environment and put public health at risk. Despite these vulnerabilities, we know very little about how WW managers are responding to current climate risks or to future climate change. This study aims to fill this critical gap in the...

In recent years, extensive attention has been paid to the passive sampling technology of diffusive gradients in thin films (DGT) due to its growing application in the measurement of a widening variety of compounds. Within any DGT device, the binding phase is a key component, and seeking novel binding phases is an issue worth studying. Cyclodextrin...

Solubility is a crucial limiting factor in pharmaceutical research and contaminated site remediation. Cyclodextrin, with its structure of hydrophilic exterior and hydrophobic cavity, has a potential ability to enhance the hydrophobic chemical’s solubility through the formation of host–guest complex. The stability of host–guest complex is often quan...