Millions of Canadians rely on private groundwater wells to access drinking water, which presents many challenges including a lack of government regulations, and limited resources for maintenance, monitoring, management, and protection. These challenges result in an increased risk of acute gastrointestinal illness in private well users. The goal of this work is to improve the understanding of drivers of E. coli fate and transport in groundwater using a data-driven approach to better inform well owners and policy makers. Specifically, the objectives include: exploratory analysis of the physical and human drivers of private well contamination; advancing the understanding of the relationships between land use-land cover and E. coli presence in wells; assessment of rainfall intermittency patterns as a driver of contamination, as an alternative to standard lag times; and, the development of data-driven explanatory models for E. coli contamination in private wells that move towards a novel coupled-systems approach.
This research utilizes a large dataset with 795,023 contamination observations, 253,136 unique wells, and over 33 variables (i.e., microbiological, hydrogeological, well characteristic, meteorological, geographical, and testing behaviour) across Ontario, Canada between 2010 and 2017. Data used includes the Well Water Information Database, Well Water Information System, Daymet, Provincial Digital Elevation Model, Ontario Land Cover Compilation, Southern Ontario Land Resource Information System, and Roads Network. Data analysis methods range from univariate and bivariate analyses to supervised and unsupervised machine learning techniques, including regression, clustering, and classification.
This work has contributed important understandings of the relationships between E. coli contamination and well and aquifer characteristics, seasonality, weather, and human behaviour. Specifically, increased well depth reduced, but did not eliminate, likelihood of contamination; wells completed in consolidated material increased likelihood of contamination; the most significant driver of contamination was identified as land use - land cover, which was categorized into four classes of E. coli contamination potential for wells, ranging from very high to low; latitude was found to drive seasonality and consequent weather patterns, leading to the creation of geographically-based seasonal models; liquid water (i.e., rainfall, snow melt) was a key driver of contamination, where increased water generally increased presence of E.coli while causing decreasing prevalence; time of year, not habit, drove user testing, generally peaking in July; and, a surrogate measure of well user stewardship was identified as driving time to closest drop-off location. Further, this work has contributed methodological advancements in identifying drivers of groundwater contamination including: utilizing literature confidence ratings alongside regression analyses to supply strategic direction to policy makers; demonstrating the value of large datasets in combination with innovative machine learning techniques, and subject matter expertise, to identify improved physically-based understandings of the system; and, highlighting the need for coupled-systems approaches as physical models alone do not capture human behaviour-based factors of contamination. / Thesis / Doctor of Engineering (DEng) / There are millions of people globally relying on private groundwater to access drinking water. Unfortunately, these wells come with many challenges including a lack of government regulations, and limited resources for maintenance, management, and protection. These challenges also result in an increased risk of illness in private well users.
Groundwater research is often limited by lack of numerical data, making it extremely difficult to understand how groundwater and contaminants are transported. This research utilizes a large dataset with 795,023 contamination observations, 253,136 unique wells, and over 33 variables (i.e., well and aquifer characteristics, human behaviours, weather-related) across Ontario, Canada between 2010 and 2017.
The work in this thesis utilizes a data-driven approach, using various machine learning techniques combined with subject matter expertise, to uncover trends and insights into when and how contamination events occur in private wells, to inform policy makers and empower well users.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29068 |
| Date | January 2023 |
| Creators | White, Katie |
| Contributors | Dickson-Anderson, Sarah, Schuster-Wallace, Corinne, Civil Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0017 seconds