Agricultural runoff is a major source of water quality impairments and is prevalent in areas where agricultural operations focus on maintaining global food security. To alleviate downstream impacts, best management practices are used to cultivate food systems and enhance soil nutrient cycling. When runoff events do occur, tracing the impairments often involves complex and costly methods to determine analyte concentrations and forecast mitigation techniques. Fluorescent dissolved organic matter (fDOM) is an innovative approach to understanding parent source materials and carbon signatures from runoff. Fluorescence and absorbance indices can distinguish intensities of the carbon molecular weight, biological activity, and humification that can trace the environmental availability of carbon sources. Comprehensive data sets can be combined using parallel factor analysis (PARAFAC) to determine parent source components. Integrating these analyses can provide real-time high-frequency data to empower policymakers and land managers to make informed decisions aimed at reducing the environmental degradation associated with modern intensive agriculture.

Waste stabilization ponds (WSPs) in humid, subtropical climates rely on stable temperatures and mechanical aeration to promote microbial activity. These critical infrastructures can lack operational resources to ensure efficient treatment, which can impact downstream communities. This study aims to use remote water quality sensor data to establish trends in a yearly dataset and correlate various water quality parameters for simplistic identification of pond health. A facultative WSP was monitored in two stages: the primary settling over a period of 14 months to evaluate partially treated water, and the secondary treatment pond for a period of 11 months to monitor final stage water quality parameters. A statistical analysis was performed on the measured parameters (dissolved oxygen, temperature, conductivity, pH, turbidity, nitrate, and ammonium) to establish a comprehensive yearly, seasonal, and monthly dataset to show fluctuations in water parameter correlations. Standard relationships in dissolved oxygen, conductivity, pH, and temperature were traced during the seasonal fluctuations, which provided insight into nitrogen processing by microbial communities. During this study, the summer period showed the most variability, specifically a deviation in the dissolved oxygen and temperature relationship from a yearly moderate negative correlation (−0.593) to a moderate positive correlation (0.459), indicating a direct relationship. The secondary treatment pond data showed more nitrogen species correlation, which can indicate final cycling during seasonal transitions. Understanding pond dynamics can lead to impactful, proactive operational decisions to address pond imbalance or chemical dosing for final treatment. By establishing parameter correlations, facilities with WSPs can strategically integrate sensor networks for real-time pond health and treatment efficiency monitoring during seasonal fluctuations.

This paper seeks to establish the impact of university experiential learning programs on small- to medium-sized enterprises while emphasizing the benefit to rural workforce development and sustainable manufacturing practices. Data were collected from diverse partners of Louisiana State University’s experiential learning program over the last 7 years to illustrate the types of recommendations and implementation statistics for sustainable manufacturing practices. The study found that rural enterprises favored the adoption of short-term, high-saving solutions to mitigate the impact of utility costs resulting from geographical isolation, while there was low implementation of long-term, large investment projects. This highlighted the practical feasibility of a project over a focus on long-term sustainability plans, which require significant capital investment, management planning, and employee training. This study outlines a university-led experiential learning program’s engagement through academic–industrial partnerships that serve student development and the economic advancement of small- to medium-sized enterprises. The data can direct future incentive opportunities for sustainability projects that have more immediate payback, to increase the adoption rate in rural facilities. The larger implication provides a framework and validation that can support the development of similar programs for extension and enterprise engagement to impact sustainable manufacturing practices.