You are currently viewing a new version of our website. To view the old version click .
MDPIMDPI
Search all articles
Sustainability
Download PDF
  • Article
  • Open Access

20 December 2025

A Streamlined Methodology for Identifying Point-Source Inputs from Rural and Agricultural Sources

,
,
,
,
,
,
and
1
Great Lakes Watershed Institute, Alma College, Alma, MI 48801, USA
2
Program of Environmental Studies, Alma College, Alma, MI 48801, USA
3
Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108, USA
*
Author to whom correspondence should be addressed.
Sustainability2026, 18(1), 74;https://doi.org/10.3390/su18010074 
(registering DOI)
This article belongs to the Section Environmental Sustainability and Applications
Version Notes
Order Reprints
Review Reports

Abstract

Rural and agricultural runoff continues to pose a threat to water quality and human health despite a plethora of research identifying likely causes. Large livestock operations and leaking septic systems have proven to be significant sources of both nutrients and bacteria in the form of algal blooms and antibiotic-resistant Escherichia coli. These impacts are often witnessed on a watershed scale. Implementing remedies is complicated, as livestock operations are defined as point-source facilities under the USA Clean Water Act (CWA) but regulated as non-point-source entities under a NPDES CAFO general permit. Non-point-source pollutant assessment of watersheds involves a wide array of sampling parameters that focus primarily on impacts after-the-fact and lack regulatory teeth. This watershed management approach is not sustainable, as evidenced by the continual degradation of our rural watersheds. This study lays out streamlined methods and techniques incorporating focused parameters that can infer point-source pollutant pathways even in already impaired waterways. We applied this methodology to the Pine River Watershed in central Lower Michigan after the appearance of an algal bloom downstream from several potential nutrient inputs. Findings show that the application of these unique methods and techniques results in the successful identification of point-source inputs. These methods are inexpensive and demand few resources, and hence they are easily reproduced and replicated. Therefore, by regulating large livestock operations as point-source discharge entities, it is possible for local communities, educational institutions, and regulatory agencies to identify likely pollutant sources in a way that promotes higher water quality and long-term sustainability.

1. Introduction

Since their inclusion in the Clean Water Act (CWA), large livestock facilities, AFOs (animal feeding operations) and CAFOs (confined or concentrated animal feeding operations) have been researched to assess their impacts on surface and groundwater [1]. A recent study showed that the number and size of these large livestock facilities have grown and that they tend to occur in clusters, increasing nutrient and bacterial loading in nearby streams. These impacts are measured on a watershed scale and are susceptible to climate-related exacerbations [2,3,4].
Specific effects of these operations include algal blooms and high concentrations of fecal coliform bacteria, including antibiotic-resistant Escherichia coli (E. coli) and other microbial organisms [5]. High concentrations of E. coli in waterways pose a real and present risk to human health in many forms [6,7]. In large part, this is why the United States Environmental Protection Agency (USEPA) has identified agricultural runoff as the leading cause of national water quality impairment [8].
Livestock operations occur in rural areas across the USA where enough water is present to meet animal demands—often, in the headwaters of riverine watersheds. Animal waste production, handling, and manure land application in headwaters result in pollutant impacts far downstream [1,9]. In glacial lake regions surrounded by industrial farming, there are growing concerns about eutrophic impediments similar to Lake Erie’s harmful algal blooms (HABs). Studies from Saginaw Bay show increased nutrient and bacterial loading from the watersheds that feed into it. Combined with climate-related impacts, such as warmer surface water and heavier rain events, this can result in long-term flushing of agricultural pollution downstream [9]. Roso [10] describes the risks of nutrient migration far downstream due to inadequate and sometimes incompatible assessment regimens and regulations by State water quality agencies. From these studies and others, it is clear that the water quality management approach for CAFOs does not promote sustainability, nor is it protective of water quality.
CAFOs, despite being identified in the CWA as point-source discharging entities, are regulated as non-point sources through the issuance of a general CAFO NPDES permit [11,12,13]. This permit is based mainly on how animal waste is handled through a nutrient management process (plan) that promotes best management practices [13]. This is because, unlike factories or wastewater treatment plants (WWTPs), it is assumed that there exist no effluent conduits from CAFO operations to nearby waterways.
It has long been known, however, that animal waste at the source (CAFO) and at manure application sites is subject to discharge into local ditches and tributaries via drain tiles or underdrains [14,15]. Underdrains are perforated pipes buried just below the root level in topographically flat areas.
Violations of CAFO permits are not subjected to the same enforcement measures as CWA violations for factories or WWTPs. In most states, CAFO permit compliance is voluntary. This has resulted in water quality degradation, algal blooms, and bacterial loading. States are required to address these pollution problems via CWA Section 303(d) Total Maximum Daily Load (TMDL) for each pollutant and each water body. If, however, CAFO operations do violate CWA standards and can be identified as point-source discharge, they are subject to the same enforcement measures as factories and WWTPs, which include fines, lawsuits, and required measures that focus on pollution prevention, abatement, and remediation [16]. In short, this kind of regulatory action would improve water quality and promote agricultural sustainability.
Changing regulatory structure at the national and state levels is difficult, but Moses and Tomaselli [14] argue that citizen groups, small rural communities, and even some discouraged state agencies can make a difference using CWA Section 505. In the central Lower Peninsula region of Michigan, several examples of watershed-level impacts from CAFOs have been observed [2,5]. Rural communities are growing frustrated where state or federal water enforcement agencies are unable or unwilling to address issues such as widespread algal blooms [17]. However, most communities—even remote, rural communities—have at their disposal multiple sources of aid. These include colleges and universities; local village and township offices; municipalities; national, state, and regional non-profit organizations; and even local community foundations. Magner [18] found that developing and promoting a systems approach between experts and end-users, as well as other stakeholders, can lead to the improved management of waterways. Additionally, the internet is a powerful and useful tool for accessing both reports and experts in the field.
A systems approach or methodology is needed that can be understood and utilized by stakeholders (university researchers, municipalities, and regulators) and focuses less on defining broad agricultural impacts and more on identifying specific point sources. This paper describes and tests a streamlined, field-based methodology that provides a systems approach to improving water quality and promotes sustainable agricultural practices. These field-based methods start from a contamination endpoint (e.g., algal bloom) and allow for a clearer picture of CWA violations from a possible point-source input. This is important because it will provide regulators with data-driven evidence that can be used to address water quality impacts through a more effective means than CAFO permit oversight.

2. Setting/Previous Research

2.1. Study Location

The Pine River Watershed occupies the upper reaches of the Saginaw River Drainage Basin, one of the largest freshwater basins in the U.S. The Saginaw River Drainage Basin ultimately feeds into Saginaw Bay, which is part of Lake Huron [19] (Figure 1).
Figure 1. Map showing the Pine River Watershed and its place within the larger Saginaw River Drainage Basin in central Lower Michigan. The Pine River Watershed is part of the Lake Huron/Saginaw Bay Drainage Basin [19].
The entire portion of the headwaters of the Pine River has been impacted by runoff, mainly due to over-storage and widespread application of animal waste from large livestock facilities. Until 2017–2018, these impacts were confined to a region of the watershed above the Alma Dam in the main trunk of the Pine River [2] (Figure 2).
Figure 2. Map of the upper Pine River Watershed. This map is divided at the Alma Dam between dominantly agricultural impacts upstream and dominantly industrial impacts downstream. The appearance of a large algal bloom in the area of the industrial impacts was relatively sudden and unexpected.

2.2. Findings from Previous Research

By 2017 and 2018, residents downstream of the Alma Dam near St. Louis, Michigan noticed a new pervasive growth of algae and aquatic vegetation in the Pine River. By 2019, there was a formal complaint lodged at a City of St. Louis Commission meeting as reported in the Gratiot County Herald. Alma College, the Great Lakes Watershed Institute, and the State of Michigan were contacted by the cities of St. Louis and Alma, Michigan, to help ascertain why algal blooms were suddenly appearing downstream of the Alma Dam. Because the upper watershed was already heavily impacted by agricultural inputs, it was assumed that nutrients from upstream sources flowing over the Alma Dam were the main sources of the algal blooms. Other potential nutrient sources were identified and presented in a report to the Office of Water Enforcement and Compliance, USEPA Region V [2].
Four sites of interest were identified in the report to USEPA (Figure 3). These included: 1. Impacted water coming over the Alma Dam; 2. The City of Alma Wastewater Treatment Plant (WWTP); 3. A tributary entering the Pine River from the South (Horse Creek); and 4. A tributary entering the Pine River from the north (Sugar Creek) [2].
Figure 3. Sample locations of likely nutrient sources causing the sudden appearance of an algal bloom in the Pine River near St. Louis, Michigan. Flow of the main river body (Pine River) is northeast.
Regarding the Alma WWTP, data taken between 2012 and 2024 showed no significant difference in SRP or E. coli concentrations between the WWTP and the Alma Dam sites (p ≤ 0.91 and p ≤ 0.88, respectively) [2]. Therefore, the Alma WWTP was eliminated as a potential source, and focus remained on water coming over the Alma Dam and two tributaries of the Pine River: Horse Creek and Sugar Creek.
When past data from the three remaining sites were compared over time, it was obvious that one site, Sugar Creek, dominated in both nutrient and bacterial inputs just upstream of the algal bloom (Figure 4) [2]. Sugar Creek provided an ideal opportunity to test whether a set of streamlined methods and techniques could identify specific inputs (point sources) contributing to the algal bloom in the Pine River. It had been monitored by Alma College before and after the appearance of the algal bloom, and the creek length is only four river miles. Also, there exists reliable information regarding adjacent land use for the majority of its length, and sample accessibility is ideal.
Figure 4. Graphs showing average concentrations of SRP and E. coli for varying timeframes. For each site (x-axis), potential nutrient sources of the algal bloom in the Pine River near St. Louis, Michigan are listed. Note: Sugar Creek dominates in both nutrient and E. coli inputs after 2018 compared with other sites.
If a point-source input could be identified, the State of Michigan could take effective and lasting action under CWA enforcement to improve water quality and even sustainability for agricultural operations that impact it.

3. Methodology

As mentioned in the introduction, regulators address agricultural pollution following guidelines established by CWA Section 303(d). CWA Section 303(d) sets USEPA and State regulatory agencies in charge of the general monitoring of waterways suspected of water quality impacts through the use of Total Maximum Daily Load (TMDL) parameters, which include a myriad of chemical parameters [20].
The results of agricultural TMDLs provide a detailed look into the type of pollution affecting waterways and even watersheds. However, they are extremely resource-intensive and less effective in identifying point-source inputs. Martin [21] found that using TMDLs as a means of identifying point sources affecting Lake Erie’s harmful algal blooms (HABs) was less effective than simply evaluating one specific chemical parameter, such as soluble reactive phosphorus (SRP).
This study outlines and applies a unique set of field methods and applied techniques, some that are not well-represented in existing literature. The unique applications of these methods provide a more simplified approach to identifying point sources, and may work well in tandem with more generalized assessment strategies like TMDLs. The goal of this study was to test these methods and techniques in order to find a potential point source in a waterway suspected of contributing to a recent algal bloom.

3.1. Simplifying Parameters

This study simplifies sampling and analytical work to only three parameters: ammonia as nitrogen (NH3-N); soluble reactive phosphorus (SRP or orthophosphate), and thermotolerant Escherichia coli (E. coli) bacteria. Three arguments provide reasons for simplification: First, these parameters are universally and uniquely representative of agricultural impacts—especially those impacts from large livestock facilities [22,23]. Second, these parameters can be used to determine the proximity of impacts to known agricultural source(s) [24,25]. Third, these parameters are also excellent indicators of the overall health of the watershed [26]. Finally, it is important to note that these parameters are relatively easy and inexpensive to incorporate into a comprehensive sampling plan, and therefore are replicable and accessible to communities and researchers.

3.2. Novel Application of Methods

The following novel approaches will help identify specific inputs, even if the waterways are already impaired. These involve the following: 1. Using statistical correlations between NH3-N and SRP in water samples to assess proximity to potential waste inputs; 2. Comparing concentrations of SRP and E. coli before and after rain events to differentiate septic system inputs from those of CAFO facilities or manure application sites; and 3. Using the presence of thermotolerant E. coli to differentiate nutrient inputs from manure applications and inorganic (chemical) fertilizers.

3.2.1. Using Statistical Correlations Between NH3-N and SRP

As manure leaves the animal host, SRP and NH3-N degrade in different ways and at different rates. Ammonia will volatilize and oxidize eventually to a more stable nitrate (NO3-N) chemical configuration. SRP does not volatilize but is taken up by micro-plants, including aquatic plants such as duckweed (i.e., Lemna minor and Lemna perpusilla) and algae. Oemke [24] and Borrello [25] demonstrated that, over the course of a sampling season, proximity to waste inputs can be determined by a simple Pearsons coefficient correlation analysis between NH3-N and SRP. Higher r values (in some cases, r values > 0.90) were observed in samples taken just downstream from suspected point-source inputs compared with random (r values < 0.10) upstream ones. This was demonstrated in multiple examples comparing upstream and downstream samples for several CAFOs between 2004 and 2016 [25].
As time and space separate suspected point sources, the r value is reduced in a linear fashion. Therefore, if a point-source input occurs along Sugar Creek, higher r values nearest to the source would be expected, with a decrease in correlation in a linear fashion downstream if no other inputs are present.

3.2.2. Differentiating Human (Septic Systems) from CAFO (Manure) Sources

Differentiating E. coli produced by livestock and humans (septic systems) is not as straightforward as it might appear. Regulatory agencies have employed a host of methods, including speciation and the presence of antibiotic resistance genes (ARGs) [27]. In an agriculturally impacted watershed, it is easy to find evidence of both human and livestock bacteria in waterways. This paper includes simple field techniques to assess the point-source origin of E. coli, such as identifying spikes in E. coli concentrations after a moderate to heavy rain event (over 1.25–2 cm in a 24 h period). Manure slurries can stagnate in underdrains until a rain event acts as a flushing agent [28]. If E. coli concentrations spike after a moderate to heavy rain event, it is likely due to discharge via underdrains.
Conversely, human waste from faulty septic systems enters surface water mainly as groundwater seeps. In Michigan, a faulty septic system is usually due to water bypassing a disconnected or non-functioning septic tank and drain field, discharging as contaminated groundwater into the nearest surface water body [29,30]. Since septic system waste is part of groundwater flow, there is a maximum flow rate, defined by Darcy’s equation, that is much longer than that for animal waste entering via an underdrain.

3.2.3. Differentiating Animal Waste and Chemical Fertilizer Nutrient Inputs

In Central Michigan, the main reason for applying manure to cropland is to relieve the storage containment systems where it resides. This is why manure is often applied to fallow land. Recent studies have shown that nitrogen isotope analyses can distinguish chemical fertilizer nutrients from those in animal waste due to the fact that animals ingest nitrogen stored in plants [31]. The mixing of nitrogen isotopes begins in the soil nutrient pool and changes depending on whether soil thaws or summer rains drive the mobilization of nutrients. This shows that it is difficult to obtain definitive results [32].
Determining whether high nutrient concentrations result from excessive chemical fertilizers or manure application is a simple matter of whether high nutrient levels are accompanied by E. coli. Consistently high concentrations of nutrients near a suspected input site without associated high concentrations of E. coli indicate chemical fertilizers as the dominant source, while excessive concentrations of E. coli correlated with high nutrient concentrations indicate animal waste as a potential source. This simple technique combined with the rain event data described above can establish a likely nutrient source as either chemical or organic in origin.

3.2.4. Other Methods

Agricultural research relies heavily on multiple sources of information, such as county drain maps, topographic maps, and plat maps. Aerial photos and GIS-generated maps are also incredibly valuable. When investigating agricultural runoff, referencing manure management plans and related permits is vital. CAFO NPDES permits have only recently become accessible online and no longer require filing Freedom of Information Act (FOIA) requests.
CAFO permits include manure management plans, which outline specifically where animal waste can be applied. The county drain commissioner and the State of Michigan determine which lands are available for manure application and which are not. These maps are available if they are part of the permits themselves. There is a stipulation in the Michigan CAFO general permits that allows CAFO operators to manifest animal waste to a third party for disposal. It is assumed that manure application will occur in legally designated areas; however, there is little follow-up by the State to ensure that happens.

3.3. Application of Methods and Techniques

The methods and techniques described above were implemented in a case study intended to identify a potential point-source input along Sugar Creek in relation to the appearance of a recent algal bloom in the Pine River downstream from the confluence of Sugar Creek. All sampling and analytical work followed protocols established by USEPA and the State of Michigan, from where samples were taken (QAPP guidelines). These protocols, for the most part, rely on those highlighted in Mitchell and Stapp [33]. Frequent discussions with Michigan’s Department of Environment, Great Lakes, and Energy (EGLE) took place when unusual incidents, such as extremely low-flow conditions or interruptions by landowners, occurred.
For field sampling, sterile and plastic containers were used to obtain water samples. Samples were placed on ice and taken to the laboratory for analysis within sixty minutes. Once in the lab, a DREL 3900 spectrophotometer (detection limit SRP and NH3 = 0.01 mg/L) was used, in which triplicate aliquots were run. Averages were calculated and outliers (those outside of 20% of the mean), were discarded which occurred three times over the course of the sampling season. In the case of the outlier, values for the two remaining aliquots were within 20% of each other, so those numbers were averaged. For three divergent samples, three new aliquots were run which occurred one time during the sampling season. For any samples over range, dilutions were prepared and re-run under the same QA/QC conditions.
The DREL 3900 spectrophotometer (HACH Co., Loveland, CO, USA) has an internal calibration feature; however, manual calibrations using factory standards and dilutions were run for each analyte at least twice weekly and found to be within 0.95 of expected values. Field probes were calibrated daily. A one-way ANOVA analysis was run, including t-tests for significance and Pearsons correlation coefficient using Excel v. 16.103.3 as specified in the results below. For thermotolerant E. coli measurements, Micrology Labs (Granger, IN, USA) Easygel® media were used and were compared with media production and plating using USEPA Procedure 1603 in the Great Lakes Watershed Institute lab. Coliscan Easygel is an easy, efficient, and inexpensive means of determining thermotolerant E. coli. Triplicate plates were created and the number of colonies on each plate was averaged. All results were assessed for significance.

4. Results and Discussion

Methods and techniques were applied to Sugar Creek in an attempt to identify potential point-source inputs that might be contributing to the algal bloom observed in the Pine River. Results are organized according to the methods and techniques applied.

4.1. Nutrients Are Coming from Animal Waste, Not Chemical Fertilizer

Figure 4 shows that, for the first three sampling sites, the nutrient concentrations in Sugar Creek in the summer of 2025 were correlated with E. coli concentrations (r = 0.89), both of which remained consistently high throughout the summer. The average SRP concentrations for 2025 were 0.74 mg/L, which is 15 times higher than the maximum concentration for a healthy stream. Thermotolerant E. coli concentrations were also extremely high—6 times higher than Alma Dam concentrations and almost 9 times higher than partial body contact criteria established by the State of Michigan. Figure 5 shows that the results for 2025 are not atypical of the past six years.
Figure 5. Sampling sites along Sugar Creek. Note: Sampling sites 1–3 exhibit higher concentrations of SRP and E. coli than the site near the confluence of Sugar Creek and Pine River (site 4).
These results indicate that nutrients are coming from animal waste (human or livestock) and not chemical fertilizers. Chemical fertilizers would be applied in early spring and the impact would not be lasting. Also, the high correlation of SRP with E. coli indicates the source of E. coli is likely the same source of nutrients. Chemical fertilizers would not release E. coli in any measurable amount.

4.2. Septic Systems Do Not Play a Significant Role in Nutrient Inputs

Figure 6 illustrates that E. coli concentrations spiked within 12 h of a rain event of over 2 cm in the summer of 2025. The differences in E. coli concentrations before and after the rain event were highly significant (p ≤ 1 × 10−4). A spike in SRP also occurred between the same rain event in 2025, with a significance of p ≤ 0.007. Sudden increases like this were observed in past years (Figure 7), suggesting rapid discharge from an underdrain source. An inspection of county maps and plat maps showed that there were few septic systems near or adjacent to Sugar Creek, where the highest concentrations of nutrients and E. coli were measured, indicating the likely source of nutrients and bacteria was a point source, not a groundwater seep.
Figure 6. Graph showing sudden spike in E. coli shortly after a rain event in 2025, indicating that the nutrient and bacterial input source is likely underdrains and not leaking septic systems.
Figure 7. Graphs from 2024 showing spikes in SRP and E. coli after a rain event from the same sampling sites as Figure 6. This indicates the input source is coming from underdrains (SRP p ≤ 0.01/E. coli p ≤ 0.006).

4.3. Statistical Correlations of NH3-N and SRP Indicate a Source Above Sugar Creek

Results of a correlative analysis between NH3-N and SRP show a stronger correlation (r = 0.38) at the start of the creek, decreasing in a clear linear fashion (r2 = 0.98) downstream (Figure 8). A second site along the Pine River was chosen as a comparison, but the correlation fluctuated, and there was a weak, linear relationship downstream (r2 = 0.69) as inputs occurred between sampling sites.
Figure 8. Graph plotting correlations between NH3-N and SRP for the first three sampling sites along Sugar Creek (upper graph). This methodology was reproduced in three sampling sites along the Pine River, where there were suspected inputs between sites (lower graph). Analysis of these results shows a stronger correlation at the headwaters of Sugar Creek than for the upper graph, decreasing in a strong linear fashion downstream. This indicates an input source is likely near the first sampling site at the very source of Sugar Creek, and no other significant inputs occur downstream. The Pine River graph shows likely nutrient inputs entering the Pine River between sites.
The correlations between NH3-N and SRP were highest at the first sampling location, but the correlation (r value) was too low to identify any point-source input directly. These results indicate that there is likely an input source beyond (upstream) of the start of Sugar Creek.

4.4. Maps and Satellite Images Help Identify Potential Point-Source Inputs

Sugar Creek is designated both as a natural tributary and a county drain, so a county drain map was consulted through the Michigan Geographic Framework (MGF). This map showed the connection of an underground pipeline (drain) at the source of Sugar Creek. As Figure 9 shows, this underground drain (noted by dotted blue lines) bifurcates but connects Sugar Creek with some potential sources not obvious from the field view or even the aerial view.
Figure 9. County drain map showing the existence of an underground drain sourced from a nearby livestock facility to the head of Sugar Creek.
Satellite imagery from a publicly available county drain website was used to see if the underground drain showed any surface indicators of inputs into the drain using simple image enhancement tools. By comparing satellite images from different timeframes, a connection between a livestock facility and a drain basin (surface access point) that linked the underground drain to Sugar Creek was observed. Adding a topographic profile GIS tool to the image, a downgradient slope from the livestock facility to the drain basin was determined. Further analysis and slight enhancement of the image characteristics show staining on the surface between the livestock facility and the drain basin. This was first identified in the 2015 image and appears to expand in size and deepen in color in the 2024 image (Figure 10). It is important to note that the timing of the surface stain linking the CAFO operation with the underground drain is commensurate with the timing of the appearance of the algal bloom in the Pine River.
Figure 10. Satellite images (enhanced) showing increased staining on the ground surface leading from a livestock facility to a drain basin that connects with the head of Sugar Creek.

4.5. Potential Second Point Source?

Though the CAFO appears to be the main source of both nutrients and bacteria, the potential impact from the rural neighborhood community itself cannot be completely discounted. The small neighborhood just south of the CAFO property is on septic systems that might be linked via a single pipe to the underground drain. Consultations with the Drain Commissioner revealed that there are many cases in which owners of older rural homes, in lieu of a permit (which may not have been available at the time), constructed their own septic systems that bypassed a septic tank and drain field and tied in with other neighboring systems. Often, these collective drains tie into an underground pipe/drain that leads to the nearest surface waterway (Figure 11).
Figure 11. Enhanced satellite image of the location of a neighborhood in the area near a livestock facility and underground drain connected to Sugar Creek. This neighborhood could represent potential secondary input from faulty septic systems. Further investigation needs to address the possibility of a connection between the neighborhood septic systems and the underground drain that ties into Sugar Creek.

5. Conclusions

Agricultural impacts are the number one source of water pollution in the USA. Rural communities struggle to address this problem due, in part, to the way agricultural impacts (specifically CAFO impacts) are regulated. What is needed is an approach that identifies and addresses impacts as point sources, as these violations carry enforcement action that has the most potential for effecting positive change. The data gathered and presented in this study are the result of novel applications of methods and techniques that successfully identified potential point-source inputs to a tributary contributing to an algal bloom in an impaired watershed. These data may provide enough information for regulatory agencies to investigate potential impacts as point-source inputs, a violation of the CWA that carries definitive and effective enforcement. These methods are simple and can be carried out by community members, university students, and faculty, as well as regulators on limited budgets. Results of these studies may inform citizen lawsuits through CWA Section 505 if regulatory agencies are reluctant to move forward.
The use of these methods and techniques, admittedly, is limited to a specific case study. In this case, the algal bloom was well-defined, as were the potential nutrient sources. The question of whether or not these methods will be useful in waterways with more diffuse impacts is a good one. Further studies should apply the proposed methods and techniques to different impacted areas, especially those with more diffuse impacts, perhaps in a larger affected geographic area. The GLWI has plans to take on this task in the coming year or two. In science, replication is key.

Author Contributions

Conceptualization, M.C.B., H.A., M.H. and J.M.; methodology, H.A., E.G., B.C., L.M., C.C. and M.C.B.; software, H.A., C.C. and B.C.; validation, M.C.B. and M.H.; formal analysis, M.C.B.; investigation, M.C.B., H.A., E.G., B.C., C.C. and L.M.; resources, M.C.B.; data curation, H.A., B.C. and C.C.; writing—original draft preparation, M.C.B.; writing—review and editing, M.C.B., H.A., J.M. and M.H.; visualization, M.C.B.; supervision, M.C.B.; project administration, M.C.B.; funding acquisition, M.C.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a Department of Education grant no: P116Z240174, and by a donation from the Healthy Pine River Group Grant No. 8900.2025.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data supporting this study, and previous data on similar areas of the watershed mentioned herein may be accessed through written request to the lead author at: borrello@alma.edu.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
CWAClean Water Act
GLWIGreat Lakes Watershed Institute
AFOAnimal feeding operations
CAFOConfined animal feeding operations
HABHarmful algal blooms
WWTPWastewater treatment plant

References

  1. Miralha, L.; Sidique, S.; Muenich, R. The spatial organization of CAFOs and its relationship to water quality in the United States. J. Hydrol. 2022, 613, 128301. [Google Scholar] [CrossRef]
  2. Borrello, M.C.; McPhail, C.; Pink, S.; Veverka, J. General Overview of the Status of the Upper Saginaw River Drainage Basin, Pine River Watershed, Central Lower Peninsula, Michigan; Report Submitted to the Office of Water Enforcement and Compliance, U.S. Environmental Protection Agency, Region, V. Water enforcement and compliance assurance, Chicago, IL, USA; U.S. Environmental Protection Agency: Washington, DC, USA, 2023. [Google Scholar]
  3. Waller, D.; Meyer, A.; Raff, Z.; Apfelbaum, S. Shifts in precipitation and agricultural intensity increase phosphorus concentrations and loads in an agricultural watershed. J. Environ. Manag. 2021, 284, 112019. [Google Scholar] [CrossRef] [PubMed]
  4. Su, H. Impact of Industrial Agriculture on the Water Environment and Human Health—Focusing on Watersheds that Feed Lake Michigan and Lake Huron. Master’s Thesis, University of Edinburgh, Edinburgh, UK, 25 August 2020. [Google Scholar]
  5. Hamilton, B.M.; Harwood, A.D.; Wilson, H.R.; Keeton, T.P.; Borrello, M.C. Are anglers exposed to Escherichia coli from an agriculturally impacted river? Environ. Monit. Assess. 2020, 192, 216. [Google Scholar] [CrossRef]
  6. Perry, J.T. Do Poultry CAFOs Contribute to Negative Human Health Outcomes: A Systematic Review. Master’s Thesis, North Carolina State University, Raleigh, NC, USA, 16 April 2024. [Google Scholar]
  7. Moore, T.C.; Fong, J.; Hernández, A.M.R.; Pogreba-Brown, K. CAFOs, novel influenza, and the need for One Health approaches. One Health 2021, 13, 100246. [Google Scholar] [CrossRef]
  8. U.S. EPA National Water Quality Inventory: Report to Congress. EPA 84-R-16011. Available online: https://www.epa.gov/sites/production/files/2017-12/documents/305brtc_finalowow_08302017.pdf (accessed on 14 July 2025).
  9. Oun, A.; Yin, Z.; Munir, M.; Xagoraraki, I. Microbial pollution characterization of water and sediment at two beaches in Saginaw Bay, Michigan. J. Great Lakes Res. 2017, 43, 64–72. [Google Scholar] [CrossRef]
  10. Rosov, K.A.; Mallin, M.A.; Cahoon, L.B. Waste nutrients from U.S. animal feeding operations: Regulations are inconsistent across states and inadequately assess nutrient export risk. J. Environ. Manag. 2020, 269, 110738. [Google Scholar] [CrossRef] [PubMed]
  11. Yager, S.; Hart, M.T. The tipping point source: Clean Water Act regulation of discharges to surface water via groundwater, and specific implications for nonpoint source agriculture. Drake J. Agric. Law 2018, 23, 439. [Google Scholar]
  12. Clean Water Act. 40 CFR. Chapter I, Subchapter D, Part 122, 412. Available online: https://www.law.cornell.edu/cfr/text/40/122.23 (accessed on 11 August 2025).
  13. State of Michigan, Department of Environment, Great Lakes, and Energy. National Pollutant Discharge Elimination System Wastewater Discharge General Permit for Concentrated Animal Feeding Operations. Available online: https://www.michigan.gov/-/media/Project/Websites/egle/Documents/Programs/WRD/CAFO/MIG010000-General-Permit-2020.pdf?rev=cb5d071f0e174361a17d69a143419f9d (accessed on 12 June 2025).
  14. Moses, A.; Tomaselli, P. Industrial animal agriculture in the United States: Concentrated animal feeding operations (CAFOs). In International Farm Animal, Wildlife and Food Safety Law; Springer International Publishing: Cham, Switzerland, 2017; pp. 185–214. [Google Scholar]
  15. Coelho, B.B.; Lapen, D.; Murray, R.; Topp, E.; Bruin, A.; Khan, B. Nitrogen loading to offsite waters from liquid swine manure application under different drainage and tillage practices. Agric. Water Manag. 2012, 104, 40–50. [Google Scholar] [CrossRef]
  16. White, S. Reducing pollution from Concentrated Animal Feeding Operations by enforcing National Pollutant Discharge Elimination System Permit requirements under the Clean Water Act. Colo. Nat. Res. Energy Environ. Law. Rev. 2025, 36, 167. [Google Scholar]
  17. Watnick, V.J. Climate change, marginalized communities and pandemics. Environ. Law 2024, 54, 381–423. [Google Scholar]
  18. Magner, J. Tailored watershed assessment and integrated management (TWAIM): A systems thinking approach. Water 2011, 3, 590–603. [Google Scholar] [CrossRef]
  19. Selzer, M.D.; Joldersma, B.; Beard, J. A Reflection on restoration progress in a Saginaw Bay watershed. J. Great Lakes Res. 2014, 40, 192–200. [Google Scholar] [CrossRef]
  20. Mayer, M.K.; Morris, J.C.; McNamara, M.W.; Zhang, X. Explaining state efforts to create Total Maximum Daily (TMDL) agreements. Soc. Sci. Q. 2024, 105, 1776–1790. [Google Scholar] [CrossRef]
  21. Martin, J.F.; Kalcic, M.M.; Aloysius, N.; Apostel, A.M.; Borrker, M.R.; Evenson, G.; Kast, J.B.; Kujawa, H.; Murumkar, A.; Becker, R.; et al. Evaluating management options to reduce Lake Erie algal blooms using an ensemble of watershed models. J. Environ. Manag. 2021, 280, 111710. [Google Scholar] [CrossRef] [PubMed]
  22. Christenson, E.C.; Serre, M.L. Integrating remote sensing with nutrient management plans to calculate nitrogen parameters for swine CAFOs at the sprayfield and sub-watershed scales. Sci. Total Environ. 2017, 580, 865–872. [Google Scholar] [CrossRef] [PubMed]
  23. Mallin, M.A.; McIver, M.R. Season matters when sampling streams for swine CAFO waste pollution impacts. J. Water. Health 2018, 16, 78–86. [Google Scholar] [CrossRef]
  24. Oemke, M.P.; Borrello, M.C. Geochemical signatures of large livestock operations on surface water. ICFAI J. Environ. Sci. 2008, 2, 7–18. [Google Scholar]
  25. Borrello, M.C.; Keeton, T.P.; Harwood, A.; St. John, L.; Jeffery, H.; DesMarais, A. Combining bacterial loading and nutrient loading to source agricultural impacts on surface water. In Proceedings of the SETAC North American World Congress Conference, Orlando, FL, USA, 6–10 November 2016. [Google Scholar]
  26. Litskas, V. Environmental impact assessment for animal waste, organic and synthetic fertilizers. Nitrogen 2023, 4, 16–25. [Google Scholar] [CrossRef]
  27. Givens, C.E.; Kolpin, D.W.; Hubbard, L.E.; Meppelink, S.M.; Cwiertny, D.M.; Thompson, D.A.; Lane, R.F.; Wilson, M.C. Simultaneous stream assessment of antibiotics, bacteria, antibiotic resistant bacteria, and antibiotic resistance genes in an agricultural region of the United States. Sci. Total Environ. 2023, 904, 166753. [Google Scholar] [CrossRef]
  28. Pandey, P.K.; Soupir, M.L. Assessing linkages between E. coli levels in streambed sediment and overlying water in an agricultural watershed in Iowa during the first heavy rain event of the season. Trans. ASABE 2014, 57, 1571–1581. [Google Scholar] [CrossRef]
  29. Baker, N.T.; Sullivan, D.J.; Selbig, W.R.; Haefner, R.J.; Lampe, D.C.; Bayless, E.R.; McHale, M.R. Green Infrastructure in the Great Lakes—Assessment of Performance, Barriers, and Unintended Consequences; US Geological Survey: Reston, VA, USA, 2022; p. 1496. [Google Scholar]
  30. Li, E.; Saleem, F.; Edge, T.A.; Schellhorn, H.E. Assessment of crAssphage as a human fecal source tracking marker in the lower Great Lakes. Sci. Total Environ. 2023, 912, 168840. [Google Scholar] [CrossRef] [PubMed]
  31. Lee, J.; Choie, H.L. The dynamics of nitrogen derived from a chemical nitrogen fertilizer with treated swine slurry in paddy soil-plant systems. PLoS ONE 2017, 12, E0174747. [Google Scholar] [CrossRef] [PubMed]
  32. Magner, J.A.; Alexander, S.E. Geochemical and isotopic tracing of water in nested southern Minnesota corn-belt watersheds. Water Sci. Tech. 2002, 45, 37–42. [Google Scholar] [CrossRef]
  33. Zhang, C. Fundamentals of Environmental Sampling and Analysis; John Wiley and Sons.: Hoboken, NJ, USA, 2024. [Google Scholar]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Article metric data becomes available approximately 24 hours after publication online.
Download PDF
Effective Strategies for Environmental Health Risk Communication
Previous
Construction Waste Documentation System in Poland: Current State and Prospects for Automation
Next