Final report for GNE19-201
Project Information
Antibiotic residues in the environment threaten soil and aquatic organisms and human and livestock health through the building of antimicrobial resistance. Manure spreading associated with animal agriculture is one source of environmental antibiotic residues. To better understand the risk of contamination from these emerging contaminants, we studied the adsorption of erythromycin, a model macrolide antibiotic used across human and animal medicine. We conducted a series of equilibrium batch experiments to determine the kinetics and extent of adsorption and a continuous-flow column adsorption experiment to observe non-equilibrium adsorption patterns. We determined adsorption equilibration time to soil was approximately 72 hr in our batch experiments. Erythromycin adsorbed to soil relatively strongly (K = 8.01 x10-2 L/mg; qmax = 1.53 x10-3 mg/mg), adsorbed to soil in the presence of manure with less affinity (K = 1.99x10-4 L/mg) at soil:manure ratio of 10:1 by mass, and did not adsorb to manure across the solids ratios tested. We observed a multi-phased adsorption of erythromycin to soil during the non-equilibrium column experiment, that was largely absent from the treatments with both soil and manure present. These results suggest erythromycin is more mobile in the environment when introduced with manure, likely the largest source of agriculturally sourced environmental antibiotics.
This work aimed to characterize the adsorption and transport processes of erythromycin and predict transport of this compound from agricultural sites to the surrounding geological and ecological systems. This work was to be done in parallel with an interview-based study on farmer perceptions of antibiotic impact on farms. The two studies together create a more holistic understanding of antibiotic impacts on dairy farms. The interview study has been fully funded elsewhere and published (Georgakakos et al., 2021) and is only discussed here where overlap applies.
Objective 1: Determine adsorption coefficients for erythromycin and ampicillin to manure and soil.
Objective 2: Model transport of erythromycin given experimental results in objective 1, and determine goodness of fit to laboratory column experiment.
Objective 3: Lower communication barriers between farmers, scientists, policy makers and the public. [Ongoing]
Sustainable farming practices contribute to farm longevity and management of environmental impacts. Economic and environmental sustainability go hand-in-hand to generate stable, productive farms that minimize negative environmental impacts while supporting the livelihoods of farmers. Animal and soil health are two sustainability focus areas that draw significant farmer attention. Maintaining good animal health translates to carefully tracking environmental conditions, reading animal health indicators, and addressing illnesses as they arise. Soil health involves a complicated fusion of understanding nutrient cycling, crop nutrient needs, organic matter incorporation and decomposition, and the microbial community’s interaction and role in each of those processes. Though animal and soil health are not generally closely tied to one another outside of nutrient management, management of manure from animals treated with antibiotics or other drugs may link the two systems more closely than previously anticipated.
Many animals absorb antibiotics poorly, with some animals excreting 50-90% of the ingested compound depending on antibiotic type (Kim et al., 2011; Alcock, 1999; Feinman & Maheson, 1978). The excreted compounds can then move with water and sediments and interact with surrounding microbial communities. The extent of this interaction, and risks it poses, is not well understood. We must therefor understand the fate and transport of these excreted compounds to trace potential interaction pathways. Understanding antibiotic transport off farms with manure will help us better understand the risks these chemicals may pose to humans, livestock, and soil and aquatic organisms.
Much of our current food system depends on the judicious use of antibiotics in livestock agriculture, and will continue to do so into the foreseeable future. However, the degree of reliance upon these compounds is highly variable across farms, management styles, and sizes, according to results from an interview-based study of farmers on the topic (Georgakakos et al, 2021). This variability suggests that risk of antibiotic contamination and spread of resistance in the environment may not be homogenous across all farms.
Antibiotic transport is relevant to livestock farms of all varieties as well as urban watersheds. I will focus on antibiotic transport on dairy farms due to dairy farm prevalence in New York State. According to the 2017 New York State Dairy Statistics Annual Summary, 623 thousand dairy cows were registered in NY State in 2017 on nearly 4,300 farms (NYS Dept. Ag. & Markets 2017). Management of the manure and the associated contaminants generated by an industry of this size is critically important. Understanding the transport of antibiotics will help us engineer our farm manure management systems to minimize the distribution of these potent compounds on a farm-by-farm basis.
The class of compounds referred to as emerging contaminants such as pesticides, pharmaceuticals, and personal care products are increasingly gaining attention for their detection in surface waters. These substances interact with the environment and humans in very impactful ways, despite their sometimes low concentrations and limited regulation. It is therefore important we understand the adsorption underpinnings of those interactions to assess potential risk and regulate sustainable usage.
This work characterizes the adsorption and transport processes of erythromycin as a model antibiotic to help predict agricultural contamination risk to surrounding ecosystems. We characterized the adsorption isotherms of erythromycin to a characteristic agricultural soil with and without manure present through adsorption equilibrium batch reactions and column experiments to better understand mobility. We fitted Langmuir adsorption models to both treatments. This research fills existing gaps in the contaminant transport literature to assess and model antibiotic contamination from animal agriculture.
It is possible that antibiotics and other emerging contaminants begin to be regulated in the near future as they continue appearing in drinking water sources. This work will help farmers adjust to those regulations should they be applied.
A parallel interview study compliments the research of this proposal by assessing farmer perspectives of the impact of antibiotics on their operations. By pairing these quantitative and qualitative research methods, a more complete perspective of antibiotics on dairy farms is developed. This complementary data will help determine risks of antibiotic contamination on farms of different sizes, and will suggest management practices that could reduce that transport
References:
Alcock, RE, Sweetman, A, Jones KC. (1999). Assessment of organic contaminant fate in waste water treatment plants 1: selected compounds and physiochemical properties. Chemosphere. 38, 2247-2262
Feinman, SE, & Matheson, JC (1978). Draft environmental impact statement: subtherapeutic antibacterial agents in animal feeds. Food and Drug Administration Department of Health, Education and Welfare Report.
Georgakakos, CB, Hicks, B, & Walter, M.T. (2021) Farmer perceptions of dairy farm antibiotic use and transport pathways as determinants of contaminant loads to the environment. Journal of Environmental Management, 281, 111880. Doi: 10.1016/j.jenvman.2020.111880
Kim, K. R., Owens, G., Kwon, S. I., So, K. H., Lee, D. B., & Ok, Y. S. (2011). Occurrence and environmental fate of veterinary antibiotics in the terrestrial environment. Water, Air, & Soil Pollution, 214(1-4), 163-174.
NYS Dept. of Agriculture & Markets. Division of Milk Control and Dairy Services. (2017) New York State Dairy Statistics 2017 Annual Summary.
Cooperators
- (Educator and Researcher)
- (Educator and Researcher)
Research
Objective 1 & 2:
Soil and manure collection
We collected soil from the top 10 cm of a fallow control plot (Cornell Recreation Connection, Freese Road, Ithaca NY) in the Caneseraga CaB soil series, an agricultural soil characteristic of New York’s Finger Lakes region. We collected manure samples from four organic dairy farms. We collected manure from organic dairy farms to reduce risk of antimicrobials present in manure (USDA organically marketed livestock cannot be treated with antibiotics) and confirmed with each farm that no animals had been treated with antibiotics or alternative organically approved therapies in the recent past). On a per cow basis, farm 1 was 100% grass-fed with 0 kg grain/day, while farms 2, 3, and 4 fed 1.4 kg grain/day (3 lb/day), 4.5 kg grain/day (10 lb/day), and 17.2 kg grain/day (38 lb grain/day), respectively, at the time of manure collection. This diversity of feeding practices is representative of farms in the region. We combined the four manures in a 1:1:1:1 by dry mass ratio to achieve an average manure composition. All manures were individually analyzed for bulk parameters. All solids were twice autoclaved at 135°C for 25 min to sterilize and oven-dried before use. Batches and column experiments were spiked with pure erythromycin, representative an unchanged, biologically reactive antimicrobial upon excretion.
Bulk parameter characterization
We measured bulk solid parameters to understand the adsorption environment and allow comparison between treatments. We obtained bulk density, pH, particle size distribution, electrical conductivity, organic matter loss-on-ignition, surface area, pore size, and zeta potential for both manure and soil substrates (Table 1). We also obtained percent sand, silt, and clay for the soil. We tested both oven-dried and autoclaved then oven-dried samples to assess the effect of substrate preparation on bulk parameters and chose to use autoclaved solids to reduce influence of biological activity (all autoclaved versus oven dried bulk parameters are recorded in Supplemental Table S1) .
We calculated soil bulk density by extracting a 270 cm3 cylindrical soil core (7.3 cm x 6.5 cm) and obtained total solids content of fresh manure by measuring 250 cm3 of fresh manure and recorded the wet weights. Samples were oven dried at 65°C for 24 hr before recording dry weights (U.S. Soil Quality Institute, 1998). Using an Accument Research AR50 Dual Channel pH/Ion Conductivity Meter (S/N AR 81202286), we measured pH and electrical conductivity. We prepared pH samples from 15 g dry soil and manure, sieved through a 2 mm sieve, and rehydrated in 30 mL deionized (DI) water after a 30 min equilibration period (Robertson et al., 1999). We prepared electrical conductivity samples in a 1:5 ratio by mass of dry solids to water and left to equilibrate for 1 hr prior to analysis (Rayment & Higginson, 1992). Particle size distribution, specific surface area, and pore size was obtained using the Brunauer-Emmett-Teller (BET) method (Micrometrics ASAP 2640) from dry, sieved (2mm) samples after subjecting the samples to a 100°C vacuum for 24 hr. We calculated percent loss on ignition by subjecting 5 g of oven dried soil or manure to 500°C for 2 hr. Suspensions of 0.1 mg/mL were prepared for zeta potential analysis following Darrow et al. (2020) for soils. We obtained zeta potential from a Malvern Panalytical (zs90) Zetasizer set to a reflective index (RI) of 2 and absorbance index (A) of 1 for soil, and a RI of 1.4 and an A of 0 for manure (RI and A adjusted from estimates in Darrow et al., 2020 and Lafon et al., 2006).
Batch equilibrium adsorption experiments
Oven-dried solids were added to each batch and volume brought up to 150 mL with deionized water after erythromycin (Cayman Chemical, item #: 16486, purity >98%) addition. To determine solids ratios for experimental use, differing solid masses (0.01 g, 0.05 g, 0.5 g, 5 g, 15 g) were tested to determine detectable adsorption of erythromycin (at 667 ppb) (Supplemental Figure S1). Preliminary manure tests resulted in no adsorption across treatments, leading to the two-phased equilibrium experiment discussed below. Soil solid ratio of 5 g/150 mL was chosen as the first solid ratio tested with significant adsorption. For combined manure and soil experiments (SM treatment), 5 g soil with 0.5 g manure was used for a soil : dry manure ratio of 10:1 by mass. This ratio would be representative of the site of surface soil - manure contact after field application using a manure spreader. All batch experiments were in 250 mL amber glass vials. All amber glass vials were sonicated and autoclaved prior to usage and left in a dark, rotating shaker for the duration of the experiment. Control reactors with no solids were prepared in the same conditions. Because preliminary experiments with manure showed no erythromycin adsorption, no additional manure-only experiments were conducted.
Soil adsorption equilibration time
For determination of adsorption equilibration time, 10 mL of 10 ppm erythromycin solution was added to each batch reactor and total volume brought to 150 mL with a final concentration of 667 ppb. Each batch was sampled at 0 hr, 3 hr, 12 hr, 24 hr, 36 hr, 48 hr, 72 hr, 96 hr, 120 hr, and 144 hr. Samples were collected using a syringe and filtered through a 0.30 μm glass fiber filter immediately. Triplicate batch reactors were prepared for each equilibration time. We diluted samples to approximately 15 ppb erythromycin for analysis using an erythromycin enzyme-linked immunosorbent assay (ELISA) kit. All samples were analyzed within 24 hrs of filtration using a MyBioSource erythromycin ELISA kit (cat #: MBS282249) and a Molecular Devices M2 microplate reader.
We defined the adsorption equilibrium time to be the first sample with no statistical difference from both the previous (t-1) and next (t+1) samples using a Wilcox Rank Sum Test (with p-value < 0.1 indicating statistical difference). We calculated first order reaction kinetics (Eq. 1),
qt = qe (1-e-k1 t) (Eq. 1)
where qt is the amount of erythromycin adsorbed at time t, qe is the amount of erythromycin adsorbed at equilibrium, and k1 is the kinetic rate constant.
Adsorption equilibrium isotherms
After establishing adsorption equilibrium time, erythromycin at 19 concentrations (10 ppb, 20 ppb, 40 ppb, 80 ppb, 100 ppb, 200 ppb, 400 ppb, 800 ppb, 1000 ppb, 2000 ppb, 5000 ppb, 10,000 ppb, 25,000 ppb, 40,000 ppb, 50,000 ppb, 65,000 ppb, 75,000 ppb, 100,000 ppb, and 150,000 ppb) was added to 5 g soil in 250 ml amber glass vials in triplicate (S treatment). Batch reactors were left to shake for 72 hr, the suspension filtered through a 0.3 μm glass fiber syringe filter, and analyzed within 24 hr using an ELISA kit and microplate reader. All samples were diluted (as needed) to the linear detection range of 0.2 - 25 ppb prior to analysis.
The SM treatment was also equilibrated for 72 hr at initial erythromycin concentrations of 10 ppb, 100 ppb, 400 ppb, 1000 ppb, 5000 ppb, 10,000 ppb, 25,000 ppb, 50,000 ppb, 100,000 ppb, and 150,000 ppb. We combined 5 g soil and 0.5 g manure by dry mass in amber glass vials for a total volume of 150 mL.
We tested five non-linear and linearized Freundlich and Langmuir models to S and SM adsorption isotherm data to determine the best model fit. If models generated non-realistic (i.e. negative) adsorption parameters they were excluded from further analysis. From the remaining models, the model with the greatest number of significant parameters and the smallest standard square error (SSE) was selected. Discussed here are the Langmuir non-linear regression and one Langmuir linearization which fit the data best (eq. 2, 3) (Additional model discussion in supplemental material).
q = qmax *K* (c/(1+K*C) (Eq. 2)
c/q = a + b*c (Eq.3)
qmax = (1/b); K = (1/qmax*a) (Eq. 3.1)
In Eq. 2 and 3, q is the equilibrium adsorption density (mg adsorbate)/(mg adsorbent), qmax is the maximum adsorption density (mg adsorbate)/(mg adsorbent), c is aqueous concentration (mg/L), and K is the equilibrium coefficient for adsorption between adsorbate (erythromycin) and adsorbent (solids) (L/mg). In the modified linear model (Eq. 3), a and b, are the intercept and slope of the linearized model, respectively. Linearized model parameters were then converted to parameters with physical meaning (Eq. 3.1).
Manure-DOM influence on adsorption
To assess manure’s impact on erythromycin adsorption to soil, we conducted a two-phased equilibration experiment. In the first phase, we equilibrated four manure concentrations (0.01 g, 0.05 g, 0.5 g, and 5 g) with erythromycin at 667 ppb for 72 hours. In the second equilibration, we added the 0.3 μm filtrate from phase one to batch reactors with 5 g soil/150 mL. The second phase equilibrated for an additional 72 hr. Samples were analyzed after phase two. Following from this experiment, SM batch experiments utilized a 10:1 soil:manure ratio (0.5 g manure solids with 5 g soil solids). We applied a semi-log transformation to the data after the second phase to generate a linear model:
c = a _ b*log(SR ) (Eq. 4)
where c is the aqueous concentration of erythromycin after second equilibration, SR is the solid ratio of soil to manure by mass used in the first equilibration, and a and b are the empirically derived intercept and slope, respectively, of the resulting model.
Soil column experiments
We used 7 cm diameter columns, continuously infiltrated from below using a pump. This design was chosen to reduce effect of preferential flow paths and flow along the column walls as is sometimes confounding with smaller columns and columns infiltrated from above (e.g. Show et al., 2022). We infiltrated about 17 liters of 500 ppb erythromycin solution through each column. A continuous, highly-concentrated input of erythromycin was chosen to represent a worst-case-scenario pollutant generating saturated adsorption site conditions. Column outflow was located 3 cm above the soil surface which continuously drained the columns. Average outflow rates ranged from 28 mL/min to 31 mL/min. Treatments were run in triplicate. All columns contained 200.00 g homogenized soil (S treatment). Columns in soil-manure (SM) treatment contained 20.00 g manure in addition to 200.00 g soil. We determined infiltration volume from initial column tests and high and low estimates of fitted adsorption isotherm models. Volumetric outflow was recorded continuously for each column independently. Samples were filtered, diluted, and analyzed within 12 hours of experiment completion. Eight samples per column were analyzed for pH, 30 samples per column were analyzed for aqueous antibiotic concentration. A non-parametric smooth local regression (loess) model was used to visualize column results.
Data analysis
The ELISA analysis method is a targeted approach, well-equipped to assess the presence of a single compound in laboratory samples. This method required a standard microplate device, making it accessible to laboratories lacking high performance liquid chromatography – mass spectrometry (HPLC-MS). Because ELISA methods are designed with a detection range of 0.2-25 ppb, this method is ideal when low concentrations are expected. We believe some of the variability between our replicates may be attributed to the high dilution ratios required to reach detection range of the ELISA.
All data were analyzed in R-studio (version 1.4.1106 for Mac) and Excel (version 16.57 for Mac). Model parameters were tested for statistical significance using a t-test.
Objective 3
Communicate the results with scientists, farmers, policy makers, and the public to help lower communication barriers between each of these entities.
Based on preliminary results from the interview-based study mentioned above, farmers feel that communication between the public, the scientific and engineering community, and policy makers is disconnected from farmers. Objective 3 aims to share information from this study with each of these entities, through the outreach activities described below. Because this manuscript has not yet been published, these outreach activities are on-going and will be completed after manuscript publication.
Scientific Community: All results will be submitted for publication in an academic journal and were included in my PhD dissertation. I presented this work at the American Geophysical Union Annual Meeting in December (2021).
Stakeholders: I will also publish the results and context of this study in a dairy trade magazine, such as Progressive Dairyman, to communicate the results of this work to dairy farmers. I will also individually send each of these documents to the farmers who were interviewed in my parallel interview-based study on perspectives of antibiotics. In conjunction with the interview study, there will be focus groups of small groups of similarly sized diary farms. After the focus group portion of the meeting, results of these studies will be shared with attendees and discussion allowed to continue.
The Public: The non-farming public will be reached through a series of newspaper articles discussing the topics of this work. Preliminary results from our interview based study indicate farmers believe the general public is ill-informed and disconnected from farms, so this series of newspaper articles is aimed to help alleviate some of that perceived disconnect.
Policy Makers: The articles and broad outcomes of this work will be shared with New York State lawmakers, especially those representing the regions where farms are located that participated in the parallel interview-based study. Letters to lawmakers will include both the scientific results of this study as well as perspectives from farmers obtained
All Figures, tables, and captions for results: NESARE_GraduateStudentGrant_FinalReportFigures
RESULTS
Bulk solid parameter data
Bulk solid characteristics such as pH, zeta potential, organic matter content, particle surface area, and pore size can all influence the adsorption equilibrium between a solid and an adsorbate. A highly negative zeta potential drives adsorption of positively charged erythromycin molecules at these pH conditions through complementary electrostatic interactions. Higher surface area allows for more adsorption sites and stronger van der Waals attractions. Hydrophobic organic matter tends to interact with organic contaminants in aqueous solutions, with compounds such as erythromycin favoring organic over aqueous phases. We therefore expect erythromycin to interact substantially with a component (dissolved or particulate) of the manure solids. The solution pH determines the charge of erythromycin from positive at low pH values to negative at high pH values, dictating which electrostatic interactions will be most influential. We obtained bulk characteristic data to understand the environment of these experiments (Table 1) and tested the effect of solid preparation methods (i.e. autoclaving versus oven-drying) (Supplemental Table S1).
Batch adsorption studies
Adsorption equilibration time
We applied a first order reaction model and determined qe and k1 (Eq. 1) to be 1.47x10-5 mg/mg (p-value = 3.64x10-5) and 6.399x10-2 hr-1 (p-value = 0.020), respectively, for the concentration tested (667 ppb), with both parameters significant predictors (p-value <0.05). We found erythromycin adsorption equilibration time to Caneserga soil to be approximately 72 hours (Figure 1). The 72 hr sample was not statistically different from either the previous (48 hr, p-value = 0.4) or the next sample (96 hr, p-value = 0.4). Some additional adsorption was observed in the 144 hr samples, and may be indicative of an additional, slower adsorption mechanism. The control reactors experienced some reduction (~50 ppb) in erythromycin concentration over the test period, but this change was not statistically significant (p-value = 0.2) in a Wilcox rank sum test between the first and last control sample, and therefore not considered in further analysis.
When testing adsorption of erythromycin to pure manure substrate, we observed no change in aqueous concentration over the range of solids ratios tested (Supplemental Figure S1). Therefore, adsorption equilibration time to manure was not determined. All subsequent tests contained a mixture of manure and soil solids.
Adsorption equilibrium isotherms
When comparing S and SM adsorption isotherms, it is apparent that the presence of manure increases the aqueous concentration of erythromycin and reduces the adsorbed mass (Figure 2). The two isotherms are distinct until the highest concentrations tested (100,000 ppb, 150,000 ppb). At these highest concentrations, adsorption of erythromycin begins to converge around the same values observed in the S treatments.
Both the S and SM isotherms were best fit by Langmuir models. In the S adsorption isotherm, the Langmuir non-linear regression model fit best (Eq. 2), with parameters of maximum adsorption capacity (qmax) and the equilibrium coefficient (K), were 1.53x10-3 mg adsorbed erythromycin/mg soil and 8.01x10-2 L/mg erythromycin respectively (Table 2). Parameters were statistically significant (p-value < 0.01) for both non-linear and linear regression models when t-test statistics were computed. The 150,000 ppb samples did not show a large enough change to be detected. The 150,000 ppb sample has therefore been removed from the analysis, and was not used to calculate best fit.
Unlike the models run on the S isotherm, the SM isotherm models had fewer statistically significant parameters. The linear regression model (Eq. 3) fit the data best considering models with non-negative parameter values, at least one parameter that was a significant predictor of the data, and standard square error (SSE) values (K =1.99x10-4 L/mg erythromycin, qmax = 4.63x10-2 mg adsorbed erythromycin/mg soil+manure; only K was a significant predictor of the data) (Table 2).
We tested the influence of manure-derived DOM on erythromycin adsorption by conducting an two-phased adsorption experiment: first between erythromycin and manure to generate an erythromycin—manure-DOM solution and then between this solution and the Caneserga soil. We found a semi-log relationship (Eq. 4) between initial manure mass and aqueous concentration of erythromycin (Figure 3). We found that manure hinders erythromycin adsorption to this soil. At the highest manure mass tested (5.00 g), samples resembled control batch experiments’ aqueous concentrations at equilibrium. While at the lowest manure mass tested (0.01 g), samples resembled S treatments’ aqueous concentrations. We found the adjusted R2 for this relationship to be 0.9629. The semi-log model coefficients were 678.11 (p-value = 0.0039*) and -93.56 (p-value = 0.0124*) for a and b, respectively. This result suggests that at soil to manure ratios of 100:1 or less, erythromycin is likely to remain in a more mobile, aqueous phase. Ratios closer to 1:1 soil : manure are exemplary of conditions at the soil-manure interface after manure application.
Soil column experiments
Initial breakthrough curves for both S and SM columns occurred between 0 and 5 L erythromycin solution passed through the columns (Figure 4). The two column treatments overlap significantly between 2.5 and 6 L infiltrated after which they diverge for the remainder of the experiment (16.6-17.6 L passed through the columns). Although there is considerable overlap between the two treatments, the SM smooth loess model resulted in a higher aqueous concentration than the S treatment model for the entire experiment. Due to the slow adsorption kinetics determined in the adsorption equilibration time experiment, these results are expected. When the columns begin to diverge, the SM columns discharged a greater amount of erythromycin, consistent with lower adsorption extents.
The S column from 5 L through the end of the experiment displayed additional adsorption in comparison with SM columns. This may be caused by a secondary, slower adsorption mechanism with less affinity for erythromycin. This multi-phased adsorption may be a result of the complex, non-homogenous nature of soil used, and appears to be much less extensive in the SM treatment, which approaches the influent concentration of 500 ppb for some final samples. This second mechanism of adsorption may have been captured by the 144 hr samples in the adsorption equilibration time experiment, which displayed additional adsorption in comparison to the 72 hr samples.
DISCUSSION
We found that erythromycin adsorbs to soil (S treatments), but in the presence of manure (SM treatments) erythromycin is characterized by an adsorption isotherm favoring its aqueous phase, leading to higher mobility and contamination risk. We found that primary adsorption equilibrium was established after approximately 72 hr in soil batch reactors. Following the recommendation by Menz et al. (2018), this work defines the soil-erythromycin adsorption interaction to better understand residue movement in the environment, especially under conditions of manure application, a likely erythromycin contamination source. We also found that erythromycin is more mobile. i.e. remains in aqueous phase, when introduced with manure than it is when introduced alone.
Differing from the organic carbon partitioning coefficient predicted by EPI Suite™ and previous soil adsorption experiments, which indicated increased erythromycin sorption to soil with organic matter presence (Kruger, 1961), we found that erythromycin became more mobile when interacting with manure-derived dissolved organic matter. Though the soils we used did have 9.8% organic matter present, the addition of manure significantly increased mobility of erythromycin. Our results suggest that adsorption sites previously taken by erythromycin are instead occupied by a component from the manure, the manure alters the form of erythromycin to make it less likely to sorb to the soil, or that erythromycin—DOM-manure complexes formed, which were small enough to pass through the filter and unable to sorb to the soil. These DOM-erythromycin complexes could have effectively made the contaminant more mobile and would be expected given the propensity of this compound to partition into organic carbon. We did not explicitly test this mechanism. DOM has been shown to influence contaminant adsorption to minerals by inhibiting mineral-catalyzed hydrolysis and oxidation reactions required for adsorption through competition for adsorption sites, and through complexation reactions yielding compounds with higher or lower affinities for adsorption sites (Polubesova & Chefez, 2014), pushing the solid – aqueous-phase contaminant equilibrium toward the aqueous phase, as observed in our study. Clarithromycin and roxithromycin, two related macrolide antibiotics, have both showed similarly reduced adsorption to minerals in the presence of DOM (Feitosa-Felizzola et al., 2009). Humic acid-DOM was reported to reduce adsorption of clarithromycin and roxithromycin onto manganese oxide and ferrihydrite minerals, likely by competing with the mineral surfaces for the macrolide antibiotics (Feitosa-Felizzola et al., 2009), a possible mechanism that may similarly influence erythromycin. To the authors knowledge, this is the first-time manure has been shown to inhibit adsorption of erythromycin to soils.
Other antibiotics have previously displayed biphasic adsorption, as seen in our column experiments. Tetracycline has shown a biphasic adsorption to a highly porous human-hair-based substrate, filling external adsorption sites prior to less accessible internal adsorption sites (Ahmed et al., 2017). Biphasic mineralization of erythromycin has been observed and associated with desorption of the compound from sediments (Kim et al, 2004b), but no prior studies have noted a biphasic adsorption of the erythromycin to soils to the authors knowledge.
Study implications
We found that erythromycin was more mobile in SM treatments in comparison with S treatments. This conclusion held across adsorption isotherm experiments and column experiments. Therefore, erythromycin contamination sourced from agricultural manure application may pose a higher risk of entering surface and ground water supplies than previously concluded from S experiments (Pan & Chun, 2016). To assess risk of antibiotic contamination in other antibiotic classes and with other dominant functional groups, manure and SM interactions should be investigated similar to and beyond those preformed here to describe mechanisms of adsorption. Additionally, organic matter derived from differing sources may interact uniquely with each contaminant due to differing DOM compositions. Reducing transport, and, more broadly antibiotic environmental impact, requires a deeper understanding of the controls of sorption of erythromycin to environmentally relevant particulate and dissolved organic compounds and soil surfaces.
Because erythromycin sorbs readily to soils, best management practices (BMPs) that reduce sediment transport may also reduce erythromycin transport. However, because of continuous flushing of sediments in riparian zones and other frequently saturated areas (such as sediment control BMPs), conditions may favor the aqueous phase of the compound, increasing mobility and reducing removal of erythromycin during high flow conditions. Control of erythromycin residue transport may be more effective on the manure management level, e.g., breaking down this compound through heat treatment (Oliver et al., 2020) or biological degradation prior to field application of manure. Kim et al. (2004b) found natural biological degradation of erythromycin was controlled by desorption of erythromycin from stream sediments, suggesting microbial degradation could be enhanced if a system was designed to allow degradation in manure storage prior to field application.
In high-concentration laboratory experiments, we found that erythromycin adsorbs more strongly to agricultural soil in the absence of manure, remaining more mobile in the presence of manure. We found that the characteristic adsorption isotherms that fit both environments were Langmuir isotherms, with the nonlinear regression fitting the soil-only (S) adsorption isotherm best and a linearization of the Langmuir fitting the SM condition best. In our column experiment we observed more passage of erythromycin in the SM condition, as would be expected from the differences in their isotherms, and possible biphasic adsorption breakthrough curve. To model in-field transport of erythromycin and similar compounds, these experiments should be repeated under additional concentration and flow regimes, and across soil types. If we are to reduce the transport of compounds such as erythromycin, we must consider these physical and chemical processes alongside biological utilization and human decision-making pathways to predict impact and risk of transport.
Education & Outreach Activities and Participation Summary
Participation Summary:
The planned outreach and education activities have not yet occurred as our manuscript is still in preparation.
Project Outcomes
Antibiotics in waste streams are not currently regulated, and very rarely do farmers make decisions to reduce transport of residues or resistant bacteria. Because each antibiotic is defined by differing transport mechanisms, it is difficult to predict off-farm transport and on-farm impact of residues spread with manure. This work aimed to address some of those questions in the context of one antibiotic: erythromycin. Under optimal management, we would reduce transport of both residues and resistant bacteria, but in order to make the best recommendations to that end, we must first understand the details of each antibiotic applied. This work aims to improve environmental sustainability of farms not only in through the lens of reduced need for antibiotic usage, but also through the lens of surrounding ecosystem health.
Inspired by this study, I applied for and was granted a USDA Postdoctoral Fellowship to continue studying antibiotics in animal agriculture, and an additional NE SARE Farmer Partnership Grant, both located in Connecticut. This study heavily influenced by career direction into emerging contaminants in both animal and human environments. This study also was delayed due to COVID restrictions in laboratory access, which encouraged broader thinking about the context of this work, and pathways to continue studying these objectives.
Over the course of this, previous, and subsequent studies, I have begun to understand that the phrasing “Sustainable Agriculture” has different meanings to different individuals. Working in the world of sustainable agriculture also requires acknowledging the importance of how others define this term, and working within both collaborator’s definitions as well as my own definitions to create collaborations that benefit everyone involved.
During the second year of my Postdoctoral Fellowship I have begun interviewing for faculty jobs to further the line of research I began with this initial project.
The ELISA method we choose to employ effectively allowed me as a graduate student to have total involvement in sample analysis from a lab without LC-MS/MS technology. I appreciated this flexibility as ran long experiments that required rapid subsequent analysis. However, where possible I would encourage others following similar pathways to choose the more robust LC-MS/MS route to enable assessment at lower concentrations and with capacity to analyze final molecular structures.
However, perhaps most importantly this project was the first external grant I was awarded as a gradate student and the first grant on which I was the primary lead. Both of these experiences were critical for my future success in acquiring larger grants and being PI of current projects. The opportunity presented by the NE SARE Graduate Student Grant gave me the skills and confidence required to write and conduct future independent work.