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Phytotoxicological Risk of pharmaceuticals in soils

Periodic Reporting for period 1 - PhytoPharm (Phytotoxicological Risk of pharmaceuticals in soils)

Reporting period: 2017-08-01 to 2019-07-31

Population growth, urbanization, and climate change contribute to increasing stress on freshwater resources in many parts of the world. To address shortages, the reuse of wastewater has become increasingly valuable as an alternative source to meet irrigation demands. However, wastewater effluent is a known reservoir for pharmaceutical contaminants that enter the sewage system in urine and faeces after being poorly metabolised by the human body. Among the pharmaceuticals of most concern are antibiotics which have been shown to contribute to the proliferation of antibiotic resistance at low concentrations. Wastewater treatment facilities have historically been designed for the removal of excess nutrients as well as human pathogens. As a result, many emerging contaminants, including antibiotics, are poorly removed and enter the environment in treated wastewater effluent. When wastewater is used for irrigation, antibiotics can be introduced into the agroecosystem directly. Upon entry, these compounds partition between soil and porewater where they can be taken up into plants. As a result, these bioactive compounds have the potential to impact both plant health and soil microbial communities. The issue is further complicated by the fact that wastewater acts as a repository of all the antibiotics consumed by population mixed together. Antibiotics represent a set of molecules with diverse physicochemical properties as well as multiple modes of action, as a result, exposure to mixtures of antibiotics via the reuse of treated wastewater has the potential to threaten the sustainability of agricultural production as well as contribute to the proliferation of antibiotic resistance in the environment. Therefore, this project aimed to develop an algorithm to predict concentrations of antibiotics in treated wastewater based upon prescription and usage data. These results were then exploited to 1) predict hotspots of antibiotic resistance at the continental, national, and catchment scales, and 2) to provide an environmentally relevant mixture for use in a mesocosm study evaluating the impacts of antibiotic mixtures of plant growth and soil function. This work revealed that antibiotic exposure is dependent upon both prescription/usage as well as hydrologic processes that determine dilution. Using barley as a model crop due to its importance in the UK, we showed that exposure to an environmentally relevant mixture of 11 antibiotics had a negative impact on germination, but that overtime, mature plants showed few negative impacts as a result of routine antibiotic exposure. Microbial analysis revealed that continued exposure increased antibiotic resistance in native microbial communities and that genes associated with multi-drug resistance and beta-lactam resistance dominated. Continuous monitoring of CO2 gas fluxes revealed reductions in the net ecosystem exchange of CO2 with increased exposure in the soil-plant system. Overall, these results suggest that although wastewater reuse has become a valuable alternative to meet irrigation demands, risks associated with the exposure of antibiotics must be considered.
An algorithm was first developed to predict expected concentrations (PECs) of antibiotics in wastewater effluent taking into account prescription rates of antibiotics across EU member states, the amount of compound excreted unmetabalised by the human body, and removal rates from wastewater treatment plants. From this, PECs of over 70 antibiotic compounds were estimated. When combined with predicted no-effect concentrations (i.e. proposed limits of concentration for the selection of antimicrobial resistance or AMR) a risk analysis was performed to prioritise the compounds most likely contributing to risk of AMR proliferation in the environment. This data was exploited in two ways. First. using a database of flow rates in receiving waters across the EU to calculate dilution rates, hotspots for AMR were developed at a continental scale. This was further refined with more detailed data from the UK’s National Health Service prescription database, where the percentage of water bodies receiving wastewater effluent at risk for AMR was identified. Finally, at the local scale, daily risk for AMR in the River Foss over the course of a year was established based on prescription practices in York area. Secondly, the prioritised compounds identified by the algorithm were used for the development of an environmentally relevant mixture of 11 antibiotic PECs in UK wastewater effluent that may be used for irrigation of crops.
The PEC mixture was then used in a 4 month mesocosm study using Spring Barley (Hordeum vulgare) as model plant species. Over the course of the experiment, mesocosms were watered twice weekly, to supplement rainfall, with synthetic wastewater effluent containing antibiotics at PEC, 0.1xPEC 10xPEC, 100xPEC, or 0. Soil samples were collected at the start of the study, prior to irrigation with antibiotics, 16 hours after the first antibiotic exposure, 8 weeks of routine antibiotic exposure, and at plant maturity at 16 weeks. The fate of antibiotics was measured in soil-pore water and mesocosm leachate. Plant development endpoints were measured in both below and above ground biomass and included biomass, stem height, tiller and leaf counts, and grain production. Soil samples were collected to monitor microbial community structure and development of AMR genes using high-throughput qPCR. Throughout the experiment, the Skyline 2-D fly-by-wire device, developed at the University of York, was employed to continuously monitor greenhouse gas fluxes including CO2, N20, and methane. Stable isotope analysis was performed on plant leaves pre and post fertilization to measure shifts in the natural abundance of N15 and C13, as indicators for plant stress. A metabolomics approach was conducted to measure differences in the plant extractable metabolome of grain produced under the different exposure regimes.
As has been repeatedly shown, the toxicological impacts of mixtures can be additive, antagonistic, or synergistic. Thus, predicting their impacts is challenging and determining the composition of mixtures is incredibly important. The algorithm developed in this project helps to resolve a significant issue in ecotoxicology by providing a data driven approach to develop complex environmentally relevant mixtures. In our mesocosm study, we demonstrated how this can be used to evaluate the risk of contaminant exposure.
Furthermore, this study is among the first to utilize plant metabolomics to investigate the impact of chemical exposure. While the tool has been demonstrated for other environmental stressors, i.e. drought and salinity, we have demonstrated that it can be useful to measure the impacts of antibiotic exposure on an economically important crop.
In totality, we have shown that barley is most sensitive to antibiotic exposure in early growth stages. Over time and in the absence of other stressors, e.g. nutrient limited conditions, that plants demonstrated little residual effects at the time of harvest and by inference were able to overcome contaminant stress. This knowledge can be used in the development of best management practices for agricultural production. Where possible, crops should be initially grown with fresh water prior to introduction of contaminants via wastewater.