G-DaM: A Distributed Data Storage with Blockchain Framework for Management of Groundwater Quality Data.

Groundwater overuse in different domains will eventually lead to global freshwater scarcity. To meet the anticipated demands, many governments worldwide are employing innovative and traditional techniques for forecasting groundwater availability by conducting research and studies. One challenging step for this type of study is collecting groundwater data from different sites and securely sending it to the nearby edges without exposure to hacking and data tampering. In the current paper, we send raw data formats from the Internet of Things to the Distributed Data Storage (DDS) and Blockchain (BC) edges. We use a distributed and decentralized architecture to store the statistics, perform double hashing, and implement access control through smart contracts. This work demonstrates a modern and innovative approach combining DDS and BC technologies to overcome traditional data sharing, and centralized storage, while addressing blockchain limitations. We have shown performance improvements with increased data quality and integrity.

agroString: Visibility and Provenance through a Private Blockchain Platform for Agricultural Dispense towards Consumers.

It is a known fact that large quantities of farm and meat products rot and are wasted if correct actions are not taken, which may lead to serious health issues if consumed. There is no proper system for tracking and communicating the status of the goods to their respective stakeholders in a secure way. Consumers have every right to know the quality of the products they consume. Using monitoring tools, such as the Internet of Agricultural Things (IoAT), and modern data protection techniques for storing and sharing, will help mitigate data integrity issues during the transmission of sensor records, increasing the data quality. The visibility state at the customer end is also improved, and they are aware of the agricultural product's conditions throughout the real-time distribution process. In this paper, we developed and implemented a CorDapp application to manage the data for the supply chain, called "agroString". We collected the temperature and humidity data using IoAT-Edge devices and various datasets from multiple sources. We then sent those readings to the CorDapp agroString and successfully shared them among the relevant parties. With the help of a Corda private blockchain, we attempted to increase data integrity, trust, visibility, provenance, and quality at each logistic step, while decreasing blockchain and central system limitations.

Nitrogen removal in vermifiltration: Mechanisms, influencing factors, and future research needs.

To meet global health and sanitation goals, there is a continued need for sustainable wastewater treatment alternatives that require minimal energy and investment. Vermifiltration, a technology gaining relevance in Africa and Asia, may be an alternative to traditional wastewater treatment systems due to its cost-effectiveness, ease of application and maintenance, and sustainability. However, nitrogen removal in vermifiltration is not well understood since most of the prior research focuses on organics removal. Thus, a state of the art review is necessary to separately focus on the mechanisms associated with nitrogen removal in vermifiltration, along with the factors affecting nitrogen removal. For the first time, this review attempts to present the types of vermifilter based on their flow pattern. The review further discusses the current status of the application of vermifiltration, along with the benefits and limitations associated with the adoption of this technology. It also explores possible strategies that could be adopted to maximize the nitrogen removal potential of vermifilters as optimizing nitrogen removal is critical for improving the performance of vermifiltration based treatment systems.

Ferrihydrite Reduction Increases Arsenic and Uranium Bioavailability in Unsaturated Soil.

Redox driven mobilization and plant uptake of contaminants under transiently saturated soil conditions need to be clarified to ensure food and water quality across different irrigation systems. We postulate that solid-phase iron reduction in anoxic microsites present in the rhizosphere of unsaturated soil is a key driver for mobilization and bioavailability of contaminants under nonflooded irrigation. To clarify this, two major crops, corn and soybean differing in iron uptake strategies, were grown in irrigated synthetic soil under semiarid conditions with gravimetric moisture content ∼12.5 ± 2.4%. 2-line ferrihydrite, which was coprecipitated with uranium and arsenic, served as the only iron source in soil. Irrespective of crop type, reduced iron was detected in pore water and postexperiment rhizosphere soil confirming ferrihydrite reduction. These results support the presence of localized anoxic microsites in the otherwise aerobic porous bulk soil causing reduction of ferrihydrite and concomitant increase in plant uptake of comobilized contaminants. Our findings indicate that reactive iron minerals undergo reductive dissolution inside anoxic microsites of primarily unsaturated soil, which may have implications on the mobility of trace element contaminants such as arsenic and uranium in irrigated unsaturated soils, accounting for 55% of the irrigated area in the US.

Comparative evaluation of machine learning models for groundwater quality assessment.

Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration ranges of nitrate and pesticide. This study evaluates the classification performance of the models for two, three, and four class scenarios and compares them with the corresponding regression models. The study also examines the issue of class imbalance and tests the efficacy of three class imbalance mitigation techniques: oversampling, weighting, and oversampling and weighting, for all the scenarios. The models' performance is reported using multiple metrics, both insensitive to class imbalance (accuracy) and sensitive to class imbalance (F1 score and MCC). Finally, the study assesses the importance of features using game-theoretic Shapley values to rank features consistently and offer model interpretability.

Water, Energy, and Carbon Footprints of Bioethanol from the U.S. and Brazil.

Driven by biofuel policies, which aim to reduce greenhouse gas (GHG) emissions and increase domestic energy supply, global production and consumption of bioethanol have doubled between 2007 and 2016, with rapid growth in corn-based bioethanol in the U.S. and sugar cane-based bioethanol in Brazil. Advances in crop yields, energy use efficiency in fertilizer production, biomass-to-ethanol conversion rates, and energy efficiency in ethanol production have improved the energy balance and GHG emission reduction potential of bioethanol. In the current study, the water, energy, and carbon footprints of bioethanol from corn in the U.S. and sugar cane in Brazil were assessed. The results show that U.S. corn bioethanol has a smaller water footprint (541 L water/L bioethanol) than Brazilian sugar cane bioethanol (1115 L water/L bioethanol). Brazilian sugar cane bioethanol has, however, a better energy balance (17.7 MJ/L bioethanol) and smaller carbon footprint (38.5 g CO2e/MJ) than U.S. bioethanol, which has an energy balance of 11.2 MJ/L bioethanol and carbon footprint of 44.9 g CO2e/MJ. The results show regional differences in the three footprints and highlight the need to take these differences into consideration to understand the implications of biofuel production for local water resources, net energy production, and climate change mitigation.

Pesticide sorption and leaching potential on three Hawaiian soils.

On the Hawaiian Islands, groundwater is the principal source of potable water and contamination of this key resource by pesticides is of great concern. To evaluate the leaching potential of four weak acid herbicides [aminocyclopyrachlor, picloram, metsulfuron-methyl, biologically active diketonitrile degradate of isoxaflutole (DKN)] and two neutral non-ionizable herbicides [oxyfluorfen, alachlor], their sorption coefficients were determined on three prevalent soils from the island of Oahu. Metsulfuron-methyl, aminocylcopyrachlor, picloram, and DKN were relatively low sorbing herbicides (K(oc) = 3-53 mL g(-1)), alachlor was intermediate (K(oc) = 120-150 mL g(-1)), and oxyfluorfen sorbed very strongly to the three soils (K(oc) > 12,000 mL g(-1)). Following determination of K(oc) values, the groundwater ubiquity score (GUS) indices for these compounds were calculated to predicted their behavior with the Comprehensive Leaching Risk Assessment System (CLEARS; Tier-1 methodology for Hawaii). Metsulfuron-methyl, aminocyclopyrachlor, picloram, and DKN would be categorized as likely leachers in all three Hawaiian soils, indicating a high risk of groundwater contamination across the island of Oahu. In contrast, oxyfluorfen, regardless of the degradation rate, would possess a low and acceptable leaching risk due to its high sorption on all three soils. The leaching potential of alachlor was more difficult to classify, with a GUS value between 1.8 and 2.8. In addition, four different biochar amendments to these soils did not significantly alter their sorption capacities for aminocyclopyrachlor, indicating a relatively low impact of black carbon additions from geologic volcanic inputs of black carbon. Due to the fact that pesticide environmental risks are chiefly dependent on local soil characteristics, this work has demonstrated that once soil specific sorption parameters are known one can assess the potential pesticide leaching risks.

Importance of snowmelt on soil nitrate leaching to groundwater - A model study.

The movement of nitrate to surface water bodies during snow accumulation and melting has been extensively studied, but there are only limited studies on the influence of snow processes on nitrate leaching to groundwater. The present study investigated the impact of snow processes on nitrate leaching to groundwater based on a simulation modeling approach using HYDRUS-1D. HYDRUS-1D model has a temperature threshold-based snow model in addition to water, solute, and heat simulation components. The snow component in HYDRUS-1D was previously not applied to snow simulation studies since the method does not consider a detailed physical and process-based representation of snow accumulation and melting. In the present study, HYDRUS-1D was used to simulate snow accumulation and melting over 30 years for a location in Waverly, Lancaster County, Nebraska, USA. From the simulations, it was observed that the calibrated temperature threshold based snow module in HYDRUS-1D is effective in simulating snow accumulation and melting, as shown by the index of agreement and root mean squared error of 0.74 and 2.70 cm for calibration (15 years) and 0.88 and 2.70 cm for validation (15 years), respectively. The impact of snow melt on nitrate leaching was studied based on a study area with corn cultivation (Waverly, Nebraska, USA). A long-term (60 years) analysis was carried out for irrigated and non-irrigated agriculture with and without precipitation as snow. A higher nitrate leaching to groundwater was observed in the order of irrigated-with snow (54,038 kg/ha), irrigated-without snow (53,516 kg/ha), non-irrigated-with snow (7,431 kg/ha), and non-irrigated-without snow (7,090 kg/ha). This displays a 0.98% and 4.81% increase in nitrate leaching due to snow in irrigated and non-irrigated conditions, respectively. When extrapolated over the corn cultivated regions in Nebraska, this resulted in a difference of 1.2E+09 kg and 6.1E+08 kg of nitrate when considering snow in irrigated and non-irrigated areas over 60 years. This is the first study that has analyzed the long-term impact of snow on nitrate transport to groundwater based on a simulation modeling approach. The results show that snow accumulation and melting plays a vital role in the nitrate leaching into the groundwater and indicates the importance of considering snow components in similar studies.

Projected long-term climate trends reveal the critical role of vapor pressure deficit for soybean yields in the US Midwest.

Extreme climate events including heat waves and droughts are projected to become more frequent under future climate change conditions. However, the mechanisms between soybean yields and climate factors, specifically involving variable rainfall and high heat episodes, are still unclear, particularly with respect to spatial trends in the United States (US) Midwest. A recently modified version of the model GLYCIM was used to evaluate rainfed soybean production across 12 states at a 10 km spatial resolution for three time periods (2011-2020, 2051-2060, 2091-2099) under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5. Results showed that except for the northernmost Midwest counties, most of the current rainfed cropping system in the Midwest would suffer a 24.6-47.4 % yield loss without considering the CO2 fertility effect. Incorporating the effect of elevated CO2 showed a smaller yield loss of 11.6-29.5 %. The increased frequency of extreme degree days (EDD) or accumulation of hourly temperatures above 30 °C associated with increased vapor pressure deficit (VPD) played a key role in contributing to water deficits and resultant crop losses under these future climate conditions. Although a relatively weak relationship between summer rainfall and crop yield was observed, decreased rainfall caused VPD to increase which induced crop water deficits. These findings suggest that it is crucial to consider VPD along with high temperature and low rainfall trends simultaneously for development of potential management or breeding-based adaptative strategies for soybean.

Interplay of legacy irrigation and nitrogen fertilizer inputs to spatial variability of arsenic and uranium within the deep vadose zone.

The vadose zone is a reservoir for geogenic and anthropogenic contaminants. Nitrogen and water infiltration can affect biogeochemical processes in this zone, ultimately affecting groundwater quality. In this large-scale field study, we evaluated the input and occurrence of water and nitrogen species in the vadose zone of a public water supply wellhead protection (WHP) area (defined by a 50-year travel time to groundwater for public supply wells) and potential transport of nitrate, ammonium, arsenic, and uranium. Thirty-two deep cores were collected and grouped by irrigation practices: pivot (n = 20), gravity (n = 4) irrigated using groundwater, and non-irrigated (n = 8) sites. Beneath pivot-irrigated sites, sediment nitrate concentrations were significantly (p < 0.05) lower, while ammonium concentrations were significantly (p < 0.05) higher than under gravity sites. The spatial distribution of sediment arsenic and uranium was evaluated against estimated nitrogen and water loading beneath cropland. Irrigation practices were randomly distributed throughout the WHP area and presented a contrasting pattern of sediment arsenic and uranium occurrence. Sediment arsenic correlated with iron (r = 0.32, p < 0.05), uranium negatively correlated to sediment nitrate (r = -0.23, p < 0.05), and ammonium (r = -0.19 p < 0.05). This study reveals that irrigation water and nitrogen influx influence vadose zone geochemistry and mobilization of geogenic contaminants affecting groundwater quality beneath intensive agricultural systems.

Improving the cotton simulation model, GOSSYM, for soil, photosynthesis, and transpiration processes.

GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO2). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM's predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil-plant-atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO2 m-2 day-1; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 L m-2 day-1; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development.

Effect of dissolved organic carbon on the transport and attachment behaviors of Cryptosporidium parvum oocysts and carboxylate-modified microspheres advected through temperate humic and tropical volcanic agricultural soil.

Transport of Cryptosporidium parvum oocysts and microspheres in two disparate (a clay- and Fe-rich, volcanic and a temperate, humic) agricultural soils were studied in the presence and absence of 100 mg L(-1) of sodium dodecyl benzene sulfonate (SDBS), and Suwannee River Humic Acid (SRHA) at pH 5.0-6.0. Transport of carboxylate-modified, 1.8 µm microspheres in soil columns was highly sensitive to the nature of the dissolved organic carbon (DOC), whereas oocysts transport was more affected by soil mineralogy. SDBS increased transport of microspheres from 48% to 87% through the tropical soil and from 43% to 93% in temperate soil. In contrast, SRHA reduced transport of microspheres from 48% to 28% in tropical soil and from 43% to 16% in temperate soil. SDBS also increased oocysts transport through the temperate soil 5-fold, whereas no oocyst transport was detected in tropical soil. SRHA had only a nominal effect in increasing oocysts transport in tropical soil, but caused a 6-fold increase in transport through the temperate soil. Amendments of only 4 mg L(-1) SRHA and SDBS decreased oocyst hydrophobicity from 66% to 20% and from 66% to 5%, respectively. However, SDBS increased microsphere hydrophobicity from 16% to 33%. Soil fines, which includes clays, and SRHA, both caused the oocysts zeta potential (ζ) to become more negative, but caused the highly hydrophilic microspheres to become less negatively charged. The disparate behaviors of the two colloids in the presence of an ionic surfactant and natural organic matter suggest that microspheres may not be suitable surrogates for oocysts in certain types of soils. These results indicate that whether or not DOC inhibits or promotes transport of oocysts and microspheres in agricultural soils and by how much, depends not only on the surface characteristics of the colloid, but the nature of the DOC and the soil mineralogy.

Ferrihydrite enrichment in the rhizosphere of unsaturated soil improves nutrient retention while limiting arsenic and uranium plant uptake.

Improvement of nutrient use efficiency and limiting trace elements such as arsenic and uranium bioavailability is critical for sustainable agriculture and food safety. Arsenic and uranium possess different properties and mobility in soils, which complicates the effort to reduce their uptake by plants. Here, we postulate that unsaturated soil amended with ferrihydrite nanominerals leads to improved nutrient retention and helps reduce uptake of these geogenic contaminants. Unsaturated soil is primarily oxic and can provide a stable environment for ferrihydrite nanominerals. To demonstrate the utility of ferrihydrite soil amendment, maize was grown in an unsaturated agricultural soil that is known to contain geogenic arsenic and uranium. The soil was maintained at a gravimetric moisture content of 15.1 ± 2.5%, typical of periodically irrigated soils of the US Corn Belt. Synthetic 2-line ferrihydrite was used in low doses as a soil amendment at three levels (0.00% w/w (control), 0.05% w/w and 0.10% w/w). Further, the irrigation water was fortified (~50 µg L-1 each) with elevated arsenic and uranium levels. Plant dry biomass at maturity was ~13.5% higher than that grown in soil not receiving ferrihydrite, indicating positive impact of ferrihydrite on plant growth. Arsenic and uranium concentrations in maize crops (root, shoot and grain combined) were ~ 20% lower in amended soils than that in control soils. Our findings suggest that the addition of low doses of iron nanomineral soil amendment can positively influence rhizosphere geochemical processes, enhancing nutrient plant availability and reduce trace contaminants plant uptake in sprinkler irrigated agroecosystem, which is 55% of total irrigated area in the United States.

Assessing the severity of rainfall-derived infiltration and inflow and sewer deterioration based on the flux stability of sewage markers.

This study investigated the flux stability of select chemical and biological sewage markers, including caffeine, total nitrogen (TN), total suspended solids (TSS), E. coli, and enterococci, and their suitability in assessing the severity of rainfall-derived infiltration and inflow (RDII) in a residential sewershed. To quantify and compare marker flux stability, concentrations of the candidate markers in two dry-weather periods were determined and the one-day lag autocorrelation coefficients (r) of their mass fluxes were calculated. TN (r = 0.82-0.88) exhibited higher and more consistent flux stability than TSS (r = 0.49-0.82), caffeine (r = 0.56-0.58), E. coli (r = 0.36-0.87), and enterococci (by culture; r = 0.40-0.52), all of which except enterococci by qPCR (r = -0.10-0.21) showed significant autocorrelation. Sewage flows and marker concentrations were also monitored in two wet-weather periods, and the severity of RDII (R(RDII)) were calculated using either flow measurements or marker concentrations independently. Corresponding to its outstanding flux stability, R(RDII) values estimated by TN predicted all severe RDII instances and gave the highest and most consistent correlation (r = 0.74-0.78) among the different sewage markers. Overall, the study illustrated the feasibility of using the flux stability of sewage markers in assessing the severity of RDII and thereby deterioration levels in sewer systems.

Nanomaterials in the environment, human exposure pathway, and health effects: A review.

Nanomaterials (NMs), both natural and synthetic, are produced, transformed, and exported into our environment daily. Natural NMs annual flux to the environment is around 97% of the total and is significantly higher than synthetic NMs. However, synthetic NMs are considered to have a detrimental effect on the environment. The extensive usage of synthetic NMs in different fields, including chemical, engineering, electronics, and medicine, makes them susceptible to be discharged into the atmosphere, various water sources, soil, and landfill waste. As ever-larger quantities of NMs end up in our environment and start interacting with the biota, it is crucial to understand their behavior under various environmental conditions, their exposure pathway, and their health effects on human beings. This review paper comprises a large portion of the latest research on NMs and the environment. The article describes the natural and synthetic NMs, covering both incidental and engineered NMs and their behavior in the natural environment. The review includes a brief discussion on sampling strategies and various analytical tools to study NMs in complex environmental matrices. The interaction of NMs in natural environments and their pathway to human exposure has been summarized. The potential of NMs to impact human health has been elaborated. The nanotoxicological effect of NMs based on their inherent properties concerning to human health is also reviewed. The knowledge gaps and future research needs on NMs are reported. The findings in this paper will be a resource for researchers working on NMs all over the world to understand better the challenges associated with NMs in the natural environment and their human health effects.

The long term effect of agricultural, vadose zone and climatic factors on nitrate contamination in the Nebraska's groundwater system.

A four-decade dataset (1974-2013) of 107,823 nitrate samples in 25,993 wells from western and eastern parts of Nebraska was used to assess long-term trends of groundwater nitrate concentration and decadal changes in the extent of groundwater nitrate-contaminated areas (NO3-N ≥ 10 mg N/L) over the entire state. Spatial statistics and regressions were used to investigate the relationships between groundwater nitrate concentrations and several potential natural and anthropogenic factors, including soil drainage capacities, vadose zone characteristics, crop production areas, and irrigation systems. The results of this study show that there is no statistically significant trend in groundwater nitrate concentrations in western Nebraska, in contrast with the increasing trend (p < .05) to the east. The spatial extent and nitrate concentrations in contaminated groundwater in center pivot-irrigated areas was less than in gravity-irrigated areas. Areas with a thicker vadose zone and larger saturated thickness of the aquifer have relatively lower nitrate concentrations. The results of a classification and regression tree (CART) model indicate the difference in the influence of physical factors on groundwater nitrate concentrations between western and eastern Nebraska, namely that groundwater nitrate concentrations correspond with vadose zone thickness, effective hydraulic conductivity, and saturated thickness in the west, while in eastern Nebraska, concentrations are correlated with average percent sand in the topsoil (0-150 cm), well depth, and effective hydraulic conductivity.

Water productivity in meat and milk production in the US from 1960 to 2016.

Global demand for livestock products is rising, resulting in a growing demand for feed and potentially burdening freshwater resources to produce this feed. To offset this increased pressure on water resources, the environmental performance of livestock sector should continue to improve. Over the last few decades, product output per animal and feedstuff yields in the US have improved, but before now it was unclear to what extent these improvements influenced the water productivity (WP) of the livestock products. In this research, we estimate changes in WP of animal products from 1960 to 2016. We consider feed conversion ratios (dry matter intake per head divided by product output per head), feed composition per animal category, and estimated the water footprint of livestock production following the Water Footprint Network's Water Footprint Assessment methodology. The current WP of all livestock products appears to be much better than in 1960. The observed improvements in WPs are due to a number of factors, including increases in livestock productivity, feed conversion ratios and feed crop yields, the latter one reducing the water footprint of feed inputs. Monogastric animals (poultry and swine) have a high feed-use efficiency compared to ruminants (cattle), but ruminants consume relatively large portion of feed that is non-edible for humans. Per unit of energy content, milk has the largest WP followed by chicken and pork. Per gram of protein, poultry products (chicken meat, egg and turkey meat) have the largest WP, followed by cattle milk and pork. Beef has the smallest WP. These data provide important information that may aid the development of strategies to improve WP of the livestock sector.

Environmental fate and microbial effects of monensin, lincomycin, and sulfamethazine residues in soil.

The impact of commonly-used livestock antibiotics on soil nitrogen transformations under varying redox conditions is largely unknown. Soil column incubations were conducted using three livestock antibiotics (monensin, lincomycin and sulfamethazine) to better understand the fate of the antibiotics, their effect on nitrogen transformation, and their impact on soil microbial communities under aerobic, anoxic, and denitrifying conditions. While monensin was not recovered in the effluent, lincomycin and sulfamethazine concentrations decreased slightly during transport through the columns. Sorption, and to a limited extent degradation, are likely to be the primary processes leading to antibiotic attenuation during leaching. Antibiotics also affected microbial respiration and clearly impacted nitrogen transformation. The occurrence of the three antibiotics as a mixture, as well as the occurrence of lincomycin alone affected, by inhibiting any nitrite reduction, the denitrification process. Discontinuing antibiotics additions restored microbial denitrification. Metagenomic analysis indicated that Proteobacteria, Bacteroidetes, Actinobacteria, and Chloroflexi were the predominant phyla observed throughout the study. Results suggested that episodic occurrence of antibiotics led to a temporal change in microbial community composition in the upper portion of the columns while only transient changes occurred in the lower portion. Thus, the occurrence of high concentrations of veterinary antibiotic residues could impact nitrogen cycling in soils receiving wastewater runoff or manure applications with potential longer-term microbial community changes possible at higher antibiotic concentrations.