Simulations of all treatments with a single parameter set.

<p>Best fit set of parameter values used (listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026955#pone-0026955-t002" target="_blank">Table 2</a>). The inset is showing the first 5 hours of the experiment.</p

Parameters, units, and fitted values.

<p>Concentration denotes an amount of per volume of substrate, density denotes an amount per structural volume of bacteria, and n.d. stands for ‘non-dimensional’. Coefficient scales initial substrate C-mol concentration to unity, and structural cell C-mol to calibrated optical density.</p

Overview of population dynamics.

<p>Top panels show dependence of maximum growth rate (left) and time to maximum growth rate (right) calculated from the model (solid line) and measured by Priester <i>et al.</i> (2009 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026955#pone.0026955-Priester1" target="_blank">[20]</a>) (dotted line). Bottom left panel shows time to maximum energy density as a function of exposure concentration. Bottom right panel shows growth rates for all treatments during the first 30 hours of the experiment.</p

Outline of the model.

<p>Bacteria assimilate substrate into energy reserves, which are utilized to fuel growth (linked to increase in cell concentration), maintenance and acclimation. Products related to respiration degrade the environment, reducing the ability of bacteria to utilize energy reserves. Both toxicants and degradation of the supernatant inhibit assimilation of the substrate, and absorbed toxicants bioaccumulate in bacterial cells. Toxicants in the cell, as well as the cell's own metabolism, increase aging acceleration (by creating damage-inducing compounds), thus increasing the hazard rate, and mortality.</p

Predicting high exposures.

<p>Exposures of 37.5, 75, 115, and 150 mg/L predicted using fits only of data on control and low exposures (10 and 20 mg/L). Data points marked with ‘x’: data used in fitting; ‘o’: data used for comparison only. Dashed line: fitted treatments (, , , and ). Solid line: predicted treatments.</p

Summary of state variables, units, and dynamic equations.

<p>Bacterial production rate and scaled functional response () are not state variables, but have been defined separately for brevity. Non-dimensional variables have been labeled ‘n.d.’. Subscript ‘+’ signifies that only positive values of the expression are considered, with the expression set to zero if its value turns out to be negative.</p

Simulating the control.

<p>Cell concentration and all state variables of the model except acclimation and bioaccumulation (not applicable for control). Upper left corner: data (circles), best fit of the standard model (dotted line) and best fit of the model extended by including environmental degradation (solid line). See text for discussion.</p

Potential Mechanisms and Environmental Controls of TiO₂ Nanoparticle Effects on Soil Bacterial Communities

It has been reported that engineered nanoparticles (ENPs) alter soil bacterial communities, but the underlying mechanisms and environmental controls of such effects remain unknown. Besides direct toxicity, ENPs may indirectly affect soil bacteria by changing soil water availability or other properties. Alternatively, soil water or other environmental factors may mediate ENP effects on soil bacterial communities. To test, we incubated nano-TiO₂-amended soils across a range of water potentials for 288 days. Following incubation, the soil water characteristics, organic matter, total carbon, total nitrogen, and respiration upon rewetting (an indicator of bioavailable organic carbon) were measured. Bacterial community shifts were characterized by terminal restriction fragment length polymorphism (T-RFLP). The endpoint soil water holding had been reported previously as not changing with this nano-TiO₂ amendment; herein, we also found that some selected soil properties were unaffected by the treatments. However, we found that nano-TiO₂ altered the bacterial community composition and reduced diversity. Nano-TiO₂-induced community dissimilarities increased but tended to approach a plateau when soils became drier. Taken together, nano-TiO₂ effects on soil bacteria appear to be a result of direct toxicity rather than indirectly through nano-TiO₂ affecting soil water and organic matter pools. However, such directs effects of nano-TiO₂ on soil bacterial communities are mediated by soil water

Long-Term Effects of Multiwalled Carbon Nanotubes and Graphene on Microbial Communities in Dry Soil

Little is known about the long-term effects of engineered carbonaceous nanomaterials (ECNMs) on soil microbial communities, especially when compared to possible effects of natural or industrial carbonaceous materials. To address these issues, we exposed dry grassland soil for 1 year to 1 mg g^–1 of either natural nanostructured material (biochar), industrial carbon black, three types of multiwalled carbon nanotubes (MWCNTs), or graphene. Soil microbial biomass was assessed by substrate induced respiration and by extractable DNA. Bacterial and fungal communities were examined by terminal restriction fragment length polymorphism (T-RFLP). Microbial activity was assessed by soil basal respiration. At day 0, there was no treatment effect on soil DNA or T-RFLP profiles, indicating negligible interference between the amended materials and the methods for DNA extraction, quantification, and community analysis. After a 1-year exposure, compared to the no amendment control, some treatments reduced soil DNA (e.g., biochar, all three MWCNT types, and graphene; <i>P</i> < 0.05) and altered bacterial communities (e.g., biochar, carbon black, narrow MWCNTs, and graphene); however, there were no significant differences across the amended treatments. These findings suggest that ECNMs may moderately affect dry soil microbial communities but that the effects are similar to those from natural and industrial carbonaceous materials, even after 1-year exposure

Evaluation of Exposure Concentrations Used in Assessing Manufactured Nanomaterial Environmental Hazards: Are They Relevant?

Manufactured nanomaterials (MNMs) are increasingly produced and used in consumer goods, yet our knowledge regarding their environmental risks is limited. Environmental risks are assessed by characterizing exposure levels and biological receptor effects. As MNMs have rarely been quantified in environmental samples, our understanding of exposure level is limited. Absent direct measurements, environmental MNM concentrations are estimated from exposure modeling. Hazard, the potential for effects on biological receptors, is measured in the laboratory using a range of administered MNM concentrations. Yet concerns have been raised regarding the “relevancy” of hazard assessments, particularly when the administered MNM concentrations exceed those predicted to occur in the environment. What MNM concentrations are administered in hazard assessments and which are “environmentally relevant”? This review regards MNM concentrations in hazard assessments, from over 600 peer-reviewed articles published between 2008 and 2013. Some administered MNM concentrations overlap with, but many diverge from, predicted environmental concentrations. Other uncertainties influence the environmental relevance of current hazard assessments and exposure models, including test conditions, bioavailable concentrations, mode of action, MNM production volumes, and model validation. Therefore, it may be premature for MNM risk research to sanction information on the basis of concentration “environmental relevance”