Correspondence/Rebuttal Cite This: Environ. Sci. Technol. XXXX, XXX, XXX−XXX
pubs.acs.org/est
Comment on “Assessing the Risk of Engineered Nanomaterials in the Environment: Development and Application of the nanoFate Model”
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misleading statement, MendNano’s formulation2 and implementation3 clearly document handling climate variability, specification of different water bodies (e.g., lake, ocean, or a river), ENM dissolution, soil types (Figures 6 and 7),3 and the suspended particle size distribution. Admittedly, Garner et al.1 report on the inclusion of soil subcompartments; however, their model formulation and simulation results do not provide details regarding the impact of this model feature.1 In another specific reference to MendNano,2,3 it is stated that “...Runof f is modeled as a simple transfer”.1 The above statement is also misleading given that MendNano utilizes the “Revised Universal Soil Loss Equation based on soil erodibility and topographic factors” and includes a fundamental model for “...the time-to-ponding...”2 Contrary to the claim that MendNano has “...no heteroaggregates, no dissolved ion...” (Table S1),1 this model does account for dissolved species (e.g., SI, eqs S14 and S18)2 and for ENMs’ association with ambient suspended particles (in water) and aerosols (in air), the latter using an attachment factor (which can be specified to vary with particle size) and accounting for the particle size distribution (Model Equations, Figure 6, Model Applicability and Figure S11 in SI).2 It is baffling that Garner et al. made the above misleading assertion while they themselves do not provide a quantitative account of the impact of heteroaggregation nor particle size distribution on intermedia transport.1 The rate of change of “dissolved concentration” is reported by Garner et al.1 (SI, Section 2.3.4) as, dC/dt = −kdisCo (kdis and C are the dissolution rate constant and dissolved concentration, respectively), in which kdis is oddly considered to be independent of particle size or hydrodynamic conditions. The above equation (unless mistyped) does not explicitly consider the solubility limit using a proper concentration driving force,6 although the authors acknowledge constraining the concentration to the solubility limit. Also, the negative sign (perhaps a typographical error) in their dissolution expression implies a dissolved concentration decline instead of increase upon dissolution. The atmospheric dry deposition velocity is estimated by Garner et al. as kdep=(2/9)(ρp−ρa)gR2p/μ, in which ρp, ρa, are the particle and air mass densities, respectively, μ is the air viscosity, g is the gravitational constant and Rp is the particle radius (SI, File 1, Section 2.1.1).1 The above expression is improper since it: (a) does not consider the dependence of kdep on wind speed and surface characteristics (e.g., soil, water, and vegetative cover), and (b) does not portray the proper functional dependence of kdep on particle size (i.e., kdep reaches a minimum value region with respect to particle size, but increases with higher or lower particle sizes relative to this region).7,8 The above approach would lead to significant underestimation of the dry deposition velocity of particles in the submicrometer and nanosize ranges. Similarly, Garner et al.
arner et al.1 recently published on modeling the fate and transport (F&T) of engineered nanomaterials (ENMs). They introduce a compartmental multimedia model (nanoFate) and claimed improvements, relative to previously reported multimedia models, with respect to treatment of intermedia transport processes, nanomaterial properties, and system dynamics. This communication intends to (a) correct improper perception introduced, perhaps inadvertently, by general statements of Garner et al.1 regarding alleged deficiencies in previous models, and (b) identify critical nanoFate deficiencies regarding the treatment of intermedia transport of nanoparticles. Previous F&T models were classified by Garner et al. (i.e., cited as references 10−12, 37, 46−50) as “...steady-state multimedia box models, spatial river/watershed models, and materials f low analysis (MFA) models.”1 Although the above classification is useful, one should note that the MendNano model (cited as reference 47 in Garner et al.1) is in fact a dynamic (i.e., unsteady state) model for simulating the multimedia distribution of ENMs considering the temporal variability of meteorological conditions and source release rates, and the long-term compartmental accumulation of ENMs (e.g., in soil and sediment, Figure 5).2 MendNano’s formulation2 and its web-based implementation3 document temporally varying input parameters and examples of simulation results of a dynamic multimedia model that accounts for the particle size distribution and dissolution in water as also noted in a recent review.4 In reference to previous studies, Garner et al.1 state that “In all instances, the prediction is based on a f ramework developed for organic chemicals that relies on chemical characteristics and processes that are not applicable to ENMs.(10−12,37,46−50)”. The above overgeneralization overlooks the fact that a number of their cited studies (references 37, 47, 48, 50) do incorporate ENMs’ specific properties and processes.4 The statements that “Most existing ENM FT models (cited as references 15,37,47,51−56) make limited use of material-specif ic descriptors” and that “They are also limited with regard to the properties, transport, and transformations they include and the spatial scale and environmental compartments they consider”,1 although may seem reasonable, are overgeneralizations. For example, MendNano and RedNano2,3 consider in detail: (i) ENMs’ intermedia transport (e.g., dry deposition, rain scavenging, sedimentation, and dissolution) as governed by their particle size distribution,5 (ii) ENMs’ association with ambient particles, (iii) temporally varying meteorological conditions (e.g., rain, wind speed, temperature), and (iv) media characteristics (e.g., dimensions, soil type, vegetation coverage, flow rates, and more). Garner et al. assess that “...the [MendNano] model is limited with regards to mass transfer processes between soil and water, use simplif ied ENM transformation processes, excludes climate variability...Only one water chemistry (i.e., fresh water or marine) and one soil type can be modeled.”1 Contrary to the above © XXXX American Chemical Society
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DOI: 10.1021/acs.est.8b00486 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
Environmental Science & Technology
Correspondence/Rebuttal
(9) Tsai, W.; Cohen, Y.; Sakugawa, H.; Kaplan, I. R. Dynamic partitioning of semivolatile organics in gas/particle/rain phases during rain scavenging. Environ. Sci. Technol. 1991, 25 (12), 2012−2023.
estimate wet scavenging via a constant (i.e., particle size and raindrop-size invariant) wet scavenging ratio (Methods Section, Page 3, paragraph 7; SI. Section 2.1.2, File 1),1 while neglecting the dependence of this parameter on both the particle size distribution and rainfall rate.7,9 Finally, it is noted that the nanoFate mass balance equations and associated intermedia transport relations presented in Garner et al. do not appear to explicitly incorporate the particle size distribution.1 ̈ to expect that a given model In summary, it would be naive would be applicable to all possible scenarios. There are now a multitude of multimedia models dealing with F&T of nanomaterials that are applicable to a range of different specific scenarios.8 While the contribution of Garner et al. is a sincere attempt to account for various F&T processes, the authors have not adequately portrayed the existing literature on multimedia fate and transport nor intermedia transport.1 It is our sincere hope that this correspondence will provide readers of Environmental Science and Technology with the added information and references to properly evaluate the literature on the environmental multimedia distribution of nanomaterials.
Yoram Cohen*,† Muhammad Bilal‡ Haoyang Liu† †
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Chemical and Biomolecular Engineering Department, Henry Samueli School of Engineering and Applied Science;‡Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, California 90095, United States
AUTHOR INFORMATION
Corresponding Author
*Phone: (310) 825-8766; e-mail:
[email protected]. ORCID
Yoram Cohen: 0000-0002-0756-4699 Notes
The authors declare no competing financial interest.
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REFERENCES
(1) Garner, K. L.; Suh, S.; Keller, A. A. Assessing the Risk of Engineered Nanomaterials in the Environment: Development and Application of the nanoFate Model,. Environ. Sci. Technol. 2017, 51 (10), 5541−5551. (2) Liu, H. H.; Cohen, Y. Multimedia environmental distribution of engineered nanomaterials,. Environ. Sci. Technol. 2014, 48 (6), 3281− 3292. (3) Liu, H. H.; Bilal, M.; Lazareva, A.; Keller, A. A.; Cohen, Y. Simulation tool for assessing the release and environmental distribution of nanomaterials,. Beilstein J. Nanotechnol. 2015, 6, 938− 951. (4) Nowack, B. Evaluation of environmental exposure models for engineered nanomaterials in a regulatory context, NanoImpact, Volume 8, 2017; pp 38−47, ISSN 2452−0748, 10.1016/j.impact.2017.06.005. (5) Cohen, Y.; Cooter, E. J. Multimedia environmental distribution of toxics (Mend-Tox). II: software implementation and case studies. Pract. Period. Hazard., Toxic, Radioact. Waste Manage. 2002, 6, 87− 101. (6) Edwin, N. L. R.; Bird, B.; Warren, E. S. Transport Phenomena, 2nd ed. Wiley International Ed, 2007. (7) Seinfeld, J. H.; Seinfeld, J. H. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 3rd ed., 2016. (8) Sehmel, G. A. Particle and Gas Dry Deposition: A Review. Atmospheric Environment. Vol. 14. pp 983−1011. B
DOI: 10.1021/acs.est.8b00486 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
