Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling


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Description

Emergency Management. The USGS provides science about natural hazards that threaten lives and livelihoods; the water, energy, minerals, and other natural resources we rely on; the health of our ecosystems and environment; and the impacts of climate and land-use change. Our scientists develop new methods and tools to supply timely, relevant, and useful information about the Earth and its processes. Learn more below. Search Search. Water Resources. Science Topics Advanced Monitoring.

Agriculture, Livestock, and Aquaculture. Climate Variability. Clouds, Precipitation, and Atmospheric Deposition. Contaminant Transport Modeling. Data Science. Drinking and Household Use. Ecosystem Functions and Processes. Ecosystem Health. Emerging Contaminants. Energy Production. Erosion and Sedimentation. Estuarine Ecosystems.


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Extreme Hydrologic Event Monitoring. Groundwater Monitoring. Groundwater, Aquifers, Wells, and Springs. Hazard and Event Monitoring. Hazards Floods, Droughts, Hurricanes, etc. Industry and Mining. Instream Use and Environmental Flows. Invasive Species. Karst, Sinkholes, and Land Subsidence. Lake and Wetland Ecosystems. The choice of mechanism really depends, then, on the science question. If the research objective is to predict complex chemistry—climate interactions and if computational resources are available, then a more complex mechanism will have the most value.

However, if the research objective is to better understand various parameterizations, then a more computationally efficient mechanism will have higher value even if it might not be capable of accurately simulating all variables in detail Hoffman et al. This is particularly the case when a baseline can be established between the simplified mechanism and the complex mechanism, as we have done here.

We feel that this parallel approach, in which a set of mechanisms with varying levels of complexity are run concurrently with a consistent set of parameters, allows us to enhance our exploration of uncertainties and thus our ability to understand the atmospheric chemistry of the Earth system. The establishment of a baseline comparison is particularly important, since the SF mechanism is a simplified mechanism and should not be blindly trusted to reproduce the behavior of more complex mechanisms. For example, if a research group is interested in precise estimates of ozone concentrations in regions where the biogenic influence is significant, the SF mechanism would prove insufficient.

If, however, the phenomenon of interest can be shown to be within the SF mechanism capabilities e. The SF mechanism may be particularly desirable with chemistry—climate simulations at higher spatial resolutions. In addition, the selection of a simplified mechanism allows for the capability to easily and efficiently test new forms and new representations of chemistry without the need to painstakingly update and test all possible interactions of any addition within a complex mechanism.

This exercise offered a significant capability to test, simulate, and further learn about improving atmospheric chemistry computations. This demonstrates that a hybrid approach or tiered approach, as recommended in Uusitalo et al. Furthermore, the selection of a simple chemical mechanism — especially when used in conjunction with more complex mechanisms within a consistent modeling framework — allows for better quantification of the uncertainties and the relative importance of particular pieces of the chemistry.

Here, for instance, the SF mechanism's representation of biogenic species chemistry is insufficient to adequately represent equatorial landmasses, but the reduced-form RH mechanism is nearly as capable as the MO mechanism over most regions and most species. This begs the following question: is there a representation of biogenic chemistry somewhere between the RH and the SF mechanisms that can approach the efficiency of the SF mechanism and the accuracy of the RH mechanism? We hope that future research will address this question, as well as others, such as more globally oriented research pertaining to ozone budgets and the interaction between OH and CH 4 lifetime.

Mechanisms of Competitive Adsorption Organic Pollutants on Hexylene-Bridged Polysilsesquioxane

In addition, comparisons of chemical mechanisms of different complexities, particularly where the simplified mechanisms fail, could potentially identify regional chemical regimes. For instance, the SF mechanism cannot adequately represent the chemistry of equatorial forests Fig. Finally, the capability to examine atmospheric chemistry complexity in a step-wise fashion could also be utilized to bridge the gap between the most complex 3-D chemical models and the more efficient models utilized by the Earth models of intermediate complexity EMIC or integrated assessment model IAM communities.

In this study, we have compared three chemical mechanisms of different levels of complexity within the CESM CAM-chem framework for present-day chemical and climatological conditions. The RH mechanism is roughly twice as efficient as the MO mechanism, and the SF mechanism is roughly 3 times as efficient as the MO mechanism, without any code optimization.

As much as possible, we kept the parameterizations consistent across all mechanisms, although we had to remap some of the MO mechanism species to match up with the RH mechanism species. Both MO and SF have been compared in other model intercomparisons, including for preindustrial conditions see the Supplement for additional information.

We hope that the analysis presented in this paper and the availability of the mechanism files Supplement will provide a baseline for continuing research on both the RH and SF mechanisms. We find that all three mechanisms successfully capture surface ozone values at the larger spatial scales, but at smaller spatial scales, and especially within the northeastern US, all three mechanisms have surface ozone biases when compared to CASTNET observations; however, the mean values for all three mechanisms are consistent with each other at a variety of spatial scales.

The RH mechanism is in close agreement with the MO mechanism for nearly every metric we examined, and any differences tend to be minor both in magnitude and in spatial extent. In addition, the SF mechanism deviates from the MO mechanism over regions of high biogenic emissions, such as equatorial Africa and South America. These large deviations within the SF mechanism are likely a result of the simplicity of the mechanism and especially of the lack of biogenic species chemistry beyond a single-species, two-reaction representation as well as a lack of PAN and N 2 O 5 chemistry Figs.

The SF mechanisms do not include NO 3 , which may also explain some of the nighttime biases. Future simulations in which NO 3 chemistry is added to the SF mechanism may correct some of these biases. We also find that although the SF mechanism differs in the magnitude of the estimated ozone from the other two mechanisms, the simulated ozone variability is similar in all three mechanisms Figs. We find that there are significant gains that can be realized by a research approach that utilizes simulations with both a complex and a simplified chemical mechanism where the complex mechanisms are used to provide a more trusted chemical result especially for the mean values and the simple mechanism could be used to efficiently simulate longer time periods to better understand the roles of meteorological variability.

The capability of the SF mechanism to simulate adequate chemistry with interactive meteorology is not examined here nor is the coupling of the SF mechanism with modal aerosols, which is left for future research. These results encourage revitalizing or creating simplified chemical mechanisms within individual modeling frameworks and examining the structural uncertainties that exist between different models with regards to simplified chemical mechanisms.

Finally, we note that there are many inherent uncertainties associated with the use and comparison of chemical mechanisms and climate—chemistry simulations, many of which are inherited with the adoption of a particular model. The CESM CAM-chem model has been used extensively to examine a variety of climate and chemistry phenomena, and uncertainties that arise from the individual choices made during the historical development of this chemical model see Brasseur et al.

Future simulations using different model versions or different choices of parameterizations, schemes, emissions, and other input datasets will need to examine the impact of those choices on the simulated chemical uncertainty and compare these to the uncertainty that arises from the selection of the different chemical mechanisms presented here. LE and ST aided in the development, preparation, and analysis of the simulations as well as in reviewing the paper. JFL advised and aided in the Reduced Hydrocarbon simulation.

PCS advised and aided in the Super-Fast simulation. This model development work was supported by the U. Computational resources for this project were provided by DOE and a consortium of other government, industry, and foundation sponsors of the Joint Program. The authors would also like to thank Daniel Rothenberg for efficient processing of the ozone files. Edited by: Fiona O'Connor Reviewed by: two anonymous referees. Abbatt, J. Aumont, B. Baker, L. Barnes, E. Bocquet, M.

Brasseur, G. Model description, J. Brown-Steiner, B. Reduced Hydrocarbon vs. Burkholder, J. Cameron-Smith, P. Cariolle, D. Collins, W. Dodge, M. Emmerson, K. Emmons, L.

Mannich reaction Mechanism / Organic Name Reactions ( csirnet, Gate, IITJAM, Barc)

Model Dev. Fiore, A. Air Waste Manage. Garcia-Menendez, F. Gery, M. Granier, C. Guenther, A. Hauglustaine, D. Model results and evaluation, J. Hoffman, R. Horowitz, L. Hourdin, F. Houweling, S. Hsu, J. Jenkin, M. Jimenez, P. Kinnison, D.

Bibliographic Information

Knote, C. Lamarque, J. Madronich, S. Marsh, D. McLinden, C. Olsen, B.


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Hannegan, O. Wild, M. Prather, and J. Milford, J. Morgenstern, O. Pfister, G. Phalitnonkiat, P. Prinn, R. Rienecker, M. Rotman, D. Saunders, S. Schnell, J. Silva, R. Squire, O. Sofen, E. Solomon, S. Stevenson, D. Stockwell, W. Szopa, S. Tilmes, S. Uusitalo, L. Val Martin, M. Voulgarakis, A. Wang, C. Wild, O.

Young, P. Annales Geophysicae. Atmospheric Measurement Techniques. Climate of the Past.

تفاصيل ال٠نتج

Earth Surface Dynamics. Earth System Dynamics. Geoscience Communication. Geoscientific Instrumentation, Methods and Data Systems.

Geoscientific Model Development. Hydrology and Earth System Sciences. Natural Hazards and Earth System Sciences. Nonlinear Processes in Geophysics. The Cryosphere. Weather and Climate Dynamics. Advances in Geosciences. Encyclopedia of Geosciences. Journal topic GMD.

Author Title Abstract Full text.

References and Recommended Reading

GMD Articles Volume 11, issue Article Assets Peer review Metrics Related articles. This work is distributed under the Creative Commons Attribution 4. Evaluating simplified chemical mechanisms within present-day simulations of the Community Earth System Model version 1.

Super-Fast chemistry Evaluating simplified chemical mechanisms within present-day simulations of the Community Earth System Model version 1. Super-Fast chemistry. Super-Fast chemistry Evaluating simplified chemical mechanisms within present-day simulations of the Community Earth Benjamin Brown-Steiner et al. Supplement KB.

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How to cite. Super-Fast chemistry, Geosci. Code availability. Data availability. Author contributions.

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Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling
Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling
Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling
Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling
Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling
Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling
Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling
Pollutant-Solid Phase Interactions Mechanisms, Chemistry and Modeling

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