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A major challenge in atmospheric modeling is balancing the number of chemical compounds and reactions included in the model with the accuracy of physical processes such as transport and mixing in the atmosphere. For example, [[box modeling]] is relatively simple and may include hundreds or even thousands of chemical reactions, but they typically use a very crude representation of atmospheric [[mixed layer]].<ref name=":2" /> This makes them useful for studying specific chemical reactions, but limited in stimulating real-world dynamics. In contrast, [[3D modeling|3D models]] are more complex, representing a variety of physical processes such as wind, convection, and atmospheric mixing. They also provide more realistic representations of transportation and mixing. However, computational limits often simply chemical reactions and typically include fewer chemical reactions than box models. The trade-off between the two approaches lies in resolution and complexity.
A major challenge in atmospheric modeling is balancing the number of chemical compounds and reactions included in the model with the accuracy of physical processes such as transport and mixing in the atmosphere. For example, [[box modeling]] is relatively simple and may include hundreds or even thousands of chemical reactions, but they typically use a very crude representation of atmospheric [[mixed layer]].<ref name=":2" /> This makes them useful for studying specific chemical reactions, but limited in stimulating real-world dynamics. In contrast, [[3D modeling|3D models]] are more complex, representing a variety of physical processes such as wind, convection, and atmospheric mixing. They also provide more realistic representations of transportation and mixing. However, computational limits often simply chemical reactions and typically include fewer chemical reactions than box models. The trade-off between the two approaches lies in resolution and complexity.


To simplify the creation of these complex models, some researchers use automatic code generators like [[Autochem]] or [[Kinetic PreProcessor]]. These tools help automate the model-building process by selecting relevant chemical reactions from databases based on a [[user-defined function]] of chemical constituents. Once the reactions are chosen, the code generator automatically constructs the [[ordinary differential equations]] that describe their time evolution, greatly reducing the time and effort required for model construction.
To simplify the creation of these complex models, some researchers use automatic code generators like [[Autochem]] or [[Kinetic PreProcessor]]. These tools help automate the model-building process by selecting relevant chemical reactions from databases based on a [[user-defined function]] of chemical constituents.<ref>{{Cite web |last=Lockard |first=David |date=November 2005 |title=AutoChem |url=https://ntrs.nasa.gov/enwiki/api/citations/20110016385/downloads/20110016385.pdf}}</ref> Once the reactions are chosen, the code generator automatically constructs the [[ordinary differential equations]] that describe their time evolution, greatly reducing the time and effort required for model construction.


Differences between model prediction and real-world observations can arise from errors in model input parameters or flaws representations of processes in the model. Some input parameters like surface emissions are often less accurately quantified from observations compared to model results. The model can be improved by adjusting poorly known parameters to better match observed data<ref name=":1" />. A formal method for applying these adjustments is through [[Bayesian optimization|Bayesian Optimization]] through an inverse modeling framework, where the results from the CTMs are inverted to optimize selected parameters. This approach has gained attention over the past decade as an effective method to interpret large amounts of data generate by models and observations from satellites.
Differences between model prediction and real-world observations can arise from errors in model input parameters or flaws representations of processes in the model. Some input parameters like surface emissions are often less accurately quantified from observations compared to model results. The model can be improved by adjusting poorly known parameters to better match observed data<ref name=":1" />. A formal method for applying these adjustments is through [[Bayesian optimization|Bayesian Optimization]] through an inverse modeling framework, where the results from the CTMs are inverted to optimize selected parameters. This approach has gained attention over the past decade as an effective method to interpret large amounts of data generate by models and observations from satellites.

Revision as of 23:39, 30 November 2024

This article is about field of academic study. For composition of the Earth's atmosphere, see Atmosphere of Earth.
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Atmospheric chemistry is a branch of atmospheric science that studies the chemistry of the Earth's atmosphere and that of other planets. This multidisciplinary approach of research draws on environmental chemistry, physics, meteorology, computer modeling, oceanography, geology and volcanology, climatology and other disciplines to understand both natural and human-induced changes in atmospheric composition. Key areas of research include the behavior of trace gasses, the formation of pollutants, and the role of aerosols and greenhouse gasses. Through a combination of observations, laboratory experiments, and computer modeling, atmospheric chemists investigate the causes and consequences of atmospheric changes.

The composition and chemistry of the Earth's atmosphere is important for several reasons, but primarily because of the interactions between the atmosphere and living organisms. Natural processes such as volcano emissions, lightning and bombardment by solar particles from corona changes the composition of the Earth's atmosphere. It has also been changed by human activity and some of these changes are harmful to human health, crops and ecosystems. Examples of problems addressed in atmospheric chemistry include acid rain, ozone depletion, photochemical smog, greenhouse gasses and global warming. Atmospheric chemists work to understand the causes of these problems. By developing a theoretical understanding, they can test potential solutions and evaluate the effects of changes in government policy.

Atmospheric composition

Visualisation of composition by volume of Earth's atmosphere. Water vapour is not included as it is highly variable. Each tiny cube (such as the one representing krypton) has one millionth of the volume of the entire block. Data is from NASA Langley.
The composition of common nitrogen oxides in dry air vs. temperature
Chemical composition of atmosphere according to altitude.[1] Axis: Altitude (km), Content of volume (%).
Average Composition of Dry Atmosphere (mole fractions)
Gas Dry air per NASA Dry clean air near sea level
(standard ISO 2533 - 1975)
Nitrogen, N2 78.084% 78.084%
Oxygen, O2[2] 20.946% 20.946%
Minor constituents (mole fractions in ppm)
Argon, Ar 9340 9340
Carbon dioxide*[a], CO2 430 430
Neon, Ne 18.18 18.18
Helium, He 5.24 5.24
Methane[a], CH4 1.9 1.9
Krypton, Kr 1.14 1.14
Hydrogen, H2 0.53 0.53
Nitrous oxide, N2O 0.34 0.34
Xenon, Xe 0.087
Nitrogen dioxide, NO2 up to 0.02
Ozone*, O3, in summer up to 0.07
Ozone*, O3, in winter up to 0.02
Sulphur dioxide*, SO2 up to 1
Iodine*, I2 0.01
Water
Water vapour* Highly variable (about 0–3%);
typically makes up about 1%
Notes
The mean molecular mass of dry air is 28.97 g/mol. *The content of the gas may undergo significant variations from time to time or from place to place. [a]The concentration of CO2 and CH4 vary by season and location.

Trace gas composition

Besides the major components listed above, the Earth's atmosphere contains many trace gas species that vary significantly depending on nearby sources and sinks. These trace gasses include compounds such as CFCs/HCFCs which are particularly damaging to the ozone layer, and H2S which has a characteristic foul odor of rotten eggs and can be smelt in concentrations as low as 0.47 ppb. Some approximate amounts near the surface of some additional gasses are listed below. In addition to gasses, the atmosphere contains particles such as aerosol, which includes examples such as droplets, ice crystals, bacteria, and dust.

Composition (ppt by volume unless otherwise stated)
Gas Clean continental, Seinfeld & Pandis (2016)[3] Simpson et al. (2010)[4]
Carbon monoxide, CO 40-200 ppb p39 97 ppb
Nitric oxide, NO 16
Ethane, C2H6 781
Propane, C3H8 200
Isoprene, C5H8 311
Benzene, C6H6 11
Methanol, CH3OH 1967
Ethanol, C2H5OH 75
Trichlorofluoromethane, CCl3F 237 p41 252.7
Dichlorodifluoromethane, CCl2F2 530 p41 532.3
Chloromethane, CH3Cl 503
Bromomethane, CH3Br 9–10 p44 7.7
Iodomethane, CH3I 0.36
Carbonyl sulfide, OCS 510 p26 413
Sulfur dioxide, SO2 70–200 p26 102
Hydrogen sulfide, H2S 15–340 p26
Carbon disulfide, CS2 15–45 p26
Formaldehyde, H2CO 9.1 ppb p37, polluted
Acetylene, C2H2 8.6 ppb p37, polluted
Ethene, C2H4 11.2 ppb p37, polluted 20
Sulfur hexafluoride, SF6 7.3 p41
Carbon tetrafluoride, CF4 79 p41
Total gaseous mercury, Hg 0.209 p55

History

Schematic of chemical and transport processes related to atmospheric composition

The first scientific studies of atmospheric composition began in the 18th century when chemists such as Joseph Priestley, Antoine Lavoisier and Henry Cavendish made the first measurements of the composition of the atmosphere.[5]

In the late 19th and early 20th centuries, researchers shifted their interest towards trace constituents with very low concentrations. An important finding from this era was the discovery of ozone by Christian Friedrich Schönbein in 1840.[6]

In the 20th century atmospheric science moved from studying the composition of air to consider how the concentrations of trace gasses in the atmosphere have changed over time and the chemical processes which create and destroy compounds in the air. Two important outcomes were the explanation by Sydney Chapman and Gordon Dobson of how the ozone layer is created and maintained, and Arie Jan Haagen-Smit’s explanation of photochemical smog. Further studies on ozone issues led to the 1995 Nobel Prize in Chemistry award shared between Paul Crutzen, Mario Molina and Frank Sherwood Rowland.

In the 21st century the focus is now shifting again. Instead of concentrating on atmospheric chemistry in isolation, it is now seen as one part of the Earth system with the rest of the atmosphere, biosphere and geosphere. A driving force for this link is the relationship between chemistry and climate. The changing climate and the recovery of the ozone hole and the interaction of the composition of the atmosphere with the oceans and terrestrial ecosystems are examples of the interdependent relationships between Earth's systems.[7]

Carbon dioxide in Earth's atmosphere if half of anthropogenic CO2 emissions[8][9] are not absorbed
(NASA simulation; 9 November 2015)
Nitrogen dioxide 2014 - global air quality levels
(released 14 December 2015)[10]

Methodology

Observations, lab measurements, and modeling are the three central elements in atmospheric chemistry. Progress in atmospheric chemistry is often driven by the interactions between these components and they form an integrated whole. For example, observations may tell us that more of a chemical compound exists than previously thought possible. This will stimulate new modeling and laboratory studies which will increase our scientific understanding to a level where we can explain the observations.[11]

Observation

Field observations of chemical systems are essential to understanding atmospheric processes and determining the accuracy of models. Atmospheric chemistry measurements can be long term to observe continuous trends or short term to observe smaller variations. Measurements can be made with observatories, satellites, and surface observations.

Routine observations of chemical composition show changes in atmospheric composition over time. Observatories such as the Mauna Loa and mobile platforms such as aircraft ships and balloons (e.g. the UK's Facility for Airborne Atmospheric Measurements) study chemical compositions and weather dynamics. An application of long term observations is the Keeling Curve - a series of measurements from 1958 to today which show a steady rise in the concentration of carbon dioxide (see also ongoing measurements of atmospheric CO2). Observations of atmospheric composition are increasingly made by satellites with important instruments such as GOME and MOPITT giving a global picture of air pollution and chemistry.

Surface observations have the advantage that they provide long term records at high time resolution but are limited in the vertical and horizontal space they provide observations from. Some surface based instruments e.g. LIDAR can provide concentration profiles of chemical compounds and aerosols but are still restricted in the horizontal region they can cover. Many observations are available online in Atmospheric Chemistry Observational Databases[12]

Laboratory studies

Laboratory studies help understand the complex interactions from Earth’s systems that can be difficult to measure on a large scale. Experiments are performed in controlled environments, such as smog chambers, that allow for the individual evaluation of specific chemical reactions or the assessment of properties of a particular atmospheric constituent.[13] A closely related subdiscipline is atmospheric photochemistry, which quantifies the rate that molecules are split apart by sunlight, determines the resulting products, and obtains thermodynamic data such as Henry's law coefficients.

Laboratory measurements are essential to understanding the sources and sinks of pollutants and naturally occurring compounds. Types of analysis that are of interest include both those on gas-phase reactions, as well as heterogeneous reactions that are relevant to the formation and growth of aerosols. Commonly used analytical instruments include ambient and particulate air samplers, scanning mobility particle sizers, gas chromatographs, and mass spectrometers.

Three boxes stacked on top of one another with vertical arrows to show elevation and horizontal arrows to show transportation. Aerosols enter the box via human, plant, and wind transport and exit via dry or wet deposition.
Schematic diagram of a one-dimensional column model depicting the movement and transformation of aerosols.[14]

Modeling

Models are essential tools for interpreting observational data, testing hypotheses about chemical reactions, and predicting future concentrations of atmospheric chemicals. To synthesize and test theoretical understanding of atmospheric chemistry, researchers commonly use computer models, such as chemical transport models (CTMs). CTMs provide realistic descriptions of the three-dimensional transport and evolution of the atmosphere.[7] Atmospheric models can be seen as mathematical representations that replicate the behavior of the atmosphere. These numerical models solve the differential equations governing the concentrations of chemicals in the atmosphere.

Depending on the complexity, these models can range from simple to highly detailed. Models can be zero-, one-, two-, or three-dimensional, each with various uses and advantages. Three-dimensional chemical transport models offer the most realistic simulations but require substantial computational resources. These models can be global e.g. GCM, simulating the atmospheric conditions across the Earth, or regional, e.g. RAMS focusing on specific areas with greater resolution. Global models typically have lower horizontal resolution and represent less complex chemical mechanisms but they cover a larger area, while regional models can represent a limited area with higher resolution and more detail.[15]

A major challenge in atmospheric modeling is balancing the number of chemical compounds and reactions included in the model with the accuracy of physical processes such as transport and mixing in the atmosphere. For example, box modeling is relatively simple and may include hundreds or even thousands of chemical reactions, but they typically use a very crude representation of atmospheric mixed layer.[14] This makes them useful for studying specific chemical reactions, but limited in stimulating real-world dynamics. In contrast, 3D models are more complex, representing a variety of physical processes such as wind, convection, and atmospheric mixing. They also provide more realistic representations of transportation and mixing. However, computational limits often simply chemical reactions and typically include fewer chemical reactions than box models. The trade-off between the two approaches lies in resolution and complexity.

To simplify the creation of these complex models, some researchers use automatic code generators like Autochem or Kinetic PreProcessor. These tools help automate the model-building process by selecting relevant chemical reactions from databases based on a user-defined function of chemical constituents.[16] Once the reactions are chosen, the code generator automatically constructs the ordinary differential equations that describe their time evolution, greatly reducing the time and effort required for model construction.

Differences between model prediction and real-world observations can arise from errors in model input parameters or flaws representations of processes in the model. Some input parameters like surface emissions are often less accurately quantified from observations compared to model results. The model can be improved by adjusting poorly known parameters to better match observed data[7]. A formal method for applying these adjustments is through Bayesian Optimization through an inverse modeling framework, where the results from the CTMs are inverted to optimize selected parameters. This approach has gained attention over the past decade as an effective method to interpret large amounts of data generate by models and observations from satellites.

One important current trend is using atmospheric chemistry as part of Earth system models. These models integrate atmospheric chemistry with other Earth system components, such as biosphere and hydrosphere, enabling the study of complex interactions between climate, atmospheric composition, and ecosystems.

See also

References

  1. ^ Cairns, Iver (23 September 1999). "Earth's Atmosphere". The University of Sydney. Retrieved 7 April 2021.
  2. ^ Zimmer, Carl (3 October 2013). "Earth's Oxygen: A Mystery Easy to Take for Granted". The New York Times. Retrieved 3 October 2013.
  3. ^ Seinfeld, John; Pandis, Spyros (2016). Atmospheric Chemistry and Physics - from Air Pollution to Climate Change, 3rd ed. Hoboken, New Jersey: Wiley. ISBN 9781119221173.
  4. ^ Simpson, I. J.; Blake, N. J.; Barletta, B.; Diskin, G. S.; Fuelberg, H. E.; Gorham, K.; Huey, L. G.; Meinardi, S.; Rowland, F. S.; Vay, S. A.; Weinheimer, A. J.; Yang, M.; Blake, D. R. (2010). "Characterization of trace gases measured over Alberta oil sands mining operations: 76 speciated C2–C10 volatile organic compounds (VOCs), CO2, CH4, CO, NO, NO2, NO, O3 and SO2". Atmospheric Chemistry and Physics. 10 (23): 11931–11954. Bibcode:2010ACP....1011931S. doi:10.5194/acp-10-11931-2010. ISSN 1680-7324. S2CID 62782723.
  5. ^ Weeks, M. E. (1934). "Daniel Rutherford and the discovery of nitrogen". Chemistry Education. 11: 101.
  6. ^ Schönbein, C. F (1840). "On the odour accompanying electricity and on the probability of its dependency on the presence of a new substance". Philosophical Magazine. 17: 293–294.
  7. ^ a b c Brasseur, Guy P.; Jacob, Daniel J. (May 2017). Modeling of Atmospheric Chemistry. Cambridge University Press. pp. 2–23. ISBN 9781316544754.
  8. ^ St. Fleur, Nicholas (10 November 2015). "Atmospheric Greenhouse Gas Levels Hit Record, Report Says". The New York Times. Retrieved 11 November 2015.
  9. ^ Ritter, Karl (9 November 2015). "UK: In 1st, global temps average could be 1 degree C higher". AP News. Retrieved 11 November 2015.
  10. ^ Cole, Steve; Gray, Ellen (14 December 2015). "New NASA Satellite Maps Show Human Fingerprint on Global Air Quality". NASA. Retrieved 14 December 2015.
  11. ^ Brasseur, Guy; Prinn, Ronald; Pszenny, Alexander (2003). Atmospheric Chemistry in a Changing World. New York: Springer-Verlag BerIin Heidelberg. ISBN 978-3-642-62396-7.
  12. ^ "Air Quality Modeling - Surface and Upper Air Databases". U.S. Environmental Protection Agency. March 19, 2024.
  13. ^ National Academies of Sciences, Engineering, and Medicine (2016). Future of Atmospheric Research: Remembering Yesterday, Understanding Today, Anticipating Tomorrow. Washington, DC: The National Academies Press. p. 15. ISBN 978-0-309-44565-8.
  14. ^ a b Jacobs, Daniel (January 1999). Introduction to Atmospheric Chemistry. Princeton University Press. ISBN 9780691001852.
  15. ^ Brasseur, Guy P.; Orlando, John J.; Tyndall, Geoffrey S. (1999). Atmospheric Chemistry and Global Change. United States: The National Academies Press. pp. 439–441. ISBN 0-19-510521-4.
  16. ^ Lockard, David (November 2005). "AutoChem" (PDF).

Further reading

  • Finlayson-Pitts, Barbara J.; Pitts, James N., Jr. (2000). Chemistry of the Upper and Lower Atmosphere. Academic Press. ISBN 0-12-257060-X.
  • Iribarne, J. V. Cho, H. R. (1980). Atmospheric Physics, D. Reidel Publishing Company.
  • Seinfeld, John H.; Pandis, Spyros N. (2006). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change (2nd Ed.). John Wiley and Sons, Inc. ISBN 0-471-82857-2.
  • Warneck, Peter (2000). Chemistry of the Natural Atmosphere (2nd Ed.). Academic Press. ISBN 0-12-735632-0.
  • Wayne, Richard P. (2000). Chemistry of Atmospheres (3rd Ed.). Oxford University Press. ISBN 0-19-850375-X.
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