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Special Issue "Air Pollution Modeling: Reviews of Science Process Algorithms"

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A special issue of Atmosphere (ISSN 2073-4433).

Deadline for manuscript submissions: closed (31 March 2011)

Special Issue Editors

Guest Editor
Dr. Daewon Byun

Air Quality Modeling Group, Air Resources Laboratory, National Oceanic & Atmospheric Administration, SSMC3, Rm 3316 (R/ARL), 1315 East West Highway, Silver Spring, MD 20910, USA
Website | E-Mail
Guest Editor
Dr. William R. Stockwell

Department of Chemistry, Room 120, Howard University, 525 College Street, NW, Washington, DC 20059, USA
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Guest Editor
Dr. Mehmet Talat Odman

School of Civil and Environmental Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, GA 30332, USA
Website | E-Mail

Special Issue Information

Dear Colleagues,

Air quality simulation models are important tools for regulatory, policy, and environmental decision making and science studies. Pollutants in the atmosphere are subject to myriad transport processes and transformation pathways that control their composition and concentration levels. The residence times of pollutants in the atmosphere can extend to multiple days to months and the corresponding spatial scales are commensurately large, ranging from local to continental scales. On these temporal and spatial scales, emissions from chemical manufacturing and other industrial activities, power generation, transportation, and waste treatment activities, as well as the natural sources, contribute to a variety of air pollution issues including visibility, ozone, particulate matter (PM), acid rain, and nutrient and toxic deposition.

This special issue is devoted to papers which provide in-depth reviews of physical and chemical process algorithms represented in the modern air quality models. This issue in Atmosphere will serve as the compendium of the state-of-science information on how these different atmospheric processes are treated in air quality models. Studies with critical reviews of pros and cons of process algorithms concerning atmospheric transport, turbulent mixing, atmospheric deposition, cloud processes, homogeneous and heterogeneous transformation of atmospheric gaseous and PM species, as well as anthropogenic and natural emission representations are welcome.

Dr. Daewon Byun
A/Prof. William R. Stockwell
Mehmet Talat Odman
Guest Editors

Keywords

  • atmospheric transport
  • turbulent mixing
  • atmospheric deposition
  • cloud processes
  • homogeneous and heterogeneous reactions
  • particulate matter
  • anthropogenic emissions
  • natural emissions

Published Papers (13 papers)

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Research

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Open AccessArticle Case Study of Pollutants Concentration Sensitivity to Meteorological Fields and Land Use Parameters over Douala (Cameroon) Using AERMOD Dispersion Model
Atmosphere 2011, 2(4), 715-741; doi:10.3390/atmos2040715
Received: 3 August 2011 / Revised: 14 October 2011 / Accepted: 14 October 2011 / Published: 14 December 2011
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Abstract
This paper deals with the simulation of the NOx concentration over Douala for the period 2002–2006 by means of the American Meteorological Society (AMS)/Environmental Protection Agency (EPA) Regulatory Model (AERMOD) model, version 07026. Its sensitivity to local meteorological fields and land use parameters
[...] Read more.
This paper deals with the simulation of the NOx concentration over Douala for the period 2002–2006 by means of the American Meteorological Society (AMS)/Environmental Protection Agency (EPA) Regulatory Model (AERMOD) model, version 07026. Its sensitivity to local meteorological fields and land use parameters are investigated by selecting different buildings (receptors) specific direction and distance from the source and by making changes in land use parameters. Results reveal variations in concentration patterns depending on the roughness length, albedo and the Bowen ratio. Changes in the albedo as well as the Bowen ratio only alter the concentration patterns during convective conditions. For a short averaging time, changes in albedo and Bowen ratio have the same effects on the concentration patterns. These results not only help to accurately choose the indicated areas for implanting industrial sites, to manage risk assessment exposure to pollutants in Douala city and addressing recommendations to policies makers. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
Open AccessArticle Cloud Processing of Gases and Aerosols in Air Quality Modeling
Atmosphere 2011, 2(4), 567-616; doi:10.3390/atmos2040567
Received: 10 June 2011 / Revised: 15 September 2011 / Accepted: 16 September 2011 / Published: 10 October 2011
Cited by 18 | PDF Full-text (943 KB) | HTML Full-text | XML Full-text
Abstract
The representations of cloud processing of gases and aerosols in some of the current state-of-the-art regional air quality models in North America and Europe are reviewed. Key processes reviewed include aerosol activation (or nucleation scavenging of aerosols), aqueous-phase chemistry, and wet deposition/removal of
[...] Read more.
The representations of cloud processing of gases and aerosols in some of the current state-of-the-art regional air quality models in North America and Europe are reviewed. Key processes reviewed include aerosol activation (or nucleation scavenging of aerosols), aqueous-phase chemistry, and wet deposition/removal of atmospheric tracers. It was found that models vary considerably in the parameterizations or algorithms used in representing these processes. As an emerging area of research, the current understanding of the uptake of water soluble organics by cloud droplets and the potential aqueous-phase reaction pathways leading to the atmospheric secondary organic aerosol (SOA) formation is also reviewed. Sensitivity tests using the AURAMS model have been conducted in order to assess the impact on modeled regional particulate matter (PM) from: (1) the different aerosol activation schemes, (2) the different below-cloud particle scavenging algorithms, and (3) the inclusion of cloud processing of water soluble organics as a potential pathway for the formation of atmospheric SOA. It was found that the modeled droplet number concentrations and ambient PM size distributions were strongly affected by the use of different aerosol activation schemes. The impact on the modeled average ambient PM mass concentration was found to be limited in terms of averaged PM2.5 concentration (~a few percents) but more significant in terms of PM1.0 (up to 10 percents). The modeled ambient PM was found to be moderately sensitive to the below-cloud particle scavenging algorithms, with relative differences up to 10% and 20% in terms of PM2.5 and PM10, respectively, when using the two different algorithms for the scavenging coefficient (Λ) corresponding to the lower and upper bounds in the parameterization for Λ. The model simulation with the additional cloud uptake and processing of water-soluble organic gases was shown to improve the evaluation statistics for modeled PM2.5 OA compared to the IMPROVE network data, and it was demonstrated that the cloud processing of water-soluble organics can indeed be an important mechanism in addition to the traditional secondary organic gas uptake to the particle organic phase. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
Open AccessArticle Chemical Mechanism Solvers in Air Quality Models
Atmosphere 2011, 2(3), 510-532; doi:10.3390/atmos2030510
Received: 18 July 2011 / Revised: 22 August 2011 / Accepted: 31 August 2011 / Published: 13 September 2011
Cited by 3 | PDF Full-text (440 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The solution of chemical kinetics is one of the most computationally intensivetasks in atmospheric chemical transport simulations. Due to the stiff nature of the system,implicit time stepping algorithms which repeatedly solve linear systems of equations arenecessary. This paper reviews the issues and challenges
[...] Read more.
The solution of chemical kinetics is one of the most computationally intensivetasks in atmospheric chemical transport simulations. Due to the stiff nature of the system,implicit time stepping algorithms which repeatedly solve linear systems of equations arenecessary. This paper reviews the issues and challenges associated with the construction ofefficient chemical solvers, discusses several families of algorithms, presents strategies forincreasing computational efficiency, and gives insight into implementing chemical solverson accelerated computer architectures. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
Open AccessArticle Coupling of Important Physical Processes in the Planetary Boundary Layer between Meteorological and Chemistry Models for Regional to Continental Scale Air Quality Forecasting: An Overview
Atmosphere 2011, 2(3), 464-483; doi:10.3390/atmos2030464
Received: 9 June 2011 / Revised: 15 August 2011 / Accepted: 16 August 2011 / Published: 31 August 2011
Cited by 9 | PDF Full-text (1119 KB) | HTML Full-text | XML Full-text
Abstract
A consensus among many Air Quality (AQ) modelers is that planetary boundary layer processes are the most influential processes for surface concentrations of air pollutants. Due to the many uncertainties intrinsically embedded in the parameterization of these processes, parameter optimization is often employed
[...] Read more.
A consensus among many Air Quality (AQ) modelers is that planetary boundary layer processes are the most influential processes for surface concentrations of air pollutants. Due to the many uncertainties intrinsically embedded in the parameterization of these processes, parameter optimization is often employed to determine an optimal set or range of values of the sensitive parameters. In this review study, we focus on the two of the most important physical processes: turbulent mixing and dry deposition. An emphasis was put on surveying AQ models that have been proven to resolve meso-scale features and cover a large geographical area, such as large regional, continental, or trans-continental boundary extents. Five AQ models were selected. Four of the models were run in real-time operational forecasting settings for continental scale AQ. The models use various forms of level 2.5 closure algorithms to calculate turbulent mixing. Tuning and parameter optimization has been used to tailor these algorithms to better suit their AQ models which are typically comprised of a coupled chemistry and meteorology model. Longer forecasts and long lead-times are inevitably under increasing demand for these models. Land Surface Models that have the capability for soil moisture and temperature data assimilation will have an advantage to constrain the key variables that govern the partitioning of surface sensible and latent heat fluxes and thus attain the potential to perform better in longer forecasts than those models that do not have this capability. Dry deposition velocity is a very significant model parameter that governs a major surface exchange activity. An exploratory study has been conducted to see the upper bound of roughness length in the similarity equation for aerodynamic resistance. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
Open AccessArticle Chemical Data Assimilation—An Overview
Atmosphere 2011, 2(3), 426-463; doi:10.3390/atmos2030426
Received: 7 June 2011 / Revised: 9 August 2011 / Accepted: 19 August 2011 / Published: 29 August 2011
Cited by 28 | PDF Full-text (1824 KB) | HTML Full-text | XML Full-text
Abstract
Chemical data assimilation is the process by which models use measurements to produce an optimal representation of the chemical composition of the atmosphere. Leveraging advances in algorithms and increases in the available computational power, the integration of numerical predictions and observations has started
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Chemical data assimilation is the process by which models use measurements to produce an optimal representation of the chemical composition of the atmosphere. Leveraging advances in algorithms and increases in the available computational power, the integration of numerical predictions and observations has started to play an important role in air quality modeling. This paper gives an overview of several methodologies used in chemical data assimilation. We discuss the Bayesian framework for developing data assimilation systems, the suboptimal and the ensemble Kalman filter approaches, the optimal interpolation (OI), and the three and four dimensional variational methods. Examples of assimilation real observations with CMAQ model are presented. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
Open AccessArticle Modeling Smoke Plume-Rise and Dispersion from Southern United States Prescribed Burns with Daysmoke
Atmosphere 2011, 2(3), 358-388; doi:10.3390/atmos2030358
Received: 1 June 2011 / Revised: 8 July 2011 / Accepted: 22 July 2011 / Published: 19 August 2011
Cited by 14 | PDF Full-text (942 KB) | HTML Full-text | XML Full-text
Abstract
We present Daysmoke, an empirical-statistical plume rise and dispersion model for simulating smoke from prescribed burns. Prescribed fires are characterized by complex plume structure including multiple-core updrafts which makes modeling with simple plume models difficult. Daysmoke accounts for plume structure in a three-dimensional
[...] Read more.
We present Daysmoke, an empirical-statistical plume rise and dispersion model for simulating smoke from prescribed burns. Prescribed fires are characterized by complex plume structure including multiple-core updrafts which makes modeling with simple plume models difficult. Daysmoke accounts for plume structure in a three-dimensional veering/sheering atmospheric environment, multiple-core updrafts, and detrainment of particulate matter. The number of empirical coefficients appearing in the model theory is reduced through a sensitivity analysis with the Fourier Amplitude Sensitivity Test (FAST). Daysmoke simulations for “bent-over” plumes compare closely with Briggs theory although the two-thirds law is not explicit in Daysmoke. However, the solutions for the “highly-tilted” plume characterized by weak buoyancy, low initial vertical velocity, and large initial plume diameter depart considerably from Briggs theory. Results from a study of weak plumes from prescribed burns at Fort Benning GA showed simulated ground-level PM2.5 comparing favorably with observations taken within the first eight kilometers of eleven prescribed burns. Daysmoke placed plume tops near the lower end of the range of observed plume tops for six prescribed burns. Daysmoke provides the levels and amounts of smoke injected into regional scale air quality models. Results from CMAQ with and without an adaptive grid are presented. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
Open AccessArticle Influence of Climatic Changes on Pollution Levels in Hungary and Surrounding Countries
Atmosphere 2011, 2(3), 201-221; doi:10.3390/atmos2030201
Received: 16 June 2011 / Revised: 28 June 2011 / Accepted: 6 July 2011 / Published: 18 July 2011
Cited by 10 | PDF Full-text (4325 KB) | HTML Full-text | XML Full-text
Abstract
The influence of future climatic changes on some high pollution levels that can cause damage to plants and human beings is studied in this paper. The particular area of interest is Hungary and its surrounding countries. Three important quantities, which are closely related
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The influence of future climatic changes on some high pollution levels that can cause damage to plants and human beings is studied in this paper. The particular area of interest is Hungary and its surrounding countries. Three important quantities, which are closely related to ozone concentrations, have been investigated. We shall mainly focus on cases where the critical values, prescribed in the directives, are exceeded. Six scenarios, which allow us to compare directly the future and the present levels, have been run over a period of sixteen years. Some of the results obtained in the selected domain by using these scenarios have been carefully studied. The major conclusion is that an increase in temperature in combination with some other factors might lead to rather considerable increases of the damaging effects of ozone on plants and humans. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
Open AccessArticle An Analytical Simple Formula for the Ground Level Concentration from a Point Source
Atmosphere 2011, 2(2), 21-35; doi:10.3390/atmos2020021
Received: 30 January 2011 / Revised: 2 March 2011 / Accepted: 8 March 2011 / Published: 24 March 2011
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Abstract
The Advection-Diffusion Equation is solved for a constant pollutant emission from a point-like source placed inside an unstable Atmospheric Boundary Layer. The solution is obtained adopting the novel analytical approach: Generalized Integral Laplace Transform Technique. The concentration solution of the equation is expressed
[...] Read more.
The Advection-Diffusion Equation is solved for a constant pollutant emission from a point-like source placed inside an unstable Atmospheric Boundary Layer. The solution is obtained adopting the novel analytical approach: Generalized Integral Laplace Transform Technique. The concentration solution of the equation is expressed through an infinite series expansion. After setting a realistic scenario through the wind and diffusivity parameterizations, the Ground Level Concentration (GLC) is determined, and an explicit approximate expression is provided for it, allowing an analytically simple expression for the position and value of the maximum. Remarks arise regarding the ability to express value and position of the GLC as explicit functions of the parameters defining the Atmospheric Boundary Layer scenario and the source height. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
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Review

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Open AccessReview A Review of Tropospheric Atmospheric Chemistry and Gas-Phase Chemical Mechanisms for Air Quality Modeling
Atmosphere 2012, 3(1), 1-32; doi:10.3390/atmos3010001
Received: 25 October 2011 / Revised: 24 November 2011 / Accepted: 12 December 2011 / Published: 21 December 2011
Cited by 13 | PDF Full-text (1590 KB) | HTML Full-text | XML Full-text
Abstract
Gas-phase chemical mechanisms are vital components of prognostic air quality models. The mechanisms are incorporated into modules that are used to calculate the chemical sources and sinks of ozone and the precursors of particulates. Fifty years ago essential atmospheric chemical processes, such as
[...] Read more.
Gas-phase chemical mechanisms are vital components of prognostic air quality models. The mechanisms are incorporated into modules that are used to calculate the chemical sources and sinks of ozone and the precursors of particulates. Fifty years ago essential atmospheric chemical processes, such as the importance of the hydroxyl radical, were unknown and crude air quality models incorporated only a few parameterized reactions obtained by fitting observations. Over the years, chemical mechanisms for air quality modeling improved and became more detailed as more experimental data and more powerful computers became available. However it will not be possible to incorporate a detailed treatment of the chemistry for all known chemical constituents because there are thousands of organic compounds emitted into the atmosphere. Some simplified method of treating atmospheric organic chemistry is required to make air quality modeling computationally possible. The majority of the significant differences between air quality mechanisms are due to the differing methods of treating this organic chemistry. The purpose of this review is to present an overview of atmospheric chemistry that is incorporated into air quality mechanisms and to suggest areas in which more research is needed. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
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Open AccessReview Adaptive Grid Use in Air Quality Modeling
Atmosphere 2011, 2(3), 484-509; doi:10.3390/atmos2030484
Received: 28 June 2011 / Revised: 31 August 2011 / Accepted: 2 September 2011 / Published: 9 September 2011
Cited by 8 | PDF Full-text (730 KB) | HTML Full-text | XML Full-text
Abstract
The predictions from air quality models are subject to many sources of uncertainty; among them, grid resolution has been viewed as one that is limited by the availability of computational resources. A large grid size can lead to unacceptable errors for many pollutants
[...] Read more.
The predictions from air quality models are subject to many sources of uncertainty; among them, grid resolution has been viewed as one that is limited by the availability of computational resources. A large grid size can lead to unacceptable errors for many pollutants formed via nonlinear chemical reactions. Further, insufficient grid resolution limits the ability to perform accurate exposure assessments. To address this issue in parallel to increasing computational power, modeling techniques that apply finer grids to areas of interest and coarser grids elsewhere have been developed. Techniques using multiple grid sizes are called nested grid or multiscale modeling techniques. These approaches are limited by uncertainty in the placement of finer grids since pertinent locations may not be known a priori, loss in solution accuracy due to grid boundary interface problems, and inability to adjust to changes in grid resolution requirements. A different approach to achieve local resolution involves using dynamic adaptive grids. Various adaptive mesh refinement techniques using structured grids as well as mesh enrichment techniques on unstructured grids have been explored in atmospheric modeling. Recently, some of these techniques have been applied to full blown air quality models. In this paper, adaptive grid methods used in air quality modeling are reviewed and categorized. The advantages and disadvantages of each adaptive grid method are discussed. Recent advances made in air quality simulation owing to the use of adaptive grids are summarized. Relevant connections to adaptive grid modeling in weather and climate modeling are also described. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
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Open AccessReview Air Quality Response Modeling for Decision Support
Atmosphere 2011, 2(3), 407-425; doi:10.3390/atmos2030407
Received: 20 June 2011 / Revised: 10 August 2011 / Accepted: 17 August 2011 / Published: 26 August 2011
Cited by 14 | PDF Full-text (269 KB) | HTML Full-text | XML Full-text
Abstract
Air quality management relies on photochemical models to predict the responses of pollutant concentrations to changes in emissions. Such modeling is especially important for secondary pollutants such as ozone and fine particulate matter which vary nonlinearly with changes in emissions. Numerous techniques for
[...] Read more.
Air quality management relies on photochemical models to predict the responses of pollutant concentrations to changes in emissions. Such modeling is especially important for secondary pollutants such as ozone and fine particulate matter which vary nonlinearly with changes in emissions. Numerous techniques for probing pollutant-emission relationships within photochemical models have been developed and deployed for a variety of decision support applications. However, atmospheric response modeling remains complicated by the challenge of validating sensitivity results against observable data. This manuscript reviews the state of the science of atmospheric response modeling as well as efforts to characterize the accuracy and uncertainty of sensitivity results. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
Open AccessReview Sub-Grid Scale Plume Modeling
Atmosphere 2011, 2(3), 389-406; doi:10.3390/atmos2030389
Received: 15 June 2011 / Revised: 16 August 2011 / Accepted: 17 August 2011 / Published: 24 August 2011
Cited by 12 | PDF Full-text (240 KB) | HTML Full-text | XML Full-text
Abstract
Multi-pollutant chemical transport models (CTMs) are being routinely used to predict the impacts of emission controls on the concentrations and deposition of primary and secondary pollutants. While these models have a fairly comprehensive treatment of the governing atmospheric processes, they are unable to
[...] Read more.
Multi-pollutant chemical transport models (CTMs) are being routinely used to predict the impacts of emission controls on the concentrations and deposition of primary and secondary pollutants. While these models have a fairly comprehensive treatment of the governing atmospheric processes, they are unable to correctly represent processes that occur at very fine scales, such as the near-source transport and chemistry of emissions from elevated point sources, because of their relatively coarse horizontal resolution. Several different approaches have been used to address this limitation, such as using fine grids, adaptive grids, hybrid modeling, or an embedded sub-grid scale plume model, i.e., plume-in-grid (PinG) modeling. In this paper, we first discuss the relative merits of these various approaches used to resolve sub-grid scale effects in grid models, and then focus on PinG modeling which has been very effective in addressing the problems listed above. We start with a history and review of PinG modeling from its initial applications for ozone modeling in the Urban Airshed Model (UAM) in the early 1980s using a relatively simple plume model, to more sophisticated and state-of-the-science plume models, that include a full treatment of gas-phase, aerosol, and cloud chemistry, embedded in contemporary models such as CMAQ, CAMx, and WRF-Chem. We present examples of some typical results from PinG modeling for a variety of applications, discuss the implications of PinG on model predictions of source attribution, and discuss possible future developments and applications for PinG modeling. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
Open AccessReview Surface Flux Modeling for Air Quality Applications
Atmosphere 2011, 2(3), 271-302; doi:10.3390/atmos2030271
Received: 14 June 2011 / Revised: 21 July 2011 / Accepted: 26 July 2011 / Published: 8 August 2011
Cited by 25 | PDF Full-text (733 KB) | HTML Full-text | XML Full-text
Abstract
For many gasses and aerosols, dry deposition is an important sink of atmospheric mass. Dry deposition fluxes are also important sources of pollutants to terrestrial and aquatic ecosystems. The surface fluxes of some gases, such as ammonia, mercury, and certain volatile organic compounds,
[...] Read more.
For many gasses and aerosols, dry deposition is an important sink of atmospheric mass. Dry deposition fluxes are also important sources of pollutants to terrestrial and aquatic ecosystems. The surface fluxes of some gases, such as ammonia, mercury, and certain volatile organic compounds, can be upward into the air as well as downward to the surface and therefore should be modeled as bi-directional fluxes. Model parameterizations of dry deposition in air quality models have been represented by simple electrical resistance analogs for almost 30 years. Uncertainties in surface flux modeling in global to mesoscale models are being slowly reduced as more field measurements provide constraints on parameterizations. However, at the same time, more chemical species are being added to surface flux models as air quality models are expanded to include more complex chemistry and are being applied to a wider array of environmental issues. Since surface flux measurements of many of these chemicals are still lacking, resistances are usually parameterized using simple scaling by water or lipid solubility and reactivity. Advances in recent years have included bi-directional flux algorithms that require a shift from pre-computation of deposition velocities to fully integrated surface flux calculations within air quality models. Improved modeling of the stomatal component of chemical surface fluxes has resulted from improved evapotranspiration modeling in land surface models and closer integration between meteorology and air quality models. Satellite-derived land use characterization and vegetation products and indices are improving model representation of spatial and temporal variations in surface flux processes. This review describes the current state of chemical dry deposition modeling, recent progress in bi-directional flux modeling, synergistic model development research with field measurements, and coupling with meteorological land surface models. Full article
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)

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