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Atmosphere, Volume 2, Issue 3 (September 2011), Pages 201-566

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Research

Jump to: Review

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 9 | 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 [...] Read more.
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 The Coupled Effect of Mid-Tropospheric Moisture and Aerosol Abundance on Deep Convective Cloud Dynamics and Microphysics
Atmosphere 2011, 2(3), 222-241; doi:10.3390/atmos2030222
Received: 1 June 2011 / Revised: 26 June 2011 / Accepted: 5 July 2011 / Published: 19 July 2011
Cited by 2 | PDF Full-text (295 KB) | HTML Full-text | XML Full-text
Abstract
The humidity of the mid troposphere has a significant effect on the development of deep convection. Dry layers (dry intrusions) can inhibit deep convection through the effect of a thermal inversion resulting from radiation and due to the reduction in buoyancy resulting [...] Read more.
The humidity of the mid troposphere has a significant effect on the development of deep convection. Dry layers (dry intrusions) can inhibit deep convection through the effect of a thermal inversion resulting from radiation and due to the reduction in buoyancy resulting from entrainment. Recent observations have shown that the sensitivity of cloud top height to changes in mid-tropospheric humidity can be larger than straightforward “parcel dilution” would lead us to expect. Here, we investigate how aerosol effects on cloud development and microphysics are coupled to the effects of mid-tropospheric dry air. The two effects are coupled because the buoyancy loss through entrainment depends on droplet evaporation, so is controlled both by the environmental humidity and by droplet sizes, which are, in turn, controlled in part by the aerosol size distribution. Previous studies have not taken these microphysical effects into account. Cloud development and microphysics are examined using a 2-D non-hydrostatic cloud model with a detailed treatment of aerosol, drop, and ice-phase hydrometeor size spectra. A moderately deep mixed-phase convective cloud that developed over the High Plains of the United States is simulated. We find that a dry layer in the mid troposphere leads to a reduction in cloud updraft strength, droplet number, liquid water content and ice mass above the layer. The effect of the dry layer on these cloud properties is greatly enhanced under elevated aerosol conditions. In an environment with doubled aerosol number (but still realistic for continental conditions) the dry layer has about a three-times larger effect on cloud drop number and 50% greater effect on ice mass compared to an environment with lower aerosol. In the case with high aerosol loading, the dry layer stops convective development for over 10 min, and the maximum cloud top height reached is lower. However, the effect of the dry layer on cloud vertical development is significantly reduced when aerosol concentrations are lower. The coupled effect of mid-tropospheric dry air and aerosol on convective development is an additional way in which long term changes in aerosol may impact planetary cloud processes and climate. Full article
(This article belongs to the Special Issue Feature Papers)
Open AccessArticle Cross-Comparison of MODIS and CloudSat Data as a Tool to Validate Local Cloud Cover Masks
Atmosphere 2011, 2(3), 242-255; doi:10.3390/atmos2030242
Received: 1 June 2011 / Revised: 26 June 2011 / Accepted: 5 July 2011 / Published: 22 July 2011
Cited by 3 | PDF Full-text (3399 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a cross-comparison of the data acquired by the MODIS and CloudSat sensors in order to understand the limit of the developed cloud-mask algorithm and to provide a quantitative validation assessment of cloud masks by using exclusively remotely sensed data. [...] Read more.
This paper presents a cross-comparison of the data acquired by the MODIS and CloudSat sensors in order to understand the limit of the developed cloud-mask algorithm and to provide a quantitative validation assessment of cloud masks by using exclusively remotely sensed data. The analysis has been carried out by comparing both the intermediate levels of the cloud mask such as the brightness temperatures and the reflectance values for different channels, and the cloud mask itself with the cloud profiles as measured by the CloudSat sensor. The comparison between MODIS cloud tests and the CloudSat profiles indicates an agreement with hit rates (H) and Hanssen-Kuiper Skill Score (KSS) varying between 0.7 and 1.0 and 0.4 and 1.0, respectively. In this case, the low values of H and KSS are found due to the limitation of CloudSat to detect low clouds. The comparison between the cloud mask and the CloudSat profile determines H and KSS values between 0.6 and 1, except for one case. The CloudSat profile has also been compared with the Standard MODIS cloud mask in order to understand the improvement obtained in the use of local adapted thresholds. A comparison of MODIS and CALIPSO data is also presented. Full article
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Open AccessArticle Nitrogen Isotope Fractionation and Origin of Ammonia Nitrogen Volatilized from Cattle Manure in Simulated Storage
Atmosphere 2011, 2(3), 256-270; doi:10.3390/atmos2030256
Received: 30 June 2011 / Revised: 20 July 2011 / Accepted: 25 July 2011 / Published: 2 August 2011
Cited by 14 | PDF Full-text (418 KB) | HTML Full-text | XML Full-text
Abstract
A series of laboratory experiments were conducted to establish the relationship between nitrogen (N) isotope composition of cattle manure and ammonia emissions, potential contribution of nitrogenous gases other than ammonia to manure N volatilization losses, and to determine the relative contribution of [...] Read more.
A series of laboratory experiments were conducted to establish the relationship between nitrogen (N) isotope composition of cattle manure and ammonia emissions, potential contribution of nitrogenous gases other than ammonia to manure N volatilization losses, and to determine the relative contribution of urinary- vs. fecal-N to ammonia emissions during the initial stage of manure storage. Data confirmed that ammonia volatilization losses from manure are most intensive during the first 2 to 3 days of storage and this coincides with a very rapid loss (hydrolysis) of urinary urea. Long-term (30 days) monitoring of δ15N of manure and emitted ammonia indicated that the dynamics of N isotope fractionation may be complicating the usefulness of the isotope approach as a tool for estimating ammonia emissions from manure in field conditions. The relationship between δ15N of manure and ammonia emission appears to be linear during the initial stages of manure storage (when most of the ammonia losses occur) and should be further investigated. These experiments demonstrated that the main source of ammonia-N volatilized from cattle manure during the initial 10 days of storage is urinary-N, representing on average 90% of the emitted ammonia-N. The contribution of fecal-N was relatively low, but gradually increased to about 10% by day 10. There appears to be substantial emissions of nitrogenous gases other than ammonia, most likely dinitrogen gas, which may account for up to 25% of N losses during the first 20 days of manure storage. This finding, which has to be confirmed in laboratory and field conditions, may be indicative of overestimation of ammonia emissions from cattle operations by the current emissions factors. Full article
(This article belongs to the Special Issue Atmospheric Emissions from Agricultural Practices)
Open AccessArticle Greenhouse Gas Emissions from Ground Level Area Sources in Dairy and Cattle Feedyard Operations
Atmosphere 2011, 2(3), 303-329; doi:10.3390/atmos2030303
Received: 13 March 2011 / Revised: 2 August 2011 / Accepted: 4 August 2011 / Published: 9 August 2011
Cited by 13 | PDF Full-text (1339 KB) | HTML Full-text | XML Full-text
Abstract
A protocol that consisted of an isolation flux chamber and a portable gas chromatograph was used to directly quantify greenhouse gas (GHG) emissions at a dairy and a feedyard operation in the Texas Panhandle. Field sampling campaigns were performed 5 consecutive days [...] Read more.
A protocol that consisted of an isolation flux chamber and a portable gas chromatograph was used to directly quantify greenhouse gas (GHG) emissions at a dairy and a feedyard operation in the Texas Panhandle. Field sampling campaigns were performed 5 consecutive days only during daylight hours from 9:00 am to 7:00 pm each day. The objective of this research was to quantify and compare GHG emission rates (ERs) from ground level area sources (GLAS) at dairy and cattle feedyard operations during the summer. A total of 74 air samples using flux chamber were collected from the barn (manure lane and bedding area), loafing pen, open lot, settling basin, lagoons, and compost pile within the dairy operation. For the cattle feedyard, a total of 87 air samples were collected from four corner pens of a large feedlot, runoff holding pond, and compost pile. Three primary GHGs (methane, carbon dioxide, and nitrous oxide) were measured and quantified from both operations. The aggregate estimated ERs for CH4, CO2, and N2O were 836, 5573, 3.4 g hd−1 d−1 (collectively 27.5 kg carbon dioxide equivalent (CO2e) hd−1 d−1), respectively, at the dairy operation. The aggregate ERs for CH4, CO2, and N2O were 3.8, 1399, 0.68 g hd−1 d−1 (1.7 kg CO2e hd−1 d−1), respectively, from the feedyard. The estimated USEPA GHG ERs were about 13.2 and 1.16 kg CO2e hd−1 d−1, respectively, for dairy and feedyard operations. Aggregate CH4, CO2 and N2O ERs at the dairy facility were about 219, 4 and 5 times higher, respectively, than those at the feedyard. At the dairy, average CH4 ERs estimated from the settling basin, primary and secondary lagoons were significantly higher than those from the other GLAS, contributing about 98% of the aggregate CH4 emission. The runoff holding pond and pen surface of the feedyard contributed about 99% of the aggregate CH4 emission. Average CO2 and N2O ERs estimated from the pen surface area were significantly higher than those estimated from the compost pile and runoff pond. The pen surface alone contributed about 93% and 84% of the aggregate CO2 and N2O emission, respectively. Abatement and management practices that address GHG emissions from these sources will likely be most effective for reducing facility emissions. Full article
(This article belongs to the Special Issue Atmospheric Emissions from Agricultural Practices)
Open AccessArticle Climate Variability and Its Impact on Forest Hydrology on South Carolina Coastal Plain, USA
Atmosphere 2011, 2(3), 330-357; doi:10.3390/atmos2030330
Received: 1 March 2011 / Revised: 26 July 2011 / Accepted: 9 August 2011 / Published: 16 August 2011
Cited by 7 | PDF Full-text (1934 KB) | HTML Full-text | XML Full-text
Abstract
Understanding the changes in hydrology of coastal forested wetlands induced by climate change is fundamental for developing strategies to sustain their functions and services. This study examined 60 years of climatic observations and 30 years of hydrological data, collected at the Santee [...] Read more.
Understanding the changes in hydrology of coastal forested wetlands induced by climate change is fundamental for developing strategies to sustain their functions and services. This study examined 60 years of climatic observations and 30 years of hydrological data, collected at the Santee Experimental Forest (SEF) in coastal South Carolina. We also applied a physically-based, distributed hydrological model (MIKE SHE) to better understand the hydrological responses to the observed climate variability. The results from both observation and simulation for the paired forested watershed systems indicated that the forest hydrology was highly susceptible to change due to climate change. The stream flow and water table depth was substantially altered with a change in precipitation. Both flow and water table level decreased with a rise in temperature. The results also showed that hurricanes substantially influenced the forest hydrological patterns for a short time period (several years) as a result of forest damage. Full article
(This article belongs to the Special Issue Regional Climate Change and Variability)
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 [...] 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 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 25 | 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 [...] Read more.
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 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 8 | 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 [...] 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 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 [...] 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 Seasonal Gradient Patterns of Polycyclic Aromatic Hydrocarbons and Particulate Matter Concentrations near a Highway
Atmosphere 2011, 2(3), 533-552; doi:10.3390/atmos2030533
Received: 21 July 2011 / Revised: 5 September 2011 / Accepted: 15 September 2011 / Published: 21 September 2011
Cited by 2 | PDF Full-text (1542 KB) | HTML Full-text | XML Full-text
Abstract
Close proximity to roadways has been associated with higher exposure to traffic-related air pollutants. However, analyses of the effects of season and meteorological parameters on horizontal gradient patterns of traffic-generated air pollutants still need to be elucidated. Our objectives were to: (1) determine [...] Read more.
Close proximity to roadways has been associated with higher exposure to traffic-related air pollutants. However, analyses of the effects of season and meteorological parameters on horizontal gradient patterns of traffic-generated air pollutants still need to be elucidated. Our objectives were to: (1) determine effects of season on horizontal gradient patterns of polycyclic aromatic hydrocarbons (PAHs), total suspended particles (TSP), and PM2.5 near a heavily trafficked highway; and (2) examine the effect of day-of-the-week variations (weekday versus weekend) associated with traffic counts on measured airborne-contaminant levels. PAHs (Σ8PAHs [MW 228–278]; gas + particulate), TSP and PM2.5 were monitored at nominal distances (50, 100, and 150 m) from the New Jersey Turnpike every 6 days for periods of 24 h, between September 2007 and September 2008. Seasonal variations in the horizontal gradient patterns of Σ8PAHs were observed. In the summer, Σ8PAHs declined significantly between 50–100 m from the highway (23% decrease), but not between the furthermost distances (100–150 m). An inverse pattern was observed in the winter: Σ8PAHs declined between 100–150 m (26% decrease), but not between the closest distances. Σ8PAHs and TSP, but not PM2.5, concentrations measured on weekends were 12–37% lower than those on weekdays, respectively, corresponding to lower diesel traffic volume. This study suggests that people living in the close proximity to highways may be exposed to varying levels of Σ8PAHs, TSP, and PM2.5 depending on distance to highway, season, and day-of-the-week variations. Full article
Open AccessArticle Measuring Trace Gas Emission from Multi-Distributed Sources Using Vertical Radial Plume Mapping (VRPM) and Backward Lagrangian Stochastic (bLS) Techniques
Atmosphere 2011, 2(3), 553-566; doi:10.3390/atmos2030553
Received: 1 July 2011 / Revised: 26 August 2011 / Accepted: 8 September 2011 / Published: 23 September 2011
Cited by 10 | PDF Full-text (264 KB) | HTML Full-text | XML Full-text
Abstract
Two micrometeorological techniques for measuring trace gas emission rates from distributed area sources were evaluated using a variety of synthetic area sources. The vertical radial plume mapping (VRPM) and the backward Lagrangian stochastic (bLS) techniques with an open-path optical spectroscopic sensor were [...] Read more.
Two micrometeorological techniques for measuring trace gas emission rates from distributed area sources were evaluated using a variety of synthetic area sources. The vertical radial plume mapping (VRPM) and the backward Lagrangian stochastic (bLS) techniques with an open-path optical spectroscopic sensor were evaluated for relative accuracy for multiple emission-source and sensor configurations. The relative accuracy was calculated by dividing the measured emission rate by the actual emission rate; thus, a relative accuracy of 1.0 represents a perfect measure. For a single area emission source, the VRPM technique yielded a somewhat high relative accuracy of 1.38 ± 0.28. The bLS technique resulted in a relative accuracy close to unity, 0.98 ± 0.24. Relative accuracies for dual source emissions for the VRPM and bLS techniques were somewhat similar to single source emissions, 1.23 ± 0.17 and 0.94 ± 0.24, respectively. When the bLS technique was used with vertical point concentrations, the relative accuracy was unacceptably low. Full article
(This article belongs to the Special Issue Atmospheric Emissions from Agricultural Practices)
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Review

Jump to: Research

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 24 | 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 [...] 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)
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 10 | 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 [...] 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 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 [...] 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 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 [...] 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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