An empirical relationship between PM(2.5) and aerosol optical depth in Delhi Metropolitan

Atmospheric remote sensing offers a unique opportunity to compute indirect estimates of air quality, which are critically important for the management and surveillance of air quality in megacities of developing countries, particularly in India and China, which have experienced elevated concentration of air pollution but lack adequate spatial-temporal coverage of air pollution monitoring. This article examines the relationship between aerosol optical depth (AOD) estimated from satellite data at 5 km spatial resolution and the mass of fine particles ≤2.5 μm in aerodynamic diameter (PM(2.5)) monitored on the ground in Delhi Metropolitan where a series of environmental laws have been instituted in recent years.PM(2.5) monitored at 113 sites were collocated by time and space with the AOD computed using the data from Moderate Resolution Imaging Spectroradiometer (MODIS onboard the Terra satellite). MODIS data were acquired from NASA's Goddard Space Flight Center Earth Sciences Distributed Active Archive Center (DAAC). Our analysis shows a significant positive association between AOD and PM(2.5). After controlling for weather conditions, a 1% change in AOD explains 0.52±0.202% and 0.39±0.15% change in PM(2.5) monitored within ±45 and 150 min intervals of AOD data. This relationship will be used to estimate air quality surface for previous years, which will allow us to examine the time-space dynamics of air pollution in Delhi following recent air quality regulations, and to assess exposure to air pollution before and after the regulations and its impact on health.     

Simulation of aerosol direct radiative forcing with RAMS-CMAQ in East Asia

The air quality modeling system RAMS-CMAQ is developed to assess aerosol direct radiative forcing by linking simulated meteorological parameters and aerosol mass concentration with the aerosol optical properties/radiative transfer module in this study. The module is capable of accounting for important factors that affect aerosol optical properties and radiative effect, such as incident wave length, aerosol size distribution, water uptake, and internal mixture. Subsequently, the modeling system is applied to simulate the temporal and spatial variations in mass burden, optical properties, and direct radiative forcing of diverse aerosols, including sulfate, nitrate, ammonium, black carbon, organic carbon, dust, and sea salt over East Asia throughout 2005. Model performance is fully evaluated using various observational data, including satellite monitoring of MODIS and surface measurements of EANET (Acid Deposition Monitoring Network), AERONET (Aerosol Robotic Network), and CSHNET (Chinese Sun Hazemeter Network). The correlation coefficients of the comparisons of daily average mass concentrations of sulfate, PM2.5, and PM10 between simulations and EANET measurements are 0.70, 0.61, and 0.64, respectively. It is also determined that the modeled aerosol optical depth (AOD) is in congruence with the observed results from the AERONET, the CSHNET, and the MODIS. The model results suggest that the high AOD values ranging from 0.8 to 1.2 are mainly distributed over the Sichuan Basin as well as over central and southeastern China, in East Asia. The aerosol direct radiative forcing patterns generally followed the AOD patterns. The strongest forcing effect ranging from −12 to −8 W m⁻² was mainly distributed over the Sichuan Basin and the eastern China’s coastal regions in the all-sky case at TOA, and the forcing effect ranging from −8 to −4 W m⁻² could be found over entire eastern China, Korea, Japan, East China Sea, and the sea areas of Japan     

Estimating PM2.5 over southern Sweden using space-borne optical measurements

In the present study Bremen aerosol retrieval (BAER) columnar aerosol optical thickness (AOT) data, according to moderate resolution imaging spectroradiometer (MODIS) and medium resolution imaging sensor (MERIS) level 1 calibrated satellite data, have been compared with AOT data obtained with the MODIS and MERIS retrieval algorithms (NASA and ESA, respectively) and by AErosol RObotic NETwork (AERONET). Relatively good agreement is found between these different instruments and algorithms. The R² and relative RMSD were 0.86 and 31% for MODIS when comparing with AERONET and 0.92 and 21% for MERIS. The aerosols investigated were influenced by low relative humidity. During this period, a relatively large range of aerosol loadings were detected; from continental background aerosol to particles emitted from agricultural fires. In this study, empirical relationships between BAER columnar AOT and ground-measured PM_2.5 have been estimated. Linear relationships, with R² values of 0.58 and 0.59, were obtained according to MERIS and MODIS data, respectively. The slopes of the regression of AOT versus PM_2.5 are lower than previous studies, but this could easily be explained by considering the effect of hygroscopic growth. The present AOT–PM_2.5 relationship has been applied on MERIS full resolution data over the urban area of Stockholm and the results have been compared with particle mass concentrations from dispersion model calculations. It seems that the satellite data with the 300 m resolution can resolve the expected increased concentrations due to emissions along the main highways close to the city. Significant uncertainties in the spatial distribution of PM_2.5 across land/ocean boundaries were particularly evident when analyzing the high resolution satellite data.     

Implementation of a windblown dust parameterization into MODELS-3/CMAQ: Application to episodic PM events in the US/Mexico border

Windblown dust is known to impede visibility, deteriorate air quality and modify the radiation budget. Arid and semiarid areas with unpaved and unvegetated land cover are particularly prone to windblown dust, which is often attributed to high particulate matter (PM) pollution in such areas. Yet, windblown dust is poorly represented in existing regulatory air quality models. In a study by the authors on modeling episodic high PM events along the US/Mexico border using the state-of-the-art CMAQ/MM5/SMOKE air quality modeling system [Choi, Y.-J., Hyde, P., Fernando, H.J.S., 2006. Modeling of episodic particulate matter events using a 3D air quality model with fine grid: applications to a pair of cities in the US/Mexico border. Atmospheric Environment 40, 5181–5201], some of the observed PM₁₀ NAAQS exceedances were inferred as due to windblown dust, but the modeling system was incapable of dealing with time-dependent episodic dust entrainment during high wind periods. In this paper, a time-dependent entrainment parameterization for windblown dust is implemented in the CMAQ/MM5/SMOKE modeling system with the hope of improving PM predictions. An approach for realizing windblown dust emission flux for each grid cell over the study domain on an hourly basis, which accounts for the influence of factors such as soil moisture content, atmospheric stability and wind speed, is presented in detail. Comparison of model predictions with observational data taken at a pair of US/Mexico border towns shows a clear improvement of model performance upon implementation of the dust emission flux parameterization.     

Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD

Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R ² is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m³ for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R ² and RMSE are 0.81 and 27.46 μg/m³, respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.     

Comparison of regional and global land cover products and the implications for biogenic emission modeling

Accurate estimates of biogenic emissions are required for air quality models that support the development of air quality management plans and attainment demonstrations. Land cover characterization is an essential driving input for most biogenic emissions models. This work contrasted the global Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product against a regional land cover product developed for the Texas Commissions on Environmental Quality (TCEQ) over four climate regions in eastern Texas, where biogenic emissions comprise a large fraction of the total inventory of volatile organic compounds (VOCs) and land cover is highly diverse. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) was utilized to investigate the influences of land cover characterization on modeled isoprene and monoterpene emissions through changes in the standard emission potential and emission activity factor, both separately and simultaneously. In Central Texas, forest coverage was significantly lower in the MODIS land cover product relative to the TCEQ data, which resulted in substantially lower estimates of isoprene and monoterpene emissions by as much as 90%. Differences in predicted isoprene and monoterpene emissions associated with variability in land cover characterization were primarily caused by differences in the standard emission potential, which is dependent on plant functional type. Photochemical modeling was conducted to investigate the effects of differences in estimated biogenic emissions associated with land cover characterization on predicted ozone concentrations using the Comprehensive Air Quality Model with Extensions (CAMx). Mean differences in maximum daily average 8-hour (MDA8) ozone concentrations were 2 to 6 ppb with maximum differences exceeding 20 ppb. Continued focus should be on reducing uncertainties in the representation of land cover through field validation.

Implications: Uncertainties in the estimation of biogenic emissions associated with the characterization of land cover in global and regional data products were examined in eastern Texas. Misclassification between trees and low-growing vegetation in central Texas resulted in substantial differences in isoprene and monoterpene emission estimates and predicted ground-level ozone concentrations. Results from this study indicate the importance of land cover validation at regional scales.     

Estimating fine particulate matter component concentrations and size distributions using satellite-retrieved fractional aerosol optical depth: part 2--a case study

We use the fractional aerosol optical depth (AOD) values derived from Multiangle Imaging Spectroradiometer (MISR) aerosol component measurements, along with aerosol transport model constraints, to estimate ground-level concentrations of fine particulate matter (PM2.5) mass and its major constituents in the continental United States. Regression models using fractional AODs predict PM2.5 mass and sulfate (SO4) concentrations in both the eastern and western United States, and nitrate (NO3) concentrations in the western United States reasonably well, compared with the available ground-level U.S. Environment Protection Agency (EPA) measurements. These models show substantially improved predictive power when compared with similar models using total-column AOD as a single predictor, especially in the western United States. The relative contributions of the MISR aerosol components in these regression models are used to estimate size distributions of EPA PM2.5 species. This method captures the overall shapes of the size distributions of PM2.5 mass and SO4 particles in the east and west, and NO3 particles in the west. However, the estimated PM2.5 and SO4 mode diameters are smaller than those previously reported by monitoring studies conducted at ground level. This is likely due to the satellite sampling bias caused by the inability to retrieve aerosols through cloud cover, and the impact of particle hygroscopicity on measured particle size distributions at ground level.     

PM source identification at Sunland Park, New Mexico, using a simple heuristic meteorological and chemical analysis

The causes for evening low-wind PM10 and PM2.5 peaks at Sunland Park, NM, were investigated by using wind sector analysis and by assessing relationships between PM loadings and meteorological parameters through canonical ordination analysis. Both PM10 and PM2.5 concentrations during the evening hours accounted for approximately 50% of their respective 24-hr averages, and the PM10 was mainly composed of coarse material (PM10-2.5 amounted to 77% of PM10). A wind sector analysis based on data from three surface meteorological monitoring stations in the region narrowed the potential source region for PM10 and PM2.5 to an area within a few kilometers south of Sunland Park. Canonical ordination analysis confirmed that the peak frequently occurred under stable conditions with weak southerly winds. Chemical analyses of PM showed that elemental and organic carbon (EC and OC, respectively) dominate PM2.5 and inorganic elements dominate PM10-2.5. The combined data for EC/OC, geologic elements, and various trace elements indicate that under low wind and stable conditions, traffic-related PM emissions (motor vehicle exhausts and re-suspended road dust) from the south of the site are the most likely sources for the evening PM10 and PM2.5 peaks.     

Measurement, analysis, and modeling of fine particulate matter in eastern North Carolina

An analysis of fine particulate data in eastern North Carolina was conducted to investigate the impact of the hog industry and its emissions of ammonia into the atmosphere. The fine particulate data are simulated using ISORROPIA, an equilibrium thermodynamic model that simulates the gas and aerosol equilibrium of inorganic atmospheric species. The observational data analyses show that the major constituents of fine particulate matter (PM2.5) are organic carbon, elemental carbon, sulfate, nitrate, and ammonium. The observed PM2.5 concentration is positively correlated with temperature but anticorrelated with wind speed. The correlation between PM2.5 and wind direction at some locations suggests an impact of ammonia emissions from hog facilities on PM2.5 formation. The modeled results are in good agreement with observations, with slightly better agreement at urban sites than at rural sites. The predicted total inorganic particulate matter (PM) concentrations are within 5% of the observed values under conditions with median initial total PM species concentrations, median relative humidity (RH), and median temperature. Ambient conditions with high PM precursor concentrations, low temperature, and high RH appear to favor the formation of secondary PM.     

Behavior of particulate matter during high concentration episodes in Seoul

The behavior of particulate matter (PM) during high-concentration episodes was investigated using monitoring data from Guui station, a comprehensive air monitoring station in Seoul, Korea, from January 2008 to March 2010. Five non-Asian dust (ND) episodes and two Asian dust (AD) episodes of high PM concentrations were selected for the study. During the ND episode, primary air pollutants accumulated due to low wind speeds, and PM_2.5 increased along with most other air pollutants. Particles larger than PM_2.5 were also high since these particles were generated by vehicular traffic rather than wind erosion. During strong AD episodes, PM_10–2.5 primarily increased and gaseous primary air pollutants decreased under high wind speeds. However, even during the AD episode, PM_2.5 and gaseous primary air pollutants increased when the effects of AD were weak and wind speeds were low. This study corroborates that accumulation of air pollutants due to a drop in surface wind speed plays an important role in short-term high-concentration occurrences. However, low wind speeds could not be directly linked to local emissions because a significant portion of accumulated air pollutants resulted from long-range transport.     

Analysis of regional and temporal characteristics of PM10 during an Asian dust episode in Korea

This study analyzes the regional and temporal distributions of PM10 concentrations observed in major metropolitan cities in Korea before, during and after a recent Asian dust episode in 2002. There were spatial and temporal variations in PM10 concentrations among the mid-western, the southwestern, the southeastern, and the southern parts of Korea during this Asian dust period due to the different air mass movement time and the different wind directions and speeds of prevailing winds in each city or region. The origins of the three-day Asian dust episode were identified by an analysis of two-day backward isentropic air trajectories. The different origins for each day also significantly contributed to the spatial and temporal variations in PM10 concentrations. A significant relationship was found between PM10 concentrations on the day preceding the first peak day and the first peak day of the Asian dust period but only in the mid-western areas. The concentrations of PM10 just after the Asian dust episode were much higher than those just before. There was a significant increase in a coarse fraction, having soil origins, of particles during the Asian dust episode. Concentrations of Mn, Fe, Ni and Cr extracted from the total suspended particulate (TSP) samples collected in 7 cities during the Asian dust episode were much higher when compared with other days in 2001. However, the Asian dust did not consistently increase the concentrations of lead, cadmium and copper as they are influenced by local sources such as local traffic or industrial emissions.     

A land use database and examples of biogenic isoprene emission estimates for the state of Texas, USA

Using data from a variety of sources, land use and vegetation in Texas were mapped with a spatial resolution of approximately 1 km. Over 600 classifications were used to characterize the land use and land cover throughout the state and field surveys were performed to assign leaf biomass densities, by species, to the land cover classifications. The total leaf biomass densities associated with these land use classifications ranged from 0 to 556 g/m², with the highest assigned total and oak leaf biomass densities located in central and eastern Texas. The land cover data were used as input to a biogenic emissions model, GLOBEIS2. Estimates of biogenic emissions of isoprene based on GLOBEIS2 and the new land cover data showed significant differences when compared to biogenic isoprene emissions estimated using previous land cover data and emission estimation procedures. For example, for one typical domain in eastern Texas, total daily isoprene emissions increased by 38% with the new modeling tools. These results may ultimately affect the way in which ozone and other photochemical pollutants are modeled and evaluated in the state of Texas.     

区域大气环境中PM_(2.5)/PM_(10)空间分布研究

提出了一种利用移动监测技术研究区域大气环境中PM2.5/PM10空间分布的方法,并在2004年12月进行了宁波市全市域PM2.5/PM10空间分布的研究.数据显示:相同路径所代表的地区PM2.5和PM10具有很好的相关性,多数路径上PM2.5与PM10数据的相关系数平方在0.95以上,而不同路径上PM2.5与PM10的比值不同.文中给出了宁波市PM2.5/PM10污染的空间分布图,直观地显示出PM2.5/PM10污染的空间分布情况,突出了污染的重点点位和地区.     

Spatial and temporal variability in urban fine particulate matter concentrations

Identification of hot spots for urban fine particulate matter (PM(2.5)) concentrations is complicated by the significant contributions from regional atmospheric transport and the dependence of spatial and temporal variability on averaging time. We focus on PM(2.5) patterns in New York City, which includes significant local sources, street canyons, and upwind contributions to concentrations. A literature synthesis demonstrates that long-term (e.g., one-year) average PM(2.5) concentrations at a small number of widely-distributed monitoring sites would not show substantial variability, whereas short-term (e.g., 1-h) average measurements with high spatial density would show significant variability. Statistical analyses of ambient monitoring data as a function of wind speed and direction reinforce the significance of regional transport but show evidence of local contributions. We conclude that current monitor siting may not adequately capture PM(2.5) variability in an urban area, especially in a mega-city, reinforcing the necessity of dispersion modeling and methods for analyzing high-resolution monitoring observations.     

Road dust resuspension in the vicinity of limestone quarries in Jordan

Many areas in Jordan suffer from elevated levels of coarse particulate matter (PM10). One potentially significant source of the observed PM is the resuspension of road dust in the vicinity of limestone quarries. To obtain data to assess the impact from this source, PM10 road dust resuspension factors near Abusiiah, a town to the north east of Amman surrounded by many quarries and brick factories, were measured. Measurements included PM10 mass, particle size distributions, wind speed, and wind direction. The results showed that PM10 concentrations could be as high as 600 microg/m3, and most of the airborne PM is in the coarse fraction. Loading trucks play a major role in resuspending road dust, with an observed PM10 emission rate of >6000 mg/km.     

区域大气环境中PM2．5／PM10空间分布研究

提出了一种利用移动监测技术研究区域大气环境中PM2.5／PM10空间分布的方法，并在2004年12月进行了宁波市全市域PM2.5／PM10空间分布的研究。数据显示：相同路径所代表的地区PM2.5和PM10具有很好的相关性，多数路径上PM2.5与PM10数据的相关系数平方在0．95以上，而不同路径上PM2.5与PM10的比值不同。文中给出了宁波市PM2.5／PM10污染的空间分布图，直观地显示出PM2.5／PM10污染的空间分布情况，突出了污染的重点点位和地区。     

Effects of wind direction on coarse and fine particulate matter concentrations in southeast Kansas

Field data for coarse particulate matter ([PM] PM10) and fine particulate matter (PM2.5) were collected at selected sites in Southeast Kansas from March 1999 to October 2000, using portable MiniVol particulate samplers. The purpose was to assess the influence on air quality of four industrial facilities that burn hazardous waste in the area located in the communities of Chanute, Independence, Fredonia, and Coffeyville. Both spatial and temporal variation were observed in the data. Variation because of sampling site was found to be statistically significant for PM10 but not for PM2.5. PM10 concentrations were typically slightly higher at sites located within the four study communities than at background sites. Sampling sites were located north and south of the four targeted sources to provide upwind and downwind monitoring pairs. No statistically significant differences were found between upwind and downwind samples for either PM10 or PM2.5, indicating that the targeted sources did not contribute significantly to PM concentrations. Wind direction can frequently contribute to temporal variation in air pollutant concentrations and was investigated in this study. Sampling days were divided into four classifications: predominantly south winds, predominantly north winds, calm/variable winds, and winds from other directions. The effect of wind direction was found to be statistically significant for both PM10 and PM2.5. For both size ranges, PM concentrations were typically highest on days with predominantly south winds; days with calm/variable winds generally produced higher concentrations than did those with predominantly north winds or those with winds from "other" directions. The significant effect of wind direction suggests that regional sources may exert a large influence on PM concentrations in the area.     

Estimation of wind blown dust emissions in Europe and its vicinity

The assessment of the wind blown dust emission for Europe and selected regions of North Africa and Southwest Asia was carried out using a mesoscale model. The mesoscale model was parameterized based on the current literature review. The model provides data on PM₁₀ emission from several dust reservoirs (anthropogenic, agriculture, semi- and natural) with spatial resolution of 10 × 10 km and temporal resolution of 1 h. The spatial variability of PM₁₀ emission depends on soil texture, land cover/land use as well as meteorological conditions. Lands covered with water or permanently wet were excluded from the model. The land covered with vegetation is treated as dust reservoir whose dust emission capacity depends on the type of vegetation and cover. The dust reservoirs are divided into reservoirs with stable and unstable surface. The changes of emission in time depend on meteorological parameters.The wind blown dust emission should be treated as a non-continuous spatio-temporal process. The emissions are estimated with high uncertainty. The estimated PM₁₀ yearly total load emitted by wind from the European territory is highly differentiated in space and time and is equal to 0.74 Tg. The total load of PM₁₀ emitted by wind from North African and Southwest Asian land surface located in the vicinity of European boundaries is assessed as nearly 50% (0.43 Tg) of the total load estimated for the whole Europe.The average yearly PM₁₀ emission factor for Europe was estimated at 0.139 Mg km^?2.The PM₁₀ emission from agricultural areas is estimated at 52% of the total wind blown emission from the domain of the European Union project “Improving and applying methods for the calculation of natural and biogenic emissions and assessment of impacts to the air quality” - NatAir.PM₁₀ emission factor for natural areas of Europe is estimated at 0.021 Mg km^?2. Appropriate factors for agricultural areas and anthropogenic areas are 0.157 Mg km^?2 and 0.118 Mg km^?2, respectively. The latter two factors are probably underestimated due to omitting in the model of other dust emission mechanisms than aeolian erosion.     

Application of a combined measurement and modeling method to quantify windblown dust emissions from the exposed playa at Mono Lake, California

Particulate matter < or =10 microm (PM10) emissions due to wind erosion can vary dramatically with changing surface conditions. Crust formation, mechanical disturbance, soil texture, moisture, and chemical content of the soil can affect the amount of dust emitted during a wind event. A refined method of quantifying windblown dust emissions was applied at Mono Lake, CA, to account for changing surface conditions. This method used a combination of real-time sand flux monitoring, ambient PM10 monitoring, and dispersion modeling to estimate dust emissions and their downwind impact. The method identified periods with high emissions and periods when the surface was stable (no sand flux), even though winds may have been high. A network of 25 Cox sand catchers (CSCs) was used to measure the mass of saltating particles to estimate sand flux rates across a 2-km2 area. Two electronic sensors (Sensits) were used to time-resolve the CSC sand mass to estimate hourly sand flux rates, and a perimeter tapered element oscillating microbalance (TEOM) monitor measured hourly PM10 concentrations. Hourly sand flux rates were related by dispersion modeling to hourly PM10 concentrations to back-calculate the ratio of vertical PM10 flux to horizontal sand flux (K-factors). Geometric mean K-factor values (K(f)) were found to change seasonally, ranging from 1.3 x 10(-5) to 5.1 x 10(-5) for sand flux measured at 15 cm above the surface (q15). Hourly PM10 emissions, F, were calculated by applying seasonal K-factors to sand flux measurements (F = K(f) x q15). The maximum hourly PM10 emission rate from the study area was 76 g/m2 x hr (10-m wind speed = 23.5 m/sec). Maximum daily PM10 emissions were estimated at 450 g/m2 x day, and annual emissions at 1095 g/m2 x yr. Hourly PM10 emissions were used by the U.S. Environmental Protection Agency (EPA) guideline AERMOD dispersion model to estimate downwind ambient impacts. Model predictions compared well with monitor concentrations, with hourly PM10 ranging from 16 to over 60,000 microg/m3 (slope = 0.89, R2 = 0.77).     

Comparison of a two-dimensional numerical dust transport model with experimental dust emissions from soil surfaces in a wind tunnel

Near-surface wind-tunnel fugitive dust concentration profiles arising from soil surfaces beds were compared to a finite difference numerical dust transport model. Comparisons of the type shown in this study were previously non-existent in the literature due to the lack of experimental wind-tunnel data for near-surface concentrations over a soil bed. However, in a previous study by the authors, near-surface steady-state concentration profiles were measured in order to obtain fugitive dust emission rates, thus allowing the comparison to models shown in this paper. The novel aspects of the current study include: comparison of concentration profiles of dust obtained experimentally in the wind tunnel with those calculated numerically; comparison of the calculated numerical fetch effect on dust emissions with that obtained in the wind tunnel; and comparison of the emission rates calculated numerically with those obtained experimentally in the wind tunnel. Initial comparisons with the model indicate good agreement implying that the physical mechanism of advection–diffusion is reasonably modeled with the choice of equations for the simple “steady-state” process near the surface. Furthermore, the numerical solutions presented in this paper provide a means to systematically explore the relative impact of varied surface boundary conditions upon the emission process and provide a potential link between wind-tunnel simulations and field scale models.