Air Quality Modeling Studies Research Articles

SANAYİ KAYNAKLI KARBONMONOKSİT SALINIMLARININ AERMOD DAĞILIM MODELİ İLE İNCELENMESİ

This study is the first air quality modeling study for downtown Balikesir where intense air pollution levels are experienced during the winter seasons. The possible effects of atmospheric carbonmonoxide (CO) emissions from an industrial operation in downtown Balikesir were examined by taking into consideration the existing meteorological and surface topography characteristics and the maps showing the distributions of the ground level concentrations for CO releases were created by using the AERMOD.According to the results, the prevailing wind direction was determined as North (N) and North-East (NE) for Balikesir province, and the maximum daily 8-hour means of CO concentration emitted from the source was estimated as 34,01 μg.m-3 for the modeling study. For this reason, it has been determined that the modeled CO concentrations do not exceed the legal limits and there is no adverse effect on the downtown area. In addition, the fact that, there was a full-scaled hospital that was located nearby the source. The location of hospital complex made the air quality modeling study more significant. According to the modeling result, the maximum daily 8-hour means of CO concentration released from the source was estimated to be about 10 μg.m-3 for the hospital area. It has also been determined that the modeled value for hospital does not exceed the legal limits as states in the regulation. Within the scope of the study, the obtained results are also important due to contribution to raising public awareness and also provide suggestions to local authority in decision-making process.

A Review of Air Quality Modeling Studies in India: Local and Regional Scale

Developing countries like India require proper control strategies for reducing the enormous premature mortality associated with air pollution. Air quality models, in addition to helping to understand the severity of air pollution by providing the pollutant concentrations, also give knowledge of the sources. Previous local and regional air quality modeling studies carried out in India are reviewed in this current study with a goal of understanding the current gaps and exploring future directions. Studies carried out in different parts of India during past decade were precisely documented in this study using methodical Scopus, Web of Science, and Google searches. Majority of the air quality studies are concentrated in megacities leaving behind the small cities which require greater attention in future. While most of the modeling studies were carried out in northern India, very few studies concentrated on central region of the country. Review of both local and regional numerical models showed the need for better emission inputs, while the statistical models inferred the need for proper selection of key tracers for source allocation. Irrespective of emission inventory and models used, particulate matter concentrations are under predicted in Delhi, which faces huge air pollution-related issues. Dust and traffic emissions are the major sources of particulate matter in India.

Development of an Emissions Processing System for Climate Scenario Inventories to Support Global and Asian Air Quality Modeling Studies

Development of an Emissions Processing System for Climate Scenario Inventories to Support Global and Asian Air Quality Modeling Studies

Spatial inter-comparison of Top-down emission inventories in European urban areas

This paper presents an inter-comparison of the main Top-down emission inventories currently used for air quality modelling studies at the European level. The comparison is developed for eleven European cities and compares the distribution of emissions of NOx, SO2, VOC and PPM2.5 from the road transport, residential combustion and industry sectors. The analysis shows that substantial differences in terms of total emissions, sectorial emission shares and spatial distribution exist between the datasets. The possible reasons in terms of downscaling approaches and choice of spatial proxies are analysed and recommendations are provided for each inventory in order to work towards the harmonisation of spatial downscaling and proxy calibration, in particular for policy purposes. The proposed methodology may be useful for the development of consistent and harmonised European-wide inventories with the aim of reducing the uncertainties in air quality modelling activities.

Global anthropogenic emissions of particulate matter including black carbon

Abstract. This paper presents a comprehensive assessment of historical (1990–2010) global anthropogenic particulate matter (PM) emissions including the consistent and harmonized calculation of mass-based size distribution (PM1, PM2. 5, PM10), as well as primary carbonaceous aerosols including black carbon (BC) and organic carbon (OC). The estimates were developed with the integrated assessment model GAINS, where source- and region-specific technology characteristics are explicitly included. This assessment includes a number of previously unaccounted or often misallocated emission sources, i.e. kerosene lamps, gas flaring, diesel generators, refuse burning; some of them were reported in the past for selected regions or in the context of a particular pollutant or sector but not included as part of a total estimate. Spatially, emissions were calculated for 172 source regions (as well as international shipping), presented for 25 global regions, and allocated to 0.5° × 0.5° longitude–latitude grids. No independent estimates of emissions from forest fires and savannah burning are provided and neither windblown dust nor unpaved roads emissions are included. We estimate that global emissions of PM have not changed significantly between 1990 and 2010, showing a strong decoupling from the global increase in energy consumption and, consequently, CO2 emissions, but there are significantly different regional trends, with a particularly strong increase in East Asia and Africa and a strong decline in Europe, North America, and the Pacific region. This in turn resulted in important changes in the spatial pattern of PM burden, e.g. European, North American, and Pacific contributions to global emissions dropped from nearly 30 % in 1990 to well below 15 % in 2010, while Asia's contribution grew from just over 50 % to nearly two-thirds of the global total in 2010. For all PM species considered, Asian sources represented over 60 % of the global anthropogenic total, and residential combustion was the most important sector, contributing about 60 % for BC and OC, 45 % for PM2. 5, and less than 40 % for PM10, where large combustion sources and industrial processes are equally important. Global anthropogenic emissions of BC were estimated at about 6.6 and 7.2 Tg in 2000 and 2010, respectively, and represent about 15 % of PM2. 5 but for some sources reach nearly 50 %, i.e. for the transport sector. Our global BC numbers are higher than previously published owing primarily to the inclusion of new sources. This PM estimate fills the gap in emission data and emission source characterization required in air quality and climate modelling studies and health impact assessments at a regional and global level, as it includes both carbonaceous and non-carbonaceous constituents of primary particulate matter emissions. The developed emission dataset has been used in several regional and global atmospheric transport and climate model simulations within the ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) project and beyond, serves better parameterization of the global integrated assessment models with respect to representation of black carbon and organic carbon emissions, and built a basis for recently published global particulate number estimates.

Spatial and temporal variability of PM2.5 and PM10 over the North China Plain and the Yangtze River Delta, China

The North China Plain (NCP) and the Yangtze River Delta (YRD) in China have been experiencing severe particulate matter (PM) pollution problems associated with the rapid economic growth and the accelerated urbanization. In this study, hourly mass concentrations of PM2.5 and PM10 during June 1st–August 31st, 2013 were collected in 13 cities located in or adjacent to the NCP region and 20 cities located in the YRD region. The overall average PM2.5 and PM10 concentrations were 77.0 μg/m3 and 136.2 μg/m3 in the NCP region, respectively, and 42.8 μg/m3 and 74.9 μg/m3 in the YRD region, respectively. The frequencies of occurrence of concentrations exceeding the China's Ambient Air Quality Standard (AAQS) (BG3095-12) Grade I standards were 83% for PM2.5 and 93% for PM10 in the NCP region, and 51% for PM2.5 and 66% for PM10 in the YRD region. Strong temporal correlation for both PM2.5 and PM10 between cities within 250 km was frequently observed. PM2.5 was found to be negatively associated with wind speed. On the PM2.5 episode days (when the 24 h PM2.5 concentration is greater than 75 μg/m3), average PM2.5 concentrations were 2–4 times greater compared to the non-episode days. The PM2.5 to PM10 ratio increased from 0.50 (0.57) on the non-episode days to 0.64 (0.64) on the episode days in the NCP (YRD) region. No distinct weekday/weekend difference was observed for PM2.5, PM10, and other gaseous pollutants (CO, SO2, NO2, and O3) in all cities. The results presented in this paper will serve as an important basis for future regional air quality modeling and source apportionment studies.

A novel method for defining hourly background no2and pm10concentrations for use in local air quality modelling studies and comparison to exisiting practises

The accuracy of air quality modelling studies is signifi cantly infl uenced by the values adopted for background concentrations. In the absence of a reliable method of combining modelled and background concentrations, it has been common practice to sum the percentiles or annual means of each contribution to obtain a value for comparison with short-term limit values. This is often not appropriate as in many cases the meteorological conditions producing high concentrations from the source do not correspond to those resulting in high background concentrations. A novel method for predicting variable hourly background NO 2 and PM 10 concentrations based on diurnal and seasonal variations and variation with wind speed and direction has been developed and compared to a baseline method. The variable method has been compared to commonly applied methods such as the annual mean or percentile method. Furthermore, the validity of a number of equations derived in the UK to add background concentrations to modelled stack contributions has been examined for Irish conditions. The equations allow a total percentile concentration to be predicted at a given receptor based on an annual mean background concentration and hourly modelled concentrations. A theoretical line source was modelled using Caline4 and corresponding meteorological data, and the addition equations applied using monitored background NO 2 and PM 10 data. The methods were also tested for a point source, modelled using the Point source Gaussian plume equation. Baseline values were calculated by addition of the relevant hourly or daily background concentration to the modelled concentrations to produce a full year of total hourly or daily concentrations. Percentiles and annual mean values, and corresponding 95% confi dence limits were calculated directly from this data set and concentrations predicted by each method assessed for agreement. The variable method was found to produce the best results for both NO 2 and PM 10 when modelling a point and line source. Of the technical addition equations, the sum of squares method performed best for PM 10 and NO 2 . The annual mean and the percentile methods performed poorly in all instances producing very large under- and overestimations highlighting the importance of this research. It is anticipated that this novel method will produce signifi cant improvements in the overall accuracy of local air quality modelling studies.

EMISSION MODELLING OF HAZARDOUS AIR POLLUTANTS FROM ROAD TRANSPORT AT URBAN SCALE

This study is focused on the development of a modelling approach to quantify emissions of traffic-related hazardous air pollutants in urban areas considering complex road network and detailed data on transport activity. In this work a new version of the Transport Emission Model for line sources has been developed for hazardous pollutants (TREM-HAP). Emission factors for benzene, 1,3-butadiene, formaldehyde, acetaldehyde, acrolein, naphthalene and also particulate matter (PM2.5) were implemented and the model was extended to integrate a probabilistic approach for the uncertainty quantification using Monte-Carlo technique. The methodology has been applied to estimate road traffic emissions in Porto Urban Area, Portugal. Hourly traffic counts provided by an automatic counting system were used to characterise the spatial and temporal variability of the number of vehicles, vehicle categories and average speed at different road segments. The data for two summer and two winter months were processed to obtain probability density functions of the input parameters required for the uncertainty analysis. For quantification of cold start excess emissions, Origin-Destination matrix for daily trips was used as additional input information. Daily emissions of hazardous air pollutants from road traffic were analysed for the study area. The uncertainty of the emission estimates related to the transport activity factors range from as small as −2 to +1.7% for acrolein and acetaldehyde on highways, to as large as −33 to +70% for 1,3-butadiene considering urban street driving. An important contribution of cold start emissions to the total daily values was estimated thus achieving 45% in case of benzene. The uncertainty in transport activity data on resulting urban emission inventory highlights the most important parameter and reveals different sensitivity of the emission quantification to the input data. The methodology presented in this work allows the development of emission inventories for hazardous air pollutants with high spatial and temporal resolution in complex urban areas required for air quality modelling and exposure studies and could be used as a decision support tool.

Development and uncertainty analysis of a high-resolution NH<sub>3</sub> emissions inventory and its implications with precipitation over the Pearl River Delta region, China

Abstract. Detailed NH3 emission inventories are important to understand various atmospheric processes, air quality modeling studies, air pollution management, and related environmental and ecological issues. A high-resolution NH3 emission inventory was developed based on state-of-the-science techniques, up-to-date information, and advanced expert knowledge for the Pearl River Delta region, China. To provide model-ready emissions input, this NH3 emissions inventory was spatially allocated to 3 km × 3 km grid cells using source-based spatial surrogates with geographical information system (GIS) technology. For NH3 emissions, 9 source categories and 45 subcategories were identified in this region, and detailed spatial and temporal characteristics were investigated. Results show that livestock is by far the most important NH3 emission source by contributing about 61.7% of the total NH3 emissions in this region, followed by nitrogen fertilizer applications (~23.7%) and non-agricultural sources (~14.6%). Uncertainty analysis reveals that the uncertainties associated with different sources vary from source to source and the magnitude of the uncertainty associated with a specific source mainly depends on the degree of accuracy of the emission factors and activity data as well as the technique used to perform the estimate. Further studies should give priority to the hog, broiler, goose subsectors of the livestock source and N fertilizer application source in order to reduce uncertainties of ammonia emission estimates in this region. The validity of the NH3 emissions inventory is justified by the trend analysis of local precipitation compositions, such as pH values, the Ca2++NH4+/SO42−+ NO3− ratios, and NH4+ concentrations which are directly or indirectly related to NH3 emissions.

Derivation of an eddy diffusivity coefficient depending on source distance for a shear dominated planetary boundary layer

In this study an integral and an algebraic formulation for the eddy diffusivities in a shear driven planetary boundary layer are derived for pollutant dispersion applications. The expressions depend on the turbulence properties and on the distance from the source. They are based on the turbulent kinetic energy spectra, Taylor’s statistical diffusion theory and measured turbulent characteristics during intense wind events. The good agreement between the algebraic and the integral formulation for the eddy diffusivities corroborate the hypothesis that using an algebraic formulation as a surrogate for the eddy diffusivities in the neutral planetary boundary layer is valid. As a consequence, the vertical eddy diffusivity provided by the algebraic formulation and its asymptotic limit for large time (diffusion time much larger than the Lagrangian integral time scale), were introduced into an analytical air pollution model and validated against data from the classic Prairie Grass project. A statistical analysis, employing specific indices shows that the results are in good agreement with the observations. Furthermore, this study suggests that the inclusion of the memory effect, which is important in regions near to a continuous point source, improves the description of the turbulent transport process of atmospheric contaminants. Therefore, the major finding of this paper is the necessity of including the downwind distance-dependent eddy diffusivity for low continuous point sources in air quality modeling studies.

Effects of improved spatial and temporal modeling of on-road vehicle emissions

Numerous emission and air quality modeling studies have suggested the need to accurately characterize the spatial and temporal variations in on-road vehicle emissions. The purpose of this study was to quantify the impact that using detailed traffic activity data has on emission estimates used to model air quality impacts. The on-road vehicle emissions are estimated by multiplying the vehicle miles traveled (VMT) by the fleet-average emission factors determined by road link and hour of day. Changes in the fraction of VMT from heavy-duty diesel vehicles (HDDVs) can have a significant impact on estimated fleet-average emissions because the emission factors for HDDV nitrogen oxides (NOx) and particulate matter (PM) are much higher than those for light-duty gas vehicles (LDGVs). Through detailed road link-level on-road vehicle emission modeling, this work investigated two scenarios for better characterizing mobile source emissions: (1) improved spatial and temporal variation of vehicle type fractions, and (2) use of Motor Vehicle Emission Simulator (MOVES2010) instead of MOBILE6 exhaust emission factors. Emissions were estimated for the Detroit and Atlanta metropolitan areas for summer and winter episodes. The VMT mix scenario demonstrated the importance of better characterizing HDDV activity by time of day, day of week, and road type. More HDDV activity occurs on restricted access road types on weekdays and at nonpeak times, compared to light-duty vehicles, resulting in 5–15% higher NOx and PM emission rates during the weekdays and 15–40% lower rates on weekend days. Use of MOVES2010 exhaust emission factors resulted in increases of more than 50% in NOx and PM for both HDDVs and LDGVs, relative to MOBILE6. Because LDGV PM emissions have been shown to increase with lower temperatures, the most dramatic increase from MOBILE6 to MOVES2010 emission rates occurred for PM2.5 from LDGVs that increased 500% during colder wintertime conditions found in Detroit, the northernmost city modeled. Implications: Air quality model performance relies partly on on-road mobile source emission inventories accurately allocated, both spatially and temporally. This work demonstrates the importance of characterizing the mix of heavy-duty diesel versus light-duty gasoline vehicle activity on an hourly basis on weekdays and weekends by road type. Incorporating detailed activity data increases weekday average NOx and PM emissions 5–15%, with early morning hour emission increases approaching 100%, compared to using one average vehicle activity mix. Application of the methodologies described in this paper will improve the accuracy of on-road emission inventories in the understanding of ozone photochemistry and PM formation.

Relating Background NO2 Concentrations in Air to Air Mass History Using Non-Parametric Regression Methods: Application at Two Background Sites in Ireland

The concentration of nitrogen dioxide (NO2) in background air varies temporally and spatially and is influenced by meteorological and anthropogenic factors. Background concentrations used in local air quality modelling studies have a significant effect on the accuracy of the overall result and when based on short-term monitoring data, variation in concentrations with air mass history is often unaccounted for. The current paper presents a powerful tool for the quantification and separation of local and regional air mass effects on background air quality. The origin of and the regions traversed by an air mass prior to reaching a receptor has been modelled using HYsplit-4. Trajectories (between 12 and 96 h duration) were defined based on the frequency with which they passed into 16 predefined compass quadrants and each represented as a vector. Using this vector as the predictor variable and the background concentration as the response variable, non-parametric regression using a Gaussian kernel function was carried out. A graphical output indicated the trajectory direction of maximum NO2 concentration, while allowing distinction to be made between spurious and true peaks. In all cases, air mass history was found to have a statistically significant effect on NO2 concentrations. Incorporating emissions data into the analysis local and regional effects were separated and quantified. It was found that emissions in the UK and Europe have a significant effect on background NO2 concentrations in Ireland and in some instances supersede domestic emissions. The methods can be used to identify source regions, separate local and regional effects and improve predictions of background concentrations based on limited monitoring data. In particular, the results highlight the importance of considering air mass history when assessing background concentration levels for use in local air quality modelling studies.

Comment on “Simulation of Surface Ozone Pollution in the Central Gulf Coast Region Using WRF/Chem Model: Sensitivity to PBL and Land Surface Physics”

A recently published meteorology and air quality modeling study has several serious deficiencies deserving comment. The study uses the weather research and forecasting/chemistry (WRF/Chem) model to compare and evaluate boundary layer and land surface modeling options. The most serious of the study's deficiencies is reporting WRF/Chem results for both meteorological and chemical quantities using the asymmetric convective model version 2 (ACM2). While the ACM2 is a valid model option for WRF, it has not yet been implemented for the chemical portion of the WRF/Chem model. Hence, the reported air quality modeling results using ACM2 are invalid. Furthermore, publication of these results gives the erroneous impression that the ACM2 model is not well suited for air quality applications when, in fact, it is the default boundary layer model in the community multiscale air quality (CMAQ) model.

Application of nonparametric regression methods to study the relationship between NO 2 concentrations and local wind direction and speed at background sites

Background concentrations of nitrogen dioxide (NO 2) are not constant but vary temporally and spatially. The current paper presents a powerful tool for the quantification of the effects of wind direction and wind speed on background NO 2 concentrations, particularly in cases where monitoring data are limited. In contrast to previous studies which applied similar methods to sites directly affected by local pollution sources, the current study focuses on background sites with the aim of improving methods for predicting background concentrations adopted in air quality modelling studies. The relationship between measured NO 2 concentration in air at three such sites in Ireland and locally measured wind direction has been quantified using nonparametric regression methods. The major aim was to analyse a method for quantifying the effects of local wind direction on background levels of NO 2 in Ireland. The method was expanded to include wind speed as an added predictor variable. A Gaussian kernel function is used in the analysis and circular statistics employed for the wind direction variable. Wind direction and wind speed were both found to have a statistically significant effect on background levels of NO 2 at all three sites. Frequently environmental impact assessments are based on short term baseline monitoring producing a limited dataset. The presented non-parametric regression methods, in contrast to the frequently used methods such as binning of the data, allow concentrations for missing data pairs to be estimated and distinction between spurious and true peaks in concentrations to be made. The methods were found to provide a realistic estimation of long term concentration variation with wind direction and speed, even for cases where the data set is limited. Accurate identification of the actual variation at each location and causative factors could be made, thus supporting the improved definition of background concentrations for use in air quality modelling studies.

An online coupled meteorological and air quality modeling study of the effect of complex terrain on the regional transport and transformation of air pollutants over the Western United States

One of the most prominent of characteristics of the western United States that affects its meteorology is the complexity of its mountainous terrain. The meteorological Mesoscale Model, version 5 with Chemistry (MM5-Chem), an online-coupled atmospheric chemistry model, was used to investigate the effect of this terrain on a high air pollution event in the free troposphere. The simulations were evaluated by comparisons with data from the North American Regional Reanalysis (NARR). Complex terrain was shown to have an important influence on the vertical transport of air pollutants on the regional scale; emissions from ground level were vertically mixed as high as 5 km above sea surface level for the wintertime conditions simulated. The simulations showed that the vertical transport of emissions from the Earth's surface could have a more significant effect on mid and upper level chemical concentrations than chemical production. The vertical transport was caused predominately by terrain forced flow over the mountains’ ridge-line and the terrain forced flow was affected by the mountain peak height and the complexity of the terrain downwind.

A Spatially and Temporally Disaggregated Anthropogenic Emission Inventory in the Southern Balkan Region

A spatially and temporally resolved atmospheric emission inventory of ozone precursor pollutants (NOx, NMVOCs and CO) was developed for the southern Balkan region in order to support air quality modelling studies focusing on Greece. The inventory is established for the reference year 2000 and it has 10 km spatial resolution. Annual anthropogenic emission data obtained from official national emission inventories and from international emission databases were spatially disaggregated and allocated to a gridded domain using the top-down methodology. Source specific temporal profiles were used in order monthly and hourly emission data to be estimated. In the study area, the highest NOx amounts are emitted by the maritime and road transport sectors. Road transport has a prominent role in anthropogenic NMVOCs emissions followed by the solvent and other product use sector. The main CO emission sources are represented by the road transport sector and the non-industrial combustion plants. In the present study, the contribution of the transport sector to the total emissions of pollutants over urban and non-urban areas of Greece is investigated. Over almost all Greek urban areas, road transport is the major NMVOCs and CO emission source. Road transport contribution to NOx emissions is greater than 50% at the large urban centres of Greece. The greater part of non-urban areas is dominated by off-road vehicle emissions.

Distribution, magnitudes, reactivities, ratios and diurnal patterns of volatile organic compounds in the Valley of Mexico during the MCMA 2002 & 2003 field campaigns

Abstract. A wide array of volatile organic compound (VOC) measurements was conducted in the Valley of Mexico during the MCMA-2002 and 2003 field campaigns. Study sites included locations in the urban core, in a heavily industrial area and at boundary sites in rural landscapes. In addition, a novel mobile-laboratory-based conditional sampling method was used to collect samples dominated by fresh on-road vehicle exhaust to identify those VOCs whose ambient concentrations were primarily due to vehicle emissions. Four distinct analytical techniques were used: whole air canister samples with Gas Chromatography/Flame Ionization Detection (GC-FID), on-line chemical ionization using a Proton Transfer Reaction Mass Spectrometer (PTR-MS), continuous real-time detection of olefins using a Fast Olefin Sensor (FOS), and long path measurements using UV Differential Optical Absorption Spectrometers (DOAS). The simultaneous use of these techniques provided a wide range of individual VOC measurements with different spatial and temporal scales. The VOC data were analyzed to understand concentration and spatial distributions, diurnal patterns, origin and reactivity in the atmosphere of Mexico City. The VOC burden (in ppbC) was dominated by alkanes (60%), followed by aromatics (15%) and olefins (5%). The remaining 20% was a mix of alkynes, halogenated hydrocarbons, oxygenated species (esters, ethers, etc.) and other unidentified VOCs. However, in terms of ozone production, olefins were the most relevant hydrocarbons. Elevated levels of toxic hydrocarbons, such as 1,3-butadiene, benzene, toluene and xylenes, were also observed. Results from these various analytical techniques showed that vehicle exhaust is the main source of VOCs in Mexico City and that diurnal patterns depend on vehicular traffic in addition to meteorological processes. Finally, examination of the VOC data in terms of lumped modeling VOC classes and its comparison to the VOC lumped emissions reported in other photochemical air quality modeling studies suggests that some alkanes are underestimated in the emissions inventory, while some olefins and aromatics are overestimated.

An online coupled meteorological and air quality modeling study of the effect of complex terrain on the regional transport and transformation of air pollutants over the Western United States

One of the most prominent of characteristics of the western United States that affects its meteorology is the complexity of its mountainous terrain. The meteorological Mesoscale Model, version 5 with Chemistry (MM5-Chem), an online-coupled atmospheric chemistry model, was used to investigate the effect of this terrain on a high air pollution event in the free troposphere. The simulations were evaluated by comparisons with data from the North American Regional Reanalysis (NARR). Complex terrain was shown to have an important influence on the vertical transport of air pollutants on the regional scale; emissions from ground level were vertically mixed as high as 5 km above sea surface level for the wintertime conditions simulated. The simulations showed that the vertical transport of emissions from the Earth's surface could have a more significant effect on mid and upper level chemical concentrations than chemical production. The vertical transport was caused predominately by terrain forced flow over the mountains’ ridge-line and the terrain forced flow was affected by the mountain peak height and the complexity of the terrain downwind.

복잡한 해안지역의 지역특성을 고려한 대기 유동장에 따른 SO2

Recently air quality modeling studies for industrial complex and large cities located in the coastal regions have been carried out Especially, the representation of atmospheric flow fields within a model domain is very important, because an adequate air quality simulation requires an accurate portrayal of the realistic three­dimensional wind fields. Therefore this study investigated effect of using high resolution terrain height data and FDDA with observational data to reflect local characteristics in numerical simulation. So the experiments were designed according to FDDA and the detail terrain height with 3sec resolution or not Case 30s was the experiment using the terrain height data of USGS without FDDA and Case 3s was the experiment using the detail terrain height data of Ministry of Environment without FDDA and Case 3sF was experiment using the detail terrain height data of Ministry of Environment with FDDA. The results of experiments were more remarkable, In Case 3s and Case 3sF, temperature indicated similar tendency comparing to observational data predicting maximum temperature during the daytime and wind speed made weakly for difference of terrain height Also Case 3sF had more adequate tendency than Case 3s at dawn.

Diurnal variability of dust aerosol optical thickness and Angström exponent over dust source regions in China

Using 22 months of Sunphotometer Aerosol Optical Thickness (AOT) data collected near the Taklamakan and Gobi dust source regions (Dunhuang, 40.09°N, 94.41°E) in China; we examine the diurnal and seasonal change of dust aerosol properties. Most dust events are during the spring through early summer months with a season‐invariant diurnal change of more than ±10% for AOT and ±30% for Angström exponent, with larger AOT and smaller Angström exponent values late in the afternoon. These values are much larger when compared to recent studies that have reported a much smaller (±5%) diurnal variability of dust AOT over various AERONET sites where dust is a major contributor to AOT. The differences are largely due to the geographical locations and meteorological conditions and such large diurnal changes of aerosol properties at or near dust source regions may be significant enough for consideration in regional radiative forcing, air quality and numerical modeling studies.