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.     

On the influence of meteorological input on photochemical modelling of a severe episode over a coastal area

The modelling reconstruction of the processes determining the transport and mixing of ozone and its precursors in complex terrain areas is a challenging task, particularly when local-scale circulations, such as sea breeze, take place. Within this frame, the ESCOMPTE European campaign took place in the vicinity of Marseille (south-east of France) in summer 2001. The main objectives of the field campaign were to document several photochemical episodes, as well as to constitute a detailed database for chemistry transport models intercomparison.CAMx model has been applied on the largest intense observation periods (IOP) (June 21–26, 2001) in order to evaluate the impacts of two state-of-the-art meteorological models, RAMS and MM5, on chemical model outputs. The meteorological models have been used as best as possible in analysis mode, thus allowing to identify the spread arising in pollutant concentrations as an indication of the intrinsic uncertainty associated to the meteorological input.Simulations have been deeply investigated and compared with a considerable subset of observations both at ground level and along vertical profiles. The analysis has shown that both models were able to reproduce the main circulation features of the IOP. The strongest discrepancies are confined to the Planetary Boundary Layer, consisting of a clear tendency to underestimate or overestimate wind speed over the whole domain.The photochemical simulations showed that variability in circulation intensity was crucial mainly for the representation of the ozone peaks and of the shape of ozone plumes at the ground that have been affected in the same way over the whole domain and all along the simulated period. As a consequence, such differences can be thought of as a possible indicator for the uncertainty related to the definition of meteorological fields in a complex terrain area.     

Evaluation of low wind modeling approaches for two tall-stack databases

The performance of the AERMOD air dispersion model under low wind speed conditions, especially for applications with only one level of meteorological data and no direct turbulence measurements or vertical temperature gradient observations, is the focus of this study. The analysis documented in this paper addresses evaluations for low wind conditions involving tall stack releases for which multiple years of concurrent emissions, meteorological data, and monitoring data are available. AERMOD was tested on two field-study databases involving several SO₂ monitors and hourly emissions data that had sub-hourly meteorological data (e.g., 10-min averages) available using several technical options: default mode, with various low wind speed beta options, and using the available sub-hourly meteorological data. These field study databases included (1) Mercer County, a North Dakota database featuring five SO₂ monitors within 10 km of the Dakota Gasification Company’s plant and the Antelope Valley Station power plant in an area of both flat and elevated terrain, and (2) a flat-terrain setting database with four SO₂ monitors within 6 km of the Gibson Generating Station in southwest Indiana. Both sites featured regionally representative 10-m meteorological databases, with no significant terrain obstacles between the meteorological site and the emission sources. The low wind beta options show improvement in model performance helping to reduce some of the overprediction biases currently present in AERMOD when run with regulatory default options. The overall findings with the low wind speed testing on these tall stack field-study databases indicate that AERMOD low wind speed options have a minor effect for flat terrain locations, but can have a significant effect for elevated terrain locations. The performance of AERMOD using low wind speed options leads to improved consistency of meteorological conditions associated with the highest observed and predicted concentration events. The available sub-hourly modeling results using the Sub-Hourly AERMOD Run Procedure (SHARP) are relatively unbiased and show that this alternative approach should be seriously considered to address situations dominated by low-wind meander conditions.

Implications: AERMOD was evaluated with two tall stack databases (in North Dakota and Indiana) in areas of both flat and elevated terrain. AERMOD cases included the regulatory default mode, low wind speed beta options, and use of the Sub-Hourly AERMOD Run Procedure (SHARP). The low wind beta options show improvement in model performance (especially in higher terrain areas), helping to reduce some of the overprediction biases currently present in regulatory default AERMOD. The SHARP results are relatively unbiased and show that this approach should be seriously considered to address situations dominated by low-wind meander conditions.     

COMPLEX I and II Model Performance Evaluation in Nevada and New Mexico

The COMPLEX I and COMPLEX II Gaussian dispersion models for complex terrain applications have been made available by EPA. Various terrain treatment options under IOPT(25) can be selected for a particular application, one of which [IOPT(25) = 1] is an algorithm similar to that of the VALLEY model. A model performance evaluation exercise involving three of the available options with both COMPLEX models was carried out using SF⁶ tracer measurements taken during worst-case stable impaction conditions in complex terrain at the Harry Allen Plant site in southern Nevada. The models did not reproduce observed concentrations on an event by event basis, as correlation coefficients for 1-h concentrations of 0-0.3 were exhibited. When observed and calculated cumulative frequency distributions for 1-h and 3-h concentrations were compared, a close correspondence between observations and concentrations calculated with COMPLEX I, IOPT(25) = 2 or 3 was noted; both options consistently overestimated observed concentrations. With IOPT(25) = 1, upper percentile (maximum) values in the calculated frequency distribution exceeded the corresponding IOPT(25) = 2 or 3 value by roughly a factor of 2, and observed values by 2.5-5. COMPLEX II typically produced maximum values 2-4 times as great as COMPLEX I for the same terrain treatment option. From these results it is concluded that: 1) the physically unrealistic sector-spread approach used in VALLEY and COMPLEX I under stable impaction conditions is a surrogate for wind direction variation, and 2) the doubling of the plume centerline concentration due to ground reflection under terrain impingement conditions that is included in IOPT(25) = 1 is inappropriate.

These findings were found to be consistent with an analysis of noncurrent observed and calculated SO₂ χ/Q frequency distributions for 1, 3, and 24 hours near the Four Corners Plant in New Mexico. The comparison involved a four-year calculated χ/Q data set and a two-year observed χ/Q data set at the worst-case high terrain impact location near the plant.     

Improving ozone modeling in complex terrain at a fine grid resolution: Part I – examination of analysis nudging and all PBL schemes associated with LSMs in meteorological model

Meteorological variables such as temperature, wind speed, wind directions, and Planetary Boundary Layer (PBL) heights have critical implications for air quality simulations. Sensitivity simulations with five different PBL schemes associated with three different Land Surface Models (LSMs) were conducted to examine the impact of meteorological variables on the predicted ozone concentrations using the Community Multiscale Air Quality (CMAQ) version 4.5 with local perspective. Additionally, the nudging analysis for winds was adopted with three different coefficients to improve the wind fields in the complex terrain at 4-km grid resolution. The simulations focus on complex terrain having valley and mountain areas at 4-km grid resolution. The ETA M–Y (Mellor–Yamada) and G–S (Gayno–Seaman) PBL schemes are identified as favorite options and promote O₃ formation causing the higher temperature, slower winds, and lower mixing height among sensitivity simulations in the area of study. It is found that PX (Pleim–Xiu) simulation does not always give optimal meteorological model performance. We also note that the PBL scheme plays a more important role in predicting daily maximum 8-h O₃ than land surface models. The results of nudging analysis for winds with three different increased coefficients' values (2.5, 4.5, and 6.0 × 10^?4 s^?1) over seven sensitivity simulations show that the meteorological model performance was enhanced due to improved wind fields, indicating the FDDA nudging analysis can improve model performance considerably at 4-km grid resolution. Specifically, the sensitivity simulations with the coefficient value (6.0 × 10^?4) yielded more substantial improvements than with the other values (2.5 and 4.5 × 10^?4). Hence, choosing the nudging coefficient of 6.0 × 10^?4 s^?1 for winds in MM5 may be the best choice to improve wind fields as an input, as well as, better model performance of CMAQ in the complex terrain area. As a result, a finer grid resolution is necessary to evaluate and access of CMAQ results for giving a detailed representation of meteorological and chemical processes in the regulatory modeling. A recommendation of optimal scheme options for simulating meteorological variables in the complex terrain area is made.     

A novel bagging ensemble approach for predicting summertime ground-level ozone concentration

Ozone pollution appears as a major air quality issue, e.g. for the protection of human health and vegetation. Formation of ground level ozone is a complex photochemical phenomenon and involves numerous intricate factors most of which are interrelated with each other. Machine learning techniques can be adopted to predict the ground level ozone. The main objective of the present study is to develop the state-of-the-art ensemble bagging approach to model the summer time ground level ozone in an industrial area comprising a hazardous waste management facility. In this study, the feasibility of using ensemble model with seven meteorological parameters as input variables to predict the surface level O3 concentration. Multilayer perceptron, RTree, REPTree, and Random forest were employed as the base learners. The error measures used for checking the performance of each model includes IoAd, R2, and PEP. The model results were validated against an independent test data set. Bagged random forest predicted the ground level ozone better with higher Nash-Sutcliffe coefficient 0.93. This study scaffolded the current research gap in big data analysis identified with air pollutant prediction.

Implications: The main focus of this paper is to model the summer time ground level O₃ concentration in an Industrial area comprising of hazardous waste management facility. Comparison study was made between the base classifiers and the ensemble classifiers. Most of the conventional models can well predict the average concentrations. In this case the peak concentrations are of importance as it has serious effect on human health and environment. The models developed should also be homoscedastic.     

Assessing spatial variability of SO2 field as detected by an air quality network using Self-Organizing Maps,cluster, and Principal Component Analysis

In Bilbao (Spain), an air quality network measures sulphur dioxide levels at 4 locations. The objective of this paper is to develop a practical methodology to identify redundant sensors and evaluate a network's capability to correctly follow and represent SO₂ fields in Bilbao, in the frame of a continuous network optimization process.The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area.To assess the spatial variability of SO₂ measured at 4 locations in the area, three different techniques have been used: Self-Organizing Maps (SOMs), cluster analysis (CA) and Principal Component Analysis (PCA). The results show that the three techniques yield the same results, but the information obtained via PCA can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of cluster analysis and SOMs is that they provide readily interpretable results. All the calculations have been carried out using the freely available software R.     

Spatial variability of sulfur dioxide and sulfate over complex terrain in East Tennessee,USA

In 2004 and 2005, the East Tennessee Ozone Study (ETOS) enhanced its regional measurement program with annular denuder systems to quantify sulfur dioxide (SO₂) and PM_2.5 sulfate (SO₄^2?) at five sampling sites that were representative of the complex terrain and physiographic features of East Tennessee. Intersite spatial variability was more defined for SO₂ than for SO₄^2?, which showed a fairly uniform structure in both daytime and nighttime measurements. Pollution roses indicated that two sites may have been influenced by the proximity of SO₂ emission sources. The data suggest that SO₂ is affected by nearby sources in the study area while the sources of SO₄^2? are regionally distributed.     

Comparison of the complex terrain algorithms incorporated into two commonly used local-scale air pollution dispersion models (ADMS and AERMOD) using a hybrid model

ADMS and AERMOD are the two most widely used dispersion models for regulatory purposes. It is, therefore, important to understand the differences in the predictions of the models and the causes of these differences. The treatment by the models of flat terrain has been discussed previously; in this paper the focus is on their treatment of complex terrain. The paper includes a discussion of the impacts of complex terrain on airflow and dispersion and how these are treated in ADMS and AERMOD, followed by calculations for two distinct cases: (i) sources above a deep valley within a relatively flat plateau area (Clifty Creek power station, USA); (ii) sources in a valley in hilly terrain where the terrain rises well above the stack tops (Ribblesdale cement works, England). In both cases the model predictions are markedly different. At Clifty Creek, ADMS suggests that the terrain markedly increases maximum surface concentrations, whereas the AERMOD complex terrain module has little impact. At Ribblesdale, AERMOD predicts very large increases (a factor of 18) in the maximum hourly average surface concentrations due to plume impaction onto the neighboring hill; although plume impaction is predicted by ADMS, the increases in concentration are much less marked as the airflow model in ADMS predicts some lateral deviation of the streamlines around the hill.     

Evaluation of a Puff Dispersion Model in Complex Terrain

California's Pacific Gas and Electric Company has many power plant operations situated in complex terrain, prominent examples being the Geysers geothermal plant in Lake and Sonoma Counties, and the Diablo Canyon nuclear plant in San Luis Obispo County. Procedures ranging from plant licensing to emergency response require a dispersion modeling capability in a complex terrain environment. This paper describes the performance evaluation of such a capability, the Pacific Gas and Electric Company Modeling System (PGEMS), a fast response Gaussian puff model with a three-dimensional wind field generator.

Performance of the model was evaluated for ground level and short stack elevated release on the basis of a special intensive tracer experiment in the complex coastal terrain surrounding the Diablo Canyon Nuclear Power Plant in San Luis Obispo County, California. The model performed well under a variety of meteorological and release conditions within the test region of 20-kilometer radius surrounding the nuclear plant, and turned in a superior performance in the wake of the nuclear plant, using a new wake correction algorithm for ground level and roof-vent releases at that location.     

Application of a model system for the study of transport and diffusion in complex terrain to the TRACT experiment

The transport and diffusion processes of a tracer gas released near the ground in the Rhine valley region, in Central Europe, during the 1992 TRACT field experiment, are simulated by a computational model system for complex terrain. This system (RMS) is composed of the prognostic mesoscale model RAMS, the Lagrangian stochastic dispersion model SPRAY and the interface code MIRS, which links RAMS to SPRAY. Three flow simulations were performed, with different initialisations and the one showing the best agreement with the measured flow was selected for the simulation of the TRACT tracer experiment. Tracer concentrations measured by an array of samplers at ground level and by an airplane aloft, are used to evaluate the 3-D concentration field simulated by the model system. The analysis of the simulation results generated by RMS shows that our model system very well reproduces the general behaviour of the contaminant plume, the temporal and spatial distribution of the concentration and the location of the concentration maxima.     

冬季沈阳市典型源排放PM_(10)浓度分布模拟分析

选取沈阳市7个典型的大气污染源2006年12月~2007年2月的PM10排放浓度资料,利用CALPUFF对PM10浓度月平均分布做模拟分析。模拟结果分析表明:冬季月平均PM10浓度分布的范围与风场、地形有直接的关系。地势平坦、风速大时,污染物扩散范围大,污染物浓度小;地势不平、风速小时,污染物扩散范围小,污染物浓度大。1月份是沈阳市冬季月平均大气污染最严重的月份,污染物分布主要集中在市区的北部、东部和南部地区,东部地区大气污染最为严重。     

Integrated chemical species analysis with source-receptor modeling results to characterize the effects of terrain and monsoon on ambient aerosols in a basin

This study integrated estimated oxidation ratio of sulfur (SOR) and oxidation ratio of nitrogen (NOR) with source-receptor modeling results to identify the effects of terrain and monsoons on ambient aerosols in an urban area (north basin) and a rural area (south basin) of the Taichung Basin. The estimated results indicate that the conversion of sulfur mainly occurs in fine particles (PM_2.5), whereas the conversion of nitrogen occurs in approximately equal quantities of PM_2.5 and coarse particles (PM_2.5–10). The results show a direct relationship for PM_2.5 between the modeling results with SOR and NOR. The high PM_2.5 SOR, NOR, and secondary aerosol values all occurred in the upwind area during both monsoons; this shows that the photochemical reaction and the terrain effect on the pollutant transmission were significant in the basin. Additionally, the urban heat island effect on the urban area and the valley effect on the rural area were significant. The results show that secondary aerosol in PM_2.5–10 contributed approximately 10 % during both monsoons, and the difference in the contribution from secondary aerosol between both areas was small. Vehicle exhaust emissions and wind-borne dust were two crucial PM_2.5–10 contributors during both monsoons; their average contributions in both areas were higher than 34 and 32 %, respectively.     

Analysis of the impact of the forest fires in August 2007 on air quality of Athens using multi-sensor aerosol remote sensing data,meteorology and surface observations

Data from multiple satellite remote sensors are integrated with ground measurements and meteorological data to study the impact of Greek forest fires in August 2007 on the air quality in Athens. Two pollution episodes were identified by ground PM₁₀ measurements between August 23 and September 4. In the first episode, Evia and Peloponnese fires contributed substantially to the air pollution levels in Athens. In the second episode, transport of industrial pollution from Italy and Western Europe as well as forest fires in Albania contributed substantially to the air pollution levels in Athens. Local air pollution sources also contributed to the observed particle levels during these episodes. Satellite data provide valuable insights into the spatial distribution of particle concentrations, thus they can be used identify pollution sources. In spite of a few weaknesses in current satellite data products identified in this analysis, combining satellite aerosol remote sensing data with trajectory models and ground measurements is a powerful tool to study intensive particle pollution events such as forest fires.     

Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization

A combination of in-situ PM_2.5, sunphotometers, upward pointing lidar and satellite aerosol optical depth (AOD) instruments have been employed to better understand variability in the correlation between AOD and PM_2.5 at the surface. Previous studies have shown good correlation between these measures, especially in the US east, and encouraged the use of satellite data for spatially interpolating between ground sensors. This work shows that cases of weak correlation can be better understood with knowledge of whether the aerosol is confined to the surface planetary boundary layer (PBL) or aloft. Lidar apportionment of the fraction of aerosol optical depth that is within the PBL can be scaled to give better agreement with surface PM_2.5 than does the total column amount. The study has shown that lidar combined with surface and remotely sensed data might be strategically used to improve our understanding of long-range or regionally transported pollutants in multiple dimensions.     

Estimating Gas Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model

Abstract

Manure storage tanks and animals in barns are important agricultural sources of methane. To examine the possibility of using an inverse dispersion technique based on a backward Lagrangian Stochastic (bLS) model to quantify methane (CH₄) emissions from multiple on-farm sources, a series of tests were carried out with four possible source configurations and three controlled area sources. The simulated configurations were: (C1) three spatially separate ground-level sources, (C2) three spatially separate sources with wind-flow disturbance, (C3) three adjacent ground-level sources to simulate a group of adjacent sources with different emission rates, and (C4) a configuration with a ground level and two elevated sources. For multiple ground-level sources without flow obstructions (C1 and C3), we can use the condition number (k, the ratio of the uncertainty in the calculated emission rate to the uncertainty in the predicted ratio of concentration to emission rate) to evaluate the applicability of this inverse dispersion technique and a preliminary threshold of k < 10 is recommended. For multiple sources with wind disturbance (C2) or an even more complex configuration including ground level and elevated sources (C4), a low k is not sufficient to provide reasonable discrete and total emission rates. The effect of flow obstructions can be neglected as long as the distance between the source and the measurement location is greater than approximately 10 times the height of the flow obstructions. This study shows that the bLS model has the potential to provide accurate discrete emission rates from multiple on-farm emissions of gases provided that certain conditions are met.     

Nitrous oxide emission from an agricultural field: Comparison between measurements by flux chamber and micrometerological techniques

The soil in a drained fjord area, reclaimed for arable farming, produced N₂O mainly at 75–105 cm depth, just above the ground water level. Surface emissions of N₂O were measured from discrete small areas by closed and open-flow chamber methods, using gas chromatographic analysis and over larger areas by integrative methods: flux gradient (analysis by FTIR), conditional sampling (analysis by TDLAS), and eddy covariance (analysis by TDLAS). The mean emission of N₂O as determined by chamber procedures during a 9-day campaign was 162–202 μg N₂ONm⁻²h⁻¹ from a wheat stubble and 328–467 μg N₂ONm⁻² h⁻¹ from a carrot field. The integrative approaches gave N₂O emissions of 149–495 μg N₂ONm⁻² h⁻¹, i.e. a range similar to those determined with the chamber methods. Wind direction affected the comparison of chamber and integrative methods because of patchiness of the N₂O emission over the area. When a uniform area with a single type of vegetation had a dominant effect on the N₂O gradient at the sampling mast, the temporal variation in N₂O emission determined by the flux gradient/FTIR method and chamber methods was very similar, with differences of only 18% or less in mean N₂O emission, well below the variation encountered with the chamber methods themselves. A detailed comparison of FTIR gradient and chamber data taking into account the precise emission footprint showed good agreement. It is concluded that there was no bias between the different approaches used to measure the N₂O emission and that the precision of the measurements was determined by the spatial variability of the N₂O emission at the site and the variability inherent in the individual techniques. These results confirm that measurements of N₂O emissions from different ecosystems obtained by the different methods can be meaningfully compared.     

Dispersion of pollutants over land–water–land interface: Study using CALPUFF model

The CALMET/CALPUFF modeling system is used to study atmospheric dispersion of pollutant over land–water–land interface. It is shown that the default scheme used by CALMET/CALPUFF to handle inhomogeneous surfaces does not take care of the different turbulence characteristics over such surfaces. An alternative method is suggested to incorporate different turbulent characteristics over inhomogeneous surfaces by using the appropriate atmospheric stability category over different surfaces. The results show that the presence of water body can increase the ground level concentration by a factor of up to 50 for the width of water body varying from 1 km to 5 km. It is also shown that the effect of water body on the ground level concentration decreases as the distance from the water body increases. The present study showed that for land–water interface, the realistic specification of turbulence characteristics over inhomogeneous surfaces significantly changes the estimation of ground level concentration as compared to the default scheme available in the CALMET/CALPUFF modeling system and is expected to give realistic results.     

Plume behaviors observed using lidar and SF6 tracer at a flat and hilly site

Two field experiments, one at Kincaid, IL, in flat terrain, the other at Bull Run, TN, in rolling terrain, were conducted under the auspices of the Electric Power Research Institute's (EPRI) Plume Model Validation and Development program. Simultaneous observations were made of ground-level SF₆ concentrations; plume cross-sections using light detection and ranging (lidar); turbulence; and routine meteorology at the surface and aloft. Due to terrain influences, surface wind-speeds at the Bull Run site were significantly lower than those at the Kincaid site, whereas thermal winds at Kincaid were generally larger than at Bull Run. At both sites, a reduction in turbulent intensity and an increase in atmospheric stability with height correlate with a substantial decrease in the rate of vertical plume dispersion. SF₆ ground-level concentration (GLC) patterns over distances of 1–50 km from the source were categorized by shape. The GLC patterns at Bull Run were frequently ‘blobby’, when significant GLCs occurred over an azimuth angle exceeding 90°, whereas patterns at Kincaid were generally coherent and nearly elliptical. Plume behavior was examined for 154 h during which both GLCs of SF₆ tracer and lidar cross-sections of the plume were of good quality. Results show that plume looping was rare at Kincaid, but occurred substantially more often at Bull Run (3%: 14%), with the reverse true for meandering (11%: 14%). Inversions that trapped plume material occurred much more often at Kincaid that at Bull Run (11%: <1%). Correlation of cross-wind concentration distributions of the plume aloft with those cross-wind SF₆ concentrations distributions at the ground were poor at both sites.     

Comparison of Predicted and Measured Concentrations for 58 Alternative models of Plume Transport in Complex Terrain

The purpose of this study was to evaluate alternative prediction models for the SO₂ concentrations produced in the vicinity of the Ohio Edison Company Sammis Power Plant. The plant is situated in the northeastern portion of the Ohio River Valley in complex terrain. Comparisons of the 16 highest predicted and measured short-term SO₂ concentrations were conducted for a one year period for 58 alternative models. Several models were found to predict reasonably accurately the 16 highest measured 24-hour SO₂ concentrations. Each of these models requires an upward adjustment in the plume centerline location as the plume is transported downwind in rising terrain. These same models overpredict by substantial margins the 16 highest measured 3-hour SO₂ concentrations. Improvements in emissions inventory data and improvements in the prediction models used are believed necessary to increase prediction accuracy further.