Assessment of 2010 air quality in two Alpine valleys from modelling: Weather type and emission scenarios

Alpine valleys are sensitive to anthropogenic emissions. Local atmospheric dynamics are a key factor that may lead to an accumulation of pollutants in the bottom of the Chamonix and Maurienne valleys. Assessment of 2010 pollutant concentrations variability needs to take these specificities into account. A meteorological data classification is combined with different emission scenarios in order to run an air quality model. Using simulations of representative scenarios rather than complete years allows for a fine spatial and temporal representation of local atmospheric dynamics and gives access to detailed chemical breakdowns. Results demonstrate the variability of primary and secondary species due to emissions and the predominance of local effects on pollutant concentrations.     

The NOAA Twin Otter and its role in BRACE: A comparison of aircraft and surface trace gas measurements

A DeHavilland DHC-6 Twin Otter, operated by the National Oceanic and Atmospheric Administration, was deployed in Tampa, FL to measure aerosols and primary and secondary trace gases in support of the Bay Regional Atmospheric Chemistry Experiment (BRACE). The Twin Otter repeatedly overflew the surface chemistry monitoring super site near Sydney, FL to assess the comparability of surface and airborne datasets and the spatial representativeness of the surface measurements. Prior to comparing the chemical datasets, we evaluated the comparability of the standards used to calibrate surface and airborne detectors, as well as the uniformity of wind fields aloft and at the surface. Under easterly flow, when the dearth of significant upwind emission sources promoted chemical homogeneity at Sydney, trace gas concentrations at the surface and aloft were generally well correlated; R² ranged from 0.4396 for H₂O₂ to 0.9738 for O₃, and was typically better than 0.70 for NO, NO₂, NO_Y, HNO₃, HCHO, and SO₂. Mean ratios of aircraft-to-surface concentrations during 10 overflights of Sydney were as follows: 1.002±0.265 (NO), 0.948±0.183 (NO₂), 1.010±0.214 (NO_Y), 0.941±0.263 (HCHO), and 0.952±0.046 (O₃). Poorer agreement and larger variability in measured ratios were noted for SO₂ (1.764±0.559), HNO₃ (1.291±0.391), and H₂O₂ (1.200±0.657). Under easterly flow, surface measurements at Sydney were representative of conditions over horizontal scales as large as 50 km and agreed well with airborne values throughout the depth of the turbulently mixed boundary layer at mid-day. Westerly flow advected the Tampa urban plume over the site; under these conditions, as well as during transitional periods associated with the development of the land–sea breeze, surface conditions were representative of smaller spatial scales. Finally, we estimate possible errors in future measurement-model comparisons likely to arise from fine scale (or subgrid;<2 km) variability of trace gas concentrations. Large subgrid variations in concentration fields were observed downwind of large emission point sources, and persisted across multiple model grid cells (distances>4 km) in coherent plumes. Variability at the edges of the well-mixed urban plume, and at the interface of the land–sea breeze circulation, was significantly smaller. This suggests that even a failure of modeled wind fields to resolve the sea breeze return can induce moderate, but not overwhelming, errors in simulated concentration fields and dependent chemical processes.     

Modeling atmospheric mercury deposition in the vicinity of power plants

Two mathematical models of the atmospheric fate and transport of mercury (Hg), an Eulerian grid-based model and a Gaussian plume model, are used to calculate the atmospheric deposition of Hg in the vicinity (i.e., within 50 km) of five coal-fired power plants. The former is applied using two different horizontal resolutions: coarse (84 km) and fine (16.7 km). More than 96% of the power plant Hg emissions are calculated with the plume model to be transported beyond 50 km from the plants. The grid-based model predicts a lower fraction to be transported beyond 50 km: >91% with a coarse resolution and >95% with a fine resolution. The contribution of the power plant emissions to total Hg deposition within a radius of 50 km from the plants is calculated to be <8% with the plume model, <14% with the Eulerian model with a coarse resolution, and <10% with the Eulerian model with a fine resolution. The Eulerian grid-based model predicts greater local impacts than the plume model because of artificially enhanced vertical dispersion; the former predicts about twice as much Hg deposition as the latter when the area considered is commensurate with the resolution of the grid-based model. If one compares the local impacts for an area that is significantly less than the grid-based model resolution, then the grid-based model may predict lower local deposition than the plume model, because two compensating errors affect the results obtained with the grid-based model: initial dilution of the power plant emissions within one or more grid cells and enhanced vertical mixing to the ground.     

Using a chemistry transport model to account for the spatial variability of exposure concentrations in epidemiologic air pollution studies

Environmental epidemiology and more specifically time-series analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM2.5 (particulate matter [PM] < or = 10 microm in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants.     

Developing a high-resolution vehicular emission inventory by integrating an emission model and a traffic model: Part 2--A case study in Beijing

A grid-based, bottom-up method has been proposed by combining a vehicle emission model and a travel demand model to develop a high-resolution vehicular emission inventory for Chinese cities. Beijing is used as a case study in which the focus is on fuel consumption and emissions from hot-stabilized activities of light-duty gasoline vehicles (LGVs) in 2005. The total quantity of emissions, emission intensity, and spatial distribution of emissions at 1- by 1-km resolution are presented and compared with results from other inventory methods commonly used in China. The results show that the total daily fuel consumption and vehicular emissions of carbon dioxide, carbon monoxide, hydrocarbons, and oxides of nitrogen from LGVs in the Beijing urban area in 2005 were 1.95 x 10(7) L, 4.28 x 10(4) t, 1.97 x 10(3) t, 0.28 x 10(3) t, and 0.14 x 10(3) t, respectively. Vehicular fuel consumption and emissions show spatial variations that are consistent with the traffic characteristics. The grid-based inventory developed in this study reflects the influence of traffic conditions on vehicle emissions at the microscale and may be applied to evaluate the effectiveness of traffic-related measures on emission control in China.     

Impact of underestimating the effects of cold temperature on motor vehicle start emissions of air toxics in the United States

Analyses of U.S. Environmental Protection Agency (EPA) certification data, California Air Resources Board surveillance testing data, and EPA research testing data indicated that EPA's MOBILE6.2 emission factor model substantially underestimates emissions of gaseous air toxics occurring during vehicle starts at cold temperatures for light-duty vehicles and trucks meeting EPA Tier 1 and later standards. An unofficial version of the MOBILE6.2 model was created to account for these underestimates. When this unofficial version of the model was used to project emissions into the future, emissions increased by almost 100% by calendar year 2030, and estimated modeled ambient air toxics concentrations increased by 6-84%, depending on the pollutant. To address these elevated emissions, EPA recently finalized standards requiring reductions of emissions when engines start at cold temperatures.     

Correlating emissions with time and temperature to predict worst-case emissions from open liquid area sources

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry's law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H2S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H2S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85-90 degrees F), the constant emission rate underestimated odor impact significantly (by 73%).     

Combined analysis of modeled and monitored SO2 concentrations at a complex smelting facility

Vale Canada Limited owns and operates a large nickel smelting facility located in Sudbury, Ontario. This is a complex facility with many sources of SO₂ emissions, including a mix of source types ranging from passive building roof vents to North America's tallest stack. In addition, as this facility performs batch operations, there is significant variability in the emission rates depending on the operations that are occurring. Although SO₂ emission rates for many of the sources have been measured by source testing, the reliability of these emission rates has not been tested from a dispersion modeling perspective. This facility is a significant source of SO₂ in the local region, making it critical that when modeling the emissions from this facility for regulatory or other purposes, that the resulting concentrations are representative of what would actually be measured or otherwise observed. To assess the accuracy of the modeling, a detailed analysis of modeled and monitored data for SO₂ at the facility was performed. A mobile SO₂ monitor sampled at five locations downwind of different source groups for different wind directions resulting in a total of 168 hr of valid data that could be used for the modeled to monitored results comparison. The facility was modeled in AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model) using site-specific meteorological data such that the modeled periods coincided with the same times as the monitored events. In addition, great effort was invested into estimating the actual SO₂ emission rates that would likely be occurring during each of the monitoring events. SO₂ concentrations were modeled for receptors around each monitoring location so that the modeled data could be directly compared with the monitored data. The modeled and monitored concentrations were compared and showed that there were no systematic biases in the modeled concentrations.

Implications:

This paper is a case study of a Combined Analysis of Modelled and Monitored Data (CAMM), which is an approach promulgated within air quality regulations in the Province of Ontario, Canada. Although combining dispersion models and monitoring data to estimate or refine estimates of source emission rates is not a new technique, this study shows how, with a high degree of rigor in the design of the monitoring and filtering of the data, it can be applied to a large industrial facility, with a variety of emission sources. The comparison of modeled and monitored SO₂ concentrations in this case study also provides an illustration of the AERMOD model performance for a large industrial complex with many sources, at short time scales in comparison with monitored data. Overall, this analysis demonstrated that the AERMOD model performed well.     

Modelling the spatial distribution of ammonia emissions in the UK

Ammonia emissions (NH3) are characterised by a high spatial variability at a local scale. When modelling the spatial distribution of NH3 emissions, it is important to provide robust emission estimates, since the model output is used to assess potential environmental impacts, e.g. exceedance of critical loads. The aim of this study was to provide a new, updated spatial NH3 emission inventory for the UK for the year 2000, based on an improved modelling approach and the use of updated input datasets. The AENEID model distributes NH3 emissions from a range of agricultural activities, such as grazing and housing of livestock, storage and spreading of manures, and fertilizer application, at a 1-km grid resolution over the most suitable landcover types. The results of the emission calculation for the year 2000 are analysed and the methodology is compared with a previous spatial emission inventory for 1996.     

Efficient retrieval of vegetation leaf area index and canopy clumping factor from satellite data to support pollutant deposition assessments

Canopy leaf area index (LAI) is an important structural parameter of the vegetation controlling pollutant uptake by terrestrial ecosystems. This paper presents a computationally efficient algorithm for retrieval of vegetation LAI and canopy clumping factor from satellite data using observed Simple Ratios (SR) of near-infrared to red reflectance. The method employs numerical inversion of a physics-based analytical canopy radiative transfer model that simulates the bi-directional reflectance distribution function (BRDF). The algorithm is independent of ecosystem type. The method is applied to 1-km resolution AVHRR satellite images to retrieve a geo-referenced data set of monthly LAI values for the conterminous USA. Satellite-based LAI estimates are compared against independent ground LAI measurements over a range of ecosystem types. Verification results suggest that the new algorithm represents a viable approach to LAI retrieval at continental scale, and can facilitate spatially explicit studies of regional pollutant deposition and trace gas exchange.     

Improving pollutant source characterization by better estimating wind direction with a genetic algorithm

In homeland security applications, it is often necessary to characterize the source location and strength of a potentially harmful contaminant. Correct source characterization requires accurate meteorological data such as wind direction. Unfortunately, available meteorological data is often inaccurate or unrepresentative, having insufficient spatial and temporal resolution for precise modeling of pollutant dispersion. To address this issue, a method is presented that simultaneously determines the surface wind direction and the pollutant source characteristics. This method compares monitored receptor data to pollutant dispersion model output and uses a genetic algorithm (GA) to find the combination of source location, source strength, and surface wind direction that best matches the dispersion model output to the receptor data. A GA optimizes variables using principles from genetics and evolution.The approach is validated with an identical twin experiment using synthetic receptor data and a Gaussian plume equation as the dispersion model. Given sufficient receptor data, the GA is able to reproduce the wind direction, source location, and source strength. Additional runs incorporating white noise into the receptor data to simulate real-world variability demonstrate that the GA is still capable of computing the correct solution, as long as the magnitude of the noise does not exceed that of the receptor data.     

Resolving local-scale emissions for modeling air quality near roadways

A large body of literature published in recent years suggests increased health risk due to exposure of people to air pollution in close proximity to roadways. As a result, there is a need to more accurately represent the spatial concentration gradients near roadways to develop mitigation strategies. In this paper, we present a practical, readily adaptable methodology, using a "bottom-up" approach to develop a detailed highway vehicle emission inventory that includes emissions for individual road links. This methodology also takes advantage of geographic information system (GIS) software to improve the spatial accuracy of the activity information obtained from a Travel Demand Model. In addition, we present an air quality modeling application of this methodology in New Haven, CT. This application uses a hybrid modeling approach, in which a regional grid-based model is used to characterize average local ambient concentrations, and a Gaussian dispersion model is used to provide texture within the modeling domain because of spatial gradients associated with highway vehicle emissions and other local sources. Modeling results show substantial heterogeneity of pollutant concentrations within the modeling domain and strong spatial gradients associated with roadways, particularly for pollutants dominated by direct emissions.     

A hybrid modeling approach to resolve pollutant concentrations in an urban area

A modeling tool that can resolve contributions from individual sources to the urban environment is critical for air-toxics exposure assessments. Air toxics are often chemically reactive and may have background concentrations originated from distant sources. Grid models are the best-suited tools to handle the regional features of these chemicals. However, these models are not designed to resolve pollutant concentrations on local scales. Moreover, for many species of interest, having reaction time scales that are longer than the travel time across an urban area, chemical reactions can be ignored in describing local dispersion from strong individual sources making Lagrangian and plume-dispersion models practical. In this study, we test the feasibility of developing an urban hybrid simulation system. In this combination, the Community Multi-scale Air Quality model (CMAQ) provides the regional background concentrations and urban-scale photochemistry, and local models such as Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) and AMS/EPA Regulatory Model (AERMOD) provide the more spatially resolved concentrations due to local emission sources. In the initial application, the HYSPLIT, AERMOD, and CMAQ models are used in combination to calculate high-resolution benzene concentrations in the Houston area. The study period is from 18 August to 4 September of 2000. The Mesoscale Model 5 (MM5) is used to create meteorological fields with a horizontal resolution of 1×1 km². In another variation to this approach, multiple HYSPLIT simulations are used to create a concentration ensemble to estimate the contribution to the concentration variability from point sources. HYSPLIT simulations are used to model two sources of concentration variability; one due to variability created by different particle trajectory pathways in the turbulent atmosphere and the other due to different flow regimes that might be introduced when using gridded data to represent meteorological data fields. The ensemble mean concentrations determined by HYSPLIT plus the concentrations estimated by AERMOD are added to the CMAQ calculated background to estimate the total mean benzene concentration. These estimated hourly mean concentrations are also compared with available field measurements.     

Correlating Emissions with Time and Temperature to Predict Worst-Case Emissions from Open Liquid Area Sources

Abstract

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry’s law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H₂S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H₂S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85–90 °F), the constant emission rate underestimated odor impact significantly (by 73%).     

Dynamic evaluation of a regional air quality model: Assessing the emissions-induced weekly ozone cycle

Air quality models are used to predict changes in pollutant concentrations resulting from envisioned emission control policies. Recognizing the need to assess the credibility of air quality models in a policy-relevant context, we perform a dynamic evaluation of the Community Multiscale Air Quality (CMAQ) modeling system for the “weekend ozone effect” to determine if observed changes in ozone due to weekday-to-weekend (WDWE) reductions in precursor emissions can be accurately simulated. The weekend ozone effect offers a unique opportunity for dynamic evaluation, as it is a widely documented phenomenon that has persisted since the 1970s. In many urban areas of the Unites States, higher ozone has been observed on weekends than weekdays, despite dramatically reduced emissions of ozone precursors (nitrogen oxides [NO_x] and volatile organic compounds [VOCs]) on weekends. More recent measurements, however, suggest shifts in the spatial extent or reductions in WDWE ozone differences. Using 18 years (1988–2005) of observed and modeled ozone and temperature data across the northeastern United States, we re-examine the long-term trends in the weekend effect and confounding factors that may be complicating the interpretation of this trend and explore whether CMAQ can replicate the temporal features of the observed weekend effect. The amplitudes of the weekly ozone cycle have decreased during the 18-year period in our study domain, but the year-to-year variability in weekend minus weekday (WEWD) ozone amplitudes is quite large. Inter-annual variability in meteorology appears to influence WEWD differences in ozone, as well as WEWD differences in VOC and NO_x emissions. Because of the large inter-annual variability, modeling strategies using a single episode lasting a few days or a few episodes in a given year may not capture the WEWD signal that exists over longer time periods. The CMAQ model showed skill in predicting the absolute values of ozone concentrations during the daytime. However, early morning NO_x concentrations were underestimated and ozone levels were overestimated. Also, the modeled response of ozone to WEWD differences in emissions was somewhat less than that observed. This study reveals that model performance may be improved by (1) properly estimating mobile source NO_x emissions and their temporal distributions, especially for diesel vehicles; (2) reducing the grid cell size in the lowest layer of CMAQ; and, (3) using time-dependent and more realistic boundary conditions for the CMAQ simulations.     

Assessing the impact of the acid deposition control program

A goal of the acidic deposition control program in the United States has been to link emissions control policies, such as those mandated under Title IV of the US Clean Air Act Amendments (CAAA) of 1990, to improvements in air and water quality. Recently, several researchers have reported trends in the time series of pollutant data in an effort to evaluate the effectiveness of the CAAA in reducing the acidic deposition problem. It is well known that pollutant concentrations are highly influenced by meteorological and climatic variations. Also, spatial and temporal inhomogeneities in time series of pollutant concentrations, induced by differences in the data collection, reduction, and reporting practices, can significantly affect the trend estimates. We present a method to discern breaks or discontinuities in the time series of pollutants stemming from emission reductions in the presence of meteorological and climatological variability. Using data from a few sites, this paper illustrates that linear trend estimates of concentrations of SO₂, aerosol SO₄²⁻, and precipitation-weighted SO₄²⁻ and NO₃⁻ can be biased because of such complex features embedded in pollutant time series.     

Numerical simulation of air pollution in urban areas: Model development

A three-dimensional, grid-based numerical air pollution model for the estimation of air pollutant concentrations in an urban area is developed. Based on the continuity equation, the modeling system incorporates the combined influences of advective transport, turbulent diffusion, chemical transformation, source emissions and surface removal of air contaminants. Recent developments in plume rise and plume penetration processes, objective wind field analysis procedures and numerical solution techniques incorporated into the model are described.