Dispersion Model Output Research Articles

Source identification of short-lived air pollutants in the Arctic using statistical analysis of measurement data and particle dispersion model output

Abstract. As a part of the IPY project POLARCAT (Polar Study using Aircraft, Remote Sensing, Surface Measurements and Models, of Climate Chemistry, Aerosols and Transport), this paper studies the sources of equivalent black carbon (EBC), sulphate, light-scattering aerosols and ozone measured at the Arctic stations Zeppelin, Alert, Barrow and Summit during the years 2000–2007. These species are important pollutants and climate forcing agents, and sulphate and EBC are main components of Arctic haze. To determine where these substances originate, the measurement data were combined with calculations using FLEXPART, a Lagrangian particle dispersion model. The climatology of atmospheric transport from surrounding regions on a twenty-day time scale modelled by FLEXPART shows that the stations Zeppelin, Alert and Barrow are highly sensitive to surface emissions in the Arctic and to emissions in high-latitude Eurasia in winter. Emission sensitivities over southern Asia and southern North America are small throughout the year. The high-altitude station Summit is an order of magnitude less sensitive to surface emissions in the Arctic whereas emissions in the southern parts of the Northern Hemisphere continents are more influential relative to the other stations. Our results show that for EBC and sulphate measured at Zeppelin, Alert and Barrow, northern Eurasia is the dominant source region. For sulphate, Eastern Europe and the metal smelting industry in Norilsk are particularly important. For EBC, boreal forest fires also contribute in summer. No evidence for any substantial contribution to EBC from sources in southern Asia is found. European air masses are associated with low ozone concentrations in winter due to titration by nitric oxides, but are associated with high ozone concentrations in summer due to photochemical ozone formation. There is also a strong influence of ozone depletion events in the Arctic boundary layer on measured ozone concentrations in spring and summer. These results will be useful for developing emission reduction strategies for the Arctic.

Exposure Assessment of Newborn Babies Near Incinerators: A Geographical Approach

ISEE-0760 Background and Objectives: The relationship between exposure to emissions from incinerators and adverse birth outcomes is controversial. In the Emilia Romagna region (northern Italy), a multisite project (the MONITER project) is focusing on the environmental and health impact of eight incinerators (municipal solid waste, MSW). The relationship between pollution attributable to these plants and birth outcomes is under investigation. The aim of the first stage of the project is the characterization of the exposure of the population, including newborn babies. Methods: Study areas were defined as the 4 km zones around the eight MSW incinerators. We collected data on all newborn residents in the study areas for the period 2003–2006 from Registry Offices. Pollution maps from the incinerators were provided by ADMS dispersion model outputs of NOX and PM10. Simulations of pollutant dispersion were also performed for all other sources in the areas (traffic, heating, industries, and agriculture). Exposure estimates at home location were made using G.I.S. Results: A total of 13251 births were included in the eight areas. We considered yearly simulations data and for each incinerator we made 5 yearly simulations (2002–2006). These models considered monthly activity of the plants within the considered time period. By means of GIS analyses, we constructed an individual-based database with 5 yearly concentration values for incinerator, and average concentration values for all the other sectors. We calculated a weighted exposure value for each first trimester and 9-month pregnancy period. The mean weighted exposure values of pollution due to incinerator during pregnancy periods are 0.57 nanograms/m3 (s.d. 0.93) for PM10 and 0.052 micrograms/m3 (s.d. 0.045) for NOX. The corresponding values for traffic pollution are 23.10 (s.d. 38.23) and 39.32 (s.d. 30.29) micrograms/m3. Conclusion: We proposed a geographical approach to exposure assessment for a population of newborn near incinerators. Collected information is the basis for exposure indicators and subsequent epidemiological analysis.

External drift kriging of NOx concentrations with dispersion model output in a reduced air quality monitoring network

In the mid nineteen eighties the Dutch NOx air quality monitoring network was reduced from 73 to 32 rural and city background stations, leading to higher spatial uncertainties. In this study, several other sources of information are being used to help reduce uncertainties in parameter estimation and spatial mapping. For parameter estimation, we used Bayesian inference. For mapping, we used kriging with external drift (KED) including secondary information from a dispersion model. The methods were applied to atmospheric NOx concentrations on rural and urban scales. We compared Bayesian estimation with restricted maximum likelihood estimation and KED with universal kriging. As a reference we also included ordinary least squares (OLS). Comparison of several parameter estimation and spatial interpolation methods was done by cross-validation. Bayesian analysis resulted in an error reduction of 10 to 20% as compared to restricted maximum likelihood, whereas KED resulted in an error reduction of 50% as compared to universal kriging. Where observations were sparse, the predictions were substantially improved by inclusion of the dispersion model output and by using available prior information. No major improvement was observed as compared to OLS, the cause presumably being that much good information is contained in the dispersion model output, so that no additional spatial residual random field is required to explain the data. In all, we conclude that reduction in the monitoring network could be compensated by modern geostatistical methods, and that a traditional simple statistical model is of an almost equal quality.

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.

Uncertainty assessment of local [formula omitted] concentrations derived from error-in-variable external drift kriging and its relationship to the 2010 air quality standard

Local NO 2 concentrations near Rotterdam (Netherlands) were assessed for the year 2010, focusing on the uncertainties and the changes in exceedance of European air quality standards. In the first step of the 2-step assessment method, the background contribution was determined by error-in-variable external drift kriging, where measurements and dispersion model output in the 1987–2003 period were combined. The result was subsequently extrapolated using dispersion model output and an emission scenario for 2010. In the second step, the local traffic contribution was added on the basis of a local generic dispersion model with use of an emission scenario for 2010. This resulted in maps showing local NO 2 concentrations, upper and lower limits, and probabilities of exceeding the 40 μ g m - 3 air quality standard. The probabilistic measures were calculated in numbers and translated into words for easier communication. Using this method and scenario we found that within about 100 m from the highways near Rotterdam the mean NO 2 concentrations are likely to exceed the standard in 2010. The chance of exceeding the standard is unlikely up to 1 km from the highways, where the mean is expected to be below the standard in 2010.

A model for external drift kriging with uncertain covariates applied to air quality measurements and dispersion model output

We present a method that combines uncertain air quality measurements with uncertain secondary information from an atmospheric dispersion model. The method combines external drift kriging and a measurement error (ME) model, and uses Bayesian techniques for inference. An illustration with simulated data shows what can theoretically be expected. The method is flexible for assigning different error variances to both the primary information and secondary information at each location. Next, we address actual NO2 data collected at an urban and a rural site in the Netherlands. Uncertainty assessments in terms of exceeding air quality standards are given. The study shows that biased uncertain secondary information can be used successfully in a spatial interpolation study at the national scale. Copyright © 2005 John Wiley & Sons, Ltd.

An evaluation of volcanic cloud detection techniques during recent significant eruptions in the western ‘Ring of Fire’

We examined recent volcanic cloud events in the Western Pacific and Indonesian area, to validate the performance of remote sensing techniques used to support the International Airways Volcano Watch (IAVW). Five events were considered, during which eruptions from eight volcanoes injected ash into the upper troposphere or lower stratosphere. For one of the eruptions, at Miyakejima, Japan, at least five aircraft encountered volcanic ash clouds, and the cost to three operators alone exceeded US $12,000,000 in aircraft repairs, diversions, and lost operating time. We performed ‘reverse’ absorption and ‘pattern analysis’ using GMS-5/VISSR, MODIS and AVHRR data, and we examined TOMS SO 2 and Aerosol Index data, surface-based observations, pilot reports, and dispersion model output. Our results verify that the introduction of ‘reverse’ absorption using the geostationary GMS-5 platform significantly enhanced our capacity to monitor volcanic ash clouds in the region. In one case, we tracked an eruption cloud for approximately 80 h. The primary impediment to remote monitoring is the presence of overlying cloud, or substantial amounts of ice within the volcanic clouds. TOMS data showed success in identifying volcanic clouds during these conditions, but was limited by the infrequency of observations. More effective future operation of the IAVW relies on developing complementary methods of volcanic cloud remote sensing, and greatly increasing the amount and quality of available surface and air observations, including observations of precursor activity. An understanding of the likely future limitations of remote sensing techniques will aid in the refining of IAVW procedures.

Air Quality Management in UK Local Authorities: Public Understanding and Participation

Science and technology are an integral part of informing public policy on air pollution and transport management. Expectations of science and technology by society and their use by air quality policy makers has led to increasing reliance upon this evidence to promote health and well-being. This can be seen, for example, in the setting of the UK national objectives for air pollutants based on exposure and health effects. This paper addresses three questions. It assesses, through the use of focus groups, the public perceptions of air pollution and the atmospheric dispersion model output from the Atmospheric Dispersion Modelling System-Urban. Then it assesses their understanding of this information and its current presentation to the layperson. Finally, it assesses how their perceptions and understanding influence their solutions to local air quality management in the London Borough of Camden. The results show that participants in the study struggled with the concept of air pollution outside their sphere of experience, and they did not understand the scientific basis or modelling used to inform local policy. This is partly a result of the way this information is presented. They did not relate the technological evidence to their opinions of acceptable solutions or they expressed reluctance to suggest specific options. It is proposed that a well-informed public would make comprehensive decisions on air quality based upon a broader range of factors, incorporating the scientific reasoning and technological information as well as emotive and socio-economic factors. A balance between science and technological understanding with the validity of local expertise will improve air quality policy making to the greater satisfaction of all stakeholders.

STEP, A statistical procedure for model results evaluation

STEP (STatistical Evaluation Procedure), a fully automated software suited for the analysis of atmospheric transport and dispersion model outputs with the results of experiments is described. The package includes the classical statistical evaluation indicators as well as the more advanced statistical procedures particularly suited to atmospheric transport and dispersion model analysis. The set of statistical parameters proposed can be used to analize model response both about hot spots of concentration and the tails of pollutant distribution. The analysis is performed using a menu-driven system, built with SAS 6.03 AF facilities, that allow the user to fully exploit the software without knowing it in detail. Several models can be compared simultaneously with the measured data. The graphical outputs are displayed on the screen and stored in a graphic SAS catalogue. The software has been implemented on a IBM PC but can be easily transported on all machines supporting SAS system. An example of the software application to Long Range transport and dispersion model is shown.

Air Pollution Model Parameter Estimation Using Simulated LIDAR Data

One way to obtain estimates of the unknown parameters in a pollution-dispersion model is to compare the model predictions with remotely sensed air-quality data. A ground-base LIDAR sensor provides relative pollution-concentration measurements as a function of space and time. The measured sensor data are compared with the dispersion-model output through a numerical estimation procedure to yield parameter estimates that best fit the data. This overall process is tested in a computer simulation to study the effects of various measurement strategies. Such a simulation is useful prior to a field measurement exercise to maximize the information content in the collected data. Parametric studies of simulated data matched to a Gaussian plumedispersion model indicate the trade offs available between estimation accuracy and data-acquisition strategy.

A new modeling technique for air pollution control

The Bay Area Air Pollution Control District of California (US of A) is a regional agency enforcing emission limits from stationary sources within a 6,000 square mile area. Monitoring stations established base line concentrations and trends relative to control effectiveness. Air quality modeling efforts are explained including the use of a climatological model and summary descriptions of dispersion models on regional and local scales and statistical modeling serving to combine the climatological model output with dispersion model output which is necessary for planning and enforcement applications.