Emission Dispersion Models Research Articles

Remote sensing of eruptions and transport of Taal volcano in January 2020

The eruption and transport of the Taal volcano (14 N, 121 E) in January 2020 have been investigated using remote sensing measurements of several satellites, including the Moderate Resolution Imaging Spectroradiometer, Ozone Monitoring Instrument (OMI), Ozone Mapping and Profiler Suite, and Himawari-8. The aerosol optical thickness (AOT), angstrom exponent (AE), and column densities of sulfur dioxide (SO2) derived from satellites are analyzed in this study. The ground-based AERONET data of Manila city and OMI SO2 are studied to validate high AOT events. Our findings indicate that ash and gases followed opposite transport paths. This differential transport is consistent with various wind data, HYSPLIT back and forward trajectory calculations, and emission dispersion models. The potential influences of deposition on ocean biology and climate are also discussed.

Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.

Effect of Mixing Height on Box Model Calculation in Sulfur Dioxide Dispersion Modeling of Oil and Gas Industries

This study was conducted to determine the effect of height in the calculation of box model dispersion modeling. The experimental method was conducted using sample data of sulfur dioxide exhaust emissions produced by the oil and gas industry. The samples were obtained from an oil and gas company that produces sulfur dioxide emissions. There are two types of data obtained, namely hourly emission data and daily emission data generated by the industry with the amount of data obtained for 360 days. The results showed that the mixing height used in the box model formulation affects the results of the calculation. The optimal mixing height obtained from the study was 75 meters where all sample data used in the calculation showed that there were only 3.5% or 11 samples that did not meet the ambient air quality standards. The results also showed that the higher the mixing height used, the lower the emission of sulfur dioxide emission dispersion modeling calculation results. While calculations with lower mixing height will produce results that are inversely proportional to the concentration of sulfur dioxide emissions, the concentration of sulfur dioxide emissions produced will be higher. Therefore, sulfur dioxide dispersion modeling using a box model can be an alternative in determining the concentration of sulfur dioxide emissions produced.

ФОРМИРОВАНИЕ ОНЛАЙН-КАРТ ЗАГРЯЗНЕНИЯ АТМОСФЕРНОГО ВОЗДУХА Г. ИЖЕВСКА, ФОРМИРУЕМОГО ВЫБРОСАМИ ПЕРЕДВИЖНЫХ ИСТОЧНИКОВ

The study deals with the assessment of spatio-temporal dynamics of atmospheric pollution caused by emissions from road traffic flows. Nitrogen dioxide concentrations were calculated in the atmospheric air near Udmurtskaya street of the city of Izhevsk. The work was based on a mathematical model created for the calculation of emissions and their dispersion taking into account the dynamics of traffic flows, wind conditions. As the initial data, we used video recordings from continuous automatic recording of the traffic flow intensity. When calculating the emission of pollutants, we applied the model presented in the Methodology for Determining Emissions of Pollutants into the Atmospheric Air from Mobile Sources for Performing Summary Calculations of Atmospheric Air Pollution (approved by Order of the Ministry of Natural Resources of the Russian Federation No. 804 of November 27, 2019). To build a scattering calculation model, we used Methods for Calculating Dispersion of Emissions of Harmful (Polluting) Substances in the Atmospheric Air (approved by Order of the Ministry of Natural Resources of the Russian Federation No. 273 of June 6, 2017). To automate the process of calculating the scattering of impurities in the atmospheric air, a program code was created using the Go programming language. The data obtained by means of the implementation of the program code were visualized in the form of a dynamic map of the concentration distribution posted on the website https://komfortsreda.udsu.ru/ in the section Izhevsk/Atmospheric air/Dynamics of NO2 concentrations. This study deals with a new promising direction in the assessment of natural environments – assessment performed through online modeling. Emission dispersion modeling and assessment of the current state of atmospheric air are important as contributing to the general characterization of the ecological state of an urban area.

Hygienic assessment of the development of non-carcinogenic risk to population health in the area of air pollution during medical waste incineration

The purpose of this study is to assess the risk of developing non-carcinogenic effects for public health due to the release of chemicals into the air from medical waste incinerators. An assessment of the public health risk observed in monitoring the growth of waste consumption is necessary to determine the likelihood of adverse effects on organs and systems. The study included the analysis and ranking of chemicals that are part of the industrial emissions of enterprises burning medical waste, emission dispersion modeling with calculation of ground concentrations and assessment of non-carcinogenic risks to public health. Analysis of noncarcinogenic health risks showed the likelihood of adverse effects on organs and systems. It has been established that the maximum levels of chronic non-carcinogenic risk during inhalation intake of pollutants from the atmospheric air (in the zone of influence of the medical waste incineration enterprise no. 1) are noted for such components such as sulfur dioxide, benzo/a/pyrene, and in the zone of influence of the medical waste incinerator no. 2 — for sulfur dioxide, benz/a/pyrene, nitrogen dioxide and suspended solids. The maximum values of the total hazard indices exceed the permissible values for the impact on the respiratory organs, cases of additional mortality, development processes, the immune system, and the hematopoietic system. These levels of risk from exposure to medical waste incinerators have been assessed as unacceptable for the public. The study confirms the need to develop practical recommendations and preventive measures to reduce the impact of chemical factors on public health.

Development of a moving point source model for shipping emission dispersion modeling in EPISODE–CityChem v1.3

Abstract. This paper demonstrates the development of a moving point source (MPS) model for simulating the atmospheric dispersion of pollutants emitted from ships under movement. The new model is integrated into the chemistry transport model EPISODE–CityChem v1.3. In the new model, ship parameters, especially speed and direction, are included to simulate the instantaneous ship positions and then the emission dispersion at different simulation time. The model was first applied to shipping emission dispersion modeling under simplified conditions, and the instantaneous and hourly averaged emission concentrations predicted by the MPS model and the commonly used line source (LS) and fixed point source (FPS) models were compared. The instantaneous calculations were quite different due to the different ways to treat the moving emission sources by different models. However, for the hourly averaged concentrations, the differences became smaller, especially for a large number of ships. The new model was applied to a real configuration from the seas around Singapore that included hundreds of ships, and their dispersion was simulated over a period of a few hours. The simulated results were compared to measured values at different locations, and it was found that reasonable emission concentrations were predicted by the moving point source model.

Global and local sensitivity analysis of the Emission Dispersion Model input parameters

PurposeThe objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO2 generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO2) emitted by an industrial fire in a plant located in an industrial region site in Algeria.Design/methodology/approachThe SA was defined for the following input parameters: wind speed, NO2 emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient.FindingsThe study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO2 initial concentration has the most influence on the predicted NO2 levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO2 concentration estimation.Originality/valueThe study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

Emission Dispersion Modelling using AERMOD on GT 1.3 PLTGU Muara Karang in Evaluate Combustor Upgrade Effect

Muara Karang power plant dominated by Gas Turbine Power Plant. One of those there is Gas Turbin 1.3 has been upgraded, thus make load ouput become increased. This study aims to see the emission dispersion generated from combustor before and after being upgraded. The emission data is obtained from combustion modelling on combustor GT 1.3 pre and post extendor using CFD methode. Then, the data will be used for emission dispersion modelling using AERMOD. The emission dispersion modelling uses two condition. First is the initial state based on environment assessment document in 2010 and second is in current environment state. The modelling shows that after the combustor upgraded, the NOx emission increase by 15%, CO emission decrease by 37%. Emission dispersion modelling result shows that there is a change in the area that has the highest concentration. In the initial environment state, area with highest concentration is on the west side of the power plant with NOx concentration is 35.46303 ug/m3, And CO concentration is 1.3362 ug/m3. In the current environment state, the area is on the east side of the power plant area with highest concentration is NOx concentration is 61.47481ug/m3 And CO concentration is 2.3163 ug/m3.

Emission dispersion modelling of Gresik power plant using Gaussian analysis to evaluate environmental impacts in receptor point

Air pollution is one of the impacts that must be concerned by Gresik Power Plant. The presence or absence of pollution can be seen in the ambient air quality around the plant location which is routinely monitored every six months. However, the monitoring data is not representative because there is no daily data conducted. Therefore, emission dispersion modeling can be carried out to obtain ambient air conditions around the plant site. In this research, the modeling is carried out using Gaussian Analysis for the HRSG chimney 3.1 and it can be seen that the area around the power plant and Pulopancikan village are significantly affected. This is indicated by the presence of a red zone in the results of the modeling and the concentration of NO2 pollutants can reach 892.65 µg/m3 exceeding the government standard (150 µg/m3). However, the validation of the model shows significant gap due to some circumstances such as meteorology condition, vehicles pollution, and human activities. In consequence, such a modeling can be developed further. For instance, the study needs to forecast emission dispersion of all active chimneys on the site in order to capture environmental condition and more concern to meteorology condition and other pollution sources.

Emission dispersion modeling and geospatial analysis of vehicular emissions in some parts of Benin City, Nigeria

Over the years, decline in air quality has been connected to the growing rate of urbanization and increasing number of vehicles on the roads. Most of the pollutants emitted from vehicular activities have been observed to have adverse effects on individuals as well as the atmosphere. Although, the focus of this study is to develop an emission dispersion model to predict the concentration of specific air pollutants with distance, the application of geostatistical technique such as Kriging interpolation to study the spatial distribution of pollutants from vehicular emissions around the study area was also exemplified. Seven (7) georeferenced points, namely, Ugbowo main gate, Ekosodin junction, Agen junction, Super D junction, Nitel junction, Okhunmwun junction and Oluku market junction were used for data collection. Pollutants from vehicular emissions, namely, nitrogen dioxide (NO2), carbon monoxide (CO) including the total radiation were monitored in the morning and evening for a period of 35 days (7th July to 12th August 2020) with the aid of portable toxic gas monitors and radiation alert meters. Other parameters of interest, which were also measured include maximum temperature and wind speed using infra-red thermometers and portable anemometer respectively. To ascertain the quality of the data, selected preliminary analysis, namely, test of normality, test of homogeneity, outlier detection and reliability test were done. Result of the study showed a high concentration of NO2, CO and total radiation around Ugbowo main gate and Okhunmwun community and environs especially during the peak hours of evening (5.0 p.m. – 6.0 p.m.) when the traffic load is high.

Integrated model for estimating odor emissions from civil wastewater treatment plants.

The objective of this research project was the design and development of an integrated model for odor emission estimation in wastewater treatment plants. The SMAT's plant, the largest wastewater treatment facility in Italy, was used as a case study. This article reports the results of the characterization phase that led to the definition and design of the proposed conceptual model for odor emission estimation. In this phase, concentrations of odor chemical tracers (VOC, H2S, NH3) and odor concentrations were monitored repeatedly. VOC screening with GC-MS analysis was also performed. VOC concentrations showed significant variability in space and magnitude. NH3 and H2S were also detected at considerable concentrations. Results were elaborated to define a spatially variable linear relationship between the sum of odor activity values (SOAV) and odor concentrations. Based on the results, a conceptual operational model was presented and discussed. The proposed system is composed by a network of continuous measurement stations, a set of algorithms for data elaboration and synchronization, and emission dispersion modeling with the application of Lagrangian atmospheric models.

Assessment of the Contamination by 14C Airborne Releases in the Vicinity of the Ignalina Nuclear Power Plant

ABSTRACTA radiocarbon (14C) activity analysis in the tree rings around Ignalina nuclear power plant (INPP) has been carried out with the aim to test the hypothesis to use 14C tree-ring analysis data as a tool for the reconstruction of gaseous releases from NPP to the environment. The INPP has been in decommissioning state since the end of 2009. Tree-ring samples for 14C analysis were collected 7 yr after final power unit shutdown from the INPP vicinity. The samples from 5 sampling locations were collected, prepared and measured using the Single Stage Accelerator Mass Spectrometer (SSAMS). Data analysis represents observable Ignalina NPP influence by 14C increase up to 15 pMC (percent modern carbon) in tree rings. Good correlations of the 14C concentrations and wind direction were obtained. The main purpose of this article was to match 14C measurement data along with the atmospheric dispersion modeling of emissions in order to retrospectively characterize the emission source.

PM10 and CO Dispersion Modeling of Emissions from the Four Thermal Power Plants in Mashhad-Iran

In this study, we present an evaluation of the (PM10) and carbon monoxide (CO) particulate matter exposure level originated from the four power plants in the area using Air Pollution Dispersion Model. Combined use of AREMOD (The American Meteorological Society/Environmental Protection Agency Regulatory Model), ArcGIS and health risk assessment were applied to estimate the level of pollution in thirteen municipal receptors in the city. The results indicated the long-range transport of the pollutants from the power plants expected to impose significant health impacts on residential receptors. Almost 80000 inhabitants of the city were exposed to PM10 concentration, ranging between 50-75 µg/m3 and 100000 were exposed to CO concentration, ranging between 40-45 µg/m3. Approximately, 1200 hectares of the city were exposed to PM10 concentration, ranging from 40 to 50 µg/m3 and 370 hectares of the city area were exposed to CO concentration between 50-75 µg/m3. Comparison between simulated and observed concentrations of pollutants shows a little overestimation by model.

Air quality assessment and modelling of pollutants emission from a major cement plant complex in Nigeria

Cement manufacturing contributes to the elevation of air pollutants in the atmosphere and thus impact on the nearby communities. This study assessed air quality in a major Cement Plant in Ibese Ogun State, Nigeria, through an ambient air quality monitoring and air emission dispersion modelling. Particulate Matter (PM) and gaseous pollutants were measured using portable samplers and AERMOD View was used for the emission dispersion modelling. Combustion products including SO 2 , NO, NO 2 , CO and VOCs were the gaseous pollutants detected along the complex fenceline and in the receptor environments. Pollutants measurements were undertaken at 23 locations within the fence line and receptor locations. The daily SO 2 and NO 2 Federal Ministry of Environment - Nigeria (FMEnv) limits were exceeded in ten (10) and five (5) locations along the fenceline, respectively. Particulates were detected in all the locations along the fenceline and in the communities. The cumulative gaseous pollutants resulting from simultaneous operations of all the identified plant air emission point sources are 0.01–276.13% of their respective 24-h limits along the fenceline, with 1-h SO 2 within the threshold limit at all fenceline locations, but 1-h NO X exceeds the threshold limit at all locations 16–21 times. The 24-h CO and VOCs are within their limits at all fenceline locations; however the 24-h SO 2 and NO X are breaching the limits at some locations 30–34 times (0.34–0.39% of the investigation period) and 44–87 times, respectively. Daily and Annual averaging concentrations of PM 10 was 14.32–31.54% and 4.90–52.60% of their respective limits. Process facilities are the major point sources of atmospheric emissions identified in the factory. Several fugitive emission sources were also identified during the field work. Comprehensive evaluation of the fugitive emission sources should be carried out in the cement plant for immediate attention. • Pollutants measurements were undertaken at 23 locations around a Nigerian major cement plant complex. • AERMOD View was used to model the impact of the cement plant activities on the receptor environment. • The daily SO 2 and NO 2 FMEnv - Nigeria and the World Bank’s limits were exceeded in 10 locations. • Process facilities are the major point sources of atmospheric emissions identified in the factory.

An adaptive semi-Lagrangian advection model for transport of volcanic emissions in the atmosphere

Abstract. The dispersion of volcanic emissions in the Earth atmosphere is of interest for climate research, air traffic control and human wellbeing. Current volcanic emission dispersion models rely on fixed-grid structures that often are not able to resolve the fine filamented structure of volcanic emissions being transported in the atmosphere. Here we extend an existing adaptive semi-Lagrangian advection model for volcanic emissions including the sedimentation of volcanic ash. The advection of volcanic emissions is driven by a precalculated wind field. For evaluation of the model, the explosive eruption of Mount Pinatubo in June 1991 is chosen, which was one of the largest eruptions in the 20th century. We compare our simulations of the climactic eruption on 15 June 1991 to satellite data of the Pinatubo ash cloud and evaluate different sets of input parameters. We could reproduce the general advection of the Pinatubo ash cloud and, owing to the adaptive mesh, simulations could be performed at a high local resolution while minimizing computational cost. Differences to the observed ash cloud are attributed to uncertainties in the input parameters and the course of Typhoon Yunya, which is probably not completely resolved in the wind data used to drive the model. The best results were achieved for simulations with multiple ash particle sizes.

Evaluating Health Co-Benefits of Climate Change Mitigation in Urban Mobility.

There is growing recognition that implementation of low-carbon policies in urban passenger transport has near-term health co-benefits through increased physical activity and improved air quality. Nevertheless, co-benefits and related cost reductions are often not taken into account in decision processes, likely because they are not easy to capture. In an interdisciplinary multi-model approach we address this gap, investigating the co-benefits resulting from increased physical activity and improved air quality due to climate mitigation policies for three urban areas. Additionally we take a (macro-)economic perspective, since that is the ultimate interest of policy-makers. Methodologically, we link a transport modelling tool, a transport emission model, an emission dispersion model, a health model and a macroeconomic Computable General Equilibrium (CGE) model to analyze three climate change mitigation scenarios. We show that higher levels of physical exercise and reduced exposure to pollutants due to mitigation measures substantially decrease morbidity and mortality. Expenditures are mainly born by the public sector but are mostly offset by the emerging co-benefits. Our macroeconomic results indicate a strong positive welfare effect, yet with slightly negative GDP and employment effects. We conclude that considering economic co-benefits of climate change mitigation policies in urban mobility can be put forward as a forceful argument for policy makers to take action.

The traffic emission-dispersion model for a Central-European city agrees with measured black carbon apportioned to traffic

The bottom-up traffic emission model EMISENS is used to calculate hourly black carbon (BC) and nitrogen oxides (NOx) emission rates on an average workday in Maribor (Slovenia), using emission factors that were previously determined with the on-road chasing measurements in Slovenia. From modeled NOx emission rates and in-situ NOx measurements we empirically determined the hourly dispersion of traffic emissions and applied it to model BC concentrations using BC emission rates. We compared the modeled BC concentrations with in-situ BC concentration measurements over three periods in winters 2011–2012 and 2012–2013, a total of 67 workdays. Measured BC concentrations were first apportioned to traffic using the top-down Aethalometer model. We found that the bottom-up modeled BC concentrations overestimated the top-down apportioned values by only 19%, 32% and 6% in each of the three investigated time periods, respectively. We did not find any influence of meteorology on the performance of the model. This is the first time that BC source apportionment results were used to evaluate traffic emissions calculated using the bottom-up modeling approach. We demonstrate that the two independent approaches yield similar results. We use thus validated emission inventory for evaluating different emission reduction scenarios. We show that excluding 10% of vehicles that are highest BC or NOx emitters would reduce the total BC or NOx emissions from traffic in Maribor by 39% and 33% respectively.

A hybrid model predictive control for traffic flow stabilization and pollution reduction of freeways

In this work a control system is developed and analyzed for the suppression of moving jamwaves and the reduction of pollutant concentrations near motorways. The system is based on the second-order macroscopic freeway traffic model METANET, joined by an emission dispersion model, introduced in a previous work of the authors. For the control tasks dedicated controllers are designed, both using the nonlinear model predictive control method. The control objectives require a distinction in the utilized control measures, thus different controllers are designed and used in predefined control modes. The first mode of the controller is responsible for keeping pollutant concentrations below prescribed limits under stable conditions. The second mode of the controller is working in case of a shockwave threat, aiming for traffic stabilization. Between the control modes switching is based on an appropriate rule set that satisfies the stability of the controlled system. The hybrid controller structure is realized by a finite automata. A complex case study is presented for the evaluation of the suggested controller.

Comparative analysis of spatio-temporal exposure assessment methods for estimating odor-related responses in non-urban populations

The assessment of air pollution exposures in epidemiological studies does not always account for spatio-temporal variability of pollutants concentrations. In the case of odor studies, a common approach is to use yearly averaged odorant exposure estimates with low spatial resolution, which may not capture the spatio-temporal variability of emissions and therefore distort the epidemiological results. This study explores the use of different exposure assessment methods for time-variant ammonia exposures with high spatial resolution, in rural communities exposed to odors from agricultural and livestock farming activities. Exposure estimations were based on monthly ammonia concentrations from emission-dispersion models. Seven time-dependent residential NH3 exposures variables were investigated: 1) Annual mean of NH3 exposures; 2) Maximum annual NH3 exposure; 3) Area under the exposure curve; 4) Peak area; 5) Peak-to-mean ratio; 6) Area above the baseline (annual mean of NH3 exposures); and 7) Maximum positive slope of the exposure curve. We developed binomial and multinomial logistic regression models for frequency of odor perception and odor annoyance responses based on each temporal exposure variable. Odor responses estimates, goodness of fit and predictive abilities derived from each model were compared. All time-dependent NH3 exposure variables, except peak-to-mean ratio, were positively associated with odor perception and odor annoyance, although the results differ considerably in terms of magnitude and precision. The best goodness of fit of the predictive binomial models was obtained when using maximum monthly NH3 exposure as exposure assessment variable, both for odor perception and annoyance. The best predictive performance for odor perception was found when annual mean was used as exposure variable (accuracy=71.82%, Cohen's Kappa=0.298) whereas odor annoyance was better predicted when using peak area (accuracy=68.07%, Cohen's Kappa=0.290). Our study highlights the importance of taking temporal variability into account when investigating odor-related responses in non-urban residential areas.

A novel approach for exposure assessment in air pollution epidemiological studies using neuro-fuzzy inference systems: Comparison of exposure estimates and exposure-health associations

Many epidemiological studies have used proximity to sources as air pollution exposure assessment method. However, proximity measures are not generally good surrogates because of their complex non-linear relationship with exposures. Neuro-fuzzy inference systems (NFIS) can be used to map complex non-linear systems, but its usefulness in exposure assessment has not been extensively explored. We present a novel approach for exposure assessment using NFIS, where the inputs of the model were easily-obtainable proximity measures, and the output was residential exposure to an air pollutant. We applied it to a case-study on NH3 pollution, and compared health effects and exposures estimated from NFIS, with those obtained from emission-dispersion models, and linear and non-linear regression proximity models, using 10-fold cross validation. The agreement between emission-dispersion and NFIS exposures was high (Root-mean-square error (RMSE) =0.275, correlation coefficient (r)=0.91) and resulted in similar health effect estimates. Linear models showed poor performance (RMSE=0.527, r=0.59), while non-linear regression models resulted in heterocedasticity, non-normality and clustered data. NFIS could be a useful tool for estimating individual air pollution exposures in epidemiological studies on large populations, when emission-dispersion data are not available. The tradeoff between simplicity and accuracy needs to be considered.