scholarly journals Modeling air-quality in complex terrain using mesoscale and dispersion models – part II: Evaluation of a dispersion model

2003 ◽  
Vol 9 (3) ◽  
Author(s):  
B. J. Abiodun ◽  
M. Mohr ◽  
L. Enger
Author(s):  
R. V. Ramos ◽  
A. C. Blanco

Abstract. Mapping of air quality are often based on ground measurements using gravimetric and air portable sensors, remote sensing methods and atmospheric dispersion models. In this study, Geographic Information Systems (GIS) and geostatistical techniques are employed to evaluate coarse particulate matter (PM10) concentrations observed in the Central Business District of Baguio City, Philippines. Baguio City has been reported as one of the most polluted cities in the country and several studies have already been conducted in monitoring its air quality. The datasets utilized in this study are based on hourly simulations from a Gaussian-based atmospheric dispersion model that considers the impacts of vehicular emissions. Dispersion modeling results, i.e., PM10 concentrations at 20-meter interval, show that high values range from 135 to 422 μg/mm3. The pollutant concentrations are evident within 40 meters from the roads. Spatial variations and PM10 estimates at unsampled locations are determined using Ordinary Kriging. Geostatistical modeling estimates are evaluated based on recommended values for mean error (ME), root mean square error (RMSE) and standardized errors. Optimal predictors for pollutant concentrations at 5-meter interval include 2 to 5 search neighbors and variable smoothing factor for night-time datasets while 2 to 10 search neighbors and smoothing factors 0.3 to 0.5 were used for daytime datasets. Results from several interpolation tests indicate small ME (0.0003 to 0.0008 μg/m3) and average standardized errors (4.24 to 8.67 μg/m3). RMSE ranged from 2.95 to 5.43 μg/m3, which are approximately 2 to 3% of the maximum pollutant concentrations in the area. The methodology presented in this paper may be integrated with atmospheric dispersion models in refining estimates of pollutant concentrations, in generating surface representations, and in understanding the spatial variations of the outputs from the model simulations.


2021 ◽  
Author(s):  
Barbara Sylvestre-Williams

Air quality is a major concern for the public; therefore, the reliability of models in predicting the air quality accurately is of a major interest. The objective of this study was to develop an air dispersion model and demonstrate that it can be successfully used in place of or in conjunction with ambient air monitoring stations in determining the local Air Quality Index (AQI). This thesis begins with a review of existing atmospheric dispersion models, specifically, the Gaussian Plume models and their capabilities to handle the atmospheric chemistry of nitrogen oxides (NOx) and sulphur dioxides (SO₂). It also includes a review of wet deposition in the form of in-cloud, below-cloud, and snow scavenging. Existing dispersion models are investigated to assess their capability of representing atmospheric chemistry, specifically in the context of NOx and SO₂x substances and their applications to urban areas. A review was completed of previous studies where Gaussian dispersion models were applied to major cities around the world such as London, Helsinki, Kanto, and Prague, to predict ground level concentrations NOx and SO₂. For the purpose of this thesis, Gaussian air dispersion model was developed, known as the Air dispersion model for the Road Sources in Urban areas (ARSUS) model, which is capable of predicting ground level concentrations for a contaminant of interest. The ARSUS model was validated against the US EPA ISC3 model before it was used to conduct the two studies in this investigation. These two studies simulated weekday morning rush hour tailpipe emissions of CO and predicted ground level concentrations. The first study used the ARSUS model ARSUS model to predict ground level concentrations of CO from the tailpipe emissions of CO for roads and highways located in the vicinity of the Toronto West ambient air monitoring station. The second study involved an expansion of the domain to predict ground level concentrations of CO from tailpipe emissions from highways located in the City of Toronto. The modelled concentrations were then compared to the Toronto West ambient air monitoring station. ARSUS model’s results indicate that air quality in the immediate vicinity of roads or highways is highly impacted by the tailpipe emissions. Higher concentrations are observed for the areas adjacent to the road and highway sources. The tailpipe emissions of CO from highways have a higher contribution to the local air quality. The predicted ground level concentration from the ARSUS model do under-predict when compared to the observed data from the monitoring station; however, despite this a predictive model is viable.


2021 ◽  
Author(s):  
Barbara Sylvestre-Williams

Air quality is a major concern for the public; therefore, the reliability of models in predicting the air quality accurately is of a major interest. The objective of this study was to develop an air dispersion model and demonstrate that it can be successfully used in place of or in conjunction with ambient air monitoring stations in determining the local Air Quality Index (AQI). This thesis begins with a review of existing atmospheric dispersion models, specifically, the Gaussian Plume models and their capabilities to handle the atmospheric chemistry of nitrogen oxides (NOx) and sulphur dioxides (SO₂). It also includes a review of wet deposition in the form of in-cloud, below-cloud, and snow scavenging. Existing dispersion models are investigated to assess their capability of representing atmospheric chemistry, specifically in the context of NOx and SO₂x substances and their applications to urban areas. A review was completed of previous studies where Gaussian dispersion models were applied to major cities around the world such as London, Helsinki, Kanto, and Prague, to predict ground level concentrations NOx and SO₂. For the purpose of this thesis, Gaussian air dispersion model was developed, known as the Air dispersion model for the Road Sources in Urban areas (ARSUS) model, which is capable of predicting ground level concentrations for a contaminant of interest. The ARSUS model was validated against the US EPA ISC3 model before it was used to conduct the two studies in this investigation. These two studies simulated weekday morning rush hour tailpipe emissions of CO and predicted ground level concentrations. The first study used the ARSUS model ARSUS model to predict ground level concentrations of CO from the tailpipe emissions of CO for roads and highways located in the vicinity of the Toronto West ambient air monitoring station. The second study involved an expansion of the domain to predict ground level concentrations of CO from tailpipe emissions from highways located in the City of Toronto. The modelled concentrations were then compared to the Toronto West ambient air monitoring station. ARSUS model’s results indicate that air quality in the immediate vicinity of roads or highways is highly impacted by the tailpipe emissions. Higher concentrations are observed for the areas adjacent to the road and highway sources. The tailpipe emissions of CO from highways have a higher contribution to the local air quality. The predicted ground level concentration from the ARSUS model do under-predict when compared to the observed data from the monitoring station; however, despite this a predictive model is viable.


2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Shin Yu ◽  
Chang Tang Chang ◽  
Chih Ming Ma

AbstractThe traffic congestion in the Hsuehshan tunnel and at the Toucheng interchange has led to traffic-related air pollution with increasing concern. To ensure the authenticity of our simulation, the concentration of the last 150 m in Hsuehshan tunnel was simulated using the computational fluid dynamics fluid model. The air quality at the Toucheng interchange along a 2 km length highway was simulated using the California Line Source Dispersion Model. The differences in air quality between rush hours and normal traffic conditions were also investigated. An unmanned aerial vehicle (UAV) with installed PM2.5 sensors was developed to obtain the three-dimensional distribution of pollutants. On different roads, during the weekend, the concentrations of pollutants such as SOx, CO, NO, and PM2.5 were observed to be in the range of 0.003–0.008, 7.5–15, 1.5–2.5 ppm, and 40–80 μg m− 3, respectively. On weekdays, the vehicle speed and the natural wind were 60 km h− 1 and 2.0 m s− 1, respectively. On weekdays, the SOx, CO, NO, and PM2.5 concentrations were found to be in the range of 0.002–0.003, 3–9, 0.7–1.8 ppm, and 35–50 μg m− 3, respectively. The UAV was used to verify that the PM2.5 concentrations of vertical changes at heights of 9.0, 7.0, 5.0, and 3.0 m were 45–48, 30–35, 25–30, and 50–52 μg m− 3, respectively. In addition, the predicted PM2.5 concentrations were 40–45, 25–30, 45–48, and 45–50 μg m− 3 on weekdays. These results provide a reference model for environmental impact assessments of long tunnels and traffic jam-prone areas. These models and data are useful for transportation planners in the context of creating traffic management plans.


2018 ◽  
Author(s):  
Matthias Karl

Abstract. This paper describes the City-scale Chemistry (CityChem) extension of the urban dispersion model EPISODE with the aim to enable chemistry/transport simulations of multiple reactive pollutants on urban scales. The new model is called CityChem-EPISODE. The primary focus is on the simulation of urban ozone concentrations. Ozone is produced in photochemical reaction cycles involving nitrogen oxides (NOx) and volatile organic compounds (VOC) emitted by various anthropogenic activities in the urban area. The performance of the new model was evaluated with a series of synthetic tests and with a first application to the air quality situation in the city of Hamburg, Germany. The model performs fairly well for ozone in terms of temporal correlation and bias at the air quality monitoring stations in Hamburg. In summer afternoons, when photochemical activity is highest, modelled median ozone at an inner-city urban background station was about 30 % lower than the observed median ozone. Inaccuracy of the computed photolysis frequency of nitrogen dioxide (NO2) is the most probable explanation for this. CityChem-EPISODE reproduces the spatial variation of annual mean NO2 concentrations between urban background, traffic and industrial stations. However, the temporal correlation between modelled and observed hourly NO2 concentrations is weak for some of the stations. For daily mean PM10, the performance of CityChem-EPISODE is moderate due to low temporal correlation. The low correlation is linked to uncertainties in the seasonal cycle of the anthropogenic particulate matter (PM) emissions within the urban area. Missing emissions from domestic heating might be an explanation for the too low modelled PM10 in winter months. Four areas of need for improvement have been identified: (1) dry and wet deposition fluxes; (2) treatment of photochemistry in the urban atmosphere; (3) formation of secondary inorganic aerosol (SIA); and (4) formation of biogenic and anthropogenic secondary organic aerosol (SOA). The inclusion of secondary aerosol formation will allow for a better sectorial attribution of observed PM levels. Envisaged applications of the CityChem-EPISODE model are urban air quality studies, environmental impact assessment, sensitivity analysis of sector-specific emission and the assessment of local and regional emission abatement policy options.


2008 ◽  
Vol 42 (32) ◽  
pp. 7384-7396 ◽  
Author(s):  
Soon-Hwan Lee ◽  
Kyoung-Hee Sung ◽  
Hwa-Woon Lee

2011 ◽  
Vol 4 ◽  
pp. ASWR.S6551
Author(s):  
Khandakar Habib Al Razi ◽  
Moritomi Hiroshi ◽  
Kambara Shinji

In Japan, mercury and its compounds were categorized as hazardous air pollutants in 1996 and are on the list of “Substances Requiring Priority Action” published by the Central Environmental Council of Japan. The Air Quality Management Division of the Environmental Bureau, Ministry of the Environment, Japan, selected the current annual mean environmental air quality standard for mercury and its compounds of 0.04 μg/m3. Long-term exposure to mercury and its compounds can have a carcinogenic effect, inducing eg, Minamata disease. This study evaluates the impact of mercury emissions on air quality in the coastal area of the Sea of Japan. Average yearly emission of mercury from an elevated point source in this area with background concentration and one-year meteorological data were used to predict the ground level concentration of mercury. The annual mean concentration distribution of mercury and its compounds were calculated for the middle part of Honshu Island, which served as a background level of mercury concentration for the coastal are of the Sea of Japan. To estimate the concentration of mercury and its compounds in air of the local area, two different simulation models have been used. The first is the National Institute of Advanced Science and Technology Atmospheric Dispersion Model for Exposure and Risk Assessment (AIST-ADMER) that estimates regional atmospheric concentration and distribution. The second is the Ministry of Economy, Trade and Industry Low Rise Industrial Source Dispersion Model (METI-LIS) that estimates the atmospheric concentration distribution in the vicinity of facilities.


2021 ◽  
Vol 13 (22) ◽  
pp. 12748
Author(s):  
Jozef Salva ◽  
Miroslav Vanek ◽  
Marián Schwarz ◽  
Milada Gajtanska ◽  
Peter Tonhauzer ◽  
...  

On-road mobile sources of emissions make important contributions to particulate matter pollution (PM2.5–PM10) in cities. The quantification of such pollution is, however, highly challenging due to the number of interacting factors that affect emissions such as vehicle category, emission standard, vehicle speed and weather conditions. The proper identification of individual sources of emission is particularly necessary for air quality management areas. In this study, we estimated exhaust and non-exhaust traffic-related PM2.5 and PM10 contributions to total ambient pollution in Banská Bystrica (Slovak republic) by simulation based on the AERMOD dispersion model. Emission rates of particular vehicle categories were obtained through vehicle population statistics, traffic data survey and emission factors from the EMEP/EEA air pollutant emission inventory guidebook. Continuous PM10 and PM2.5 data from air quality monitoring stations were analysed for the years 2019–2020 and compared with modelled concentrations. The annual concentration values of PM2.5 and PM10 in the study area reached 16.71 μg/m3 and 15.57 μg/m3, respectively. We found that modelled PM2.5 peak concentration values exceeded the WHO air quality guideline annual mean limit. Traffic-related PM2.5 and PM10 contributions to ambient pollution at the reference point located nearby to a busy traffic route were approximately 25% and 17%, respectively. The reference point located outside the main transport corridors showed an approximately 11% contribution, both for PM2.5 and PM10 concentrations. The simulations showed that PM pollution is greatly contributed to by on-road mobile sources of emissions in the study area, and especially non-exhaust emissions, which require serious attention in association with their health impacts and the selection of Banská Bystrica as an air quality management area.


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