The TOMPs ambient air monitoring network – Continuous data on UK air quality for over 20 years

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.

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Development of a hazardous materials dispersion modeling capability with application to ambient air monitoring network design

10.2172/5410605 ◽

1989 ◽

Author(s):

C. Hodgin

Keyword(s):

Network Design ◽

Monitoring Network ◽

Hazardous Materials ◽

Ambient Air ◽

Air Monitoring ◽

Dispersion Modeling ◽

Monitoring Network Design ◽

Ambient Air Monitoring

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Ambient air monitoring of nitrogen dioxide at Kalimati, Tirtomartani, Kalasan, Sleman, Yogyakarta

MATEC Web of Conferences ◽

10.1051/matecconf/201815402005 ◽

2018 ◽

Vol 154 ◽

pp. 02005

Author(s):

Supriyanto ◽

Indah Suci Ramadhani

Keyword(s):

Air Quality ◽

Nitrogen Dioxide ◽

Ambient Air ◽

Threshold Level ◽

Air Monitoring ◽

Sampling Location ◽

Human Being ◽

Main Road ◽

Plywood Industry ◽

Ambient Air Monitoring

Nitrogen dioxide (NO2) is one of parameters in air quality according to Indonesia Government No. 41/1999. NO2 has influence in respiratory problem of human being if it exceeds threshold level (400 μgNO2/Nm3). This study is air monitoring of NO2 concentration in ambient air and it uses a Griess Saltzman method using the spectrophotometer (Indonesia Standard SNI 19-7119.2-2005) near of Plywood Industry and Main Road. The sampling location has two points at Kalimati Village, Tirtomartani, Kalasan, Sleman, D.I Yogyakarta. On December 2016, the results show which NO2 concentration is below threshold level with 0,469 μg NO2/Nm3 at first point and 0,234 μg NO2/Nm3 at second point.

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An air dispersion model for the city of Toronto, Ontario, Canada

10.32920/ryerson.14653017.v1 ◽

2021 ◽

Author(s):

Barbara Sylvestre-Williams

Keyword(s):

Air Quality ◽

Dispersion Model ◽

Ground Level ◽

Ambient Air ◽

Air Monitoring ◽

Monitoring Station ◽

Dispersion Models ◽

Air Dispersion ◽

Air Dispersion Model ◽

Ambient Air Monitoring

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.

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