Statistical and diagnostic evaluation of the ADMS-Urban model compared with an urban air quality monitoring network

Righi, Serena; Lucialli, Patrizia; Pollini, Elisa

doi:10.1016/j.atmosenv.2009.05.016

Cited by 60 publications

(31 citation statements)

References 16 publications

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“…Consistent with results reported by Righi et al (2009) using ADMS-Urban, the performance of the ADMS4 model decreased with increasing wind speed for CO, PM 10 , and SO 2 , leading subsequently to an increasing level of underpredicted concentrations. At high wind speeds, ADMS assumes high turbulence coefficients that disperse pollutants faster than reality.…”

Section: Influence Of Wind Speedsupporting

confidence: 79%

“…A diagnostic analysis method concentrated on the value of FAC2 was conducted to assess the impact of wind speed on model performance (Chang and Hanna, 2004;Kukkonen et al, 2001;Oettl et al, 2001;Righi et al, 2009). For this purpose, a two-level grouping was applied on the predicted and observed data pairs, the first being based on the location of the pair (LO1 to LO5), and the second on the wind speed recorded at the time of field measurement (WS1 to WS4).…”

Section: Influence Of Wind Speedmentioning

confidence: 99%

“…Thus, appropriate similarity scaling factors can be used to simulate the boundary layer and to calculate a vertical profile of the corresponding turbulence (Carruthers, 2010;Leroy et al, 2010;Righi et al, 2009). These models can define the atmosphere as a continuum with meteorological parameters and turbulence fluctuations allowed to vary with height, leading to a more physically realistic description of the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

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Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

El‐Fadel

Abi-Esber

2012

Journal of the Air & Waste Management Association

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In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NO X ), carbon monoxide (CO), fine particulate matter (PM 10 ) and sulfur dioxide (SO 2 )) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement 'd' values reaching 0.58, fractional bias 'FB' and geometric mean bias 'MG' values approaching 0 and 1, respectively, and normalized mean square error 'NMSE' values as low as 2.17. However, median ratios of predicted to observed concentrations 'Cp/Co' at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NO X , CO, PM 10 and SO 2 , respectively, and the fraction of predictions within a factor of two of observations 'FAC2' values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.Implications: In the absence of site-specific source emission factors, the use of internationally reported emission factors without testing their validity may generate significant errors. Instead, recorded field measurements and meteorological data may be combined with atmospheric transport and dispersion models to better estimate source emissions, particularly in regulatory compliance studies. In this context, lower model performance is expected at higher wind speeds for most indicators such as CO, PM 10 , and SO 2 .

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Section: Influence Of Wind Speedsupporting

confidence: 79%

Section: Influence Of Wind Speedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

El‐Fadel

Abi-Esber

2012

Journal of the Air & Waste Management Association

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“…Concentrations of pollutants related to road transport most highly are emitted during morning and afternoon rush hours when traffic intensity in the city is the highest. Concentrations are also higher in narrow streets with buildings on both sides (Baltrėnas et al 2008;Brauer et al 2000;Cyrys et al 2000;Righi et al 2009). …”

Section: Introductionmentioning

confidence: 94%

Estimation of inter-seasonal differences in NO2 concentrations using a dispersion ADMS-Urban model and measurements

Dėdelė

Miškinytė

2014

Air Qual Atmos Health

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Air pollution dispersion modeling is one of the methods for the ambient air quality assessment. The mathematical models are widely used tools for predicting air pollution and accurately assessing the spatial distribution of air pollutants in the city. Road transport is one of the main sources of pollution affecting air quality in urban areas, and nitrogen dioxide is considered to be one of the most common with traffic emission-related pollutant, which concentrations are strongly correlated to the distance from the roadways. The study aim was to determine the dispersion of nitrogen dioxide (NO 2 ) concentration and to estimate the inter-seasonal differences in these concentrations at different sites (rural, urban, traffic) using a ADMS-Urban model and measurements in Kaunas city. For the modeling of nitrogen dioxide pollution, the year 2011 was selected. Average concentrations of nitrogen dioxide for the winter, summer, and intermediate spring seasons were modeled. Nitrogen dioxide concentrations calculated by model have been verified against 41 Ogawa passive sampling results. The modeling results showed that the highest average seasonal NO 2 concentration was modeled in the winter season (21.79 μg/m 3 ), while the lowest concentration was determined in summer (12.28 μg/m 3 ). Correlation coefficient between modeled NO 2 concentrations using the ADMS-Urban model and measured by Ogawa passive samplers was significantly high for all seasons and confirmed the generally good performance of the model.

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“…ADMS urban metoda omogućava predviđanje aerozagađenja (Righi, 2009) dok upravljanje aerozagađenjem omogućava trenutno očitanje aerozagađenja i disperziju istog putem umreženih stanica. Daljim razvojem modela (primjena baze podataka o polutantima, hidrometerološkim parametrima, broju vozila itd...) moguće je nakon perioda (ne manjeg od 5 godina) predvidjeti, odnosno davati prognoze za aerozagađenje.…”

Section: /271unclassified

Model of sustainable air pollution management in urban areas

Stankovic¹

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Statistical and diagnostic evaluation of the ADMS-Urban model compared with an urban air quality monitoring network

Cited by 60 publications

References 16 publications

Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

Estimation of inter-seasonal differences in NO2 concentrations using a dispersion ADMS-Urban model and measurements

Model of sustainable air pollution management in urban areas

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