Simulating the production and dispersion of environmental pollutants in aerosol phase in an urban area of great historical and cultural value

Librando, Vito; Tringali, Giuseppe; Calastrini, F.; Gualtieri, Giovanni

doi:10.1007/s10661-008-0598-6

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Actual information, such as emission standards (from EURO-III to EURO-VI), and additional mitigation measures (e.g., diesel particulate filter) fitted at the tailpipe for each vehicle are very difficult (if not impossible) to obtain during routine monitoring. Some studies adopted a vehicular emission model (e.g., Mobile 6 and COPERT4) to better mimic the variation in road traffic emissions and then to feed the information into an air dispersion model, including a CFD model [39][40][41]. However, this kind of model requires many inputs, such as fuel consumption, fleet configuration, trip length, distribution of vehicle miles traveled by road types, average speed distribution by road types, annual mileage, which are not available in many areas/countries; thus, large uncertainty in the simulated traffic emissions and, in turn, the air quality simulations results.…”

Section: Results Of Cfd Modelmentioning

confidence: 99%

Application of a Machine Learning Method for Prediction of Urban Neighborhood-Scale Air Pollution

Wai

2023

IJERPH

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Urban air pollution has aroused growing attention due to its associated adverse health effects. A model which could promptly predict urban air quality with considerable accuracy is, therefore, important and will benefit the development of smart cities. However, only a computational fluid dynamics (CFD) model could better resolve the dispersion behavior within an urban canyon layer. A machine learning (ML) model using the Artificial Neural Network (ANN) approach was formulated in the current study to investigate vehicle-derived airborne particulate (PM10) dispersion within a compact high-rise-built environment. Various measured meteorological parameters and PM10 concentrations were adopted as the model inputs to train the ANN model. A building-resolved CFD model under the same environmental settings was also set up to compare its model performance with the ANN model. Our results showed that the ANN model exhibited promising performance (r = 0.82, fractional bias = 0.002) when comparing the > 1000 h PM10 measurements. When comparing the diurnal hourly measured PM10 variations in a clear-sky day, both the ANN and CFD models performed well (r > 0.8). The good performance of the CFD model relied on the knowledge of the in situ diurnal traffic profile, the adoption of suitable mobile source emission factor(s) (e.g., from MOBILE 6 and COPERT4), and the use of urban thermal and dynamical variables to capture PM10 variations in both neutral and unstable atmospheric conditions. These requirements/constraints make it impractical for daily operation. On the contrary, the ML (ANN) model adopted here is free from these constraints and is fast (less than 0.1% computational time relative to the CFD model). These results demonstrate that the ANN model is a superior option for a smart city application.

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Section: Results Of Cfd Modelmentioning

confidence: 99%

Application of a Machine Learning Method for Prediction of Urban Neighborhood-Scale Air Pollution

Wai

2023

IJERPH

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“…In particular, this was the case of the test and validation of STREET model ) for a 3-month period to calculate the concentrations of carbon monoxide (CO; Tartaglia et al 1995), nitrogen oxides (NO x ; Gualtieri and Tartaglia 1997) and total suspended particles (TSP; Librando et al 2009). Furthermore, the CO and NO x validated STREET model eventually became part of a geographic information system purposely developed for predicting urban traffic air pollution in the city of Florence (Gualtieri and Tartaglia 1998).…”

Section: Scope Of the Workmentioning

confidence: 99%

“…Therefore, the final result of the modelling tool is an output file including 1-h CO concentrations sorted by wind sector calculated by each dispersion model. The CALINE4 model application was made based on FORTRAN modelling tool formerly implemented in Librando et al (2009) with the only change in the analyzed pollutant, i.e., CO in place of TSP.…”

Section: Concentrationsmentioning

confidence: 99%

A Street Canyon Model Intercomparison in Florence, Italy

Gualtieri

2010

Water Air Soil Pollut

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Assessing air quality in street canyons is a crucial concern, as the highest pollution levels and threshold exceedances are usually experienced within this kind of urban streets. A brief overview based on experimental studies and modelling techniques undertaken in literature is presented, including characteristic features affecting wind flow and pollutant dispersion within street canyons. In this work, a numerical street canyon model intercomparison has been performed in a typical urban canyon in Florence, Italy. In particular, STREET, Canyon Plume Box Model (CPBM) and Operational Street Pollution Model (OSPM) have been applied to compute the street-level 1-h carbon monoxide (CO) concentrations. In addition, the CALINE4 model has been applied to test the site morphology. Input data cover a 1-year time period and include meteorological observations as well as measured traffic volumes and driving speeds. Hourly road emissions have been calculated using the COPERT methodology taking into account vehicle fleet, traffic flows and driving speed, as well as ambient temperature to account for cold overemissions. A preliminary experimental data analysis has been carried out in order to investigate the dependence of observed CO concentrations on meteorological and traffic parameters. Hourly CO concentrations observed over the full year have been used to compare the STREET, CPB and OSP models, resulting in a detailed statistical analysis carried out by wind sector as well as on a seasonal basis.

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