Air Quality and Sustainable Development of Urban Agglomerations in the Mediterranean Area: Science, Technology and Policies

Murena, Fabio; Salizzoni, Pietro

doi:10.3390/atmos12040487

Cited by 1 publication

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“…In the SIRANE model, the emission data is evaluated by using an average emission factor weighted by vehicle type on the vehicle fleet registered in one year. Thus, SIRANE does not take into account the presence of any high emitters as high-emission vehicles (HEVs), which could cause concentration peaks [41], as those captured by MONICA during the mobile monitoring campaigns did.…”

Section: Resultsmentioning

confidence: 99%

Using Mobile Monitoring and Atmospheric Dispersion Modeling for Capturing High Spatial Air Pollutant Variability in Cities

et al. 2022

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Air pollution is still one of the biggest environmental threats to human health on a global scale. In urban environments, exposure to air pollution is largely influenced by the activity patterns of the population as well as by the high spatial and temporal variability in air pollutant concentrations. Over the last years, several studies have attempted to better characterize the spatial variations in air pollutant concentrations within a city by deploying dense, fixed as well as mobile, low-cost sensor networks and more recently opportunistic sampling and by improving the spatial resolution of air quality models up to a few meters. The purpose of this work has been to investigate the use of properly designed mobile monitoring campaigns along the streets of an urban neighborhood to assess the capability of an operational air dispersion model as SIRANE at the district scale to capture the local variability of pollutant concentrations. To this end, an IoT ecosystem—MONICA (an Italian acronym for Cooperative Air Quality Monitoring), developed by ENEA, has been used for mobile measurements of CO and NO2 concentration in the urban area of the City of Portici (Naples, Southern Italy). By comparing the mean concentrations of CO and NO2 pollutants measured by MONICA devices and those simulated by SIRANE along the urban streets, the former appeared to exceed the simulated ones by a factor of 3 and 2 for CO and NO2, respectively. Furthermore, for each pollutant, this factor is higher within the street canyons than in open roads. However, the mobile and simulated mean concentration profiles largely adapt, although the simulated profiles appear smoother than the mobile ones. These results can be explained by the uncertainty in the estimation of vehicle emissions in SIRANE as well as the different temporal resolution of measurements of MONICA able to capture local high concentrations.

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Section: Resultsmentioning

confidence: 99%

Using Mobile Monitoring and Atmospheric Dispersion Modeling for Capturing High Spatial Air Pollutant Variability in Cities

et al. 2022

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Air Quality and Sustainable Development of Urban Agglomerations in the Mediterranean Area: Science, Technology and Policies

Abstract: Air quality in urban areas is one of the main challenges of sustainable development [...]

Cited by 1 publication

References 10 publications

Using Mobile Monitoring and Atmospheric Dispersion Modeling for Capturing High Spatial Air Pollutant Variability in Cities

Using Mobile Monitoring and Atmospheric Dispersion Modeling for Capturing High Spatial Air Pollutant Variability in Cities

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