Modeling of an air quality monitoring network with high space-time resolution

Sofia, Daniele; Giuliano, Aristide; Gioiella, Filomena; Poletto, Massimo

doi:10.1016/b978-0-444-64235-6.50035-8

Cited by 16 publications

(12 citation statements)

References 6 publications

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“…Air pollution monitoring networks offer the possibility to measure the spatiotemporal distribution of air pollution in the urban environment for the health and safety of citizens (Singla et al 2018) (Sofia et al 2018a). For example, sensor networks offer the potential to focus on air pollution monitoring reflecting high spatial and temporal variability in pollutant levels (Knox et al 2013) (Sofia et al 2018b). In this way, if a particular pollutant exceeds the target limit, efficient strategies should be adopted to mitigate the air pollution issue and find the pollution sources.…”

Section: Advisory and Preventionmentioning

confidence: 99%

Mitigation strategies for reducing air pollution

Sofia

Gioiella²,

Lotrecchiano

et al. 2020

Environ Sci Pollut Res

Self Cite

134

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Today, it is increasingly recognized that air pollution hurts human health. Consequently, efficient mitigation strategies need to be implemented for substantial environmental and health co-benefits. A valid approach to reducing the air pollution effects on the environment and human health is proposed. Specific guidelines have been elucidated by differentiating them on the base of the final stakeholders (citizens, enterprises, and public authorities), of the emission sources (transport, household energy, industry, and energy generation sector, agriculture, and shipping area), and of the field of implementation (urban and extra-urban context). This paper can provide useful information for governments for the implementation of a strategic plan focused on emphasizing multi-pollutant emission reductions and overall air pollution-related risk.

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Section: Advisory and Preventionmentioning

confidence: 99%

Mitigation strategies for reducing air pollution

Sofia

Gioiella²,

Lotrecchiano

et al. 2020

Environ Sci Pollut Res

Self Cite

134

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show abstract

“…Even if it is always possible to identify some recurring phases of the monitoring activity connected to the evolution of the fire, the measurement campaign will still have to be designed based on the event, the materials and substances affected by the fire, the place and its environmental characteristics and sensitivity. In order to have a complete view of the environmental situation, it is recommended to install in urban areas air quality monitoring networks, which, beside fixed monitoring stations [3], could also include mobile monitoring stations of the new generations, like Real-time on-road monitoring stations [4]. Such kind of network ensures high space-time resolution of air quality measurements, which can be used not only to define the levels of pollution but also to trace back the pollution and define its sources [5].…”

Section: Introductionmentioning

confidence: 99%

Pollution Dispersion from a Fire Using a Gaussian Plume Model

Lotrecchiano¹,

Sofia²,

Giuliano³

et al. 2020

IJSSE

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A deterministic approach was used in this work to assess the PM2.5 pollutant dispersion in the air during a fire event. The pollution data were recorded with high time resolution by a monitoring station located 4 km SouthWest from the fire. The pollutant emission due to the fire was described as an equivalent stack having the height of the observed cloud of generated smoke. The pollutant dispersion was modelled by means of a Gaussian plume dispersion model. To this purpose, the unknown equivalent emission mass flow rate at the stack in the model was found out using the available experimental data of PM2.5 measured on the ground far away, considering the changing of the air stability between nighttime and daytime and the variable wind direction. Model results highlighted that the predicted maximum pollutant concentration was larger of an order of magnitude than the data value recorded at the monitoring station and exceeded the law limit value. A sensitivity analysis on the wind speed and the atmospheric stability conditions was performed as well to identify the worst case scenarios in case of a fire event. The main conclusion is that a dense network of measurement stations with high time resolution is necessary to properly monitor an area or to provide validation data for any predicting dispersion model in case of a pollutant release.

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“…In particular, the levels of air pollution have changed in recent years, attracting the interest of governance, municipalities, and citizens [2]. As is well known, air pollution has a direct impact on urban pollution [3] and human health [4], especially in developing and industrialized countries [5] that should, instead, implement air quality management.…”

Section: Introductionmentioning

confidence: 99%

Novel Air Pollution Measurement System Based on Ethereum Blockchain

Sofia

Lotrecchiano

Giuliano

et al. 2020

JSAN

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The need to protect sensitive data is growing, and environmental data are now considered sensitive. The application of last-generation procedures such as blockchains coupled with the implementation of new air quality monitoring technology allows the data protection and validation. In this work, the use of a blockchain applied to air pollution data is proposed. A blockchain procedure has been designed and tested. An Internet of Things (IoT)-based sensor network provides air quality data in terms of particulate matter of two different diameters, particulate matter (PM)10 and PM2.5, volatile organic compounds (VOC), and nitrogen dioxide (NO2) concentrations. The dataset also includes meteorological parameters and vehicular traffic information. This work foresees that the data, recovered from traditional Not Structured Query Language (NoSQL) database, and organized according to some specifications, are sent to the Ethereum blockchain daily automatically and with the possibility to choose the period of interest manually. There was also the development of a transaction management and recovery system aimed at retrieving data, formatting it according to the specifications and organizing it into files of various formats. The blockchain procedure has therefore been used to track data provided by air quality monitoring networks unequivocally.

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Modeling of an air quality monitoring network with high space-time resolution

Cited by 16 publications

References 6 publications

Mitigation strategies for reducing air pollution

Mitigation strategies for reducing air pollution

Pollution Dispersion from a Fire Using a Gaussian Plume Model

Novel Air Pollution Measurement System Based on Ethereum Blockchain

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