On Assessing the Accuracy of Air Pollution Models Exploiting a Strategic Sensors Deployment

Tse, Rita; Monti, Lorenzo; Prandi, Catia; Aguiari, Davide; Pau, Giovanni; Salomoni, Paola

doi:10.1145/3284869.3284880

Cited by 11 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system includes a sensors infrastructure deployed in the Campus green areas where the trees will be planted to collect data of environmental conditions such as particulate matter (PM 1.0, PM 2.5 and PM 10), carbon dioxide (CO 2 ) and carbon monoxide (CO), nitrogen dioxide (NO 2 ) and ozone (O 3 ), formaldehyde, temperature, related humidity, and air pressure. All these sensors are encapsulated in the CANARIN II sensors station [50]. The idea is to use these data to provide up-to-date information about the environmental conditions, through the online contents (exploitable from different devices) and an in-situ installation.…”

Section: System Overviewmentioning

confidence: 99%

Designing interactive infographics to stimulate environmental awareness: an exploration with a University community

Prandi

Ceccarini

Nisi

et al. 2020

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

The Sustainable Development Goals (SDGs) are a universal call to action addressed not only to governments but also to businesses and civil society. In this scenario, Universities play a key role in achieving the SDGs and, also, in creating awareness about these global issues. As an example, the University of Bologna is carrying out several initiatives related to sustainability matters. Considering, in particular, the environmental dimension, the University undertook different strategies to reduce the waste of paper, embracing the dematerialization and paperless movements. To make these initiatives visible to the University community and to increase awareness about these issues, we designed a system that intends to provide data related to the implications of such initiatives in terms of paper waste avoided and benefits for the environment, to be exploited using different devices. In particular, this study focuses on the design and the evaluation of two infographics, conceived by two different groups of experts and researchers: one "animated" and one "aesthetic". The evaluation intends to present the differences in terms of perceived design and informative dimensions, with the final aim to present insights on the design of infographics able to foster awareness, targeting specific communities.

show abstract

Section: System Overviewmentioning

confidence: 99%

Designing interactive infographics to stimulate environmental awareness: an exploration with a University community

Prandi

Ceccarini

Nisi

et al. 2020

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, the measures of the environmental components collected by our Canarin II include: temperature, relative humidity, PM10, PM1.0, PM2.5, and pressure. A more detailed description of the Canarin II platform, its characteristics and performances can be found in [ 55 ] and in [ 68 ].…”

Section: Hardware Equipment and Deploymentmentioning

confidence: 99%

RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning

Monti

Vincenzi

Mirri

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time–space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring.

show abstract

“…Finally, according to the third group of approaches, a smart campus is developed through the effective use of resources, by providing services to environmental communities [3], reducing costs and improving the quality of life (inside and outside the campus) [6]. In this sense, collecting data about environmental aspects (i.e., air quality, by monitoring pollutants, such as CO2 and Particulate Matters, or PM, [5], [24]) can play a fundamental role and can be improved by the adoption of the first two concepts too.…”

Section: Related Workmentioning

confidence: 99%

“…Even if these data are interesting to analyzed, we were intrigued by augmenting such infrastructure by including other sensors, both for indoor and outdoor measurements. We took this approach with three goals in mind: i) to collect data about other environmental conditions and phenomena; ii) to validate the collected data comparing the different data sources; iii) to make the data collected with our sensors available thought open-data repositories [24]. At the current stage, our sensors infrastructure is composed of environmental sensors (indoor and outdoor), noise sensors, infrared and thermal cameras.…”

Section: The Sensors Layermentioning

confidence: 99%

Smart Campus: Fostering the Community Awareness Through an Intelligent Environment

et al. 2019

Self Cite

View full text Add to dashboard Cite

Interconnected computational devices in the Internet of Things (IoT) context make possible to collect real-time data about a specific environment. The IoT paradigm can be exploited together with data visualization techniques to put into effect intelligent environments, where pervasive technologies enable people to experience and interact with the generated data. In this paper, we present a case study where these emerging areas and related technologies have been explored to benefit communities, making their members actively involved as central players of such an intelligent environment. To give practical effect to our approach, we designed and developed a system, named Smart Campus, composed of: i) an infrastructure made of sensors to collect real-time data in a University Campus, and ii) a rich web-based application to interact with spatio-temporal data, available in a public interactive touch monitor. To validate the system and grasp insights, we involved 135 students through a survey, and we extracted meaningful data from the interactive sessions with the public display. Results show that this Campus community understood the potential of the system and students are willing to actively contribute to it, pushing us to better investigate future scenarios where students can participate with ideas, visualizations/services to integrate into the web-based system, as well as sensors to plug into the infrastructure.

show abstract

On Assessing the Accuracy of Air Pollution Models Exploiting a Strategic Sensors Deployment

Cited by 11 publications

References 16 publications

Designing interactive infographics to stimulate environmental awareness: an exploration with a University community

Designing interactive infographics to stimulate environmental awareness: an exploration with a University community

RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning

Smart Campus: Fostering the Community Awareness Through an Intelligent Environment

Contact Info

Product

Resources

About