Virtual Sensors: Towards High-Resolution Air Pollution Monitoring using AI and IoT

Martha Arbayani Zaidan; Naser Hossein Motlagh; Brandon E. Boor; David Lu; Petteri Nurmi; Tuukka Petäjä; Aijun Ding; Markku Kulmala; Sasu Tarkoma; Tareq Hussein

Virtual Sensors: Towards High-Resolution Air Pollution Monitoring using AI and IoT

Martha Arbayani Zaidan, Naser Hossein Motlagh, Brandon E. Boor, David Lu, Petteri Nurmi, Tuukka Petäjä, Aijun Ding, Markku Kulmala, Sasu Tarkoma, Tareq Hussein

Tutkimustuotos: Artikkelijulkaisu › Artikkeli › Tieteellinen › vertaisarvioitu

Abstrakti

The present paper contributes a research vision for virtual sensing that combines Artificial Intelligence (AI) and Internet of Things (IoT) to increase the coverage of air quality information. Virtual sensors take advantage of correlations between different pollutants to estimate the concentrations of pollutants for which no affordable sensors are available. We cover key requirements and challenges, reflecting on current state-of-the-art and identifying key research challenges. We also demonstrate the potential and feasibility of virtual sensing through experiments conducted with data from Helsinki, Finland, which show how standard PM2.5 and temperature measurements can be used to provide reliable estimates of CO2 and black carbon concentrations. We also discuss potential applications that can benefit from the implementation of virtual air pollution sensors and establish a research roadmap for the path forward.

Alkuperäiskieli	englanti
Lehti	IEEE Internet of Things Magazine
Sivumäärä	8
ISSN	2576-3180
Tila	Hyväksytty/In press - 2023
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

Pääsy asiakirjaan

Virtual Sensors_Towards High-Resolution Air Pollution Monitoring using AI and IoT

Siteeraa tätä

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title = "Virtual Sensors: Towards High-Resolution Air Pollution Monitoring using AI and IoT",

abstract = "The present paper contributes a research vision for virtual sensing that combines Artificial Intelligence (AI) and Internet of Things (IoT) to increase the coverage of air quality information. Virtual sensors take advantage of correlations between different pollutants to estimate the concentrations of pollutants for which no affordable sensors are available. We cover key requirements and challenges, reflecting on current state-of-the-art and identifying key research challenges. We also demonstrate the potential and feasibility of virtual sensing through experiments conducted with data from Helsinki, Finland, which show how standard PM2.5 and temperature measurements can be used to provide reliable estimates of CO2 and black carbon concentrations. We also discuss potential applications that can benefit from the implementation of virtual air pollution sensors and establish a research roadmap for the path forward.",

author = "Zaidan, {Martha Arbayani} and {Hossein Motlagh}, Naser and Boor, {Brandon E.} and David Lu and Petteri Nurmi and Tuukka Pet{\"a}j{\"a} and Aijun Ding and Markku Kulmala and Sasu Tarkoma and Tareq Hussein",

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T1 - Virtual Sensors: Towards High-Resolution Air Pollution Monitoring using AI and IoT

AU - Zaidan, Martha Arbayani

AU - Hossein Motlagh, Naser

AU - Boor, Brandon E.

AU - Lu, David

AU - Nurmi, Petteri

AU - Petäjä, Tuukka

AU - Ding, Aijun

AU - Kulmala, Markku

AU - Tarkoma, Sasu

AU - Hussein, Tareq

PY - 2023

Y1 - 2023

N2 - The present paper contributes a research vision for virtual sensing that combines Artificial Intelligence (AI) and Internet of Things (IoT) to increase the coverage of air quality information. Virtual sensors take advantage of correlations between different pollutants to estimate the concentrations of pollutants for which no affordable sensors are available. We cover key requirements and challenges, reflecting on current state-of-the-art and identifying key research challenges. We also demonstrate the potential and feasibility of virtual sensing through experiments conducted with data from Helsinki, Finland, which show how standard PM2.5 and temperature measurements can be used to provide reliable estimates of CO2 and black carbon concentrations. We also discuss potential applications that can benefit from the implementation of virtual air pollution sensors and establish a research roadmap for the path forward.

AB - The present paper contributes a research vision for virtual sensing that combines Artificial Intelligence (AI) and Internet of Things (IoT) to increase the coverage of air quality information. Virtual sensors take advantage of correlations between different pollutants to estimate the concentrations of pollutants for which no affordable sensors are available. We cover key requirements and challenges, reflecting on current state-of-the-art and identifying key research challenges. We also demonstrate the potential and feasibility of virtual sensing through experiments conducted with data from Helsinki, Finland, which show how standard PM2.5 and temperature measurements can be used to provide reliable estimates of CO2 and black carbon concentrations. We also discuss potential applications that can benefit from the implementation of virtual air pollution sensors and establish a research roadmap for the path forward.

M3 - Article

JO - IEEE Internet of Things Magazine

JF - IEEE Internet of Things Magazine

SN - 2576-3180

ER -