Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling

Mihăiţă, AS; Dupont, L; Chery, O; Camargo, M; Cai, C

Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling

Mihăiţă, AS

Dupont, L Chery, O Camargo, M Cai, C

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Publication Type:: Journal Article
Citation:: Journal of Cleaner Production, 2019, 221 pp. 398 - 418
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Mihăiţă, AS https://orcid.org/0000-0001-7670-5777	en_US
dc.contributor.author	Dupont, L	en_US
dc.contributor.author	Chery, O	en_US
dc.contributor.author	Camargo, M	en_US
dc.contributor.author	Cai, C	en_US
dc.date.available	2021-08-12T19:01:04Z
dc.date.issued	2019-06-01	en_US
dc.identifier.citation	Journal of Cleaner Production, 2019, 221 pp. 398 - 418	en_US
dc.identifier.issn	0959-6526	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132807
dc.description.abstract	© 2019 Elsevier Ltd Air pollution impact assessment is a major objective for various community councils in large cities, which have lately redirected their attention towards using more low-cost sensing units supported by citizen involvement. However, there is a lack of research studies investigating real-time mobile air-quality measurement through smart sensing units and even more of any data-driven modelling techniques that could be deployed to predict air quality accurately from the generated data-sets. This paper addresses these challenges by: a) proposing a comparative and detailed investigation of various air quality monitoring devices (both fixed and mobile), tested through field measurements and citizen sensing in an eco-neighbourhood from Lorraine, France, and by b) proposing a machine learning approach to evaluate the accuracy and potential of such mobile generated data for air quality prediction. The air quality evaluation consists of three experimenting protocols: a) first, we installed fixed passive tubes for monitoring the nitrogen dioxide concentrations placed in strategic locations highly affected by traffic circulation in an eco-neighbourhood, b) second, we monitored the nitrogen dioxide registered by citizens using smart and mobile pollution units carried at breathing level; results revealed that mobile-captured concentrations were 3–5 times higher than the ones registered by passive-static monitoring tubes and c) third, we compared different mobile pollution stations working simultaneously, which revealed noticeable differences in terms of result variability and sensitivity. Finally, we applied a machine learning modelling by using decision trees and neural networks on the mobile-generated data and show that humidity and noise are the most important factors influencing the prediction of nitrogen dioxide concentrations of mobile stations.	en_US
dc.relation.ispartof	Journal of Cleaner Production	en_US
dc.relation.isbasedon	10.1016/j.jclepro.2019.02.179	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Environmental Sciences	en_US
dc.title	Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling	en_US
dc.type	Journal Article
utslib.citation.volume	221	en_US
utslib.for	0907 Environmental Engineering	en_US
utslib.for	0910 Manufacturing Engineering	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	221	en_US

Abstract:

© 2019 Elsevier Ltd Air pollution impact assessment is a major objective for various community councils in large cities, which have lately redirected their attention towards using more low-cost sensing units supported by citizen involvement. However, there is a lack of research studies investigating real-time mobile air-quality measurement through smart sensing units and even more of any data-driven modelling techniques that could be deployed to predict air quality accurately from the generated data-sets. This paper addresses these challenges by: a) proposing a comparative and detailed investigation of various air quality monitoring devices (both fixed and mobile), tested through field measurements and citizen sensing in an eco-neighbourhood from Lorraine, France, and by b) proposing a machine learning approach to evaluate the accuracy and potential of such mobile generated data for air quality prediction. The air quality evaluation consists of three experimenting protocols: a) first, we installed fixed passive tubes for monitoring the nitrogen dioxide concentrations placed in strategic locations highly affected by traffic circulation in an eco-neighbourhood, b) second, we monitored the nitrogen dioxide registered by citizens using smart and mobile pollution units carried at breathing level; results revealed that mobile-captured concentrations were 3–5 times higher than the ones registered by passive-static monitoring tubes and c) third, we compared different mobile pollution stations working simultaneously, which revealed noticeable differences in terms of result variability and sensitivity. Finally, we applied a machine learning modelling by using decision trees and neural networks on the mobile-generated data and show that humidity and noise are the most important factors influencing the prediction of nitrogen dioxide concentrations of mobile stations.

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http://hdl.handle.net/10453/132807