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Spatiotemporal correlation of urban pollutants by long-term measurements on a mobile observation platform

Spatiotemporal correlation of urban pollutants by long-term measurements on a mobile observation platform
Spatiotemporal correlation of urban pollutants by long-term measurements on a mobile observation platform
We conducted a three-year campaign of atmospheric pollutant measurements exploiting portable instrumentation deployed on a mobile cabin of a public transport system. Size selected particulate matter (PM) and nitrogen monoxide (NO) were measured at high temporal and spatial resolution. The dataset was complemented with measurements of vehicular traffic counts and a comprehensive set of meteorological covariates. Pollutants showed a distinctive spatiotemporal structure in the urban environment. Spatiotemporal autocorrelations were analyzed by a hierarchical spatiotemporal statistical model. Specifically, particles smaller than 1.1µm exhibited a robust temporal autocorrelation with those at the previous hour and tended to accumulate steadily during the week with a maximum on Fridays. The smallest particles (mean diameter 340 nm) showed a spatial correlation distance of 600 m. The spatial correlation distance reduces to 60 m for particle diameters larger than 1.1µm, which also showed peaks at the stations correlated with the transport system itself. NO showed a temporal correlation comparable to that of particles of 5.0µm of diameter and a correlating distance of 155 m. The spatial structure of NO correlated with that of the smallest sized particles. A generalized additive mixed model was employed to disentangle the effects of traffic and other covariates on PM concentrations. A reduction of 50% of the vehicles produces a reduction of the fine particles of -13% and of the coarse particle number of -7.5%. The atmospheric stability was responsible for the most significant effect on fine particle concentration.
Cable train measurement platform,, Nitrogen monoxide,, Size segregated particulate matter,, Spatiotemporal structure,, Vehicular traffic
0269-7491
Crocchiantia, Stefano
0b33e5ce-e074-4c16-9079-83ad030123de
Del Sartob, Simone
d7409224-93b0-4a78-a014-530e0a8150bc
Giovanna Ranallic, Maria
372a0aaf-c399-48c1-aa16-b1f00765e408
Castellini, Silvia
aa041922-4213-46d3-aeac-c8e92bd1ceb1
Petroselli, Chiara
19266726-2dc0-4790-af77-7ccdc45865eb
Cappelletti, David
a01705e5-ab93-4231-ae3c-1093813a5727
Crocchiantia, Stefano
0b33e5ce-e074-4c16-9079-83ad030123de
Del Sartob, Simone
d7409224-93b0-4a78-a014-530e0a8150bc
Giovanna Ranallic, Maria
372a0aaf-c399-48c1-aa16-b1f00765e408
Castellini, Silvia
aa041922-4213-46d3-aeac-c8e92bd1ceb1
Petroselli, Chiara
19266726-2dc0-4790-af77-7ccdc45865eb
Cappelletti, David
a01705e5-ab93-4231-ae3c-1093813a5727

Crocchiantia, Stefano, Del Sartob, Simone, Giovanna Ranallic, Maria, Castellini, Silvia, Petroselli, Chiara and Cappelletti, David (2021) Spatiotemporal correlation of urban pollutants by long-term measurements on a mobile observation platform. Environmental Pollution, 268, [115645]. (doi:10.1016/j.envpol.2020.115645).

Record type: Article

Abstract

We conducted a three-year campaign of atmospheric pollutant measurements exploiting portable instrumentation deployed on a mobile cabin of a public transport system. Size selected particulate matter (PM) and nitrogen monoxide (NO) were measured at high temporal and spatial resolution. The dataset was complemented with measurements of vehicular traffic counts and a comprehensive set of meteorological covariates. Pollutants showed a distinctive spatiotemporal structure in the urban environment. Spatiotemporal autocorrelations were analyzed by a hierarchical spatiotemporal statistical model. Specifically, particles smaller than 1.1µm exhibited a robust temporal autocorrelation with those at the previous hour and tended to accumulate steadily during the week with a maximum on Fridays. The smallest particles (mean diameter 340 nm) showed a spatial correlation distance of 600 m. The spatial correlation distance reduces to 60 m for particle diameters larger than 1.1µm, which also showed peaks at the stations correlated with the transport system itself. NO showed a temporal correlation comparable to that of particles of 5.0µm of diameter and a correlating distance of 155 m. The spatial structure of NO correlated with that of the smallest sized particles. A generalized additive mixed model was employed to disentangle the effects of traffic and other covariates on PM concentrations. A reduction of 50% of the vehicles produces a reduction of the fine particles of -13% and of the coarse particle number of -7.5%. The atmospheric stability was responsible for the most significant effect on fine particle concentration.

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Spatiotemporal correlation of urban pollutants - Accepted Manuscript
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Accepted/In Press date: 10 September 2020
e-pub ahead of print date: 5 October 2020
Published date: 1 January 2021
Additional Information: Funding Information: We thank MIUR and the Università degli Studi di Perugia for financial support to the project AMIS and to project LEPA, through the program “Dipartimenti di Eccellenza (2018–2022)". We are indebted with people at FAI Instruments which supported with eagerness and friendships the project for the three years of campaign. Comune di Perugia, ARPA Umbria and Leitner spa are also acknowledged for supporting the experimental campaign. We gratefully thank the three anonymous Referees for the constructive comments and recommendations which definitely helped to improve the readability and quality of the paper. Publisher Copyright: © 2020 Elsevier Ltd
Keywords: Cable train measurement platform,, Nitrogen monoxide,, Size segregated particulate matter,, Spatiotemporal structure,, Vehicular traffic

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Local EPrints ID: 444899
URI: http://eprints.soton.ac.uk/id/eprint/444899
ISSN: 0269-7491
PURE UUID: 31ab7270-53f2-481d-a5dd-f3f01ecf1dcb

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Date deposited: 10 Nov 2020 17:31
Last modified: 17 Mar 2024 06:02

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Contributors

Author: Stefano Crocchiantia
Author: Simone Del Sartob
Author: Maria Giovanna Ranallic
Author: Silvia Castellini
Author: Chiara Petroselli
Author: David Cappelletti

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