A surrogate-assisted measurement correction method for accurate and low-cost monitoring of particulate matter pollutants

Air pollution involves multiple health and economic challenges. Its accurate and low-cost monitoring is important for developing services dedicated to reduce the exposure of living beings to the pollution. Particulate matter (PM) measurement sensors belong to the key components that support operation of these systems. In this work, a modular, mobile Internet of Things sensor for PM measurements has been proposed. Due to a limited accuracy of the PM detector, the measurement data are refined using a two-stage procedure that involves elimination of the non-physical signal spikes followed by a non-linear correction of the responses using a multiplicative surrogate model. The correction layer is derived from the sparse and non-uniform calibration data, i.e., a combination of the measurements from the PM monitoring station and the sensor obtained in the same location over a specified (relatively short) interval. The device and the method have been both demonstrated based on the data obtained during three measurement campaigns. The proposed correction scheme improves the fidelity of PM measurements by around two orders of magnitude w.r.t. the responses for which the post-processing has not been considered. Performance of the proposed surrogate-assisted technique has been favorably compared against the benchmark approaches from the literature

Teknisk løsning for nasjonalt beregningsverktøy. Dataflyt, systemdesign og krav til infrastruktur.

Rapporten beskriver dataflyt, systemdesign og krav til infrastruktur for et system som modellerer og beregner luftkvalitet. Rapporten inneholder også beskrivelse av brukergrensesnittet i en webløsning

Example of image recognition and classification of wood-based heating technology for fireplaces, new stoves and old stoves.

<p>Example of image recognition and classification of wood-based heating technology for fireplaces, new stoves and old stoves.</p

Turnover rate (% y-1) per county estimated from the webcrawled sampling.

<p>Turnover rate (% y-1) per county estimated from the webcrawled sampling.</p

Geo-positioned data collected with the webcrawler.

<p>“Without RWC” refers to properties without residential wood combustion technology, and “With RWC” refers to properties with residential wood combustion technology. The rectangle in A corresponds to the area represented in the module B. Reprinted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200650#pone.0200650.ref024" target="_blank">24</a>] under a CC BY license, with permission from NILU - Norwegian Institute for Air Research, original copyright (2018), <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200650#pone.0200650.s001" target="_blank">S1 Fig</a>.</p

Webcrawled data in Oslo municipality.

<p>Red dots represent dwellings provided with wood-based technologies for residential heating, blue dots represent dwellings provided with a technology for residential heating different from wood-based technology. The grids represent the relative share of wood-based technologies regarding other heating systems. The rectangle corresponds to the area represented in the module B of the figure. Reprinted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200650#pone.0200650.ref023" target="_blank">23</a>] under a CC BY license, with permission from NILU - Norwegian Institute for Air Research, original copyright (2018), <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0200650#pone.0200650.s002" target="_blank">S2 Fig</a>.</p

Average detection precision for the wood burning technologies.

<p>Average detection precision for the wood burning technologies.</p

A surrogate-assisted measurement correction method for accurate and low-cost monitoring of particulate matter pollutants

Air pollution involves multiple health and economic challenges. Its accurate and low-cost monitoring is important for developing services dedicated to reduce the exposure of living beings to the pollution. Particulate matter (PM) measurement sensors belong to the key components that support operation of these systems. In this work, a modular, mobile Internet of Things sensor for PM measurements has been proposed. Due to a limited accuracy of the PM detector, the measurement data are refined using a two-stage procedure that involves elimination of the non-physical signal spikes followed by a non-linear correction of the responses using a multiplicative surrogate model. The correction layer is derived from the sparse and non-uniform calibration data, i.e., a combination of the measurements from the PM monitoring station and the sensor obtained in the same location over a specified (relatively short) interval. The device and the method have been both demonstrated based on the data obtained during three measurement campaigns. The proposed correction scheme improves the fidelity of PM measurements by around two orders of magnitude w.r.t. the responses for which the post-processing has not been considered. Performance of the proposed surrogate-assisted technique has been favorably compared against the benchmark approaches from the literature

The influence of vegetation drought stress on formaldehyde and ozone distributions over a central European city

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