English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

. Low-cost sensors (LCSs) are an appealing solution to the problem of spatialresolution in air quality measurement, but they currently do not have thesame analytical performance as regulatory reference methods. Individualsensors can be susceptible to analytical cross-interferences; have randomsignal variability; and experience drift over short, medium and longtimescales. To overcome some of the performance limitations of individualsensors we use a clustering approach using the instantaneous median signalfrom six identical electrochemical sensors to minimize the randomized driftsand inter-sensor differences. We report here on a low-power analytical device(  W) that is comprised of clusters of sensors for NO2 , Ox , CO and total volatile organic compounds(VOCs) and that measures supporting parameters such as water vapour and temperature.This was tested in the field against reference monitors, collecting ambientair pollution data in Beijing, China. Comparisons were made of NO2 and Ox clustered sensor data against reference methods forcalibrations derived from factory settings, in-field simple linear regression(SLR) and then against three machine learning (ML) algorithms. The parametricsupervised ML algorithms, boosted regression trees (BRTs) and boosted linearregression (BLR), and the non-parametric technique, Gaussian process (GP),used all available sensor data to improve the measurement estimate of NO2 and Ox . In all cases ML produced anobservational value that was closer to reference measurements than SLR alone.In combination, sensor clustering and ML generated sensor data of a qualitythat was close to that of regulatory measurements (using the RMSE metric) yetretained a very substantial cost and power advantage.

atmospheric measurement techniques

An improved low-power measurement of ambient NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; combining electrochemical sensor clusters and machine learning