Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise - Part II

被引：80

作者：

Borrego, C. ^{[1
,2
,3
]}

Ginja, J. ^{[1
]}

Coutinho, M. ^{[1
]}

Ribeiro, C. ^{[1
]}

Karatzas, K. ^{[4
]}

Sioumis, Th ^{[4
]}

Katsifarakis, N. ^{[4
]}

Konstantinidis, K. ^{[4
]}

De Vito, S. ^{[5
]}

Esposito, E. ^{[5
]}

Salvato, M. ^{[5
]}

Smith, P. ^{[6
,15
]}

Andre, N. ^{[7
]}

Gerard, P. ^{[7
]}

Francis, L. A. ^{[7
]}

Castell, N. ^{[8
]}

Schneider, P. ^{[8
]}

Viana, M. ^{[9
]}

Minguillon, M. C. ^{[9
]}

Reimringer, W. ^{[10
]}

Otjes, R. P. ^{[11
]}

von Sicard, O. ^{[12
]}

Pohle, R. ^{[12
]}

Elen, B. ^{[13
]}

Suriano, D. ^{[14
]}

Pfister, V ^{[14
]}

Prato, M. ^{[14
]}

Dipinto, S. ^{[14
]}

Penza, M. ^{[14
]}

机构：

[1] IDAD Inst Environm & Dev, Campus Univ, P-3810193 Aveiro, Portugal

[2] Univ Aveiro, CESAM, P-3810193 Aveiro, Portugal

[3] Univ Aveiro, Dept Environm & Planning, P-3810193 Aveiro, Portugal

[4] Aristotle Univ Thessaloniki, Dept Mech Engn, Thessaloniki 54124, Greece

[5] CR Portici, Smart Networks & Photovolta Div, ENEA, I-80055 Portici, NA, Italy

[6] Univ Cambridge, Dept Chem, Cambridge, England

[7] Catholic Univ Louvain, Inst Informat & Commun Technol, Ottignies, Belgium

[8] NILU Norwegian Inst Air Res, Inst Veien 18, N-2027 Kjeller, Norway

[9] Spanish Natl Res Council, IDAEA CSIC, Jordi Girona 18, Barcelona 08034, Spain

[10] 3S Sensors Signal Proc Syst GmbH, D-66121 Saarbrucken, Germany

[11] ECN Energy Res Ctr Netherlands, Petten, Netherlands

[12] Siemens AG, Corp Technol, Munich, Germany

[13] VITO, Mol, Belgium

[14] ENEA, Lab Funct Mat & Technol Sustainable Applicat, I-72100 Brindisi, Italy

[15] IQFR CSIC, Calle Serrano 119, Madrid 28004, Spain

来源：

ATMOSPHERIC ENVIRONMENT | 2018年 / 193卷

关键词：

Air quality monitoring; Reference methods; Low-cost microsensors; Experimental campaign; Measurement uncertainty; Machine learning; FIELD CALIBRATION; AVAILABLE SENSORS; MONITORING; PART; POLLUTION; NETWORK; OLFACTION; EXPOSURE; CLUSTER;

D O I：

10.1016/j.atmosenv.2018.08.028

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The EuNetAir Joint Exercise focused on the evaluation and assessment of environmental gaseous, particulate matter (PM) and meteorological microsensors versus standard air quality reference methods through an experimental urban air quality monitoring campaign. This work presents the second part of the results, including evaluation of parameter dependencies, measurement uncertainty of sensors and the use of machine learning approaches to improve the abilities and limitations of sensors. The results confirm that the microsensor platforms, supported by post processing and data modelling tools, have considerable potential in new strategies for air quality control. In terms of pollutants, improved correlations were obtained between sensors and reference methods through calibration with machine learning techniques for CO (r(2) = 0.13-0.83), NO2 (r(2) = 0.24-0.93), 03 (r(2) = 0.22-0.84), PM10 (r(2) = 0.54-0.83), PM2.5 (r(2) = 0.33-0.40) and SO2 (r(2) = 0.49-0.84). Additionally, the analysis performed suggests the possibility of compliance with the data quality objectives (DQO) defined by the European Air Quality Directive (2008/50/EC) for indicative measurements.

引用

页码：127 / 142

页数：16

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