Low-processing data enrichment and calibration for PM2.5 low-cost sensors-Reference-Cited by-同舟云学术

Low-processing data enrichment and calibration for PM2.5 low-cost sensors

Published:2023 Issue:3 Part B Volume:27 Page:2229-2240
ISSN:0354-9836
Container-title:Thermal Science
language:en
Short-container-title:THERM SCI

Author:

Stojanovic Danka¹^ORCID,Kleut Duska¹^ORCID,Davidovic Milos¹^ORCID,de Vito²^ORCID,Jovasevic-Stojanovic Milena¹^ORCID,Bartonova Alena³^ORCID,Lepioufle Jean-Marie³^ORCID

Affiliation:

1. Vinca Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia

2. ENEA- Agenzia per le Nuove Tecnologie, l’ Energia e lo Sviluppo Economico Sostenibile, C.R. Portici, Portici (Naples), Italy

3. NILU-Norwegian Institute for Air Research, Kjeller, Norway

Abstract

Particulate matter (PM) in air has been proven to be hazardous to human health. Here we focused on analysis of PM data we obtained from the same campaign which was presented in our previous study. Multivariate linear and random forest models were used for the calibration and analysis. In our linear regression model the inputs were PM, temperature and humidity measured with low-cost sensors, and the target was the reference PM measurements obtained from SEPA in the same timeframe.

Funder

Ministry of Education, Science and Technological Development of the Republic of Serbia

Publisher

National Library of Serbia

Subject

Renewable Energy, Sustainability and the Environment

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