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Title: Monitoring and prediction of particulate matter (PM2.5 and PM10) around the IPBeja Campus
Authors: Silva, Flávia Matias Oliveira da
Alexandrina, Eduardo Carlos
Pardal, Ana Cristina
Carvalhos, Maria Teresa
Lui, Elaine Schornobay
Keywords: Particulate matter
Air quality
Neural networks
Issue Date: 16-Dec-2021
Publisher: MDPI
Citation: Silva, F., Alexandrina, E., Pardal, A., Carvalhos, M. & Lui, E. (2022). Monitoring and prediction of particulate matter (PM2.5 and PM10) around the IPBeja Campus. Sustainability, 14(24), 1-9.
Abstract: Nowadays, most of the world’s population lives in urban centers, where air quality stand- 12 ards are not strictly observed; citizens are exposed to air quality levels over the limits of the World 13 Health Organization. The interaction between the issuing and atmospheric sources influences the 14 air quality or level. The local climatic conditions (temperature, humidity, winds, rainfall) determine 15 a greater or less dispersion of the pollutants present. In this sense, this work aimed to build a math 16 modelling prediction to monitor the air quality around the campus of IPBeja, which is in the vicinity 17 of a car traffic zone. The study analyzed the data from the last months, particulate matter (PM10 18 and PM2.5), and meteorological parameters for prediction using NARX. The device contains a par- 19 ticle sensor (NOVA SDS011), a microcontroller ESP8266 NodeMCU v3, a temperature sensor, hu- 20 midity, pressure BME280, and a suction tube. The results show a considerable increase in particles 21 in occasional periods, reaching average values of 135 μg/m3 for PM10 and 52 μg/m3 for PM2.5. 22 Thus, the monitoring and prediction serve as a warning to perceive these changes and be able to 23 relate them to natural phenomena or issuing sources in specific cases.
Peer reviewed: yes
ISSN: 2071-1050
Publisher version:
Appears in Collections:D-TCA - Artigos em revistas indexadas à WoS/Scopus

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