Statistical PM_2.5 Prediction in an Urban Area Using Vertical Meteorological Factors

Autor(en)

Jutapas Saiohai, Surat Bualert, Thunyapat Thongyen, Kittichai Duangmal, Parkpoom Choomanee, Wladyslaw W. Szymanski

Abstrakt

A key concern related to particulate air pollution is the development of an early warning system that can predict local PM2.5 levels and excessive PM2.5 concentration episodes using vertical meteorological factors. Machine learning (ML) algorithms, particularly those with recognition tasks, show great potential for this purpose. The objective of this study was to compare the performance of multiple linear regression (MLR) and multilayer perceptron (MLP) in predicting PM2.5 levels. The software was trained to predict PM2.5 levels up to 7 days in advance using data from long-term measurements of vertical meteorological factors taken at five heights above ground level (AGL)—10, 30, 50, 75, and 110 m—and PM2.5 concentrations measured 30 m AGL. The data used were collected between 2015 and 2020 at the Microclimate and Air Pollutants Monitoring Tower station at Kasetsart University, Bangkok, Thailand. The results showed that the correlation coefficients of PM2.5 predicted and observed using MLR and MLP were in the range of 0.69–0.86 and 0.64–0.82, respectively, for 1–3 days ahead. Both models showed satisfactory agreement with the measured data, and MLR performed better than MLP at PM2.5 prediction. In conclusion, this study demonstrates that the proposed approach can be used as a component of an early warning system in cities, contributing to sustainable air quality management in urban areas.

Organisation(en)

Aerosolphysik und Umweltphysik

Externe Organisation(en)

Kasetsart University

Journal

Atmosphere

Band

14

Anzahl der Seiten

15

ISSN

2073-4433

DOI

https://doi.org/10.3390/atmos14030589

Publikationsdatum

03-2023

Peer-reviewed

Ja

ÖFOS 2012

103037 Umweltphysik, 105208 Atmosphärenchemie

Schlagwörter

ASJC Scopus Sachgebiete

Environmental Science (miscellaneous), Atmospheric Science

Sustainable Development Goals

SDG 11 – Nachhaltige Städte und Gemeinden

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/d8d1bb3b-c627-4c49-a955-3a6b67e54b86