Artificial Intelligence-assisted Air Quality Monitoring For Smart City Management

Background. The environment has been significantly impacted by rapid urbaniza-
tion, leading to a need for changes in climate change and pollution indicators. The
4IR offers a potential solution to efficiently manage these impacts. Smart city ecosys-
tems can provide well-designed, sustainable, and safe cities that enable holistic climate
change and global warming solutions through various community-centred initiatives.
These include smart planning techniques, smart environment monitoring, and smart
governance. An air quality intelligence platform, which operates as a complete mea-
surement site for monitoring and governing air quality, has shown promising results
in providing actionable insights. This article aims to highlight the potential of ma-
chine learning models in predicting air quality, providing data-driven strategic and
sustainable solutions for smart cities.
Methods. This study proposed an end-to-end air quality predictive model for smart
city applications, utilizing four machine learning techniques and two deep learning
techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The
study was conducted in four different urban cities in Selangor, Malaysia, including
Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air qual-
ity data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally,
meteorological data including wind speed and wind direction were also considered,
and their interactions with the pollutant markers were quantified. The study aimed
to determine the correlation variance of the dependent variable in predicting air pol-
lution and proposed a feature optimization process to reduce dimensionality and re-
move irrelevant features to enhance the prediction of PM2.5, improving the existing
LSTM model. The study estimates the concentration of pollutants in the air based on
training and highlights the contribution of feature optimization in air quality predic-
tions through feature dimension reductions.
Results. In this section, the results of predicting the concentration of pollutants
(PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting
the PM10 and PM2.5 concentration, LSTM performed the best overall high R2 values
in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting,
How to cite this article Neo EX, Hasikin K, Lai KW, Mokhtar MI, Azizan MM, Hizaddin HF, Razak SA, Yanto . 2023. Artificial
intelligence-assisted air quality monitoring for smart city management. PeerJ Comput. Sci. 9:e1306 http://doi.org/10.7717/peerj-cs.1306
Petaling, Klang and Shah Alam stations, respectively. The study indicated that among
the studied pollution markers, PM2.5, PM10, NO2, wind speed and humidity are the
most important elements to monitor. By reducing the number of features used in
the model the proposed feature optimization process can make the model more
interpretable and provide insights into the most critical factor affecting air quality.
Findings from this study can aid policymakers in understanding the underlying causes
of air pollution and develop more effective smart strategies for reducing pollution
levels.