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GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms

  • Research on Sustainable Developments for Environment Management
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Abstract

Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia’s air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.

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Data availability

The data that aid the outcomes of this study are available from the Department of Environment (DOE), Malaysia. Some constraints apply to these data’s availability, which were only used under license for this study. Data are available from the first and/or corresponding author with the Department of Environment (DOE) permission.

Abbreviations

GIS:

Geographical information system

ML:

Machine learning

PMs:

Particulate matters

PM2.5:

Particulate matter finer than 2.5 μm

PM10:

Particulate matter finer than 10 μm

OECD:

Organization for Economic Cooperation and Development

XGBoost:

Extreme Gradient Boosting machine

KNN:

K-nearest neighbour

NB:

Naive Bayes

RF:

Random forest

CMAQ:

Community multi-scale air quality

WRF:

Weather Research Forecasting

AQI:

Air quality index

API:

Air pollution index

ANN:

Artificial neural network

DOE:

Department of Environment

O3:

Ozone

CO:

Carbon monoxide

SO2:

Sulfur dioxide

NO2:

Nitrogen dioxide

BU:

Built-up index

NDVI:

Normalized Difference Vegetation Index

SAVI:

Soil-adjusted vegetation index

LST:

Land surface temperature

Ws:

Wind speed

CARET:

Classification And REgression Training

APIMS:

Air pollution index of Malaysia

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Acknowledgements

The authors would like to thank the department of environment (DOE), Malaysia, for providing the PM10 data for this study. The contribution of Dr. Omar Althuwaynee is much appreciated.

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  1. Geospatial Analysis and Modelling (GAM) Research Laboratory, Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Perak, Malaysia

    Abdulwaheed Tella & Abdul-Lateef Balogun

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Abdulwaheed Tella: software; writing, original draft; methodology; conceptualization; visualization; writing, review and editing; investigation. Abdul-Lateef Balogun: conceptualization, visualization, supervision, writing — review and editing.

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Tella, A., Balogun, AL. GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms. Environ Sci Pollut Res 29, 86109–86125 (2022). https://doi.org/10.1007/s11356-021-16150-0

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