Comparison of Spatial Modelling Approaches on PM10 and NO2 Concentration Variations: A Case Study in Surabaya City, Indonesia.

Liadira Kusuma Widya,Liadira Kusuma Widya,Huey-Jen Su,Chih-Da Wu,Chih-Da Wu,Hsiao-Yun Lee,Lalu Muhamad Jaelani,Chin-Yu Hsu,Shih-Chun Candice Lung,Shih-Chun Candice Lung

doi:10.3390/ijerph17238883

Abstract

Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) models, to assess variations in particulate matter (PM10) and nitrogen dioxide (NO2) concentrations in Surabaya City, Indonesia. This is the first study to implement spatiotemporal variability of air pollution concentrations in Surabaya City, Indonesia. To develop the prediction models, air pollution data collected from seven monitoring stations from 2010 to 2018 were used as dependent variables, while land-use/land cover allocations within a 250 m to 5000 m circular buffer range surrounding the monitoring stations were collected as independent variables. A supervised stepwise variable selection procedure was applied to identify the important predictor variables for developing the LUR, GWR, and GTWR models. The developed models of LUR, GWR, and GTWR accounted for 49%, 50%, and 51% of PM10 variations and 46%, 47%, and 48% of NO2 variations, respectively. The GTWR model performed better (R2 = 0.51 for PM10 and 0.48 for NO2) than the other two models (R2 = 0.49–0.50 for PM10 and 0.46–0.47 for NO2), LUR and GWR. In the PM10 model four predictor variables, public facility, industry and warehousing, paddy field, and normalized difference vegetation index (NDVI), were selected during the variable selection procedure. Meanwhile, paddy field, residential area, rainfall, and temperature played important roles in explaining NO2 variations. Because of biomass burning issues in South Asia, the paddy field, which has a positive correlation with PM10 and NO2, was selected as a predictor. By using long-term monitoring data to establish prediction models, this model may better depict PM10 and NO2 concentration variations within areas across Asia.

Highlights

It is well documented that exposure to ambient air pollution can lead to increased mortality and morbidity and a shortened life expectancy [1,2]
The results demonstrated that temporally weighted regression (TWR) and geographic and temporal weighted regression (GTWR)
Regarding PM10, the annual mean concentration of PM10 in Surabaya was 41.31 μg/m3, well beyond the 20 μg/m3 limit recommended by World Health Organization (WHO) [29]

Summary

Introduction

It is well documented that exposure to ambient air pollution can lead to increased mortality and morbidity and a shortened life expectancy [1,2]. 10 μm) and nitrogen dioxide (NO2 ) are commonly used as indicators of ambient air pollution [3,4,5] This is because these pollutants are believed to be associated with several acute and chronic health conditions [6]. They have demonstrated associations with increased levels of mortality and morbidity in megacities [7,8,9,10]. Previous studies have confirmed that in Indonesia exposure to air pollution can worsen health conditions, including respiratory diseases and lung cancer, and even general health burdens. [13,14,15]

Methods

Results

Discussion

Conclusion