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

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.

[1]  R. Letz,et al.  Personal exposure to nitrogen dioxide: relationship to indoor/outdoor air quality and activity patterns. , 1986, Environmental science & technology.

[2]  John D. Spengler,et al.  Sequential box models for indoor air quality: Application to airliner cabin air quality , 1988 .

[3]  D. Dockery,et al.  An association between air pollution and mortality in six U.S. cities. , 1993, The New England journal of medicine.

[4]  A. Tri-Tugaswati.  Review of Air Pollution and Its Health Impact in Indonesia1 , 1994 .

[5]  D. Dockery,et al.  Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. , 1995, American journal of respiratory and critical care medicine.

[6]  Shosuke Suzuki,et al.  Effect of Air Pollution on Respiratory Health in Indonesia and Its Economic Cost , 2003, Archives of environmental health.

[7]  Qihao Weng,et al.  Urban Air Pollution Patterns, Land Use, and Thermal Landscape: An Examination of the Linkage Using GIS , 2006, Environmental monitoring and assessment.

[8]  Zhiyong Hu,et al.  Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease , 2009, International journal of health geographics.

[9]  Zhihai Ma,et al.  An Evaluation of Spatial Autocorrelation and Heterogeneity in the Residuals of Six Regression Models , 2009 .

[10]  Daniel J. Jacob,et al.  Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: implications for the sensitivity of PM2.5 to climate change. , 2010 .

[11]  L. Anselin.  Local Indicators of Spatial Association—LISA , 2010 .

[12]  Bo Wu,et al.  Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices , 2010, Int. J. Geogr. Inf. Sci..

[13]  A. Stewart Fotheringham,et al.  Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity , 2010 .

[14]  Jie Cao,et al.  Ambient Temperature and Mortality: An International Study in 13 Cities of East Asia , 2010, The Science of the total environment.

[15]  C. Simpson,et al.  CHARACTERIZATION OF PARTICULATE MATTER EMISSION FROM OPEN BURNING OF RICE STRAW. , 2011, Atmospheric environment.

[16]  Junyi Zhang,et al.  Spatial and Temporal Factors of Air Quality in Surabaya City: An Analysis based on a Multilevel Model , 2014 .

[17]  K. Matthews,et al.  Chronic PM2.5 exposure and inflammation: determining sensitive subgroups in mid-life women. , 2014, Environmental research.

[18]  Hsiao-Lan Liu,et al.  The Impact of Green Space Changes on Air Pollution and Microclimates: A Case Study of the Taipei Metropolitan Area , 2014 .

[19]  Weiqi Zhou,et al.  Meteorological and urban landscape factors on severe air pollution in Beijing , 2015, Journal of the Air & Waste Management Association.

[20]  Kai Zhang,et al.  An assessment of air pollutant exposure methods in Mexico City, Mexico , 2015, Journal of the Air & Waste Management Association.

[21]  Guangming Zeng,et al.  Land use regression models coupled with meteorology to model spatial and temporal variability of NO2 and PM10 in Changsha, China , 2015 .

[22]  Giorgio Cattani,et al.  Development of nitrogen dioxide and volatile organic compounds land use regression models to estimate air pollution exposure near an Italian airport , 2016 .

[23]  Marloes Eeftens,et al.  Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the Swiss SAPALDIA regions , 2016, Environmental Health.

[24]  Yoanita Eka Rahayu,et al.  Land Use Development and its Impact on Airport Access Road , 2016 .

[25]  Yuan Zhou,et al.  A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD , 2016, Remote. Sens..

[26]  J. Schwartz,et al.  Impact of Particulate Matter Exposure and Surrounding “Greenness” on Chronic Absenteeism in Massachusetts Public Schools , 2017, International journal of environmental research and public health.

[27]  Shih-Chun Candice Lung,et al.  Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability. , 2017, Environmental pollution.

[28]  Ha-Young Kwak,et al.  Identifying the Correlation Between Rainfall, Traffic Flow Performance and Air Pollution Concentration in Seoul Using a Path Analysis , 2017 .

[29]  Yi Yang,et al.  A Mixed Geographically and Temporally Weighted Regression: Exploring Spatial-Temporal Variations from Global and Local Perspectives , 2017, Entropy.

[30]  Qiuhong Tang,et al.  Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model , 2017 .

[31]  M. Franchini,et al.  Health Effects of Ambient Air Pollution in Developing Countries , 2017, International journal of environmental research and public health.

[32]  M. B. Burroughs Peña,et al.  Environmental Exposures and Cardiovascular Disease: A Challenge for Health and Development in Low- and Middle-Income Countries. , 2017, Cardiology clinics.

[33]  Tao Liu,et al.  The Interaction between Ambient PM10 and NO2 on Mortality in Guangzhou, China , 2017, International journal of environmental research and public health.

[34]  Seunghoon Park,et al.  Investigating the Effects of the Built Environment on PM2.5 and PM10: A Case Study of Seoul Metropolitan City, South Korea , 2018, Sustainability.

[35]  Chen Zhao,et al.  National scale spatiotemporal land-use regression model for PM2.5, PM10 and NO2 concentration in China , 2018, Atmospheric Environment.

[36]  Chuan Ding,et al.  A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership , 2018, Comput. Environ. Urban Syst..

[37]  K. Vadrevu,et al.  Improved rice residue burning emissions estimates: Accounting for practice-specific emission factors in air pollution assessments of Vietnam. , 2018, Environmental pollution.

[38]  Z. Zhen,et al.  Global and Geographically and Temporally Weighted Regression Models for Modeling PM2.5 in Heilongjiang, China from 2015 to 2018 , 2019, International journal of environmental research and public health.

[39]  M. Brauer,et al.  Global, national, and urban burdens of paediatric asthma incidence attributable to ambient NO2 pollution: estimates from global datasets. , 2019, The Lancet. Planetary health.

[40]  R. V. Suryadinata,et al.  Risk Factors and Early Symptoms Related to Respiratory Disease in Pedicab Drivers in Surabaya , 2019 .

[41]  Hua Li,et al.  A Geographically and Temporally Weighted Regression Model for Spatial Downscaling of MODIS Land Surface Temperatures Over Urban Heterogeneous Regions , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Chih-Da Wu,et al.  Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration , 2020, International journal of environmental research and public health.

[43]  Agus Dwi Susanto,et al.  Air pollution and human health , 2020, Medical Journal of Indonesia.