TY - JOUR
T1 - Identifying the best spatial interpolation method for estimating spatial distribution of PM2.5 in Jakarta
AU - Solihah, K. I.
AU - Martono, D. N.
AU - Haryanto, B.
N1 - Funding Information:
The authors are grateful to Universitas Indonesia for funding this research through PUTI Grant with contract number NKB-2585/UN2.RST/HKP.05.00/2020. The authors also express their gratitude to the Department of Environment of DKI Jakarta Province, the Indonesian Ministry of Environment and Forestry, and the US Embassy for helping in providing PM2.5 data.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2021/11/30
Y1 - 2021/11/30
N2 - Nowadays, many researchers are focused on analyzing the association between PM2.5 concentration and respiratory diseases. PM2.5 is one of the most threatening air pollutant for human health in cities and causes an increasing number of deaths. However, obtaining detailed PM2.5 concentration data constitutes one of the problems in analyzing its relationship with the human health effect. This study aims to select the best model for predicting PM2.5, spatially explicit in Jakarta, and estimate its spatial distribution in this region over the 2019-2020 period. The observation data of PM2.5 measurement results were in eight points spread across Jakarta. Furthermore, the data is a two-year daily time series from 2019-2020, which was then be processed into annual average data. Seven spatial interpolations of different methods were selected to identify which is most realistic in generating the estimated concentration value of PM2.5. From the results, we conclude that the Spline with Tension was the best interpolation method based on 2D visualization and model evaluation. Based on the model evaluation, the Spline with Tension method generated the best model with minimum error, where RMSE, MSE, MAE, and MAP had values of 0.0533,0.0028, 0.0400, 0.0008, respectively. Meanwhile, Ordinary Kriging with spherical had the most significant.
AB - Nowadays, many researchers are focused on analyzing the association between PM2.5 concentration and respiratory diseases. PM2.5 is one of the most threatening air pollutant for human health in cities and causes an increasing number of deaths. However, obtaining detailed PM2.5 concentration data constitutes one of the problems in analyzing its relationship with the human health effect. This study aims to select the best model for predicting PM2.5, spatially explicit in Jakarta, and estimate its spatial distribution in this region over the 2019-2020 period. The observation data of PM2.5 measurement results were in eight points spread across Jakarta. Furthermore, the data is a two-year daily time series from 2019-2020, which was then be processed into annual average data. Seven spatial interpolations of different methods were selected to identify which is most realistic in generating the estimated concentration value of PM2.5. From the results, we conclude that the Spline with Tension was the best interpolation method based on 2D visualization and model evaluation. Based on the model evaluation, the Spline with Tension method generated the best model with minimum error, where RMSE, MSE, MAE, and MAP had values of 0.0533,0.0028, 0.0400, 0.0008, respectively. Meanwhile, Ordinary Kriging with spherical had the most significant.
UR - http://www.scopus.com/inward/record.url?scp=85120880352&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/893/1/012043
DO - 10.1088/1755-1315/893/1/012043
M3 - Conference article
AN - SCOPUS:85120880352
SN - 1755-1307
VL - 893
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
IS - 1
M1 - 012043
T2 - 2nd International Conference on Tropical Meteorology and Atmospheric Sciences, ICTMAS 2021
Y2 - 23 March 2021 through 25 March 2021
ER -
