Downscaling MERRA-2 Reanalysis PM Series over the Arabian Gulf by Inverse Distance Weighting, Bicubic Spline Smoothing, and Spatio-Temporal Kriging

Overview

Journal Toxics

Date 2024 Mar 27

PMID 38535910

Authors

Youssef Saliba

Alina Barbulescu

Affiliations

Soon will be listed here.

Abstract

This study offers a detailed analysis of the fine particulate matter (PM) series in the Arabian Gulf zone, employing three interpolation models, Inverse Distance Weighting (IDW), Bicubic Spline Smoothing (BSS) and Spatio-Temporal Kriging (STK). Unique advancements include the use of complete temporal records in IDW, the management of edge effects in S with synthetic buffer points, and the application of STK to detrended data residuals. The results indicated that the BBS, particularly adept at handling boundary conditions, significantly outperformed the other methods. Compared to IDW, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) decreased by 21%, 15%, and 21%, respectively, in BSS. Compared to STK, MAE, RMSE, and MAPE were lower with around 60%, 61%, and 58%, respectively in BSS. These findings underscore the efficacy of the BSS method in spatial interpolation for environmental monitoring, contributing to enhanced PM analysis and public health management in the region.

Citing Articles

Assessing Pollution with Heavy Metals and Its Impact on Population Health.

Saliba Y, Barbulescu A Toxics. 2025; 13(1).

PMID: 39853050 PMC: 11768440. DOI: 10.3390/toxics13010052.

References

Randles C, da Silva A, Buchard V, Colarco P, Darmenov A, Govindaraju R . The MERRA-2 Aerosol Reanalysis, 1980 - onward, Part I: System Description and Data Assimilation Evaluation. J Clim. 2018; 30(17):6823-6850. PMC: 5859955. DOI: 10.1175/JCLI-D-16-0609.1. View

Schlesinger R . The health impact of common inorganic components of fine particulate matter (PM2.5) in ambient air: a critical review. Inhal Toxicol. 2007; 19(10):811-32. DOI: 10.1080/08958370701402382. View

Lee S, Serre M, van Donkelaar A, Martin R, Burnett R, Jerrett M . Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States. Environ Health Perspect. 2012; 120(12):1727-32. PMC: 3546366. DOI: 10.1289/ehp.1205006. View

Chae S, Shin J, Kwon S, Lee S, Kang S, Lee D . PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network. Sci Rep. 2021; 11(1):11952. PMC: 8185114. DOI: 10.1038/s41598-021-91253-9. View

Susanto F, de Souza P, He J . Spatiotemporal Interpolation for Environmental Modelling. Sensors (Basel). 2016; 16(8). PMC: 5017410. DOI: 10.3390/s16081245. View

Barbulescu A, Nazzal Y, Howari F . Statistical analysis and estimation of the regional trend of aerosol size over the Arabian Gulf Region during 2002-2016. Sci Rep. 2018; 8(1):9571. PMC: 6015092. DOI: 10.1038/s41598-018-27727-0. View

Chang C, Lo S, Yu S . The parameter optimization in the inverse distance method by genetic algorithm for estimating precipitation. Environ Monit Assess. 2006; 117(1-3):145-55. DOI: 10.1007/s10661-006-8498-0. View

Ryou H, Heo J, Kim S . Source apportionment of PM and PM air pollution, and possible impacts of study characteristics in South Korea. Environ Pollut. 2018; 240:963-972. DOI: 10.1016/j.envpol.2018.03.066. View

Meng X, Hand J, Schichtel B, Liu Y . Space-time trends of PM constituents in the conterminous United States estimated by a machine learning approach, 2005-2015. Environ Int. 2018; 121(Pt 2):1137-1147. DOI: 10.1016/j.envint.2018.10.029. View

10.

Yanosky J, Paciorek C, Suh H . Predicting chronic fine and coarse particulate exposures using spatiotemporal models for the Northeastern and Midwestern United States. Environ Health Perspect. 2009; 117(4):522-9. PMC: 2679594. DOI: 10.1289/ehp.11692. View

11.

Meng X, Garay M, Diner D, Kalashnikova O, Xu J, Liu Y . Estimating PM speciation concentrations using prototype 4.4 km-resolution MISR aerosol properties over Southern California. Atmos Environ (1994). 2018; 181:70-81. PMC: 6288801. DOI: 10.1016/j.atmosenv.2018.03.019. View

12.

Tang Y, Chai T, Pan L, Lee P, Tong D, Kim H . Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for July 2011. J Air Waste Manag Assoc. 2015; 65(10):1206-16. DOI: 10.1080/10962247.2015.1062439. View

13.

Xiao F, Yang M, Fan H, Fan G, Al-Qaness M . An improved deep learning model for predicting daily PM2.5 concentration. Sci Rep. 2020; 10(1):20988. PMC: 7710732. DOI: 10.1038/s41598-020-77757-w. View

14.

Al-Taani A, Nazzal Y, Howari F, Yousef A . Long-term trends in ambient fine particulate matter from 1980 to 2016 in United Arab Emirates. Environ Monit Assess. 2019; 191(3):143. DOI: 10.1007/s10661-019-7259-9. View

15.

Li L, Losser T, Yorke C, Piltner R . Fast inverse distance weighting-based spatiotemporal interpolation: a web-based application of interpolating daily fine particulate matter PM2:5 in the contiguous U.S. using parallel programming and k-d tree. Int J Environ Res Public Health. 2014; 11(9):9101-41. PMC: 4199009. DOI: 10.3390/ijerph110909101. View

16.

Valencia A, Serre M, Arunachalam S . A hyperlocal hybrid data fusion near-road PM2.5 and NO2 annual risk and environmental justice assessment across the United States. PLoS One. 2023; 18(6):e0286406. PMC: 10234552. DOI: 10.1371/journal.pone.0286406. View

17.

Tsai D, Jhuang J, Su S, Chiang C, Yang Y, Lee W . A stabilized spatiotemporal kriging method for disease mapping and application to male oral cancer and female breast cancer in Taiwan. BMC Med Res Methodol. 2022; 22(1):270. PMC: 9563856. DOI: 10.1186/s12874-022-01749-9. View

18.

MORAN P . Notes on continuous stochastic phenomena. Biometrika. 1950; 37(1-2):17-23. View

19.

Liu S, Geng G, Xiao Q, Zheng Y, Liu X, Cheng J . Tracking Daily Concentrations of PM Chemical Composition in China since 2000. Environ Sci Technol. 2022; 56(22):16517-16527. PMC: 9670839. DOI: 10.1021/acs.est.2c06510. View

20.

Goudarzi G, Hopke P, Yazdani M . Forecasting PM concentration using artificial neural network and its health effects in Ahvaz, Iran. Chemosphere. 2021; 283:131285. DOI: 10.1016/j.chemosphere.2021.131285. View