Izvorni znanstveni članak
https://doi.org/10.13044/j.sdewes.d11.0482
Evaluating Microplastic Pollution Along the Dubai Coast: An Empirical Model Combining On-Site Sampling and Sentinel-2 Remote Sensing Data
Tarig Ali
; American University of Sharjah, Sharjah, United Arab Emirates
Md Mortula
; American University of Sharjah, Sharjah, United Arab Emirates
Batoul Mohsen
; American University of Sharjah, Sharjah, United Arab Emirates
Lara Dronjak
; University Rovira and Virgily, Tarragona, Spain
Rahul Gawai
; American University of Sharjah, Sharjah, United Arab Emirates
Serter Atabay
; American University of Sharjah, Sharjah, United Arab Emirates
Zahid Khan
; AECOM, Aecom, Canada
Kazi Fattah
; Department of Civil, Environmental & Architectural Engineering, University of, Kansas, United States
Sažetak
The study addresses the growing concern of microplastic pollution in environmental matrices, emphasizing the significance of monitoring for understanding their distribution, sources, and mitigation. Laboratory-based spectral reflectance analysis of water samples containing visible microplastics revealed distinctive spectral signatures. Coastal water samples collected over two campaigns were subjected to pre-treatment in order to extract microplastics and microscopic inspection followed by spectroscopic confirmation. Results indicated average microplastics concentrations of 0.633 and 0.324mg/L, along with 7.85 and 5.30 items/L in the datasets. Leveraging these findings, along with Sentinel-2 (Level-1C) data and spectral signatures, an empirical spectral microplastics model was developed to convert Sentinel-2's reflectance into microplastics concentrations. This model displayed an 87.30% R2 and ±0.015mg/L RMSE. Subsequently, the model was employed to estimate microplastics concentrations in 2018, 2019, 2020, and 2021, showcasing its potential for monitoring microplastics pollution in the study area and similar regions.
Ključne riječi
microplastics; remote sensing; Sentinel-2; regression
Hrčak ID:
315387
URI
Datum izdavanja:
3.7.2024.
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