Dust;
Chlorophyll a;
Mann-Kendall;
Google Earth Engine;
Persian Gulf;
ARABIAN GULF;
RED TIDE;
MODIS;
VARIABILITY;
PRODUCTIVITY;
DYNAMICS;
GROWTH;
EAST;
SEA;
SST;
D O I:
10.1016/j.marpolbul.2023.115564
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
Persian Gulf is surrounded by the most active dust source regions of the world. Every year, millions of tons of dust are deposited in this area causing significant adverse effects. This research aimed to fill the gap in the field of dust impact quantification regarding different and important regions in the Persian Gulf. We evaluated aerosol optical thickness (AOT) as dust, chlorophyll a (Chl a) as phytoplankton biomass, sea surface temperature (SST), and particulate organic carbon (POC) using Moderate Resolution Imaging Spectroradiometer (MODIS) data to quantify and analyze the impacts of dust. According to dust frequency maps over 21 years, 4 critical regions were selected including the Northern, Central, and Southern regions of the Persian Gulf and Strait of Hormuz. Slightly lower Chl a values were noted in the southern region and Strait of Hormuz compared to the Northern and Central regions. The Mann-Kendall (MK) test and Sen's slope estimator, which can determine trend parameters, were used to analyze 4 selected regions using 8 days, monthly, and yearly averaged data. The results of the MK test showed a significant positive trend for SST and a significant negative trend for POC. AOT and Chl a trends varied based on their locations. The cross-correlation test with a time lag showed maximum correlations between Chl a and dust (AOT) with a delay of 6-7 months. Also, to quantify the impacts of dust on phytoplankton biomass, R2 and analysis of variance (ANOVA) regression were used for data with a 6-7-month time lag. The results showed that the contribution of dust in the amount of Chl a variation was about 10 %-20 %. This study showed that the simultaneous use of remote sensing and statistical methods could provide necessary and timely warnings (regarding management) to prevent algal blooms and aquatic loss in this important and strategic area.
机构:
Marine Science and Technology College, Zhejiang Ocean University, ZhoushanMarine Science and Technology College, Zhejiang Ocean University, Zhoushan
Liang J.
Chen C.
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机构:
School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, SuzhouMarine Science and Technology College, Zhejiang Ocean University, Zhoushan
Chen C.
Song Y.
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h-index: 0
机构:
School of Design and the Built Environment, Curtin University, PerthMarine Science and Technology College, Zhejiang Ocean University, Zhoushan
Song Y.
Sun W.
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h-index: 0
机构:
Department of Geography and Spatial Information Techniques, Ningbo University, Zhejiang, NingboMarine Science and Technology College, Zhejiang Ocean University, Zhoushan
Sun W.
Yang G.
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h-index: 0
机构:
Department of Geography and Spatial Information Techniques, Ningbo University, Zhejiang, NingboMarine Science and Technology College, Zhejiang Ocean University, Zhoushan
机构:
Zhejiang Univ Finance & Econ, Inst Land & Urbanrural Dev, Hangzhou 310058, Peoples R China
Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China
Zhejiang Univ Finance & Econ, Inst Eight Eight Strategy, Hangzhou 310058, Peoples R ChinaZhejiang Univ Finance & Econ, Inst Land & Urbanrural Dev, Hangzhou 310058, Peoples R China
Li, Guangyu
He, Tingting
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机构:
Zhejiang Univ, Sch Publ Affairs, Hangzhou 310058, Peoples R China
Yuhangtang Rd 866, Hangzhou, Peoples R ChinaZhejiang Univ Finance & Econ, Inst Land & Urbanrural Dev, Hangzhou 310058, Peoples R China
He, Tingting
Zhang, Maoxin
论文数: 0引用数: 0
h-index: 0
机构:
Zhejiang Univ, Sch Publ Affairs, Hangzhou 310058, Peoples R China
Zhejiang Univ, Inst Fiscal Big Data & Policy, Hangzhou 310058, Peoples R ChinaZhejiang Univ Finance & Econ, Inst Land & Urbanrural Dev, Hangzhou 310058, Peoples R China
Zhang, Maoxin
Wu, Cifang
论文数: 0引用数: 0
h-index: 0
机构:
Zhejiang Univ, Sch Publ Affairs, Hangzhou 310058, Peoples R ChinaZhejiang Univ Finance & Econ, Inst Land & Urbanrural Dev, Hangzhou 310058, Peoples R China