The impact of Asian dust events on the chlorophyll a in the Yellow Sea: a preliminary analysis based on remote sensing

2011 ◽  
Author(s):  
Qianguang Tu ◽  
Zengzhou Hao ◽  
Fang Gong ◽  
Delu Pan ◽  
Zhihua Mao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Yellow Sea ◽  
Preliminary Analysis ◽  
Asian Dust ◽  
The Yellow Sea ◽  
Dust Events ◽  
The Impact

scholarly journals The Use of Remote Sensing Analysis for Evaluating the Impact of Development Projects in the Yellow Sea Large Marine Ecosystem

2020 ◽  
Vol 12 (9) ◽  
pp. 3628
Author(s):  
Gabriel Sidman ◽  
Sydney Fuhrig ◽  
Geeta Batra
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Yellow Sea ◽  
Marine Pollution ◽  
Marine Ecosystem ◽  
Environmental Indicators ◽  
The Yellow Sea ◽  
Series Data ◽  
Large Marine Ecosystem ◽  
The Impact

Remote sensing has long been valued as a data source for monitoring environmental indicators and detecting trends in ecosystem stress from anthropogenic causes such as deforestation, river dams and air and water pollution. More recently, remote sensing analyses have been applied to evaluate the impacts of environmental projects and programs on reducing environmental stresses. Such evaluation has focused primarily on the change in above-surface vegetation such as forests. This study uses remote sensing ocean color products to evaluate the impact on reducing marine pollution of the Global Environment Facility’s (GEF) portfolio of projects in the Yellow Sea Large Marine Ecosystem. Chlorophyll concentration was derived from satellite images over a time series from the 1990s, when GEF projects began, until the present. Results show a 50% increase in chlorophyll until 2011 followed by a 34% decrease until 2019, showing a potential delayed effect of pollution control efforts. The rich time series data is a major advantage to using geospatial analysis for evaluating the impacts of environmental interventions on marine pollution. However, one drawback to the method is that it provides insights into correlations but cannot attribute the results to any particular cause, such as GEF interventions.

Dry deposition of PM10 over the Yellow Sea during Asian dust events from 2001 to 2007

2014 ◽  
Vol 26 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Han Yan ◽  
Xiaohuan Liu ◽  
Jianhua Qi ◽  
Huiwang Gao
Keyword(s):  
Dry Deposition ◽  
Yellow Sea ◽  
Asian Dust ◽  
The Yellow Sea ◽  
Dust Events

Biogeochemistry of dimethylsulfoniopropionate, dimethylsulfide and acrylic acid in the Yellow Sea and the Bohai Sea during autumn

2016 ◽  
Vol 13 (1) ◽  
pp. 127 ◽  
Author(s):  
Yue Liu ◽  
Chun-Ying Liu ◽  
Gui-Peng Yang ◽  
Hong-Hai Zhang ◽  
Sheng-hui Zhang
Keyword(s):  
Acrylic Acid ◽  
Chlorophyll A ◽  
Yellow Sea ◽  
Bohai Sea ◽  
Sulfur Cycle ◽  
The Yellow Sea ◽  
Trace Gas ◽  
The Bohai Sea ◽  
Marginal Seas ◽  
High Concentrations

Environmental context Dimethylsulfide (DMS) is a climatically important biogenic trace gas that is emitted from oceans. This research focuses on the spatiotemporal distributions of DMS and its related compounds, i.e. dimethylsulfoniopropionate (DMSP) and acrylic acid (AA), and the influencing factors in the Yellow Sea and the Bohai Sea during autumn. In addition, the sea-to-air flux of DMS, kinetic responses of DMSP consumption as well as DMS and AA production are also investigated. This study is helpful in understanding the marine sulfur cycle in marginal seas in China. Abstract The biogeochemistry of dimethylsulfoniopropionate (DMSP), dimethylsulfide (DMS) and acrylic acid (AA) in the Yellow Sea (YS) and the Bohai Sea (BS) was investigated in November 2013. The concentrations (and ranges) of total DMSP (DMSPt), dissolved DMSP (DMSPd), DMS and AA in surface waters were 30.71 (1.07–122.50), 6.60 (0.85–35.67), 1.48 (0.53–5.32) and 42.2 (13.8–352.8) nmol L–1 respectively. The concentrations of DMSPd and AA were positively correlated with chlorophyll-a levels, which suggests that phytoplankton biomass has an important function in controlling DMSPd and AA distributions. Furthermore, DMS and AA concentrations revealed significant positive relationships with DMSPd concentrations. The average ratios of AA/(DMSP+AA) and DMS/AA were 53.98 and 7.62% respectively. The vertical profiles of DMSP, DMS and AA were characterised by high concentrations that mostly occur near the surface. Even under highly variable hydrographic conditions, a positive relationship was observed between DMSPt and chlorophyll-a concentrations. The rates of DMSPd consumption, as well as DMS and AA production, significantly varied with marine environments. The sea-to-air fluxes of DMS from the YS and the BS to the atmosphere were estimated to be in the range of 3.01 to 6.91μmol m–2day–1.

scholarly journals Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign

2018 ◽  
Vol 133 ◽  
pp. 150-156 ◽  
Author(s):  
Qianguo Xing ◽  
Lingling Wu ◽  
Liqiao Tian ◽  
Tingwei Cui ◽  
Lin Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Early Stage ◽  
The Yellow Sea ◽  
Green Tide

scholarly journals A Lagrangian-based Floating Macroalgal Growth and Drift Model (FMGDM v1.0): application in the green tides of the Yellow Sea

2021 ◽  
Author(s):  
Fucang Zhou ◽  
Jianzhong Ge ◽  
Dongyan Liu ◽  
Pingxing Ding ◽  
Changsheng Chen
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Surface Wind ◽  
Ocean Model ◽  
Biological Data ◽  
The Yellow Sea ◽  
Green Tide ◽  
Observation Data ◽  
Green Tides ◽  
Drift Model

Abstract. Massive floating macroalgal blooms in the ocean have had an array of ecological consequences; thus, tracking their drifting pattern and predicting their biomass are important for their effective management. However, a high-resolution ecological dynamics model is lacking. In this study, a physical–ecological model, Floating Macroalgal Growth and Drift Model (FMGDM v1.0), was developed to determine the dynamic growth and drift pattern of floating macroalgal, based on the tracking, replication and extinction of Lagrangian particles. The position, velocity, quantity and represented biomass of particles are updated synchronously between the tracking module and the ecological module. The former is driven by ocean flows and sea surface wind, while the latter is controlled by the temperature, salinity, and irradiation. Based on the hydrodynamic models of the Finite-Volume Community Ocean Model and parameterized using a culture experiment of Ulva prolifera, which caused the largest bloom worldwide of the green tide in the Yellow Sea, China, this model was applied to simulate the green tides around the Yellow Sea in 2014 and 2015. The simulation result, distribution and biomass of green tides, was validated using remote sensing observation data and reasonably modeled the entire process of green tide bloom and its extinction from early spring to late summer. Given the prescribed spatial initialization from remote sensing observation, the model could provide accurate short-term (7–8 d) predictions of the spatial and temporal developments of the green tide. With the support of the hydrodynamic model and biological data of macroalgae, this model can forecast floating macroalgae blooms in other regions.

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