The temporal distribution of organochlorine pesticides (OCPs) was examined in the (210)Pb dated sediment core from the Beibu Gulf, South China Sea. The total OCPs concentrations were in the range of 0.93-26.6 ng g(-1) dry weight. Dichlorodiphenyltrichloroethanes (DDTs) (0.17-24.8 ng g(-1)), Hexachlorocyclohexanes (HCHs) (0.04-0.51 ng g(-1)), Chlordane related compounds (CHLs) (0.22-1.72 ng g(-1)) and endosulfan (n.d.-0.91 ng g(-1)) were the predominant compounds. Similar to most Chinese coastal areas, the levels of DDTs in the Beibu Gulf became elevated since the early 1990s, especially since 2000 despite the ban in 1983 in China. This suggests that the concentrations of DDTs were controlled by several processes, such as land reclamation and soil runoff. The isomer ratios of (p,p'-DDE + p,p'-DDD)/p,p'-DDT, p,p'-DDT/DDTs along with construction land expansion indicated that economic activities, land reclamation, soil runoff and the use of DDT-containing antifouling paints might be responsible for the input of DDT. The ratios of α-HCH/γ-HCH (and γ-HCH/HCHs) and trans-chlordane/cis-chlordane (TC/CC) indicated fresh inputs of lindane and chlordane, respectively. In addition, CC was found to be degraded faster than TC under anaerobic conditions in sediments from the Beibu Gulf.
Remote sensing for the monitoring of chlorophyll-a (Chl-a) is essential to compensate for the shortcomings of traditional water quality monitoring, strengthen red tide disaster monitoring and early warnings, and reduce marine environmental risks. In this study, a machine learning approach called the Gradient-Boosting Decision Tree (GBDT) was employed to develop an algorithm for estimating the Chl-a concentrations of the coastal waters of the Beibu Gulf in Guangxi, using Landsat 8 OLI image data as the image source in combination with field measurements of Chl-a concentrations. The GBDT model with B4, B3 + B4, B3, B1 − B4, B2 + B4, B1 + B4, and B2 − B4 as input features exhibited higher accuracy (MAE = 0.998 μg/L, MAPE = 19.413%, and RMSE = 1.626 μg/L) compared with different physics models, providing a new method for remote sensing inversion of water quality parameters. The GBDT model was used to study the spatial distribution and temporal variation of Chl-a concentrations in the coastal sea surface of the Beibu Gulf of Guangxi from 2013 to 2020. The results showed a spatial distribution with high concentrations in nearshore waters and low concentrations in offshore waters. The Chl-a concentration exhibited seasonal changes (concentration in summer > autumn > spring ≈ winter).
Chlorophyll-a (Chl-a) concentration is a measure of phytoplankton biomass, and has been used to identify ‘red tide’ events. However, nearshore waters are optically complex, making the accurate determination of the chlorophyll-a concentration challenging. Therefore, in this study, a typical area affected by the Phaeocystis ‘red tide’ bloom, Qinzhou Bay, was selected as the study area. Based on the Gaofen-1 remote sensing satellite image and water quality monitoring data, the sensitive bands and band combinations of the nearshore Chl-a concentration of Qinzhou Bay were screened, and a Qinzhou Bay Chl-a retrieval model was constructed through stepwise regression analysis. The main conclusions of this work are as follows: (1) The Chl-a concentration retrieval regression model based on 1/B4 (near-infrared band (NIR)) has the best accuracy (R2 = 0.67, root-mean-square-error = 0.70 μg/L, and mean absolute percentage error = 0.23) for the remote sensing of Chl-a concentration in Qinzhou Bay. (2) The spatiotemporal distribution of Chl-a in Qinzhou Bay is varied, with lower concentrations (0.50 μg/L) observed near the shore and higher concentrations (6.70 μg/L) observed offshore, with a gradual decreasing trend over time (−0.8).
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