Coastal water eutrophication and resource depletion have become serious issues in many coastal regions of the world. Seaweed aquaculture in Chinese coastal waters is a very important industry. This work assessed the major environmental benefits of seaweed cultivation, mapped the spatial distributions of various species and analyzed their environmental impacts during 2015 in China. This study found that seaweed aquaculture had brought in considerable environmental benefits, including the removal of about 75,563 t of nitrogen and 9592 t of phosphate from coastal waters, the sequestration of about 539,555 t of carbon, the absorption of 1,980,167 t of CO2, the release of 1,440,612 t of O2 into coastal waters, and the production of 5809 t of Iodine. In addition, seaweed cultivation saved approximately 29,313 t of chemical fertilizers, 1873 t of pesticide and 62,492 ha of farmland compared with terrestrial vegetable cultivation. These results demonstrate that Chinese seaweed aquaculture has turned the pollutants that cause eutrophication into nutrients, which generates considerable environmental benefits as well as socio-economic values. However, seaweed species, as well as coastal water qualities, vary by regions, spatial management of seaweed farms related to the coastal water quality is necessary. In the eutrophic coastal waters, more seaweed species that are highly capable of removing nitrogen and phosphate should be cultivated. Besides, the potentially negative effects of seaweed farming should also be considered before performing spatial management to ensure the healthy development of seaweed industry. As Chinese seaweed production continues to boom, it is a unique opportunity to strengthen the seaweed industry and to reach a win–win situation that both gains considerable environmental benefits and generates substantial socio-economic returns. This is also a promising way for other coastal developing nations/regions to follow.

The considerable environmental benefits of seaweed aquaculture in China

Geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR) are classic methods for estimating non-stationary relationships. Although these methods have be...

Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships

Evaluation of ecosystem health and potential human health hazards in the Hangzhou Bay and Qiantang Estuary region through multiple assessment approaches.

Eutrophication, mainly caused by the oversupply of nitrate (NO3−) and phosphate, has become a serious environmental problem in Xiangshan Bay (east China), but the sources of the nitrate are poorly understood. In this study, the estuarine distributions of dual stable isotopes (δ15N and δ18O) of NO3− measured in May 2012 were used in combination with concurrent measurements of salinity, pH, dissolved oxygen, and nutrients (nitrate, nitrite, ammonium, and phosphate) to investigate NO3− sources and the processes that control NO3− distributions within the bay. In the lower bay, high concentrations of NO3− (up to 60.6 μmol L−1), an N/P ratio of 52, and low values of nitrate δ15N (δ15NNO3 = 5.7‰–6.7‰) and δ18O (δ18ONO3 = 1.8‰–3.5‰) indicate that NO3− in this area was likely from the Changjiang and Qiantangjiang rivers as well as local nitrification-associated inputs. In the upper bay, constituent concentrations and high values of δ15NNO3 (up to 8.8‰) and δ18ONO3 (up to 6.8‰) suggest that NO3− distributions here were controlled mainly by the mixing of coastal and fresh waters, phytoplankton assimilation, and inputs of riverine NO3−, possibly derived from manure and sewage. Spatial variations of Δ(15,18), the nitrate δ15N-to-δ18O anomaly, is possibly resulted from the different water residence times across the bay, which could in turn lead to different degrees of nitrification or variable amounts of NO3− from atmospheric deposition.

Sources of nitrate in Xiangshan Bay (China), as identified using nitrogen and oxygen isotopes

Modeling spatially anisotropic nonstationary processes in coastal environments based on a directional geographically neural network weighted regression

The Hangzhou Bay (HZB) and Xiangshan Bay (XSB), in northern Zhejiang Province and connect to the East China Sea (ECS) were considerably affected by the consequence of water quality degradation. In this study, we analyzed physical and biogeochemical properties of water quality via multivariate statistical techniques. Hierarchical cluster analysis (HCA) grouped HZB and XSB into two subareas of different pollution sources based on similar physical and biogeochemical properties. Principal component analysis (PCA) identified three latent pollution sources in HZB and XSB respectively and emphasized the importance of terrestrial inputs, coastal industries as well as natural processes in determining the water quality of the two bays. Therefore, proper measurement for the protection of aquatic ecoenvironment in HZB and XSB were of great urgency.

Identification of coastal water quality by multivariate statistical techniques in two typical bays of northern Zhejiang Province, East China Sea

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Identification of coastal water quality by multivariate statistical techniques in two typical bays of northern Zhejiang Province, East China Sea