China is facing intense coastal eutrophication. Large-scale seaweed aquaculture in China is popular, now accounting for over 2/3’s of global production. Here, we estimate the nutrient removal capability of large-scale Chinese seaweed farms to determine its significance in mitigating eutrophication. We combined estimates of yield and nutrient concentration of Chinese seaweed aquaculture to quantify that one hectare of seaweed aquaculture removes the equivalent nutrient inputs entering 17.8 ha for nitrogen and 126.7 ha for phosphorus of Chinese coastal waters, respectively. Chinese seaweed aquaculture annually removes approximately 75,000 t nitrogen and 9,500 t phosphorus. Whereas removal of the total N inputs to Chinese coastal waters requires a seaweed farming area 17 times larger than the extant area, one and a half times more of the seaweed area would be able to remove close to 100% of the P inputs. With the current growth rate of seaweed aquaculture, we project this industry will remove 100% of the current phosphorus inputs to Chinese coastal waters by 2026. Hence, seaweed aquaculture already plays a hitherto unrealized role in mitigating coastal eutrophication, a role that may be greatly expanded with future growth of seaweed aquaculture.

Eutrophication is a serious environmental problem in Qiantang River, the largest river in the Zhejiang Province of southeast China. Increased phosphorus concentration is thought to be the major cause of water eutrophication. The objective of this study was to develop an empirical remote sensing model using Landsat Thematic Mapper (TM) data to estimate phosphorus concentration and characterize the spatial variability of the phosphorus concentration in the mainstream of Qiantang River. Field water quality data were collected across a spatial gradient along the river and geospatially overlaid with Landsat satellite images. Various statistical regression models were tested to correlate phosphorus concentration with a combination of other water quality indicators and remotely sensed spectral reflectance, including Secchi depth (SD) and chlorophyll-a (Chl-a) concentration. The optimal regression model was subsequently used to map and characterize the spatial variability of the total phosphorus (TP) concentration in the mainstream of Qiantang River. The results suggest that spectral reflectance from the Landsat satellite is spatially and implicitly correlated with phosphorus concentration (R ² = 0.77). The approach proved to be effective and has the potential to be applied over large areas for water quality monitoring.