Seasonal patterns of water quality and phytoplankton dynamics in surface waters in Guangzhou and Foshan, China.

During 2015, we studied the temporal patterns of nutrient concentrations and turbidity in water bodies with different degrees of agricultural and urban pressures across Guangzhou and Foshan (China). Data and observations were made by trained citizen scientists and professional researchers. Our study shows that all monitored water bodies, with the exception of Qiandeng Lake and Fengjiang River, had elevated NO3--N concentrations, which ranged from 0.10 to 6.83mg/L and peaked in late winter and early spring and reached a minimum in summer and mid-autumn. PO43-P concentrations ranged from 0.01 to 0.25mg/L and peaked during the winter, late-summer and late autumn. Turbidity values were highest at sites with agricultural activities, with maximums in the late winter and autumn, and the highest frequency (16% and 25%) of algae presence occurred in the spring and autumn. To better understand the characteristics and drivers of the algae occurrences, measurements of phytoplankton composition and physicochemical characteristics were conducted in three key seasons in the agricultural process, fallow, sowing and rainy season in 2016. Our focused study found that the occurrence of Bacillariophyta, Euglenophyta, Xanthophyta, Cryptophyta, Chrysophyta were positively correlated with dissolved oxygen and phosphorus concentrations, while Chlorophyta and Cyanophyta had positive correlations with turbidity, oxygen demand and nitrogen concentrations. Bacillariophyceae counted for the highest proportion of phytoplankton during the fallow season, comprising up to 60+% of the phytoplankton among the sites. During the rainy season, Chlorophyceae species were the majority, comprising up to 90+% of phytoplankton among the sampled sites. Our results pointed to the complexity of nutrient and phytoplankton dynamics in water bodies under multiple pressures, and to the value of using citizen scientists to determine contextual information to benefit more focused studies.

Prioritising local action for water quality improvement using citizen science; a study across three major metropolitan areas of China.

Streams in urban areas are prone to degradation. While urbanization-induced poor water quality is a widely observed and well documented phenomenon, the mechanism to pinpoint local drivers of urban stream degradation, and their relative influence on water quality, is still lacking. Utilizing data from the citizen science project FreshWater Watch, we use a machine learning approach to identify key indicators, potential drivers, and potential controls to water quality across the metropolitan areas of Shanghai, Guangzhou and Hong Kong. Partial dependencies were examined to establish the direction of relationships between predictors and water quality. A random forest classification model indicated that predictors of stream water colour (drivers related to artificial land coverage and agricultural land use coverage) and potential controls related to the presence of bankside vegetation were found to be important in identifying basins with degraded water quality conditions, based on individual measurements of turbidity and nutrient (N-NO3 and P-PO4) concentrations.