Primary productivity plays a key role in aquatic lake ecosystems. This study addresses the characteristics of primary phytoplankton productivity and its relationship with environmental factors in a large, shallow, and eutrophic lake (Lake Taihu, China). Surface water samples were collected in wet and dry seasons from eight lake areas to investigate physicochemical factors and primary productivity. The results show obvious seasonal differences in phytoplankton primary productivity and physicochemical factors in Lake Taihu. The primary productivity in the wet season is about five times larger than that in the dry season, and the spatial distribution of primary productivity is obviously inhomogeneous in the wet season, while in the dry season, there are no significant differences in different lake areas. Most of the lake areas are in the middle eutrophic state regardless of the season; the northwest region has the heaviest degree of eutrophication, while the southeast region has the lightest degree of eutrophication. Pearson correlation indicated that nutrients are the main factors affecting primary productivity in the wet season, while temperature is the most important factor affecting primary productivity in the dry season. Multiple stepwise regression suggested that chlorophyll-a (Chl-a), temperature (T), and water transparency (SD) can be used to estimate the phytoplankton primary productivity in Lake Taihu in different seasons, and the main influencing factors for primary productivity are Chl-a, nutrients, and SD/total suspended solids (TSS) in the wet season and T, Chl-a, and SD/TSS in the dry season.

Water quality monitoring (WQM) of urban rivers has been a reliable method to supervise the urban water environment. Indiscriminate WQM strategies can hardly emphasize the concerning pollution and usually require high costs of money, time, and manpower. To tackle these issues, this work carried out a multi-dimensional study (large spatial scale, multiple monitoring parameters, and long time scale) on the water quality of two urban rivers in Jiujiang City, China, which can provide indicative information for the optimization of WQM. Of note, the spatial distribution of NH₃-N concentration varied significantly both in terms of the two different rivers as well as the different sections (i.e., much higher in the northern section), with a maximal difference, on average greater, than five times. Statistical methods and machine learning algorithms were applied to optimize the monitoring objects, parameters, and frequency. The sharp decrease in water quality of adjacent sections was identified by Analytical Hierarchy Process of water quality assessment indexes. After correlation analysis, principal component analysis, and cluster analysis, the various WQM parameters could be divided into three principal components and four clusters. With the machine learning algorithm of Random Forest, the relation between concentration of pollutants and rainfall depth was fitted using quadratic functions (calculated Pearson correlation coefficients ≥ 0.89), which could help predict the pollution after precipitation and further determine the appropriate WQM frequency. Generally, this work provides a novel thought for efficient, smart, and low-cost water quality investigation and monitoring strategy determination, which contributes to the construction of smart water systems and sustainable water source management.

In lake aquatic ecosystems, which form the material and energy base of lakes, primary production is critical. The present study addresses the characteristics of primary phytoplankton productivity and its relationship with environmental physicochemical factors in three typical zones (algae-type, transition, and grass-type) of the eutrophic Lake Taihu. Seasonal sampling was conducted, and black–white bottle oxygen measurement was used to determine the primary productivity in different water layers in the lake. The results show obvious temporal and spatial differences in the physicochemical factors and phytoplankton productivity in Lake Taihu. The water column productivity and respiration conformed to the following seasonal descending order, summer > fall > spring > winter, and the following regional descending order, algae-type zone > transition zone > grass-type zone. The seasonal proportions of primary productivity were approximately 40% in the summer, 25% in the fall, 20% in the spring, and less than 15% in the winter. The highest values of water layer productivity and respiration were mainly at a depth of 0.2 m and decreased with the water depth. The percentage of productivity at different water layers was 23% (0 m), 31% (0.2 m), 23% (0.4 m), 11% (0.6 m), 7% (0.8 m), and 5% (1 m). The optical compensation depth for Lake Taihu was about 0.8 times the transparency (SD). Spearman correlation indicated that the temperature (T) and water depth (D) had an obvious impact on productivity in all three lake zones. Multiple stepwise regression suggested that T, D, SD, and chlorophyll-a (Chl-a) can be used to estimate productivity in different seasons/regions. The main influencing factors on phytoplankton productivity are T, D, Chl-a, and SD in the algae-type and transition zones and T, D, and total suspended solids (TSSs) in the grass-type zone.