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The monitoring of harmful phytoplankton is very important for the maintenance of the aquatic ecological environment. Traditional algae monitoring methods require professionals with substantial experience in algae species, which are time-consuming, expensive, and limited in practice. The automatic classification of algae cell images and the identification of harmful phytoplankton images were realized by the combination of multiple convolutional neural networks (CNNs) and deep learning techniques based on transfer learning in this work. Eleven common harmful and 31 harmless phytoplankton genera were collected as input samples; the five CNNs classification models of AlexNet, VGG16, GoogLeNet, ResNet50, and MobileNetV2 were fine-tuned to automatically classify phytoplankton images; and the average accuracy was improved 11.9% when compared to models without fine-tuning. In order to monitor harmful phytoplankton which can cause red tides or produce toxins severely polluting drinking water, a new identification method of harmful phytoplankton which combines the recognition results of five CNN models was proposed, and the recall rate reached 98.0%. The experimental results validate that the recognition performance of harmful phytoplankton could be significantly improved by transfer learning, and the proposed identification method is effective in the preliminary screening of harmful phytoplankton and greatly reduces the workload of professional personnel.

Combined sewer overflows (CSOs) are a dominant contributor to urban river pollution. Therefore, reducing the environmental impacts of CSOs and improving the self-purification capacity of water bodies are essential. In this study, the side-stream supersaturation (SSS) oxygenation was applied to restore microbial function of rivers which are affected by CSOs to improve the self-purification capacity. The results showed that apart from the dissolved organic matter inputs from CSO event, the sediment had become an important contributor to pollution in the studied river. After the long-term (46 d) implementation of SSS oxygenation, dissolved oxygen and the oxidation-reduction potential of the river water increased by 98% and 238%, respectively, compared to emergency control measures implemented following individual CSO events. The NH3-N concentrations and the chemical oxygen demand also decreased by 20% and 45%, respectively. In addition, the occurrence of microbial functions related to information storage and processing, and cellular process and signaling, increased by 1.87% and 0.82% in response to SSS oxygenation, respectively, and the Shannon index of the sediment microbial community increased by more than 15%. The frequencies of genes related to nitrification and sulfur oxidation also increased by 20-450% and >50%, respectively. This research provides new insights into the ecological restoration of rivers affected by CSOs.

In this work, two deep subsurface wastewater infiltration systems (SWISs) were constructed and fed with domestic sewage (control system, S1) and mixed wastewater consisting of old landfill leachate and domestic sewage (experimental system, S2). S1 and S2 exhibited favorable removal efficiencies, with TP (98.8%, 98.7%), COD (87.6%, 86.9%), NH4+-N (99.8%, 99.9%) and TN (99.2%, 98.9%). Even when increasing the pollutant load in S2 by adding old landfill leachate, the almost complete removal performance could be maintained in terms of low effluent concentrations and even increased in terms of load removal capabilities, which included COD (19.4, 25.9 g∙m−2·d−1), NH4+-N (8.2, 19.9 g∙m−2·d−1), TN (8.9, 20.6 g∙m−2·d−1). To investigate the transformation of dissolved organic matter along depth, Three-Dimensional Excitation Emission Matrix fluorescence spectroscopy combined with Fluorescence Regional Integration analysis was applied. The results showed that PⅠ,n and PⅡ,n (the proportions of biodegradable fractions) increased gradually from 6.59% to 21.8% at S2_20 to 10.8% and 27.7% at S2_110, but PⅢ,n and PⅤ,n (the proportions of refractory organics) declined from 23.1% to 27.8% at S2_20 to 21.1% and 16.4% at S2_110, respectively. In addition, high-throughput sequencing technology was employed to observe the bacterial community at different depths, and the predicted functional potential of the bacterial community was analyzed by PICRUSt. The results showed that the genera Flavobacterium, Pseudomonas, Vogesella, Acinetobacter and Aquabacterium might be responsible for refractory organic degradation and that their products might serve as the carbon source for denitrifiers to achieve simultaneous nitrate and refractory organic removal. PICRUSt further demonstrated that there was a mutual response between refractory organic degradation and denitrification. Overall, the combined treatment of domestic sewage and old leachate in rural areas by SWIS is a promising approach to achieve comprehensive treatment.