Deep-Learning-Based Automated Tracking and Counting of Living Plankton in Natural Aquatic Environments

Silver nanoparticles alter plankton-mediated carbon cycle processes in freshwater mesocosms

Understanding the ecological impacts of nanoparticle exposure has become increasingly urgent, as these materials are now widespread in aquatic environments. However, the effects of nanoparticle exposure on plankton community-mediated carbon cycling remain poorly understood. This study investigated the effects of long-term (28-day) exposure to environmentally relevant concentrations (10 and 100 µg/L) of silver nanoparticles (AgNPs) on the plankton-mediated carbon cycling processes of freshwater ecosystems by constructing a mesocosm ecosystem. The results showed that AgNP exposure enhanced the photosynthetic activity and biomass of both phytoplankton and zooplankton, thereby promoting carbon fixation. AgNP exposure also increased the ecological niche breadth and carbon source utilization of planktonic microorganisms while disrupting lipid metabolism, which facilitated carbon decomposition and utilization. Furthermore, AgNPs promoted the transformation of organic carbon by reducing the content and chemical composition of dissolved organic carbon and increasing the sedimentation of particulate organic carbon in the plankton community. Notably, compared with the control, exposure to 10 and 100 µg/L AgNPs reduced CO2 release at the water-air interface by 9 % and 17 %, respectively. Overall, this study provides the first evidence that long-term exposure to AgNPs can alter plankton-mediated carbon cycling processes in aquatic ecosystems, offering new insights into the ecotoxicological effects and risk assessment of nanoparticles.

Machine learning-based prediction of non-aeration linear alkylbenzene sulfonate mineralization in an oxygenic microalgal-bacteria biofilm

Microalgal-bacteria biofilm shows great potential in low-cost greywater treatment. Accurately predicting treated greywater quality is of great significance for water reuse. In this work, machine learning models were developed for simulating and predicting linear alkylbenzene sulfonate (LAS) removal using 152-days collected data from a battled oxygenic microalgal-bacteria biofilm reactor (MBBfR). By using nine variables including influent LAS, hydraulic retention time (HRT), biofilm density and thickness, specific oxygen production and consumption rates, microalgae and bacteria concentrations, and dissolved oxygen (DO), the support vector machine (SVM) model enabled the accurate LAS removal prediction (training set: R2 = 0.995, (root mean square error, RMSE) = 0.076, (mean absolute error, MAE) = 0.069; testing set: R2 = 0.961, RMSE = 0.251, MAE = 0.153). SVM can be also successfully applied for MBBfR operation optimization (HRT = 4.28 h, DO = 0.25 mg/L) that achieving accurate prediction of LAS mineralization.

A novel spectroscopy-deep learning approach for aqueous multi-heavy metal detection

Addressing heavy metal contamination in water bodies is a critical concern for environmental scientists. Traditional detection methods are often complex and costly. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have shown significant potential in analytical chemistry. However, these AI models require extensive spectral data, which traditional methods struggle to provide quickly. To overcome this challenge, we developed a new digital spectral imaging system and rapidly collected 3000 digital spectra from mixed heavy metal samples. We then created an end-to-end regression model for predicting heavy metal concentrations in mixed water samples using deep convolutional neural networks (ResNet-50, Inception V1, and SqueezeNet V1.1). The results indicated that the trained ResNet-50 model can effectively detect arsenic, chromium, and copper simultaneously, with a linear fitting coefficient exceeding 0.99 between true and predicted values. This study offers an efficient approach for rapid heavy metal detection in complex water environments and serves as a reference for developing intelligent analytical techniques.

Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies

The environment faces increasing anthropogenic impacts, resulting in a rapid increase in environmental issues that undermine the natural capital essential for human wellbeing. These issues are complex and often influenced by various factors represented by data with different modalities. While machine learning (ML) provides data-driven tools for addressing the environmental issues, the current ML models in environmental science and engineering (ES&E) often neglect the utilization of multimodal data. With the advancement in deep learning, multimodal learning (MML) holds promise for comprehensive descriptions of the environmental issues by harnessing data from diverse modalities. This advancement has the potential to significantly elevate the accuracy and robustness of prediction models in ES&E studies, providing enhanced solutions for various environmental modeling tasks. This perspective summarizes MML methodologies and proposes potential applications of MML models in ES&E studies, including environmental quality assessment, prediction of chemical hazards, and optimization of pollution control techniques. Additionally, we discuss the challenges associated with implementing MML in ES&E and propose future research directions in this domain.

Rapid Response of Daphnia magna Motor Behavior to Mercury Chloride Toxicity Based on Target Tracking

A rapid and timely response to the impacts of mercury chloride, which is indispensable to the chemical industry, on aquatic organisms is of great significance. Here, we investigated whether the YOLOX (improvements to the YOLO series, forming a new high-performance detector) observation system can be used for the rapid detection of the response of Daphnia magna targets to mercury chloride stress. Thus, we used this system for the real-time tracking and observation of the multidimensional motional behavior of D. magna. The results obtained showed that the average velocity (v¯), average acceleration (a¯), and cumulative travel (L) values of D. magna exposed to mercury chloride stress changed significantly under different exposure times and concentrations. Further, we observed that v¯, a¯ and L values of D. magna could be used as indexes of toxicity response. Analysis also showed evident D. magna inhibition at exposure concentrations of 0.08 and 0.02 mg/L after exposure for 10 and 25 min, respectively. However, under 0.06 and 0.04 mg/L toxic stress, v¯ and L showed faster toxic response than a¯, and overall, v¯ was identified as the most sensitive index for the rapid detection of D. magna response to toxicity stress. Therefore, we provide a strategy for tracking the motile behavior of D. magna in response to toxic stress and lay the foundations for the comprehensive screening of toxicity in water based on motile behavior.

Deep learning in water protection of resources, environment, and ecology: achievement and challenges

The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Efficient water resource management has a long-term influence on the sustainable development of the economy and society. Economic development and ecology preservation are tangled together, and the growth of one is not possible without the other. Deep learning (DL) is ubiquitous in autonomous driving, medical imaging, speech recognition, etc. The spectacular success of deep learning comes from its power of richer representation of data. In view of the bright prospects of DL, this review comprehensively focuses on the development of DL applications in water resources management, water environment protection, and water ecology. First, the concept and modeling steps of DL are briefly introduced, including data preparation, algorithm selection, and model evaluation. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of DL algorithms for different studies, as well as prospects for the application and development of DL in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.