Mapping Long-Term Spatiotemporal Dynamics of Pen Aquaculture in a Shallow Lake: Less Aquaculture Coming along Better Water Quality

A data synthesis on air-water exchange flux of plasticizers of phthalates and organophosphates in surface water

Microplastic pollution has become an emerging environmental issue in the past decades. Plasticizers are organic compounds applied during the manufacturing process and are of particular interests to researchers. Phthalate esters (PAEs) and organophosphate esters (OPEs) are two common types of plasticizers that have been found to be prevalent in water and the atmosphere. Investigating their air-water exchange process is crucial for understanding their sources and fate as pollutants. This study employs a systematic review and data synthesis to evaluate the air-water exchange and dry deposition flux of PAEs and OPEs on a global scale, aiming to identify the factors influencing their exchange process. Results showed that the air-water exchange and dry deposition flux vary among different types of plasticizers, and climatic conditions may also have impact on the air-water exchange flux. Future research is needed to explore more mechanisms related to the influencing factors. PRACTITIONER POINTS: The study presents a data synthesis of air-water exchange and dry deposition flux of plasticizers. Results indicate the sources and sinks of plasticizers on surface water, which helps to understand the consequence of plastic pollution. The air-water exchange and dry deposition flux of PAEs and OPEs vary due to different chemical properties and climatic conditions. Further research is needed to better understand the influencing factors of this process.

Significant Temporal and Spatial Variability in Nutrient Concentrations in a Chinese Eutrophic Shallow Lake and Its Major Tributaries

Sediment nutrients can be released to the surface water when hydraulic disturbance becomes strong in shallow lakes, which contributes to nutrient enrichment and subsequent lake eutrophication in the water column. To explore the seasonal variations and spatial distributions exhibited by nutrients in the water column, surface sediment, and pore water of Lake Yangcheng and its major tributaries, we determined the concentrations of nitrogen (N) and phosphorus (P) throughout the lake in different seasons of 2018. Total N (TN) and total P (TP) concentrations in the connected rivers were much greater than those in the lake, indicating that external loading greatly contributed to the nutrient enrichment. TN concentration in the water column was highest in the winter, whereas TP peaked in the summer. A similar temporal pattern was observed for TN and TP in the sediment with maxima in the winter and minima in the summer; however, nutrients in the pore water were highest in the summer, in contrast to the temporal variation in the sediment. Additionally, high TN values in the water column and high TP in the three compartments were distributed primarily in the west part of the lake, while high TN concentrations in the sediment and pore water were observed mainly in the east portion of the lake. According to the enrichment factor index (an indicator evaluating the nutrient enrichment by comparing the detected contents and standard values), nutrients in the lake sediment were severely enriched with TN and TP averaging 2195.8 mg/kg and 543.0 mg/kg, respectively. The vertical distribution of TN and TP generally exhibited similar decreasing patterns with an increase in sediment depth, suggesting mineralization of TN and TP by microbes and benthic organisms. More attention and research are needed to understand the seasonality of nutrient exchange across the sediment–water interface, especially in eutrophic lakes.

Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

RCSANet: A Full Convolutional Network for Extracting Inland Aquaculture Ponds from High-Spatial-Resolution Images

Numerous aquaculture ponds are intensively distributed around inland natural lakes and mixed with cropland, especially in areas with high population density in Asia. Information about the distribution of aquaculture ponds is essential for monitoring the impact of human activities on inland lakes. Accurate and efficient mapping of inland aquaculture ponds using high-spatial-resolution remote-sensing images is a challenging task because aquaculture ponds are mingled with other land cover types. Considering that aquaculture ponds have intertwining regular embankments and that these salient features are prominent at different scales, a Row-wise and Column-wise Self-Attention (RCSA) mechanism that adaptively exploits the identical directional dependency among pixels is proposed. Then a fully convolutional network (FCN) combined with the RCSA mechanism (RCSANet) is proposed for large-scale extraction of aquaculture ponds from high-spatial-resolution remote-sensing imagery. In addition, a fusion strategy is implemented using a water index and the RCSANet prediction to further improve extraction quality. Experiments on high-spatial-resolution images using pansharpened multispectral and 2 m panchromatic images show that the proposed methods gain at least 2–4% overall accuracy over other state-of-the-art methods regardless of regions and achieve an overall accuracy of 85% at Lake Hong region and 83% at Lake Liangzi region in aquaculture pond extraction.