Global occurrence characteristics, drivers, and environmental risk assessment of microplastics in lakes: A meta-analysis

Microplastic (MP) pollution in lakes has received much attention as an increasing amount of plastic waste enters aquatic ecosystems. However, there is still a lack of comprehensive understanding of the global distribution patterns, environmental hazards, factors driving their presence, and the relationships between sources and sinks of MPs. In this study, we conducted a meta-analysis of drivers of lake MP pollution based on 42 articles on MP pollution from three different aspects: geographical distribution, driving factors and environmental risks. The results revealed differences in the MP pollution levels across the different sampling sites in the global lakes. Moreover, there is significant heterogeneity in the abundance of MPs among various lakes, whose distribution pattern is affected by geographical location, sampling method and extraction method. The size of the MPs differed significantly between water and sediment, and the proportion of small (<1 mm) MPs in sediment was significantly greater than that in water (72% > 46%). Environmental risk assessment reveals that the risk level of MP pollution in most lakes worldwide is low, and the environmental risk of pollution in lake water is higher than that in sediment. Based on the risk assessment and geographical location of the lake, the risk of MP pollution is related not only to human activities and economic development but also to local waste management practices, which directly impact the accumulation of MPs. Therefore, we suggest that the production of biodegradable low-risk polymer plastics instead of high-risk materials, and plastic solid waste recycling management should be strengthened to effectively mitigate the presence of MPs in the environment.

Dynamic monitoring and analysis of chlorophyll-a concentrations in global lakes using Sentinel-2 images in Google Earth Engine

Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km2) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m3 for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.

Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with Landsat OLI images

Information regarding water clarity at large spatiotemporal scales is critical for understanding comprehensive changes in the water quality and status of ecosystems. Previous studies have suggested that satellite observation is an effective means of obtaining such information. However, a reliable model for accurately mapping the water clarity of global lakes (reservoirs) is still lacking due to the high optical complexity of lake waters. In this study, by using gated recurrent units (GRU) layers instead of full-connected layers from Artificial Neural Networks (ANNs) to capture the efficient sequence information of in-situ datasets, we propose a novel and transferrable hybrid deep-learning-based recurrent model (DGRN) to map the water clarity of global lakes with Landsat 8 Operational Land Imager (OLI) images. We trained and further validated the model using 1260 pairs of in-situ measured water clarity and surface reflectance of Landsat 8 OLI images with Google Earth Engine. The model was subsequently utilized to construct the global pattern of temporal and spatial changes in water clarity (lake area>10 km²) from 2014 to 2020. The results show that the model can estimate water clarity with good performance (R² = 0.84, MAE = 0.55, RMSE = 0.83, MAPE = 45.13%). The multi-year average of water clarity for global lakes (16,475 lakes) ranged from 0.0004 to 9.51 m, with an average value of 1.88 ± 1.24 m. Compared to the lake area, elevation, discharge, residence time, and the ratio of area to depth, water depth was the most important factor that determined the global spatial distribution pattern of water clarity. Water clarity of 15,840 global lakes between 2014 and 2020 remained stable (P ≥ 0.05); while there was a significant increase in 243 lakes (P < 0.05) and a decrease in 392 lakes (P < 0.05). However, water clarity in 2020 (COVID-19 period) showed a significant increase in most global lakes, especially in China and Canada, suggesting that the worldwide lockdown strategy due to COVID-19 might have improved water quality, espically water clarity, dueto the apparent reduction of anthropogenic activities.

Estimation of total flux of polycyclic aromatic hydrocarbons facilitated by methane ebullition into water column from global lake sediments

Methane ebullition and contamination are two typical characteristics from lakes, however, these two are generally studied independently. In fact, the exchange of matter and energy between methane bubbles and their surrounding environment can be very active to enhance the contaminant transport. There is limited research on understanding the characteristics and trends of gas ebullition facilitated contaminant emissions in large areas considering water and air as receptors. We herein estimate the transport capacity of methane ebullition for polycyclic aromatic hydrocarbons (PAHs) out of the sediment from global lakes, which may reach an average of 71 (up to 159) t yr^-1. Methane bubbles could transfer one third of the total PAH flux from sediments, or equivalent of 1.3-3.0 ng L^-1 of additional PAHs, into the water column with the rest going into air, offsetting from 52 to 118% of dry PAH deposition flux into global lakes sediment per year. Given the PAH concentration in lake water is often in the range of 0.1-100 ng L^-1, ebullition facilitated PAH flux may increase PAH concentration by a factor of 1.4 to 2.4 until 2,100, being a significant contributor for the PAH increment in lake waters.