Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning

Abstract

Thermal discharge from nuclear power plants poses a threat to the received natural water bodies, but the long-term extent and intensity of their surface thermal plumes remain unclear. In this study, we proposed a method to determine the background area for each drainage outlet and delineate the mixed surface thermal plumes based on 7,172 Landsat thermal infrared images. We further used a deep convolutional neural network integrated with prior location knowledge to extract core surface thermal plumes for 74 drainage outlets of 66 nuclear power plants worldwide. Our final model achieved a mean Intersection over Union (mIoU) of 0.8998 and an F1 score of 0.8886. We found that the mean maximal water surface temperature (WST) increment of the studied plants globally was 4.80 K. The Tianwan plant in China experienced the highest WST increase (8.51 K), followed by the Gravelines plant in France and the Ohi plant in Japan (7.91 K and 7.71 K, respectively). The Bruce plant in Canada had the largest thermal-polluted surface area (7.22 km2). We also provided the dataset, Global Coastal Nuclear power plant Thermal Plume (GCNT-Plume), to describe the long-term occurrence of water surface thermal plumes. Three influencing factors of the water surface thermal plume were further analyzed in this study, including total capacity, drainage type, and location type, which were associated with operating power, drainage method, and geographical features, respectively. Total capacity was more statistically related to the maximum of WST increment under shallow drainage condition. The mean WST increment of shallow drainage was 1.22 K higher than that of deep drainage. Surface plumes larger than 4 km2 frequently occurred in the Great Lakes, while small surface thermal plumes (< 1 km2) were primarily found in estuaries. The proposed method provides an important framework for future operational water surface thermal plume detection using remotely sensed observations and deep learning.