Abstract

Monitoring the drain-off water from nuclear power stations by high-resolution remote sensing satellites is of great significance for ensuring the safe operation of nuclear power stations and monitoring environmental changes. In order to select the optimal algorithm for Landsat 8 Thermal Infrared Sensor (TIRS) data to monitor warm drain-off water from the Daya Bay Nuclear Power Station (DNPS) and the Ling Ao Nuclear Power Station (LNPS) located on the southern coast of China, this study applies the edge detection method to remove stripes and produces estimates of four Sea Surface Temperature (SST) inversion methods, the Radiation Transfer Equation Method (RTM), the Single Channel algorithm (SC), the Mono Window algorithm (MW) and the Split Window algorithm (SW), using the buoy and Minimum Orbit Intersection Distances (MOIDS) SST data. Among the four algorithms, the SST from the SW algorithm is the most consistent with the buoy, the MODIS SST, the ERA-Interim and the Optimum Interpolation Sea Surface Temperature (OISST). Based on the SST retrieved from the SW algorithm, the tidal currents calculated by the Finite-Volume Coastal Ocean Model (FVCOM) and winds from ERA-Interim, the distribution of the warm drain-off from the two nuclear power stations is analyzed. First, warm drain-off water is mainly distributed in a fan-shaped area from the two nuclear power stations to the center of the Daya Bay. The SST of the warm drain-off is about 1–4 °C higher than the surrounding water and exceeds 6 °C at the drain-off outfall. Second, the tide determines the shape and distribution characteristics of the warm drain-off area. The warm drain-off water flows to the northeast during the flood tide. During the ebb tide, the warm drain-off water flows toward the southwest direction as the tide flows toward the bay mouth, forming a fan-shaped area. Moreover, the temperature increase intensity in the combined discharge channel during the flood tide is lower than that during the ebb tide, and the low temperature rising area during the flood tide is smaller than that during the ebb tide.

Highlights

  • With the increase in energy demands and the development of nuclear energy technology, China has established a number of nuclear power stations in coastal areas, which have greatly promoted the economic development of those regions

  • Tang et al [4] used the Advanced Very High-Resolution Radiometer (AVHRR) data for monitoring the warm plume of the Daya Bay Nuclear Power Station (DNPS) in different seasons, and the results showed that warm plumes could make the Sea Surface Temperature (SST) increase by 1~3 ◦C

  • The results show that the SST from the Split Window algorithm (SW) algorithm is more consistent with the MODIS SST, ERA SST and Optimum Interpolation Sea Surface Temperature (OISST) than the other algorithms (Tables 7–9)

Read more

Summary

Introduction

With the increase in energy demands and the development of nuclear energy technology, China has established a number of nuclear power stations in coastal areas, which have greatly promoted the economic development of those regions. Due to its advantages of rapid, economical and multi-scale analysis of heat distribution change regions, thermal infrared remote sensing technology can reflect the spatial and temporal differences and changes of water temperature This method is an effective means to monitor and estimate the warm plumes of nuclear power stations. TIR data with a high spatial resolution have been used to investigate the warm drain-off water from coastal power stations by analyzing the spatiotemporal distribution of the SSTs retrieved from HJ-1B/IRS, FY-3A/MERSI, Landsat5/TM and Landsat7/ETM+. These sensors with a single-channel design are subject to an immature single-channel algorithm, which leads to uncertainty in their results.

TIRS Data Pre-Processing
Sea Surface Temperature Inversion Algorithm
Radiation Transfer Equation Method
The Single Channel Algorithm
The Mono Window Algorithm
The Split Window Algorithm
TIRS Image Destriping Results
Validation of Sea Surface Temperature Algorithms
Characteristics of SST
Distribution of SST Rising Intensity
Background
Discussion
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call