Abstract
Biofouling is one of the main factors affecting the efficiency and safety of cooling water systems in coastal nuclear power plants. Understanding the population dynamics, succession rules and cumulative effects of major fouling organisms is the basis for targeted prevention and control. A 1-year simulated concrete panel test was conducted from December 2020 to November 2021 in Xinghua Bay, China. A total of 78 species of fouling organisms were recorded by combining the monthly, seasonal, semiannual, annual and monthly cumulative panels, and the community composition was dominated by nearshore warm-water species, making for a typical subtropical inner bay-type community. The fouling organisms had a peak attachment period from June to October. Significantly more attachment was observed during summer (from June to August) than during the other three seasons. The attachment amount in the second half-year (from June to November) was much higher than that in the first half-year (from December to May). The attachment thickness, density, and biomass of the bottom summer panels reached 20 cm, 105,150 ind./m 2 , and 19,274.50 g/m 2 , respectively, while those of the bottom annual panels were 40 cm, 27,300 ind./m 2 , and 17,762.50 g/m 2 , respectively. The dominant fouling organisms with calcified shells mainly included Amphibalanus reticulatus and Pernaviridis . These species had high attachment amounts,could accumulate attachments for a long time, and even might cause secondary blockage, making them the most detrimental to the safety of a cooling system. Moreover,the seasonal upward growth of hydroids and bryozoans can also significantly reduce the efficiency of cooling water intake. We suggest that targeted prevention and control should be carried out according to the larval attachment period of different dominant groups of fouling organisms during June-October, which can greatly improve the prevention and control efficiency. Strengthening the research on the biological cycle phenomenon of the main species and their main environmental impact factors, and establishing a scientific and effective early-warning model are the governance direction of formulating and implementing scientific pollution prevention and control in the future.