Initial estuarine response to the nutrient-rich Piney Point release into Tampa Bay, Florida

From March 30th to April 9th, 2021, 814 million liters of legacy phosphate mining wastewater and marine dredge water from the Piney Point facility were released into lower Tampa Bay (Florida, USA). This resulted in an estimated addition of 186 metric tons of total nitrogen, exceeding typical annual external nitrogen load estimates to lower Tampa Bay in a matter of days. Elevated levels of phytoplankton (non-harmful diatoms) were first observed in April in the lower Bay. Filamentous cyanobacteria blooms (Dapis spp.) peaked in June, followed by a bloom of the red tide organism Karenia brevis. Reported fish kills tracked K. brevis concentrations, prompting cleanup of over 1600 metric tons of dead fish. Seagrasses had minimal changes over the study period. By comparing these results to baseline environmental monitoring data, we conclude that many of the biological responses observed after the release from Piney Point are abnormal relative to historic conditions.

ESTIMATING EFFICIENCY OF POND ELECTRIC SEINE NETS OPERATION

The article considers the problem of low productivity of many fish farms, especially non-drainage ponds against the background of a growing increase in the volume of fish farming, which is typical for pond fish farming in Russia. It has been found that one of the main factors of low productivity (1–5 c/ha) is the imperfection of existing tools and methods of fishing. With significant costs of manual labor and time, the catch of farmed fish is no more than 50%. The remaining fish die mostly during winter fish kills. To reduce the proportion of manual labor and improve the efficiency of fishing in non-drainage ponds, many countries are looking for the methods to modernize existing or develop new fishing gear. The most promising in this respect are electric breeders for fishing in the non-drainage ponds, small lakes and water reservoirs. The advantages of this method are the coverage of a large fishing zone, the possibility of catching fish leaving ordi-nary seines, and work in silted and tied water bodies. Studying the regularities of the electric field impact on fish is necessary to explain and predict its behavior in electric fields, to develop technical means that provide the necessary control activity, to develop measures to protect ichthyofauna from the harmful effects of electric fields, etc.

A Mechanistic Dissolved Oxygen Modeling for Riverine Fish Kill Prevention

<p>Dissolved oxygen (DO) is a critical factor that controls the health and survival of the aquatic life. In the lower Danshuei River of Taiwan, DO was occasionally lower than 2 mg/L leading to several fish kill events. Since 2018, the Taipei city government started to continuously monitor hourly DO and other water quality factors at sites of Cheng-Mei Bridge and Cheng-De Bridge. However, at most sites, the monitoring has been conducted once a month. To provide sufficient DO predictions for preventing the occurrence of fish kills, a mechanistic DO modeling is required. As a result, in this study, we developed a system dynamic DO modeling considering oxygen exchange between the air-water and up/downstream interfaces with instream interactions of reaeration, photosynthesis, sediment oxygen demand (SOD), biochemical oxygen demand (BOD), respiration, and deoxygenation using the STELLA Architect software. In the model, we used meteorological data, water quality data, and hydrological data (flow rate, cross-section area, and hydraulic depth) simulated by HEC-RAS as input data to simulate daily DO at Cheng-Mei Bridge. Field measurements ranging from 0.21 to 10.34 mg/L were used to calibrate and validate the simulation results during Jan. to Aug. 2018, and Sep. to Dec. 2018, respectively. Our simulation results appeared reasonably good accuracy, in which the root mean square error (RMSE) ranging from 0.5 to 1.5 mg/L, and the percentage root mean square error (PRMSE) ranging from 5 to 15%. Moreover, results showed that DO was most sensitive to hydrological data, deoxygenation coefficient, and reaeration coefficient such that the meteorological conditions, like temperature and wind speed, were also important variables triggering hypoxia or anoxia that caused fish kills. Consequently, to better avoid or mitigate the occurrence of fish kills, we believe this physically-based DO modeling coupled with meteorological variables will offer useful information in predicting the condition of DO along the lower Danshuei River for managers to take preventative actions.</p>

What Happened in 1950?

This chapter discusses what happened around 1950 that led to the expansion of dead zones. For the Gulf of Mexico, there are many reasons to think the flow of the Mississippi River has changed since the days of Mark Twain, considering the construction of so many levees, dikes, floodways, spillways, weirs, and revetments. Rain-absorbing grasslands and forests have been replaced by asphalt, roof shingles, and other hydrophobic material that hasten rainwater to the Gulf. But the flow of the Mississippi has not changed enough to explain why the Gulf dead zone grew around 1950. As the chapter discusses, what did change was nutrients. It shows that concentrations doubled in the Mississippi River from the 1930s to the 1990s, which stimulated algal growth and production of organic material that eventually led to depletion of dissolved oxygen. In addition to creating dead zones, the increase in nutrients has stimulated harmful algal blooms, leading to fish kills and beach closings.

Using machine learning to understand the implications of meteorological conditions for fish kills

Fish kills, often caused by low levels of dissolved oxygen (DO), involve with complex interactions and dynamics in the environment. In many places the precise cause of massive fish kills remains uncertain due to a lack of continuous water quality monitoring. In this study, we tested if meteorological conditions could act as a proxy for low levels of DO by relating readily available meteorological data to fish kills of grey mullet (Mugil cephalus) using a machine learning technique, the self-organizing map (SOM). Driven by different meteorological patterns, fish kills were classified into summer and non-summer types by the SOM. Summer fish kills were associated with extended periods of lower air pressure and higher temperature, and concentrated storm events 2–3 days before the fish kills. In contrast, non-summer fish kills followed a combination of relatively low air pressure, continuous lower wind speed, and successive storm events 5 days before the fish kills. Our findings suggest that abnormal meteorological conditions can serve as warning signals for managers to avoid fish kills by taking preventative actions. While not replacing water monitoring programs, meteorological data can support fishery management to safeguard the health of the riverine ecosystems.