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During the summer of 2020, NASA returned to launching astronauts to the International Space Station (ISS) from American soil. By 2024, NASA's mission is to return to the Moon, and by 2028 create a sustainable presence. Long duration missions come with obstacles, especially when trying to create a sustainable environment in a location where "living off the land" is impossible. Some resources on the Moon can be recovered or resupplied; however, many resources such as those needed for sustaining life must be recycled or grown to support humans. To achieve sustainability, food and water must be grown and recycled using elements found within the habitat. NASA's current work focuses on food resupply and growing plants as supplemental nutrient content. This paper examines the possibility for using aquaculture systems to purify water while growing nutrient-rich species as food sources, which aquatic food sources would be ideal for a habitat environment, and which species might provide an ideal test case for future studies aboard ISS. The aquatic species should be rapidly grown with high protein content and low launch mass requirements. Although there are numerous challenges and unknown technology gaps for maintaining aquaculture systems in reduced gravity environments, the benefit of employing such systems would be of great advantage towards creating a sustainable presence beyond Earth's orbit for sustainable aquaculture.

The adverse economic impacts of harmful algal blooms can be mitigated via tailored forecasting methods. Adequate provision of these services requires knowledge of the losses avoided, or, in other words, the economic benefits they generate. The latter can be difficult to measure for broader population segments, especially if forecasting services or features do not yet exist. We illustrate how Stated Preference tools and Choice Experiments are well-suited for this case. Using as example forecasts of respiratory irritation levels associated with airborne toxins caused by Florida red tide, we show that 24-hour predictions of spatially and temporally refined air quality conditions are valued highly by the underlying population. This reflects the numerous channels and magnitude of red tide impacts on locals' life and activities, which are also highlighted by our study. Our approach is broadly applicable to any type of air quality impediment with risk of human exposure. Submitted version

An accurate forecast of the red tide respiratory irritation level would improve the lives of many people living in areas affected by algal blooms. Using a decades-long database of daily beach conditions, two conceptually different models to forecast the respiratory irritation risk level one day ahead of time are trained. One model is wind-based, using the current days' respiratory level and the predicted wind direction of the following day. The other model is a probabilistic self-exciting Hawkes process model. Both models are trained on beaches in Florida during 2011-2017 and applied to the red tide bloom during 2018-2019. For beaches where there is enough historical data to develop a model, the model which performs best depends on the beach. The wind-based model is the most accurate at half the beaches, correctly predicting the respiratory risk level on average about 84% of the time. The Hawkes model is the most accurate (81% accuracy) at nearly all of the remaining beaches., Comment: 31 pages, 9 figures