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Extensive dampness and mold growth in buildings are some of the most common, yet overlooked indirect impacts of floods, which adversely affect human respiratory health, particularly among asthmatic individuals. There is currently a lack of understanding on interrelationships among flood characteristics and drivers, building and HVAC system properties (e.g., ventilation rates), human behaviors (e.g., time spent in homes) and vulnerability to mold growth (e.g., asthma symptoms) in the built environment, particularly in residential buildings. This project collects data in the aftermath of two recent catastrophic hurricane events - Ida and Ian - from affected residential buildings to study the relationships among flood characteristics, mold growth, building properties, human behavior and human respiratory health. Our interdisciplinary team uses survey questionnaires, laboratory experiments and machine learning modeling to answer the following scientific questions: (1) what flood characteristics and drivers, building and HVAC system properties and human behaviors cause higher levels of mold growth in residential buildings? and (2) how does living in submerged or water-damaged houses after floods affect asthma symptoms among the residents? The developed empirical database and identified relationships can be used to guide building designers and occupational health scientists to establish resilient indoor environments, provide a foundation to develop flood-induced mold growth and asthma risk models, assist public health officials and emergency managers to have a better understanding of indirect health-related impacts of floods and support the development of timely strategies for disaster management in population centers. 

Extensive dampness and mold growth in buildings are some of the most common, yet overlooked indirect impacts of floods, which adversely affect human respiratory health, particularly among asthmatic individuals. There is currently a lack of understanding on interrelationships among flood characteristics and drivers, building and HVAC system properties (e.g., ventilation rates), human behaviors (e.g., time spent in homes) and vulnerability to mold growth (e.g., asthma symptoms) in the built environment, particularly in residential buildings. This project collects data in the aftermath of two recent catastrophic hurricane events - Ida and Ian - from affected residential buildings to study the relationships among flood characteristics, mold growth, building properties, human behavior and human respiratory health. Our interdisciplinary team uses survey questionnaires, laboratory experiments and machine learning modeling to answer the following scientific questions: (1) what flood characteristics and drivers, building and HVAC system properties and human behaviors cause higher levels of mold growth in residential buildings? and (2) how does living in submerged or water-damaged houses after floods affect asthma symptoms among the residents? The developed empirical database and identified relationships can be used to guide building designers and occupational health scientists to establish resilient indoor environments, provide a foundation to develop flood-induced mold growth and asthma risk models, assist public health officials and emergency managers to have a better understanding of indirect health-related impacts of floods and support the development of timely strategies for disaster management in population centers. 

Extensive dampness and mold growth in buildings are some of the most common, yet overlooked indirect impacts of floods, which adversely affect human respiratory health, particularly among asthmatic individuals. There is currently a lack of understanding on interrelationships among flood characteristics and drivers, building and HVAC system properties (e.g., ventilation rates), human behaviors (e.g., time spent in homes) and vulnerability to mold growth (e.g., asthma symptoms) in the built environment, particularly in residential buildings. This project collects data in the aftermath of two recent catastrophic hurricane events - Ida and Ian - from affected residential buildings to study the relationships among flood characteristics, mold growth, building properties, human behavior and human respiratory health. Our interdisciplinary team uses survey questionnaires, laboratory experiments and machine learning modeling to answer the following scientific questions: (1) what flood characteristics and drivers, building and HVAC system properties and human behaviors cause higher levels of mold growth in residential buildings? and (2) how does living in submerged or water-damaged houses after floods affect asthma symptoms among the residents? The developed empirical database and identified relationships can be used to guide building designers and occupational health scientists to establish resilient indoor environments, provide a foundation to develop flood-induced mold growth and asthma risk models, assist public health officials and emergency managers to have a better understanding of indirect health-related impacts of floods and support the development of timely strategies for disaster management in population centers. 

ABSTRACT Microplastics have received increased attention due to their negative impacts on the environment and human health. To minimize these impacts, mitigation strategies that are efficient and cost‐effective for a range of plausible conditions need to be developed. Models can be used to support these mitigation‐related decisions. However, modeling studies related to the export of microplastics from terrestrial to aquatic systems have been limited. Here, we review such modeling studies, the trends over time and geography of focus, and discuss pertinent concepts and the underlying physical, chemical, and biological processes. We categorize the published modeling studies, discuss their limitations, and provide recommendations for future research to fill key knowledge gaps. Future modeling efforts should focus on collecting more comprehensive field data for validation, developing continuous models over event‐based, conducting experimental studies to better understand the fundamental processes, developing hybrid modeling frameworks, adopting sediment transport modeling approaches, incorporating land management practices in the models, integrating surface and sub‐surface processes at the watershed scale, and utilizing advanced data‐driven models like foundation models. 

Abstract This study proposed a framework to evaluate multivariate return periods of hurricanes using event‐based frequency analysis techniques. The applicability of the proposed framework was demonstrated using point‐based and spatial analyses on a recent catastrophic event, Hurricane Ian. Univariate, bivariate, and trivariate frequency analyses were performed by applying generalized extreme value distribution and copula on annual maximum series of flood volume, peak discharge, total rainfall depth, maximum wind speed, wave height and storm surge. As a result of point‐based analyses, return periods of Hurricane Ian was investigated by using our framework; univariate return periods were estimated from 39.2 to 60.2 years, bivariate from 824.1 to 1,592.6 years, and trivariate from 332.1 to 1,722.9 years for the Daytona‐St. Augustine Basin. In the Florida Bay‐Florida Keys Basin, univariate return periods were calculated from 7.5 to 32.9 years, bivariate from 36.5 to 114.9 years, and trivariate from 25.0 to 214.8 years. Using the spatial analyses, we were able to generate the return period map of Hurricane Ian across Florida. Based on bivariate frequency analyses, 18% of hydrologic unit code 8 (HUC8) basins had an average return period of more than 30 years. Sources of uncertainty, due to the scarcity of analysis data, stationarity assumption and impact of other weather systems such as strong frontal passages, were also discussed. Despite these limitations, our framework and results will be valuable in disaster response and recovery.