Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the former impacts. Of the indirect impacts, those on mold growth in indoor environments that affect human respiratory health (e.g. asthma) have received limited attention. Models can be used to predict these impacts and support development of mitigation and preventive actions. Despite the presence of models for some other impacts of flooding, quantitative models for estimating the impacts of flooding on indoor mold spores are lacking. In this article, we studied the aftermath of two recent hurricanes-Ida and Ian-in the United States and applied machine learning algorithms to develop the first quantitative model for predicting mold spores in buildings. A comprehensive fine-scale database (building level), consisting of flood characteristics, building properties, human indoor activities and existing mold spores, prepared through survey questionnaires, home inspections, laboratory analyses and flood hindcasting, from 60 homes was utilized. The modeling results suggested satisfactory performance for regression-based predictions of indoor mold spores (coefficient of determination or R² of 0.83 and 0.38). This is the first quantitative model for predicting the impacts of flooding on mold spores. Our study provides a foundation for quantitative assessments of flood impacts on indoor mold spores in residential buildings. This supports insurance companies, public health officials and emergency managers to better assess the impacts of hurricanes and extreme flooding on human respiratory health.

Food security is always a pressing agenda worldwide. The grain production in many areas has decreased due to the reduction in agricultural research funding and infrastructure investment. In this paper, we employed the Extreme-Tree algorithm to determine the main effectors in grain production in Hexi Corridor, Gansu, China, during 2002–2018. First, we applied the three-stage super-SBM DEA to precisely assess agricultural production. Then, we used the Extremely randomized trees algorithm to quantify the importance of each factor. Our results show that the variant of average efficiency score at the first stage was minimal. After removing the influence of environmental factors on production efficiency, the more accurate efficiency score was decreasing from 2002 to 2018. The R² value of the Extra-Tree model was 0.989 in the grain production analysis. Our research shows that grain production in the Hexi Corridor was controlled by human-driven but not nature-driven during our research period. Based on the importance attribution analysis of each model, it showed that the importance of human-driven investment occupied 93.7% of grain production. The importance of nature-driving was about 6.3%. Accordingly, we proposed corresponding opinions and suggestions to government and growers.