Persistence and fate of 17beta-estradiol and testosterone in agricultural soils

Steroidal hormones are constantly released into the environment by man-made and natural sources. The goal of this study was to examine the persistence and fate of 17beta-estradiol and testosterone, the two primary natural sex hormones. Incubation experiments were conducted under aerobic and anaerobic conditions using [4-(14)C]-radiolabeled 17beta-estradiol and testosterone. The results indicated that 6% of 17beta-estradiol and 63% of testosterone could be mineralized to (14)CO(2) in native soils under aerobic conditions. In native soils under anaerobic conditions, 2% of testosterone and no 17beta-estradiol was methanogenized to (14)CH(4). Essentially, no mineralization of either testosterone or 17beta-estradiol to (14)CO(2) occurred in autoclaved soils under aerobic or anaerobic condition. Results also indicated that 17beta-estradiol could be transformed to an unidentified polar compound through abiotic chemical processes; however, 17beta-estradiol was only oxidized to estrone via biological processes. The TLC results also indicated that testosterone was degraded, not by physical-chemical processes but by biological processes. Results also indicated that the assumed risks of estrogenic hormones in the environment might be over-estimated due to the soil's humic substances, which can immobilize majority of estrogenic hormones, and thereby reduce their bioavailability and toxicity.     

Drivers of future streamflow changes in watersheds across the Northeastern United States

Accurate projections of streamflow, which have implications for flooding, water resources, hydropower, and ecosystems, are critical to climate change adaptation and require an understanding of streamflow sensitivity to climate drivers. The northeastern United States has experienced a dramatic increase in extreme precipitation over the past 25 years; however, the effects of these changes, as well as changes in other drivers of streamflow, remain unclear. Here, we use a random forest model forced with a regional climate model to examine historical and future streamflow dynamics of four watersheds across the Northeast. We find that streamflow in the cold season (November–May) is primarily driven by 3-day rainfall and antecedent wetness (Antecedent Precipitation Index) in three rainfall-dominant watersheds, and 30-day rainfall, antecedent wetness, and 30-day snowmelt in the fourth, more snowmelt-dominated watershed. In the warm season (June–October), streamflow is driven by antecedent wetness and rainfall in all watersheds. By the end of the century (2070–2099), cold season streamflow depends on the importance placed on snow in the machine learning model, with changes ranging from −7% (with snow) to +40% (without snow) in a single watershed. Simulated future warm season streamflow increases in two watersheds (56% and 193%) due to increased precipitation and antecedent soil wetness, but decreases in the other two watersheds (−6% and −27%) due to reduced precipitation.