Brehob Meredith M, Pennino Michael J, Handler Amalia M, Compton Jana E, Lee Sylvia S, Sabo Robert D
Oak Ridge Institute for Science and Education (ORISE), U.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division, Washington, DC, USA.
U.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division, Washington, DC, USA.
Earths Future. 2024 Aug 26;12(8):e2024EF004493. doi: 10.1029/2024EF004493.
Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. Efforts to quantify the occurrence, drivers, and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll- concentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in-lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll- across the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll- concentrations across US EPA's National Lakes Assessment ( = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of variation in chlorophyll-. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll- for ∼112,000 lakes across the CONUS. Risk for high chlorophyll- concentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in nutrient pollution hot spots across the country. These catchment and lake-specific results can help managers identify potential nutrient pollution and chlorophyll- hot spots that may fuel blooms, prioritize at-risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals.
过量的营养物质污染会导致有害藻华（HABs）的形成，这会损害渔业和娱乐活动，并可能通过蓝藻毒素直接危及人类和动物健康。由于对湖泊营养物质和叶绿素浓度进行实地监测需要大量资源，因此很难对大面积的有害藻华的发生、驱动因素和严重程度进行量化。为了更好地描述营养物质如何与其他环境因素相互作用，从而在淡水系统中产生藻华，我们使用了空间明确且时间匹配的气候、景观、湖泊特征和营养物质清单数据集，来预测美国本土（CONUS）的营养物质和叶绿素。我们采用嵌套建模方法，训练了三个随机森林（RF）模型，以解释美国环境保护局国家湖泊评估（ = 2,062）中总氮（TN）、总磷（TP）和叶绿素浓度的时空变化。TN和TP浓度是最重要的预测因子，与其他变量一起，RF模型解释了叶绿素变化的68%。然后，我们使用这些RF模型，将湖泊TN和TP预测值外推到没有营养物质观测数据的湖泊，并预测CONUS地区约112,000个湖泊的叶绿素。叶绿素浓度高的风险在以农业为主的中西部地区最高，但在全国的营养物质污染热点地区也出现了其他风险区域。这些流域和特定湖泊的结果可以帮助管理者识别可能引发藻华的潜在营养物质污染和叶绿素热点，确定需要额外监测的风险湖泊的优先级，并优化管理措施以保护人类健康和其他环境目标。
