| 基于贝叶斯网络的太湖叶绿素a影响因素分析 |
| 摘要点击 1624 全文点击 1320 投稿时间：2022-06-29 修订日期：2022-08-09 |
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| 中文关键词 太湖 温风比 叶绿素a 总磷 连续型贝叶斯网络模型 富营养化 |
| 英文关键词 Lake Taihu ratio of air temperature to wind speed chlorophyll-a total phosphorus Bayesian networkmodel with continuous variables eutrophication |
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| 中文摘要 |
| 全球变暖加剧了湖泊富营养化问题.太湖作为中国典型的大型富营养化浅水湖泊，有害蓝藻水华问题尤为突出.以太湖作为研究对象，利用1992~2020年气象（气温、风速、降雨量、日照时长）、水质［总氮、总磷（TP）、电导率、pH、化学需氧量］和生物［叶绿素a （Chla）］监测数据，基于连续型贝叶斯网络模型构建了Chla的模拟模型，研究太湖不同气象和水质条件下的Chla水平.结果表明，春季"温风比"平均水平为6.67℃·s ·m-1，ρ（TP）低于0.130 mg ·L-1左右时，Chla偏高（>75分位数，下同）的概率小于75%；夏季"温风比"平均水平为10.52℃·s ·m-1，ρ（TP）低于0.257 mg ·L-1左右时，Chla偏高的概率小于75%；秋季ρ（TP）平均水平为0.154 mg ·L-1，"温风比"小于6.30℃·s ·m-1左右时，Chla偏高的概率小于75%.基于以上研究，进一步利用连续型贝叶斯网络模型构建的Chla模型模拟了不同气候变化背景下的营养盐控制目标.为控制太湖Chla处于有害蓝藻水华暴发"低风险"水平，在1992~2000、2001~2010和2011~2020年各年代气候水平背景下TP需要控制的目标浓度阈值分别为春季ρ（TP）：0.135、0.115和0.059 mg ·L-1；夏季ρ（TP）：0.174、0.164和0.145 mg ·L-1和秋季ρ（TP）：0.171、0.162和0.145 mg ·L-1.结果表明随着全球气候变暖，控制富营养化湖泊有害蓝藻水华需要更加严格的营养盐控制策略，可为太湖开展有害蓝藻水华防控和富营养化治理评估营养盐控制目标提供参考. |
| 英文摘要 |
| Global warming has aggravated the problem of lake eutrophication. As a typical large, eutrophic, shallow lake in China, the issue of cyanobacterial harmful algal blooms (cyanoHABs) was particularly prominent in Lake Taihu. We took Lake Taihu as the study area, using the meteorological (temperature, wind speed, rainfall, and sunshine hours), water quality (total nitrogen, total phosphorus, conductivity, pH, and chemical oxygen demand), and biological (chlorophyll-a in phytoplankton) monitoring data from 1992 to 2020. We built a simulation model of chlorophyll-a based on the Bayesian network model with continuous variables to study the chlorophyll-a level of Lake Taihu under different meteorological and water quality conditions. The 75th percentile of chlorophyll-a concentration was used as the threshold to judge the risk of cyanobacterial bloom. When the probability of chlorophyll-a concentration below this threshold was greater than 75%, it was regarded as "low risk" of cyanobacterial bloom outbreak. The results showed that the average level of "temperature wind ratio" (ratio of air temperature to wind speed) in spring was 6.67℃·s·m-1, and the probability of high chlorophyll-a was less than 75% when the total phosphorus concentration was less than 0.130 mg·L-1. The average "temperature wind ratio" level in summer was 10.52℃·s·m-1, and the probability of high chlorophyll-a was less than 75% when the total phosphorus concentration was less than 0.257 mg·L-1. The average level of total phosphorus concentration in autumn was 0.154 mg·L-1, and the probability of high chlorophyll-a was less than 75% when the "temperature wind ratio" was less than 6.30℃·s·m-1. Based on the above research, the chlorophyll-a model constructed by the Bayesian network model with continuous variables was further used to simulate the nutrient control objectives under different climate change backgrounds. In order to control chlorophyll-a in Lake Taihu at the:"low risk" level of cyanoHABs, the target concentration thresholds of total phosphorus needed to be controlled under the climate level background from 1992 to 2000, 2001 to 2010, and 2011 to 2020 were given. From 1992 to 2000, the threshold value of total phosphorus concentration was 0.135 mg·L-1 in spring, 0.174 mg·L-1 in summer, and 0.171 mg·L-1 in autumn. From 2001 to 2010, the threshold value of total phosphorus concentration was 0.115 mg·L-1 in spring, 0.164 mg·L-1 in summer, and 0.162 mg·L-1 in autumn. From 2011 to 2020, the threshold value of total phosphorus concentration was 0.059 mg·L-1 in spring, 0.145 mg·L-1 in summer, and 0.145 mg·L-1 in autumn. The results showed that the control of cyanoHABs in eutrophic lakes required more stringent nutrient control strategies with global warming. It provided a reference for preventing and controlling cyanoHABs and eutrophication in Lake Taihu. Previous studies have used multiple regression models, hydrodynamic numerical models, and other methods to predict chlorophyll-a concentrations or cyanobacterial blooms in lakes. However, there has been no study on the prediction of cyanoHABs in lakes based on the Bayesian network model with continuous variables and the "dynamic" evaluation of nutrient thresholds. Therefore, based on the seasonal meteorological, water quality, and biological monitoring data of Lake Taihu from 1992 to 2020, the chlorophyll-a model of Lake Taihu was constructed for the first time based on the Bayesian network model with continuous variables to simulate the chlorophyll-a concentration of Lake Taihu under different climate indicators and total phosphorus concentrations. The weight of its influencing factors was also analyzed, and the nutrient control objectives under different climate scenarios were "dynamically" evaluated. |
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