氮、磷等环境因子对太湖微囊藻与水华鱼腥藻生长的影响

为探索太湖主要水华藻类（微囊藻与水华鱼腥藻）在多种环境因子作用下的生长变化机理，在实验室内对部分水华藻类（微囊藻、鱼腥藻）进行分离培养，研究氮、磷、温度等环境因子对水华藻类生长增殖的影响。研究表明，高水温（30℃）是微囊藻的最适生长温度；随着氮、磷浓度的提高，微囊藻的生长速率加快；低磷是鱼腥藻生长的限制因子。同时，通过野外测定的各项指标发现，当藻类密度较低时，其与总氮、总磷呈正相关。     

三峡库区大宁河2010年春季水华特征

以大宁河春季水华期间调查数据为基础,运用数理统计分析手段,通过描述大宁河春季水华期藻类及主要理化因子分布特征,揭示出影响藻类生长的主要因子。结果表明:大宁河春季水华期水华河段水体氮、磷含量较高,总氮浓度为1.2～4.11mg/L,平均值为1.748mg/L,总磷浓度为0.027～0.615mg/L,氮磷比均值为17.5。春季水华藻类适宜的光照强度为1400～3800lx,水温为13.0～14.0℃时叶绿素a含量有最大增长,平均水温为13.4℃,藻密度与总氮、总磷、水温、DO、pH、浊度、高锰酸盐指数呈显著正相关关系,与透明度呈负相关关系。回水河段流速小于0.05m/s,流速是藻类生长最主要的限制因子。大宁河回水河段春季水华藻类分布较广,主要有甲藻门、绿藻门、硅藻门、隐藻门、蓝藻门、裸藻门和黄藻门7门28属,其中甲藻门分布最广,其次是绿藻门。春季水华优势种主要有甲藻门的拟多甲藻,绿藻门的衣藻、小球藻,硅藻门的直链藻,蓝藻门的色球藻等。     

基于多元回归理论的太湖湖泛预警模型研究

在太湖宜兴段藻源性湖泛高发区设立4个监测点,以湖泛发生的物质基础"藻类生物量"为研究对象,运用数据分析软件SPSS对监测点的藻类生物量、水质、气温等数据进行相关分析,建立了以藻密度为因变量的多元逐步回归模型。结合往年太湖藻源性湖泛发生时的气象条件等历史资料以及相关藻密度阈值的报道,构建了太湖宜兴段藻源性湖泛高发区监测预警模型系统,该模型能够基于监测点的实时水质数据和气象预报数据,对监控区域湖水在未来某时间段内发生湖泛风险的可能性进行分级预警。     

藻型湖泊大量藻类衰亡后致嗅物的分析以及水质变化的研究

藻类大量死亡后极易产生致嗅物.我们模拟了藻类的生长死亡过程,观测除藻后水体理化性质和生物性质的改变情况以及致嗅物的成分及浓度,以确定致嗅物质产生的途径.由于藻类死亡后细胞解体,藻类细胞中的氮、磷物质释放到水体中,导致水体的富营养化程度反而升高,而叶绿素-a也呈现下降趋势,整个试验过程后期溶解氧为0,水体产生嗅味物质,可采用吹扫捕集/固相微萃取—气相色谱—质谱法和顶空固相微萃取气质联用法分析致嗅物质.实验证明,当藻型湖泊的藻类被基本去除后,整个水体的初级生产力受到严重的破坏,威胁到水生态系统的安全性,可导致水体进一步恶化.     

水源水除藻研究中藻类监测方法的选用

对显微计数和叶绿素α测定两种主要藻类的监测方法进行了简要评述，提出在水源水除藻研究中应针对不同的除藻机制，采取不同的藻类监测方法。化学氧化除藻大都使用强氧化剂，将它们投入含藻的水体后，能穿透藻类细胞壁，扩散至细胞内部氧化叶绿素，使藻类代谢终止、死亡，故宜采用叶绿素α法。生物法除藻是利用生物对藻类的吸附、捕食和分解等作用去除藻类，则应采用计数法。     

三峡175米蓄水期间春季嘉陵江出口段藻类变化

为认识三峡大坝175m蓄水对春季嘉陵江藻类的影响,开展了春季嘉陵江出口段藻类活动频繁时期的现场调研。结果表明,与2007年春季相比,三峡大坝175m实验性蓄水期间,2009年春季嘉陵江出口段水位上涨、流速减缓、水体容量增大,营养盐受到一定程度稀释;硅藻为嘉陵江出口段绝对优势藻种,其中星肋小环藻与极小冠盘藻为水华藻种,星肋小环藻具有快速增长性,流速变缓不利于硅藻繁殖,致使总藻密度降低,总藻种数增加,其中绿、蓝、硅藻增加百分比较大,藻类多样性增大。     

南京南湖底泥疏浚后水质演化规律调查

对疏浚后的南京南湖底泥的TP、TN和COD释放规律、补水后的水质状况以及藻类演替规律进行了调查。结果表明,上覆水中TP平均质量浓度基本不随自来水补入量的增加而发生变化,TN和COD质量浓度随自来水补入量的增加而增大;水体中的TP、TN和COD含量总体呈上升趋势;从2005年3月中旬起,出现藻类的大量繁殖,在2005年7月发生水华,藻类优势种由裸藻、隐藻和小环藻演替为裸藻、栅藻和韦斯藻,藻类总量由2005年3月的3.7×106L-1上升到2006年4月的1.5×107L-1。     

基于人工神经网络的夜光藻密度预测模型

利用人工神经网络 BP算法 ,对各种理化因子与赤潮中夜光藻密度建立了人工神经网络预报模型 ,并利用该模型对各种理化因子与夜光藻密度的非线性对应规律进行了研究。结果表明 ,模型较好地反映了存在的对应规律     

渔洞水库控藻技术初探

去除藻类与控制其生长是湖泊水库水体恢复与保护的难题。打破藻类组成元素碳、氮、磷之间的比例,是有效抑制藻类细胞繁殖的前提和基础。文章结合工作实际,分析渔洞水库藻类数量和优势种群的变化,藻类与富营养化之间的关系,探讨物理、化学控藻技术存在的问题,提出生物控藻技术。     

7种化合物对新月藻的毒性效应及敏感度差异研究

以新月藻为受试生物,研究了阿特拉津、马拉硫磷、草甘膦、甲苯4种有机化合物和HgCl₂、NaAsO₂、K₂Cr₂O₇ 3种金属化合物对新月藻的毒性效应,探讨了新月藻对这些化合物的敏感度差异与特点及其机理,为这些物质的藻类急性毒性提供基础数据。结果表明,新月藻对阿特拉津、草甘膦、HgCl₂、NaAsO₂、K₂Cr₂O₇敏感度较高,而且响应大小与化合物浓度呈正相关趋势,对马拉硫磷和甲苯有一定响应,但随浓度变化趋势不明显;基于藻类光合作用的急性毒性检测方法,适用于检测可阻碍或抑制藻类光合作用中电子迁移、氧化还原、自由基等反应的毒物。     

t分布受控遗传算法优化BP神经网络的PM2.5质量浓度预测

根据齐齐哈尔大学监测点2014年3—5月PM2?5质量浓度及其对应的每小时的气象因素、气体污染物浓度，建立基于t分布受控遗传算法的BP神经网络模型（ BPM?TCG），对PM2?5质量浓度进行模拟预测。并将其与BP神经网络模型、遗传算法优化BP神经网络模型（ BP?GA）进行对比分析。3种模型预测结果表明：BPM?TCG模型预测精度最高，泛化能力最好。 BPM?TCG模型对PM2?5质量浓度的准确预测为预防和控制PM2?5提供依据。     

Study of harmful algal blooms in a eutrophic pond, Bangladesh

The purpose of this research was to analyze the underlying mechanisms and contributing factors related to the seasonal dynamic of harmful algal blooms in a shallow eutrophic pond, Bangladesh during September 2005–July 2006. Two conspicuous events were noted simultaneously throughout the study period: high concentration of phosphate–phosphorus (>3.03; SD 1.29 mg l^???1) and permanent cyanobacterial blooms {>3,981.88 × 10³ cells l^???1 (SD 508.73)}. Cyanobacterial blooms were characterized by three abundance phases, each of which was associated with different ecological processes. High nitrate–nitrogen (>2.35; SD 0.83 mg l^???1), for example, was associated with high cyanobacterial abundance, while low nitrate–nitrogen (0.36; SD 0.2 mg l^???1) was recorded during moderate abundance phase. Extremely low NO₃–N/PO₄–P ratio (>3.55, SD 2.31) was recorded, and all blooming taxa were negatively correlated with this ratio. Cyanobacterial blooms were positively correlated with temperature (r?=?0.345) and pH (0.833; p?=?0.05) and negatively correlated with transparency (r?=???0.956; p?=?0.01). Although Anabaena showed similar relationship with water quality parameters as cyanobacteria, the co-dominant Microcystis exhibited negative relationship with temperature (r?=???0.386) and nitrate–nitrogen (r?=???0.172). This was attributed to excessive growth of Anabaena that suppressed Microcystis’s growth. Planktothrix was the third most dominant taxa, while Euglena was regarded as opportunistic.     

基于AGNES算法优化BP神经网络和GIS系统的大气污染物浓度预测

建立了大气污染物浓度与影响因子之间的BP神经网络,对城市中各监测点位的次日大气污染物浓度进行预测,采用GIS的插值分析进行污染物空间分布预测,其中BP神经网络的输入向量采用AGNES算法进行处理。以太原市区SO2、PM10浓度预测为例,选择气温、湿度、降水量、大气压强、风速和前5天的污染物浓度等10个参数训练BP神经网络,结果表明,BP神经网络的训练效果较好,预测结果与实际浓度显著相关,R2分别为0.988、0.976;结合太原市8个监测点位的污染物浓度预测值,运用GIS空间差值法绘出SO2、PM10的浓度分布预测图,该图与实际情况大体符合,并且与国控大气污染企业的分布显著相关,Pearson相关系数分别为0.969、0.949。     

Multiple Effects of Environmental Factors on Algal Growth and Nutrient Thresholds for Harmful Algal Blooms: Application of Response Surface Methodology

Systematic understanding of the co-effects of environmental factors on phytoplankton proliferation can enable more effective control of harmful algal blooms in eutrophic lakes and reservoirs. A batch of statistically designed experiments using response surface methodology was recently conducted on mixed algae samples collected from Changtan Reservoir. The central composite designed response surface model was established to evaluate multiple effects of various physical and chemical factors (total nitrogen, total phosphorus, temperature, and light intensity) on algal density and chlorophyll a content. Analysis of variance indicated an excellent correlation between modeling results and experimental responses. Among the selected environmental variables, promotion of the interactive effects of nitrogen and phosphorus together with the optimum total nitrogen/phosphorus mass ratio (between 7.9 and 10.1) was determined to be the most significant stimulating parameter associated with algal blooming development dominated by non-nitrogen-fixing species. The favorable effects of strong illumination were shown to be greater than those of high temperature. The border values of total nitrogen and phosphorus concentrations leading to a critical value of algal density under different water temperatures and light intensities could be predicted as nutrient loading thresholds for harmful algal blooms by our second-order polynomial regression model.     

Function Approximation and Documentation of Sampling Data Using Artificial Neural Networks

Biodiversity studies in ecology often begin with the fitting and documentation of sampling data. This study is conducted to make function approximation on sampling data and to document the sampling information using artificial neural network algorithms, based on the invertebrate data sampled in the irrigated rice field.Three types of sampling data, i.e., the curve species richness vs. the sample size, the curve rarefaction, and the curve mean abundance of newly sampled species vs.the sample size, are fitted and documented using BP (Backpropagation) network and RBF (Radial Basis Function) network. As the comparisons, The Arrhenius model, and rarefaction model, and power function are tested for their ability to fit these data. The results show that the BP network and RBF network fit the data better than these models with smaller errors.BP network and RBF network can fit non-linear functions (sampling data) with specified accuracy and don't require mathematical assumptions. In addition to the interpolation, BP network is used to extrapolate the functions and the asymptote of the sampling data can be drawn. BP network cost a longer time to train the network and the results are always less stable compared to the RBF network. RBF network require more neurons to fit functions and generally it may not be used to extrapolate the functions. The mathematical function for sampling data can be exactly fitted using artificial neural network algorithms by adjusting the desired accuracy and maximum iterations. The total numbers of functional species of invertebrates in the tropical irrigated rice field are extrapolated as 140 to 149 using trained BP network, which are similar to the observed richness.     

Dynamic response of the scenic beauty value of different forests to various thinning intensities in central eastern China

Forest management has a significant influence on the preferences of people for forest landscapes. This study sought to evaluate the dynamic effects of thinning intensities on the landscape value of forests over time. Five typical stands in Wuxiangsi National Forest Park in Nanjing, China, were subjected to a thinning experiment designed with four intensities: unthinned, light thinning, moderate thinning, and heavy thinning. People’s preferences for landscape photographs taken in plots under various thinning intensities were assessed through scenic beauty estimation (SBE) at 2 and 5 years after thinning. The differences in scenic beauty value between different thinning intensities were then analyzed with a paired samples t test for the two periods. The results indicated that the landscape value of all of the thinned plots significantly exceeded that of the unthinned plots 2 years after thinning (p?相似文献   

基于BP神经网络的贵阳市空气质量指数预报模型

采用贵阳市2013年1月1日—2015年12月31日的空气质量指数(AQI)日均值,常规的地面和高空观测资料,基于不同季节,调整BP神经网络的隐藏层个数和隐藏层节点数,建立不同的BP神经网络预报模型,进行参数检验,最终选取预报效果最好的模型带入实况进行检验。结果表明,夏季的预报效果最好,采用的模型TS评分为81.6%,平均绝对误差为9.1,正确率为97.4%,用该模型检验预报效果,实况和预报的相关系数为0.71,平均误差为9;而冬季的预报效果明显低于其他季节,采用的模型TS评分为65.7%,平均绝对误差为19.5,正确率为72.9%,用该模型检验预报效果,实况和预报的相关系数为0.79,平均误差为19。而且BP神经网络模型的预报效果同隐藏层个数与隐藏层节点数没有显著关系。     

Use of fuzzy logic models for prediction of taste and odor compounds in algal bloom-affected inland water bodies

Mechanistic modeling of how algal species produce metabolites (e.g., taste and odor compounds geosmin and 2-methyl isoborneol (2-MIB)) as a biological response is currently not well understood. However, water managers and water utilities using these reservoirs often need methods for predicting metabolite production, so that appropriate water treatment procedures can be implemented. In this research, a heuristic approach using Adaptive Network-based Fuzzy Inference System (ANFIS) was developed to determine the underlying nonlinear and uncertain quantitative relationship between observed cyanobacterial metabolites (2-MIB and geosmin), various algal species, and physical and chemical variables. The model is proposed to be used in conjunction with numerical water quality models that can predict spatial–temporal distribution of flows, velocities, water quality parameters, and algal functional groups. The coupling of the proposed metabolite model with the numerical water quality models would assist various utilities which use mechanistic water quality models to also be able to predict distribution of taste and odor metabolites, especially when monitoring of metabolites is limited. The proposed metabolite model was developed and tested for the Eagle Creek Reservoir in Indiana (USA) using observations over a 3-year period (2008–2010). Results show that the developed models performed well for geosmin (R ²?=?0.83 for all training data and R ²?=?0.78 for validation of all 10 data points in the validation dataset) and reasonably well for the 2-MIB (R ²?=?0.82 for all training data and R ²?=?0.70 for 7 out of 10 data points in the validation dataset).     

基于随机森林与神经网络的高光谱土壤重金属Zn含量反演

在河北省保定市白洋淀区域采集115个土壤样品进行重金属含量分析和室内光谱测量,分别将BP神经网络、随机森林、决策树、多元线性回归、K近邻回归、AdaBoost回归和偏最小二乘回归法应用于全部原始波谱数据和基于双层随机森林选择后的波段数据。结果表明,基于原始波谱数据的土壤重金属Zn元素含量的反演模型精度较低,而通过双层随机森林选择出光谱数据中与土壤重金属Zn信息相关的波段,减轻了网络模型的过拟合问题,提高了模型预测精度;与其他模型比较,结合双层随机森林和BP神经网络构建的反演模型对研究区土壤重金属Zn含量预测效果最佳。     

太湖蓝藻水华暴发程度年度预测

近年来,尽管太湖主要水质指标有所改善,但蓝藻水华暴发的频次和面积并未明显减少.为了探讨太湖蓝藻水华暴发的环境驱动因子,统计了2012—2020年历年4—10月预警期间的太湖蓝藻水华发生规模与频次,结合同步浮标自动监测数据和实验室分析数据,构建了蓝藻水华预测模型.以太湖蓝藻水华综合指数(Ic)表征蓝藻水华强度,并通过Ic...