Remote quantification of phycocyanin in potable water sources through an adaptive model

Cyanobacterial blooms in water supply sources in both central Indiana USA (CIN) and South Australia (SA) are a cause of great concerns for toxin production and water quality deterioration. Remote sensing provides an effective approach for quick assessment of cyanobacteria through quantification of phycocyanin (PC) concentration. In total, 363 samples spanning a large variation of optically active constituents (OACs) in CIN and SA waters were collected during 24 field surveys. Concurrently, remote sensing reflectance spectra (R_rs) were measured. A partial least squares–artificial neural network (PLS–ANN) model, artificial neural network (ANN) and three-band model (TBM) were developed or tuned by relating the R_rs with PC concentration. Our results indicate that the PLS–ANN model outperformed the ANN and TBM with both the original spectra and simulated ESA/Sentinel-3/Ocean and Land Color Instrument (OLCI) and EO-1/Hyperion spectra. The PLS–ANN model resulted in a high coefficient of determination (R²) for CIN dataset (R² = 0.92, R: 0.3–220.7 μg/L) and SA (R² = 0.98, R: 0.2–13.2 μg/L). In comparison, the TBM model yielded an R² = 0.77 and 0.94 for the CIN and SA datasets, respectively; while the ANN obtained an intermediate modeling accuracy (CIN: R² = 0.86; SA: R² = 0.95). Applying the simulated OLCI and Hyperion aggregated datasets, the PLS–ANN model still achieved good performance (OLCI: R² = 0.84; Hyperion: R² = 0.90); the TBM also presented acceptable performance for PC estimations (OLCI: R² = 0.65, Hyperion: R² = 0.70). Based on the results, the PLS–ANN is an effective modeling approach for the quantification of PC in productive water supplies based on its effectiveness in solving the non-linearity of PC with other OACs. Furthermore, our investigation indicates that the ratio of inorganic suspended matter (ISM) to PC concentration has close relationship to modeling relative errors (CIN: R² = 0.81; SA: R² = 0.92), indicating that ISM concentration exert significant impact on PC estimation accuracy.     

对数比变换和偏最小二乘法在地球化学组合异常提取中的应用——以湘西北铅锌矿为例

以湘西北水系沉积物测量常量元素含量数据为研究对象,针对湘西北铅锌矿床的层控特征,以研究区含矿层地球化学常量元素含量的统计规律为基础,利用对数比变换(alr、clr、ilr变换)对原始数据进行预处理,并在此基础上进行偏最小二乘(PLS)降维分析,提取了地球化学组合异常,并与常用的主成分分析、因子分析的结果进行对比。分析结果表明,等距对数比变换(ilr变换)相对于其他变换方法有明显的优势,变换后的数据对称特征明显,更趋于正态分布。相对于其他两种降维方法,偏最小二乘法提取的元素组合与研究区铅锌矿的地球化学基本特征更为接近,组合异常与已知矿床分布和构造特征耦合良好,与研究区地质特征吻合,比主成分分析与因子分析得到的结果更易于地质解释。     

富营养深水水库底栖动物群落与浮游生物相关性分析

底栖动物和浮游生物通过食物网发生复杂的交互作用,是水库生态系统物质循环的重要组分.南湾水库和汤浦水库均为存在富营养化现象的深水水库,具有较长时间的水温分层期.本文选取这两座供水水库为对象,比较其水体营养状态、底栖动物的群落结构、现存量和多样性.通过群落状态指数(IICS)定量评估底栖动物群落状态,并采用PLS模型、Spearman秩相关以及回归图探讨浮游生物各类群密度与IICS的关系.研究结果显示,南湾水库富营养程度高于汤浦水库,寡毛类在南湾水库占据优势地位,摇蚊类在汤浦水库数量上占优.南湾水库以收集者为主,其数量占比远高于汤浦水库.深水水库通常存在较强的热分层,富营养化会加重水库底部的缺氧或厌氧状态,导致底栖动物遭受周期性的缺氧扰动,影响种类多样性和现存量,改变浮游生物类群与底栖动物群落的相关性,因此底栖动物群落与浮游生物的相关性能够提供判断水库富营养化程度的重要信号,进而为水库管理方提供更多有价值的参考信息.     

偏最小二乘法回归 (PLS)研究酸性森林土壤中铝的释放过程

采集了华南和西南酸性森林小流域中的土壤和土壤水并分析其化学组成,利用偏最小二乘法回归(PLS)研究了土壤中铝的释放过程,以综合评价各种影响因素的相对重要性。连续3年(2000～2002)的现场监测数据表明,尽管土壤水中铝的浓度存在显著差异,但铝的形态分布在各流域之间基本相似。大多数土壤水中单体铝(Ala)主要以无机态(Ali)存在,有机铝(Alo)一般不足10%。在pH3.6～5.6范围内,土壤水中Al3 活度(pAl)与土壤水pH值呈显著正相关关系(P<0.001),上层和下层土壤中pH-pAl拟合直线的斜率分别为1.28和2.00,明显偏离三水铝石(gibbsite)的溶解平衡模式。偏最小二乘法回归分析显示,上层和下层土壤中铝具有不同的来源和主导影响因素。上层土壤中,铝主要来源于有机键合铝(Alorg),土壤水离子强度(I)是造成铝浓度差异的主导因素。与此同时,铝的释放过程明显受到离子交换反应的影响。下层土壤中,铝近乎全部来源于无机键合铝(Alinorg),土壤水H 浓度成为影响铝释放过程的最主要因素。与上层土壤相比,离子交换对土壤铝释放过程影响明显减小。     

基于镀铜镉柱海水中硝酸盐氮的检测技术的研究

镀铜镉柱还原——重氮偶氮染色法在检测海水领域具有重要的位置,算法的不断创新优化成为海水水质检测质量的的关键.本研究以硝酸钾作为标准溶液,海水作溶剂做相关实验,并且在实际海水检测应用中得到良好的效果.通过对实验原理的分析及实验过程的探究,确定了本研究的最佳还原与显色条件.并采用偏最小二乘法(partial least s...