Pollution history and recovery of a boreal lake exposed to a heavy bleached pulping effluent load

We examined the effects of heavy pulp mill discharges on the Lake Lievestuoreenjärvi ecosystem and the later recovery of diatom and chironomid communities from age-dated short core samples. Beginning in 1927 the lake received a heavy effluent load from a sulphite pulp mill. Except for the recession during the Second World War and the temporary closure of the mill from 1967 to 1971, the industrial load, containing large quantities of nutrients, organic matter and toxic compounds, increased continuously. In the early 1980s, laboratory documents were falsified by the directors of the mill and the systematic illegal effluent overload led to a collapse of the whole lake ecosystem. In 1985, the outdated plant was finally closed down. Based on the assessment of chemical properties and biological remains of the sediment, we distinguished five developmental phases in the ecological state of the lake. In the pre-industrial phase, the pelagic and profundal benthic communities were dominated by species preferring ultraoligotrophic or oligotrophic lakes. Concomitant with the increasing discharge and deposition of chlorine compounds, resin acids, and mercury, as well as strong acidity and hypolimnetic and epilimnetic anoxia, the ecological status changed in a short period from excellent to bad. Finally, in the early 1960s, the majority of the lake was virtually dead and the aquatic life survived only in the uppermost littoral zone. Since 1985, a fast recovery in the water quality has led to a strong, but temporary eutrophy in pelagic communities. The main peak of eutrophication was caused by the invasion of a species new to the lake,Aulacoseira granulata var.angustissima. Later, the pelagic communities shifted towards oligotrophy, but the original, pre-industrial status has not been re-established. The profundal benthic communities have not achieved the pre-industrial structure, but at present indicate mesotrophy.     

基于二维小波变换的FMI图象分割

为了从FMI资料中定量提取参数，一个重要的步骤是从实际FMI资料中分离出反映溶孔、溶洞、裂缝的子图像。本文给出的方法，考虑图像像元邻域的特征，应用二维小波变换求出目标与背景边缘的点集，按这个边缘点集的坐标点所对应的原图像像素灰度值的平均值作为分割阈值进行图像分割。实际资料处理表明，应用这种方法可以从实际的FMI资料中准确地分割出孔洞、裂缝的子图像并且可以按深度段连续自动处理，为后续定量计算参数奠定了良好基础。     

Assessing the Comparability of Ammonium,Nitrate and Sulfate Concentrations Measured by Three Air Quality Monitoring Networks

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciation and Trend Network (STN). If combined, these three networks provide speciated fine particulate data at several hundred locations throughout the United States. Yet, differences in sampling protocols and samples handling may not allow their joint use. With these concerns in mind, the objective of this study is to assess the spatial and temporal comparability of the sulfate, nitrate and ammonium concentrations reported by each of these networks. One of the major differences between networks is the sampling frequency they adopted. While CASTNet measures pollution levels on seven-day integrated samples, STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and IMPROVE data therefore exhibit considerably more short-term variability than their CASTNet counterpart. We show that, despite their apparent incongruity, averaging the data with a window size of four to six weeks is sufficient to remove the effects of differences in sampling frequency and duration and allow meaningful comparison of the signals reported by the three networks of concern. After averaging, all the sulfate and, to a lesser degree, ammonium concentrations reported are fairly similar. Nitrate concentrations, on the other hand, are still divergent. We speculate that this divergence originates from the different types of filters used to collect particulate nitrate. Finally, using a rotated principal component technique (RPCA), we determined the number and the geographical organization of the significant temporal modes of variation (clusters) detected by each network for the three pollutants of interest. For sulfate and ammonium, the clusters’ geographical boundaries established for each network and the modes of variations within each cluster seem to correspond. RPCA erformed on nitrate concentrations revealed that, for the CASTNet and IMPROVE networks, the modes of variation do not correspond to unified geographical regions but are found more sporadically. For STN, the clustered areas are unified and easily delineable. We conclude that the possibility of jointly using the data collected by CASTNet, IMPROVE and STN has to be weighed pollutant by pollutant. While sulfate and ammonium data show some potential for joint use, at this point, combining the nitrate data from these monitoring networks may not be a judicious choice.     

Correcting method of eddy covariance fluxes over non-flat surfaces and its application in ChinaFLUX

Although Eddy Covariance (EC) technique is one of the best methods for estimating the energy and mass exchanges between underlying surface and atmosphere in micrometeorology, errors and uncertainties still exist without necessary corrections. In this paper, we will focus on the effect of coordinate system on the eddy fluxes. Based on the data observed over four sites (one farmland site, one grassland site and two forest sites), the effects of three coordinate system transforming methods (Double Rotation-DR, Triple Rotation-TR and Planar Fit-PF)on the turbulent fluxes are analyzed. It shows that (i) the corrected fluxes are more or less than the uncorrected fluxes, which is related mainly to the sloping degree of surface, wind speed and wind direction; and (ii) pitch angle has a sinusoidal dependence on wind direction, especially in the regular sloping terrain; and (iii) PF method is something like the simplification of TR or DR,and there are not obvious distinctions in correction in sloping grassland and flat farmland, but PF method is not suitable for uneven and irregular forest sites.     

Least-squares wave-path migration

We present a new least-squares migration method called least-squares wave-path migration. The proposed method combines an iterative conjugate-gradient solver with a stationary-phase wave-path migration operator. Numerical tests demonstrate that (i) least-squares wave-path migration is computationally more efficient than and almost as accurate as Kirchhoff least-squares migration, and (ii) many of the artefacts seen in wave-path migration images are suppressed after several conjugate-gradient iterations. Previous results have shown that 3D wave-path migration is up to 100 times faster than a standard 3D Kirchhoff migration, but sometimes at the cost of reduced quality. With the proposed least-squares wave-path migration method, the image quality in wave-path migration can be improved at an acceptable increase in computational cost.     

纳米比亚罗辛地区矿化白岗岩铀矿物学研究及意义

罗辛矿床为世界级白岗岩型铀矿床,存在SJ、SH、SK、Z17、Z19等多个矿（化）带,各矿（化）带矿物组合存在较大差异,但系统的矿物学研究程度不高。本次工作通过岩矿鉴定、扫描电镜观察、电子探针分析等方法,系统研究罗辛地区主要矿（化）带铀矿物共生组合及赋存状态。结果显示,白岗岩矿化按矿物组合可划分为3类: ①晶质铀矿+锆石+磷灰石组合,主要分布在Z17、Z19、SJ矿床（带）;②铀石 、铀钍石+榍石+磷灰石组合,主要分布在SH矿床;③贝塔石组合,主要分布在SK、SH矿床。研究区铀成矿可划分为3个期次：岩浆期、热液期、表生淋积期。岩浆期为主成矿期,研究区晶质铀矿电子探针化学年龄为505±11 Ma,代表岩浆期成矿年龄。SH应进一步寻找铀石、铀钍石+榍石+磷灰石组合矿化白岗岩。     

可解释性长短期记忆模型用于预测湖泊总磷浓度变化

对湖泊总磷的变化预测和来源识别对水资源调度和流域生态治理有着重要的意义,然而复杂的生化反应和水动力条件导致的非平稳性给湖泊总磷浓度的准确预测带来极大的困难。为克服这一挑战,本文引入了基于加权回归的季节趋势分解(seasonal and trend decomposition using Loess,STL)技术和夏普利加法(SHapley additive exPlanations,SHAP)结合长短期记忆网络(long short-term memory neural network,LSTM)和门控循环单元(gated recurrent unit,GRU)构建了一个可解释的预测框架,以增强对湖泊总磷浓度演变的预测并提高其可解释性。研究表明:(1)在骆马湖总磷浓度的预测中,该框架拥有较好的预报精度(R²=0.878),优于LSTM和卷积长短期记忆模型(convolutional neural networks and long short term memory network,CNN-LSTM)。当预测时间步长增加到8 h时,该框架有效提高了总磷浓度的预测精度,平均相对误差和均方根误差分别降低了47.1%和33.3%。从预测趋势来看,骆马湖在汛期的总磷平均浓度为0.158 mg/L,相较于非汛期的平均浓度,增加了202.1%。(2)运河来水是骆马湖总磷浓度最重要的影响因素,贡献权重为60.0%,并且不同断面(三湾、三场)的污染源受水动力、气象等因素的影响存在显著的时空差异。本文凸显了神经网络模型在预警水体污染方面的可实施性,并且为提高传统神经网络的学习能力和可解释性的开发与验证提供了重要方向。