The dynamics of toxic and nontoxic Microcystis during bloom in the large shallow lake,Lake Taihu,China

Lake Taihu is a large shallow freshwater lake (surface area 2,338 km², mean depth 1.9 m) in China, which has experienced toxic cyanobacterial bloom dominated by Microcystis annually during the last few decades. In the present study, the dynamics of toxic and nontoxic Microcystis in three sampling stations (Meiliang Bay (site N2), Gonghu Bay (site N4), and the lake center area (site S4)) were quantified using quantitative real-time PCR (qPCR) during bloom periods from April to September, 2010. Our data showed that the abundance of toxic Microcystis and the toxic proportion gradually increased from April to August in water samples and reached the peak in August. During the study period, toxic Microcystis genotypes comprised between 26.2 and 64.3, between 4.4 and 22.1, and between 10.4 and 20.6 % of the total Microcystis populations in the three sampling sites, respectively. Correlation analysis suggested that there was a strong positive relationship between total Microcystis, toxic Microcystis and the toxic proportion. Chlorophyll a, total phosphorus, and water temperature were positively correlated with the abundances of total Microcystis and toxic Microcystis. Furthermore, the toxic proportion was positively correlated with total phosphorus (P?<?0.05) and water temperature (P?<?0.01), showing that global warming together with eutrophication could promote more frequent toxic blooms.     

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.     

Occurrence of organotins in the Yangtze River and the Jialing River in the urban section of Chongqing, China

The occurrence of organotins in the Yangtze River and the Jialing River in the urban section of Chongqing, China and their impact on drinking water waterworks are reported in this study. Water samples were extracted by solid-phase microextraction and measured using a gas chromatograph with mass spectrometer. The results showed that the rivers studied were polluted by both butyltins and phenyltins and that the butyltin species was the dominant pollutant. Butyltins, especially monobutyltin, were detected in all 18 sampling stations, and phenyltins were detected only in 11 sampling stations. Majority of the organotins were MBTs with concentrations varying from 27.3 to 1,145.8 ng Sn L^?1. Diphenyltin and dibutyltin were the second most common with the highest levels of 113.7 and 202.5 ng Sn L^?1, respectively. Monophenyltin, tributyltin, and triphenyltin had the lowest detection rates with concentration levels of 9.7, 161.8, and 37.2 ng Sn L^?1, respectively. Some of the organotins were also detected in drinking water waterworks, which posed a threat to the water quality of Chongqing.     

Mapping afforestation and deforestation from 1974 to 2012 using Landsat time-series stacks in Yulin District, a key region of the Three-North Shelter region, China

The Three-North Shelter Forest Program is the largest afforestation reconstruction project in the world. Remote sensing is a crucial tool to map land use and land cover change, but it is still challenging to accurately quantify the change in forest extent from time-series satellite images. In this paper, 30 Landsat MSS/TM/ETM+ epochs from 1974 to 2012 were collected, and the high-quality ground surface reflectance (GSR) time-series images were processed by integrating the 6S atmosphere transfer model and a relative reflectance normalization algorithm. Subsequently, we developed a vegetation change tracking method to reconstruct the forest change history (afforestation and deforestation) from the time-series Landsat GSR images based on the integrated forest z-score (IFZ) model by Huang et al. (2009a), which was improved by multi-phenological IFZ models and the smoothing processing of IFZ data for afforestation mapping. The mapping result showed a large increase in the extent of forest, from 380,394 ha (14.8 % of total district area) in 1974 to 1,128,380 ha (43.9 %) in 2010. Finally, the land cover and forest change map was validated with an overall accuracy of 89.1 % and a kappa coefficient of 0.858. The forest change time was also successfully retrieved, with 22.2 % and 86.5 % of the change pixels attributed to the correct epoch and within three epochs, respectively. The results confirmed a great achievement of the ecological revegetation projects in Yulin district over the last 40 years and also illustrated the potential of the time-series of Landsat images for detecting forest changes and estimating tree age for the artificial forest in a semi-arid zone strongly influenced by human activities.     

Development of a multi-criteria index ranking system for urban runoff best management practices (BMPs) selection

Low impact development best management practices (LID-BMPs) are considered to be cost-effective measures for mitigating the water quantity and quality impact of urban runoff. Currently, there are many types of LID-BMPs, and each type has its own intrinsic technical and/or economical characteristics and limitations for implementation. The selection of the most appropriate BMP type(s) for a specific installation site is therefore a very important planning step. In the present study, a multi-criteria selection index system (MCIS) for LID-BMP planning was developed. The selection indexes include 12 first-level indices and 26 second-level indices which reflect the specific installation site characteristics pertaining to site suitability, runoff control performance, and economics of implementation. A mechanism for ranking the BMPs was devised. First, each individual second-level index was assigned a numeric value that was based on site characteristics and information on LID-BMPs. The quantified indices were normalized and then integrated to obtain the score for each of the first-level index. The final evaluation scores of each LID-BMP were then calculated based on the scores for the first-level indices. Finally, the appropriate BMP types for a specific installation site were determined according to the rank of the final evaluation scores. In order to facilitate the application of the MCIS BMP ranking system, the computational process has been coded into a software program, BMPSELEC. A case study demonstrating the MCIS methodology, using an LID-BMP implementation planning at a college campus in Foshan, Guangdong Province, is presented.     

电镀企业竣工环保验收监测及后续监管中存在问题探讨

结合电镀企业竣工环保验收监测工作实践,对单位产品基准排水量和排气量的计算和评价,电镀标准中对污染物处理设施排气量的规定,第一类污染物在不同采样点评价标准的选择,重金属排放总量计算以及后续电镀企业废水、废气、污泥监管中存在的问题进行了探讨,并提出了相应的建议。     

Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations

We introduce robust procedures for analyzing water quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years.     

基于GF-1号遥感影像的武汉市及周边湖泊综合营养状态指数反演

湖泊水体富营养化的监测评价是湖泊水资源管理和水环境保护的基础性工作。基于GF-1号WFV遥感影像和综合营养状态指数法,通过82个站点实测数据建立多元线性回归和RBF神经网络模型,对武汉市及其周边地区主要湖泊综合营养状态指数进行了反演。反演的结果显示,武汉市及周边大部分湖泊水域处于轻度富营养和中营养状态,局部湖区为中度富营养状态。验证结果表明:GF-1号WFV多光谱数据用于监测大面积湖群水质变化是可行的;两种模型都可以建立实测数据与遥感信息的函数关系,根据函数可以反演湖泊水质综合营养状态指数,进而实现大面积湖泊水质动态监测;而RBF神经网络模型预测的R2为0.742 3,均方根误差为3.72,其反演精度更高,更适合于监测内陆湖泊水质变化。