Andreas Holbach

Dechlorination and organohalide-respiring bacteria dynamics in sediment samples of the Yangtze Three Gorges Reservoir

Several groups of bacteria such as Dehalococcoides spp., Dehalobacter spp., Desulfomonile spp., Desulfuromonas spp., or Desulfitobacterium spp. are able to dehalogenate chlorinated pollutants such as chloroethenes, chlorobenzenes, or polychlorinated biphenyls under anaerobic conditions. In order to assess the dechlorination potential in Yangtze sediment samples, the presence and activity of the reductively dechlorinating bacteria were studied in anaerobic batch tests. Eighteen sediment samples were taken in the Three Gorges Reservoir catchment area of the Yangtze River, including the tributaries Jialing River, Daning River, and Xiangxi River. Polymerase chain reaction analysis indicated the presence of dechlorinating bacteria in most samples, with varying dechlorinating microbial community compositions at different sampling locations. Subsequently, anaerobic reductive dechlorination of tetrachloroethene (PCE) was tested after the addition of electron donors. Most cultures dechlorinated PCE completely to ethene via cis-dichloroethene (cis-DCE) or trans-dichloroethene. Dehalogenating activity corresponded to increasing numbers of Dehalobacter spp., Desulfomonile spp., Desulfitobacterium spp., or Dehalococcoides spp. If no bacteria of the genus Dehalococcoides spp. were present in the sediment, reductive dechlorination stopped at cis-DCE. Our results demonstrate the presence of viable dechlorinating bacteria in Yangtze samples, indicating their relevance for pollutant turnover.

The Yangtze-Hydro Project: a Chinese–German environmental program

Water of good quality is one of the basic needs of human life. Worldwide, great efforts are being undertaken for an assured water supply. In this respect, one of the largest water technology projects worldwide is the Yangtze Three Gorges Dam in China. There is a need for extensive scientific and technical understanding of the challenges arising from this large hydrological engineering project. German and Chinese groups from various scientific fields are collaborating to provide knowledge for the sustainable management of the reservoir. In this project description, the Yangtze Three Gorges Dam Project, its goals and challenges, are described in brief, and the contributions of the German research projects are presented.

Three Gorges Reservoir: Density Pump Amplification of Pollutant Transport into Tributaries

The impoundment of the Three Gorges Reservoir (TGR) on the Yangtze River in China burdened its tributary backwaters with severe environmental problems.1 Confluence zones of reservoir tributaries with the Yangtze River main channel are main drivers of pollutant dynamics in the TGR2 and are thus keys to develop mitigation measures. Here, we show a novel experimental approach of spatiotemporal water quality analysis to trace water mass movements and identify pollutant transport pathways in reservoir water bodies. Our results show the movements of density currents in a major tributary backwater of the TGR. A huge interflow density current from the Yangtze River main channel transported its heavy metal carriage to the upstream reaches of the tributary backwater. Water from the upstream backwater moved counterwise and carried less but pollutant-enriched suspended sediments. This scenario illustrates the importance of confluence zone hydrodynamics for fates and pathways of pollutants through the widely unknown hydrodynamics of new reservoirs.

An integrated approach to model the biomagnification of organic pollutants in aquatic food webs of the Yangtze Three Gorges Reservoir ecosystem using adapted pollution scenarios

The impounding of the Three Gorges Reservoir (TGR) at the Yangtze River caused large flooding of urban, industrial, and agricultural areas, and profound land use changes took place. Consequently, substantial amounts of organic and inorganic pollutants were released into the reservoir. Additionally, contaminants and nutrients are entering the reservoir by drift, drainage, and runoff from adjacent agricultural areas as well as from sewage of industry, aquacultures, and households. The main aim of the presented research project is a deeper understanding of the processes that determines the bioaccumulation and biomagnification of organic pollutants, i.e., mainly pesticides, in aquatic food webs under the newly developing conditions of the TGR. The project is part of the Yangtze-Hydro environmental program, financed by the German Ministry of Education and Science. In order to test combinations of environmental factors like nutrients and pollution, we use an integrated modeling approach to study the potential accumulation and biomagnification. We describe the integrative modeling approach and the consecutive adaption of the AQUATOX model, used as modeling framework for ecological risk assessment. As a starting point, pre-calibrated simulations were adapted to Yangtze-specific conditions (regionalization). Two exemplary food webs were developed by a thorough review of the pertinent literature. The first typical for the flowing conditions of the original Yangtze River and the Daning River near the city of Wushan, and the second for the stagnant reservoir characteristics of the aforementioned region that is marked by an intermediate between lake and large river communities of aquatic organisms. In close cooperation with German and Chinese partners of the Yangtze-Hydro Research Association, other site-specific parameters were estimated. The MINIBAT project contributed to the calibration of physicochemical and bathymetric parameters, and the TRANSMIC project delivered hydrodynamic models for water volume and flow velocity conditions. The research questions were firstly focused on the definition of scenarios that could depict representative situations regarding food webs, pollution, and flow conditions in the TGR. The food webs and the abiotic site conditions in the main study area near the city of Wushan that determine the environmental preconditions for the organisms were defined. In our conceptual approach, we used the pesticide propanil as a model substance.

Since 2015 the SinoGerman research project SIGN supports water quality improvement in the Taihu region, China

The Taihu (Tai lake) region is one of the most economically prospering areas of China. Due to its location within this district of high anthropogenic activities, Taihu represents a drastic example of water pollution with nutrients (nitrogen, phosphate), organic contaminants and heavy metals. High nutrient levels combined with very shallow water create large eutrophication problems, threatening the drinking water supply of the surrounding cities. Within the international research project SIGN (SinoGerman Water Supply Network, www.water-sign.de), funded by the German Federal Ministry of Education and Research (BMBF), a powerful consortium of fifteen German partners is working on the overall aim of assuring good water quality from the source to the tap by taking the whole water cycle into account: The diverse research topics range from future proof strategies for urban catchment, innovative monitoring and early warning approaches for lake and drinking water, control and use of biological degradation processes, efficient water treatment technologies, adapted water distribution up to promoting sector policy by good governance. The implementation in China is warranted, since the leading Chinese research institutes as well as the most important local stakeholders, e.g. water suppliers, are involved.

Total Nitrogen Sources of the Three Gorges Reservoir — A Spatio-Temporal Approach

Understanding the spatial and temporal variation of nutrient concentrations, loads, and their distribution from upstream tributaries is important for the management of large lakes and reservoirs. The Three Gorges Dam was built on the Yangtze River in China, the world’s third longest river, and impounded the famous Three Gorges Reservoir (TGR). In this study, we analyzed total nitrogen (TN) concentrations and inflow data from 2003 till 2010 for the main upstream tributaries of the TGR that contribute about 82% of the TGR’s total inflow. We used time series analysis for seasonal decomposition of TN concentrations and used non-parametric statistical tests (Kruskal-Walli H, Mann-Whitney U) as well as base flow segmentation to analyze significant spatial and temporal patterns of TN pollution input into the TGR. Our results show that TN concentrations had significant spatial heterogeneity across the study area (Tuo River> Yangtze River> Wu River> Min River> Jialing River>Jinsha River). Furthermore, we derived apparent seasonal changes in three out of five upstream tributaries of the TGR rivers (Kruskal-Walli H ρ = 0.009, 0.030 and 0.029 for Tuo River, Jinsha River and Min River in sequence). TN pollution from non-point sources in the upstream tributaries accounted for 68.9% of the total TN input into the TGR. Non-point source pollution of TN revealed increasing trends for 4 out of five upstream tributaries of the TGR. Land use/cover and soil type were identified as the dominant driving factors for the spatial distribution of TN. Intensifying agriculture and increasing urbanization in the upstream catchments of the TGR were the main driving factors for non-point source pollution of TN increase from 2003 till 2010. Land use and land cover management as well as chemical fertilizer use restriction were needed to overcome the threats of increasing TN pollution.

Dilution of pollution? Processes affecting the water quality in the river-style Three Gorges Reservoir.

The Three Gorges Reservoir (TGR) in the Yangtze River is a unique water body. There is no comparative river-style reservoir in the world with similar characteristics and magnitudes of shape, size, water level fluctuation, and discharge. This highly dynamic system is located in a rapidly developing area of China covering parts of the Hubei Province and Chongqing Municipality. Increasing efforts for waste water treatment and pollution management can hardly compete with growing urban areas, industry, and infrastructure. Thus, huge amounts of various pollutants enter the TGR water bodies through point and non-point sources. Serious eutrophication problems manifest themselves in increasing numbers of algal blooms, especially in tributary backwaters of TGR. Besides nutrient loads, absolute concentrations of anthropogenic pollutants like heavy metals and organic chemical compounds rarely reach critical levels according to the Chinese, German, and WHO water quality guidelines. The massive dilution of these compounds in the mean discharging water of around 30,000 m 3 /s causes low absolute concentrations. But overall, tremendous total loads are transported further downstream and may remain a threat to connected ecosystems. At Tongji University in Shanghai, China, the 3rd conference entitled “Workshop on Processes in the Yangtze River System” was held for scientific exchangeon the recent cutting edgeenvironmentalresearchintheYangtzeRiverwithafocus

Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity

Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June–12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. This allows the regression performance of each model and variable to be evaluated. The best performing model for each variable results in a coefficient of determination R2 in the range of 89.9% to 94.6%. That clearly reveals the potential of the machine learning approaches with hyperspectral data. In further investigations, we focus on the generalization of the regression framework to prepare its application to different types of inland waters.

Urban Pollutant Plumes around Wushan and Dachang City in the Three Gorges Reservoir

The Three Gorges Reservoir (TGR) in the Yangtze River was among other reasons meant to improve and strengthen socio-economic development of the involved regions. Considerable economic growth and urbanization now pose additional threat to the water quality of the TGR due to urban pollutant inflows. The changed hydrological conditions in the TGR have considerably altered pollutant transport dynamics now causing higher susceptibility of the backwater areas to eutrophication and algal blooms. The Yangtze River is also widely used as a source for drinking water production. The assessment of the urban impact on the water quality in the TGR is, thus, crucial for sustainable future development planning. Measurements with an underwater multi-sensor system (MINIBAT) were performed in the frame of the “Yangtze-Project” [1] around the cities of Wushan and Dachang on the Daning River in the backwater reaches of the TGR in December 2012. Hydrological conditions were stable at constant water level between 174–175 m.a.s.l and low discharge conditions in the TGR during sampling period. 3D distribution patterns of the parameters temperature, conductivity, and turbidity in the water bodies were modeled using geostatistics. Selected water samples from different depths were analyzed for dissolved and particulate constituents using ICP-MS. Results show plumes of higher temperature, conductivity and turbidity in the epilimnion around the investigated urban areas. The range of influence was larger for temperature and conductivity plumes. Significant increase of turbidity was detected around Dachang City during few hours. Urban impacts on the water quality were significant at the encountered conditions and need to be further investigated for risk assessment, especially concerning drinking water production and eutrophication problems.