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Phthalate esters (PAEs) are widely used in the manufacturing of plastics, and the demand for PAEs has grown rapidly, especially in China. This trend will lead to much more environmental PAE contamination. PAEs are listed as priority substances in the European Union and are therefore subject to ecological risk assessments. This paper reviews the literature concerning the pollution status of PAEs and their ecological risk to aquatic environments. Risk quotients (RQs) based on the predicted no effect concentration and PAE concentrations in aquatic environments demonstrated significant (10 ≤ RQ < 100) or expected (RQ ≥ 100) potential adverse effects for algae, Daphnia, and fish in aquatic environments near PAE-based industrial and urban areas. Thus, the ecological risk of PAEs in Chinese aquatic environments should be considered, especially in areas where commercial plastics are produced.

The variations and associations of abiotic and biotic variables in Baiyangdian Lake, China, were analyzed in situ. Abiotic variables included eleven water quality parameters, and were characterized by water quality index (WQI). Biotic variables included periphyton function and diversity metrics. WQI differed in different seasons at sampling sites and the highest value of WQI was observed in October 2009. Periphyton function metrics, expressed by extracellular enzyme activities of alkaline phosphatase, β-glucosidase and leucine aminopeptidase, gross primary productivity and daily respiration rate, and diversity indices, in terms of Shannon diversity index and Berger-Parker abundance index, showed significantly temporal and spatial variations. Regression linear analysis illustrated a fairly good correlation of WQI with periphyton function and diversity indices, Shannon diversity index was the best correlated with WQI (r = 0.904, P < 0.01), followed by leucine aminopeptidase (r = -0.847, P < 0.01) and Berger-Parker abundance index (r = -0.840, P < 0.01), alkaline phosphatase, β-glucosidase and gross primary productivity also showed a good inverse correlation with WQI. Redundancy analysis suggested that eleven environmental variables explained a significant amount of the variation in the periphyton community data. The study was helpful for us to understand chemical and ecological status of water quality, and give us messages for monitoring water quality accurately.

The Haihe River Basin is a semiarid water resources area of China. River ecosystem was degraded for high population density and intensive water resources development activities. To assist in the improvement of the ecological integrity of this river ecosystem, an environmental flow assessment model was developed that consider both spatial structure and dominant eco-function parameters. River ecosystem was divided into three sub-ecosystems which including river reach, wetland and estuary based on the spatial structure of river ecosystem. River reach was divided into three types which including habitat restoration type (HR), water quality restoration type (WQR) and vegetation restoration substitute water quantity restoration type (VRSWQR) according to their dominant eco-function. The spatio-temporal distribution of environmental flow (EF) for the river ecosystem in the Haihe River Basin was assessed based on the model. The results indicate that the EF for the river reach, wetland, and estuary are 2.267, 1.532, and 0.972 billion m(3), respectively. The EF for HR type of river reach, the WQR type of river reach and VRSWQR type of river reach are 1.140, 1.138, and 0.154 billion m(3), which are equal to 4.320, 4.312, and 0.584 % of the average annual flow of 26.39 billion m(3), respectively. EF for river ecosystem in wet period (June-September), normal period (October-January) and dry period (February-May) are 2.999, 0.951, and 0.821 billion m(3), respectively. Annual EF for river ecosystem of the Basin are 4.771 billion m(3), which accounts for 18 % of the average annual flows of 26.39 billion m(3).

Heavy metals may adversely affect the structure and function of the periphyton community in lake ecosystems. We carried out samplings of three habitats at eight sites located in the Lake Baiyangdian that is strongly influenced by wastewater discharge (Sites 1 and 2), aquaculture and densely populated villages (Sites 3, 6, and 8), and the least disturbed (Sites 4, 5, and 7). Cu, Ni, Pb, Zn, Hg, Cd, and Cr were determined in these samples, and the periphyton community was simultaneously studied. The contamination factor (C f (i) ) was estimated for every metal as the ratio between pre-industrial records from sediments (C n (i) ) and present concentration values (C (i) ), and the individual potential risk (E r (i) ) was calculated by multiply the toxic response factor (Tr (i) ) and C f (i) for a given substance were based on Hakanson's methodology. The RI was obtained for each sampling site by summing the values of E r (i) first and the average was calculated across the sampling sites. The results showed that the RI for all three habitats was lower than 94, and they are in decreasing order: wastewater discharge, aquaculture and densely populated villages, and the least anthropogenic impacted. When the three sampling seasons were compared, August appeared to show the highest risk, followed by April and November. The RI values showed negative correlations (r = -0.444 to -0.851, p < 0.05) with the structural and functional metrics. The best correlation was detected between chlorophyll c/chlorophyll a (Chl c/a) ratio and E r (i) Hg (r = -0.851, p < 0.01). Our results suggest the periphyton community can be used in bio-monitoring.

A comprehensive indicator model for risk assessment and a multiple-level theoretical indicator system of the water quality-quantity-ecosystem (WQQE) for the Haihe River basin were constructed in this research. A fuzzy optimization model was used to assess risks for the four water systems of the Haihe River basin, and their risk order from high to lower risk was southern Haihe River system (SH), northern Haihe River system (NH), Tuhaimajiahe River system (TH) and Luanjiyanhai River system (LJ). The highest risk value (SH) was 0.8737. In terms of the WQQE, the secondary parameters for assessment of the four water system risks were 0.3579, 0.7226, 0.9547, and 0.5428 respectively. The results indicated that the main control factors for pollution for LJ, TH, SH and NH differed from each other and involved pollutant level, development of water resources, water flow and quality, ecosystem health and the hydrologic structure.

Although there are many studies of heavy metal contaminations of sediments, attention has seldom been paid to the problem in developing countries. The purpose of this article is to find the distribution and ecological risk of As, Hg, Cr, Cd, Pb, Cu, and Zn in surface sediment of Baiyangdian which is the biggest wetland in Northeast China. We apply three methodologies. The first is literature analysis comparing total concentrations of heavy metals with other water bodies around world. The second is Chinese Environmental Quality Standard for Soils (EQSS), National Environmental Protection Agency of China 1995, and the third is Soil and Aquatic Sediment Guidelines and Standards issued by New York Department of Environmental Conservation (NYSDEC). The results show that compared to other water bodies around the world, the seven heavy metals are low. However, Cd was found in the most polluting level of EQSS near a village and was second grade some distance from it. The village was also the most polluted site of Zn, which was in the second grade. When assessed by NYSDEC, Cu, Cr, and As contaminated the sediment and with moderate impacts on benthic life while Pb, Hg, and Zn were found at tolerable levels throughout Baiyangdian. The centre of Cu and Cr contamination was also near the village. As is the most polluting heavy metals with a major occurrence in the middle of the wetland. There were no heavy metals creating severe disturbance to the benthic communities. Based on the assessment, this article proposes different options for more sustainable management.

The structural and functional attributes of biofilms from Lake Baiyangdian, China, were tested to evaluate the utility of biofilms as indicators of macrophyte-dominated lake health. Biofilms on artificial substrata were sampled at 8 sites in the lake during different seasons. The responses of biofilms to land use and water quality changes were analyzed. The biofilm structural and functional attributes fluctuated seasonally. All attributes we tested differed significantly with different land use. Generally, biomass and enzyme activities increased, whereas chl c/a decreased with human disturbance. Attributes were strongly correlated to environmental variables, especially the trophic status of the lake. Overall, biofilms were good indicators of macrophyte-dominated lake health. The proportions of Bacillariophyta and Cyanophyta, Chlorophyll-a, b, chlorophyll-b/a, chlorophyll-c/a, algal density, β-glucosidase, aminopeptidase and ash-free dry weight of biofilms are recommended as tools for assessing responses to land use changes, and for monitoring the effectiveness of ecosystem rehabilitation.

The relative risk model (RRM) was applied in regional ecological risk assessments successfully. In this study, the RRM was developed through increasing the data of risk source and introducing the source-stressor-habitat exposure filter (SSH), the endpoint-habitat exposure filter (EH) and the stressor-endpoint effect filter (SE) to reflect the meaning of exposure and effect more explicit. Water environment which include water quality, water quantity and aquatic ecosystems was selected as the ecological risk assessment endpoints. The Luanhe River Basin located in the North China was selected as model case. The results showed that there were three low risk regions, one medium risk region and two high risk regions in the Luanhe River Basin. The results also indicated habitat destruction was the largest stressor with the risk scores as high as 11.87 for the Luanhe water environment, the second was oxygen consuming organic pollutants (9.28) and the third was nutrients (7.78). So these three stressors were the main influencing factors of the ecological pressure in the study area. Furthermore, animal husbandry was the biggest source with the risk scores as high as 20.38, the second was domestic sewage (14.00), and the third was polluting industry (9.96). For habitats, waters and farmland were enduring the bigger pressure and should be taken considerable attention. Water deterioration and ecological service values damaged were facing the biggest risk pressure, and secondly was biodiversity decreased and landscape fragmentation.

The distribution and ecosystem risk of 16 polycyclic aromatic hydrocarbons (PAHs) in the Luan River, China, has been investigated. summation operatorPAHs levels ranged from 37.3 to 234 ng L(-1) in water, from 20.9 to 287 ng g(-1) in sediment and from 36.9 to 378 ng g(-1) in bank soil, respectively. A method based on toxic equivalency factors (TEFs) and risk quotient (RQ) which can be used to assess the ecosystem risk of summation operatorPAHs sensitively and accurately was invented and a new ecosystem risk classification of summation operatorPAHs was suggested. The results indicated that the PAHs in aquatic environment of the Luan River resulted in low ecosystem risk and at S9 the ecosystem risk of PAHs in water was moderate. Low and moderate molecular PAHs presented much more ecosystem risk than high molecular PAHs in the Luan River basin and the mean ecosystem risk in water was higher than that in sediment and bank soil.

Evaluating six main rivers and six lakes in Beihuan water system (BWS) and diagnosing the limiting factors of eco-health were conducted for the ecohealth assessment and ecological restoration division of urban water system (UWS) for Beijing. The results indicated that Jingmi River and Nanchang River were in a healthy state, the degree of membership to unhealthy were 0.358, 0.392, respectively; while Yongding River, Beihucheng River, Liangma River, Tongzi River and six lakes were in an unhealthy state, their degree of membership to unhealthy were between 0.459 and 0.927. The order of that was Liangma > Beihucheng > Tongzi > Yongding > six lakes > Jingmi > Nanchang, in which Liangma Rivers of that was over 0.8. The problems of Rivers and lakes in BWS are different. Jingmi River and Nanchang River were ecotype limiting; Yongding River, Tongzi River and six lakes were water quality and ecotype limiting. Beihucheng River and Liangma River were water quantity, water quality and ecotype limiting. BWS could be divided into 3 restoration divisions, pollution control division including Yongding River, Tongzi River and six lakes; Jingmi River and Nanchang River were ecological restoration zone, while Beihucheng River and Liangma River were in comprehensive improvement zone. Restoration potentiality of Jingmi River and Nanchang River were higher, and Liangma River was hardest to restore. The results suggest a new idea to evaluate the impact of human and environmental factors on UWS.

Distribution and characteristics of heavy metals enrichment in sediment were surveyed including the bio-available form analyzed for assessment of the Luan River source water quality. The approaches of sediment quality guidelines (SQG), risk assessment code and Hakanson potential ecological risk index were used for the ecological risk assessment. According to SQG, The results show that in animal bodies, Hg at the sampling site of Wuliehexia was 1.39 mg/kg, Cr at Sandaohezi was 152.37 mg/kg and Cu at Hanjiaying was 178.61 mg/kg exceeding the severe effect screening level. There were 90% of sampling sites of Cr and Pb and 50% sites of Cu exceeded the lowest effect screening level. At Boluonuo and Wuliehexia, the exchangeable and carbonate fractions for above 50% of sites were at high risk levels and that for above 30% of sites at Xiahenan and Wulieheshang were also at high risk levels. Other sites were at medium risk level. Compared to soil background values of China, Hg and Cd showed very strong ecological risk, and the seven heavy metals of Hg, Cd, Cu, As, Pb, Cr, Zn at ecological risk levels were in the descending order. The results could give insight into risk assessment of environmental pollution and decision-making for water source security.