Plants play an important role as sinks for or indicators of semivolatile organic pollutants, however most studies have focused on terrestrial plants and insufficient information has been obtained on aquatic plants to clarify the accumulation of organic pollutants via air-to-leaf vs. water-to-leaf pathways. The presence of p, p'-dichlorodiphenyldichloroethylene (p, p'-DDE), hexachlorobenzene (HCB), 15 polycyclic aromatic hydrocarbons (PAHs), and 9 substituted PAHs (s-PAHs), including oxy-PAHs and sulfur-PAHs, in 10 submerged and emergent plants collected from Lake Dianchi was analyzed in this study. Relatively low concentrations of p, p'-DDE (ND to 2.22 ngig wet weight [ww]) and HCB (0.24-0.84 ng/g ww) and high levels of PAHs (46-244 ng/g ww) and s-PAHs (6.0-46.8 ng/g ww) were observed in the aquatic plants. Significantly higher concentrations of most of the compounds were detected in the leaves of the submerged plants than in those of the emergent plants. The percentages of concentration difference relative to the concentrations in the submerged plants were estimated at 55%, 40%, 10%-69% and 0.5% 79% for p, p'-DDE, HCB, PAHs, and s-PAHs, respectively. The percentages were found to increase significantly with an increase in log Kow, suggesting that the high level of phytoaccumulation of pollutants in aquatic plants is due to hydrophobicity-dependent diffusion via the water-to-leaf pathway and the mesophyll morphology of submerged plants. (C) 2018 Elsevier Ltd. All rights reserved.
Targeting nonpoint source (NPS) pollution hot spots is of vital importance for placement of best management practices (BMPs). Although physically-based watershed models have been widely used to estimate nutrient emissions, connections between nutrient abatement and compliance of water quality standards have been rarely considered in NPS hotspot ranking, which may lead to ineffective decision-making. It's critical to develop a strategy to identify priority management areas (PMAs) based on water quality response to nutrient load mitigation. A water quality constrained PMA identification framework was thereby proposed in this study, based on the simulation-optimization approach with ideal load reduction (ILR-SO). It integrates the physically-based Soil and Water Assessment Tool (SWAT) model and an optimization model under constraints of site-specific water quality standards. To our knowledge, it was the first effort to identify PMAs with simulation-based optimization. The SWAT model was established to simulate temporal and spatial nutrient loading and evaluate effectiveness of pollution mitigation. A metamodel was trained to establish a quantitative relationship between sources and water quality. Ranking of priority areas is based on required nutrient load reduction in each sub-watershed targeting to satisfy water quality standards in waterbodies, which was calculated with genetic algorithm (GA). The proposed approach was used for identification of PMAs on the basis of diffuse total phosphorus (TP) in Lake Dianchi Watershed, one of the three most eutrophic large lakes in China. The modeling results demonstrated that 85% of diffuse TP came from 30% of the watershed area. Compared with the two conventional targeting strategies based on overland nutrient loss and instream nutrient loading, the ILR-SO model identified distinct PMAs and narrowed down the coverage of management areas. This study addressed the urgent need to incorporate water quality response into PMA identification and showed that the ILR-SO approach is effective to guide watershed management for aquatic ecosystem restoration.
Ammonia oxidation, performed by both ammonia oxidizing bacteria (AOB) and archaea (AOA), is an important step for nitrogen removal in constructed wetlands (CWs). However, little is known about the distribution of these ammonia oxidizing organisms in CWs and the associated wetland environmental variables. Their relative importance to nitrification in CWs remains still controversial. The present study investigated the seasonal dynamics of AOA and AOB communities in a free water surface flow CW (FWSF-CW) used to ameliorate the quality of polluted river water. Strong seasonality effects on potential nitrification rate (PNR) and the abundance, richness, diversity and structure of AOA and AOB communities were observed in the river water treatment FWSF-CW. PNR was positively correlated to AOB abundance. AOB (6.76 x 10(5)-6.01 x 10(7) bacterial amoA gene copies per gram dry sed-iment/soil) tended to be much more abundant than AOA (from below quantitative PCR detection limit to 9.62 x 10(6) archaeal amoA gene copies per gram dry sediment/soil). Both AOA and AOB abundance were regulated by the levels of nitrogen, phosphorus and organic carbon. Different wetland environmental variables determined the diversity and structure of AOA and AOB communities. Wetland AOA communities were mainly composed of unknown species and Nitrosopumilus-like organisms, while AOB communities were mainly represented by both Nitrosospira and Nitrosomonas. (C) 2018 Elsevier B.V. All rights reserved.
Worldwide there has been deterioration of lakeshore habitat and increasing eutrophication. These stresses have impacted littoral macroinvertebrate communities. However, bioassessment and rehabilitation have been largely carried out offshore, and the inshore macroinvertebrates have received less attention especially in shallow plateau lakes. In this study, we compared inshore and offshore macroinvertebrate communities in a shallow plateau lake, Lake Dianchi, China. The environmental parameters determining the distribution of macroinvertebrates were analyzed with partial redundancy analysis. Our results showed that macroinvertebrate communities differed significantly between inshore and offshore. Taxonomic richness was much higher inshore than offshore, due to higher habitat heterogeneity. By contrast, both density and biomass inshore were significantly lower than those of offshore. Generally, vegetation and substrate type were the key environmental parameters shaping macroinvertebrate communities. Eutrophication exerted great effect on offshore communities, while its impacts on inshore communities varied spatially. Shoreline degradation and seasonal eutrophication effects resulted in the limited density and biomass of inshore communities. Our results emphasized the significance of inshore habitats for macroinvertebrates in Lake Dianchi, and provided important implications for bioassessment and ecological rehabilitation in shallow lakes.
China is an agricultural country with the largest population in the world. However, intensification of droughts and floods has substantial impacts on agricultural production. For effective agricultural disaster management, it is significant to understand and quantify the influence of droughts and floods on crop production. Compared with droughts, the influence of floods on crop production and a comprehensive evaluation of effects of droughts and floods are given relatively less attention. The impact of droughts and floods on crop production is therefore investigated in this study, considering spatial heterogeneity with disaster and yield datasets for 1949-2015 in China mainland. The empirical relationships between drought and flood intensity and yield fluctuation for grain, rice, wheat, maize and soybean are identified using a Bayesian hierarchical model. They are then used to explore what social-economic factors influenced the grain sensitivity to droughts and floods by the Pearson's coefficient and locally weighted regression (LOSEE) plots. The modeling results indicate that: (a) droughts significantly reduce grain yields in 28 of 31 provinces and obvious spatial variability in drought sensitivity exists, with Loess Plateau having highest probability of crop failure caused by droughts; (b) floods significantly reduce grain yield in 20 provinces, while show positive effect in the northwestern and southwestern China; (c) the spatial patterns of influence direction of droughts and floods on rice, maize and soybean are consistent with the grain's results; and (d) promoting capital investments and improving access to technical inputs (fertilizer, pesticide, and irrigation) can help effectively buffer grain yield lose from droughts.
Digital Elevation Model (DEM) is one of the most important controlling factors determining the simulation accuracy of hydraulic models. However, the currently available global topographic data is confronted with limitations for application in 2-D hydraulic modeling, mainly due to the existence of vegetation bias, random errors and insufficient spatial resolution. A hydraulic correction method (HCM) for the SRTM DEM is proposed in this study to improve modeling accuracy. Firstly, we employ the global vegetation corrected DEM (i.e. Bare-Earth DEM), developed from the SRTM DEM to include both vegetation height and SRTM vegetation signal. Then, a newly released DEM, removing both vegetation bias and random errors (i.e. Multi-Error Removed DEM), is employed to overcome the limitation of height errors. Last, an approach to correct the Multi-Error Removed DEM is presented to account for the insufficiency of spatial resolution, ensuring flow connectivity of the river networks. The approach involves: (a) extracting river networks from the Multi-Error Removed DEM using an automated algorithm in ArcGIS; (b) correcting the location and layout of extracted streams with the aid of Google Earth platform and Remote Sensing imagery; and (c) removing the positive biases of the raised segment in the river networks based on bed slope to generate the hydraulically corrected DEM. The proposed HCM utilizes easily available data and tools to improve the flow connectivity of river networks without manual adjustment. To demonstrate the advantages of HCM, an extreme flood event in Huifa River Basin (China) is simulated on the original DEM, Bare-Earth DEM, Multi-Error removed DEM, and hydraulically corrected DEM using an integrated hydrologic-hydraulic model. A comparative analysis is subsequently performed to assess the simulation accuracy and performance of four different DEMs and favorable results have been obtained on the corrected DEM. (C) 2018 Elsevier B.V. All rights reserved.
Determination of the limiting nutrient of phytoplankton is critical to the lake eutrophication management. The average value of total nitrogen/total phosphorus (TN/TP) ratio is widely used to determine the limiting nutrient; while it suffers from the risk of the incorrect description of data and neglecting dynamics of the nutrient limitation. A probabilistic method was thereby proposed in this study to explore dynamics of nutrient limitation, including (a) indicator definition as the probability of TN/TP ratio failing in Redfield ratio line (PFR), indicating the possibility of TN limitation, to improve a probabilistic measure for the nutrient limitation; (b) Bayesian ANOVA analysis for posterior distributions of different treatments; and (c) dynamics determination as PFRs to show dynamics of nutrient limitation. Lake Xingyun in Southwestern China was taken as a case to explore the interannual and seasonal dynamics of the nutrient limitation. According to modeling results, we deducted that (a) for the interannual dynamics, the limiting nutrient shifted from TP to TN; and (b) for the seasonal dynamics, TN and TP were co-limiting. Deductions were further confirmed by the observed data. With the proposed probabilistic method, the co-limitation of TN and TP was identified for the seasonal dynamics; while using the average ratio solely denied the possibility of co-limitation. The current study also revealed that, due to neglecting the interannual and seasonal dynamics of nutrient limitation, the average ratio might mislead the eutrophication management strategy by recommending reducing TN and TP concentration together. The proposed probabilistic method demonstrated that TN was the limiting nutrient during the growing season of the phytoplankton in recent years and actions should focus on the TN concentration reduction. (C) 2017 Elsevier B.V. All rights reserved.
Lake eutrophication has become a worldwide challenge, and the empirical chlorophyll a-total phosphorus (Chla-TP) relationship provides a management target for TP concentrations. Neglecting the dynamics of the relationship at the lake-specific scale would mislead the eutrophication control strategy. The Bayesian hierarchical model (BHM) is a flexible tool to explore dynamics of the Chla-TP relationship and improves the overall estimation accuracy by partial pooling of data. In this study, we used the BHMto show the spatial and seasonal dynamics of the Chla-TP relationship in one of themost eutrophic lakes in China (Lake Dianchi). We defined an indicator (the Chla/TP ratio, CPR), to represent the susceptibility of Chla to TP. We conducted a model selection process and used the CPR-TP curves to show the spatial and seasonal dynamics of the ChlaTP relationships. We determined that the wind caused the spatial dynamics due to the horizontal transport of phytoplankton, while the water temperature and the percentage of soluble reactive phosphorus led to the seasonal dynamics via increasing the growth rate of phytoplankton. These findings helped the eutrophication control in Lake Dianchi. We found that compared with the strategy to decrease the TP concentration, decreasing the susceptibility is expected to be more effective. Finally, we concluded that exploring the dynamics of the Chla-TP relationship provided an important basis for eutrophication control at the lake-specific scale.
Denitrification community in wetland plays an important role in nitrogen removal. The present study investigated the seasonal and spatial dynamics of denitrification rate and nirS-denitrifier communities and the potential influential factors in a large wetland system treating polluted river water. Wetland denitrification rate and the abundance, richness, diversity and composition of nirS-denitrifier community were found to vary with season and sampling site. Both wetland denitrification rate and denitrifier community were related to plant type. Wetland soils and sediments differed greatly in either denitrification rate or denitrifier community structure. Wetland generally had lower denitrification rate and denitrifier abundance in summer than in spring and winter. Denitrification rate showed no direct correlation to denitrifier abundance but was positively correlated to denitrifier diversity. Denitrification rate could be mediated by denitrifier community structure. Moreover, Spearman rank correlation analysis suggested that denitrification rate was significantly correlated to sediment/soil ammonia, nitrate, nitrite, total phosphorus and pH, while denitrifier abundance was significantly correlated to total phosphorus and temperature. Nitrite, total nitrogen, total organic carbon, and the ratio of total organic carbon to total nitrogen showed significant correlations with wetland denitrifier diversity, while ammonia, nitrate, total nitrogen and total phosphorus might have important roles in shaping wetland denitrifier community structure. In addition, for each wetland sediment or soil, 0.8-46.2% of the retrieved nirS sequences could be related to the sequences from cultivated denitrifiers. Dechloromonas-like denitrifiers were more abundant in wetland sediments than in wetland soils.