Comprehensive modelling and cost-benefit optimization for joint regulation of algae in urban water system.

Algal blooms in urban water system is an international concern, which especially in China, have become a major obstacle to the urban water environment improvement since the preliminary achievements were made in the treatment of black and odorous water bodies. The complex blooming mechanisms require a joint regulation plan. This study established a framework that consisted of three steps, i.e., simulation, optimization, and verification, to build an optimal joint regulation plan. By taking the urban river network in Suzhou Pingjiang Xincheng as a case study, the cost-benefits of six alternative regulation measures were assessed using an algal bloom mechanism model and the discounted cash flow model based on 70 regulation scenarios. The joint regulation plan was optimized using the marginal-cost-based greedy strategy on the basis of the cost-benefits of different measures. The optimized joint plans, which were verified to be global optima, were more cost-effective than the designed regulation scenarios, and reduced the average chlorophyll-a concentrations by 55.3%-60.1% compared with the status quo. Applying the optimized cost allocation ratios of each measure to adjust the existing regulation scheme of another similar case verified that the optimization results had great generalizability.

Multi-factor analysis of algal blooms in gate-controlled urban water bodies by data mining.

Intense human disturbance has made algal bloom a prominent environmental problem in gate-controlled urban water bodies. Urban water bodies present the characteristics of natural rivers and lakes simultaneously, whose algal blooms may manifest multi-factor interactions. Hence, effective regulation strategies require a multi-factor analysis to understand local blooming mechanisms. This study designed a holistic multi-factor analysis framework by integrating five data mining techniques. First, the Kolmogorov-Smirnov test was conducted to screen out the possible explanatory variables. Then, correlation analyses and principal component analyses were performed to identify variable collinearity and mutual causality, respectively. After collinearity and mutual causality were treated prudently by using orthogonalization and instrumental variables, multilinear regression can be properly conducted to quantify factor contributions to algae growth. Lastly, a decision tree was used innovatively to depict the limiting threshold curves of each driving factor that restricts algae growth under different circumstances. The driving factors, their contributions, and the limiting threshold curves compose the complete blooming mechanisms, thus providing a clear direction for the targeted regulation task. A typical case study was performed in Suzhou, a Chinese city with an intricate gate-controlled river network. Results confirmed that climatic factors (i.e., water temperature and solar radiation), hydrodynamic factors (i.e., flow velocity), nutrients (i.e., phosphorus and nitrogen), and external loadings contributed 49.3%, 21.7%, 21.3%, and 7.7%, respectively, to algae growth. These results indicate that a joint regulation strategy is urgently required. Future studies can focus on coupling the revealed mechanisms with an ecological model to provide a comprehensive toolkit for the optimization of an adaptive joint regulation plan under the background of global warming.

Slow Development Restores the Fertility of Photoperiod-Sensitive Male-Sterile Plant Lines.

Photoperiod- and thermosensitive genic male sterility (P/TGMS) lines are widely used in crop breeding. The fertility conversion of Arabidopsis (Arabidopsis thaliana) TGMS lines including cals5-2, which is defective in callose wall formation, relies on slow development under low temperatures. In this study, we discovered that cals5-2 also exhibits PGMS. Fertility of cals5-2 was restored when pollen development was slowed under short-day photoperiods or low light intensity, suggesting that slow development restores the fertility of cals5-2 under these conditions. We found that several other TGMS lines with defects in pollen wall formation also exhibited PGMS characteristics. This similarity indicates that slow development is a general mechanism of PGMS fertility restoration. Notably, slow development also underlies the fertility recovery of TGMS lines. Further analysis revealed the pollen wall features during the formation of functional pollens of these P/TGMS lines under permissive conditions. We conclude that slow development is a general mechanism for fertility restoration of P/TGMS lines and allows these plants to take different strategies to overcome pollen formation defects.

Slowing development restores the fertility of thermo-sensitive male-sterile plant lines.

Temperature-sensitive genic male sterility (TGMS) lines are widely used in the breeding of hybrid crops1,2, but by what means temperature as a general environmental factor reverses the fertility of different TGMS lines remains unknown. Here, we identified an Arabidopsis TGMS line named reversible male sterile (rvms) that is fertile at low temperature (17 °C) and encodes a GDSL lipase. Cytological observations and statistical analysis showed that low temperature slows pollen development. Further screening of restorers of rvms, as well as crossing with a slow-growth line at normal temperature (24 °C), demonstrate that slowing of development overcomes the defects of rvms microspores and allows them to develop into functional pollen. Several other Arabidopsis TGMS lines were identified, and their fertility was also restored by slowing of development. Given that male reproductive development is conserved3, we propose that slowing of development is a general mechanism applicable to the sterility-fertility conversion of TGMS lines from different plant species.

A Bayesian approach for evaluation of the effect of water quality model parameter uncertainty on TMDLs: A case study of Miyun Reservoir.

Facing increasingly serious water pollution, the Chinese government is changing the environmental management strategy from solely pollutant concentration control to a Total Maximum Daily Load (TMDL) program, and water quality models are increasingly being applied to determine the allowable pollutant load in the TMDL. Despite the frequent use of models, few studies have focused on how parameter uncertainty in water quality models affect the allowable pollutant loads in the TMDL program, particularly for complicated and high-dimension water quality models. Uncertainty analysis for such models is limited by time-consuming simulation and high-dimensionality and nonlinearity in parameter spaces. In this study, an allowable pollutant load calculation platform was established using the Environmental Fluid Dynamics Code (EFDC), which is a widely applied hydrodynamic-water quality model. A Bayesian approach, i.e. the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, which is a high-efficiency, multi-chain Markov Chain Monte Carlo (MCMC) method, was applied to assess the effects of parameter uncertainty on the water quality model simulations and its influence on the allowable pollutant load calculation in the TMDL program. Miyun Reservoir, which is the most important surface drinking water source for Beijing, suffers from eutrophication and was selected as a case study. The relations between pollutant loads and water quality indicators are obtained through a graphical method in the simulation platform. Ranges of allowable pollutant loads were obtained according to the results of parameter uncertainty analysis, i.e. Total Organic Carbon (TOC): 581.5-1030.6t·yr(-1); Total Phosphorus (TP): 23.3-31.0t·yr(-1); and Total Nitrogen (TN): 480-1918.0t·yr(-1). The wide ranges of allowable pollutant loads reveal the importance of parameter uncertainty analysis in a TMDL program for allowable pollutant load calculation and margin of safety (MOS) determination. The sources of uncertainty are discussed and ways to reduce the uncertainties are proposed.