Apoplastic histochemical features of plant root walls that may facilitate ion uptake and retention.

We used brightfield and epifluorescence microscopy, as well as permeability tests, to investigate the apoplastic histochemical features of plant roots associated with ion hyperaccumulation, invasion, and tolerance of oligotrophic conditions. In hyperaccumulator species with a hypodermis (exodermis absent), ions penetrated the root apex, including the root cap. By contrast, in non-hyperaccumulator species possessing an exodermis, ions did not penetrate the root cap. In vivo, the lignified hypodermis blocked the entry of ions into the cortex, while root exodermis absorbed ions and restricted them to the cortex. The roots of the hyperaccumulators Pteris vittata and Cardamine hupingshanensis, as well as the aquatic invasives Alternanthera philoxeroides, Eichhornia crassipes, and Pistia stratiotes, contained lignin and pectins. These compounds may trap and store ions before hypodermis maturation, facilitating ion hyperaccumulation and retention in the apoplastic spaces of the roots. These apoplastic histochemical features were consistent with certain species-specific characters, including ion hyperaccumulation, invasive behaviors in aquatic environments, or tolerance of oligotrophic conditions. We suggest that apoplastic histochemical features of the root may act as invasion mechanisms, allowing these invasive aquatic plants to outcompete indigenous plants for ions.

[Tempo-spatial Variations and Influential Factors of Carbon Dioxide Emissions from the Geheyan Reservoir over the Qingjiang River Basin, China].

Greenhouse gas emission from aquatic ecosystem will affect climate change on the regional and global scale. So large river interception project which affects the carbon cycle model and greenhouse gas emission from aquatic ecosystem has provoked more and more attentions in recent years. In order to understand and evaluate the effects of typical hydropower project construction on the aquatic ecosystem and carbon cycle, the Geheyan Reservoir, a typical river interception project, was selected as a typical case in this paper for measuring carbon dioxide fluxes from interface between water and atmosphere from March, 2015 to February, 2016. The integration of the online gas analyzer and floating box was used to obtain carbon dioxide fluxes from interface between water and atmosphere. Data was obtained over the before dam, upstream, tributary, drawdown area and bay area respectively for understanding the spatial Heterogeneity of carbon dioxide fluxes. Data analysis showed that yearly mean fluxes of carbon dioxide from the Geheyan Reservoir was (55.6918±66.3329) mg·(m2·h)-1 during measurement, which indicated that the temporal distribution was higher in winter and lower in other seasons and the spatial variation was higher in typical bay and lower before dam as well as drawdown zone over the reservoir. Moreover, the seasonal variation of carbon dioxide fluxes from the interface between water and atmosphere was very stable at Yuxiakou measurement points as a typical background area of the reservoir, which was unexpectedly higher than those before dam and drawdown zone in the most months during measurement. The results from data analysis also indicated that the spatial and temporal variation of carbon dioxide fluxes from the Geheyan Reservoir was affected by water quality parameters such as dissolved oxygen, chlorophyll, pH, water temperature and conductivity and carbon in water, but the relationship between carbon dioxide fluxes and other parameter was very different within each season and impound period. So the results above will provide more supports for understanding the import pathway and transfer of aquatic carbon cycle caused by large river interception project in China.

Simulated annealing based hybrid forecast for improving daily municipal solid waste generation prediction.

A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%.