Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks

Enhanced aqueous phosphorus removal and mechanism by water spinach (Ipomoea aquatica Forsk) pretreated with lanthanum nitrate

Excess nutrients such as phosphate (PO43-) entering surface waters promote eutrophication, and phosphorous (P) removal is important to clear the water. Phytoremediation efforts have been used to improve water quality by varieties of P removal plants, such as water spinach (Ipomoea aquatica Forsk). Water spinach can reduce both internal and external resources of phosphorus from waterbody. The ion of lanthanum (La), one rare earth element (REE), is an immobilization substance for aqueous phosphate and also a fertilizer for plants. Therefore, lanthanum nitrate La (NO3)3 was used further to improve the phytoextraction of P from the polluted water. This study investigated the effects of La on the aqueous P removal by two genotypes of water spinach, green stem large leaves (GSLL) and green stem willow leaves (GSWL). The low concentration La (NO3)3 helped the plant to remove more phosphorous from eutrophic water, but La at high concentration lowered the removal of P. Under La (NO3)3 treatments, the optimum concentration for maximum P removal in GSLL is 3 mg/L, and for GSWL, it is 10 mg/L and P removal rates were enhanced to 95% and 96%, respectively. When the concentration of La (NO3)3 is 100 mg/L, the removal percentage of P was only 10% for both genotypes. The very high concentration of La will impose toxicity and even cause the death of the water spinach and produce secondary pollution; for example, under some specific circumstances, the bond between lanthanum and nitrates dissociates into lanthanum ions (La3⁺) and nitrate ions (NO₃⁻). If the concentration is high, then it accumulates in the aquatic water organisms and plants and causes toxicity in their bodies. If humans eat up these plants and fish, it causes toxic effects in humans. The La (NO3)3 positively affects different parameters of plants. La (NO3)3 increases the growth, pigments, enzyme activity, and malondialdehyde (MDA) of plants which were also discussed in this study. The biological mechanism should be responsible for the enhanced aqueous phosphorus removal by water spinach using lanthanum nitrate.

Modelling assisted phytoremediation of soils contaminated with heavy metals - Main opportunities, limitations, decision making and future prospects

The heavy metals (HMs) soils contamination is a growing concern since HMs are not biodegradable and can accumulate in all living organisms causing a threat to plants and animals, including humans. Phytoremediation is a cost-efficient technology that uses plants to remove, transform or detoxify contaminants. In recent years, phytoremediation is entering the stage of large-scale modelling via various mathematical models. Such models can be useful tools to further our understanding and predicting of the processes that influence the efficiency of phytoremediation and to precisely plan such actions on a large-scale. When dealing with extremely complicated and challenging variables like the interactions between the climate, soil and plants, modelling before starting an operation can significantly reduce the time and cost of such process by granting us an accurate prediction of possible outcomes. Research on the applicability of different modelling approaches is ongoing and presented work compares and discusses available models in order to point out their specific strengths and weaknesses in given scenarios. The main aim of this paper is to critically evaluate the main advantages and limitations of available models for large-scale phytoremediation including, among others, the Decision Support System (DSS), Response Surface Methodology (RSM), BALANS, PLANTIX and various regression models. Study compares their applicability and highlight existing gaps in current knowledge with a special reference to improving the efficiency of large-scale phytoremediation of sites contaminated with heavy-metals. The presented work can serve as a useful tool when choosing the most suitable model for the phytoremediation of contaminated sites.

Modeling and optimizing the removal of cadmium by Sinapis alba L. from contaminated soil via Response Surface Methodology and Artificial Neural Networks during assisted phytoremediation with sewage sludge

The study was aimed to model and optimize the removal of cadmium from contaminated post-industrial soil via Sinapis alba L. by comparing two modeling approaches: Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). The experimental design was done using the Box-Behnken Design method. In the RSM model, the quadratic model was shown to predict the closest results in comparison to our experimental data. For ANN approach, a two-layer Feed-Forward Back-Propagation Neural Network model was designed. The results showed that sewage sludge supplementation increased the efficiency of the Sinapis alba plant in removing Cd from the soil. After 28 days of exposure, the removal rate varied from 10.96% without any supplementation to 65.9% after supplementation with the highest possible (law allowed) dose of sewage sludge. The comparison proved that the prediction capability of the ANN model was much higher than that of the RSM model (adjusted R-square: 0.98, standard error of the Cd prediction removal: 0.85 ± 0.02). Thus, the ANN model could be used for the prediction of heavy metal removal during assisted phytoremediation with sewage sludge. Moreover, such approach could also be used to determinate the dose of sewage sludge that will ensure highest process efficiency.

Application Research of Artificial Neural Network in Environmental Quality Monitoring

With the steady growth of the economy and the rapid development of modern industrial technology, the problem of environmental pollution has increased. To continue to develop, it is necessary to thoroughly implement the sustainable development strategy, and we must pay more attention to environmental issues. One of the important management tools implemented in China for environmental management is environmental quality monitoring and evaluation. Environmental quality monitoring can scientifically evaluate the environmental quality of a region, scientifically evaluate and forecast the environmental management and environmental engineering, and provide scientific basis for environmental management, environmental engineering, formulation of environmental standards, environmental planning, comprehensive prevention and control of environmental pollution, and ecological environment construction. This paper will discuss the basic principles of neural network and the implementation process of MATLAB and in the MATLAB software implementation and display process. At the same time, the results of different parameters are analyzed through experiments, and the network parameters are constantly adjusted to improve the accuracy of the evaluation results. Taking the regional environment as an example, two monitoring methods are proposed, and a variety of neural network models are used to analyze each prediction method. Case study results show that the latter method has a better prediction effect.

Phytoremediation of Palm Oil Mill Effluent (POME) Using Eichhornia crassipes

It is inevitable that the manufacturing process of palm oil is accompanied by the generation of a massive amount of high strength wastewater, namely palm oil mill effluent (POME), which could pose serious threat to the aquatic environment. POME which contains high organic compounds originating from biodegradable materials causes water pollution if not properly managed. Palm oil industries are facing the challenges to make ends meet in the aspects of natural assurance, financial reasonability and development sustainability. It is therefore crucial to seek a practical solution to achieve the goal of environmental protection while continuing the economic sustainability. Phytoremediation has been proven as a potential method for removal or degradation of various hazardous contaminants. However, research on phytoremediation of POME using Eichhornia crassipes (E. crassipes) is still limited. This study aims to determine the feasibility of applying phytoremediation technique using E. crassipes for POME treatment. The effects of pH, plant:POME ratio and retention time on the biochemical oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solid (TSS) of POME were investigated. The highest BOD removal of 92.6% was achieved after 21 days retention time at pH 4 with plant:POME ratio of 1:20 kg/L. The highest COD removal of 20.7% was achieved after 14 days retention time at pH 6 with plant:POME ratio of 1:20 kg/L. Phytoremediation using E. crassipes was shown to be a promising eco-friendly technique for POME treatment, and is therefore recommended as a good alternative treatment solution for this industrial effluent.

Phytoremediation potential and control of Phragmites australis as a green phytomass: an overview

Phragmites australis (common reed) is one of the most extensively distributed emergent plant species in the world. This plant has been used for phytoremediation of different types of wastewater, soil, and sediments since the 1970s. Published research confirms that P. australis is a great accumulator for different types of nutrients and heavy metals than other aquatic plants. Therefore, a comprehensive review is needed to have a better understanding of the suitability of this plant for removal of different types of nutrients and heavy metals. This review investigates the existing literature on the removal of nutrients and heavy metals from wastewater, soil, and sediment using P. australis. In addition, after phytoremediation, P. australis has the potential to be used for additional benefits such as the production of bioenergy and animal feedstock due to its specific characteristics. Determination of adaptive strategies is vital to reduce the invasive growth of P. australis in the environment and its economic effects. Future research is suggested to better understand the plant's physiology and biochemistry for increasing its pollutant removal efficiency.

A paper-text perspective

Purpose

In the era of Big Data, network digital resources are growing rapidly, especially the short-text resources, such as tweets, comments, messages and so on, are showing a vigorous vitality. This study aims to compare the categories discriminative capacity (CDC) of Chinese language fragments with different granularities and to explore and verify feasibility, rationality and effectiveness of the low-granularity feature, such as Chinese characters in Chinese short-text classification (CSTC).

Design/methodology/approach

This study takes discipline classification of journal articles from CSSCI as a simulation environment. On the basis of sorting out the distribution rules of classification features with various granularities, including keywords, terms and characters, the classification effects accessed by the SVM algorithm are comprehensively compared and evaluated from three angles of using the same experiment samples, testing before and after feature optimization, and introducing external data.

Findings

The granularity of a classification feature has an important impact on CSTC. In general, the larger the granularity is, the better the classification result is, and vice versa. However, a low-granularity feature is also feasible, and its CDC could be improved by reasonable weight setting, even exceeding a high-granularity feature if synthetically considering classification precision, computational complexity and text coverage.

Originality/value

This is the first study to propose that Chinese characters are more suitable as descriptive features in CSTC than terms and keywords and to demonstrate that CDC of Chinese character features could be strengthened by mixing frequency and position as weight.