Effects of Different Heat Treatment Methods on Organic Pollutants and Heavy Metal Content in Oil Sludge Waste and Ecotoxicological Evaluation

Unveiling the resistance of native weed communities: insights for managing invasive weed species in disturbed environments

Weed communities influence the dynamics of ecosystems, particularly in disturbed environments where anthropogenic activities often result in higher pollution. Understanding the dynamics existing between native weed communities and invasive species in disturbed environments is crucial for effective management and normal ecosystem functioning. Recognising the potential resistance of native weed communities to invasion in disturbed environments can help identify suitable native plants for restoration operations. This review aims to investigate the adaptations exhibited by native and non-native weeds that may affect invasions within disturbed environments. Factors such as ecological characteristics, altered soil conditions, and adaptations of native weed communities that potentially confer a competitive advantage relative to non-native or invasive weeds in disturbed environments are analysed. Moreover, the roles of biotic interactions such as competition, mutualistic relationships, and allelopathy in shaping the invasion resistance of native weed communities are described. Emphasis is given to the consideration of the resistance of native weeds as a key factor in invasion dynamics that provides insights for conservation and restoration efforts in disturbed environments. Additionally, this review underscores the need for further research to unravel the underlying mechanisms and to devise targeted management strategies. These strategies aim to promote the resistance of native weed communities and mitigate the negative effects of invasive weed species in disturbed environments. By delving deeper into these insights, we can gain an understanding of the ecological dynamics within disturbed ecosystems and develop valuable insights for the management of invasive species, and to restore long-term ecosystem sustainability.

The methods for improving the biodegradability of oily sludge: a critical review

Biological degradation method, as an environmentally friendly, low-carbon, and clean pollution treatment technology, is widely used for the harmless disposal of oily sludge. The biodegradability of oily sludge with stable emulsification system, high oil, and water content is poor. Therefore, it is necessary to pre-treat the oily sludge to improve its biodegradability, including recover the petroleum resources and remove heavy metals and bio-toxic organic matters. This review systematically summarizes five oily sludge treatment methods and their influences on sludge biodegradability, including pyrolysis, chemical hot washing, solvent extraction, chemical oxidation, and hydrothermal. Pyrolysis at temperatures above 750 °C produces high molecular weight polycyclic aromatic hydrocarbons, chemical hot washing and chemical oxidation would cause secondary pollution, solvent extraction method could not be applied due to the high cost and high toxicity of the extractant, and the oil removal of hydrothermal method is inefficient. Additionally, the principles, advantages, and disadvantages of those treatments and the factors affecting microbial degradation were analyzed, which provide the development direction of pretreatment technology to improve the biodegradability of oily sludge.

Comparative Methods for Quantification of Sulfate-Reducing Bacteria in Environmental and Engineered Sludge Samples

This study aimed to compare microscopic counting, culture, and quantitative or real-time PCR (qPCR) to quantify sulfate-reducing bacteria in environmental and engineered sludge samples. Four sets of primers that amplified the dsrA and apsA gene encoding the two key enzymes of the sulfate-reduction pathway were initially tested. qPCR standard curves were constructed using genomic DNA from an SRB suspension and dilutions of an enriched sulfate-reducing sludge. According to specificity and reproducibility, the DSR1F/RH3-dsr-R primer set ensured a good quantification based on dsrA gene amplification; however, it exhibited inconsistencies at low and high levels of SRB concentrations in environmental and sulfate-reducing sludge samples. Ultimately, we conducted a qPCR method normalized to dsrA gene copies, using a synthetic double-stranded DNA fragment as a calibrator. This method fulfilled all validation criteria and proved to be specific, accurate, and precise. The enumeration of metabolically active SRB populations through culture methods differed from dsrA gene copies but showed a plausible positive correlation. Conversely, microscopic counting had limitations due to distinguishing densely clustered organisms, impacting precision. Hence, this study proves that a qPCR-based method optimized with dsrA gene copies as a calibrator is a sensitive molecular tool for the absolute enumeration of SRB populations in engineered and environmental sludge samples.

Prediction Model of Soil Heavy Metal Content Based on Particle Swarm Algorithm Optimized Neural Network

In 2014, the relevant research data from the Ministry of Environmental Protection and the Ministry of Land and Resources showed that the total exceedance rate of soil heavy metal pollution in China had reached 16.1%, and in the construction of ecological civilization in the 13th Five-Year Plan, China has made the prevention and control of soil heavy metal pollution as the focus of prevention and control. Therefore, in this paper, four neural optimization network models, that is, radial basis neural network (RBFNN), generalized regression neural network (GRNN), wavelet neural network (WNN), and fuzzy neural network (FNN), are simulated and created to measure and correlate the soil heavy metal content in a city in northwest China and a city in central China from the actual situation in China. The simulations were conducted. Finally, by analyzing the comparison of predicted and true values of these four models on the test data of two sets of experimental data, the distribution of predicted differences to true values, and the calculation results of three error indicators, we found that WNN has the best prediction performance when using RBFNN, GRNN, WNN, and FNN for soil heavy metal content prediction.

Industrial sludge valorization and decontamination via lipid extraction and heavy metals removal using low-cost protic ionic liquid

Sludge is a heterogenous organic-rich matter that comprise of highly valuable biopolymers along with various contaminants including heavy metals. Sludge valorization as a renewable resource and inexpensive feedstock is key for sludge realization in circular economy context. This study presents the use of low-cost protic ionic liquid (PIL) as an integrated process medium to decontaminate heavy metal contaminated industrial sludge while selectively extract the lipid content. The treatment process focused on the use of 1-methylimidazole chloride for its higher heavy metal extraction performance compared to other screened ionic liquids (ILs). The treatment was also able to selectively extract lipids from industrial sludge, leaving a protein/carbohydrate rich solid product. Process temperature was shown to have a key impact on the biopolymers' fractionation. Operating at temperatures above 120 °C resulted in higher recovery of proteins in the lipid-rich fraction, compromising the quality of the lipid stream. Variation of the PIL acid/base (a/b) ratio also had a significant impact on the deconstruction of the sludge biopolymers, with a/b ratio of 1 resulting in highest recovery of all biopolymers. Optimal water concentration as co-solvent was found at 30 wt%, with lipid recovery reaching 60% and heavy metals extraction ranging between 29 and 89%.