Journal of Environmental Health Science and Engineering最新文献

Abstract

Wastewater stabilization ponds (WSPs) rely on the metabolic activities of the inhabiting microbiota to treat wastewater. A selected consortium of Chlorella vulgaris and Chlorella protothecoides were used to manipulate the natural resident microalgae assemblage to improve the treatment performance of a domestic wastewater pond treatment system in a coastal region. Since information is lacking about the resulting influence on the composition or succession of the phytoplankton or associated microbiota assemblage, the current study aimed to determine how dosing with the microalgae C. vulgaris and C. protothecoides change the efficiency of wastewater effluent treatment, as well as the composition and succession of the natural occurring phytoplankton and microbial assemblage throughout WSP system. After a year of specific microalgae inoculations, the effluent in part complied with the standards set by the Department of Water Affairs and Forestry (DWAF) and the USA, Environmental Protection Agency (EPA). The cyanobacteria Microcystis aeruginosa dominated the sixth (75%) and seventh pond (97%) before the inoculation with C. vulgaris and C. protothecoide commenced. After 12 inoculation events C. vulgaris and C. protothecoides were dominant in ponds three to seven while the dominant microbial groups were Bacteroidetes, Cyanobacteria, Firmicutes, Planctomycetes, Proteobacteria, Spirochaetes, Synergistetes and Verrucomicrobia. After the microalgae treatment, the WSP effluent were more compliant regarding to the set guidelines for effluent than prior to microalgae treatment. Based on the ability of the C. vulgaris and C. protothecoides to improve the effluent water quality, it was evident that the consortium of microalgae can be use improve domestic wastewater effluent in rural nutrient sensitive catchments.

In recent years, artificial intelligence (AI) techniques have been recognized as powerful techniques. In this work, AI techniques such as artificial neural networks (ANNs), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithms (GA), and particle swarm optimization (PSO), used in water and wastewater treatment processes, are reviewed. This paper describes applications of the mentioned AI techniques for the modelling and optimization of electrochemical processes for water and wastewater treatment processes. Most research in the mentioned scope of study consists of electrooxidation, electrocoagulation, electro-Fenton, and electrodialysis. Also, ANNs have been the most frequent technique used for modelling and optimization of these processes. It was shown that most of the AI models have been built with a relatively low number of samples (< 150) in data sets. This points out the importance of reliability and robustness of the AI models derived from these techniques. We show how to improve the performance and reduce the uncertainty of these developed black-box data-driven models. From the perspectives of both experiment and theory, this review demonstrates how AI techniques can be effectively adapted to electrochemical processes for water and wastewater treatment to model and optimize these processes.

Introduction

The relationship between pesticide exposure and the occurrence of many chronic diseases, including cancer, is confirmed by literature data.

Methods

In this review, through the analysis of more than 70 papers, we explore an increase in oxidative stress level caused by exposure to environmental pollutants and the protective effects of plant-origin antioxidants.

Results and discussion

One of the molecular mechanisms, by which pesticides affect living organisms is the induction of oxidative stress. However, recently many plant-based dietary ingredients with antioxidant properties have been considered as a chemopreventive substances due to their ability to remove free radicals. Such a food component must meet several conditions: eliminate free radicals, be easily absorbed and function at an appropriate physiological level. Its main function is to maintain the redox balance and minimize the cellular damage caused by ROS. Therefore, it should be active in aqueous solutions and membrane domains. These properties are characteristic for phenolic compounds and selected plant hormones. Phenolic compounds have proven antioxidant properties, while increasing number of compounds from the group of plant hormones with a very diverse chemical structure turn out to act as antioxidants, being potential food ingredients that can eliminate negative effects of pesticides.

Purpose

The use of nanomaterials like quantum dots (QDs) with optical properties has increased in the last decade because of their electronics, medicine, and environmental applications. The lack of recycling and appropriate disposal causes these materials to be considered new emerging contaminants. In this research, the extraction and translocation (phytoextraction) of cadmium as QDs by Ocimum basilicum “basil” in the presence of EDTA (chelating agent) was studied. For edible plants are essential to know where the contaminants are located to minimize human consumption.

Methods

In this work, the phytoextraction, distribution, and translocation of cadmium (under ionic solution-CdIS and CdS/Te QDs) at 25 and 50 mg/kg-soil and in the presence of 1,000 mg/kg-soil EDTA was studied in O. basilicum (a commercialized culinary herb) for three and six weeks. Basil seedlings were grown in an environmentally controlled chamber at 24-28^o C and 12/12 hours periods of light and darkness.

Results

The highest cadmium concentration was found in the roots from where it is translocated to stems and leaves. CdS/Te QDs at low concentration (25 mg/kg and 3 weeks of exposure) had the highest translocation factor, indicating that cadmium’s nanocrystal (QDs) forms can affect the phytoextraction mechanism. The highest bioconcentration of cadmium was reached at a high contaminant concentration.

Conclusion

The bioconcentration factors confirm that O. basilicum phytoextracts cadmium from soils contaminated with CdS/Te QDs and CdIS. The bioconcentration factors of cadmium (under both species CdS/Te QDs and CdIS) increase in the presence of EDTA. The bioconcentration and translocation factors suggest that consuming “basil” from contaminated soils with CdS/Te QDs at low concentrations increases the exposition to this metal.

Water is an essential compound on earth and necessary for life. The presence of highly toxic contaminants such as arsenic and others, in many cases, represents one of the biggest problems facing the earth´s population. Treatment of contaminated water with magnetite (Fe₃O₄) nanoparticles (NPs) can play a crucial role in arsenic removal. In this report, we demonstrate arsenic removal from an aqueous solution and natural water taken from the Peruvian river (Tambo River in Arequipa, Peru) using magnetite NPs synthesized by the coprecipitation method. XRD data analysis of Fe₃O₄ NPs revealed the formation of the cubic-spinel phase of magnetite with an average crystallite size of ~ 13 nm, which is found in good agreement with the physical size assessed from TEM image analysis. Magnetic results evidence that our NPs show a superparamagnetic-like behavior with a thermal relaxation of magnetic moments mediated by strong particle-particle interactions. FTIR absorption band shows the interactions between arsenate anions and Fe-O and Fe-OH groups through a complex mechanism. The experimental results showed that arsenic adsorption is fast during the first 10 min; while the equilibrium is reached within 60 min, providing an arsenic removal efficiency of ~ 97%. Adsorption kinetics is well modeled using the pseudo-second-order kinetic equation, suggesting that the adsorption process is related to the chemisorption model. According to Langmuir’s model, the maximum arsenic adsorption capacity of 81.04 mg·g^− 1 at pH = 2.5 was estimated, which describes the adsorption process as being monolayer, However, our results suggest that multilayer adsorption can be produced after monolayer saturation in agreement with the Freundlich model. This finding was corroborated by the Sips model, which showed a good correlation to the experimental data. Tests using natural water taken from Tambo River indicate a significant reduction of arsenic concentration from 356 µg L^− 1 to 7.38 µg L^− 1, the latter is below the limit imposed by World Health Organization (10 µg L^− 1), suggesting that magnetite NPs show great potential for the arsenic removal.

Purpose

This study aims to investigate the prospective associations of neighborhood environmental exposure trajectories with asthma symptom trajectories during childhood developmental stages.

Methods

We considered asthma symptom, neighborhood environmental factors, and socio-demographic data from the “Longitudinal Study of Australian Children (LSAC)”. Group-based trajectory modeling was applied to identify the trajectories of asthma symptom, neighborhood traffic conditions, and neighborhood livability scales (considered for safety and facilities). We used multivariable logistic regression models to assess associations between various neighborhood environmental factors and asthma symptom trajectories.

Results

We included 4,174 children from the LSAC cohort in our study. Three distinct trajectories for asthma symptom were the outcome variables of this study. Among the neighborhood environmental factors, we identified two distinct trajectories for the prevalence of heavy traffic on street, and two trajectories of neighborhood liveability scale. Compared to the ‘Low/no’ asthma symptoms trajectory group, children exposed to a ‘persistently high’ prevalence of heavy traffic on street was also significantly associated with both ‘transient high’ [relative risk ratio (RRR):1.40, 95% CI:1.25,1.58) and ‘persistent high’ (RRR: 1.33, 95% CI:1.17,1.50)] asthma symptom trajectory groups. Trajectory of moderate and static neighborhood liveability score was at increased risk of being classified as ‘transient high’ (RRR:1.16, 95% CI:1.07,1.25) and ‘persistent high’ (RRR:1.38, 95% CI:1.27,1.50) trajectories of asthma symptom.

Conclusion

Exposure to heavy traffic and poor neighborhood liveability increased the risk of having an unfavourable asthma symptom trajectory in childhood. Reducing neighborhood traffic load and improving neighborhood safety and amenities may facilitate a favorable asthma symptom trajectory among these children.

In this review, several adsorbents were studied for the elimination of heavy metal ions from multi-component wastewaters. These utilized sorbents are mineral materials, microbes, waste materials, and polymers. It was attempted to probe the structure and chemistry characteristics such as surface morphology, main functional groups, participated elements, surface area, and the adsorbent charges by SEM, FTIR, EDX, and BET tests. The uptake efficiency for metal ions, reusability studies, isotherm models, and kinetic relations for recognizing the adsorbent potentials. Besides, the influential factors such as acidity, initial concentration, time, and heat degree were investigated for selecting the optimum operating conditions in each of the adsorbents. According to the results, polymers especially chitosan, have displayed a higher adsorption capacity relative to the other common adsorbents owing to the excellent surface area and more functional groups such as amine, hydroxyl, and carboxyl species. The high surface area generates the possible active sites for trapping the particles, and the more effective functional groups can complex more metal ions from the polluted water. Also, it was observed that the uptake capacity of each metal ion in the multi-component solutions was different because the ionic radii of each metal ion were different, which influence the competition of metal ions for filling the active sites. Finally, the reusability of the polymers was suitable, because they can use several cycles which proves the economic aspect of the polymers as the adsorbent.