Characteristics and degradation mechanisms of polychlorinated naphthalenes in surface soil in Yangtze River Delta, China.

Both thermal and environmental processes are significant factors influencing the existing characteristics, e.g., congener distributions, and existing levels, of polychlorinated naphthalenes (PCNs) in the environment. Soil plays an important role in the life cycle of PCNs, but degradation of PCNs in soils has never been reported. In this study, we collected surface soil samples from 13 cities in the Yangtze River Delta, which is one of the most crowded areas of China and analyzed the samples for 75 PCNs. The long-range transportation from polluted areas was the major source for PCNs in remote areas, but the PCN profiles in remote areas reported in our previous studies were different from those in human settlement in this study, indicating there is a transformation of PCNs after emissions from anthropogenic activities. Two experiments were then designed to reveal the degradation mechanisms, including influencing factors, products, and pathways, of PCNs in surface soils. Based on the experiments, we found that the major factor driving the losses of PCNs in surface soils was volatilization, followed by photo irradiation and microbial metabolism. Under photo-irradiation, the PCN structures would be destroyed through a process of dechlorination followed by oxidation. In addition, the dechlorination pathways of PCNs have been established and found to be significantly influenced by the structure-related parameters.

Target Analysis of Polychlorinated Naphthalenes and Nontarget Screening of Organic Chemicals in Bovine Milk, Infant Formula, and Adult Milk Powder by High-Resolution Mass Spectrometry.

Infant formula is intended as an effective substitute for breast milk but is the main source of polychlorinated naphthalenes (PCNs) to nonbreastfed infants. We performed target and nontarget analyses to determine PCNs and identify other organic contaminants in infant formula. The mean PCN concentrations in infant formula, milk powder, and bovine milk were 106.1, 88.8, and 78.2 µg kg-1 of dry weight, respectively. The PCN congener profiles indicated that thermal processes and raw materials were probably the main sources of PCNs in infant formula. A health risk assessment indicated that PCNs in infant formula do not pose health risks to infants. Using gas chromatography-Orbitrap mass spectrometry, 352, 372, and 161 organic chemicals were identified in the infant formula, milk powder, and bovine milk samples, respectively. Phthalate esters were detected in all four plastic-packed milk powder samples. The results indicated milk becomes more contaminated with organic chemicals during manufacturing, processing, and packaging.

Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN.

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s2 to -1.2646 × 10-7 m/s2, while the variance decreases from 0.0133 m/s2 to 1.0975 × 10-5 m/s2. In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.

Enhanced phosphate remediation of contaminated natural water by magnetic zeolitic imidazolate framework-8@engineering nanomaterials (ZIF8@ENMs).

The efficient enrichment and reutilization of phosphate from natural water still remains a daunting challenge to satisfy the increasingly stringent phosphate discharge criteria. In response to this problem, the presented study successfully synthesizes a series of magnetic zeolitic imidazolate framework-8@engineering nanomaterials (ZIF8@ENMs) via a two-step hydrothermal and coprecipitation method by facilely growing ZIF8 and/or Fe3O4 on various functional ENMs. Structure morphology, chemical composition and hysteresis curve characterizations demonstrate the successful formation of magnetic Fe3O4-ZIF8@ENM. Amongst the prepared magnetic ZIF8@ENMs hybrids, the Fe3O4-ZIF8@ENMs possessing massive hydroxyl groups is demonstrated to harvest the maximum adsorption capacity of 441.7 mg g-1 under neutral condition. Such-acquired adsorption capacity evidently surpass state-of-the-art adsorbents. Systematic assessment of the chemical condition effects on phosphate removal, revealing its conspicuous merits of robust pH independence (94.63-98.20%), high selectivity pinpointing phosphate within complex cations, ease-of-separation and satisfactory recycle. The outstanding performance of magnetic ZIF8@ENMs are mainly derived from the formed strong ZnOP, FeOP and electrostatic interactions between phosphate and adsorbents. Along this line, designing magnetic MOFs-based hybrids towards phosphate are anticipated to be promising avenues for advanced treatment of phosphate-like contaminants and efficient recycle in practical applications.

Phosphate removal by ZIF-8@MWCNT hybrids in presence of effluent organic matter: Adsorbent structure, wastewater quality, and DFT analysis.

Recently, zeolitic imidazolate framework-8 (ZIF8) and its derivatives have been applied in aqueous contaminant removal. Herein, three types of ZIF8@carbon nanotube (CNT) hybrids harvesting different pore structures and chemical bonding information are utilized for phosphate removal in the typical wastewater of activated sludge system (SW) and partial nitrification-denitrification treatment system (PND). Effluent organic matter (EfOM) is found to compete with phosphate for adsorption sites on adsorbents, resulting in reducing adsorptive capacities for phosphate, and the negative effect trend to become severer with increasing EfOM concentrations. Thus adverse impact are highly to be relieved by using ZIF8@CNT-2 (hybrids with CNT dosage of 120 mg) with novel structure design, the hybrid of which harvests the highest phosphate removal of 92.8-100%, the largest Partition coefficient (PC) of 9119.05 mg g-1 µM with initial concentration of 0.96 mg L-1, pH independence in the range from 4 to 10. Analyses of the XPS characterization and first-principles calculations demonstrate the dominant interactions of Zn-O-P and H-bond during phosphate adsorption process by ZIF8@CNT hybrids. Such interactions are suppressed in presence of EfOM by weakening the above-stated binding energy at different adsorption sites according to first-principles simulation, resulting in declined phosphate adsorption capacity. In this regard, the less sensitivity to co-existing EfOM of ZIF8@CNT-2 may be due to the increased P=O, Zn-O-P and P-OH and the strengthened tolerance of nanostructure. These results suggest the promising enhanced phosphate removal in presence of EfOM could be obtained by specifically designing adsorbent structure.