The influence of biochar on the soil structure and aggregate stability has been debated in previous studies. To probe the action of biochar on soil aggregates, a 5-year field experiment was implemented in the brown earth soil of northeastern China. We determined the aggregate distribution (> 2000 μm, 250–2000 μm, 53–250 μm, and < 53 μm) and organic carbon (OC) and organo-mineral complex contents both in the topsoil (0–20 cm) and within the soil aggregates. Three treatments were studied as follows: control (basal application of mineral NPK fertilizer), biochar (biochar applied at a rate of 2.625 t ha⁻¹), and stover (maize stover applied at a rate of 7.5 t ha⁻¹), and all treatments received the same fertilization. The biochar and stover applications decreased the soil bulk and particle densities significantly (p < 0.05) and enhanced the soil total porosity. Both amendments significantly (p < 0.05) enhanced the total OC, heavy OC fractions, and organo-mineral complex quantities in the bulk soil as well as in all the studied aggregate fractions. Biochar and stover applications promoted the formation of small macroaggregates. A greater amount of organic matter was contained in the macroaggregates, which led to the formation of more organo-mineral complexes, thereby improving soil aggregate stability. However, the different mechanisms underlying the effect of biochar and stover on organo-mineral complexes need further research. Biochar and stover applications are both effective methods of improving the soil structure in Northeast China.

In the present study, millimeter CS-TPP@MnFe₂O₄ gel beads (particle size 3–4 mm) were prepared by the sol-gel process using the embedding method, and its performance of Cu(II) and influence factors were studied. The effect of various parameters such as the gel bead addition amount, adsorption time, temperature, pH, and competitive substances (anion and cationic) was studied. The surface and properties of gel beads were characterized by X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR), and transmission electron microscopy (TEM). The experimental results showed that the optimal pH for adsorption of Cu(II) by CS-TPP@MnFe₂O₄ was 5–7, the adsorption of Cu(II) reached equilibrium at 24 h, and the maximum adsorption capacity could reach 125.70 mg g⁻¹ at 298.15 K by Langmuir isotherm model. K⁺, Na⁺, Cl⁻, NO₃⁻, and SO₄²⁻ had little effect on the adsorption, and Ca²⁺, Mg²⁺, and H₂PO₄⁻ inhibited the adsorption, and SiO₃²⁻ and humic acid (HA) promoted the adsorption of Cu(II) by the adsorbent. After five adsorption-desorption experiments, the desorption rate of gel beads reached 89.3%, and the adsorption capacity of Cu(II) was still high. In conclusion, the CS-TPP@MnFe₂O₄ gel beads are a type of stable and effective materials to remove Cu(II) from water.

Air pollution can significantly accelerate the process of material corrosion, which may cause significant economic losses and serious safety incidents. Atmospheric corrosion maps provide atmospheric corrosivity in a given geographic scope, which can guide the designers to select the most suitable anti-corrosion materials for outdoor projects, also provide useful information for maintenance. This article investigated mapping of atmospheric corrosivity in Shandong Province, China. In order to obtain atmospheric corrosivity data, 100 exposure corrosion test sites were established in Shandong according to International Standard Organization (ISO) 8565. Hot-dip galvanized steel samples were exposed for 1 year in the test sites. Taking the results of exposure corrosion test as the data, inverse distance weighting (IDW) and ordinary kriging (OK) interpolation algorithm were used to estimate the atmospheric corrosivity of Shandong Province according to ISO 9223. The validity of OK and IDW was compared in developing atmospheric corrosion maps of Shandong Province on a 1 × 1 km resolution. The cross-validation results showed that OK interpolation algorithm with Gaussian semivariogram model get the best result in the prediction of corrosion rate. When the corrosion category was used as the criterion, the IDW interpolation algorithm of power 4 performed best, predicted results of 74 sites (n = 100) were consistent with observed. However, high mean relative errors (MRE more than 37%) and relatively low correlation (R² about 24%) indicated that the prediction results of the two interpolation algorithms had a large error, which was caused by the low data density and the complicated corrosive factors of the atmosphere.

Sulfamethoxazole (SMX), a representative sulfonamide antibiotic, has been identified as a new kind of persistent pollutant with property of hard biodegradation and hydrolyzation. Conventional methods cannot remove it well. In this study, the performances and mechanisms for SMX degradation were examined by persulfate (PS) activation with nanoscale zero-valent iron (nZVI) at various conditions including dosages of nZVI and PS, pH value, and initial SMX concentration. Results showed that about 88.4% SMX (10 mg/L) was removed by nZVI/PS system (0.10 g/L nZVI, 1.0 mM PS) within 120 min compared to 63.1% by nZVI alone system under room temperature. Lower initial SMX concentration and higher PS concentration were beneficial to the degradation of SMX, while pH (from 3.11 to 9.33) and nZVI dosage (from 0.05 to 0.30 g/L) had little effect. Radical quenching experiment and electron spin resonance test demonstrated that the degradation of SMX was attributed to sulfate radicals (SO₄·⁻) and hydroxyl radicals (·OH) produced in this system. SMX reduction reaction by nZVI in nZVI/PS process was proved by reductive-oxidative degradation experiment and HPLC test, and the reduction product could be oxidized by SO₄·⁻ and ∙OH to other products even to H₂O and CO₂. Further, probable removal mechanisms have also been proposed. This study manifests that nZVI/PS system is effective for SMX removal and may provide some ideas for understanding the transformation process of antibiotic in iron-based advanced oxidation processes.

Over the past 40 years since China’s reform and opening up, the industrial structure has undergone tremendous changes. The rapid development of the economy has been accompanied by a surge in carbon emissions. How to achieve a win-win situation for economic growth and carbon emissions reduction has aroused widespread concern from all sectors of society. Here, this paper discusses the dynamic relationship of industrial structure upgrading, economic growth, and carbon emission reduction. Results show that there is a long-term equilibrium relationship among industrial structure upgrading, economic growth, and carbon emissions. In the short term, when the three variables deviate from the long-term equilibrium state, the non-equilibrium state will be pulled back to equilibrium with the adjustment strength of − 0.0633, − 0.0097, and 0.0013. Carbon emission reduction promotes industrial structure upgrading. Industrial structure upgrading has a greater positive impact on economic growth. Industrial structure upgrading and economic growth have a negative impact on carbon emissions, thereby promoting emission reduction. And at the 10% significance level, there is a one-way Granger causality from carbon emissions to industrial structure upgrading, economic growth can cause one-way changes in carbon emissions, and industrial structure upgrading is a one-way Granger cause of economic growth. Finally, several carbon emission reduction policies are proposed promote industrial restructuring and sustainable economic development.

In this paper, folic acid–coated graphene oxide nanocomposite (FA-GO) is used as an adsorbent for the treatment of heavy metals including cadmium (Cd²⁺) and copper (Cu²⁺) ions. As such, graphene oxide (GO) is modified by folic acid (FA) to synthesize FA-GO nanocomposite and characterized by the atomic force microscopy (AFM), Fourier transform-infrared (FT-IR) spectrophotometry, scanning electron microscopy (SEM), and C/H/N elemental analyses. Also, computational intelligence tests are used to study the mechanism of the interaction of FA molecules with GO. Based on the results, FA molecules formed a strong π-π stacking, chemical, and hydrogen bond interactions with functional groups of GO. Main parameters including pH of the sample solution, amounts of adsorbent, and contact time are studied and optimized by the Response Surface Methodology Based on Central Composite Design (RSM-CCD). In this study, the equilibrium of adsorption is appraised by two (Langmuir and Freundlich and Temkin and D-R models) and three parameter (Sips, Toth, and Khan models) isotherms. Based on the two parameter evaluations, Langmuir and Freundlich models have high accuracy according to the R² coefficient (more than 0.9) in experimental curve fittings of each pollutant adsorption. But, multilayer adsorption of each contaminant onto the FA-GO adsorbent (Freundlich equation) is demonstrated by three parameter isotherm analysis. Also, isotherm calculations express maximum computational adsorption capacities of 103.1 and 116.3 mg g⁻¹ for Cd²⁺ and Cu²⁺ ions, correspondingly. Kinetic models are scrutinized and the outcomes depict the adsorption of both Cd²⁺ and Cu²⁺ followed by the pseudo-second-order equation. Meanwhile, the results of the geometric model illustrate that the variation of adsorption and desorption rates do not have any interfering during the adsorption process. Finally, thermodynamic studies show that the adsorption of Cu²⁺ and Cd²⁺ onto the FA-GO nanocomposite is an endothermic and spontaneous process.

The green tide in the Yellow Sea is the world’s largest macroalgal bloom. The maximum area affected by green tide can reach tens of thousands of square kilometers. Previous studies have shown that green tide drifts northward during the outbreak, yet the location of its northernmost drift and the characteristics of interannual variations have not been explored in detail. In this paper, we use the Moderate Resolution Imaging Spectrometer (MODIS) data, GaoFen-1 (GF-1) satellite data, unmanned aerial vehicle (UAV) aerial data, and field trips for extraction and monitoring of green tide and then analyze the emergence, development, and dissipation of the green tide in Rongcheng coastal waters and the change of the northernmost drift position and its related factors from the year of 2013 to 2018. The results show that green tide have drifted to the Rongcheng coastal area over the years and the northernmost drift position can reach 37.662° N in 2014. Interannual difference of the northernmost position of the drift of the green tide is obvious, it is mainly affected by the wind, and there is no certain connection with the maximum coverage area, the coverage area when reaching the northernmost position, and the existence days of the green tide. These results can help to understand the drift range and dissipation process of green tide in the Yellow Sea.

Pyrene, a toxic four-benzene-ring that persists in the ecosystem, is highly resistant to degradation. The goal of the research is to screen, isolate, and identify pyrene-degrading filamentous fungi via the molecular biological identification method. The capabilities of identified isolates in biodegradation and transformation of pyrene were also evaluated. Based on the morphological characterization and sequence alignments, results of neighbor-joining phylogenetic tree from 18S rRNA of F03 revealed that genetic similarity had achieved 99% of homology percentage and identified as Trichoderma sp. Trichoderma sp. F03 was able to degrade pyrene (78%) when culture conditions were set at 100 mg/L initial pyrene concentration in culture medium with pH 5 at 27 °C, the use of glucose as a carbon source and polyethylene glycol sorbitan monooleate as a biosurfactant without agitation. Finally, three metabolites, benzoic acid, 3-hydroxybenzoic acid, and acetic acid, were detected during the pyrene degradation process by using gas chromatography–mass spectrometry (GCMS).

Studies from our group and others have reported that 30 mW/cm² microwave could damage the structures of rat hippocampus, as well as impair the neuronal functions. The neuroprotective effects of astragaloside, purified from Astragalus membranaceus, have been demonstrated in animal models of neurodegenerative diseases. In this study, we found that 30 mW/cm² microwave impaired spatial learning and memory ability in rats, while astragaloside could significantly alleviate the injuries. The pathological analysis also showed that astragaloside protected neurons from microwave-induced damages, such as mitochondrial swelling and cavitation, rough endoplasmic reticulum swelling and dilation, synaptic gap disappearing, and vesicle aggregation. Moreover, microwave-induced structural damage of synapse resulted in downregulation of acetylcholine, an important neurotransmitter for information transmission, while astragaloside could protect the structure of synapse, as well as restore the acetylcholine level in rat hippocampus. Furthermore, astragaloside also accelerated the recovery of brain electroencephalogram (EEG) after microwave exposure, indicating that astragaloside could promote the normalization of neuronal functions. In conclusion, astragaloside protected the morphological structures and restored acetylcholine level in rat hippocampus, which could improve brain functions via normalizing brain EEG. Therefore, astragaloside might be a promising candidate to treat microwave-induced injuries of central nervous system (CNS).

In the process of identifying groundwater pollution sources, in order to solve the problem that the monitoring data of monitoring wells was insufficient or the correlation between monitoring data and model parameters was weak, a monitoring well optimization method based on Bayesian formula and information entropy was proposed. Two-dimensional phreatic groundwater solute transport model was built and solved by using GMS software. To reduce the computational load of calling the numerical model repeatedly in the optimization design of the monitoring schemes and the identification process of the pollution sources, the Kriging method was used to establish the surrogate model of the numerical model. Under the condition of single well monitoring and determined monitoring frequency, with the target of optimization of monitoring position number D and monitoring time interval ∆t, both the single-objective monitoring scheme with the minimum information entropy of the model parameter posterior distribution and the multi-objective monitoring scheme with the minimum information entropy and the shortest monitoring time were optimized respectively. According to the above-optimized monitoring schemes, the delayed rejection adaptive Metropolis algorithm was used to identify the pollution source parameters. The case study results showed that under the condition of pre-set single well monitoring with monitoring frequency of 10 times, the single-objective optimized monitoring scheme was D = 37 and Δt = 20 days. Under this monitoring scheme, the mean errors of inversion pollution source parameters α = (XS, YS, T₁, T₂, QS) were 0.09%, 0.4%, 4.72%, 2.43%, and 9.29%, respectively. The multi-objective optimized monitoring scheme was D = 37 and Δt = 2 days. Under this monitoring scheme, the mean errors of the inversion parameters α = (XS, YS, T₁, T₂, QS) were 12.76%, 3.77%, 5.13%, 1.36%, and 7.68%, respectively. Compared with the monitoring scheme based on the single-objective optimization, although the inversion mean error of the five parameters based on the multi-objective optimized monitoring scheme increased by 2.75%, the monitoring time significantly reduced from 180 to 18 days.