It's time to reevaluate the list of priority polycyclic aromatic compounds: Evidence from a large urban shallow lake.

Monitoring only 16 priority PAHs (Pri-PAHs) may greatly underestimate the pollutant load and toxicity of polycyclic aromatic compounds (PACs) in aquatic environments. There is an urgent need to reevaluate the list of priority PACs. To determine which PACs deserve priority monitoring, the occurrence, sources, and toxicity of 78 PACs, including 24 parent PAHs (Par-PAHs), 49 alkylated PAHs (Alk-PAHs), 3 oxygenated PAHs (OPAHs), carbazole, and dibenzothiophene were investigated for the first time in Lake Chaohu sediments, China. Concentrations of ∑Par-PAHs, ∑Alk-PAHs, and ∑OPAHs ranged from 35 to 165, 3.4-26, and 7.7-26 ng g-1, respectively. Concentrations of 16 Pri-PAHs have decreased by 1-2 orders of magnitude compared to a decade ago, owing to the effective implementation of PAHs emission control measures. Comparisons with the sediment quality guidelines indicated that 16 Pri-PAHs have negligible adverse effects on benthic organisms. Positive matrix factorization (PMF) model results showed that coal combustion was the major source of PACs (accounting for 23.5 %), followed by traffic emissions (23.4 %), petroleum volatilization (21.9 %), wood/biomass combustion (18.2 %), and biological/microbial transformation (13.1 %). The toxicity of PACs was assessed by calculating the BaP toxic equivalent concentrations (TEQBaP) and toxic units. It was found that Par-PAHs were the predominant toxic substances. In addition, monomethyl-BaPs, OPAHs, BeP, and 7,12-DMBaA should be prioritized for monitoring due to their noticeable contributions to overall toxicity. The contributions of different sources to the toxicity of PACs were determined based on PMF model results and TEQBaP values, which revealed that combustion sources mainly contributed to the comprehensive toxicity of PACs in Lake Chaohu sediments.

Transporters and phytohormones analysis reveals differential regulation of ryegrass (Lolium perenne L.) in response to cadmium and arsenic stresses.

Cadmium (Cd) and arsenic (As) are two highly toxic heavy metals and metalloids that coexist in many situations posing severe threats to plants. Our investigation was conducted to explore the different regulatory mechanisms of ryegrass (Lolium perenne L.) responding to individual and combined Cd and As stresses in hydroponics. Results showed that the ryegrass well-growth phenotype was not affected by Cd stress of 10 mg·L-1. However, As of 10 mg·L-1 caused rapid water loss, proline surge, and chlorosis in shoots, suggesting that ryegrass was highly sensitive to As. Transcriptomic analysis revealed that the transcription factor LpIRO2 mediated the upregulation of ZIP1 and YSL6 that played an important role in Cd tolerance. We found that the presence of As caused the overexpression of LpSWT12, a process potentially regulated by bHLH14, to mitigate hyperosmolarity. Indoleacetic acid (IAA) and abscisic acid (ABA) contents and expression of their signaling-related genes were significantly affected by As stress rather than Cd. We predict a regulatory network to illustrate the interaction between transporters, transcription factors, and signaling transduction, and explain the antagonism of Cd and As toxicity. This present work provides a research basis for plant protection from Cd and As pollution.

Direct electrochemistry of hemoglobin at a graphene gold nanoparticle composite film for nitric oxide biosensing.

A simple two-step method was employed for preparing nano-sized gold nanoparticles-graphene composite to construct a GNPs-GR-SDS modified electrode. Hemoglobin (Hb) was successfully immobilized on the surface of a basal plane graphite (BPG) electrode through a simple dropping technique. Direct electrochemistry and electrocatalysis of the hemoglobin-modified electrode was investigated. The as-prepared composites showed an obvious promotion of the direct electro-transfer between hemoglobin and the electrode. A couple of well-defined and quasi-reversible Hb CV peaks can be observed in a phosphate buffer solution (pH 7.0). The separation of anodic and cathodic peak potentials is 81 mV, indicating a fast electron transfer reaction. The experimental results also clarified that the immobilized Hb retained its biological activity for the catalysis toward NO. The biosensor showed high sensitivity and fast response upon the addition of NO, under the conditions of pH 7.0, potential -0.82 V. The time to reach the stable-state current was less than 3 s, and the linear response range of NO was 0.72-7.92 µM, with a correlation coefficient of 0.9991.

The spatiotemporal variation of PM2.5-O3 association and its influencing factors across China: Dynamic Simil-Hu lines.

In recent years, PM2.5 and O3 composite airborne pollution has become one of the most severe environment issues in China. To get a better understanding and tackle these problems, we employed multi-year data to explore the spatiotemporal variation of the PM2.5-O3 relationship in China and investigated its major driving factors. Firstly, interesting patterns were found that named dynamic Simil-Hu lines, which presented a combined effect of natural and anthropogenic influences, were closely related to the spatial patterns of PM2.5-O3 association across seasons. Furthermore, regions with lower altitudes, higher humidity, higher atmospheric pressure, higher temperature, fewer sunshine hours, more accumulated precipitation, denser population and higher GDP often show positive PM2.5-O3 associations, regardless of seasonal variations. Amongst these factors, humidity, temperature and precipitation were dominant factors. This research suggests that the collaborative governance of composite atmospheric pollution should be implemented dynamically, in consideration of geographical locations, meteorological conditions and socioeconomic conditions.

Causation inference in complicated atmospheric environment.

Reliable attribution is crucial for understanding various climate change issues. However, complicated inner-interactions between various factors make causation inference in atmospheric environment highly challenging. Taking PM2.5-Meteorology causation, which involves a large number of non-Linear and uncertain interactions between many meteorological factors and PM2.5, as a case, we examined the performance of a series of mainstream statistical models, including Correlation Analysis (CA), Partial Correlation Analysis (PCA), Structural Equation Model (SEM), Convergent Cross Mapping (CCM), Partial Cross Mapping (PCM) and Geographical Detector (GD). From a coarse perspective, the Top 3 major meteorological factors for PM2.5 in 190 cities across China extracted using different models were generally consistent. From a strict perspective, the extracted dominant meteorological factor for PM2.5 demonstrated large model variations and shared a limited consistence. Such models as SEM and PCM, which are capable of further separating direct and indirect causation in simple systems, performed poorly to identify the direct and indirect PM2.5-Meteorology causation. The notable inconsistence denied the feasibility of employing multiple models for better causation inference in atmospheric environment. Instead, the sole use of CCM, which is advantageous in dealing with non-linear causation and removing disturbing factors, is a preferable strategy for causation inference in complicated ecosystems. Meanwhile, given the multi-direction, uncertain interactions between many variables, we should be more cautious and less ambitious on the separation of direct and indirect causation. For better causation inference in the complicated atmospheric environment, the combination of statistical models and atmospheric models, and the further exploration of Deep Neural Network can be promising strategies.

Magnetic GO/Fe3O4 for rapid malachite green (MG) removal from aqueous solutions: a reversible adsorption.

Magnetic GO/Fe3O4 was synthesized using co-precipitation of Fe2+ and Fe3+ composited with graphene oxide (GO) in alkaline conditions. SEM, XPS, FTIR, N2 adsorption and VSM techniques were employed to characterize the surface peculiarities of GO/Fe3O4 and it was then used for removal of malachite green (MG). The key influencing factors on adsorption, such as mass ratio of GO, pH value and dosage of GO/Fe3O4, were investigated. The Freundlich isotherm was well fitted to the experimental data, suggesting GO/Fe3O4 has more than one type of reactive site. By comparing the adsorption of anionic dyes and cationic dyes onto GO/Fe3O4, it was concluded that GO/Fe3O4 could be extensively applied to take up cationic dyes mainly for electrostatic interaction. In addition, the spent GO/Fe3O4 was almost 100% recovered in a water bath at 80 °C. An ultraviolet-visible (UV-vis) spectrophotometer and an atom adsorption spectrophotometer (AAS) were used to determine leached GO and Fe ions discharged into the treated solutions. Low leaching showed that magnetic GO/Fe3O4 is a stable environmentally-friendly material.

Estimating the impact of ground ozone concentrations on crop yields across China from 2014 to 2018: A multi-model comparison.

Ground level ozone exerts a strong impact on crop yields, yet how to properly quantify ozone induced yield losses in China remains challenging. To this end, we employed a series of O3-crop models to estimate ozone induced yield losses in China from 2014 to 2018. The outputs from all models suggested that the total Relative Yield Losses (RYL) of wheat in China from 2014 to 2018 was 18.4%-49.3% and the total RYL of rice was 6.2%-52.9%. Consequently, the total Crop Production Losses (CPL) of wheat and rice could reach 63.9-130.4 and 28.3-35.4 million tons, and the corresponding Total Economic Losses (TEL) could reach 20.5-44.7 and 11.0-15.3 billion dollars, stressing the great importance and urgency of national ozone management. Meanwhile, the estimation outputs highlighted the large variations between different regional O3-crop models when applying to large scales. Instead of applying one unified O3-crop models to all regions across China, we also explored the strategy of employing specific O3-crop models in corresponding (and neighboring) regions to estimate ozone induced yield loss in China. The comparison of two strategies suggested that the mean value from multiple models may still present an inconsistent over/underestimation trend for different crops. Therefore, it is a preferable strategy to employ corresponding O3-crop models in different regions for estimating the national crop losses caused by ozone pollution. However, the severe lack of regional O3-crop models in most regions across China makes a robust estimation of national yield losses highly challenging. Given the large variations between O3-crop interactions across regions, a systematic framework with massive regional O3-crop models should be properly designed and implemented.