A novel bio-based fluorescent N, P-CDs@CMC/PEI composite hydrogel for sensitive detection and efficient capture of toxic heavy metal ions.

To address the serious environmental pollution problems of toxic heavy metal ions in water bodies, a novel fluorescent composite hydrogel N, P-CDs@CMC/PEI with a bio-based polymer matrix of carboxylmethyl cellulose (CMC), polyethylenimine (PEI) as a second interpenetrating network and N, P-doped carbon dots (N, P-CDs) as a fluorescent probe was prepared for simultaneous detection and capture of HMIs by a facile and simple one-step approach. The morphology, chemical structure, swelling ratio, mechanical strength and fluorescence property of these composite hydrogels were studied through varied characterization methods. The composite hydrogel showed sensitive and selective fluorescence response with Hg(II) and Fe(III) and the according LOD values were 0.48 and 0.27 mg L-1, respectively. The relationship between the types of the adsorbent, pH value, HMIs concentration and temperature on the adsorption capacity of these composite hydrogels were studied. The pseudo-second-order model and Langmuir model were applicable to explain the adsorption process of CPH2 for Hg(II) and Cr(VI). The maximum calculated adsorption capacities for the above targeted HMIs by Langmuir model were 846.7 and 289.5 mg g-1, respectively. Coexisting inorganic salts and organic acids in low concentration had little effects on Hg(II) and Cr(VI) removal and the composite hydrogel showed good recyclability and stability for Hg(II) and Cr(VI) removal after four cycles. The electrostatic attraction and coordination covalent bonds were responsible for the adsorption process.

Identification and characterization of candidate detoxification genes in Pharsalia antennata Gahan (Coleoptera: Cerambycidae).

The wood-boring beetles, including the majority of Cerambycidae, have developed the ability to metabolize a variety of toxic compounds derived from host plants and the surrounding environment. However, detoxification mechanisms underlying the evolutionary adaptation of a cerambycid beetle Pharsalia antennata to hosts and habitats are largely unexplored. Here, we characterized three key gene families in relation to detoxification (cytochrome P450 monooxygenases: P450s, carboxylesterases: COEs and glutathione-S-transferases: GSTs), by combinations of transcriptomics, gene identification, phylogenetics and expression profiles. Illumina sequencing generated 668,701,566 filtered reads in 12 tissues of P. antennata, summing to 100.28 gigabases data. From the transcriptome, 215 genes encoding 106 P450s, 77 COEs and 32 GSTs were identified, of which 107 relatives were differentially expressed genes. Of the identified 215 genes, a number of relatives showed the orthology to those in Anoplophora glabripennis, revealing 1:1 relationships in 94 phylogenetic clades. In the trees, P. antennata detoxification genes mainly clustered into one or two subfamilies, including 64 P450s in the CYP3 clan, 33 COEs in clade A, and 20 GSTs in Delta and Epsilon subclasses. Combining transcriptomic data and PCR approaches, the numbers of detoxification genes expressed in abdomens, antennae and legs were 188, 148 and 141, respectively. Notably, some genes exhibited significantly sex-biased levels in antennae or legs of both sexes. The findings provide valuable reference resources for further exploring xenobiotics metabolism and odorant detection in P. antennata.

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.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism.

Air pollution over China has attracted wide interest from public and academic community. PM2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM2.5 concentrations are essential to understand the variability of PM2.5 and seek methods to control PM2.5. Since 2013, the measurement of PM2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM2.5 concentrations. We start with an introduction of general meteorological conditions and PM2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM2.5 concentrations. Next, major methods used to quantify meteorological influences on PM2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM2.5. The feedback effects of PM2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM2.5 pollution are made finally.

Understanding long-term variations of meteorological influences on ground ozone concentrations in Beijing During 2006-2016.

Recently, ground ozone has become one major airborne pollutant and the frequency of ozone-induced pollution episodes has increased rapidly across China. However, due to the lack of long-term observation data, relevant research on the characteristics and influencing factors of urban ozone concentrations remains limited. Based on ground ozone observation data during 2006-2016, we quantified the causality influence of individual meteorological factors on ozone concentrations in Beijing using a convergent cross mapping (CCM) method. The result indicated that the influence of each meteorological factor on ozone concentrations varied significantly across seasons and years. At the inter-annual scale, all-year meteorological influences on ozone concentrations were much more stable than seasonal meteorological influences. At the seasonal scale, meteorological influences on ozone concentrations were stronger in spring and autumn. Amongst multiple individual factors, temperature was the key meteorological influencing factor for ozone concentrations in all seasons except winter, when wind, humidity and SSD exerted major influences on ozone concentrations. In addition to temperature, air pressure was another meteorological factor that exerted strong influences on ozone concentrations. At both the inter-annual and seasonal scale, the influence of temperature and humidity on ozone concentrations was generally stable whilst that of other factors experienced large variations. Different from PM2.5, meteorological influences on ozone concentrations were relatively weak in summer, when ozone concentrations were the highest in Beijing. Given the generally stable meteorological influences on ozone concentrations and human-induced emissions of VOCs and NOx across seasons, warming induced notable increase in summertime biogenic emissions of VOCs and NOx can be a major driver for the increasing ozone pollution episodes. This research provides useful references for understanding long-term meteorological influences on ozone concentrations in mega cities in China.

Understanding the Influence of Crop Residue Burning on PM2.5 and PM10 Concentrations in China from 2013 to 2017 Using MODIS Data.

In recent years, particulate matter (PM) pollution has increasingly affected public life and health. Therefore, crop residue burning, as a significant source of PM pollution in China, should be effectively controlled. This study attempts to understand variations and characteristics of PM10 and PM2.5 concentrations and discuss correlations between the variation of PM concentrations and crop residue burning using ground observation and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The results revealed that the overall PM concentration in China from 2013 to 2017 was in a downward tendency with regional variations. Correlation analysis demonstrated that the PM10 concentration was more closely related to crop residue burning than the PM2.5 concentration. From a spatial perspective, the strongest correlation between PM concentration and crop residue burning existed in Northeast China (NEC). From a temporal perspective, the strongest correlation usually appeared in autumn for most regions. The total amount of crop residue burning spots in autumn was relatively large, and NEC was the region with the most intense crop residue burning in China. We compared the correlation between PM concentrations and crop residue burning at inter-annual and seasonal scales, and during burning-concentrated periods. We found that correlations between PM concentrations and crop residue burning increased significantly with the narrowing temporal scales and was the strongest during burning-concentrated periods, indicating that intense crop residue burning leads to instant deterioration of PM concentrations. The methodology and findings from this study provide meaningful reference for better understanding the influence of crop residue burning on PM pollution across China.