Soil heavy metal pollution has been becoming serious and widespread in China. To date, there are few studies assessing the nationwide soil heavy metal pollution induced by industrial and agricultural ...activities in China. This review obtained heavy metal concentrations in soils of 402 industrial sites and 1041 agricultural sites in China throughout the document retrieval. Based on the database, this review assessed soil heavy metal concentration and estimated the ecological and health risks on a national scale. The results revealed that heavy metal pollution and associated risks posed by cadmium (Cd), lead (Pb) and arsenic (As) are more serious. Besides, heavy metal pollution and associated risks in industrial regions are severer than those in agricultural regions, meanwhile, those in southeast China are severer than those in northwest China. It is worth noting that children are more likely to be affected by heavy metal pollution than adults. Based on the assessment results, Cd, Pb and As are determined as the priority control heavy metals; mining areas are the priority control areas compared to other areas in industrial regions; food crop plantations are the priority control areas in agricultural regions; and children are determined as the priority protection population group. This paper provides a comprehensive ecological and health risk assessment on the heavy metals in soils in Chinese industrial and agricultural regions and thus provides insights for the policymakers regarding exposure reduction and management.
Display omitted
•402 industrial and 1041 agricultural sites are reviewed.•Pollution and risks in industrial regions were severer than agricultural regions.•30% of industrial sites pose potential non-carcinogenic risk.•The majority of As carcinogenic risks are at a relatively unacceptable range.•The priority control components were identified.
In this study, a novel method combining microplate fluorescence imaging (FI) and high-throughput screening (HTS) technology was applied to screen and evaluate the multicomponent metal (Zn, Cd, Ni) ...sulfides-modified g-C
3
N
4
with high-activity photocatalytic performance. Glass screen printing was creatively used in preparing a photocatalytic reaction microplate containing 225 independent micro-reaction chambers (μRCs) as experiment carriers. A photocatalyst chip comprising 225 Zn
x
Cd
y
Ni
1−x−y
S/g-C
3
N
4
multicomponent photocatalysts was made via chemical ink-jet printing (IJP) technology, at last 23 high-efficiency M
3
S/g-C
3
N
4
were screened out from the photocatalyst chip by the optical density (OD) method.
Graphic Abstract
Well‐defined nanostructures are crucial for precisely understanding nano‐bio interactions. However, nanoparticles (NPs) fabricated through conventional synthesis approaches often lack poor ...controllability and reproducibility. Herein, a synthetic biology‐based strategy is introduced to fabricate uniformly reproducible protein‐based NPs, achieving precise control over heterogeneous components of the NPs. Specifically, a ferritin assembly toolbox system is developed that enables intracellular assembly of ferritin subunits/variants in Escherichia coli. Using this strategy, a proof‐of‐concept study is provided to explore the interplay between ligand density of NPs and their tumor targets/penetration. Various ferritin hybrid nanocages (FHn) containing human ferritin heavy chains (FH) and light chains are accurately assembled, leveraging their intrinsic binding with tumor cells and prolonged circulation time in blood, respectively. Further studies reveal that tumor cell uptake is FH density‐dependent through active binding with transferrin receptor 1, whereas in vivo tumor accumulation and tissue penetration are found to be correlated to heterogeneous assembly of FHn and vascular permeability of tumors. Densities of 3.7 FH/100 nm2 on the nanoparticle surface exhibit the highest degree of tumor accumulation and penetration, particularly in tumors with high permeability compared to those with low permeability. This study underscores the significance of nanoparticle heterogeneity in determining particle fate in biological systems.
Creating heterogeneous nanoparticles with controllability and reproducibility remains challenging using conventional synthesis approaches. Herein, a synthetic biology‐based approach is introduced that enables the creation of a highly uniform and controllable living nanosystem, allowing for the tailoring of heterogeneous ferritin nanocages through spontaneous assembly. A proof‐of‐concept study illustrates the significant impact of nanoparticle heterogeneity on both tumor uptake and penetration.
In grass, the lemma is a unique floral organ structure that directly determines grain size and yield. Despite a great deal of research on grain enlargement caused by changes in glume cells, the ...importance of normal development of the glume for normal grain development has been poorly studied. In this study, we investigated a rice spikelet mutant,
degenerated lemma
(
del
), which developed florets with a slightly degenerated or rod-like lemma. More importantly,
del
also showed a significant reduction in grain length and width, seed setting rate, and 1000-grain weight, which led to a reduction in yield. The results indicate that the mutation of the
DEL
gene further affects rice grain yield. Map-based cloning shows a single-nucleotide substitution from T to A within
Os01g0527600/DEL/OsRDR6
, causing an amino acid mutation of Leu-34 to His-34 in the
del
mutant. Compared with the wild type, the expression of
DEL
in
del
was significantly reduced, which might be caused by single base substitution. In addition, the expression level of
tasiR-ARF
in
del
was lower than that of the wild type. RT-qPCR results show that the expression of some floral organ identity genes was changed, which indicates that the
DEL
gene regulates lemma development by modulating the expression of these genes. The present results suggest that the normal expression of
DEL
is necessary for the formation of lemma and the normal development of grain morphology and therefore has an important effect on the yield.
Complex systems and their various effects are always of concern in environmental chemistry (EC). With the increasing number of research dimensions, traditional paradigms based on methodological ...reductionism have failed to help us determine the accurate effects or behaviors of chemical pollutants in multi-media environments. As an adept means of handling complicated objects, artificial intelligence (AI) supported by various machine learning (ML) algorithms is one of the best ways to cope with this problem. In this perspective, we try to explain some similarities between the complex matter in EC and the AI networks and provide some suggestions for combining the two networks for EC data mining.
Machine learning will radically accelerate analysis of complex material networks in environmental chemistry.
In this study, multiwalled carbon nanotubes (MWCNTs)/TiO2 nanocomposites were obtained by constant volumetric wet impregnation processes. The prepared catalysts were characterized by scanning ...electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). The effect of reaction conditions on photocatalytic performance of the catalysts was investigated by the degradation of methyl orange (MO) under UV irradiation, in a new type of reactor with unique structure. The results showed that the prepared nanocomposite exhibited higher MO degradation efficiency than that of pure nano-TiO2. Besides, in batch experiments of influencing factors, including ionic strength, oxidant amount, and response times, the presence of H2O2 would contribute to increasing the MO degradation rate of MWCNTs/TiO2 samples. Ionic concentration and long reaction times are adverse to the MO degradation in the processes.
Soil pollution in China is one of most wide and severe in the world. Although environmental researchers are well aware of the acuteness of soil pollution in China, a precise and comprehensive mapping ...system of soil pollution has never been released. By compiling, integrating and processing nearly a decade of soil pollution data, we have created cornerstone maps that illustrate the distribution and concentration of cadmium, lead, zinc, arsenic, copper and chromium in surficial soil across the nation. These summarized maps and the integrated data provide precise geographic coordinates and heavy metal concentrations; they are also the first ones to provide such thorough and comprehensive details about heavy metal soil pollution in China. In this study, we focus on some of the most polluted areas to illustrate the severity of this pressing environmental problem and demonstrate that most developed and populous areas have been subjected to heavy metal pollution.
A machine learning (ML) strategy based on color-spectral images for mixed amino acid (AA) analysis is presented. The results showed that a well-trained ML model could accurately predict multiple AAs ...at the same time, suggesting its value for facilitating quantitative analysis of mixed AA systems.
A data path between mixed amino acid analysis and machine learning.
Heavy metals (HMs) represent pervasive and highly toxic environmental pollutants, known for their long latency periods and high toxicity levels, which pose significant challenges for their removal ...and degradation. Therefore, the removal of heavy metals from the environment is crucial to ensure the water safety. Biochar materials, known for their intricate pore structures and abundant oxygen-containing functional groups, are frequently harnessed for their effectiveness in mitigating heavy metal contamination. However, conventional tests for optimizing biochar synthesis and assessing their heavy metal adsorption capabilities can be both costly and tedious. To address this challenge, this paper proposes a data-driven machine learning (ML) approach to identify the optimal biochar preparation and adsorption reaction conditions, with the ultimate goal of maximizing their adsorption capacity. By utilizing a data set comprising 476 instances of heavy metal absorption by biochar, seven classical integrated models and one stacking model were trained to rapidly predict the efficiency of heavy metal adsorption by biochar. These predictions were based on diverse physicochemical properties of biochar and the specific adsorption reaction conditions. The results demonstrate that the stacking model, which integrates multiple algorithms, allows for training with fewer samples to achieve higher prediction accuracy and improved generalization ability.
Determination of complex pollutants often involves many high-cost and laborious operations. Today’s pop machine-learning (ML) technology has exhibited their amazing successes in image recognition, ...drug designing, disease detection, natural language understanding, etc. ML-driven samples testing will inevitably promote the development of related subjects and fields, but the biggest challenge ahead for this process is how to provide some intelligible and sufficient data for various algorithms. In this work, we present a full strategy for rapid detecting mixed pollutants through the synergistic application of holographic spectrum and convolutional neural network (CNN). The results have shown that a well-trained CNN model could realize quantitative analysis of the mixed pollutants by extracting spectral information of matters, suggesting the strategy’s value in facilitating the study of complex chemical systems.
A data path between pollutants analysis and machine learning (ML). Display omitted
•A novel imaging method was proposed to characterize mixed pollutant samples.•A specialized data-set was customized for meet the need of machine learning.•Multiple pollutants were synchronously measured in a very wide concentration range.
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