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The rapid development of the economy in China resulted in increasingly serious water pollution problem. A lot of water pollution treatment projects have been launched to improve the water environment quality. Water pollution treatment is a complex and long-term task. Considering the concept of water pollution with fuzziness and the factors affecting the effect of water pollution treatment (EWPT), this study constructs a multi-dimensional dynamic fuzzy comprehensive monitoring model. The model considers the vague boundaries in the representation of water pollution and various factors affecting the treatment effect, such as monitoring time, monitoring index, and monitoring location. In detail, firstly, existing methods for evaluating the EWPT are analyzed and reviewed. Then a multi-dimensional dynamic model is developed for monitoring the EWPT. Finally, the Yueya Lake of Henan Province in China is taken as an example to demonstrate the effectiveness and practicability of the proposed method. From the analysis of the results, to maintain the cleanliness of the water, efforts should still be made to eliminate and completely block the pollutants on the shore in order to fundamentally solve the problem. [ABSTRACT FROM AUTHOR]

Clarifying the impact of human activities on polycyclic aromatic hydrocarbons (PAHs) in soil can provide a scientific basis to reduce PAHs pollution and formulate control measures to manage soil PAHs pollution, which are important to reduce global warming and support China's implementation of carbon neutrality goals. Here, we characterize the PAHs in 1055 samples of urban soil collected in China from 2000 to 2020. The total concentration of PAHs ranged from 2.75 to 38,865 ng g−1. PAHs concentrations in the Yangtze River Delta (YangtzeD) and Pearl River Delta exhibited an inverted U-shaped curve over time, and PAHs concentrations in Beijing have decreased. Air pollution prevention policies and vehicle emission standards in these regions reduced PAHs in the soil. We measured PAHs in soils of 18 major cities in YangtzeD, representing a typical area. Total concentration of PAHs was 3.88–2153 ng g−1 in YangtzeD, with 40.5%, 36.3%, and 23.2% of PAHs coming from industry and transportation, coal combustion, and biomass combustion, respectively by Positive matrix factorization (PMF) model. To explore how human activities affect the PAH concentrations, we screened three machine learning models and selected SHapley Additive explanation-extreme gradient boosting (XGB-SHAP) as the best model. The results of XGB-SHAP (R2 = 0.64) show that the main human activities affecting PAHs were carbon emissions, population size, and economic development (MAS = 709.1) and industrial waste gas emissions (MAS = 577.1), and these were positively correlated with PAHs. Therefore, effective ways to reduce PAHs may largely rely on industrial co-control. With the prevention and control of air pollution, clean energy, and green and low-carbon development policies constantly being implemented, PAH concentrations will continue to be reduced in the future. [ABSTRACT FROM AUTHOR]

• Colloform pyrite has a concentric-banded texture with the core composed of granular microcrystals (~300 nm). • Colloform pyrite has abrupt changes between a random CPO and <100>, or <110> CPOs. • The growth of pyrite transform from disordered microcrystals into elongated crystals is controlled by geometrical selection. Colloform pyrite textures have been detected in various geological bodies and have received much attention in recent years. Although these textures have been proved to be formed by the aggregation of micro-grains with different sizes, further work is needed to understand the mechanism for developing them. We carried out a detailed study on the micro-textures, crystal preferred orientations (CPOs), trace element, and sulfur isotope compositions of colloform pyrite aggregates from the Baiyunpu Pb–Zn deposit in Hunan, China. Results showed that the inner core of colloform pyrite had an irregular outline with flattening at their contact boundaries and was commonly formed by the compaction of multiple spherical aggregates. The outer layers concentrically grew around the core. Field emission-scanning electron microscope (FE-SEM) suggested both the inner core and the outer layers were composed of a large number of nano- or micron-scale grains (~300 nm granular crystals to acicular and elongated crystals up to ~100 μm in length). Electron backscatter diffraction (EBSD) revealed an initial random CPO in the core and either 〈1 0 0〉 or 〈1 1 0〉 CPOs in subsequent layers. In situ sulfur isotope compositions obtained by secondary-ion mass spectrometry (SIMS) showed that the δ34S values of colloform pyrite ranged from −8.4 to −0.2‰, which were interpreted to be mainly derived from thermochemical sulfate reduction (TSR). Colloform pyrite aggregates are considered to grow from the core outward through the accretion of layers, including (1) formation and aggregation of microcrystals in core A 1 and (2) growth and geometrical selection of crystals in outer layers. The growth and transformation of pyrite from disorderly granular microcrystals in the core to elongated crystals radiating from the aggregated center in the outer concentric zones is thought to be controlled by geometrical selection. Factors affecting the epitaxial relationship between the substrate and the growing crystals, such as interface energy, ambient temperature and degree of sulfur supersaturation, may control the abrupt changes in CPO recorded in colloform pyrite aggregates. [ABSTRACT FROM AUTHOR]

To estimate spatial distribution, source analysis and uncertainty of heavy metals (Pb, Cd, Cr, Hg, As, Cu, Zn, and Ni) based on geographic information system (GIS), positive matrix factorization model (PMF) and bootstrap (BS) using 382 soil samples collected from cultivated soils in Lanzhou. The mean contents of Cd, Hg, Cu, Zn and Ni were high as 1.7,1.7, 2.1, 1.5 and 1.3 times local background values, mean contents of Pb, Cr and As were lower than local background values. However, the mean contents of eight heavy metals were lower environmental quality risk control standard for soil contamination of agricultural soil. Proportions of four sources were identified: Cr was predominantly contributed by natural sources (29.14%), Cu, Zn and Ni was primarily from industrial sources (25.26%), Hg and As were mainly of agricultural sources (27.49%), Pb and Cd mainly came from traffic source and smelting-related activities (18.09%). Uncertainties analysis contained three aspects: bootstrap runs, factor contributions in the PMF solution, and coefficient of variation (CV) values. By combining the four pollution source factors with bootstrap runs, the accuracy of the four pollution source factors were reliable based on PMF model. The median values in the BS runs was considered the most true factor contribution, and the 5th-95th quartile interval represents the variability of each factor, Factor 4 (traffic source) R 2 was 0.70 and lower variability. The highest CV value usually means a significantly deviation degree. In this study, the CV values of Cr in Factor 1, Cu, Zn, and Ni in Factor 2, Hg, and As in Factor 3, Pb, and Cd in Factor 4 were lower, indicates a lower deviation degree. and with the lowest content among heavy metals usually was also with the greatest uncertainties. In this study improves understanding of the reduction of heavy metal pollution in cultivated soil, and also serves as reference for pollution source apportionment in other regions., (Copyright © 2021. Published by Elsevier Inc.)