Contents and sources of polycyclic aromatic hydrocarbons and organochlorine pesticides in vegetable soils of Guangzhou, China

We investigated contents, distribution and possible sources of PAHs and organochlorine pesticides (Ops) in 43 surface and subsurface soils around the urban Guangzhou where variable kinds of vegetables are grown. The results indicate that the contents of PAHs (16 US EPA priority PAHs) range from 42 to 3077 microg/kg and the pollution extent is classified as a moderate level in comparison with other investigations and soil quality standards. The ratios of methylphenanthrenes to phenanthrene(MP/P), anthracene to anthracene plus phenanthrene (An/178), benz[a]anthracene to benz[a]anthracene plus chrysene (BaA/228), indeno[1,2,3-cd]pyrene to indeno[1,2,3-cd]pyrene plus benzo[ghi]perylene (In/In+BP) suggest that the sources of PAHs in the soil samples are mixed with a dominant contribution from petroleum and combustion of fossil fuel. The correlation analysis shows that the PAHs contents are significantly related to total organic carbon contents (TOC) (R2=0.75) and black carbon contents (BC) (R2=0.62) in the soil samples. Dichlorodiphenyltrichloroethane and metabolites (DDTs) and hexachlorocyclohexanes and metabolites (HCHs) account largely for the contaminants of OPs. The concentrations of DDTs range from 3.58 to 831 microg/kg and the ratios for DDT/(DDD+DDE) are higher than 2 in some soil samples, suggesting that DDT contamination still exists and may be caused by its persistence in soils and/or impurity in the pesticide dicofol. The concentrations of HCHs are 0.19-42.3 microg/kg.     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.     

Analysis of the mechanism of the impact of internet development on green economic growth: evidence from 269 prefecture cities in China

As the digital economy develops rapidly and the network information technology advances, new development models represented by the network economy have emerged, which have a crucial impact on green economic growth. However, the relevant previous studies lacked the role of analyzing the direct and indirect effects of internet development on green economic growth at the prefecture-level city level. For this purpose, this paper aims to examine the intrinsic mechanism of the impact of internet development on green economic growth and provide empirical support for cities and regions in China to increase internet construction. Furthermore, the mixed model (EBM), which includes both radial and non-radial distance functions, is applied to calculate the green economic growth index. Fixed effect model and mediation effect model are also employed to test influence mechanisms of the internet development on green economic growth using panel data of 269 prefecture-level cities in China from 2004 to 2019. The statistical results reveal that internet development has contributed significantly to green economic growth. When the internet development level increases by 1 unit, the green economic growth level increases by an average of 5.0372 units. However, regional heterogeneity is evident between internet development and green economic growth, that is, the promoting effect of internet development on green economic growth is gradually enhanced from the eastern region to the western region. We also find that internet development guides industrial structure upgrading improves environmental quality and accelerates enterprise innovation, which indirectly contributes to green economic growth. And internet development mainly achieves green economic growth through enterprise innovation. Based on the above findings, we concluded that policymakers should not only strengthen the guiding role of social actors to promote the stable development of the internet industry, but also foster the construction of the three models of “internet+industry integration,” “internet+environmental governance,” and “internet+enterprise innovation” to promote green economic growth.

     

Spatiotemporal changes and driving factors of vegetation in 14 different climatic regions in the global from 1981 to 2018

Environmental Science and Pollution Research - Climate change affects the change of vegetation, and the analysis of vegetation change and its drivers in different globe climate zones is important...     

Spatiotemporal dynamics of vegetation in China from 1981 to 2100 from the perspective of hydrothermal factor analysis

Environmental Science and Pollution Research - The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of...     

Numerical simulation and experimental research of fractal suspended carrier based on nonlinear equation

Environmental Science and Pollution Research - The geometric structure of the suspended carrier is an important factor that directly affects the effluent quality of the moving bed biofilm reactor,...     

附加电场对中空纤维膜污染的减缓作用

通过对新型附加电场的中空纤维膜组件的实验室研究,探讨了电场对缓解微滤膜污染的作用.在恒定流量下过滤含有20mg/L腐殖酸的原水,无电压时,跨膜压力从0.0200MPa上升到0.0300MPa耗时2h,而施加60V电压时该时间段延长至10h.研究表明,原水中的腐殖酸在电场中发生电泳迁移,减少了向膜表面的移动,其对膜的污染被减缓;同时腐殖酸在电场中发生凝聚现象,沉积在膜表面形成疏松的滤饼层,是跨膜压力增加缓慢的另一原因.     

Drivers and barriers to engage enterprises in environmental management initiatives in Suzhou Industrial Park, China

Small- and medium-sized enterprises (SMEs) play an important role in sustainable development not only for their significant contribution to China’s economy, but also for their large share of total discharged pollutants. Therefore, this research takes the enterprises in Suzhou Industrial Park, China as the case study to investigate the environmental management practices of SMEs, and identify drivers and barriers to engaging businesses in environmental management initiatives. It is shown that, as in other countries, SMEs are less active in adopting environmental management initiatives than larger companies. Legislation remains the key driver to engage SMEs in environmental management initiatives. Based on the analysis, policy recommendations are also presented.     

表面活性剂异位淋洗法修复石油污染土壤

综述了表面活性剂在石油污染土壤修复中的应用情况,介绍了表面活性剂的卷缩和增溶作用机理,分析了在异位淋洗修复中的表面活性剂浓度、复配等影响因素,并对表面活性剂在石油污染土壤修复领域的研究和应用前景进行了展望。     

高含水油泥调质脱稳用剂

高含水油泥主要来自于页岩气田、油气田和炼厂的污水处理场（站）,含水率一般高达90%以上,成分复杂、乳化严重,是性质稳定的多相体系。破胶脱稳是高含水油泥处理处置与利用的前提,也是减量化的瓶颈,其核心是调质脱稳用剂的优选。介绍了高含水油泥的性质及调质脱稳的原理和方法,阐述了高含水油泥调质脱稳用剂的研究及应用现状,总结了助滤剂、氧化剂、电解质、破乳剂、絮凝剂和微乳化剂等常用药剂的调质机理、适应范围、研究实例和优缺点,并提出了调质脱稳用剂未来的发展方向。