Environmental decentralization,environmental public service,and public health: evidence from 289 cities in China

Environmental Geochemistry and Health - Effective supply of environmental public services (EPS) is important to guarantee the mitigation of residential pollution exposure risk. This study analyzes...     

A comprehensive review on green buildings research: bibliometric analysis during 1998–2018

Environmental Science and Pollution Research - Buildings account for nearly 2/5ths of global energy expenditure. Due to this figure, the 90s witnessed the rise of green buildings (GBs) that were...     

A comparison analysis of the decoupling carbon emissions from economic growth in three industries of Heilongjiang province in China

Environmental Science and Pollution Research - Growing environmental pressure urges China to develop in a sustainable and low carbon way, and thus China strives to achieve a carbon peak by 2030 and...     

Correction to: Impact of precipitation disparity on groundwater fluctuation in a semi-arid region (Vellore district) of southern India using geospatial techniques

Environmental Science and Pollution Research - A Correction to this paper has been published: https://doi.org/10.1007/s11356-021-13406-7     

Research progress and hot spots of hydrothermal liquefaction for bio-oil production based on bibliometric analysis

Environmental Science and Pollution Research - Hydrothermal liquefaction (HTL) of biomass used HTL reaction under high temperature and pressure to produce bio-oil. This technology is considered as...     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Diversity of antibiotic resistance genes and encoding ribosomal protection proteins gene in livestock waste polluted environment

The rapid development and increase of antibiotic resistance are global phenomena resulting from the extensive use of antibiotics in human clinics and animal feeding operations. Antibiotics can promote the occurrence of antibiotic resistance genes (ARGs), which can be transferred horizontally to humans and animals through water and the food chain. In this study, the presence and abundance of ARGs in livestock waste was monitored by quantitative PCR. A diverse set of bacteria and tetracycline resistance genes encoding ribosomal protection proteins (RPPs) from three livestock farms and a river were analyzed through denaturing gradient gel electrophoresis (DGGE). The abundance of sul(I) was 10³ to 10⁵ orders of magnitude higher than that of sul(II). Among 11 tet-ARGs, the most abundant was tet(O). The results regarding bacterial diversity indicated that the presence of antibiotics might have an evident impact on bacterial diversity at every site, particularly at the investigated swine producer. The effect of livestock waste on the bacterial diversity of soil was stronger than that of water. Furthermore, a sequencing analysis showed that tet(M) exhibited two genotypes, while the other RPPs-encoding genes exhibited at least three genotypes. This study showed that various ARGs and RPPs-encoding genes are particularly widespread among livestock.     

Mitochondrial toxicity and in vitro biological effects of ambient PM2.5 from an urban site in China

Abstract

The roles of PM_2.5-induced mitochondrial damage and oxidative stress on mast cell degranulation were examined in vitro. Mast cells were treated with suspensions of PM_2.5 in Dulbecco’s modified Eagle’s medium at concentrations from 25 to 200?mg/L in the absence or presence of 10?mmol/L N-acetyl-L-cysteine. Biological effects and mitochondrial function were assessed by determining cell viability, β-hexosaminidase release, interleukin-4 secretion, reactive oxygen species generation, adenosine triphosphate production, potential alteration of mitochondrial membrane, and activities of mitochondrial electron transport chain complexes I and III. Exposure of mast cells to PM_2.5 induced reduction of adenosine triphosphate production, collapse of mitochondrial membrane potential, and inhibition of the activity of complex III. Co-treatment of mast cells exposed to PM_2.5 with N-acetyl-L-cysteine attenuated cytotoxicity and the production of reactive oxygen species, and decreased the release of β-hexosaminidase and interleukin-4. Evidently, PM_2.5-induced oxidative stress plays an essential role in mitochondrial toxicity and mast cell activation.     

Determination of 27 pharmaceuticals and personal care products (PPCPs) in water: The benefit of isotope dilution

• Isotope dilution method was developed for the determination of 27 PPCPs in water. • The established method was successfully applied to different types of water samples. • The correction effect of corresponding 27 ILSs over 70 d was investigated. • Benefit of isotopic dilution method was illustrated for three examples. Pharmaceuticals and personal care products (PPCPs) are a unique group of emerging and non-persistent contaminants. In this study, 27 PPCPs in various water samples were extracted by solid phase extraction (SPE), and determined by isotope dilution method using liquid chromatography coupled to tandem triple quadruple mass spectrometer (LC-MS/MS). A total of 27 isotopically labeled standards (ILSs) were applied to correct the concentration of PPCPs in spiked ultrapure water, drinking water, river, effluent and influent sewage. The corrected recoveries were 73%–122% with the relative standard deviation (RSD)<16%, except for acetaminophen. The matrix effect for all kinds of water samples was<22% and the method quantitation limits (MQLs) were 0.45–8.6 ng/L. The developed method was successfully applied on environmental water samples. The SPE extracts of spiked ultrapure water, drinking water, river and wastewater effluent were stored for 70 days, and the ILSs-corrected recoveries of 27 PPCPs were obtained to evaluate the correction ability of ILSs in the presence of variety interferences. The recoveries of 27 PPCPs over 70 days were within the scope of 72%–140% with the recovery variation<37% in all cases. The isotope dilution method seems to be of benefit when the extract has to be stored for long time before the instrument analysis.