Long-term study of heavy metal pollution in the northern Hangzhou Bay of China: temporal and spatial distribution,contamination evaluation,and potential ecological risk

Environmental Science and Pollution Research - Coastal ecosystem is vulnerable to heavy metal contamination. The northern Hangzhou Bay is under intensifying impact of anthropogenic activities. To...     

Iodine enrichment and the underlying mechanism in deep groundwater in the Cangzhou Region,North China

Environmental Science and Pollution Research - The lack of information on the origin and behavior of iodine in deep groundwater restricts the development and use of groundwater resources. To...     

A comparison of antibiotics,antibiotic resistance genes,and bacterial community in broiler and layer manure following composting

Animal manure is an important source of antibiotics and antibiotic resistance genes (ARGs) in the environment. However, the difference of antibiotic residues and ARG profiles in layer and broiler manure as well as their compost remains unexplored. In this study, we investigated the profiles of twelve antibiotics, seventeen ARGs, and class 1 integrase gene (intI1) in layer and broiler manure, and the corresponding compost at large-scale. Compared with layer manure, broiler manure exhibited approximately six times more residual tetracyclines, especially chlortetracycline. The relative abundances of qnrS and ermA genes in broiler manure were significantly higher than those in layer manure. The concentration of tetracyclines not only had a significantly positive correlation with tetracycline resistance genes (tetA and tetC) but was also positively correlated with quinolone resistance (qepA, qnrB, and qnrS) and macrolide resistance (ermA and ermT). Most ARGs in manure were reduced after composting. However, the relative abundance of sulfonamide resistance gene sul1 increased up to 2.41% after composting, which was significantly higher than that of broiler (0.41%) and layer (0.62%) manure. The associated bacterial community was characterized by high-throughput 16S rRNA gene sequencing. The relative abundances of thermophilic bacteria had significant positive correlations with the abundance of sul1 in compost. The composting has a significant impact on the ARG-associated gut microbes in poultry manure. Variation partitioning analysis indicated that the change of bacterial community compositions and antibiotics contributed partially to the shift in ARG profiles. The results indicate that at industry-scale production broiler manure had more antibiotics and ARGs than layer manure did, and composting decreased most ARG abundances in poultry manure except for sulfonamide resistance genes.

     

Transcriptome analysis of Plantago major as a phytoremediator to identify some genes related to cypermethrin detoxification

Environmental Science and Pollution Research - Cypermethrin (CYP) is a toxic manmade chemical compound belonging to pyrethroid insecticides contaminating the environment. Plantago major (PM) has...     

Synthesis of Ni-doped anatase TiO2 single crystals loaded on wood-based activated carbon for enhanced photodegradation of triphenylmethane dyes

Environmental Science and Pollution Research - In this work, the Ni-doped anatase TiO2 single crystals loaded on activated carbon (Ni-T/AC) were synthesized by a sol–gel method. The chemical...     

Mercury uptake by Paspalum distichum L. in relation to the mercury distribution pattern in rhizosphere soil

Environmental Science and Pollution Research - Paspalum distichum L. was tested to evaluate their phytoremediation capacity for Hg contaminated soil through analyzing the dissipation of Hg in soil...     

Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR)

Environmental Science and Pollution Research - Since COVID-19 is extremely threatening to human health, it is significant to determine its impact factors to curb the virus spread. To tackle the...     

Characterization of a novel marine aerobic denitrifier Vibrio spp. AD2 for efficient nitrate reduction without nitrite accumulation

Environmental Science and Pollution Research - Aerobic denitrifiers have the potential to reduce nitrate in polluted water under aerobic conditions. A salt-tolerant aerobic denitrifier was newly...     

Asian Urban Environment and Climate Change: Preface

The Asian Network on Climate Science and Technology (www.ancst.org), in collaboration with Tsinghua University, held a conference on environmental and climate science, air pollution, urban planning and transportation in July 2015, with over 40 Asian experts participating and presentation. This was followed by a meeting with local government and community experts on the practical conclusions of the conference. Of the papers presented at the conference a selection are included in this special issue of Journal of Environmental Science, which also reflects the conclusions of the Paris Climate meeting in Dec 2015, when the major nations of the world agreed about the compelling need to reduce the upward trend of adverse impacts associated with global climate change. Now is the time for urban areas to work out the serious consequences for their populations, but also how they should work together to take action to reduce global warming to benefit their own communities and also the whole planet!     

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers'' information and vehicles'' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.