A historical record of trace metal deposition in northeastern Qinghai-Tibetan Plateau for the last two centuries

Environmental Science and Pollution Research - Owing to rapid socio-economic development in China, trace metal emissions have increased and lakes even in remote areas have experienced marked...     

Numerical model for static chamber measurement of multi-component landfill gas emissions and its application

The quantitative assessment of landfill gas emissions is essential to assess the performance of the landfill cover and gas collection system. The relative error of the measured surface emission of landfill gas may be induced by the static flux chamber technique. This study aims to quantify effects of the size of the chamber, the insertion depth, pressure differential on the relative errors by using an integrated approach of in situ tests, and numerical modeling. A field experiment study of landfill gas emission is conducted by using a static chamber at one landfill site in Xi’an, Northwest China. Additionally, a two-dimensional axisymmetric numerical model for multi-component gas transport in the soil and the static chamber is developed based on the dusty-gas model (DGM). The proposed model is validated by the field data obtained in this study and a set of experimental data in the literature. The results show that DGM model has a better capacity to predict gas transport under a wider range of permeability compared to Blanc’s method. This is due to the fact that DGM model can explain the interaction among gases (e.g., CH₄, CO₂, O₂, and N₂) and the Knudsen diffusion process while these mechanisms are not included in Blanc’s model. Increasing the size and the insertion depth of static chambers can reduce the relative error for the flux of CH₄ and CO₂. For example, increasing the height of chambers from 0.55 to 1.1 m can decrease relative errors of CH₄ and CO₂ flux by 17% and 18%, respectively. Moreover, we find that gas emission fluxes for the case with positive pressure differential (?P_in-out) are greater than that of the case without considering pressure fluctuations. The Monte Carlo method was adopted to carry out the statistical analysis for quantifying the range of relative errors. The agreement of the measured field data and predicted results demonstrated that the proposed model has the capacity to quantify the emission of landfill gas from the landfill cover systems.

     

Mechanistic insight into the inhibitory effect of artemisinin sustained-release inhibitors with different particle sizes on Microcystis aeruginosa

Environmental Science and Pollution Research - Environment-friendly algaecides based on allelopathy have been widely used to control harmful algal blooms. In this research, micro and nano scale...     

Combining the multivariate statistics and dual stable isotopes methods for nitrogen source identification in coastal rivers of Hangzhou Bay,China

Coastal rivers contributed the majority of anthropogenic nitrogen (N) loads to coastal waters, often resulting in eutrophication and hypoxia zones. Accurate N source identification is critical for optimizing coastal river N pollution control strategies. Based on a 2-year seasonal record of dual stable isotopes (\({\updelta}^{15}\mathrm{N}-{\mathrm{NO}}_3^{\hbox{-} }\) and \({\updelta}^{18}\mathrm{O}-{\mathrm{NO}}_3^{\hbox{-} }\)) and water quality parameters, this study combined the dual stable isotope-based MixSIAR model and the absolute principal component score-multiple linear regression (APCS-MLR) model to elucidate N dynamics and sources in two coastal rivers of Hangzhou Bay. Water quality/trophic level indices indicated light-to-moderate eutrophication status for the studied rivers. Spatio-temporal variability of water quality was associated with seasonal agricultural, aquaculture, and domestic activities, as well as the seasonal precipitation pattern. The APCS-MLR model identified soil + domestic wastewater (69.5%) and aquaculture tailwater (22.2%) as the major nitrogen pollution sources. The dual stable isotope-based MixSIAR model identified soil N, aquaculture tailwater, domestic wastewater, and atmospheric deposition N contributions of 35.3 ±21.1%, 29.7 ±17.2%, 27.9 ±14.5%, and 7.2 ±11.4% to riverine \({\mathrm{NO}}_3^{\hbox{-} }\) in the Cao’e River (CER) and 34.4 ±21.3%, 29.5 ±17.2%, 27.4 ±14.7%, and 8.7 ±12.8% in the Jiantang River (JTR), respectively. The APCS-MLR model and the dual stable isotope-based MixSIAR model showed consistent results for riverine N source identification. Combining these two methods for riverine N source identifications effectively distinguished the mix-source components from the APCS-MLR method and alleviated the high cost of stable isotope analysis, thereby providing reliable N source apportionment results with low requirements for water quality sampling and isotope analysis costs. This study highlights the importance of soil N management and aquaculture tailwater treatment in coastal river N pollution control.

     

Driving factors of consumption-based PM2.5 emissions in China: an application of the generalized Divisia index

Environment, Development and Sustainability - Analyzing the driving factors of PM2.5 pollution in different industries is of great significance for developing energy conservation and emission...     

Study on the degradation mechanism of 2-amino-4-acetaminoanisole from wastewater by nano-Fe3O4-catalyzed Fenton system

Environmental Science and Pollution Research - 2-Amino-4-acetaminoanisole (AMA) is an intermediate product in the synthesis of many commercial dyes, and its wide application has led to the...