China’s sponge cities alleviate urban flooding and water shortage: a review

Environmental Chemistry Letters - About one tenth of humans are impacted by water shortages around the globe. Water resilience is worsening under climate change because intensifying weather...     

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...     

Airborne hydrophilic microplastics in cloud water at high altitudes and their role in cloud formation

Microplastic pollution is occurring in most ecosystem, yet their presence in high altitude clouds and their influence on cloud formation and climate change are poorly known. Here we analyzed microplastics in cloud water sampled at the summits of Japan mountains at 1300–3776 m altitude by attenuated total reflection imaging and micro-Fourier transform infrared spectroscopy. We observed nine microplastics including polyethylene, polypropylene, polyethylene terephthalate, polymethyl methacrylate, polyamide 6, polycarbonate, ethylene–propylene copolymer or polyethylene–polypropylene alloy, polyurethane, and epoxy resin. Microplastic were fragmented, with mean concentrations ranging from 6.7 to 13.9 pieces per liter, and with Feret diameters ranging from 7.1 to 94.6 μm. Microplastics bearing hydrophilic groups such as carbonyl and/or hydroxyl groups were abundant, suggesting that they might have acted as condensation nuclei of cloud ice and water. Overall, our finding suggest that high-altitude microplastics cloud influence cloud formation and, in turn, might modify the climate.

     

Recycling sewage sludge into ceramic materials: A review

Environmental Chemistry Letters - Huge amounts of unwanted sewage sludge are generated in the process of wastewater treatment, calling for methods to recycle sewage sludges into value-added...     

Personal exposure to PM2.5-bound heavy metals associated with cardiopulmonary function in general population

Environmental Science and Pollution Research - To better understand the cardiopulmonary alterations associated with personal exposed PM2.5-bound heavy meals, we conducted a cross-sectional study in...     

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...     

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.     

Understanding and addressing the environmental risk of microplastics

Over the past decades, the plastic production has been dramatically increased. Indeed, a category of small plastic particles mainly with the shapes of fragments, fibers, or spheres, called microplastics (particles smaller than 5 mm) and nanoplastics (particles smaller than 1 μm) have attracted particular attention. Because of its wide distribution in the environment and potential adverse effects to animal and human, microplastic pollution has been reported as a serious environment problem receiving increased attention in recent years. As one of the commonly detected emerging contaminants in the environment, recent evidence indicates that the concentration of microplastics show an increasing trend, for the reason that up to 12.7 million metric tons of plastic litter is released into aquatic environment from land-based sources each year. Furthermore, microplastic exposure levels of model organisms in laboratory studies are usually several orders of magnitude higher than those found in environment, and the microplastics exposure conditions are also different with those observed in the environment. Additionally, the detection of microplastics in feces indicates that they can be excreted out of the bodies of animal and human. Hence, great uncertainties might exist in microplastics exposure and health risk assessment based on current studies, which might be exaggerated. Policies reduce microplastic emission sources and hence minimize their environmental risks are determined. To promote the above policies, we must first overcome the technical obstacles of detecting microplastics in various samples.     

Conversion of waste plastics into value-added carbon materials

Plastic pollution is a major environmental issue worldwide, calling for advanced methods to recycle waste plastics in the context of the circular economy. Here we review methods and strategies to convert waste plastics into value-added carbon materials, with focus on sources, properties, pretreatment of waste plastics, and on preparation of carbon materials. Pretreatment techniques include mechanical crushing, plastic stabilization and electrospinning. Carbon materials such as carbon nanotubes, graphene, carbon nanosheets, carbon spheres and porous carbon are prepared by oxygen-limited carbonization, catalytic carbonization, the template-based method, and pressure carbonization. We emphasize the conversion of polyethene terephthalate, polyethylene, polypropylene, polystyrene, halogenated plastics, polyurethane and mixed plastics.

     

Mechanism of complexation of toxic arsenate,selenate, and molybdate with hydrotalcites

Environmental Chemistry Letters - Wastewater from the uranium mining industry contains toxic arsenate (AsO43–), selenate (SeO42–), and molybdate (MoO42–) that can be removed by...