The impact trilemma of energy prices,taxation, and population on industrial and residential greenhouse gas emissions in Europe

Environmental Science and Pollution Research - As a major source of pollution and the cause of climate change, greenhouse gas emissions are attracting the attention of scholars, policymakers, and...     

武汉地区夏季和秋冬季大气污染的天气概念模型

利用2013～2019年武汉市生态环境局监测数据、L波段雷达探空资料、NCEP/NCAR逐日再分析资料,对夏季和秋冬季武汉地区污染日的大气污染特征、边界层结构、环流形势、物理量场进行研究,建立了武汉地区大气污染的天气概念模型.主要结论如下:(1)武汉市空气质量具有季节性变化特征,大气污染程度四季分布表现为冬＞秋＞春＞夏.夏季首要污染物是臭氧,冬季首要污染物是PM2.5.(2)比较挑选出的夏季清洁日和污染日的气象要素特征,污染日逆温的平均强度约为清洁日的一倍,逆温底高一般在600 m以下,空气质量一般为轻度-中度污染;静风频率(37.1％)明显高于清洁日的静风频率(2.9％);污染日平均风速小(0.8 m/s),边界层内相对湿度较低.同样比较秋冬季两类天气的气象要素特征,污染日逆温底高低、厚度小,不及清洁日的一半,不利于污染物的扩散,易出现重度污染天气.静风频率(20％)高于清洁日的静风频率(7.5％),风速小(1.6 m/s),污染日边界层内呈明显上千下湿的格局.(3)建立了夏季大气污染的天气概念模型,污染日副高偏弱位置偏东,长江流域易少雨干旱;地面我国东部大范围地区处于均压场中,武汉地区为偏东北异常小风,不利于大气污染物的扩散.(4)建立了秋冬季大气污染的天气概念模型,长江流域环流平直少波动,配合地面弱低压的天气形势和较强的逆温使得大气污染物聚集在近地面.蒙古冷高压强度偏弱,使得入侵我国的冷空气强度偏弱;武汉地区为偏北小风,对雾霾的移除和稀释扩散作用差.该研究结论可供大气污染预测预警研究和环境管理部门大气污染的联防联控参考.     

Magnetic graphene-based nanocomposites as highly efficient absorbents for Cr(VI) removal from wastewater

Environmental Science and Pollution Research - Due to the merits of their high adsorption and convenient separation, magnetic graphene-based composites have become a promising adsorbent in terms of...     

Could biochar amendment be a tool to improve soil availability and plant uptake of phosphorus? A meta-analysis of published experiments

Environmental Science and Pollution Research - As one of the most important nutrients for plant growth, phosphorus was often poorly available in soil. While biochar addition induced improvement of...     

Lithium extraction from salt lake brines with high magnesium/lithium ratio: a review

Environmental Chemistry Letters - The demand for lithium is growing rapidly with the increase in electric vehicles, batteries and electronic equipments. Lithium can be extracted from brines, yet...     

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.     

Enhanced arsenate removal from aqueous solution by Mn-doped MgAl-layered double hydroxides

Environmental Science and Pollution Research - In this study, Mn-doped MgAl-layered double hydroxides (LDHs) were successfully synthesized for efficient removal arsenate from aqueous solution. The...     

Combination with catalyzed Fe(0)-carbon microelectrolysis and activated carbon adsorption for advanced reclaimed water treatment: simultaneous nitrate and biorefractory organics removal

A process combining catalyzed Fe(0)-carbon microelectrolysis (IC-ME) with activated carbon (AC) adsorption was developed for advanced reclaimed water treatment. Simultaneous nitrate reduction and chemical oxygen demand (COD) removal were achieved, and the effects of composite catalyst (CC) addition, AC addition, and initial pH were investigated. The reaction kinetics and reaction mechanisms were calculated and analyzed. The results showed that CC addition could enhance the reduction rate of nitrate and effectively inhibit the production of ammonia. Moreover, AC addition increased the adsorption capacity of biorefractory organic compounds (BROs) and enhanced the degradation of BRO. The reduction of NO₃^?–N at different pH values was consistently greater than 96.9%, and NH₄⁺–N was suppressed by high pH. The presence of CC ensured the reaction rate of IC-ME at high pH. The reaction kinetics orders and constants were calculated. Catalyzed iron scrap (IS)-AC showed much better nitrate reduction and BRO degradation performances than IS-AC and AC. The IC-ME showed great potential for application to nitrate and BRO reduction in reclaimed water.

     

Probabilistic health risk assessment of nitrosamines in drinking water of Shaoxing,Zhejiang, China

Environmental Science and Pollution Research - Nitrosamines (NAms) are potent genotoxic and carcinogenic but widely detected in drinking water. This study aimed to investigate the occurrence of...     

Comparative effect of organic amendments on physio-biochemical traits of young and old bean leaves grown under cadmium stress: a multivariate analysis

Environmental Science and Pollution Research - The current study investigated the influence of organic amendments on cadmium (Cd) uptake and its effects on biochemical attributes of young and old...