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...     

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.     

Effects of climate variables on the transmission of COVID-19: a systematic review of 62 ecological studies

Environmental Science and Pollution Research - The new severe acute respiratory syndrome coronavirus 2 was initially discovered at the end of 2019 in Wuhan City in China and has caused one of the...     

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...     

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.     

报废汽车拆解工艺的比对与研究

报废汽车具有良好的资源属性,处理企业选取合理的处理工艺对其进行处置,不仅能够提高经济效益,也可以有效保护生态环境。通过对当前报废汽车处理工艺技术进行分析比对,针对不同拆解企业提出相应的工艺选取建议,为产业发展提供帮助。     

Numerical model of aerobic bioreactor landfill considering aerobic-anaerobic condition and bio-stable zone development

Environmental Science and Pollution Research - Aeration by airflow technology is a reliable method to accelerate waste biodegradation and stabilization and hence shorten the aftercare period of a...     

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...     

Physiological responses involved in cadmium tolerance in a high-cadmium-accumulating rice (Oryza sativa L.) line

Environmental Science and Pollution Research - The disparity of tolerance in plants in response to Cd stress is associated with multiple physiological processes. A pot experiment was conducted to...     

武汉市一次污染过程的局地流场和边界层结构的数值模拟

利用2013年11月武汉市逐日空气质量资料、地面气象观测资料、NCEP/NCAR再分析资料和L波段雷达探空资料，通过WRF模式模拟空气污染生消过程中的局地气象条件变化，探讨特殊地形条件下边界层结构变化和局地环流在污染物生消过程中的作用和影响。结果表明：（1）武汉地区当背景环流场强的时候，由地形引起的局地流场对污染物扩散的影响就弱，反之当背景环流场弱的时候, 地形对流场影响明显:夜间为山风，白天为谷风。夜间山风与偏西北气流及偏东气流在武汉及周边地区辐合，形成气流汇聚带，在武汉地区形成一个反复污染带,即由地形引起的局地流场对污染物扩散的贡献就大；（2）武汉地区发生空气污染时，地面湿度较高，边界层呈上干下湿状态，其特征为暖而干且有偏东小风，这导致污染物不断堆积和重污染过程的形成。