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.     

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.

     

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.     

两种水生昆虫头宽与基于图像分析的大小参数关系性分析

对于水生昆虫及其它生物而言,大小是一个重要的功能性状,但是头宽的测量特别耗时和受人为因素影响.随着计算机技术的发展,基于图像分析技术(一种快速测量大小的策略)可获得传统方式难以获得的大小参数,明确这些大小参数与头宽的关系,对推动这些参数的应用具有重要意义.选择两种个体大小差异较大的蜉蝣(梧州蜉和光滑细蜉)作为研究对象,对其头宽与基于数字图像获得的面积,周长,最小矩形的长和宽,最适合椭圆的长轴和短轴和粒径等个体大小参数的相关性进行研究.数字图像采用改进后的描述仪扫描获取;图像分析基于ImageJ软件完成;基于二元直线回归和多元直线回归分析各参数之间的相关性.各种层次的回归分析结果均表明:基于图像分析获得的两种蜉蝣所有个体大小参数均与头宽大小呈极其显著的正相关性,其中面积的相关性最高.并且面积与其它大小参数相关性也极其显著,对个体大小具有较好的代表性.研究结果为推动数字图像分析技术应用在水生昆虫及其它生物的生物学和生态学研究中提供了技术支持和理论依据.     

Waste landfill plant and waste disposal plant efficiencies in China

Journal of Material Cycles and Waste Management - As resident awareness of environmental issues has grown, waste disposal has become an important part of urban environmental governance. Most...     

Effects of intercropping with floricultural accumulator plants on cadmium accumulation in grapevine

Environmental Science and Pollution Research - Grapevine (Vitis vinifera L.) intercropping with floricultural cadmium (Cd) accumulator plants (Helianthus annuus L., Cosmos sulphureus Cav., Cosmos...     

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...     

Control of NOx emissions by air staging in small- and medium-scale biomass pellet boilers

Environmental Science and Pollution Research - The effect of air staging strategies on NOx control was investigated on a 210-kW small-scale biomass boiler (SBB) and a 1.4-MW medium-scale biomass...     

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...     

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。