The dynamics and drivers of environmental performance in Chinese cities: a decomposition analysis

Environmental Science and Pollution Research - This paper examines urban environmental performance using a unique database of 286 Chinese cities for the period 2002–2014. Both the dynamics of...     

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.     

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.     

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...     

Characteristic and mechanism of sorption and desorption of benzene on humic acid

Environmental Science and Pollution Research - The sorption/desorption behaviors of benzene, toluene, ethylbenzene, and xylene (BTEX) on soil organic matter (SOM) have a significant influence on...     

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。     

峡山地不同垂直带土壤层的水文功能及其影响因子

渗透性能和持水能力是土壤重要的水力学性质，是土壤调节径流、保持水土和涵养水源等水文功能的基础。以三峡库首的夷陵-大老岭山地为对象，采集亚高山棕壤针叶林地、中山黄棕壤针阔混交林与茶园地和低山黄壤针叶林地等4个样点剖面的土壤样品，在室内进行土壤饱和导水率、水分特征曲线和理化性质测定，量化了不同样地土壤渗透性能、持水能力和水分库容等水文功能参数，并明确了其主要影响因子。结果表明: 研究区山地土壤饱和导水率在0.06～14.78 mm/min之间，亚高山棕壤和中山黄棕壤林地土壤渗透性能较好，其平均饱和导水率在7.15～14.78 mm/ min之间，低山黄壤次之（1.3 9 mm/min），中山黄棕壤茶园土壤渗透能力最差（1.17 mm/min）。不同类型土壤的饱和含水量、毛管含水量、田间持水量存在较大差异，凋萎含水量差异较小。同一类型土壤的不同发生层内，土壤饱和含水量随土壤深度的增加而递减，毛管持水量和田间持水量随土层深度的增加波动上升。不同样地间土壤水分总库容差异较大，随着海拔的升高，土壤水分总库容增加。土壤水分特征参数与土壤性质的相关分析表明，饱和导水率与土壤总孔隙度呈显著正相关，与容重呈显著负相关；饱和含水量、毛管持水量、田间持水量均与土壤总孔隙度、粘粒含量呈显著性正相关，与容重呈显著负相关；饱和含水量与根系重量呈显著正相关；毛管持水量、田间持水量与砂粒含量呈显著负相关。与中山茶园地和低山黄壤林地相比，亚高山棕壤和中山黄棕壤林地渗透性能较好且持水性更强，具有更好的调蓄径流和涵养水源的水文功能。 关键词： 山地土壤；土壤孔隙度；饱和导水率；持水能力；三峡库区     

PVC/PES-g-PEGMA共混膜的制备及其耐污染性能

首先将聚乙二醇单甲基甲醚酯(PEGMA)接枝到聚醚砜(PES)上得到PES-g-PEGMA,然后利用溶液共混的方法,将聚氯乙烯(PVC)与PES-g-PEGMA共混,通过溶剂-非溶剂扩散诱导相分离法(NIPS)制备PVC/PES-g-PEGMA共混膜。在此基础上对PVC/PES-g-PEGMA共混膜的断面和表面微观结构、水通量、截留率、机械性能及耐污染等性能进行测试,并采用接触角,含水率(EWC),X射线光电子能谱仪(XPS)来表征PES-g-PEGMA的质量百分含量对PVC/PES-g-PEGMA共混膜亲水性的影响。结果表明,PVC/PES-g-PEGMA共混膜水通量,亲水性较纯PVC有很大程度的提高。耐污染性实验表明,随着PES-g-PEGMA的增加,耐污染性逐渐增强。实验结果同时也表明,该共混体系最佳共混比为7∶3。