A combined radical and non-radical oxidation processes for efficient degradation of Acid Orange 7 in the homogeneous Cu(II)/PMS system: important role of chloride

Environmental Science and Pollution Research - Trace copper ion (Cu(II)) in water and wastewater can trigger peroxymonosulfate (PMS) activation to oxidize organic compounds, but it only works under...     

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.     

湖北省耕地利用转型对农民增收的空间溢出效应研究

基于2005～2017年湖北省74个县域面板数据,构建基于交通权重的空间权重矩阵,利用空间杜宾模型,实证研究耕地利用转型影响农民增收的空间溢出效应,并进一步揭示该溢出效应呈现的具体规律与特征.研究表明:(1)耕地利用转型各形态和农民收入水平都具有显著的正向空间自相关性;(2)农民收入水平的空间自回归系数ρ为0.65,表明相邻县域农民增收存在正向空间溢出效应,即本县域农民收入增长1％,相邻县域农民收入将会增长0.65％;(3)相邻县域间的空间溢出效应主要靠耕地数量、农业劳动力和基础设施投入形态变化驱动,其中,农业劳动力形态变化产生的空间溢出效应占总效应的69％,耕地数量形态占15％,基础设施投入形态占12％.因此,应注意提升县域间耕地利用系统协同性,利用其空间溢出效应促进湖北省各县域共同发展、农民收入协同提高.     

Association between air pollution and COVID-19 infection: evidence from data at national and municipal levels

Environmental Science and Pollution Research - The impact of high concentrations of air pollution on COVID-19 has been a major air quality and life safety issue in recent studies. This study aimed...     

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.     

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...     

The pollution halo effect of technology spillover and pollution haven effect of economic growth in agricultural foreign trade: two sides of the same coin?

Environmental Science and Pollution Research - The existing literature on the environmental Kuznets curve (EKC) fails to investigate the spatial attribute of the “pollution halo” effect...     

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...     

长江源区浮游植物群落结构及分布特征

地处青藏高原腹地的长江源区是长江流域的水源涵养地和生态系统的天然屏障,同时也是自然生态系统最敏感、生态环境十分脆弱的地区,浮游植物作为水体生态系统中的初级生产者,其群落结构对反映水体质量具有重要的指示作用。为探究长江源区水环境特性和浮游植物群落特征,于2018年3月份与10月份对长江源区10个典型河段的水环境因子与浮游植物群落组成进行了系统调查。结果显示:10月份各调查河段流速、浊度、水温及电导率等水环境参数均比3月份高;同一月份中,楚玛尔河的浊度、盐度、电导率等水环境参数均显著高于其余河段;两个月份各调查河段的pH、溶解氧以及亚硝酸盐氮等指标差异不显著。采样调查共鉴定出浮游植物4门28属58种,其中以硅藻、绿藻和蓝藻物种数占优,分别占总数的79.3%、10.4%和8.6%。调查期间,长江源区浮游植物密度在6.06×10~4～39.90×10~4 cells·L~(-1)之间,平均生物量为0.59 mg·L~(-1)。浮游植物优势种有美丽颤藻(Oscillatoria formosa)、普通等片藻(Diatoma vulgare)、脆杆藻(Fragilaria sp.)、针杆藻(Synedra sp.)、舟形藻(Navicula sp.)、桥弯藻(Cymbella sp.)、小球藻(Chlorella sp.)等。两个月份Shannon-Wiener多样性指数(H’)均值分别为3.06和3.12,Margalef丰富度指数(D)均值为1.17和1.52,Pielou均匀度指数(J)均值为0.90和0.82。结合水体营养盐指标、浮游植物密度及多样性指数等指标对水质评价,长江源区水质总体呈无污染或轻度污染状态。     

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...