An automated (novel) algorithm for estimating green vegetation cover fraction from digital image: UIP-MGMEP

Green vegetation cover fraction (VCF) is an important indicator of vegetation status in ecology and agronomy. Digital image analysis (DIA) has been widely accepted as a new VCF measurement technique. In this study, we present a novel fully automatic threshold segmentation algorithm for VCF measurements, which is named as upper inflection point plus mean gradient magnitude of edge pixels (UIP-MGMEP). The algorithm performs VCF estimation upon the vegetation index Excess Green (EXG). UIP-MGMEP optimizes the EXG threshold by searching the upper inflection point (UIP) of the M-Et curve (mean gradient magnitude of edge pixels (MGMEP) vs. EXG threshold), based on the assumption that EXG variance of the boundary pixels between vegetation and background is larger than the variance of the background. Five typical sample images are used to illustrate how ground complexity reduces the distinctness of the UIP. Three controlled experiments are illustrated to test the robustness of UIP-MGMEP to resolution, exposure, and ground complexity. The results show that UIP-MGMEP is a promising algorithm for automatic VCF estimation upon digital images. Compared to broad-leaved grass, narrow-leaved grass is more sensitive to resolution and exposure. To reduce ground complexity, smaller footprint size while more images to cover the same area may be better than one image with large footprint size. UIP-MGMEP is fully automatic, making it promising for batch processing of VCF measurements that is very difficult in any wide-range field survey in the past. UIP-MGMEP algorithm can only extract green vegetation and is not suitable for non-green (even grayish-green) vegetation, due to the limits of vegetation index EXG. In addition, UIP-MGMEP is not recommended for images with VCF less than 0.5% or greater than 99.5%.     

Comparisons of three plant species in accumulating polycyclic aromatic hydrocarbons (PAHs) from the atmosphere: a review

Environmental Science and Pollution Research - Plant leaves play a key role in the accumulation of PAHs, as they are able to capture PAHs from the air. In this paper, the mechanism, including...     

近53 a来长江流域极端降水指数特征

利用1963~2015年长江流域115个气象站点逐日降水数据,分析了不同极端降水指标的空间变化特点和时间变化趋势。结果表明,近53 a来,长江流域多年平均年极端降水量与年降水量从下游到上游逐渐递减,两者变化趋势大致呈现“增-减-增”的空间分布格局。年极端降水量对年降水量贡献（PEP）存在明显的空间分布差异,但贡献比例在流域内普遍呈现增加的趋势。持续1 d的极端降水事件的降水量分布及其变化趋势与年极端降水量的分布特征类似,其对年极端降水量的贡献比例高达65%以上,说明长江流域极端降水以持续1 d的极端降水事件为主。持续2 d及以上的极端降水事件主要集在中皖苏赣局部地区和四川中部地区,但其降水量对年极端降水量的贡献比例较小。从上游到下游,年最大日降水量（MDP）逐渐增大。其中,上游源头地区的沱沱河、曲麻莱和玉树3个站点MDP主要集中在0~25 mm之间,其他站点均以25~50 mm量级为主;长江流域中部地区的MDP大部分以50~100 mm的量级为主,处于100~150 mm之间的次之;长江流域东部地区主要以100~150 mm量级的MDP为主。 关键词： 极端降水;降水贡献;不同历时;长江流域     

电石渣－铁屑法去除硫酸废水中的氟和砷

对各种处理含氟、砷废水的方法进行了探讨，选择了以电石渣和废铁屑为药剂去除硫酸废水中氟和砷的方法，取得了较好的效果。该法以废治废、工艺简便、运行费用低，处理后的废水可达排放标准。     

微波强化CWPO工艺CuO_x-CeO_2/GAC催化剂的制备及其对二甲亚砜的降解

以颗粒活性炭(GAC)为载体、铜为活性组分、铈为助剂组分、草酸钠为沉淀剂,采用浸渍焙烧法制得CuO_x-CeO_2/GAC催化剂。以H_2O_2为氧化剂,微波强化催化湿式过氧化氢氧化(CWPO)处理二甲亚砜(DMSO)初始质量浓度为1 000 mg/L的废水,处理3 min后DMSO去除率达93.8%。催化剂第7次使用时DMSO去除率仍保持在75%以上。初始废水pH在3~9范围内,DMSO去除率均在85%以上。助剂Ce的加入提高了催化剂表面活性组分的分散性和稳定性,使催化剂的活性稳定性和使用寿命显著提高。     

Sb release characteristics of the solid waste produced in antimony mining smelting process

Sb release characteristics of blast furnace slag, mining waste rock and tailing sand were investigated in static immersion and dynamic leaching test. These three kinds of waste samples were collected from the antimony mine in Lengshuijiang, China, produced in mining smelting process. Effects of solid/liquid ratio, sample size and pH of leaching solution on Sb release characteristics were inspected based on the analysis of scanning electron microscope, pH and EC of leachate. The optimal parameters for Sb leaching of each sample were analyzed. For blast furnace slag and mining waste rock, Sb release contents increased along with the decline of solid/liquid ratio. The maximum accumulative release contents were 42.13, 34.26 mg/kg at the solid/liquid ratio of 1:20. While Sb release content for tailing sand decreased first and then increased with the reduction of solid/liquid ratio. When the solid/liquid ratio was 1:5, the accumulative Sb release content reached the most (24.30 mg/kg). Sb release content of mining waste rock increased with the drop of leaching solution pH, with the highest accumulative release content of 26.01 mg/kg at pH 2.0. Sb release contents of blast furnace slag and tailing sand showed positive correlation with the variation of leaching solution pH. The maximum accumulative release contents of these two samples were 215.91 and 147.83 mg/kg, respectively, when leaching solution pH was 7.0. In summary, Sb release capacity of the three samples in descending order was tailing sand, blast furnace slag and mining waste rock. pH and EC of the leachate in dynamic test varied independently with the initial pH of leaching solution while showing close relationship with mineral hydrolysis in the waste.     

通风口高度对太阳能烟囱建筑自然通风和自然排烟性能的影响研究

安装太阳能烟囱后的建筑具有效果优良、节能环保的自然通风系统。室内通风口高度是太阳能烟囱建筑性能的关键参数之一。提出通过改变太阳能烟囱建筑室内通风口高度位置,使太阳能烟囱既可用于建筑的日常自然通风,也能实现室内火灾场景下的自然排烟。利用缩比例实验台研究了不同室内通风口高度对太阳能烟囱建筑自然通风与自然排烟效果的影响,发现自然通风和自然排烟效果随通风口高度增加呈现先增大后减小的趋势,且最佳室内通风口高度位于窗户上檐位置附近。研究结果对太阳能烟囱建筑的建筑结构关键参数设计具有指导意义。     

固相萃取-气相色谱法测定水样中马拉硫磷残留

采用C-18小柱萃取、毛细管柱分离、气相色谱氢火焰离子化检测器（FID）测定水样中的马拉硫磷,检测限为0．12μg／L。试验了样品流量和洗脱剂对回收率的影响,结果表明样品流量为6mL／min、二氯甲烷作洗脱剂时,回收率较好。测定蒸馏水、地下水和河水样品,相对标准偏差〈2．2％。加标回收率在79．0％-109％之间。     

华东某铀矿区稻米中放射性核素铀污染特征及健康风险评价

为查明华东某铀矿区稻米中放射性核素铀污染现状及健康风险问题,测定铀矿区和对照区共136件稻米样品中放射性核素U含量,采用单因子污染指数法评价放射性铀污染,并开展U元素健康风险评价。结果表明:(1)研究区稻米中U含量平均值为1.46 ng·g~(–1),各亚区稻米中U含量平均值从大到小顺序为:开采矿井区水冶场区含矿未采区废弃矿井区江西省背景值对照区;(2)稻米单因子污染指数为1.25,属于轻度污染。其中,开采矿井区和水冶厂区为轻度污染,废弃矿井区和对照区未受污染;(3)首次计算提出江西省大米U元素致癌风险最大斜率系数为1.04×10~3(d·kg)·mg~(–1)。各亚区稻米中成人和儿童致癌风险指数高低顺序均为:开采矿井区水冶厂区含矿未采区废弃矿井区对照区。儿童直接饮食稻米具有一定的致癌风险;开采矿井区和水冶厂区的成人存在一定致癌风险,含矿未采区和废弃矿井区以及对照区均无致癌风险。     

CO2 emission driving forces and corresponding mitigation strategies under low-carbon economy mode: evidence from China’s Beijing–Tianjin–Hebei region

On account of the background of China’s “new normal” characterized by slower economic growth, this paper analyses the low-carbon economy status quo in the Beijing–Tianjin–Hebei region and empirically investigates the relationship between carbon dioxide (CO₂) emissions and its various factors for China’s Beijing–Tianjin–Hebei region using panel data econometric technique. We find evidence of existence of Environmental Kuznets Curve. Results also show that economic scale, industrial structure, and urbanization rate are crucial factors to promote CO₂ emissions. However, technological progress, especially the domestic independent research and development, plays a key role in CO₂ emissions abatement. Next, we further analyze the correlation between each subregion and various factors according to Grey Relation Analysis. Thereby, our findings provide important implications for policymakers in air pollution control and CO₂ emissions reduction for this region.