Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing,China

A method was proposed to identify the main influence factors of soil heavy metals. The influence degree of different environmental factors was ranked. Parent material, soil type, land use and industrial activity were main factors. Interactions between some factors obviously affected soil heavy metal distribution. Identifying the factors that influence the heavy metal contents of soil could reveal the sources of soil heavy metal pollution. In this study, a categorical regression was used to identify the factors that influence soil heavy metals. First, environmental factors were associated with soil heavy metal data, and then, the degree of influence of different factors on the soil heavy metal contents in Beijing was analyzed using a categorical regression. The results showed that the soil parent material, soil type, land use type, and industrial activity were the main influencing factors, which suggested that these four factors were important sources of soil heavy metals in Beijing. In addition, population density had a certain influence on the soil Pb and Zn contents. The distribution of soil As, Cd, Pb, and Zn was markedly influenced by interactions, such as traffic activity and land use type, industrial activity and population density. The spatial distribution of soil heavy metal hotspots corresponded well with the influencing factors, such as industrial activity, population density, and soil parent material. In this study, the main factors affecting soil heavy metals were identified, and the degree of their influence was ranked. A categorical regression represents a suitable method for identifying the factors that influence soil heavy metal contents and could be used to study the genetic process of regional soil heavy metal pollution.     

沈阳市大气中PM2.5来源解析研究

通过2015年在沈阳市采集PM2.5样品及源类样品,分析样品的质量浓度和化学组成,用化学质量平衡(CMB)模型对该市PM2.5来源进行解析。结果表明:沈阳市大气中PM2.5浓度时空变化特征明显;各主要源类对沈阳市PM2.5的分担率依次为煤烟尘(28.03%)、二次无机离子(22.63%)、机动车尾气尘(17.27%)、城市扬尘(13.28%)、建筑尘(5.94%)、土壤风沙尘(5.82%)、道路尘(3.04%)、生物质燃烧尘(2.74%)和冶金尘(1.25%)。燃煤和机动车的有效控制既能降低本类源的贡献,也能降低二次无机离子,体现了多源类综合治理原则。     

丙烯酰胺对水环境中典型微藻的毒性效应

作为可疑致癌物,丙烯酰胺(acrylamide,AM)是目前各国政府和广大民众普遍关注的重要污染物.为探究AM对水体生物的毒性效应及可能存在的生态风险,本文以海洋微藻东海原甲藻(Prorocentrum donghaiense)和淡水微藻莱茵衣藻(Chlamydomonas reinhardtii)为研究对象,采用室内培养法,测定了不同暴露浓度的AM对2种微藻生长、形态和生理状态的影响.结果显示,AM对2种藻类生长均有显著的抑制作用(P0.05),96 h半抑制质量浓度(EC_(50))分别为22.79 mg·L~(-1)和161.8 mg·L~(-1);最高无抑制浓度(NOEC)分别为1.04 mg·L~(-1)和9.84 mg·L~(-1).不同微藻对AM胁迫的响应存在较大的差异性,与莱茵衣藻相比,东海原甲藻对AM更敏感.扫描电镜(SEM)和透射电镜(TEM)结果显示,当受到AM影响时,2种藻细胞严重变形,表现出塌陷、质壁分离、空泡数量增多和叶绿体片层结构少量断裂等现象;叶绿素含量和F_v/F_m(PSⅡ最大光化学量子产量)测试表明AM可以通过破坏微藻的光合系统而抑制光合作用.AM对东海原甲藻属于中毒性物质,对莱茵衣藻属于低毒性物质.     

西北地区昼夜增温的不对称性对植被动态的影响

全球变暖有很大的差异性,表现在昼夜增温速度和各个季节的温度增加的速率具有不一致性,夜间增温的速度要快于白天增温的速度,夜间气温的增温对植被变化的影响更为明显。文章利用34年逐月最高温、最低温以及降水量数据,用一元线性回归分析法、反距离权重插值以及二阶偏相关分析法,分析了西北地区白天和夜间气温的非对称时空变化和非对称增温趋势对植被动态的影响,结果表明:西北地区昼夜温度都呈现出上升的趋势,且上升趋势比较明显,白天增温的速度和夜间增温的速度呈现出不对称性,夜间温度升高的速率是白天温度升高速率的1.2倍,昼夜的温差呈现出减小的趋势;西北地区昼夜增温趋势存在不一致性,白天气温的增温速率在-0.02-0.11℃?a-1,夜间气温的增温速率在-0.06-0.2℃?a-1。tmax和tmin在绝大部分地区都呈现上升趋势,但在空间变化的区域上具有不对称性;西北地区植被生长对昼夜增温的响应具有明显的空间差异性,大部分地区的植被对昼夜增温表现为积极的响应,夜间增温对植被的影响要比白天增温对植被的影响是比较明显的;白天和夜间增温的不对称性影响着不同类型的植被,白天气温的升高有利于针叶林植被、草地和阔叶林植被的生长,而夜间气温的升高对灌丛和荒漠植被的生长有积极的影响。     

分散液液微萃取气相色谱-质谱法测定水中15种硝基苯类化合物

建立了超声辅助基质分散液液微萃取（UA-DLLME）作为前处理联合气相色谱串联质谱（GC-MS）同时测定地下水和地表水中15种硝基苯类化合物的分析方法。萃取剂、分散剂、萃取温度、萃取时间和离子强度等影响因素采用Plackett-Burman设计,快速筛选出最显著影响因素,利用中心组合设计（CCD）简化实验步骤优化显著因素,结合响应曲面图最终确定最佳的萃取条件：5 mL水样在3%氯化钠条件下迅速加入40 μL四氯化碳（萃取剂）和0.5 mL乙腈（分散剂）,50 ℃下超声4 min,混合液4 000 r/min离心3 min。结果表明：15种硝基苯类化合物在50.0~1 000.0 μg/L的浓度范围内线性相关系数均大于0.995;采用超声辅助基质分散液液微萃取时,方法检出限（MDL）为0.018~0.039 μg/L;15种目标物的加标平均回收率为83.31%~99.08%,相对标准偏差均不高于5.0%（n=6）。     

中国可再生能源政策演化、效果评估与未来展望

发展可再生能源已成为中国能源转型及减缓气候变化的关键途径。2006年《可再生能源法》实施至今,中国可再生发展取得举世瞩目成绩。然而可再生能源在中国整个能源结构中的占比仍然偏低,其未来可持续发展仍面临挑战,如可再生能源发展资金缺口持续扩大,可再生能源电力消纳仍然面临一定障碍。在未来新的发展背景下,有必要对中国可再生能源政策进行系统的评估和总结,进一步探讨未来可再生能源政策的优化方案。本文首先对中国2005—2019年可再生能源政策的发展历程及演化路径进行总结梳理,基于不同时期的发展特点及面临的主要矛盾将中国可再生能源政策历程划分为四个阶段;然后在综述相关研究基础上,对中国可再生能源政策的实施效果进行评估,包括政策有效性、政策成本、技术进步等。结果表明,中国过去十年的可再生能源政策对促进可再生能源快速发展是积极有效的,而促进可再生能源发展的政策成本仍然有进一步下降的空间;另外,虽然中国可再生能源发电成本下降显著,然而基于历史经验的技术进步率仍然不足以支撑实现中国2020年平价上网的目标,未来需要进一步推动可再生能源技术的进步和发电成本的下降。结合当前及未来中国可再生能源发展所处阶段的特点及面临挑战,我们提出进一步完善中国可再生能源政策以促进可再生能源持续稳定发展的政策建议,一是完善可再生能源绿色电力证书交易制度,增强可再生能源发展的内生激励并降低政策实施成本;二是建立储能技术市场机制,为减少弃电以及推动可再生能源消纳提供政策支持;三是推进碳金融体系建设,为可再生能源发展提供新的融资来源并降低发电成本。     

雅砻江流域中上游径流变化归因分析

在全球气候不断变化和人类活动的影响下,开展河川径流变化的归因研究对水资源的合理利用和分配具有现实意义。以雅砻江流域雅江水文站以上区域作为研究区,采用线性分析、Mann-Kendall检验法和累积距平法等方法分析了研究区1961～2015年水文气候要素的变化特征,进而基于Budyko假设水热耦合平衡原理的弹性系数法估算了径流变化对各影响因子的弹性系数,并对研究区的径流变化进行归因分析。结果表明:近55年来,研究区年径流深整体呈上升趋势,上升速率为0.496 mm/a,并在1999年发生了由低到高的显著突变。年降水量和潜在蒸散量分别呈上升和下降趋势,变化率分别为0.994和-0.246 mm/a。降水、潜在蒸散发和下垫面弹性系数分别为1.40、-0.40和-0.83,说明年径流对降水量的变化最为敏感,其次是下垫面变化,对潜在蒸散量变化的敏感度最低。年径流对气候变化的敏感度呈微增强趋势,对下垫面变化的敏感度逐步减弱。气候变化是年径流增加的主要因素,总贡献率为60.66%,其中降水量和潜在蒸散量的贡献率分别占59.21%和1.45%,下垫面变化的贡献率为39.34%,反映下垫面变化的NDVI自1999年后呈下降趋势,是导致径流增加的重要原因。     

四川省国家级自然保护区管理现状与对策建议

以2011年开展的四川省内23个国家级自然保护区管理状况的评估结果为基础,本文总结了四川省国家级自然保护区管理工作中的主要成绩经验和不足,并提出了相应的对策与建议。     

Understanding of regional air pollution over China using CMAQ,part I performance evaluation and seasonal variation

The U.S. EPA Models-3 Community Multiscale Air Quality (CMAQ) modeling system with the process analysis tool is applied to China to study the seasonal variations and formation mechanisms of major air pollutants. Simulations show distinct seasonal variations, with higher surface concentrations of sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter with aerodynamic diameter less than or equal to 10 μm (PM₁₀), column mass of carbon monoxide (CO) and NO₂, and aerosol optical depth (AOD) in winter and fall than other seasons, and higher 1-h O₃ and troposphere ozone residual (TOR) in spring and summer than other seasons. Higher concentrations of most species occur over the eastern China, where the air pollutant emissions are the highest in China. Compared with surface observations, the simulated SO₂, NO₂, and PM₁₀ concentrations are underpredicted throughout the year with NMBs of up to ?51.8%, ?32.0%, and ?54.2%, respectively. Such large discrepancies can be attributed to the uncertainties in emissions, simulated meteorology, and deviation of observations based on air pollution index. Max. 1-h O₃ concentrations in Jan. and Jul. at 36-km are overpredicted with NMBs of 12.0% and 19.3% and agree well in Apr. and Oct. Simulated column variables can capture the high concentrations over the eastern China and low values in the central and western China. Underpredictions occur over the northeastern China for column CO in Apr., TOR in Jul., and AODs in both Apr. and Jul.; and overpredictions occur over the eastern China for column CO in Oct., NO₂ in Jan. and Oct., and AODs in Jan. and Oct. The simulations at 12-km show a finer structure in simulated concentrations than that at 36-km over higher polluted areas, but do not always give better performance than 36-km. Surface concentrations are more sensitive to grid resolution than column variables except for column NO₂, with higher sensitivity over mountain and coastal areas than other regions.