南岳森林土壤的研究——Ⅱ.化学性状和土壤有机质

本文介绍了南岳森林土壤与母岩的化学性状,以了解气候和植被对土壤的影响;介绍了土壤有机质的作用、含量、分布;讨论了影响土壤有机质含量的因素并给出了循环模型。     

杭州城市大气消光系数和能见度的影响因子研究

为了解杭州市能见度下降与大气污染之间的关系，在2001年5月至2002年5月对不同粒径的颗粒物（PM10、PM2.5）的质量浓度进行了观测，结合晴天天气条件下的大气能见度，推算污染物和水汽分子对大气的消光散射，发现细微颗粒物的散射消光特性对杭州市能见度下降起主要作用，并得到能见度与细微颗粒物浓度比值（PM2．5／PM10）的关系；分析了大气能见度和消光系数与PM2．5／PM10和相对湿度的相关系数。     

Synergistic effects of particulate matter (PM10) and SO2 on human non-small cell lung cancer A549 via ROS-mediated NF-κB activation

Since a real atmospheric scenario usually represents a system involving multiple pollutants, air pollution studies typically focused on describing adverse effects associated with exposure to individual pollutants cannot reflect actual health risk. Particulate matter(PM10) and sulfur dioxide(SO2) are two major pollutants derived from coal combustion processes and co-existing in coal-smoke air pollution, but their potentially synergistic toxicity remains elusive thus far. In this study, we investigated the cytotoxic responses of PM10 and SO2, singly and in binary mixtures, using human non-small cell lung cancer A549 cells, followed by clarifying the possible mechanisms for their interaction. The results indicated that the concomitant treatment of PM10 and SO2 at low concentrations led to synergistic injury in terms of cell survival and apoptosis occurrence, while PM10 and SO2 alone at the same concentrations did not cause damage to the cells. Also, radical oxygen species(ROS) production followed by nuclear factor kappa B(NF-κB) activation was involved in the above synergistic cytotoxicity, which was confirmed by the repression of the actions by an ROS inhibitor(NAC). This implies that assessment of health risk should consider the interactions between ambient PM and gaseous copollutants.     

Comparison of Four Machine Learning Methods for Predicting PM10 Concentrations in Helsinki, Finland

Machine learning methods can offer a practicalalternative to deterministic and statistical methods forpredicting air pollution concentrations. However, for agiven data set, it is often not clear beforehand whichmachine learning method will yield the best predictionperformance. This study compares the variable selection andprediction performance of four machine-learning methods ofdifferent complexity: logistic regression, decision tree,multivariate adaptive regression splines and neuralnetwork. The methods are applied to the task of predictingthe exceedance of the European PM₁₀ daily averageobjective of 50 g m^-3 for a station in Helsinki,Finland. Our study shows that some predictors were selectedby all models but that the different models also pickeddifferent variables. The performance of three of the fourmethods investigated was very similar, however, performanceof the decision tree method was significantly inferior.Performance was sensitive to the learning sample size andtime period used.     

蒸汽相变作用下不可溶PM10增长性能的数值模拟

目前,对颗粒凝结增长性能的数值模拟均忽略了其碰撞效应。为了对其进行补充,在相关研究的基础上,将颗粒群平衡模型（population balance module,PBM）与欧拉多相流模型进行耦合,通过用户自定义函数（user-defined function,UDF）同时引入碰撞团聚核函数和凝结增长速率函数,采用数值模拟,分析了初始蒸汽饱和度、停留时间、初始颗粒数浓度和初始粒径对不可溶PM10凝结增长性能的影响。模拟结果表明,初始蒸汽饱和度越大,颗粒凝结增长后的最终粒径越大;在一定范围内,延长停留时间,能够促进颗粒的凝结增长;初始颗粒数浓度的增大不利于颗粒的凝结增长;初始粒径越小,颗粒凝结增长性能越好,相变凝结的效果越明显。上述模拟结果与实验结果一致。     

Determination and analysis of PM10 source apportionment during episodes of air pollution in Central Eastern European urban areas: The case of wintertime 2006

Source apportionment of air pollution due to particulate matter with an aerodynamic diameter <10 μm (PM₁₀) was investigated in Central Eastern European urban areas. A combination of four methods was developed to distinguish long-range transport (LRT) and regional transport (RT) from local pollution (LP) sources as well as to discern the involvement of traffic or residential sources in LP. Sources of PM₁₀ events of pollution were determined in January 2006 in representative Polish cities using monitored air quality and meteorological data, backward air mass trajectories, correlation and principal component analysis (PCA). Daily patterns of PM₁₀ levels show that several peak episodes were registered in Poland; January 21–30th being the most polluted days. Air mass back-trajectory analysis shows that all cities were under the influence of LRT from North-eastern origins (Russia–Belarus–Ukraine), most were also under LRT from Southern origin (Slovakia, Czech Republic), and northern cities were under national RT influence. PCA analysis shows that ion-sums of secondary inorganic aerosols account for LRT pollution while arsenic and chromium represents markers of RT (industrial) and LP (residential) sources of PM₁₀, respectively. Determination of several ratios (REG/UB, REG/TRAF, TRAF/UB) calculated between PM₁₀ levels measured at regional background (REG); urban background (UB) and traffic (TRAF) monitoring sites shows that, with ratios REG/UB ≥ 0.57, PM₁₀ episodes in both Szczecin and Warsaw bore a marked RT origin. The lower REG/UB ≤ 0.35 in the Southern cities of Cracow and Zabrze indicates that LP was the main contributor to the observed episodes. Only PM₁₀ episodes in Southern-western Poland (Jelenia Góra) were clearly of LP origin as characterized, by the lowest REG/UB ratio (<0.2). The high TRAF/UB ratios obtained for all cities (close to 1) indicate that there was a great uniformity of PM levels on an urban scale owing to the meteorologically stagnant conditions. A high correlation between PM₁₀, NO₂ and CO confirms that traffic emission represented a common and an important LP source of urban pollution in most Polish cities during January 2006. On the other hand PM₁₀ which is also highly correlated with SO₂ in 4 cities out of 6, indicates that coal combustion through domestic heating or industrial activities was also an important LP source of PM₁₀. Finally, extremely unfavourable meteorological conditions caused by the influence of a Siberian high-pressure system were found to be associated with the occurrence of severe PM₁₀ episodes of pollution.     

大连市“十五”期间大气环境中SO2、NO2、PM10污染状况分析

根据大连市大气污染特点，通过对SO2、NO2、PM10年均值、月均值以及各功能区年均值和采暖期与非采暖期的污染状况进行玢析。得出大连市大气环境中SO2、NO2、PM10的主要来源及污染程度。结果表明，3项污染指标的年均值均符合国家环境空气质量二级标准：SO2呈缓慢增长趋势，NO2、PM10无明显变化趋势。     

某高密度街区大气颗粒物质量浓度分布特征实测研究

为研究城市高密度街区大气颗粒物浓度分布特征,2019年秋季对上海市某高密度街区道路大气颗粒物浓度、空气温度、相对湿度、地理位置、车辆与道路图像视频信息进行了同步移动在线监测,并结合街区内固定站数据和后向轨迹模拟结果,总结了影响街区大气颗粒物浓度变化的主要因素。结果表明:城市大气颗粒物背景拟合值处于较低水平时,街区内的大气颗粒物浓度变化和影响因素易被识别;机动车污染源对大气颗粒物浓度贡献大,其中大型机动车的影响明显;户外施工和道路清扫会引起大气颗粒物浓度上升,其中PM₁₀上升更明显;交通密度大的十字路口大气颗粒物浓度通常较高;城市高架的盖状结构会阻碍大气颗粒物在垂直方向上的扩散,引起局部大气颗粒物浓度上升;街区内高大浓密的乔木对近地面的大气颗粒物屏蔽效果不理想,甚至有助于颗粒物累积;早晚高峰时段大气颗粒物浓度较非高峰时段高。     

多模式模拟评估奥运赛事期间可吸入颗粒物减排效果

以空气质量多模式系统为工具,分析奥运赛事期间可吸入颗粒物(PM10)浓度大幅减小特征,从气象场和排放源两方面研究PM10浓度大幅减小的主要原因.多模式系统由嵌套网格空气质量模式(NAQPMS)、通用空气质量多尺度模式(CMAQ)和复杂大气空气质量三维模式(CAMx)3个空气质量复合模型组成,并以中尺度气象模式(MM5)和稀疏矩阵排放处理模型(SMOKE)提供统一气象场及排放源.研究对比2006年8月、2008年8月两组气象条件下北京PM10浓度水平及模拟效果,结果表明奥运赛事期间PM10浓度大幅减小的主要原因不是气象因素,而是由于额外措施引起的PM10排放减少.同时采用多模式系统数值模拟反向评估,获得北京奥运赛事期间奥运控制及额外减排措施引起的PM10减排量,结果表明,奥运赛事期间所有额外控制措施对颗粒物浓度效果相当于在2008年8月气象条件下,削减大约200t.d-1的无组织PM10排放,相当于北京正常时期PM10排放的50%.     

不同海拔高寒放牧草甸的生态系统呼吸与环境因子的关系

在全球变化的背景下,为了研究藏北高寒放牧草甸的生态系统呼吸和土壤呼吸特征,沿着3个海拔高度（4 300、4 500和4 700 m）观测了2010年7-9月白天的呼吸通量。同时,观测了同期的土壤温度、土壤水分含量、空气温度和相对湿度,在定性分析土壤水分含量和呼吸通量关系的基础上,将其分成低、中和高3个水平,在此基础上,分析生态系统呼吸、土壤呼吸与土壤温度、土壤水分含量、空气温度以及相对湿度的关系。结果表明,空气温度是决定生态系统呼吸和土壤呼吸变异的主导因子;生态系统呼吸、土壤呼吸以及裸地的土壤呼吸的Q10值分别为1.83～3.07、1.54～4.13和1.29～2.89;总体而言,生态系统呼吸和土壤呼吸Q10值随着海拔的升高和土壤水分含量的增加而增大。