RESEARCH: Shrub Propagation Techniques for Biological Control of Invading Tree Species

Cornus stolonifera, Salix petiolaris, and Spiraea alba clones already located within the corridor of an electrical power line. To establish the efficiency of treatments, we examined the statistical differences of growth traits between species and treatments. An analysis of the effects of layering shows, after the first growth season, differences for all growth traits in only one species, Spiraea alba. After the second growth season, we observed the development of new aerial stems. Layering favors horizontal expansion of shrubs over height development. The third year after treatment, the effect of layering is reduced except for Cornus stolonifera, which continuously increases, as shown by the significant progression of the clone issued from the layer even five years after treatments. With the cutting back technique, we expected a distinct vertical growth of the shrubs at the expense of increasing the crown diameter. This technique would be best associated with the rejuvenation of clones, followed by a layering of new shoots to allow a horizontal expansion of the shrubs. Therefore, the formation of a dense shrub community by layering should be considered a valuable approach for the biological control of undesirable trees in powerline rights-of-way.     

Exploring Chemical Variables in Ligustrum lucidum Ait. F. Tricolor (Rehd.) Rehd. in Relation to Air Pollutants and Environmental Conditions

Ligustrum lucidum Ait. f. tricolor (Rehd.) Rehd. in relation to atmospheric pollutants in Córdoba city, Argentina. The study area receives regional pollutants and was categorized taking into account traffic level, industrial density, type of industry, location of the sample point in relation to the street corner, treeless condition, and topographic level. Dried weight/fresh weight ratio (DW/FW) and specific leaf area (SLA) were calculated, and concentrations of chlorophylls, carotenoids, total sulfur, soluble proteins, malondialdehyde (MDA), and hydroperoxy conjugated dienes (HPCD) were determined in leaf samples. Sulfur content correlates positively with traffic density and SLA correlates negatively with some combinations of the categorical variables; MDA correlates positively with topographic level and total protein concentration correlates negatively with treeless condition. On the basis of our results, traffic, location of trees, type of industry, situation of a tree with respect to others, and topographic level are the environmental variables to bear in mind when selecting analogous sampling points in a passive monitoring program. An approximation to predict tree injury may be obtained by measuring DW/FW ratio, proteins, pigments, HPCD, and MDA as they are responsible for the major variability of data.     

Dynamics and Causation of Environmental Equity, Locally Unwanted Land Uses, and Neighborhood Changes

/ Why are some environmental risks distributed disproportionately in the neighborhoods of the minorities and the poor? A hypothesis was proposed in a recent study that market dynamics contributed to the current environmental inequity. That is, locally unwanted land uses (LULUs) make the host communities home to more poor people and people of color. This hypothesis was allegedly supported by a Houston case study, whereby its author analyzed the postsiting changes of the socioeconomic characteristics of the neighborhoods surrounding solid waste facilities. I argue that such an analysis of postsiting changes alone is insufficient to test the causation hypothesis. Instead, I propose a conceptual framework for analysis of environmental equity dynamics and causation. I suggest that the presiting neighborhood dynamics and the characteristics of control neighborhoods be analyzed as the first test for the causation hypothesis. Furthermore, I present theories of neighborhood change and then examine alternative hypotheses that these theories offer for explaining neighborhood changes and for the roles of LULUs in neighborhood changes. These alternative hypotheses should be examined when analyzing the relationship between LULUs and neighborhood changes in a metropolitan area. Using this framework of analysis, I revisited the Houston case. First, I found no evidence that provided support for the hypothesis that the presence of LULUs made the neighborhoods home to more blacks and poor people, contrary to the conclusion made by the previous study. Second, I examined alternative hypotheses for explaining neighborhood changes-invasion-succession, other push forces, and neighborhood life-cycle; the former two might offer better explanation.KEY WORDS: Environmental equity and justice; Locally unwanted lane uses; Siting; Market dynamics; Invasion-succession; Neighborhood changes     

长春秋季生物质燃烧对PM2.5中WSOC吸光性的影响

为探究长春秋季生物质燃烧对PM_(2.5)中水溶性有机碳(water-soluble organic carbon,WSOC)吸光性的影响,于2017年10～11月进行PM_(2.5)样品采集,对PM_(2.5)中碳质组分、糖类化合物和WSOC的光吸收特征参数进行分析.研究表明:长春秋季PM_(2.5)中WSOC、有机碳(organic carbon,OC)、元素碳(elemental carbon,EC)的平均浓度分别为(10.12±3.47)、(17.07±5.64)和(1.34±0.75)μg·m~(-3),二次有机碳(secondary organic carbon,SOC)对OC的平均贡献率为38.93%.长春秋季总糖浓度为(1 049.39±958.85)ng·m~(-3),其中作为生物质燃烧示踪剂的脱水糖含量(左旋葡聚糖、半乳聚糖和甘露聚糖)在总糖中占比为91.69%,糖类相关性分析结果显示生物质燃烧源为长春秋季大气中糖类物质的主要贡献源.糖类物质的相关性分析及3种脱水糖的特征比值研究显示,作为长春秋季大气主要污染源的生物质燃烧的类型是硬木和作物残渣的燃烧.长春秋季WSOC的光吸收波长指数(AAE)为5.75±1.06,单位质量吸收效率(MAE)为(1.23±0.28)m~2·g~(-1),表明生物质燃烧对WSOC吸光性具有重要影响.利用生物质燃烧特征源参数量化计算生物质燃烧对WSOC浓度的贡献达58.82%,对总WSOC光吸收的贡献达40.92%.     

郑州市春季大气污染过程VOCs特征、臭氧生成潜势及源解析

利用GC5000在线气相色谱仪于2018年4月15日～5月15日对郑州市城区环境大气挥发性有机物(VOCs)进行监测,开展其污染特征、臭氧生成潜势(OFP)和来源解析研究.结果表明,监测期间,郑州市春季VOCs平均体积分数为40.26×10~(-9),非污染日和污染日VOCs平均体积分数分别为35.82×10~(-9)和44.12×10~(-9),污染日相较非污染日增长23%;VOCs物种对OFP的贡献表现为烯烃芳香烃烷烃炔烃;源解析结果显示监测期间郑州市VOCs主要来源是LPG源(66.05%)、机动车源(47.39%)、工业溶剂源(37.51%)、燃烧源(37.80%)和植物排放源(11.25%),且污染日的LPG源和植物排放源的贡献率较非污染日增长22.92%和68.50%.     

供水管网生物膜有机物的二氯乙腈与二氯乙酰胺生成特性

在饮用水输配系统中,来源于管壁生物膜的有机物可能耗氯并生成消毒副产物(DBPs),包括二氯乙腈(DCAN)与二氯乙酰胺(DCAcAm)等高毒性含氮DBPs(N-DBPs).研究考察管网常见的细菌与其胞外聚合物(EPS)以及模拟管壁生物膜氯化与氯胺化后DCAN与DCAcAm的生成,并与天然有机物(NOM)和水源水有机物进行比较.结果显示,铜绿假单胞菌、恶臭假单胞菌与藤黄微球菌与氯反应生成的DCAN、DCAcAm浓度分别为1.48～2.02、0.21～0.38μg·mg~(-1)(mg~(-1)以TOC计),高于同反应条件下NOM的生成量;相比于氯化反应,3株细菌细胞氯胺化生成的DCAN与DCAcAm浓度明显更低.3株菌的EPS也是氯与氯胺化反应生成DCAN与DCAcAm的前体物,且其氯胺化反应生成的DCAcAm浓度高于氯化反应生成的.与NOM、水源水相比,模拟管壁生物膜氯化后生成的N-DBPs与三氯甲烷(TCM)浓度比更高,表明生物膜有机物比NOM与水源水有机物更倾向生成DCAN与DCAcAm类N-DBPs,且模拟管壁生物膜氯胺化的DCAcAm生成量高于氯化反应的,说明管壁生物膜有机物是供水管网系统中DCAN与DCAcAm类N-DBPs的重要前体物.     

北京南部城区PM2.5中碳质组分特征

为了解《大气污染防治行动计划》实施后北京市大气PM_2.5中碳质组分特征,于2017年12月至2018年12月在北京污染较重的南部城区进行了PM_2.5连续采样,对其中的有机碳（OC）和元素碳（EC）进行了全面研究.结果表明,北京大气PM_2.5、OC和EC浓度变化范围分别为4.2~366.3、0.9~74.5和0.0~5.5 μg ·m^-3,平均浓度分别为（77.1±52.1）、（11.2±7.8）和（1.2±0.8）μg ·m^-3,碳质组分（OC和EC）整体占PM_2.5的16.1%.OC质量浓度季节特征表现为：冬季[（13.8±8.7）μg ·m^-3] > 春季[（12.7±9.6）μg ·m^-3] > 秋季[（11.8±6.2）μg ·m^-3] > 夏季[（6.5±2.1）μg ·m^-3],EC四季质量浓度水平均较低,范围为0.8~1.5 μg ·m^-3.二次有机碳（SOC）年均质量浓度为（5.4±5.8）μg ·m^-3,四季贡献比例范围为45.7%~52.3%,年均贡献为48.2%,凸显了二次形成的重要贡献.随污染加重,尽管OC和EC贡献比例均降低,但浓度水平却成倍升高,OC和EC浓度在严重污染天分别是空气质量为优天的6.3和3.2倍.与非供暖时段相比,供暖时段PM_2.5、OC和SOC浓度分别增加了14.4%、47.9%和72.1%,体现了OC对供暖季PM_2.5污染的重要贡献.PSCF分析表明,位于北京西南的山西省和河南省部分区域是PM_2.5和OC的主要潜在源区,且PM_2.5潜在源区更为集中;EC的PSCF高值（>0.7）区域较少,主要位于北京南部,如山东省和河南省部分地区,且北京市及周边地区贡献明显.     

深圳大气PM2.5中水溶性有机物的来源特征

对2017年9月~2018年8月深圳市北部大气PM_2.5中水溶性有机物（WSOM）的质量浓度、质谱及来源结构进行测量和分析.结果表明：PM_2.5的质量浓度为（32.3±18.4）μg/m³,WSOM的质量浓度为（9.4±5.7）μg/m³,占颗粒物总有机物的（77.6%±14.0%）.质谱分析显示,WSOM的氧碳比（O/C）平均值达到（0.57±0.09）,属于二次有机物的O/C值范围,且生物质燃烧排放的离子碎片C₂H₄O₂⁺的丰度显著,说明WSOM的来源中有显著的生物质燃烧排放的有机气溶胶.为了明确WSOM的来源结构,利用正矩阵因子分解法（PMF）模型进行来源解析,发现3个合理因子：高氧化态有机气溶胶（MO-OOA）,低氧化态有机气溶胶（LO-OOA）和生物质燃烧（BBOA）,贡献比例分别为51.7%,31.8%和16.5%.MO-OOA和BBOA贡献浓度均呈现秋冬高、春夏低的季节变化特征,反向轨迹分析显示其与内陆污染传输关系密切.LO-OOA的变化相对稳定,本地源的贡献较大.结合¹⁴C同位素示踪法对秋冬季WSOM样品分析,发现机动车等化石源二次有机物是WSOM的主要来源,贡献比例达到53.9%,需继续加强对化石燃料控制来降低WSOM污染.     

NOx存在条件下相对湿度对甲苯氧化产物的影响

利用流动管反应器模拟甲苯与氧化剂×OH在NO_x存在条件下的反应,定量测定了不同相对湿度条件下（17.5%、35%、50%、70%）反应生成的部分气相产物和颗粒相产物,计算了不同相中产物产率,测量了不同相对湿度下的颗粒相有机碳（OC）产率,推导了相对湿度对甲苯氧化反应的影响机制.结果表明,相对湿度不仅对甲苯与×OH反应途径比例有影响,还对产物的产率及氧化程度有影响.     

金属表面涂装行业VOCs排放特征及排放系数

对比了浙江省2014和2018年金属表面涂装企业的有机废气排放及治理情况,分析了该行业涂料及稀释剂的使用、主要污染因子,测算了溶剂型、水性涂料的挥发性有机物（VOCs）产生系数和排放系数.结果表明：2018年VOCs治理水平明显高于2014年,水性涂料使用企业比例由18%上升至36%,纯溶剂型企业由82%下降至64%;金属表面涂装行业的主要排放污染物为二甲苯、丁醇、乙酸乙酯、乙酸丁酯、甲苯、丙二醇、乙苯、苯乙烯等8种有机物.溶剂型和水性涂料的VOCs产生系数分别为0.72和0.31kg/kg;溶剂型和水性涂料2014年VOCs排放系数为0.64和0.29kg/kg,2018年为0.48和0.21kg/kg.