阳澄湖水源中典型嗅味物质测定与来源分析

以阳澄湖水源为研究对象,采用固相微萃取-气质联用法测定水中2-甲基异莰醇(2-MIB)、土臭素(GSM)、2,4,6-三氯苯甲醚(2,4,6-TAC)、2,3,4-三氯苯甲醚(2,3,4-TAC)、2,3,6-三氯苯甲醚(2,3,6-TAC)、2-异丙基-3甲氧基吡嗪(IPMP)、2-异丁基-3甲氧基吡嗪(IBMP)、β-环柠檬醛、β-紫罗兰酮、异氟尔酮等10种典型嗅味物质,并分析可能的来源藻类.以2018年1月-12月阳澄湖水源中优势藻种为基础,建立以上述嗅味物质为变量的多元线性回归模型.结果表明,水源中2-MIB、GSM、β-环柠檬醛、β-紫罗兰酮、异氟尔酮5种嗅味物质与直链藻、针杆藻、鱼腥藻、色球藻、颤藻、微囊藻、束丝藻7种藻类有较强的相关性.     

达州市城区环境空气质量变化趋势及CMAQ模型预报分析

利用Spearman秩相关系数法、污染日历图、浓度分析法和CMAQ预测模型研究了达州市城区2015—2019年空气质量状况。结果表明:2015—2019年,达州市城区O_3浓度变化趋势为显著上升(P0.05),季度变化明显,8月易发生因O_3超标导致的轻度污染状况;CO年均值变化趋势为显著降低(P0.05);NO_2年均值呈上升趋势,但尚未达到显著水平(P0.05);SO_2、PM_(10)和PM_(2.5)年均值呈下降趋势,但变化趋势不明显(P0.05)。2019年,1月和12月污染最重,PM_(2.5)超标是主因,PM_(10)和PM_(2.5)年均值达标形势严峻,全年一半以上天数的PM_(2.5)浓度超过年均值二级标准限值,PM_(10)也近半;NO_2年均值达标形势严峻,全年212 d超过年均值二级标准限值。CMAQ模型对不同污染指标的预测准确率不同,预测PM_(2.5)浓度、首要污染物和空气质量等级时的准确率不及人工预测,预测AQI时的准确率高于人工预测,更多污染指标的预测比较还有待进一步研究。     

贵阳市城区PM10和PM2.5水溶性离子特征分析

分别于2013年10月和2014年2月、5月、7月在贵阳市城区3个环境空气质量监测国控点位（南明区市监测站、云岩区黔灵公园马鞍山和观山湖区贵阳一中）进行PM₁₀、PM_2.5样品采集，并对10种水溶性离子（SO₄^2-、NO₂^-、NO₃^-、NH₄⁺、Cl^-、F^-、Na⁺、K⁺、Mg²⁺、Ca²⁺）的含量进行了分析。结果表明，研究时段内，贵阳市3个点位PM₁₀、PM_2.5平均质量浓度分别为（64.8±25.5）、（46.6±21.2）μg/m³。其中，云岩区黔灵公园马鞍山点位的颗粒物浓度最低，南明区市监测站点位最高。3个点位PM_2.5平均浓度与PM₁₀平均浓度的比值为0.719，表明贵阳市城区PM₁₀中，PM_2.5占主导地位。水溶性离子分析显示，SO₄^2-、NO₂^-、NO₃^-、NH₄⁺、Cl^-、F^-、Na⁺、K⁺主要分布在PM_2.5中，Mg²⁺、Ca²⁺主要分布在PM₁₀中。3个点位PM₁₀和PM_2.5中的水溶性离子均表现为SO₄^2-、NH₄⁺、Ca²⁺浓度较大，F^-、NO₂^-较小，表明3个点位的污染源总体相同，且水溶性离子占PM₁₀、PM_2.5含量的比例达33.6%~48.1%。贵阳市城区大气中的SO₂转化率在5月、7月、10月较高，2月最低，主要是由于5月、7月、10月的高温、高湿、强辐射环境条件促进了SO₂向SO₄^2-的转化。阴阳离子平衡分析表明，贵阳市城区PM₁₀、PM_2.5呈现出偏碱性的特征。水溶性离子主成分分析表明，贵阳市城区PM₁₀中的水溶性离子主要来源于城市扬尘、生物质燃烧尘、煤烟尘、建筑尘以及二次粒子，PM_2.5中水溶性离子的来源与PM₁₀较为相似。     

北京市城区挥发性有机物污染特征及其对臭氧影响分析

对2020年4月—2021年3月北京市建成区挥发性有机物（VOCs）的化学特征、污染来源及其对臭氧（O₃）污染的影响进行了研究。结果显示：O₃日最大8 h滑动平均值在臭氧季（4—9月）均值为134 μg/m³，是非臭氧季（10月至次年3月）均值（59.6 μg/m³）的2.2倍。臭氧季VOCs体积浓度均值为14.3×10^-9，明显低于非臭氧季（21.1×10^-9），可能与光化学反应速率和VOCs来源的季节性差异有关。臭氧生成潜势（OFP）贡献率排名前10位的物种在臭氧季和非臭氧季相似，均包括间/对-二甲苯、甲苯、乙烯、邻二甲苯、异戊烷、正丁烷、丙烯、反式-2-丁烯和1，2，4-三甲基苯，但排名有所差异，燃煤源特征明显的乙烯等物种在非臭氧季上升明显，与溶剂使用、油气挥发相关的间/对二甲苯、甲苯、异戊烷和正丁烷等物种在臭氧季上升明显。甲苯/苯的值和异戊烷/正戊烷的值在臭氧季明显高于非臭氧季，反映出机动车排放和油气挥发等在臭氧季影响突出，非臭氧季是燃煤影响显著。基于正交矩阵因子分解模型（PMF），臭氧季解析出机动车尾气排放（40.9%）、溶剂使用（20%）、油气挥发（16.4%）、工业排放（17.6%）和植物排放（5.1%）等5种污染源；非臭氧季解析出机动车尾气（38.9%）、燃烧源（26.3%）、工业排放（17.8%）和溶剂使用（17%）等4种污染源。     

Identifying robust strategies for assisted migration in a competitive stochastic metacommunity

Assisted migration (AM) is the translocation of species beyond their historical range to locations that are expected to be more suitable under future climate change. However, a relocated population may fail to establish in its donor community if there is high uncertainty in decision-making, climate, and interactions with the recipient ecological community. To quantify the benefit to persistence and risk of establishment failure of AM under different management scenarios (e.g., choosing target species, proportion of population to relocate, and optimal location to relocate), we built a stochastic metacommunity model to simulate several species reproducing, dispersing, and competing on a temperature gradient as temperature increases over time. Without AM, the species were vulnerable to climate change when they had low population sizes, short dispersal, and strong poleward competition. When relocating species that exemplified these traits, AM increased the long-term persistence of the species most when relocating a fraction of the donor population, even if the remaining population was very small or rapidly declining. This suggests that leaving behind a fraction of the population could be a robust approach, allowing managers to repeat AM in case they move the species to the wrong place and at the wrong time, especially when it is difficult to identify a species’ optimal climate. We found that AM most benefitted species with low dispersal ability and least benefited species with narrow thermal tolerances, for which AM increased extinction risk on average. Although relocation did not affect the persistence of nontarget species in our simple competitive model, researchers will need to consider a more complete set of community interactions to comprehensively understand invasion potential.     

三峡库区笋溪河流域面源污染及其与土壤可蚀性k值的关系

研究三峡库区面源污染特征及其与水土流失的关系,可为库区氮磷污染和土壤侵蚀控制提供依据.选择三峡库区库尾笋溪河流域,在流域内分园地、林地和耕地3种土地利用类型共采集126个土壤样品,并在主干和支流采集52个水质样品.根据EPIC模型计算土壤可蚀性k值,分析流域内土壤可蚀性k值对面源污染的影响.结果表明,笋溪河流域面源污染主要是氮污染,总氮均值达1.37 mg/L,氮素的主要形态为硝态氮,占总氮的71.2%;总磷浓度为0.1 mg/L.流域内土壤可蚀性k值均值为0.040,随着土层加深土壤可蚀性k值呈上升趋势;林地土壤可蚀性k值显著低于园地和耕地.笋溪河流域总氮浓度与园地和耕地0-20 cm土壤可蚀性k值有关,硝态氮浓度与耕地0-40 cm土壤可蚀性k值有关.因此,笋溪河流域面源污染严重,主要来源是耕地和园地,应实行免耕、植物篱等措施,同时减少化肥施用,增加有机肥比例,以增加土壤抗侵蚀能力,进而控制流域水土流失和面源污染.(图6参37)     

2016—2017年武汉市城区大气PM2.5污染特征及来源解析

利用2016年1月至2017年9月湖北省环境监测中心站大气复合污染自动监测站的在线监测数据，对武汉市城区PM_2.5的污染特征及主要来源进行解析。结果表明，武汉市城区PM_2.5质量浓度呈现出明显的季节差异，季节变化规律为冬季>春季>秋季>夏季。水溶性离子的主要成分SO₄^2-、NO₃^-和NH₄⁺占总离子质量浓度的82.0%。PM_2.5中阴离子相对阳离子较为亏损，颗粒整体呈碱性。夏季气态污染物的氧化程度较高且SO₂较NO₂氧化程度高。后向轨迹分析结果表明，区域传输是武汉市PM_2.5的一个重要来源，在4个典型重污染阶段，武汉市分别受到局地、东北、西北及西南方向气团传输的影响。PMF模型解析出武汉市PM_2.5五大主要来源及平均贡献率：扬尘22.0%、机动车排放27.7%、二次气溶胶21.6%、重油燃烧14.9%和生物质燃烧13.8%。     

Effects of site-selection bias on estimates of biodiversity change

Estimates of biodiversity change are essential for the management and conservation of ecosystems. Accurate estimates rely on selecting representative sites, but monitoring often focuses on sites of special interest. How such site-selection biases influence estimates of biodiversity change is largely unknown. Site-selection bias potentially occurs across four major sources of biodiversity data, decreasing in likelihood from citizen science, museums, national park monitoring, and academic research. We defined site-selection bias as a preference for sites that are either densely populated (i.e., abundance bias) or species rich (i.e., richness bias). We simulated biodiversity change in a virtual landscape and tracked the observed biodiversity at a sampled site. The site was selected either randomly or with a site-selection bias. We used a simple spatially resolved, individual-based model to predict the movement or dispersal of individuals in and out of the chosen sampling site. Site-selection bias exaggerated estimates of biodiversity loss in sites selected with a bias by on average 300–400% compared with randomly selected sites. Based on our simulations, site-selection bias resulted in positive trends being estimated as negative trends: richness increase was estimated as 0.1 in randomly selected sites, whereas sites selected with a bias showed a richness change of −0.1 to −0.2 on average. Thus, site-selection bias may falsely indicate decreases in biodiversity. We varied sampling design and characteristics of the species and found that site-selection biases were strongest in short time series, for small grains, organisms with low dispersal ability, large regional species pools, and strong spatial aggregation. Based on these findings, to minimize site-selection bias, we recommend use of systematic site-selection schemes; maximizing sampling area; calculating biodiversity measures cumulatively across plots; and use of biodiversity measures that are less sensitive to rare species, such as the effective number of species. Awareness of the potential impact of site-selection bias is needed for biodiversity monitoring, the design of new studies on biodiversity change, and the interpretation of existing data.     

我国居住建筑室内PM_(2.5)研究现状及进展

分析了我国居住建筑室内PM_(2.5)污染水平,总结了现阶段PM_(2.5)的研究方法,主要包括理论分析法、数值模拟法和实验测试法。指出室外源是室内PM_(2.5)的主要污染来源,而室内烟草烟雾、烹饪及人员活动也会严重影响室内PM_(2.5)浓度。针对目前研究中存在的问题,提出了标定典型建筑在不同影响因素下的I/O比范围、研究家具和家电的颗粒物释放状况、加强农村室内外空气颗粒物污染调查等建议。