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.     

The performance of nitrate-reducing Fe(II) oxidation processes under variable initial Fe/N ratios: The fate of nitrogen and iron species

•Bacterially-mediated coupled N and Fe processes examined in incubation experiments. •NO₃⁻ reduction was considerably inhibited as initial Fe/N ratio increased. •The maximum production of N₂ occurred at an initial Fe/N molar ratio of 6. •Fe minerals produced at Fe/N ratios of 1–2 were mainly easily reducible oxides. The Fe/N ratio is an important control on nitrate-reducing Fe(II) oxidation processes that occur both in the aquatic environment and in wastewater treatment systems. The response of nitrate reduction, Fe oxidation, and mineral production to different initial Fe/N molar ratios in the presence of Paracoccus denitrificans was investigated in 132 h incubation experiments. A decrease in the nitrate reduction rate at 12 h occurred as the Fe/N ratio increased. Accumulated nitrite concentration at Fe/N ratios of 2–10 peaked at 12–84 h, and then decreased continuously to less than 0.1 mmol/L at the end of incubation. N₂O emission was promoted by high Fe/N ratios. Maximum production of N₂ occurred at a Fe/N ratio of 6, in parallel with the highest mole proportion of N₂ resulting from the reduction of nitrate (81.2%). XRD analysis and sequential extraction demonstrated that the main Fe minerals obtained from Fe(II) oxidation were easily reducible oxides such as ferrihydrite (at Fe/N ratios of 1–2), and easily reducible oxides and reducible oxides (at Fe/N ratios of 3–10). The results suggest that Fe/N ratio potentially plays a critical role in regulating N₂, N₂O emissions and Fe mineral formation in nitrate-reducing Fe(II) oxidation processes.     

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。     

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.     

北京市典型废弃物焚烧源2010—2017年二NFDA1英排放特征和减排效果分析

分析2010—2017年北京市三类典型废弃物焚烧源的废气二NFDA1英排放监测数据,计算排放因子和排放量,评估减排政策成效,并分析不同排放源达标排放时同类物分布特征异同和变化规律,探讨影响排放的重要因素。结果表明: 5家焚烧源平均排放浓度为 0.008～0.069 ng/m³(以TEQ计,下同),废气二NFDA1英排放因子为 0.027～1.7 μg/t,2016年向空气中排放的二NFDA1英量为 0.002 5～0.058 g；生活垃圾、危险废物和医疗废物焚烧源的低、高氯代同类物质量分数比的平均值分别为接近于 0.5、大于0.5和小于0.5,危险废物焚烧源的 ∑PCDFs、∑PCDDs质量分数比的平均值大于2； 123478-HxCDF和123678-HxCDF质量浓度接近且线性相关,具有相近的生成机理和去除效率； I-TEQ变化趋势与∑PCDFs质量分数的变化趋势基本一致,活性炭喷射和布袋除尘的去除效率是影响二NFDA1英排放的重要因素之一；危险废物焚烧源HWI1随运行时间增加排放浓度增加,而及时更换烟道管壁有助于消除“记忆效应”的不良影响。     

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)     

美缝剂TVOC释放量与释放规律的测试与分析

提出了环境舱法测试美缝剂总挥发性有机化合物(TVOC)释放量的方法,对市场常见的美缝剂产品的TVOC进行了测试,并研究了美缝剂的TVOC释放规律和主要污染物.同时对美缝剂的甲醛释放量进行了测试.通过对16种环氧树脂美缝剂产品的数据分析显示,美缝剂产品的甲醛释放量风险较低;75%的产品在第7天的TVOC释放量可达到500μg·m^-3以下.但个别产品在28 d时仍有明显的VOC释放,显示其具有长期缓慢释放的特点.美缝剂释放的主要污染物为苯甲醛与苯甲醇,个别产品检出有甲苯和邻苯二甲酸酯类物质.     

资阳市大气污染源排放清单研究

采用实地调研、资料收集等方式获得了2017年资阳市典型污染源的活动水平数据,参照城市大气污染物排放清单编制技术手册建立了基于排放因子法和物料衡算法的资阳市大气污染源排放清单,分析了主要污染物的行业排放特征和空间分布特征。结果表明,2017年资阳市SO₂、NO_X、CO、PM₁₀、PM_2.5、VOCs、NH₃总排放量分别为3.58kt、13.91kt、94.91kt、25.51kt、8.67kt、23.84kt和46.44kt。SO₂排放主要来自工业源;NOX排放主要来自移动源;CO排放主要来自工业过程及移动源;PM₁₀和PM_2.5、排放来自扬尘源和露天秸秆焚烧;VOCs主要来自溶剂使用源;NH₃主要来自农业活动。资阳市主要污染物排放分布在工业点源较为集中的雁江区和安岳县,乐至县污染物排放量相对较小。     

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.