Polycyclic aromatic hydrocarbons in benthos of the northern Bering Sea Shelf and Chukchi Sea Shelf

Eighteen polycyclic aromatic hydrocarbons (PAHs) were detected in benthos collected onboard the ‘Snow Dragon’ in the Northern Bering Sea Shelf and Chukchi Sea Shelf during the 6^th Chinese National Arctic Research Expedition (CHINARE 2014). Σ₁₈PAHs for all biota samples ranged from 34.2 to 128.1 ng/g dry weight (dw), with the highest concentration observed in fish muscle (Boreogadus saida) samples close to St. Lawrence Island. The PAH composition pattern was dominated by the presence of lighter 3 ring (57%) and 2 ring (28%) PAHs, indicating oil-related or petrogenic sources as important origins of PAH contamination. Concentrations of alkyl-PAHs (1-methylnaphthalene and 2-methylnaphthalene) were lower than their parent PAH (naphthalene) in all biological tissue, and their percentage also decreased significantly (p<0.05) compared with those in the corresponding sediment. There were no significant relationships between PAH concentrations and trophic levels, which is possibly due to the combined results of the complex benthic foodweb in the subarctic/Arctic shelf region, as well as a low assimilation/effective metabolism for PAHs. According to toxic potency evaluation results from TCDD toxic equivalents (TEQs) and BaP-equivalent (BaPE) values, whelk (Neptunea heros) and starfish (Ctenodiscus crispatus) are two macroinvertebrate species showing relatively higher dioxin-like toxicity and carcinogenic risk.     

Particulate matter exchange between atmosphere and roads surfaces in urban areas

The deposition and the re-suspension of particulate matter (PM) in urban areas are the key processes that contribute not only to stormwater pollution, but also to air pollution. However, investigation of the deposition and the re-suspension of PM is challenging because of the difficulties in distinguishing between the resuspended and the deposited PM. This study created two Bayesian Networks (BN) models to explore the deposition and the re-suspension of PM as well as the important influential factors. The outcomes of BN modelling revealed that deposition and re-suspension of PM10 occurred under both, high-traffic and low-traffic conditions, and the re-suspension of PM2.5 occurred under low-traffic conditions. The deposition of PM10 under low-volume traffic condition is 1.6 times higher than under high-volume traffic condition, which is attributed to the decrease in PM10 caused by relatively higher turbulence under high-volume traffic conditions. PM10 is more easily resuspended from road surfaces compared to PM2.5 as the particles which larger than the thickness of the laminar airflow over the road surface are more easily removed from road surfaces. The increase in wind speed contributes to the increase in PM build-up by transporting particulates from roadside areas to the road surfaces and the airborne PM2.5 and PM10 increases with the increase in relative humidity. The study outcomes provide a step improvement in the understanding of the transfer processes of PM2.5 and PM10 between atmosphere and urban road surfaces, which in turn will contribute to the effective design of mitigation measures for urban stormwater and air pollution.     

Analytical methods and environmental processes of nanoplastics

The degradation of plastic debris may result in the generation of nanoplastics (NPs). Their high specific surface area for the sorption of organic pollutions and toxic heavy metals and possible transfer between organisms at different nutrient levels make the study of NPs an urgent priority. However, there is very limited understanding on the occurrence, distribution, abundant, and fate of NPs in the environment, partially due to the lack of suitable techniques for the separation and identification of NPs from complex environmental matrices. In this review, we first overviewed the state-of-the-art methods for the extraction, separation, identification and quantification of NPs in the environment. Some of them have been successfully applied for the field determination of NPs, while some are borrowed from the detection of microplastics or engineered nanomaterials. Then the possible fate and transport of NPs in the environment are thoroughly described. Although great efforts have been made during the recent years, large knowledge gaps still exist, such as the relatively high detection limit of existing method failing to detect ultralow masses of NPs in the environment, and spherical polystyrene NP models failing to represent the various compositions of NPs with different irregular shapes, which needs further investigation.     

基于底栖动物的松花江流域不同地形分区水质指标阈值研究

为全面了解松花江流域不同地形分区内底栖动物群落对水质指标的响应规律,识别不同分区水质指标指示物种的差异,于2016—2018年对松花江流域97个采样点的水质指标〔EC、ρ（DO）、ρ（COD_Mn）、ρ（NH₃-N）、ρ（TN）、ρ（TP）〕和大型底栖动物群落进行调查分析,采用临界指示物种分析法（threshold indicator taxa analysis,TITAN）分别探讨松花江流域山区、丘陵区和平原区水质指标的生态阈值,当污染物浓度超过负响应阈值时敏感种密度降低,当超过正响应阈值时耐受种也会受到明显影响,底栖动物群落结构会发生显著变化.将TITAN法所得的负响应阈值作为触发底栖动物群落发生变化的最低值,正响应阈值为底栖动物群落的耐受极限值.结果表明:①松花江流域水质指标在不同地形分区内的阈值不同,除ρ（DO）和ρ（COD_Mn）外,其他指标负响应阈值均表现为山区 < 丘陵区 < 平原区,ρ（DO）则表现相反,ρ（COD_Mn）在丘陵区出现最高阈值（5.46 mg/L）、山区出现最低阈值（4.01 mg/L）.除ρ（DO）以外,其他指标的正响应阈值均呈山区 < 丘陵区 < 平原区的趋势,ρ（DO）正响应阈值的变化趋势则与之相反.②松花江流域内超过50%的采样点水质指标值均超过其负响应阈值,超出正响应阈值的采样点比例在6%~40%之间,说明流域受到一定的干扰,但干扰程度不严重.③同一物种在不同地形分区内对水体理化指标的指示方向可能相反.萝卜螺属在丘陵区为ρ（NH₃-N）的正响应指示物种,在平原区则转变为负响应指示物种;短沟蜷属在丘陵区为ρ（TN）和ρ（TP）的正响应物种,在平原区则转变为负响应物种.研究显示,大型底栖动物群落结构的分布特征是影响水质指标阈值指示物种识别的主要原因,而不同分区的自然地理状况、栖境状况和水质状况则是造成大型底栖动物群落结构分布差异的主要因素.      

北京市典型汽修企业VOCs排放特征与臭氧影响分析

对北京地区27家汽修企业进行调研,选取2家典型汽修企业进行气袋采样-GC-MS-FID采集及分析,定量分析其VOCs的排放特征,并计算其臭氧生成潜势（OFP）。结果表明:使用不同漆料的汽修企业排放特征不同,水性漆企业非甲烷总烃的排放浓度为0.62~36.49 mg/m3,油性漆企业的排放浓度为0~100.39 mg/m3;水性漆排放的VOCs以烷烃为主,占比高达57.16%,丙烷（39.65%）和甲苯（11.41%）是首要污染物;卤代烃（55.51%）是油性漆企业的主要VOCs排放物种,主要组分为1,2-二氯丙烷和1,2-二氯乙烷;水性漆企业的OFP值为144.78 mg/m3,油性漆企业的OFP值为664.43 mg/m3,大气反应活性最大的物种多为芳香烃,芳香烃对OFP的贡献率分别为52.18%和88.44%。     

UASB启动运行特征及互营丙酸氧化菌定量分析

为揭示升流式厌氧污泥床（UASB）反应器启动运行效能与互营丙酸降解菌群数量之间的关系,以稀释的玉米淀粉生产废水为底物,考察了UASB启动期的运行特征,并采用实时荧光定量PCR技术（qPCR）分析了互营丙酸降解菌群（丙酸氧化菌和产甲烷菌）的演替规律.结果表明,在进水COD 2000mg/L和水力停留时间（HRT）24h条件下,经过38d的连续运行,COD去除率达到了91.9%.当HRT分阶段缩短至8h时,比产甲烷速率达到了315LCH₄/（kg COD·d）,且形成了沉降性能良好的颗粒污泥.qPCR检测结果表明,至少有5种已鉴定的丙酸氧化菌存在于UASB反应器中.Pelotomaculum propionicum为接种污泥中的主要丙酸氧化菌,约占检测到丙酸氧化菌总数的45.7%.它的数量随着HRT缩短不断减少.而Syntrophobacter sulfatireducens和S.wolinii的数量随着HRT缩短不断增加,并在启动完成时达到最大值,分别为1.3×10³,5.5×10³个16S rRNA基因拷贝数/ng DNA,演替成为成熟颗粒污泥中的优势丙酸氧化菌群.Methanobacterium和Methanosarcina为接种污泥中的主要氢营养型产甲烷菌和乙酸营养型产甲烷菌,其数量随着HRT的缩短而逐渐减少,而Methanospirillum和Methanosaeta的数量随HRT的缩短逐渐增加,成为成熟颗粒污泥中的优势产甲烷菌群.     

烟花爆竹燃放对大气污染物及PM2.5组分影响

为了解烟花爆竹燃放对保定市大气污染物和PM_2.5中水溶性离子及有机碳（OC）、元素碳（EC）浓度的影响,对保定市春节期间大气污染物和颗粒物组分的浓度特征进行了分析,并评估了烟花爆竹的贡献.结果表明： 2019年春节期间烟花爆竹集中燃放期PM_2.5、PM₁₀、SO₂、NO₂、CO平均浓度比非集中燃放期分别增加了1.3、1.0、1.1、0.4、0.02倍;保定市春节期间禁燃措施施行后,除夕、初一2d污染物平均浓度、最高浓度和高浓度持续时间均明显下降,集中燃放期烟花爆竹燃放对PM_2.5、PM₁₀和SO₂浓度贡献量从50%左右（2018年、2017年）下降至30%左右（2019年）,其中SO₂贡献量下降幅度超过PM_2.5和PM₁₀;组分分析表明,接待中心站点（主城区）、涿州站点（区县建成区）烟花爆竹燃放期K⁺、Mg²⁺、Cl^-浓度在水溶性离子中的总占比分别为39.3%、51.1%,比非燃放期的占比显著上升;烟花爆竹燃放对PM_2.5中K⁺、Mg²⁺、Cl^-浓度贡献率在50%以上,其中对K⁺贡献占比高达89.0%,涿州站点SO₄^2-、K⁺、Mg²⁺、Cl^-的贡献量分别是接待中心站点的2.2、2.1、1.9、1.8倍,燃放期硫氧化率（SOR）、氮氧化率（NOR）相比于非燃放期均有一定程度的升高;集中燃放期OC、EC浓度较非集中燃放期分别升高了2.5、2.1倍,烟花爆竹燃放对OC影响大于EC.     

典型西南工业城市春冬季PM2.5来源与潜在源区分析——以柳州市为例

为了揭示柳州城区春冬季PM_2.5的来源及其潜在源区分布和贡献,利用2018年24h自动监测数据和气象数据对柳州市大气污染物浓度变化特征进行了分析,并且使用后向轨迹模型（HYSPLIT）对春冬季柳州市PM_2.5逐日72h气流后向轨迹和前向轨迹进行聚类分析,同时结合潜在源贡献因子分析法（WPSCF）和轨迹浓度权重法（WCWT）对其潜在源区和浓度贡献进行了分析.结果显示,（1）在研究期内,不利的主导风向和工业区布局导致研究区PM_2.5在春冬季污染较严重,且工业源和交通源是其主要本地来源;（2）春冬季PM_2.5高值主要来源于西北和东南方向,其中,西北向PM_2.5主要来源于本地排放,且浓度在空间上呈现西高东低的趋势;（3）春季后向轨迹PM_2.5浓度整体大于冬季,春冬季中对柳州市PM_2.5影响最大轨迹均来自东部的短距离输送,而来自西北的气流轨迹输对PM_2.5贡献最低.春冬季柳州市大气PM_2.5通过气流传输对贵州地区大气环境有较大影响;（4）春季,柳州市PM_2.5的主要潜在源区分布在广西东南部、广东中西部、南海沿岸海域、湖南中部、江西西北部、湖北东部及安徽西北部;冬季,主要分布在广西东南部、广东西南部和南海沿岸海域.     

洪泽湖不同入湖河流沉积物磷形态特征及生物有效性

为揭示洪泽湖入湖河流沉积物磷形态空间差异性及影响因素,分析了洪泽湖自西北向西南7条入湖河流65个表层沉积物中不同磷（P）形态,并探讨了磷形态空间赋存特征的影响因素及环境意义.研究表明：沉积物总磷（TP）含量为488.90mg/kg~960.22mg/kg,无机磷（Pi）为主要形态,相对含量为65.81%~76.16%.西部入湖流域沉积物有机磷（Po）以非活性有机磷（NLOP）为主,汴河最高,相对含量约占Po的50.41%,生物有效态无机磷（BAP）相对含量最高,占Pi的66.84%,污染程度最高;西南和西北入湖流域Po则以中活性有机磷（MLOP）为主,Pi以钙结合态无机磷（HCl-Pi）为主.西北入湖流域由于受当地地质背景的影响,HCl-Pi所占Pi相对含量最高（43.02%）,从而减缓了磷的移动能力,污染程度最低.随着沉积物污染程度的增加,生物有效态Po含量增加,但所占Po相对含量降低;HCl-Pi含量增加,所占Pi相对含量降低,这一现象和我国其它典型地区沉积物磷形态空间分布类似.西部和西南入湖流域主要受水土流失、有机面源污染及藻类生长的影响,有机质环境较高,水交换能力弱,可被有机质降解的Po组分高于可被矿化的Po组分,大部分难降解Po组分易沉积,导致西部和西南入湖流域较高的BAP和NLOP含量,富营养化程度较高.沉积物OM是各形态磷之间相互转化的关键因素,和沉积物内源磷地球生物化学循环密切相关.洪泽湖入湖流域沉积物磷形态空间差异性主要由农业面源污染物的输入而导致内源磷负荷加剧.洪泽湖西部和西南入湖流域应重点控制农田水土流失及养殖业面源污染,建设滨岸修复带,遵循少量多次增施有机肥原则,减少农用地水土流失.健全农村养殖业废水废渣处理;划定科学养殖区;提倡铜围网箱,增加水体交换率.而对于洪泽湖西北入湖流域则应重点防止过度城镇化带来的水土流失及对生态功能保护区过高的污染负荷.     

融合Lamb-Jenkinson分型法和LSTM神经网络的PM2.5预测研究

文章采用NCEP/NCAR逐日海平面气压场资料,利用Lamb-Jenkinson环流分型法对张家口大气环流进行分型,分析环流型与PM_(2.5)质量浓度之间的关系,并针对PM_(2.5)浓度的预测,提出一种融合Lamb-Jenkinson环流分型和LSTM神经网络混合模型的方法,即以环流指数为预测因子基于LSTM方法搭建PM_(2.5)质量浓度的预测模型。结果表明:影响张家口地区的主要环流型有反气旋型、气旋型、偏北平直型、西南平直型、偏西平直型、东北平直型等、西北平直型、偏东平直型。PM_(2.5)污染日出现的主要环流型为南气旋平直型、东南平直型、偏南平直型、偏东气旋型、西南气旋平直型、偏东平直型、气旋型等,而反气旋型和反气旋式平直环流型不利于污染出现。张家口地区的PM_(2.5)污染与地面环流有着密切的联系,当存在PM_(2.5)污染时,张家口地区处于日本海高压后部的均压场区域,污染越严重,日本海高压中心强度越强。模型预测结果的均方根误差为9.88、平均绝对误差为5.84、拟合优度达0.80,表明该模型具有一定的预报能力。