COVID-19疫情期间雄安地区VOCs的变化特征、臭氧生成潜势及来源解析

利用挥发性有机物(VOCs)在线监测数据对新冠肺炎疫情(COVID-19)期间(2019年12月25日～2020年2月24日)雄安地区环境空气中VOCs进行监测,探讨了疫情防控前、后VOCs的变化特征、臭氧生成潜势及来源解析.结果表明,疫情防控后φ(TVOCs)平均值为45.1×10-9,约为疫情防控前φ(TVOCs)...     

基于特定源风险评估模型的小麦籽粒铅超标风险预测

系统分析重金属铅（Pb）在"源-土壤-小麦"传输途径中的累积特征是小麦Pb污染防治的关键.以河南省济源市为例,在区域调查的基础上,耦合正定矩阵因子分解法、Freundlich回归方程和Monte Carlo随机模拟方法,构建特定源风险评估模型（SRAM）,预测不同场景下小麦籽粒Pb累积风险,并结合空间分析方法对区域污染防治措施进行评估和优化.结果表明,大气沉降和磷肥应用是区域农田土壤Pb污染的主要来源,贡献了小麦籽粒Pb超标累积的29.0%.土壤pH和阳离子交换量（CEC）是影响小麦籽粒Pb累积的关键土壤因子.在受大气污染影响显著的高风险区域（研究区西北和西部）,通过相关措施提升土壤阳离子交换量（~20 cmol·kg^-1）可将大气沉降源导致小麦籽粒Pb超标风险从10.5%显著降低至2.39%.     

Evaluation of Poly(lactic acid) and Sugar Beet Pulp Green Composites

Poly(lactic acid) (PLA) and sugar beet pulp (SBP) were compounded by twin-screw extrusion and injection molded into composite forms. Specific mechanical energy decreased with the addition of SBP during processing. PLA–SBP composites retained more tensile strength than expected based on the Nicolais–Narkis model especially at high levels of SBP suggesting adhesion between SBP and PLA. The thermal characteristics of PLA were not affected by thermo-mechanical processing or by the incorporation of SBP up to 30% weight basis. PLA and PLA–SBP composites had similar tensile properties to other thermoplastic resins and may be used as a cost-competitive replacement.

区域大气重污染过程识别指标及算法研究

重点区域秋冬季大气重污染应对是大气污染防治工作的重中之重,但针对区域大气重污染过程尚无明确、统一的判定依据,给空气质量管理造成不便.以京津冀大气污染传输通道"2+26"城市为研究对象,依据秋冬季大气重污染过程的区域性特征,以及"发生—发展—消散"的演变规律,将区域内3个及以上相邻城市至少 1d空气质量达到重污染水平判定...     

液相色谱-质谱法直接进样测定水中丙烯酰胺的基质效应

采用柱后注射法和前处理后添加法评定高效液相色谱-串联质谱法(LC-MS-MS)的基质效应,研究了不同色谱条件对水样丙烯酰胺测定中基质效应的影响,提供了有效削减基质效应的方法,并建立了直接进样测定水中丙烯酰胺的LC-MS-MS法。研究表明,流动相溶剂强度、添加剂种类及浓度对LC-MS-MS直接进样测定水中丙烯酰胺的基质效应影响显著;通过调节流动相溶剂强度和甲酸高度使丙烯酰胺保留时间与基质效应出现的时间段错开,可避开基质效应;在10%甲醇-90%水流动相中添加0.01%~0.02%甲酸可显著降低基质效应,对水中丙烯酰胺的检出限为0.03μg/L,对自来水、地表水、地下水的加标回收率为96.7%~105%,相对标准偏差为1.7%~6.4%。建立的LC-MS-MS直接进样法简便、快速、灵敏度高、定性定量准确可靠,适合水中丙烯酰胺的超痕量分析。     

上海浦东新区环境空气重金属污染特征及来源解析

于2012年3月—2013年2月在浦东新区城区点和郊区点采集TSP样品,利用双酸消解原子荧光法、ICP法分析了样品中的13种元素。结果表明,除As略超标外其他元素均达标,城区点和郊区点环境空气重金属年均值排在前3位的元素是:Fe、Sn、Zn,占样品金属总量的83%~87%;排在末4位的元素是:Ni、Cr、As、Cd,占金属总量的1%~2%。通过EPA PMF 5.0模型运算结果,在城区中6个因子贡献比最高的31.5%为Sn+Fe+Mn,贡献比最低的2.1%为Hg;在郊区中6个因子贡献比最高的36.4%为Pb+Cu,贡献比最低的9.2%为Fe+Sn+Mn。浦东新区富集因子E≤10的元素有Cr、V、Ba、Mn、Ni、Fe、Hg城、Cu郊、As;富集因子E10的元素有Hg郊、Cu城、Zn、Pb、Cd、Sn。     

Improving land use inference by factorizing mobile phone call activity matrix

Land use is shaped by human activities. Traditional methods of measuring land uses (e.g. surveys and remote sensing techniques) often have difficulties in capturing human activities. The near-ubiquitous coverage of mobile phones opens up a new way to investigate land use through human activities. We propose to analyze land use by characterizing human activity patterns based on the aggregated call volume, and apply non-negative matrix factorization to identify fundamental behavioral classes. Using tower-based call data from Dakar, Senegal, we discover two fundamental land use patterns: commercial/business/industrial (C/B/I) and residential. Then, the land use of the reception area of each cell tower can be inferred based on the weights obtained for each basis vector. To evaluate the proposed approach, the results are compared with two points-of-interest (POI) data sets obtained from OpenStreetMap and Facebook’s Graph API. We have found that a majority of POIs like embassies, offices, and hotels are located in the predicted C/B/I areas; specifically, there is a strong positive correlation between estimated land use weights and the number of related POIs. Furthermore, we have shown analyzing 24-h call pattern matrix can track daily land use changes.     

Methods to summarize change among land categories across time intervals

Time-series maps have become more detailed in terms of numbers of categories and time points. Our paper proposes methods for raster datasets where detailed analysis of all categorical transitions would be initially overwhelming. We create two measurements: Incidents and States. The former is the number of times a pixel’s category changes across time intervals; the latter is the number of categories that a pixel represents across time points. The combinations of Incidents and States summarize change trajectories. We also describe categorical transitions in terms of annual flow matrices, which quantify the additional information generated by intermediate time points within the temporal extent. Our approach summarizes change at the pixel and landscape levels in ways that communicate where and how categories transition over time. These methods are useful to detect hotspots of change and to consider whether the apparent changes are real or due to map error.     

汛期暴雨径流对饮用水水库溶解性有机质(DOM)光谱特征的影响

为探究汛期暴雨径流对水库有机质的影响,以李家河水库为例,采用三维荧光光谱-平行因子法（EEMs-PARAFAC）及紫外-可见（UV-Vis）吸收光谱法分析径流过程中4个时期（径流前、径流洪峰期、径流1周后和6周后）溶解性有机质（DOM）含量及组分变化.结果表明：①径流洪峰期水库水体浊度、DOC含量显著升高（P<0.01）,洪峰期后逐步下降;②紫外-可见光谱特征参数显示,径流洪峰期a（254）和a（355）显著升高（P<0.01）,而E2/E3和E3/E4显著降低（P<0.01）,表明暴雨径流使水库水体DOM的浓度、相对分子质量和腐殖化程度升高;③李家河水库共解析出4种荧光DOM组分,分别为陆源腐殖质组分（C1、C2）、微生物腐殖质组分（C3）和类色氨酸组分（C4）,径流洪峰期C1~C3组分荧光强度显著升高（P<0.05）,表明暴雨径流大幅增加了水库DOM类腐殖质组分含量,洪峰期后C1~C4组分荧光强度不断降低,表明径流后DOM不断沉降和降解.④ Pearson相关性分析显示暴雨径流后水体DOM荧光强度和浊度呈现显著的相关性（r>0.467,P<0.05）,表明DOM的减少和悬浮物沉降有关,主成分分析（PCA）表明暴雨径流事件的各个时期水库水质呈现出不同的特征.本研究阐明了暴雨径流对李家河水库DOM含量和组分短期及后续影响,为饮用水水库水质管理提供了科学支撑.     

利用SPAMS研究西安市重污染天气细颗粒物污染特征及来源

为了对西安市冬季重污染过程中的细颗粒物进行动态源解析,于2016年12月5-22日,利用SPAMS（单颗粒气溶胶质谱仪）在西安市城市运动公园开展连续观测.将观测期分为4个阶段,结合气象条件对不同阶段细颗粒物的污染特征进行分析比较.依据质谱特征,将所采集到的颗粒分为EC（元素碳）、OC（有机碳）、ECOC（混合碳）、HM（重金属）、LEV（左旋葡聚糖）、SiO₃（矿尘）、K（钾）、Na（钠）、HOC（有机大分子）及Other（其他）类.结果表明:观测期间所采集到的OC类颗粒物数量最多,在重污染阶段OC、K和EC类颗粒物占颗粒总数的70%以上,是重污染天气的主要组成颗粒.在雾霾消散期,OC、LEV和SiO₃类颗粒是主要类型颗粒物.根据颗粒物的化学类型及离子特征,利用PMF（正交矩阵因子分解）模型法得到6种污染源贡献率分别为27.7%（燃煤源）、22.3%（二次污染源）、20.4%（交通源）、10.4%（生物质燃烧源）、9.7%（工艺过程源）、6.5%（扬尘源）及3.0%（其他未知源）.研究显示:在重污染阶段,燃煤源与交通源占比大幅上升,与二次污染源共同造成了此次重污染天气;在雾霾消散期,扬尘源及生物质燃烧源成为大气细颗粒物的主要污染源.