石家庄市冬季大气中VOCs污染特征分析

为弄清石家庄市冬季大气中VOCs的污染特征,采用美国环保局TO-15方法对石家庄市冬季大气中VOCs组成进行了定性和定量分析。在此基础上,进行了VOCs的月度变化分析、春节期间的变化分析,并进行了VOCs与空气质量指数AQI、PM2.5等之间的相关性分析;根据VOCs组成及变化情况和相关性,分析了其可能的来源。结果表明,石家庄市冬季大气中VOCs的质量浓度为145.7~1 410.7μg/m3,VOCs组分主要有丙酮、二氯甲烷、苯、乙酸乙酯、甲苯、1,2-二氯丙烷、三氯甲烷。春节期间,大气中VOCs的浓度有大幅的下降,比日常均值下降了40.9%。AQI较高时,大气中VOCs浓度有所升高。石家庄市冬季大气中丙酮、二氯甲烷、乙酸乙酯等主要来源于医药化工生产活动,苯、甲苯主要来源于煤燃烧。     

高效率颗粒物微物理模型简介与重污染过程应用分析

中国细颗粒物(PM2.5)导致的空气污染和雾霾天气问题越来越突出,在低层对流层中很多地方都有显著的二次颗粒物生成过程,因此对颗粒物物理化学转化过程的研究具有重要意义,其转化过程对空气质量模式模拟和预报的准确性有重要影响。高效率颗粒物微物理(APM)模型经过近20多年的研究和发展,在全球化学传输模型(GEOS-Chem)、区域性气象和化学预报模型(WRF-Chem)通用地球系统模型(CESM-CAM5)等气象及环境空气质量预报模型中取得了很好的应用验证,能够在全球/区域等不同尺度对粒子生成、长大、凝聚、消除等微物理过程以及气溶胶光学厚度(AOD)、直接/间接辐射强迫等进行模拟。应用GEOS-Chem-APM模式模拟了2014年2月京津冀区域的重污染过程。结果显示,湿度模拟的准确性对其他物理和污染物浓度等模拟结果有重要作用;2月20—26日的重污染过程模拟结果与实际观测较为吻合,主要污染特点是较高的湿度(90%以上),同时,二次颗粒物和包裹了二次物种的一次颗粒物(黑碳/有机碳)是低能见度的最大贡献者。     

大数据解析技术在大气环境监测中的应用研究

针对近年来新兴的大数据及挖掘、分析技术,对大数据解析技术在环境科学研究中的应用进行了分析和探析。以城市局部地区大气细颗粒污染物PM2.5浓度计算为例,筛选了PM2.5浓度历史数据、气象条件、交通状况、人群活动情况、网格道路状况等数据为影响特征量,分析了用神经网络法进行大数据解析和推演的原理和数学过程,是用数学、计算机、统计等方法研究解决多元、非线性复杂环境问题的一种新的探索。     

SPE-HPLC法测定水中硝基苯酚类化合物

建立了以固相萃取为前处理条件,用液相色谱法测定水中10种硝基苯酚类化合物的分析方法。实验使用HLB(6 L/150 g)固相萃取柱富集水样中的目标化合物,二氯甲烷与乙酸乙酯体积比1:2的混合溶剂洗脱,采用Phenyl柱(4.6 mm×250 mm,5 μm)分离目标化合物,以乙腈(1%甲酸)/水(1%甲酸)为流动相进行梯度洗脱,二极管阵列检测器检测。10种硝基苯酚类物质在0.02~10 mg/L范围内呈现良好的线性。方法检出限为0.1~0.3 μg/L,水样加标相对标准偏差为5.19%~18.2%,平均加标回收率为49.8%~124%。该方法适用于水中10种硝基苯酚类化合物的测定。     

重点流域水系沉积物中多环芳烃标准样品制备

环境标准样品是监测过程中重要的质量控制手段,中国受天然基体标准样品制备技术的制约,尚未拥有自己的沉积物中多环芳烃标准样品。为制备符合中国重点流域沉积物类型及多环芳烃浓度水平的沉积物标准样品,对中国6个重点流域的沉积物进行了采样,获得了制备多环芳烃标准样品的原料。分析常温及冷冻2种干燥方式对多环芳烃的影响,为制备大批量多环芳烃标准样品提供数据支持。对11个点位样品采用气相色谱质谱法测定16种优控多环芳烃化合物浓度,结果表明,样品均匀性良好,样品中多环芳烃检出率在99%以上,江河沉积物样品中多环芳烃浓度区间为664~2.91×103μg/kg,湖泊样品最高值达到1.25×105μg/kg,其主要污染源是化学燃料的燃烧。为中国制备水系沉积物中多环芳烃标准样品积累了技术基础。     

大气细颗粒物在线源解析方法研究进展

基于对国内外关于PM2.5化学组分特征、二次气溶胶生成机理、源解析模型的研究综述,引入实现PM2.5在线源解析的2种可能方法,即气溶胶质谱+解析算法与3类化学组分组合观测+受体模型。重点阐述了气溶胶质谱及在线水溶性离子、EC/OC和金属元素仪器的应用研究现状,并分析了不同仪器测定结果的可靠性、优势及缺陷,在此基础上提出加强城市尺度的大气化学二次反应机理及关键技术参数研究、大气颗粒物化学组分连续观测应用于在线源解析的研究、基于多种信息来源和技术手段的在线源解析方法集成研究等建议。     

固相萃取-气质联用法测定固体废物浸出液中的二硝基苯

建立了一种利用固相萃取法对固体废物浸出液(TCLP)中二硝基苯进行萃取,DB-5石英毛细管柱(30 m×0.25 mm×0.25μm)进行分离,质谱检测器检测二硝基苯的方法。方法在0.002 0~0.020 0 mg/L之间线性关系良好,二硝基苯三种同分异构体的检出限均为0.5μg/L,模拟样品加标回收率为93%~96%,RSD≤2%;实际固体废物样品测定的加标回收率为95%~98%。     

基于EFDC的长江常州段污水排江浓度场数值模拟

长江感潮河段水流与污染排放引起的浓度场复杂多变,基于EFDC(Environmental Fluid Dynamic Code)模型,采用正交曲线网格拟合自然河道边界,建立了长江常州段的水动力和水质模型。采用现场同步实测资料,与计算结果比对,证明该模型在水动力和水质两个方面都与实测数据吻合较好,满足了实际工程的需要;同时由于长江水流运动较快,一阶降解动力学已经满足了化学需氧量(COD)、氨氮(NH3-N)和总磷(TP)浓度计算要求,可以不考虑它们复杂的生化反应过程。     

Evaluating the difference between the normalized difference vegetation index and net primary productivity as the indicators of vegetation vigor assessment at landscape scale

Both the net primary productivity (NPP) and the normalized difference vegetation index (NDVI) are commonly used as indicators to characterize vegetation vigor, and NDVI has been used as a surrogate estimator of NPP in some cases. To evaluate the reliability of such surrogation, here we examined the quantitative difference between NPP and NDVI in their outcomes of vegetation vigor assessment at a landscape scale. Using Landsat ETM+ data and a process model, the Boreal Ecosystem Productivity Simulator, NPP distribution was mapped at a resolution of 90 m, and total NDVI during the growing season was calculated in Heihe River Basin, Northwest China in 2002. The results from a comparison between the NPP and NDVI classification maps show that there existed a substantial difference in terms of both area and spatial distribution between the assessment outcomes of these two indicators, despite that they are strongly correlated. The degree of difference can be influenced by assessment schemes, as well as the type of vegetation and ecozone. Overall, NDVI is not a good surrogate of NPP as the indicators of vegetation vigor assessment in the study area. Nonetheless, NDVI could serve as a fairish surrogate indicator under the condition that the target region has low vegetation cover and the assessment has relatively coarse classification schemes (i.e., the class number is small). It is suggested that the use of NPP and NDVI should be carefully selected in landscape assessment. Their differences need to be further evaluated across geographic areas and biomes.     

Spatial and seasonal distribution of nitrogen in marsh soils of a typical floodplain wetland in Northeast China

Horizontal and profile distributions of nitrogen in marsh soils in different seasons were studied in a typical site within the Erbaifangzi wetland in Northeast China. Results showed that there was higher spatial heterogeneity for nitrate nitrogen (NO₃^--_{3}^{-}-N) and ammonium nitrogen (NH₄⁺_{4}^{+}–N), as well as available nitrogen (AN), in surface soils in July compared to that in September. Relative to July, the mean nitrogen contents in surface soils were slightly higher in September; however, in November, soils contained significantly lower NO₃^--_{3}^{-}-N and NH₄⁺_{4}^{+}–N, higher AN, organic nitrogen (Org-N), and total nitrogen (TN). Except for mineral nitrogen, no significant differences were observed between Org-N and TN contents in September and November. Nitrogen contents generally declined exponentially with depth along soil profiles in three sampling dates (July, September, and November), except for a significant accumulation peak of NO₃^--_{3}^{-}-N at the 20–30 cm depth in September. However, NH₄⁺_{4}^{+}–N contents showed a vertical alternation of “increasing and decreasing” in both July and September, while nearly kept constant with depth in November. The depth ranking of nitrogen showed the shallowest distribution for AN, followed by Org-N and TN, while deeper distributions for NO₃^--_{3}^{-}-N and NH₄⁺_{4}^{+}–N. TN, Org-N, and AN were significantly correlated with soil organic matter and total phosphorus. Soil pH values were significantly correlated with TN and AN contents in surface soils. Clay contents showed significant correlations with nitrogen contents except for NO₃^--_{3}^{-}-N in surface soils and NH₄⁺_{4}^{+}–N in profile soils. However, soil moisture was not significantly correlated with nitrogen contents among all soil samples.