Ambio’s legacy on monitoring,impact, and management of acid rain: This article belongs to Ambio’s 50th Anniversary Collection. Theme: Acidification

Early studies published in Ambio showed large-scale acidification of lakes in southern Sweden and Norway from acid rain. These studies were important for delimiting various scientific issues and thus for eventually contributing to legislation, which reduced emissions of sulfur dioxide and nitrogen oxides and helped to mitigate this major environmental problem. Long-term studies and monitoring in Sweden and Norway and at Hubbard Brook Experimental Forest in New Hampshire helped guide this legislation in Europe and in the USA.     

Determination of Q1, an unknown organochlorine contaminant, in human milk, Antarctic air, and further environmental samples

Q1, an organochlorine component with the molecular formula C(9)H(3)Cl(7)N(2) and of unknown origin was recently identified in seal blubber samples from the Namibian coast (southwest of Africa) and the Antarctic. In these samples, Q1 was more abundant than PCBs and on the level of DDT residues. Furthermore, Q1 was more abundant in seals from the Antarctic than the Arctic. To prove this assumption, gas chromatography-electron-capture negative ion mass spectrometry (GC/ECNI-MS), which is sensitive and selective for Q1, allowed for screening of traces of Q1 even in samples with particularly high levels of other organochlorine contaminants. Q1 was isolated by high-performance liquid chromatography (HPLC) from a skua liver sample. A 1:1 mixture with trans-nonachlor in electron-capture detectors (ECDs) was used to determine the relative response factor with ECNI-MS. The ECNI-MS response of Q1 turned out to be 4.5 times higher than that of trans-nonachlor in an ECD. With GC/ECNI-MS in the selected ion-monitoring mode, four Antarctic and four Arctic air samples were investigated for the presence of Q1. In the Antarctic air samples, Q1 levels ranged from 0.7 to 0.9 fg/m(3). In Arctic air samples, however, Q1 was below the detection limit (<0.06 fg/m(3) or 60 ag/m(3)). We also report on high Q1 levels in selected human milk samples (12-230 microg/kg lipid) and, therefore, suggested that the unknown Q1 is an environmental compound whose origin and distribution should be investigated in detail. Our data confirm that Q1 is a bioaccumulative natural organochlorine product. Detection of a highly chlorinated natural organochlorine compound in air and human milk is novel.     

切圆燃烧煤粉炉的空气分级燃烧改造

阐述了分级燃烧抑制NOx形成的基本原理,介绍了切圆燃烧煤粉炉的空气分级燃烧改造的基本特点,考察了轴向和径向空气分级、炉内空气浓度等因素对NOx排放量的影响;控制适当条件,使脱氮效率达到30％－50％,对煤粉炉的空气分级燃烧改造进行了费用效益分析。     

Progress in developing an ANN model for air pollution index forecast

An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO₂ and NO_x, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM₁₀, SO₂, and NO₂. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.     

Environmental forensic investigation of the air pollution from a cement manufacturing unit

Abstract

Cement manufacturing is a process that results in the emission of significant quantities of suspended particulate matter (SPM) to the ambient air. An environmental forensic investigation was carried out in the surroundings of a major cement manufacturing unit at a place called Coimbatore in the southern Indian state of Tamil Nadu. The investigation was carried out to identify the contribution of the cement manufacturing unit to the SPM concentration of the surrounding air environment. The sampling points’ selection and sample collection were done following the principles outlined in the INTERPOL Manual for Pollution Crime Forensic Investigation. On-site monitoring of the air samples was carried out using Mini Laser Aerosol Spectrometer (GRIMM, Mini-LAS Model 11R). The instrument was capable of measuring particles ranging from 0.25 to 32 µm and classifying them into 31 size channels. The test results at majority of the monitoring locations were well above the limits specified in the National Ambient Air Quality Standards of India. Microscopic studies of the dust samples were carried out for surface texture and particle shape. The spatial distribution of particles was analysed using geographic information system (GIS) for the visual identification of the extent of the pollution by keeping the cement factory as the focal point. The results from the GIS and microscopic analysis established the role of the cement factory in the particulate matter pollution of its surroundings, specifically in the areas North-West of the factory. The successfully adopted procedure can serve as a guideline for the environmental forensic investigation of similar pollution incidences.     

粤北地区一次臭氧污染过程分析及数值模拟

2020年7月1日,粤北地区出现了一次O₃污染过程,其中韶关市录得全省最高值162μg·m^-3.本文利用常规观测资料以及再分析资料对影响此次韶关地区O₃污染过程的主要气象场特征进行分析,结果表明：6月28日—7月2日天气形势总体处于静稳状态,地面受到均压场控制,同时副热带高压较强,脊线位于韶关上空,导致中高层有下沉气流,抑制了垂直方向污染物的扩散.6月28—30日(污染积累阶段),风速较大,韶关在外来输送和污染扩散条件的共同作用下,O₃浓度上升但未超标.7月1日(污染持续阶段),韶关和清远两地风速减小,污染扩散条件不利;同时气温有所上升,光化学反应作用增强,有利于O₃浓度攀升;同时广州和佛山两地的风向从偏南风转为偏北风,韶关市维持偏南风,主导风向的差异是全省只有韶关O₃超标的主要原因.7月2日(污染消散阶段),O₃外来传输及本地生成作用减弱,全省O₃浓度下降.进一步利用HYSPLIT后向轨迹以及WRF-CMAQ空...     

基于轨迹模型的和田绿洲沙尘移动轨迹及气象特征分析

以塔克拉玛干沙漠南缘的和田绿洲为研究区域,使用HYSPLIT后向轨迹模型和NCEP的GDAS全球气象要素数据,将和田绿洲西北部的墨玉县城作为模拟受点(79.72°E,37.26°N),对2004—2018年墨玉县发生的2 891次沙尘天气进行36 h的后向轨迹模拟,并通过k-means聚类分析法,定量分析沙尘天气期间气流的传输路径及气象特征。结果表明：2004—2018年,研究区沙尘天气的发生频率变化幅度不大,没有明显的增减趋势;年内沙尘天气主要集中在春季和夏季（3—7月）,占全年沙尘天气总数的70.23%,5、6月是强沙尘暴最活跃期。在沙尘天气期间气流的移动高度和轨迹相差较大,按照气流的起源地和到达墨玉县的方向将沙尘暴分为NW-W簇、N-N簇、NE-E簇和E-ES簇4簇轨迹类型,其发生天数占比分别为18.9%、12.3%、60.1%和7.8%。由东向西的沙尘天气发生天数占比最高（60.1%）,但主要以浮尘天气为主;由西北向东南方向的沙尘发生频率不高,但移动速度最快,强沙尘暴和扬沙主要来自西北和西部方向;由北向南的沙尘暴速度最慢（1.36 m/s）,空气湿度最低（26.4%）,但气温最高（292.58 K）。定量印证了墨玉县沙尘暴不同传输路径,可为绿洲区沙尘暴研究提供参考。

     

室内空气中污染物的检测技术

室内环境空气污染对人体健康影响很大,文中指出了影响人体健康的致病因子,分析了单个微粒的光通量和粒径之间的关系。鉴于单下颗粒的散射光信号很弱,本文提出一种方法,把激光器的内腔作为颗粒注入区,利用激光器的内腔功率谱密度远大于腔外功率密度的特点,结合先进的激光散控制理论,对空气中的颗粒进行检测。     

地下水DNAPLs污染的研究进展

地下水有机污染特别是DNAPLs污染是当前国际上地下水污染控制的热点问题。本文综述了DNAPLs的监测技术、DNAPL在含水层的迁移及DNAPb污染场地修复技术的研究进展。     

混凝沉淀—气浮—生化处理洗毛废水

采用混凝沉淀－气浮－生化处理洗毛废水。混凝沉淀－气浮在进水ＣＯＤ１４５００ｍｇ／Ｌ,处理水量２０００ｍ＾３／ｄ,ＣＯＤ去除率９０％;ＨＲＴ ３２ｈ生化处理时,出水ＣＯＤ为１８７ｍｇ／ｌ,可达到污水综合排放二级标准（洗毛行业）。