Characterization of submicron particles during autumn in Beijing, China

In this study, we performed a highly time-resolved chemical characterization of nonrefractory submicron particles(NR-PM_1) in Beijing by using an Aerodyne high-resolution time-of-flight aerosol mass spectrometer(HR-ToF-AMS). The results showed the average NR-PM_1 mass concentration to be 56.4 ± 58.0 μg/m~3, with a peak at 307.4 μg/m~3. Due to the high frequency of biomass burning in autumn, submicron particles significantly increased in organic content, which accounted for 51% of NR-PM_1 on average. Secondary inorganic aerosols(sulfate + nitrate + ammonium) accounted for 46% of NR-PM_1, of which sulfate,nitrate, and ammonium contributed 15%, 20%, and 11%, respectively. To determine the intrinsic relationships between the organic and inorganic species, we used the positive matrix factorization(PMF) model to merge the high-resolution mass spectra of the organic species and NO+and NO_2~+ions. The PMF analysis separated the mixed organic and nitrate(NO+and NO_2~+) spectra into four organic factors, including hydrocarbon-like organic aerosol(HOA), oxygenated organic aerosol(OOA), cooking organic aerosol(COA), and biomass burning organic aerosol(BBOA), as well as one nitrate inorganic aerosol(NIA) factor. COA(33%) and OOA(30%) contributed the most to the total organic aerosol(OA) mass, followed by BBOA(20%) and HOA(17%). We successfully quantified the mass concentrations of the organic and inorganic nitrates by the NO+and NO2+ions signal in the organic and NIA factors. The organic nitrate mass varied from 0.01-6.8 μg/m~3, with an average of 1.0 ±1.1 μg/m~3, and organic nitrate components accounted for 10% of the total nitrate mass in this observation.     

Particulate matter pollution in Kunshan High-Tech zone: Source apportionment with trace elements, plume evolution and its monitoring

Particulate matter(PM) in the Kunshan High-Tech zone is studied during a three-month campaign. PM and trace elements are measured by the online pollution monitoring, forecastwarning and source term retrieval system AS3. Hourly measured concentrations of PM_(10), PM_(2.5) and 16 trace elements in the PM_(2.5) section(Ca, Pb, Cu, Cl, V, Cr, Fe, Ti, Mn, Ni, Zn, Ga, As, Se, Sr, Ba)are focused. Source apportionment of trace elements by Positive Matrix Factorization modeling indicates that there are five major sources, including dust, industrial processing, traffic,combustion, and sea salt with contribution rate of 23.68%, 21.66%, 14.30%, 22.03%, and 6.89%,respectively. Prediction of plume dispersion from concrete plant and traffic emissions shows that PM_(10) pollution of concrete plant is three orders of magnitude more than that of the traffic. The influence range can extend to more than 3 km in 1 hr. Because the footprint of the industrial plumes is constantly moving according to the local meteorological conditions, the fixed monitoring sites scattered in a few hundred meters haven't captured the heaviest pollution plume at the local scale of a few km~2. As a more intensive monitoring network is not operationally possible, the use of online modeling gives accurate and quantitative information of plume location, which increases the spatial pollution monitoring capacity and improves the understanding of measurement data. These results indicate that the development of the AS3 system, which combines monitoring equipment and air pollution modeling systems, is beneficial to the real-time pollution monitoring in the industrial zone.     

Quantifying the characteristics of particulate matters captured by urban plants using an automatic approach

It is widely accepted that urban plant leaves can capture airborne particles. Previous studies on the particle capture capacity of plant leaves have mostly focused on particle mass and/or size distribution. Fewer studies, however, have examined the particle density, and the size and shape characteristics of particles, which may have important implications for evaluating the particle capture efficiency of plants, and identifying the particle sources. In addition, the role of different vegetation types is as yet unclear. Here, we chose three species of different vegetation types, and firstly applied an object-based classification approach to automatically identify the particles from scanning electron microscope(SEM)micrographs. We then quantified the particle capture efficiency, and the major sources of particles were identified. We found(1) Rosa xanthina Lindl(shrub species) had greater retention efficiency than Broussonetia papyrifera(broadleaf species) and Pinus bungeana Zucc.(coniferous species), in terms of particle number and particle area cover.(2) 97.9% of the identified particles had diameter ≤10 μm, and 67.1% of them had diameter ≤2.5 μm. 89.8% of the particles had smooth boundaries, with 23.4% of them being nearly spherical.(3) 32.4%–74.1% of the particles were generated from bare soil and construction activities, and 15.5%–23.0% were mainly from vehicle exhaust and cooking fumes.     

Identification and elucidation of anthropogenic source contribution in PM10 pollutant: Insight gain from dispersion and receptor models

Source apportionment study of PM₁₀ (Particulate Matter) in a critically polluted area of Jharia coalfield, India has been carried out using Dispersion model, Principle Component Analysis (PCA) and Chemical Mass Balance (CMB) techniques. Dispersion model Atmospheric Dispersion Model (AERMOD) was introduced to simplify the complexity of sources in Jharia coalfield. PCA and CMB analysis indicates that monitoring stations near the mining area were mainly affected by the emission from open coal mining and its associated activities such as coal transportation, loading and unloading of coal. Mine fire emission also contributed a considerable amount of particulate matters in monitoring stations. Locations in the city area were mostly affected by vehicular, Liquid Petroleum Gas (LPG) & Diesel Generator (DG) set emissions, residential, and commercial activities. The experimental data sampling and their analysis could aid understanding how dispersion based model technique along with receptor model based concept can be strategically used for quantitative analysis of Natural and Anthropogenic sources of PM₁₀.     

制氧企业安全评价方法探讨

本文针对制氧行业的生产特点以及安全管理情况,对作业过程中存在的危险、有害因素进行了分析,综合运用安全检查表法(SCL)与故障模式、影响及危险度分析法(FMECA)对制氧企业安全现状进行了评价,并提出了相应的对策措施.结果表明:安全检查表法简便、易于掌握、较为实用;故障模式、影响及危险度分析法对评价人员的要求较高,但可得...     

列表分析法在开发区回顾性环境影响评价中的应用

结合已开发区域的现状调查,对列表分析法在开发区回顾性环境影响评价中的应用进行了介绍。应用结果表明,列表分析法可充分体现回顾性环境影响评价的内容和特点,精炼报告书的编制,同时能很好地指导开发区进一步的开发建设和优化布局。     

AHP-FUZZY综合评价法在确定清洁生产审核重点中的应用研究——以武汉市某电源公司为例

清洁生产审核是将清洁生产理念转变为企业层次上操作的具体程序,在进行审核时首要步骤是确定审核重点。现阶段确定审核重点主要运用权重总和计分排序法,该方法易受审核者主观影响,审核质量不易控制。AHP-Fuzzy综合评价法作为一种系统分析方法很好地弥补了权重总和计分排序法的不足,在系统工程的很多领域都有广泛应用。通过确定银泰电源公司清洁生产审核重点,建立七工段层次模型:确立目标,明确问题;建立系统的层次结构;构造判断矩阵;进行层次的单排序及一致性检验;进行层次总排序一致性检验;根据排序结果,提出决策建议,验证了该综合评价法在清洁生产审核应用中的科学性、可行性和准确性,能将比较复杂、不够确定的多因素问题转化为有数字依据的简单易行的评价模式,为企业清洁生产的定量考核和指标化管理提供了一个科学的评价方法。     

低中放废物处置FEPs清单的构建——以飞凤山处置场为例

识别所有影响其长期安全的有关因素,即特征(Features)、事件(Events)和过程(Processes)(FEPs),是放射性废物处置场安全全过程系统分析的关键过程之一。我国目前低中放固体废物处置环境影响评价中未开展FEPs识别和景象开发。拟以飞凤山处置场为例,选择自下而上方法的构建方法,对如何建立我国的低中放废物处置FEPs清单进行了探讨。并以最新发布的NEA国际FEPs清单(3.0版)中提出的基于外部因素和处置组成(废物包、处置场、岩石圈和生物圈)的分类方案为基础,结合飞凤山处置场的近地表处置及其环境特征,建立了该处置场的FEPs清单。     

太原市多环芳烃(PAHs)排放清单与分布特征分析

根据太原市11种主要排放源的排放因子和活动量数据,估算了美国国家环境保护局(US EPA)优先控制污染物清单中16种多环芳烃(PAHs)的年排放量.结果表明2010年太原市16种PAHs的排放量约为332.10t,其中7种致癌性PAHs排放总量为35.11t.从排放源看,生活燃煤和炼焦煤是太原市排放PAHs的主要来源,占总排放量的65%以上.从各地区的PAHs排放情况看,排放量最大的地区是清徐县(87t/a),占总排放的27%.其次为古交市(54t/a)、晋源区(44t/a)、尖草坪区(40t/a).各地区人均收入与单位GDP排放量之间呈负相关 (R2=0.727);各地区PAHs排放量与农村人口之间呈正相关(R2=0.813),从排放谱看,排放以低环PAHs为主(81%),致癌性PAHs占总排放量的10.6%.结果表明,太原市PAHs的排放与太原市特殊的能源结构和人群结构有关.     

An approach to calculating allowable watershed pollutant loads

To improve the management of discharge pollutants loads in the reservoirs’ watershed, an approach of the allowable pollutants loads calculation and its allocation, based on the water environment model, was proposed. Establishment of the approach framework was described at first. Under the guidance of this framework, two major steps were as follows: modeling and scenario analysis were involved and should be applied to support the decision of discharge loads management; Environmental Fluid Dynamic Code (EFDC) model was selected as the kernel model in this framework. In modeling step, spatial discretization for establishing cell map in model, data preprocessing, parameter calibration and uncertainty analysis (which is considered as the significantly relevant factor of the margin of safety (MOS)), were conducted. As a result of the research, the model-based approach presented as a combination of estimation and precise calculation, which contributed to scenario analysis step. Some integrated modules, such as scenario simulation, result analysis and plan optimization were implemented as cycles in the scenario analysis. Finally, allowable pollutant loads under various conditions were calculated. The Chaihe Reservoir in Liaoning Province, China was used as a case study for an application of the approach described above. Results of the Chaihe reservoir water quality simulation, show good agreement with field data and demonstrated that the approach used in the present study provide an efficient and appropriate methodology for pollutant load allocation.