遵义市PM2.5中水溶性离子的污染特征及来源解析

为探究遵义市PM_2.5中水溶性离子的污染特征及来源,于2018年6月~2019年5月采集了遵义市两个采样点共120个PM_2.5样品,并利用离子色谱法对样品中8种水溶性离子进行了分析。结果表明：采样期间,遵义市PM_2.5平均值为47.6±19.3 μg/m³,呈现冬春高、夏秋低的季节变化特征;8种水溶性离子平均质量浓度顺序为SO₄^2- > NO₃^- > NH₄⁺ > Ca²⁺ > K⁺ > Cl^- > Na⁺ > Mg²⁺,平均值为13.74 μg/m³,水溶性离子质量浓度的季节变化与PM_2.5变化趋势相似;SO₄^2-、NO₃^-、NH₄⁺（SNA）是PM_2.5中主要水溶性离子,占比为83.8%,说明遵义市大气PM_2.5二次污染较严重;相关性分析表明,PM_2.5中NH₄⁺主要以（NH₄）₂SO₄、NH₄HSO₄的形式存在,部分以NH₄NO₃的形式存在;[NO₃^-]/[SO₄^2-]小于1,表明固定源为主要污染源;主成分分析结果表明,PM_2.5中水溶性离子主要来源于燃煤、交通混合源、土壤、建筑扬尘及农业源。     

广州市天河区2016年雨季挥发性有机物污染特征及来源解析

采用"GCMS/FID"在线分析方法,对广州市区2016年7月大气VOCs的污染特征及来源进行了研究,共检出了73种VOCs组分.结果表明,观测期间总VOCs的小时平均浓度为（118.83±79.40）μg·m^-3,最高值为492.42 μg·m^-3,最低值为10.54 μg·m^-3.07：00左右TVOC浓度出现高峰,说明早高峰的机动车污染对该站点的VOCs具有较大贡献;14：00左右浓度最低,与光化学损耗相关;21：00~24：00间VOCs浓度又出现高值,可能和污染源排放或边界层压缩有关.运用PMF模型解析出VOCs的5个主要来源分别是：交通污染源、溶剂使用污染、加油站污染、植物排放和餐厨废气,其贡献分别为29.79%、26.61%、24.86%、9.91%、8.84%;白天交通废气源贡献最大,而中午植物排放的贡献也明显增大;夜间溶剂污染源和加油站污染源占比上升,为该时段VOCs的主要来源.     

Molecular characterization of effluent organic matter in secondary effluent and reclaimed water: Comparison to natural organic matter in source water

Municipal wastewater reclamation is becoming of increasing importance in the world to solve the problem of water scarcity. A better understanding of the molecular composition of effluent organic matter(Ef OM) in the treated effluents of municipal wastewater treatment plants(WWTPs) is crucial for ensuring the safety of water reuse. In this study, the molecular composition of Ef OM in the secondary effluent of a WWTP in Beijing and the reclaimed water further treated with a coagulation–sedimentation–ozonation process were characterized using a non-target Fourier transform ion cyclotron resonance mass spectrometry(FT-ICR MS) method and compared to that of natural organic matter(NOM) in the local source water from a reservoir. It was found that the molecular composition of Ef OM in the secondary effluent and reclaimed water was dominated by CHOS formulas, while NOM in the source water was dominated by CHO formulas. The CHO formulas of the three samples had similar origins. Anthropogenic surfactants were responsible for the CHOS formulas in Ef OM of the secondary effluent and were not well removed by the coagulation-sedimentation-ozonation treatment process adopted.     

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.     

嘉兴市城市河网区多环芳烃污染源解析及生态风险评价

为研究嘉兴市城市河网区水体中多环芳烃的污染水平和来源并进行生态风险评价,采用气相色谱-质谱法（GC-MS）对环境优控多环芳烃（PAHs）进行分析检测.结果表明,枯水期和丰水期分别检测出10种和16种优控PAHs,质量浓度范围分别为77.32~283.76ng ·L^-1和13.05~133.02ng ·L^-1,平均质量浓度分别为143.83ng ·L^-1和73.47ng ·L^-1;枯水期低环（2环和3环）占比79.18%,丰水期低环占比73.60%;嘉兴市河网区水体多环芳烃污染情况与国内外其他地区相比处于较低水平;采用同分异构比值法和主成分分析法进行污染来源分析,结果表明嘉兴市枯水期和丰水期河网水体中多环芳烃污染主要来源为城市面源污染、燃烧源以及交通污染源;Kalf风险熵值法评价结果表明,枯水期：萘（Nap）、苊烯（Acy）、二氢苊（Ace）、芴（Flu）、菲（Phe）、蒽（Ant）、荧蒽（Fla）、芘（Pyr）和苯并［a］蒽（BaA）以及∑PAHs为中等生态风险水平,丰水期：萘（Nap）、苊烯（Acy）、芴（Flu）、菲（Phe）、荧蒽（Fla）、芘（Pyr）、苯并［a］蒽（BaA）、苯并［b］荧蒽（BbF）、苯并［k］荧蒽（BkF）、苯并［a］芘（BaP）、茚苯［1,2,3-cd］芘（InP）和苯并［ghi］苝（BghiP）属于中等生态风险水平,∑PAHs为低生态风险水平;总体而言,嘉兴市河网水体中PAHs生态风险呈中等水平,有关部门需采取措施降低河网水体中PAHs的生态风险.     

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.     

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。     

泉州市表层土中多环芳烃的含量、来源及其生态风险评价

采集了泉州市不同功能区的33个表层土壤（0～10 cm）样品,利用超高效液相色谱系统（UPLC）-荧光检测器法,研究了土壤中15种优控多环芳烃的含量和分布特征,并利用比值法、因子分析和多元线性回归法对其来源进行了分析,以及采用苯并（a）芘的毒性当量浓度（TEQBaP）评价了土壤中PAHs的生态风险.结果表明,土壤中15...