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.     

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。     

秦皇岛市大气颗粒物来源解析研究

对秦皇岛海港区和山海关区的颗粒物来源进行了定性识别和定量解析。并考察了重点污染源——我国最大的煤码头秦皇岛港煤码头对市区扬尘和TSP中的碳的影响。得出秦皇岛市海港区和山海关区TSP主要来源为风沙、扬尘,其次为燃煤飞灰和海盐尘。气象条件特别是风向对TSP各源分担率的影响很大,秦皇岛市煤码头是该区域TSP中碳的主要来源。不同气象条件下,煤码头对海港区和山海关区的影响程度明显不一样。      

城市污水处理厂中水回用初探

以沈阳市污水处理厂实际工程为例 ,提出了中水回用项目的可行性、项目方案及工程投资预测     

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.     

草地退化对三江源区高寒草甸生态系统CO2通量的影响及其原因

为了揭示草地退化对三江源地区高寒草甸生态系统碳通量的影响,利用涡度相关技术,于2006年12月1日~2007年11月30日对三江源地区的退化高寒草甸生态系统的碳通量及生物和环境因子进行观测.结果发现:草地退化对高寒草甸生态系统的碳通量产生了深刻影响,与未退化的高寒草甸生态系统相比,退化高寒草甸生态系统全年总初级生产力(GPP)下降了36.6%,全年生态系统呼吸(Reco)下降了7.9%,全年净生态系统CO2交换(NEE)也由退化前的负值(碳吸收)转变为正值(碳排放),二者相差132.5gC/(m2·a),生态系统由原来的碳汇转变为目前的碳源.这些变化与高寒草甸退化后,生态系统植物地上生物量锐减、植物生长期缩短(NEE<0的天数)、植物多样性下降、土壤含水量降低等因素密切相关.     

沈阳地区冬季空气颗粒物输送路径及潜在源分析

文章利用2016~2017年冬季沈阳地区气象数据和环境空气污染物浓度数据,综合分析沈阳地区冬季环境空气污染特征,分析颗粒物输送路径以及潜在源区贡献情况。结果表明,沈阳地区冬季污染时段内PM2.5是主要污染物,PM2.5平均浓度达到149μg/m^3,最大值达到273μg/m^3。沈阳地区环境空气颗粒物除来源于本地排放外,还主要受到内蒙古东北部和京津冀北部2个方向传输影响,内蒙古东北部方向传输气量约占总气量的70%。潜在源区分析表明,辽宁中西部、内蒙古东北部及京津冀北部是沈阳地区环境空气颗粒物潜在源区。浓度权重轨迹分析表明,辽宁大部、内蒙古东北部、京津冀北部和山东局部地区对沈阳地区的颗粒物浓度贡献较大,PM2.5修正后的浓度权重值WCWT>50μg/m^3,PM10的WCWT值>100μg/m^3。