Detection of polycyclic aromatic hydrocarbons using a high performance-single particle aerosol mass spectrometer

The real-time detection of the mixing states of polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs in ambient particles is of great significance for analyzing the source, aging process, and health effects of PAHs and nitro-PAHs; yet there is still few effective technology to achieve this type of detection. In this study, 11 types of PAH and nitro-PAH standard samples were analyzed using a high performance-single particle aerosol mass spectrometer (HP-SPAMS) in lab studies. The identification principles ‘parent ions’ and ‘mass-to-charge (m/z) = 77’ of each compound were obtained in this study. It was found that different laser energies did not affect the identification of the parent ions. The comparative experiments of ambient atmospheric particles, cooking and biomass burning emitted particles with and without the addition of PAHs were conducted and ruled out the interferences from primary and secondary organics on the identification of PAHs. Besides, the reliability of the characteristic ions extraction method was evaluated through the comparative study of similarity algorithm and deep learning algorithm. In addition, the real PAH-containing particles from vehicle exhaust emissions and ambient particles were also analyzed. This study improves the ability of single particle mass spectrometry technology to detect PAHs and nitro-PAHs, and HP-SPAMS was superior to SPAMS for detecting single particles containing PAHs and nitro-PAHs. This study provides support for subsequent ambient observations to identify the characteristic spectrum of single particles containing PAHs and nitro-PAHs.     

Progressively narrow the gap of PM2.5 pollution characteristics at urban and suburban sites in a megacity of Sichuan Basin, China

Nowadays, the fine particle pollution is still severe in some megacities of China, especially in the Sichuan Basin, southwestern China. In order to understand the causes, sources, and impacts of fine particles, we collected PM_2.5 samples and analyzed their chemical composition in typical months from July 2018 to May 2019 at an urban and a suburban (background) site of Chengdu, a megacity in this region. The daily average concentrations of PM_2.5 ranged from 5.6-102.3 µg/m³ and 4.3-110.4 µg/m³ at each site. Secondary inorganics and organic matters were the major components in PM_2.5 at both sites. The proportion of nitrate in PM_2.5 has exceeded sulfate and become the primary inorganic component. SO₂ was easier to transform into sulfate in urban areas because of Mn-catalytic heterogeneous reactions. In contrast, NO₂ was easily converted in suburbs with high aerosol water content. Furthermore, organic carbon in urban was much greater than that in rural, other than elemental carbon. Element Cr and As were the key cancer risk drivers. The main sources of PM_2.5 in urban and suburban areas were all secondary aerosols (42.9%, 32.1%), combustion (16.0%, 25.2%) and vehicle emission (15.2%, 19.2%). From clean period to pollution period, the contributions from combustion and secondary aerosols increased markedly. In addition to tightening vehicle controls, urban areas need to restrict emissions from steel smelters, and suburbs need to minimize coal and biomass combustion in autumn and winter.     

PM2.5 chemical source profiles for vehicle exhaust, vegetative burning, geological material, and coal burning in Northwestern Colorado during 1995

PM_2.5 (particles with aerodynamic diameters less than 2.5 μm) chemical source profiles applicable to speciated emissions inventories and receptor model source apportionment are reported for geological material, motor vehicle exhaust, residential coal (RCC) and wood combustion (RWC), forest fires, geothermal hot springs; and coal-fired power generation units from northwestern Colorado during 1995. Fuels and combustion conditions are similar to those of other communities of the inland western US. Coal-fired power station profiles differed substantially between different units using similar coals, with the major difference being lack of selenium in emissions from the only unit that was equipped with a dry limestone sulfur dioxide (SO₂) scrubber. SO₂ abundances relative to fine particle mass emissions in power plant emissions were seven to nine times higher than hydrogen sulfide (H₂S) abundances from geothermal springs, and one to two orders of magnitude higher than SO₂ abundances in RCC emissions, implying that the SO₂ abundance is an important marker for primary particle contributions of non-aged coal-fired power station contributions. The sum of organic and elemental carbon ranged from 1% to 10% of fine particle mass in coal-fired power plant emissions, from 5% to 10% in geological material, >50% in forest fire emissions, >60% in RWC emissions, and >95% in RCC and vehicle exhaust emissions. Water-soluble potassium (K⁺) was most abundant in vegetative burning profiles. K⁺/K ratios ranged from 0.1 in geological material profiles to 0.9 in vegetative burning emissions, confirming previous observations that soluble potassium is a good marker for vegetative burning.     

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.     

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。     

煤矿危险源分类分级与预警

关于危险源概念目前尚无准确的理论界定,为此,笔者试图阐明危险源分为固有型危险源和触发型危险源的新观点,提出并说明了对不同类型危险源应采用的不同的辩识与分级方法。在此基础上,结合企业实际,提出了危险源预警思想和模块设计方法。所有这些,对完善安全管理理论,提高煤矿安全管理水平具有理论意义和实用价值     

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.