Size distribution, directional source contributions and pollution status of PM from Chengdu, China during a long-term sampling campaign.

Long-term and synchronous monitoring of PM10 and PM2.5 was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by two-way and three-way receptor models (PMF2, ME2-2way and ME2-3way). Consistent results were found: the primary source categories contributed 63.4% (PMF2), 64.8% (ME2-2way) and 66.8% (ME2-3way) to PM10, and contributed 60.9% (PMF2), 65.5% (ME2-2way) and 61.0% (ME2-3way) to PM2.5. Secondary sources contributed 31.8% (PMF2), 32.9% (ME2-2way) and 31.7% (ME2-3way) to PM10, and 35.0% (PMF2), 33.8% (ME2-2way) and 36.0% (ME2-3way) to PM2.5. The size distribution of source categories was estimated better by the ME2-3way method. The three-way model can simultaneously consider chemical species, temporal variability and PM sizes, while a two-way model independently computes datasets of different sizes. A method called source directional apportionment (SDA) was employed to quantify the contributions from various directions for each source category. Crustal dust from east-north-east (ENE) contributed the highest to both PM10 (12.7%) and PM2.5 (9.7%) in Chengdu, followed by the crustal dust from south-east (SE) for PM10 (9.8%) and secondary nitrate & secondary organic carbon from ENE for PM2.5 (9.6%). Source contributions from different directions are associated with meteorological conditions, source locations and emission patterns during the sampling period. These findings and methods provide useful tools to better understand PM pollution status and to develop effective pollution control strategies.

[Spatial Variability and Source Apportionment of PM2.5 Carbon Components in Tianjin].

The contents of eight carbonaceous subfractions were determined by simultaneously collecting PM2.5 samples from four sites in different functional areas of Tianjin in 2021. The results showed that the organic carbon (OC) concentration was 3.7 µg·m-3 to 4.4 µg·m-3, and the elemental carbon (EC) concentration was 1.6 µg·m-3 to 1.7 µg·m-3, with the highest OC concentration in the central urban area. There was no significant difference in EC concentration. The concentration of PM2.5 showed the distribution characteristics of the surrounding city>central city>peripheral area. The OC/EC minimum ratio method was used to estimate the concentrations of secondary organic carbon (SOC) in PM2.5, and the results showed that the secondary pollution was more prominent in the surrounding city, with SOC accounting for 48.8%. The correlation between carbon subcomponents in each functional area showed the characteristics of the peripheral area>central area>surrounding area, all showing the strongest correlation between EC1 and OC2 and EC1 and OC4. By including the carbon component concentration into the positive definite matrix factorization (PMF) model for source apportionment, the results showed that road dust sources(9.7%-23.5%), coal-combustion sources (10.2%-13.3%), diesel vehicle exhaust (12.6%-20.2%)and gasoline vehicle exhaust (18.9%-38.8%)were the main sources of carbon components in PM2.5 in Tianjin. The pollution sources of carbon components were different in different functional areas, with the central city and peripheral areas mainly affected by gasoline vehicle exhaust; the surrounding city was more prominently affected by the secondary pollution and diesel vehicle exhaust.

Heavy metals in urban ambient PM10 and soil background in eight cities around China.

The ambient PM(10) and background soil samples were collected and analyzed with ICP-AES in eight cities around China to investigate the levels of ten heavy metals (Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, and Pb). The mean concentrations of ten heavy metals in PM(10) of the eight cities of China followed the order of Zn > Pb > Mn > Cu > Ni > Cr > Co > V. The metals in the ambient PM(10) and soil were compared in each city to evaluate the heavy metal mass fraction from anthropogenic sources in ambient air. The CD values in these cities were all above 0.2, indicating that the ingredients spectrums of PM(10) and soil vary markedly. Most heavy metals were enriched in PM(10), except Fe and Ti. The results showed that almost all the cities suffer important heavy metal pollution from anthropogenic sources. The eight cities were also grouped according to their similarity in heavy metals of ambient PM(10) by cluster analysis to investigate the relationship between the heavy metals and the pollution sources of each city. The conclusion was that the eight cities were divided into three clusters which had similar industrial type and economy scale: the first cluster consisted of Shenzhen, Wuxi, and Guiyang; followed by Jinan and Zhengzhou as the second grouping; and the third group had Taiyuan, Urumqi, and Luoyang.

[Spatial-temporal Variation and Driving Factors of Ozone in China from 2019 to 2021 Based on EOF Technique and KZ Filter].

Based on the hourly O3 concentration data of 337 prefectural-level divisions and simultaneous surface meteorological data in China, we applied empirical orthogonal function (EOF) analysis to analyze the main spatial patterns, variation trends, and main meteorological driving factors of O3 concentration in China from March to August in 2019-2021. In this study, a KZ (Kolmogorov-Zurbenko) filter was used to decompose the time series of O3 concentration and simultaneous meteorological factors into corresponding short-term, seasonal, and long-term components in 31 provincial capitals.Then, the stepwise regression was used to establish the relationship between O3 and meteorological factors. Ultimately, the long-term component of O3 concentration after "meteorological adjustment" was reconstructed. The results indicated that the first spatial patterns of O3 concentration showed a convergent change, that is, the volatility of O3 concentration was weakened in the high-value region of variability and enhanced in the low-value region.Before and after the meteorological adjustment, the variation trend of O3 concentration in different cities was different to some extent. The adjusted curve was "flatter" in most cities. Among them, Fuzhou, Haikou, Changsha, Taiyuan, Harbin, and Urumqi were greatly affected by emissions. Shijiazhuang, Jinan, and Guangzhou were greatly affected by meteorological conditions. Beijing, Tianjin, Changchun, and Kunming were greatly affected by emissions and meteorological conditions.

[Health Impacts of Air Pollution in Tianjin].

Ambient air pollution is a dominant determinant of health. The health effects and economic losses due to air pollution are very important for decision-making. Since the implementation of the "Air Pollution Prevention and Control Action Plan" and "blue sky defense war" policies, the air quality of Tianjin has changed significantly. Here, the health effects and economic losses attributable to ambient air pollution in Tianjin from 2013 to 2020 wereestimated. For the particulate matter which has complex components, we assessed the inhalation health risks of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in PM2.5. The variation in the concentration of the main components of PM2.5 was also analyzed. The results showed that improved air quality had positive health benefits. The health benefits from SO2 were the highest among the six air pollutants, and 3786 deaths were avoided in 2020 compared to in 2013 due to lower SO2 concentration. The economic losses caused by air pollutants ranged from several billion to ten billion yuan. Among the six air pollutants, particulate matter and ozone had higher health losses in recent years. The health risks of heavy metals and PAHs in PM2.5 showed a decreasing trend. However, Cr(Ⅵ), As, Cd, and Ni in PM2.5in the winter of 2020 still had respiratorysystem carcinogenic risk, whereas there was no health risk of PAHs in PM2.5in 2019-2020. The concentrations of main components of PM2.5 have decreased significantly. In the future, the reduction of health loss caused by air pollution depends on synergy governance of particulate matter and ozone and further research on health effects.

Concentrations and sources of PAHs in surface sediments of the Fenhe reservoir and watershed, China.

Sixteen PAHs in surface sediments at 28 sites throughout Fenhe reservoir and watershed were measured. The ∑PAHs concentrations ranged from 539.0 to 6281.7 with the mean of 2214.8ng/g. The 2-3 rings PAHs, contributing 55 percent to ∑PAHs, were the dominant species. Twenty-eight sites were grouped into three segments: Fenhe principal stream, estuaries of main branch streams, and Fenhe reservoir. ∑PAHs was highest in the estuaries of main branch streams. The ecological risk assessment was studied by biological thresholds. The results showed levels of PAHs might cause mild but not acute adverse biological effects. In addition, PAHs ratios, PCA/MLR and hierarchical clustering analysis were applied to evaluate the possible sources. Coal combustion (35 percent), diesel and gasoline emissions (29 percent and 16 percent, respectively) might be the important sources. For sites in Fenhe reservoir, the major sources were complex, while other two segments were mainly influenced by coal combustion source.

Source and risk assessment of PCBs in sediments of Fenhe reservoir and watershed, China.

The concentrations of polychlorinated biphenyls (PCBs) in sediments from the Fenhe reservoir and watershed were detected at 28 sites in wet and dry seasons. The ∑(123)PCBs ranged from n.d. to 126.49 ng g(-1) dw. The dominated congeners were tri-PCBs (34.29%) and tetra-PCBs (24.05%). In the Fenhe reservoir, ∑(123)PCBs presented a decreasing trend, while percentages of low chlorinated congeners showed an increasing trend. For the temporal variations, PCBs homologues profiles of sediment samples and spatial distribution of ∑(123)PCBs for the two periods were similar (with CD = 0.021 and r(2) = 0.999 respectively), although PCBs concentrations in the wet season were significantly higher than in the dry season. PCA was applied to analyze the possible sources for PCBs, suggesting that PCBs might be mainly influenced by Aroclor 1016 and 1242. Compared with 3 established sediment quality guidelines, levels of PCBs in sediments of the investigated watershed might have a potential biological impact, especially in the wet season.

[Main Problems and Refined Solutions of Urban Fugitive Dust Pollution in China].

Fugitive dust poses an important contribution to urban air particulate matter in China. To further improve the level of dust pollution prevention and control, the emission and contribution characteristics of urban fugitive dust were summarized; the main causes of dust pollution were analyzed; and the key links, key indicators, and main measures for prevention and control were clarified, so as to further improve the concept of "accurate dust control." Among all types of fugitive dust sources, road dust and construction dust were the main emission and contribution sources, among which road dust was more prominent. Production activities, vehicle disturbances, and wind erosion were the main dust-generating links of various dust sources. Silt loading was taken as the key control index for road dust prevention and control, whereas silt loading and bare soil (or material) areas were taken as the key control index for construction and other dust sources. Around the key indicators, three main ways to control the road dust and six main measures to control the construction and other dust sources were defined. In addition, some suggestions on the necessary supporting measures for dust control were put forward, so as to provide a comprehensive and beneficial reference for the practical application of dust control in Chinese cities.

[PM2.5 Source Apportionment Based on a Variety of New Receptor Models].

In order to understand the applicability of various new receptor models, four receptor models, including the positive matrix factorization/multilinear engine 2-species ratio (PMF/ME2-SR), partial target transformation-positive matrix factorization (PTT-PMF), positive matrix factorization (PMF), and chemical mass balance (CMB), were used to analyze and verify the atmospheric fine particulate matter (PM2.5) data of a typical city in northern China. It was found that coal combustion (25%-26%), dust (19%-21%), secondary nitrate (17%-19%), secondary sulfate (16%), vehicle emissions (13%-15%), biomass burning (4%-7%), and steel (1%-2%) had a contribution to PM2.5. By comparing the source profiles and source contributions obtained by different models and calculating the coefficient of differences (CD) and average absolute error (AAE) of each source, we found that although the source apportionment results of the four models were in good agreement (the average CD value was between 0.6 and 0.7), there were still slight differences in the identification of some components in each source. Compared with the traditional model (PMF), the PMF/ME2-SR model can better identify sources with similar source profile characteristics, which is due to the component ratios of sources that are introduced. For example, the CD and AAE of dust sources were 15% and 54% lower than those of PMF, respectively. The PTT-PMF model takes the measured primary source profiles and virtual secondary source profiles as a constraint target, and the calculated CD and AAE of secondary sulfate were 0.25 and 17%, respectively, which were 55% and 23% lower than PMF. The PTT-PMF model can obtain more "pure" secondary sources and identify the pollution sources that are not identified by other models, which has more advantages in the refined identification of sources.

Determination of buffering capacity of total suspended particle and its source apportionment using the chemical mass balance approach.

The samples of total suspended particle (TSP) from sources and TSP in the ambient atmosphere were collected in 2006 at Tianjin, People's Republic of China and analyzed for 16 chemical elements, two water-soluble ions, total carbon, and organic carbon. On the basis of the chemical mass balance (CMB) model, the contributions of different TSP sources to the ambient TSP were identified. The results showed that resuspended dust has the biggest contributions to the concentration of ambient TSP. The buffering capacity of each TSP source was also determined by an analytical chemistry method, and the result showed that the constructive dust (the dust emitted from construction work) had the strongest buffering capacity among the measured sources, whereas the coal combustion dust had the weakest buffering capacity. A calculation formula of the source of buffering capacity of ambient TSP was developed based on the result of TSP source apportionment and the identification of the buffering capacity of each TSP source in this study. The results of the source apportionment of the buffering capacity of ambient TSP indicated that open sources (including soil dust, resuspended dust, and constructive dust) were the dominant sources of the buffering capacity of the ambient TSP. Acid rain pollution in cities in Northern China might become serious with a decrease of open source pollution without reducing acidic sources. More efforts must be made to evaluate this potential risk, and countermeasures should be proposed as early as possible.

Chemical composition of precipitation and its sources in Hangzhou, China.

To understand the origin and chemical characteristics of precipitation in Hangzhou, rainwater samples were collected from June 2006 to May 2008. All samples were analyzed for pH, electrical conductivity, and major ions (NH4⁺, Ca²âº, Mg²âº, Na⁺, K⁺, SO4²â», NO3⁻, F⁻, and Cl⁻). Acidification of precipitation in Hangzhou was serious with volume-weighted mean pH value of 4.5, while frequency of acid rain was 95%. The calculated SO4²â»/NO3⁻ ratio in Hangzhou precipitation was 2.87, which indicated that the precipitation of Hangzhou belonged to sulfate-based acid rain. The results of acid neutralization analysis showed that not all the acidity in the precipitation of Hangzhou was neutralized by alkaline constituents. The results of sea salt contribution analysis showed that nearly all SO4²â», Ca²âº, and Mg²âº and 33.7% of K⁺ were of non-sea origins, while all Na⁺ and Cl⁻ and 66.3% of K⁺ originated from sea sources. The principal component analysis which was used to analyze the sources of various ions indicated that chemical compositions of precipitation in Hangzhou mainly came from terrestrial sources, factory emissions, fuel wood burning, and marine sources.

[Emission Performance Quantitative Evaluation and Application of Industrial Air Pollution Sources].

Treatment of industrial atmospheric emission sources is an important way to improve air quality, but accurate pollution control remains still an urgent challenge. Taking Xiqing District of Tianjin as an example, based on the second national pollution source census, this study carried out a quantitative evaluation of the pollutant emission performance of industrial enterprises and explored the significance, feasibility, and challenges facing emission performance evaluation. The results show that the emission performance of various industries in Xiqing District vary greatly. The pollutant emission performance level is closely related to an industry's own attributes, development scale, and management level. On the whole, the emission performance level of industries with high production process emission coefficients and a high proportion of small and medium-sized enterprises (such as furniture manufacturing, the metal products industry, ferrous metal smelting, and the rolling processing industry) is worse, while the emission performance of high-end industries represented by computer communication and other electronic equipment manufacturing and automobile manufacturing is generally better. The emission performance of different enterprises in the same industry also varies greatly. For example, the 11 enterprises with the worst performance in the metal machinery manufacturing industry only contributed 0.06% of industrial output yet their PM emission contribution reached 8.50%. The 19 worst-performing enterprises in the rubber and plastic industry contributed 4.76% of industrial output yet their VOCs emissions accounted for 43.59% of the total. At the same time, this study presents an emissions reduction plan according to the relevant technical guidelines of the Ministry of Ecology and Environment. Based on this, the cost of emissions reduction could be cut by as much as 90% when the pollutant emissions reductions of the same scale are reduced. The gap in the pollutant emissions performance of various industries and enterprises, the incongruity between economic benefits and environmental costs, and the important guiding role of emission performance evaluation for emissions reductions demonstrate the necessity of performance evaluation. Overall, this research shows that pollutant emission performance evaluation can effectively support macro-industrial structure adjustment and the environmental governance of meso-micro industrial enterprises, providing an important reference for pollution control interventions.

[Characteristics of Ozone and Source Apportionment of the Precursor VOCs in Tianjin Suburbs in Summer].

From June to August 2018, a 1-hr resolution concentration dataset of ozone and its gaseous precursors (volatile organic compounds(VOCs) and NOx), and meteorological parameters were synchronously monitored by online instruments of the Nankai University Air Quality Research Supersite. The relationships and variation characteristics between ozone and its precursors were analyzed. According to the photochemical age, the initial concentrations of VOCs were calculated, and the photochemical loss of the concentration of VOCs during the daytime (06:00-24:00) was corrected. The initial and directly monitored concentrations of VOCs were incorporated into the PMF model for source apportionment. The results indicated that the mean concentration of O3 in Tianjin in summer was (41.3±25.7)×10-9, while that of VOCs was (13.9±12.3)×10-9. The average concentration of alkane (7.0±6.8)×10-9 was clearly higher than that of other VOC species. The species with high concentrations of alkanes were propane and ethane, accounting for 47% of the total alkane concentration. The average ozone formation potential (OFP) in summer was 52.1×10-9, and the OFP value of alkene was the highest and its contribution reached 57%. During the daytime, alkene loss accounted for 75% of the total VOC loss. The major sources of VOCs that were calculated based on the initial concentration data were the chemical industry and solvent usage (25%), automobile exhaust (22%), combustion source (19%), LPG/NG (19%), and gasoline volatilization (15%), respectively. Compared with the apportionment results based on directly monitored concentrations, the contribution of the chemical industry and solvent usage decreased by 4%, while automobile exhaust decreased by 5%. By combining the results of PMF apportionment and the OFP model to analyze the relative contributions of emission sources to ozone formation, and we found that the highest contribution source of ozone was the chemical industry and solvent usage (26%) in summer. Compared with the analysis results based on the directly monitored concentrations, the OFP values of the chemical industry and solvent usage decreased by 7%, while that of NG/LPG apparently decreased by 13%.

[Size Distribution of Aerosol Hygroscopic Growth Factors in Winter in Tianjin].

Aerosol hygroscopic growth factors[g(RH)] are key for evaluating aerosol light extinction and direct radiative forcing. The hygroscopic tandem differential mobility analyzer (HTDMA) was utilized to measure the size-resolved gm(RH) under different polluted conditions in winter in Tianjin. Furthermore, based on the size distribution of aerosol water-soluble ions, the gκ(RH) across a wide size range (60 nm to 9.8 µm) was estimated using the κ-Köhler theory, which provides a basis for the estimation of aerosol optical parameters and direct radiative forcing under ambient conditions. Under clean conditions, ultrafine particles (<100 nm) were more hygroscopic and gm(RH=80%) was higher than 1.30 due to the active photolysis reaction. However, under severely polluted conditions, the proportion of water-soluble ions in aerosols increased with the increasing size; gm(RH) increased with particle size, where gm(RH=80%) and gm(RH=85%) for 300 nm particles was 1.39 and 1.46, respectively. For a wide size range (60 nm to 9.8 µm), the aerosols in the accumulation mode were more hygroscopic and aerosols in the Aitken mode were less hygroscopic, with coarse mode aerosols being the least hygroscopic. During the polluted period, the particulate size notably increased, and the mass fraction of NO3- and SO42- in the accumulation mode aerosols was significantly higher than during the clean period. Accordingly, the hygroscopicity of accumulation mode aerosols was strongly enhanced during the polluted period[gκ(RH)=1.3-1.4] and aerosols in the 0.18-3.1 µm size range all had a strong hygroscopicity. On polluted days, the synergistic effect of the increase in particle size, water-soluble ions, and aerosol hygroscopicity results in the considerable deterioration of visibility.

[Construction and Evaluation on Size Resolved Source Apportionment Methods Based on Particle Size Distribution of Chemical Species].

The analysis of the sources of atmospheric particulate pollution can provide scientific support for the prevention and control of air pollution. Most particulate matter (PM) source analysis studies are based on the chemical composition of PM. In addition, particle size characteristics are also one of the important properties of PM. The accuracy of analytical results can be improved by analyzing the particle size characteristics of chemical components. In this study we aim to to solve the problem of insufficient utilization of component particle size information by using a the three-dimensional multi-particle size factor analysis model (ABB), where the particle size distribution of marked components is regarded as the constraint limit, and a multi-particle size source analytical model (SDABB) based on the characteristics of the components particle size distribution is constructed. The sensitivity of the SDABB model to the collinearity of the source spectrum and the similarity of the particle size distribution of the source contributions are investigated by evaluating the model through the simulation of the data set. The results showed that the ABB model was sensitive to the collinearity of the source spectrum and to the similarity of the particle size distribution of the source contributions. When particle size distribution rules were incorporated into the SDABB model, the effects of the two scenarios were significantly improved, that is, the SDABB model was able to better analyze collinear source spectrum and was insensitive to the similarity of the contribution particle size distribution.

[Evaluation of Different ISORROPIA-â ¡ Modes and the Influencing Factors of Aerosol pH Based on Tianjin Online Data].

Aerosol acidity is closely related to particle properties and the explosive growth of secondary particles. Aerosol pH is difficult to measure directly but can be estimated indirectly by thermodynamic equilibrium modeling. ISORROPIA-Ⅱ is one of the most commonly used thermodynamic models and includes different modes (forward and reverse) and aerosol states (stable and metastable). Studies have shown that the calculated pH results vary with the selected mode and phase state. In addition to the selection of modes and phases, there are also other factors that influence the modeling results. In order to explore the appropriate mode and phase selection of ISORROPIA-Ⅱ as well as the factors influencing the model results under the air pollution characteristics of typical Chinese cities, the simulation results of different modes and aerosol states were analyzed by using online hourly data for Tianjin. The results showed that the pH calculations using the forward mode and metastable state were satisfactory at a higher RH. With increased temperature, the pH, aerosol water content, and concentration proportion in the aerosol phase of semi-volatile components all decreased. RH affected aerosol pH by influencing the aerosol water content and concentration of semi-volatile components. An increased cation concentration led to an increased pH and NH3 concentration but a decreased HNO3 concentration, whereas an increased anion concentration had the opposite effect. Ca2+, SO42-, NO3-, and NH4+ had a great influence on pH. Compared with SO42-, NO3- had less effect on pH. Sensitive areas exist in the influence of NH4+ on pH, and a high NH4+ concentration did not cause a continuous pH increase. This study can improve the understanding of aerosol pH simulation using ISORROPIA-Ⅱ, and provides reference for research on the pH-related secondary generation mechanism, semi-volatile component gas-particle distribution, and pollution control measures.

[Influence of Typical Desulfurization Process on Flue Gas Particulate Matter of Coal-fired Boilers].

As flue gas desulfurization (FGD) was one of the most important purification processes of coal-fired boilers, we selected four boilers, which were equipped with wet limestone, furnace calcium injection, ammonia-based, and double-alkali FGDs, to research the influence of FGDs on the flue particulate matter (PM). The flue PM before and after the FGD were sampled using laboratory resuspension and dilution tunnel sampling methods, respectively, and the PM was analyzed for its chemical composition (i.e., ions, elements, and carbon). The results showed that the types of desulfurizers could influence the composition of the flue PM. After passing through the wet limestone, ammonia-based, and double-alkali FGDs, the proportion of Ca, NH4+, and Na in PM2.5 increased from 5.1% to 24.8%, from 0.8% to 7.3%, and from 0.9% to 1.7%, respectively. The influence of wet and dry FGDs on the flue PM were different. The fraction of ions in the PM emitted from the wet FGD were higher than those from the dry FGD. The proportion of SO42- in the flue PM2.5 increased from 2.0% and 6.7% to 9.6% and 11.9% using the wet limestone and ammonia-based FGDs, respectively, and Cl- increased from 0.4% and 1.2% to 3.8% and 5.2%. In addition, the amount of heavy metals (e.g., Cr, Pb, Cu, Ti, and Mn) in PM2.5 declined after the wet FGDs. The PM2.5 emitted from the dry FGD boiler was richer in crustal elements, such as Al, Si, and Fe, than that from the wet FGDs. The wet FGDs also effected the carbonaceous components of the flue PM. After passing through the wet limestone and ammonia-based FGDs, the proportion of elemental carbon in the flue PM2.5 decreased from 6.1% to 0.9% and from 3.6% to 0.7% respectively, but the organic carbon content did not decrease.

[Mixed State and Sources of Fine Particulate Matter in the Summer in Tianjin City Based on Single Particle Aerosol Mass Spectrometer (SPAMS)].

Tianjin is located in the Beijing-Tianjin-Hebei region. Recently, particulate matter pollution has received wide attention; therefore, studying the chemical composition and sources of particulate matter in the atmospheric environment is of great significance. To clarify the mixed state and possible sources of particulate matter in the summer ambient air in Tianjin, this study used single particle aerosol mass spectrometer (SPAMS) to collect 209887 samples. Particle size and complete spectrometry information were collected in July 2017. A total of 369 particle classes were obtained with respect to clustering particles with similarities in mass spectrometry characteristics using ART-2a. Then, according to the similarity in the chemical composition (mass spectrometry) of the categories, 19 particulate matter categories were artificially merged: K-EC (0.20%), K-EC-Sec (0.18%), K-NO3-PO3(12.00%), K-NO3-SiO3(2.98%), K-Sec (0.16%), EC (39.60%), EC-Sec (3.46%), EC-HM-Sec (3.93%), HEC (1.49%), HEC-Sec (1.38%), OC-Amine-Sec (3.58%), OC-Sec (0.36%), OCEC-Sec (0.71%), Dust-HEC (21.35%), Dust-Sec (0.72%), Cl-EC-NO3(1.22%), Na-Cl-NO3(3.20%), HM-Sec (2.58%), and PAH-Sec (0.90%). The obtained particle classes can be attributed to different sources of aerosol particles and different transmission and reaction processes. According to comprehensive analysis, the collected particle contribution sources were found to mainly include motor vehicle emission sources, biomass combustion sources, process sources, dust sources, and secondary processes. Among them, K-EC, EC, HEC, and Dust-HEC particles were mainly from direct emissions of primary sources. K-Sec, OC-Amine-Sec, OC-Sec, OCEC-Sec, Na-Cl-NO3, and PAH-Sec particles mainly undergo different degrees of aging or mixed with secondary components.

[PM2.5 Pollution Characteristics During Winter and Summer in the Hohhot-Baotou-Ordos Region, China].

Based on the source apportionment by positive matrix factorization (PMF) model, we analyze the main sources and characteristics of aerosol fine particulate matter (PM2.5) during winter and summer in the Hohhot-Baotou-Ordos region, China. We found that organic (19.9%-44.6%) and crustal compositions (9.7%-46.2%) accounted for a large proportion of aerosol PM2.5 according to the results of mass closure. The results of source apportionment showed that the contribution of sources rank as:secondary inorganic aerosol (26.7%) > coal (26.1%) > motor vehicle (19.1%) > dust (18.1%) during winter, and as:secondary inorganic aerosol (26.7%) > dust (22.3%) > coal (16.6%) > vehicle exhaust (15.1%) > SOC (8.7%) during summer. Findings suggest that the contribution of sources with secondary inorganic aerosol were the largest sources both in winter and summer, and that the Hohhot-Baotou-Ordos region was also affected by coal during the winter and dust during the summer. Corresponding to the source apportionment, analysis of typical heavy pollution episodes in winter and summer showed that the pollution sources during the winter were mainly secondary inorganic aerosol and coal, whereas they were mainly secondary inorganic aerosol during the summer.

[Characteristics of Component Particle Size Distributions of Particulate Matter Emitted from a Waste Incineration Plant].

There are few analyses on the components of particulate matter emitted from waste incineration plants. In past studies, analyses of particle size distribution characteristics of the components were mainly targeted at particles with larger particle sizes. An electrical low pressure impactor (ELPI) was used in this study to collect the particulate matter emitted from a waste incineration plant, and the elements and carbonaceous components of these samples were analyzed. The particle size characteristics of organic carbon (OC), elemental carbon (EC), and heavy metal elements in 14 particle size segments were analyzed and composition profiles of elements and carbonaceous components of PM1, PM2.5, and PM10 from the waste incineration plant were established to provide a reference for refined source apportionment research. The results showed that the main components of the waste incineration plant included Al, Si, S, Ca, Cr, Fe, OC, EC, etc. OC and Ca were dominating components, and mass fractions of these components in the PM2.5 profile were 10.15% and 12.37%, respectively. The contents of heavy metals were ranked as Cr > Pb > Zn > Mn > Cu > Cd > Ni, and the mass fractions of Cr and Pb in PM2.5 amounted to 1.83% and 0.74%, respectively. OC in the range of 2.39-3.99 and 6.68-9.91 µm accounted for 15.02% and 20.45% of the total OC content, respectively, and the content of OC in fine particles was higher than that in coarse particles. The content of EC in fine particles was much higher than that in coarse particles, and it accounted for 14.8% in the 0.382-0.613 µm particle size. Heavy metal elements such as Cr, Mn, Ni, Cu, Zn, Cd, and Pb were mainly concentrated in the fine particles.