A refined source apportionment study of atmospheric PM2.5 during winter heating period in Shijiazhuang, China, using a receptor model coupled with a source-oriented model

2020 ◽  
Author(s):  
Baoshuang Liu ◽  
Yufen Zhang ◽  
Yinchang Feng ◽  
Qili Dai ◽  
Congbo Song
Keyword(s):  
Source Apportionment ◽  
Coal Combustion ◽  
Road Dust ◽  
Source Control ◽  
Receptor Model ◽  
Heating Period ◽  
Vehicle Exhaust ◽  
Industrial Emissions ◽  
Secondary Sources ◽  
Source Category

<p>With the intensification of Chinese source control of air pollution, there is an urgent need for refined and rapid source apportionment techniques. A refined source apportionment method was constructed based on an off-line sampling dataset using a receptor model coupled with a source-oriented model, and the method was implemented in Shijiazhuang during the heating period. The refined results for source apportionment mainly included temporal, spatial, and source-category refinement data. The results indicated that the mean concentration of PM<sub>2.5</sub> during the heating period was 96 μg/m<sup>3</sup>. Organic carbon (OC) and NO<sub>3</sub><sup>-</sup> were found to be the dominant species of PM<sub>2.5</sub> during the study. A high correlation was detected between elemental carbon (EC) and NO<sub>3</sub><sup>–</sup> on polluted days, which was suggestive of the stagnant condition that accumulates EC and nitrate simultaneously. Secondary particle formation greatly promoted the occurrence of haze events. Secondary sources (34.9%), vehicle exhaust (18.6%), coal combustion (20.0%), industrial emissions (9.2%), crustal dust (9.7%), and biomass burning (7.6%) were the major sources during the heating period. The contributions of secondary sources and vehicle exhaust increased on polluted days, while those of coal combustion, industrial emissions and crustal dust decreased significantly. The contribution percentage of secondary sources from the southeast direction was basically the highest, while those of vehicle exhaust from the northwest or southeast directions were relatively higher as well, likely due to the distribution of traffic arteries. Based on the refined results for the source-category assessment, we found that the heating boilers (17.0%), non-road mobile (13.8%), diesel vehicles (10.4%), residential combustion (6.7%), road dust (5.5%), and architectural material industry (4.9%) were the major contributors to PM<sub>2.5</sub>. There was some uncertainty in the distribution proportions of the refined results, which were derived based on the emission inventory and the results of CALPUFF model.</p>

scholarly journals Chemical Composition and Source Apportionment of PM2.5 in Urban Areas of Xiangtan, Central South China

2019 ◽  
Vol 16 (4) ◽  
pp. 539 ◽  
Author(s):  
Xiaoyao Ma ◽  
Zhenghui Xiao ◽  
Lizhi He ◽  
Zongbo Shi ◽  
Yunjiang Cao ◽  
...  
Keyword(s):  
Source Apportionment ◽  
South China ◽  
Urban Areas ◽  
Inorganic Ions ◽  
Water Soluble ◽  
Total Carbon ◽  
Anthropogenic Sources ◽  
Vehicle Exhaust ◽  
Industrial Emissions ◽  
Wind Speeds

Xiangtan, South China, is characterized by year-round high relative humidity and very low wind speeds. To assess levels of PM2.5, daily samples were collected from 2016 to 2017 at two urban sites. The mass concentrations of PM2.5 were in the range of 30–217 µg/m3, with the highest concentrations in winter and the lowest in spring. Major water-soluble ions (WSIIs) and total carbon (TC) accounted for 58–59% and 21–24% of the PM2.5 mass, respectively. Secondary inorganic ions (SO42−, NO3−, and NH4+) dominated the WSIIs and accounted for 73% and 74% at the two sites. The concentrations of K, Fe, Al, Sb, Ca, Zn, Mg, Pb, Ba, As, and Mn in the PM2.5 at the two sites were higher than 40 ng/m3, and decreased in the order of winter > autumn > spring. Enrichment factor analysis indicates that Co, Cu, Zn, As, Se, Cd, Sb, Tl, and Pb mainly originates from anthropogenic sources. Source apportionment analysis showed that secondary inorganic aerosols, vehicle exhaust, coal combustion and secondary aerosols, fugitive dust, industrial emissions, steel industry are the major sources of PM2.5, contributing 25–27%, 21–22%, 19–21%, 16–18%, 6–9%, and 8–9% to PM2.5 mass.

Source Apportionment of PM10 in the Open Air of Urban Area in Tangshan

2012 ◽  
Vol 209-211 ◽  
pp. 1545-1548
Author(s):  
Ai Min Ji ◽  
Shu Mei Yan ◽  
Ying Gao ◽  
Shu Ran Wan ◽  
Hong Ya Liu
Keyword(s):  
Particulate Matter ◽  
Mass Balance ◽  
Coal Combustion ◽  
Chemical Mass Balance ◽  
Receptor Model ◽  
Inorganic Elements ◽  
Soil Dust ◽  
Vehicle Exhaust ◽  
Water Solvent ◽  
Suspended Dust

All sources of the particulate matter less than 10 micrometers (PM10) were collected in Tangshan. Inorganic elements, water-solvent ions and carbon species of PM10 samples were investigated to identify the sources of PM10. Contribution and sharing rate of suspended dust, soil dust, coal smoke dust, construction dust, vehicles exhaust, SO42-, NO3- and sea dust to PM10 was determined based on the chemical mass balance (CMB) receptor model. The results showed that suspended dust was the most important PM10 source with a contribution of 42%; flying ash from coal combustion, particle dust from soil, vehicle exhaust were also important sources with contributions of 20%, 12% and 11%, respectively.

scholarly journals Insight into PM<sub>2.5</sub> Sources by Applying Positive Matrix Factorization (PMF) at an Urban and Rural Site of Beijing

2021 ◽  
Author(s):  
Deepchandra Srivastava ◽  
Jingsha Xu ◽  
Tuan V. Vu ◽  
Di Liu ◽  
Linjie Li ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Biomass Burning ◽  
Coal Combustion ◽  
Road Dust ◽  
Rural Site ◽  
Winter Period ◽  
Soil Dust ◽  
Beijing Area ◽  
Aerosol Mass ◽  
Inorganic Species

Abstract. This study presents the source apportionment of PM2.5 performed by PMF on data collected at an urban (Institute of Atmospheric Physics – IAP) and a rural site (Pinggu-PG) in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-Beijing) field campaigns. The campaigns were carried out from 9th November to 11th December 2016 and 22nd May to 24th June 2017. The PMF included both organic and inorganic species, and a seven-factor output provided the most reasonable solution for the PM2.5 source apportionment. These factors are interpreted to be traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion and secondary inorganics. Major contributors to PM2.5 mass were secondary inorganics (22–24 %), biomass burning (30–36 %), and coal combustion (20–21 %) sources during the winter period at both sites. Secondary inorganics (48 %), road dust (20 %) and coal combustion (17 %) showed the highest contribution during summer at PG, while PM2.5 particles were mainly composed of soil dust (35 %) and secondary inorganics (40 %) at IAP. Despite this, factors that were resolved based on metal signatures were not fully resolved and indicate a mixing of two or more sources. PMF results were also compared with sources resolved from another receptor model (i.e. CMB) and PMF performed on other measurements (i.e. online and offline aerosol mass spectrometry (AMS)) and showed a good agreement for some but not all sources. The biomass burning factor in PMF may contain aged aerosols as a good correlation was observed between biomass burning and oxygenated fractions (r2 = 0.6–0.7) from AMS. The PMF failed to resolve some sources identified by the CMB and AMS, and appears to overestimate the dust sources. A comparison with earlier PMF source apportionment studies from the Beijing area highlights the very divergent findings from application of this method.

scholarly journals Characteristics of the main primary source profiles of particulate matter across China: from 1987 to 2017

2018 ◽  
Author(s):  
Xiaohui Bi ◽  
Yuan Cheng ◽  
Qili Dai ◽  
Jianhui Wu ◽  
Jiaying Zhang ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Biomass Burning ◽  
Coal Combustion ◽  
Vehicle Emissions ◽  
Sampling Methods ◽  
Road Dust ◽  
Fugitive Dust ◽  
Soil Dust ◽  
Industrial Emissions ◽  
Source Profiles

Abstract. Based on the published literatures and typical profiles from the source library of Nankai University, a total of 3244 chemical profiles of the main primary sources of ambient particulate matter across China from 1987 to 2017, including coal combustion, industrial emissions, vehicle emissions, fugitive dust, biomass burning, and cooking emissions, were investigated and reviewed to trace the evolution of their main components and identify the main influencing factors to the evolution. As a result, the most complicated profiles are likely attributed to coal combustion and industrial emissions, which are evidently influenced by the decontamination processes and sampling techniques as well as the coal nature and the boiler types. The profiles of vehicle emissions are dominated by OC and EC, and varied with the changing standard of sulfur and additives in the gasoline and diesel as well as the sampling methods. The profiles of fugitive dust, such as soil dust and road dust, are dominated by the crustal materials and influenced by the sampling methods to some extent. The profiles of biomass burning is impacted mainly by the biomass categories and sampling methods. As expected, the profiles of cooking emissions is impacted mainly by the cooking types and materials. The uncertainty analysis and cluster analysis of all these source profiles are conducted to reveal the variations of the different source profiles in the same source category and evaluate the differences between source categories. A relatively large variation has been founded in the source profiles of coal combustion, vehicle emissions, industry emissions and biomass burning, indicating that it is necessary to establish the local profiles for these sources due to their high uncertainties. While the profiles of road dust and soil dust present a less variation with the stable chemical characteristics among the different profiles in the same category, suggesting that the profiles of these sources could be referenced for the cities in China when such local profiles are not available. The presented results highlight the need for increased investigation of more specific markers beyond routine measured components (e.g., isotopes, organic compounds and gaseous precursors) to discriminate sources. Additionally, specific focus should be placed on the sub-type of source profiles in the future, especially for local industrial emissions and geographical areas in China, to support the air quality research communities in their efforts to develop high resolution source apportionment for making a more effective control strategies.

scholarly journals Volatile Organic Compounds in a Petrochemical Region in Arid of NW China: Chemical Reactivity and Source Apportionment

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 641 ◽  
Author(s):  
Xiaoxiao Zhang ◽  
Xiang Ding ◽  
Xinming Wang ◽  
Dilinuer Talifu ◽  
Guo Wang ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Source Apportionment ◽  
Organic Compounds ◽  
Coal Combustion ◽  
Chemical Reactivity ◽  
Abundant Species ◽  
Heating Period ◽  
Nw China ◽  
Average Maximum ◽  
Volatile Organic

We measured volatile organic compounds (VOCs) during the heating, non-heating, and sandstorm periods in the air of the Dushanzi district in NW China and investigated their concentrations, chemical reactivity, and sources. The observed concentrations of total VOCs (TVOCs) were 22.35 ± 17.60, 33.20 ± 34.15, and 17.05 ± 13.61 ppbv in non-heating, heating, and sandstorm periods, respectively. C2-C5 alkanes, C2-C3 alkenes, benzene, and toluene were the most abundant species, contributing more than 60% of the TVOCs. Among these VOCs, alkenes such as propene had the highest chemical reactivity, accounting for more than 60% of total hydroxyl radical loss rate (LOH) and ozone formation potential (OFP). Chemical reactivity was the highest in the heating period. The average reaction rate constant (KOH-avg) and average maximum incremental reactivity coefficient (MIR-avg) of the total observed VOCs were (8.72 ± 1.42) × 10−12 cm3/mol∙s and 2.42 ± 0.16 mol/mol, respectively. The results of the source apportionment via the Positive Matrix Factorization (PMF) model showed that coal combustion (43.08%) and industrial processes (38.86%) were the major sources of VOCs in the air of the Dushanzi district. The contribution of coal combustion to VOCs was the highest in the heating period, while that of industrial solvents and oil volatilization was the lowest.

scholarly journals Effectiveness evaluation of temporary emission control action in 2016 in winter in Shijiazhuang, China

2018 ◽  
Vol 18 (10) ◽  
pp. 7019-7039 ◽  
Author(s):  
Baoshuang Liu ◽  
Yuan Cheng ◽  
Ming Zhou ◽  
Danni Liang ◽  
Qili Dai ◽  
...  
Keyword(s):  
Atmospheric Pollution ◽  
Coal Combustion ◽  
Meteorological Conditions ◽  
Control Measures ◽  
Atmospheric Pollutants ◽  
Control Action ◽  
Emission Sources ◽  
Heating Period ◽  
Industrial Emissions ◽  
Potential Source

Abstract. To evaluate the environmental effectiveness of the control measures for atmospheric pollution in Shijiazhuang, China, a large-scale controlling experiment for emission sources of atmospheric pollutants (i.e. a temporary emission control action, TECA) was designed and implemented during 1 November 2016 to 9 January 2017. Compared to the no-control action and heating period (NCAHP), under unfavourable meteorological conditions, the mean concentrations of PM2.5, PM10, SO2, NO2, and chemical species (Si, Al, Ca2+, Mg2+) in PM2.5 during the control action and heating period (CAHP) still decreased by 8, 8, 5, 19, 30.3, 4.5, 47.0, and 45.2 %, respectively, indicating that the control measures for atmospheric pollution were effective. The effects of control measures in suburbs were better than those in urban area, especially for the control effects of particulate matter sources. The control effects for emission sources of carbon monoxide (CO) were not apparent during the TECA period, especially in suburbs, likely due to the increasing usage of domestic coal in suburbs along with the temperature decreasing.The results of positive matrix factorization (PMF) analysis showed that crustal dust, secondary sources, vehicle emissions, coal combustion and industrial emissions were main PM2.5 sources. Compared to the whole year (WY) and the no-control action and no-heating period (NCANHP), the contribution concentrations and proportions of coal combustion to PM2.5 increased significantly during other stages of the TECA period. The contribution concentrations and proportions of crustal dust and vehicle emissions to PM2.5 decreased noticeably during the CAHP compared to other stages of the TECA period. The contribution concentrations and proportions of industrial emissions to PM2.5 during the CAHP decreased noticeably compared to the NCAHP. The pollutants' emission sources during the CAHP were in effective control, especially for crustal dust and vehicles. However, the necessary coal heating for the cold winter and the unfavourable meteorological conditions had an offset effect on the control measures for emission sources to some degree. The results also illustrated that the discharge of pollutants might still be enormous even under such strict control measures.The backward trajectory and potential source contribution function (PSCF) analysis in the light of atmospheric pollutants suggested that the potential source areas mainly involved the surrounding regions of Shijiazhuang, i.e. south of Hebei and north of Henan and Shanxi. The regional nature of the atmospheric pollution in the North China Plain revealed that there is an urgent need for making cross-boundary control policies in addition to local control measures given the high background level of pollutants.The TECA is an important practical exercise but it cannot be advocated for as the normalized control measures for atmospheric pollution in China. The direct cause of atmospheric pollution in China is the emission of pollutants exceeding the air environment's self-purification capacity, which is caused by an unreasonable and unhealthy pattern for economic development in China.

scholarly journals Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

2021 ◽  
Vol 21 (19) ◽  
pp. 14703-14724
Author(s):  
Deepchandra Srivastava ◽  
Jingsha Xu ◽  
Tuan V. Vu ◽  
Di Liu ◽  
Linjie Li ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Biomass Burning ◽  
Matrix Factorization ◽  
Coal Combustion ◽  
Road Dust ◽  
Positive Matrix Factorization ◽  
Winter Period ◽  
Soil Dust ◽  
Positive Matrix ◽  
Inorganic Species

Abstract. This study presents the source apportionment of PM2.5 performed by positive matrix factorization (PMF) on data presented here which were collected at urban (Institute of Atmospheric Physics – IAP) and rural (Pinggu – PG) sites in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-Beijing) field campaigns. The campaigns were carried out from 9 November to 11 December 2016 and from 22 May to 24 June 2017. The PMF analysis included both organic and inorganic species, and a seven-factor output provided the most reasonable solution for the PM2.5 source apportionment. These factors are interpreted as traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion, and secondary inorganics. Major contributors to PM2.5 mass were secondary inorganics (IAP: 22 %; PG: 24 %), biomass burning (IAP: 36 %; PG: 30 %), and coal combustion (IAP: 20 %; PG: 21 %) sources during the winter period at both sites. Secondary inorganics (48 %), road dust (20 %), and coal combustion (17 %) showed the highest contribution during summer at PG, while PM2.5 particles were mainly composed of soil dust (35 %) and secondary inorganics (40 %) at IAP. Despite this, factors that were resolved based on metal signatures were not fully resolved and indicate a mixing of two or more sources. PMF results were also compared with sources resolved from another receptor model (i.e. chemical mass balance – CMB) and PMF performed on other measurements (i.e. online and offline aerosol mass spectrometry, AMS) and showed good agreement for some but not all sources. The biomass burning factor in PMF may contain aged aerosols as a good correlation was observed between biomass burning and oxygenated fractions (r2= 0.6–0.7) from AMS. The PMF failed to resolve some sources identified by the CMB and AMS and appears to overestimate the dust sources. A comparison with earlier PMF source apportionment studies from the Beijing area highlights the very divergent findings from application of this method.

scholarly journals Source Analysis and Human Health Risk Assessment Based on Entropy Weight Method Modification of PM2.5 Heavy Metal in an Industrial Area in the Northeast of China

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 852
Author(s):  
Siyu Sun ◽  
Na Zheng ◽  
Sujing Wang ◽  
Yunyang Li ◽  
Shengnan Hou ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Human Health ◽  
Coal Combustion ◽  
Human Health Risk ◽  
Traffic Emissions ◽  
Adult Males ◽  
Heating Period ◽  
Industrial Emissions ◽  
Entropy Weight ◽  
Entropy Weight Method

In this study, PM2.5 was analyzed for heavy metals at two sites in industrial northeast China to determine their sources and human health risks during heating and non-heating periods. A positive matrix factorization (PMF) model determined sources, and US Environmental Protection Agency (USEPA) and entropy weight methods were used to assess human health risk. PM2.5 heavy metal concentrations were higher in the heating period than in the non-heating period. In the heating period, coal combustion (59.64%) was the primary heavy metal source at Huagong Hospitals, and the contribution rates of industrial emissions and traffic emissions were 21.06% and 19.30%, respectively. Industrial emissions (42.14%) were the primary source at Xinqu Park, and the contribution rates of coal combustion and traffic emissions were 34.03% and 23.83%, respectively. During the non-heating period, coal combustion (45.29%) and industrial emissions 45.29% and 44.59%, respectively, were the primary sources at Huagong Hospital, and the traffic emissions were 10.12%. Industrial emissions (43.64%) were the primary sources at Xinqu Park, where the coal combustion and traffic emissions were 25.35% and 31.00%, respectively. In the heating period, PM2.5 heavy metals at Xinqu Park had noncarcinogenic and carcinogenic risks, and the hazard index of children (5.74) was higher than that of adult males (5.28) and females (4.49). However, adult males and females had the highest lifetime carcinogenic risk (1.38 × 10−3 and 1.17 × 10−3) than children (3.00 × 10−4). The traditional USEPA and entropy weight methods both produced reasonable results. However, when there is a difference between the two methods, the entropy weight method is recommended to assess noncarcinogenic health risks.

scholarly journals Determination and Similarity Analysis of PM2.5 Emission Source Profiles Based on Organic Markers for Monterrey, Mexico

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 554
Author(s):  
Yasmany Mancilla ◽  
Gerardo Medina ◽  
Lucy T. González ◽  
Pierre Herckes ◽  
Matthew P. Fraser ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Ad Hoc ◽  
Pollution Abatement ◽  
Chemical Species ◽  
Airborne Particulate Matter ◽  
Source Attribution ◽  
Vehicle Exhaust ◽  
Industrial Emissions ◽  
Source Profiles ◽  
Organic Markers

Source attribution of airborne particulate matter (PM) relies on a host of different chemical species. Organic molecular markers are a set of particularly useful marker compounds for estimating source contributions to the fine PM fraction (i.e., PM2.5). Although there are many source apportionment studies based on organic markers, these studies heavily rely on the few studies that report region-specific emission profiles. Source attribution efforts, particularly those conducted in countries with emerging economies, benefit from ad hoc information to conduct the corresponding analyses. In this study, we report organic molecular marker source profiles for PM2.5 emitted from 12 major sources types from five general source categories (meat cooking operations, vehicle exhausts, industries, biomass and trash burning, and urban background) for the Monterrey Metropolitan Area (Mexico). Source emission samples were obtained from a ground-based source-dominated sampling approach. Filter-based instruments were utilized, and the loaded filters were chemically characterized for organic markers by GC-MS. Levoglucosan and cholesterol dominate charbroiled-cooking operation sources while methoxyphenols, PAHs and hopanes dominate open-waste burning, vehicle exhaust and industrial emissions, respectively. A statistical analysis showed values of the Pearson distance < 0.4 and the similarity identity distance > 0.8 in all cases, indicating dissimilar source profiles. This was supported by the coefficient of divergence average values that ranged from 0.62 to 0.72. These profiles could further be utilized in receptor models to conduct source apportionment in regions with similar characteristics and can also be used to develop air pollution abatement strategies.

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