Chemical characterization of atmospheric particles and source apportionment in the vicinity of a steelmaking industry

Almeida, Susana Marta; Lage, Joana; Fernández, Beatriz; Garcia, S.; Reis, M.A.; Chaves, P.C.

doi:10.1016/j.scitotenv.2015.03.112

Cited by 84 publications

(33 citation statements)

References 40 publications

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“…4, the mean EFs of Ca, Ba, K, Mn, V and Cr are below 10, which suggest that these elements would be more likely originated from natural sources such as crustal soil and re-suspended soil and have no obvious enrichment in particle matter (Xu et al, 2013b). It is worth to notice for K the 90% percentage is over 10, and there are outliers over 10, and K is associated to biomass burning as reported in many other studies (e.g., Hleis et al, 2013;Almeida et al, 2015). Zn has different range of enrichment factor with the mean of 164.46 (Fig.…”

Section: Enrichment Factormentioning

confidence: 68%

Characteristics and Source Analysis of Trace Elements in PM2.5 in the Urban Atmosphere of Wuhan in Spring

Acciai

Zhang

Wang

et al. 2017

Aerosol Air Qual. Res.

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The concentrations of PM 2.5 and trace elements with hourly resolution were measured during May 2014 in urban residential area of Wuhan, the biggest city in central China. During the period of measurement, the average temperature was approximate 25°C without domestic heating and cooling. The average concentration of PM 2.5 was 95.53 µg m -3 , which was higher than the limit of Ambient Air Quality Standard of China GB3095-2012 (75 µg m -3 , Level 2). A sand storm original from Northwestern China was also recorded. Concentrations of major trace elements (K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, As, Se, Pd, Ag, Cd, Au, Hg, Pb, Co, Sn, Sb, Tl) were comparable to previous studies, except for Ba and Ca with more than doubled concentrations (103 ng m -3 , 1,792.49 ng m -3 ) due to the storm. Enrichment Factor (EF) and Positive Matrix Factorization (PMF) were employed to characterize the emission sources. The computation of EF showed that Zincs was highly enriched. Four sources, biomass burning (63.2%) might mainly related to power plants using biowaste burning and bio-related cooking activities, metallurgical and steel industries (14%), dust crustal (12.5%) and dust associated with vehicular traffic (10.4%), were identified in decreasing order of average percentage contribution to the PM 2.5 mass with the aid of trace elements. The orientations of emission sources were also addressed.

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Section: Enrichment Factormentioning

confidence: 68%

Characteristics and Source Analysis of Trace Elements in PM2.5 in the Urban Atmosphere of Wuhan in Spring

Acciai

Zhang

Wang

et al. 2017

Aerosol Air Qual. Res.

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“…The contribution proportions of factor 4 to PM 2.5 increased from 26.2 % (51.7 µg m −3 ) during the WY, 28.0 % (63.2 µg m −3 ) during the NCANHP, and 29.5 % (84.0 µg m −3 ) during the NCAHP to 31.7 % (85.2 µg m −3 ) during the CAHP, and lightly increased to 32.6 % (96.3 µg m −3 ) during the ACA. Factor 5 was identified as industrial emissions, with high loadings of Cr (66.7 %), Cu (63.7 %), Fe (83.2 %), Mn (51.3 %), Ti (70.0 %), Zn (69.2 %), Pb (42.1 %), and Cl − (41.0 %) (Morishita et al, 2011;Mansha et al, 2012;Almeida et al, 2015;Liu et al, 2015;Liu et al, 2016;Yao et al, 2016). The contribution proportions of factor 5 to PM 2.5 ranged from 5.0 % (11.3 µg m −3 ) during the NCANHP and 5.1 % (10.0 µg m −3 ) during the WY to 5.9 % (16.7 µg m −3 ) during the NCAHP, and decreased to 5.3 % (14.2 µg m −3 ) during the CAHP and 4.9 % (14.4 µg m −3 ) during the ACA.…”

Section: Variations In Pm 25 Source Contributionsmentioning

confidence: 99%

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

et al. 2018

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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 PM 2.5 , PM 10 , SO 2 , NO 2 , and chemical species (Si, Al, Ca 2+ , Mg 2+ ) in PM 2.5 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 PM 2.5 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 PM 2.5 increased significantly during other stages of the TECA period. The contribution concentrations and proportions of crustal dust and vehicle emissions to PM 2.5 decreased noticeably during the CAHP compared to other stages of the TECA period. The contribution concentrations and proportions of industrial emissions to PM 2.5 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.

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“…In the current study source apportionment was performed using positive matrix factorization (PMF)-EPA-PMF 3.0 [24], which was developed by Paatero [25]. PMF can decompose the data matrix into two sub-data matrixes-the factor profiles and factor contributions without detailed prior knowledge on source inventories [26]. Moreover, non-negative constrains are implemented in order to obtain more physically explainable factors [22].…”

Section: Source Apportionmentmentioning

confidence: 99%

“…Data below the limit of quantification (LOQ) were replaced by half of the LOQ and the uncertainties were set to 5/6 the LOQ. Missing data were replaced by geometric mean of the measured values and their accompanying uncertainties were set as four times these geometric mean values [26].…”

Section: Source Apportionmentmentioning

confidence: 99%

Source apportionment by positive matrix factorization on elemental concentration obtained in PM10 and biomonitors collected in the vicinities of a steelworks

Lage

Wolterbeek

Reis

et al. 2016

J Radioanal Nucl Chem

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The objective of this study was to assess the impact of steelworks emissions in its vicinity through chemical element analysis. Two approaches were used: instrumental monitoring and biomonitoring using transplanted lichens. Element contents in filters and lichens were determined by k 0 -INAA and PIXE and sources identification was performed by the receptor model positive matrix factorization. PM10 data indicated that the steelworks has an important impact on the air quality, having several sources associated with its processes been identified. Lichen analyses showed that this impact decrease significantly with the distance to the factory.

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Chemical characterization of atmospheric particles and source apportionment in the vicinity of a steelmaking industry

Cited by 84 publications

References 40 publications

Characteristics and Source Analysis of Trace Elements in PM2.5 in the Urban Atmosphere of Wuhan in Spring

Characteristics and Source Analysis of Trace Elements in PM2.5 in the Urban Atmosphere of Wuhan in Spring

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

Source apportionment by positive matrix factorization on elemental concentration obtained in PM10 and biomonitors collected in the vicinities of a steelworks

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