Investigation of sources of atmospheric aerosol at urban and semi-urban areas in Bangladesh

Begum, Bilkis A.; Kim, Eugene; Biswas, Swapan Kumar; Hopke, Philip K.

doi:10.1016/j.atmosenv.2004.02.042

Cited by 268 publications

(148 citation statements)

References 31 publications

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“…Developed by Ashbaugh et al (1985), a CPF plot can be specifically used to determine possible local source regions using wind direction data (Begum et al 2004;Kim and Hopke 2004a;Kim et al 2004;Nicolás et al 2009a). This method combines the contributions of each source from the PMF and meteorological data (wind direction and velocity) to evaluate the influence of a local source upon the receptor site.…”

Section: Positive Matrix Factorization and Conditional Probability Fumentioning

confidence: 99%

“…Positive matrix factorization (PMF) is a new model based on a weighted non-negative least squares algorithm which overcomes this problem as it beforehand restricts the mentioned values in such a way that only positive values are allowed; moreover, experimental concentrations are weighted using their uncertainties. PMF has already been applied in numerous studies (Polissar et al 1998;Kim et al 2004;Kim and Hopke 2004b;Begum et al 2004;Nicolás et al 2008). The disadvantages of PCA (or FA) and advantages of advanced receptor models like PMF have been compared in different studies (e.g., Qin et al 2002;Viana et al 2008;Tauler et al 2009).…”

Section: Introductionmentioning

confidence: 99%

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PM10 source apportionment in the surroundings of the San Vicente del Raspeig cement plant complex in southeastern Spain

Yubero

Carratalá

Crespo

et al. 2010

Environ Sci Pollut Res

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Introduction The concentrations of trace metals, ionic species, and carbonaceous components in PM 10 (particulate matter with aerodynamic diameters smaller than 10 µm) were measured from samples collected near an industrial complex, primarily composed of cement plants, in southeastern Spain, from September 2005 to August 2006. Materials and methods Positive matrix factorization and conditional probability function were applied to this data set to identify different types of sources. Results Six significant sources were identified: crustal matter, traffic, aged sea salt, industrial emissions, secondary aerosol, and sea salt. The difficulty of separating anthropogenic sources from those of natural origin is highlighted in this study; in particular, the crustal source can be connected with both natural (African outbreaks, wind resuspension) and man-made emissions, like fugitive emissions in an industrial environment.

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Section: Positive Matrix Factorization and Conditional Probability Fumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PM10 source apportionment in the surroundings of the San Vicente del Raspeig cement plant complex in southeastern Spain

Yubero

Carratalá

Crespo

et al. 2010

Environ Sci Pollut Res

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show abstract

“…Factor 3 was represented by relatively high loadings of OC (55.9 %), EC (70.9 %), Cu (26.9 %), and Zn (26.5 %). OC and EC are generally predominant in the reported source profile of vehicle exhaust (Yao et al, 2016;Liu et al, 2016Liu et al, , 2017a, Zn is widely used as an additive for lubricant in two-stroke engines, and Cu is closely associated with brake wear (Begum et al, 2004;Canha et al, 2012;Shafer et al, 2012;Lin et al, 2015;Liu et al, 2017a). Therefore, factor 3 was identified as vehicle emissions.…”

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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“…All the hourly endpoints in back trajectories generated by the HYSPLIT 4.8 were classified into 1°l atitude by 1°longitude grid cells, which correspond to the area of Southeastern China, MSA, India and the Bay of Bengal. The PSCF value for the ijth cell was defined as (Begum et al, 2004):…”

Section: Potential Source Contribution Function (Pscf)mentioning

confidence: 99%

Atmospheric polybrominated diphenyl ethers (PBDEs) and Pb isotopes at a remote site in Southwestern China: Implications for monsoon-associated transport

Yue

Zhang

et al. 2011

Science of The Total Environment

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Investigation of sources of atmospheric aerosol at urban and semi-urban areas in Bangladesh

Cited by 268 publications

References 31 publications

PM10 source apportionment in the surroundings of the San Vicente del Raspeig cement plant complex in southeastern Spain

PM10 source apportionment in the surroundings of the San Vicente del Raspeig cement plant complex in southeastern Spain

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

Atmospheric polybrominated diphenyl ethers (PBDEs) and Pb isotopes at a remote site in Southwestern China: Implications for monsoon-associated transport

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