Araştırma Makalesi
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Organik Aerosolün Çok Boyutlu Sıvı Kromatografi Sistemleri ile Günlük Dağılımının Gösterimi

Yıl 2021, Cilt: 33 Sayı: 2, 277 - 286, 31.03.2021
https://doi.org/10.7240/jeps.808964

Öz

Aerosols have various effects on human health, climate, and ecosystems. Aerosols also have an important role in climate change by altering the radiation balance and lifetime and properties of clouds. Organic aerosol (OA) is composed of a mixture of hundreds to thousands of organic compounds that varies geographically, diurnally, and seasonally according to several factors, such as type and concentration of precursor, type and concentration of oxidants, temperature, relative humidity, among others. The type and number of functional groups composing individual organic species influence their interaction with water vapor and sunlight, therefore affecting both the water cycle and radiation balance. Great efforts have been made to develop analytical techniques that allow the identification and quantification of individual species composing this complex mixture of organic matter and decrease the uncertainty of models to accurately predict OA formation and evolution. It was found that most of the column combinations were not adequate and the best system was provided by methanol: water (1:1). The combination of columns that provided the best separation of the standard compounds was provided by Cyclohexyl and Methyl as primary and secondary columns. However, when this combination of columns was used to represent the evolution of ambient air organic aerosols, significant co-elution was observed. The positive outcome of this study was helpful to evaluate 2D-LC as a potential technique for more accurate determination of organic aerosol components and to understand organic aerosol formation and transformation pathways that can be used in secondary organic aerosol production models.

Destekleyen Kurum

TÜBITAK

Proje Numarası

116Y041

Kaynakça

  • [1] Kim, S.-Y., J.L. Peel, M.P. Hannigan, S.J. Dutton, L. Sheppard, M.L. Clark, and S. Vedal, (2012). The temporal lag structure of short-term associations of fine particulate matter chemical constituents and cardiovascular and respiratory hospitalizations. Environ Health Persp, 120 (8), 1094-1099.
  • [2] Lu, Z., D.G. Streets, E. Winijkul, F. Yan, Y. Chen, T.C. Bond, Y. Feng, M.K. Dubey, S. Liu, and J.P. Pinto, (2015). Light absorption properties and radiative effects of primary organic aerosol emissions. Environmental science & technology, 49 (8), 4868-4877.
  • [3] Seinfeld, J.H. and J.F. Pankow, (2003). Organic atmospheric particulate material. Annu Rev Phys Chem, 54 (1), 121-140.
  • [4] Daumit, K.E., S.H. Kessler, and J.H. Kroll, (2013). Average chemical properties and potential formation pathways of highly oxidized organic aerosol. Faraday Discuss 165 181-202.
  • [5] Poole, C.F., Chapter 1 - General Concepts in Column Chromatography, in The Essence of Chromatography, C.F. Poole, Editor. 2003, Elsevier Science: Amsterdam. p. 1-78.
  • [6] Abraham, M.H., H.S. Chadha, R.A. Leitao, R.C. Mitchell, W.J. Lambert, R. Kaliszan, A. Nasal, and P. Haber, (1997). Determination of solute lipophilicity, as log P (octanol) and log P (alkane) using poly (styrene–divinylbenzene) and immobilised artificial membrane stationary phases in reversed-phase high-performance liquid chromatography. J Chromatogr A, 766 (1-2), 35-47.
  • [7] Abraham, M.H., M. Rosés, C.F. Poole, and S.K. Poole, (1997). Hydrogen bonding. 42. Characterization of reversed‐phase high‐performance liquid chromatographic C18 stationary phases. J Phys Org Chem, 10 (5), 358-368.
  • [8] Poole, C.F. and S.K. Poole, (2002). Column selectivity from the perspective of the solvation parameter model. J Chromatogr A, 965 (1-2), 263-299.
  • [9] Reta, M., P.W. Carr, P.C. Sadek, and S.C. Rutan, (1999). Comparative study of hydrocarbon, fluorocarbon, and aromatic bonded RP-HPLC stationary phases by linear solvation energy relationships. Anal Chem, 71 (16), 3484-3496.
  • [10] Sándi, Á. and L. Szepesy, (1998). Characterization of various reversed-phase columns using the linear free energy relationship: II. Evaluation of selectivity. J Chromatogr A, 818 (1), 19-30.
  • [11] Tan, L.C., P.W. Carr, and M.H. Abraham, (1996). Study of retention in reversed-phase liquid chromatography using linear solvation energy relationships I. The stationary phase. J Chromatogr A, 752 (1-2), 1-18.
  • [12] Zhao, J. and P.W. Carr, (1998). Comparison of the retention characteristics of aromatic and aliphatic reversed phases for HPLC using linear solvation energy relationships. Anal Chem, 70 (17), 3619-3628.
  • [13] Zhao, J. and P.W. Carr, (1999). An approach to the concept of resolution optimization through changes in the effective chromatographic selectivity. Anal Chem, 71 (14), 2623-2632.
  • [14] Flores, R.M. and P.V. Doskey, (2014). Using multidimensional gas chromatography to group secondary organic aerosol species by functionality. Atmos Environ, 96 (0), 310-321.
  • [15] Seeley, J.V., E.M. Libby, K.A.H. Edwards, and S.K. Seeley, (2009). Solvation parameter model of comprehensive two-dimensional gas chromatography separations. J Chromatogr A, 1216 (10), 1650-1657.
  • [16] Smith, R.M., Chapter 3 Retention index scales used in high-performance liquid chromatography, in Journal of Chromatography Library, R.M. Smith, Editor. 1995, Elsevier. p. 93-144.

Representation of the Diurnal Distribution of Organic Aerosol by Multidimensional Liquid Chromatography Systems

Yıl 2021, Cilt: 33 Sayı: 2, 277 - 286, 31.03.2021
https://doi.org/10.7240/jeps.808964

Öz

Aerosols have various effects on human health, climate, and ecosystems. Aerosols also have an important role in climate change by altering the radiation balance and lifetime and properties of clouds. Organic aerosol (OA) is composed of a mixture of hundreds to thousands of organic compounds that varies geographically, diurnally, and seasonally according to several factors, such as type and concentration of precursor, type and concentration of oxidants, temperature, relative humidity, among others. The type and number of functional groups composing individual organic species influence their interaction with water vapor and sunlight, therefore affecting both the water cycle and radiation balance. Great efforts have been made to develop analytical techniques that allow the identification and quantification of individual species composing this complex mixture of organic matter and decrease the uncertainty of models to accurately predict OA formation and evolution. It was found that most of the column combinations were not adequate and the best system was provided by methanol: water (1:1). The combination of columns that provided the best separation of the standard compounds was provided by Cyclohexyl and Methyl as primary and secondary columns. However, when this combination of columns was used to represent the evolution of ambient air organic aerosols, significant co-elution was observed. The positive outcome of this study was helpful to evaluate 2D-LC as a potential technique for more accurate determination of organic aerosol components and to understand organic aerosol formation and transformation pathways that can be used in secondary organic aerosol production models.

Proje Numarası

116Y041

Kaynakça

  • [1] Kim, S.-Y., J.L. Peel, M.P. Hannigan, S.J. Dutton, L. Sheppard, M.L. Clark, and S. Vedal, (2012). The temporal lag structure of short-term associations of fine particulate matter chemical constituents and cardiovascular and respiratory hospitalizations. Environ Health Persp, 120 (8), 1094-1099.
  • [2] Lu, Z., D.G. Streets, E. Winijkul, F. Yan, Y. Chen, T.C. Bond, Y. Feng, M.K. Dubey, S. Liu, and J.P. Pinto, (2015). Light absorption properties and radiative effects of primary organic aerosol emissions. Environmental science & technology, 49 (8), 4868-4877.
  • [3] Seinfeld, J.H. and J.F. Pankow, (2003). Organic atmospheric particulate material. Annu Rev Phys Chem, 54 (1), 121-140.
  • [4] Daumit, K.E., S.H. Kessler, and J.H. Kroll, (2013). Average chemical properties and potential formation pathways of highly oxidized organic aerosol. Faraday Discuss 165 181-202.
  • [5] Poole, C.F., Chapter 1 - General Concepts in Column Chromatography, in The Essence of Chromatography, C.F. Poole, Editor. 2003, Elsevier Science: Amsterdam. p. 1-78.
  • [6] Abraham, M.H., H.S. Chadha, R.A. Leitao, R.C. Mitchell, W.J. Lambert, R. Kaliszan, A. Nasal, and P. Haber, (1997). Determination of solute lipophilicity, as log P (octanol) and log P (alkane) using poly (styrene–divinylbenzene) and immobilised artificial membrane stationary phases in reversed-phase high-performance liquid chromatography. J Chromatogr A, 766 (1-2), 35-47.
  • [7] Abraham, M.H., M. Rosés, C.F. Poole, and S.K. Poole, (1997). Hydrogen bonding. 42. Characterization of reversed‐phase high‐performance liquid chromatographic C18 stationary phases. J Phys Org Chem, 10 (5), 358-368.
  • [8] Poole, C.F. and S.K. Poole, (2002). Column selectivity from the perspective of the solvation parameter model. J Chromatogr A, 965 (1-2), 263-299.
  • [9] Reta, M., P.W. Carr, P.C. Sadek, and S.C. Rutan, (1999). Comparative study of hydrocarbon, fluorocarbon, and aromatic bonded RP-HPLC stationary phases by linear solvation energy relationships. Anal Chem, 71 (16), 3484-3496.
  • [10] Sándi, Á. and L. Szepesy, (1998). Characterization of various reversed-phase columns using the linear free energy relationship: II. Evaluation of selectivity. J Chromatogr A, 818 (1), 19-30.
  • [11] Tan, L.C., P.W. Carr, and M.H. Abraham, (1996). Study of retention in reversed-phase liquid chromatography using linear solvation energy relationships I. The stationary phase. J Chromatogr A, 752 (1-2), 1-18.
  • [12] Zhao, J. and P.W. Carr, (1998). Comparison of the retention characteristics of aromatic and aliphatic reversed phases for HPLC using linear solvation energy relationships. Anal Chem, 70 (17), 3619-3628.
  • [13] Zhao, J. and P.W. Carr, (1999). An approach to the concept of resolution optimization through changes in the effective chromatographic selectivity. Anal Chem, 71 (14), 2623-2632.
  • [14] Flores, R.M. and P.V. Doskey, (2014). Using multidimensional gas chromatography to group secondary organic aerosol species by functionality. Atmos Environ, 96 (0), 310-321.
  • [15] Seeley, J.V., E.M. Libby, K.A.H. Edwards, and S.K. Seeley, (2009). Solvation parameter model of comprehensive two-dimensional gas chromatography separations. J Chromatogr A, 1216 (10), 1650-1657.
  • [16] Smith, R.M., Chapter 3 Retention index scales used in high-performance liquid chromatography, in Journal of Chromatography Library, R.M. Smith, Editor. 1995, Elsevier. p. 93-144.
Toplam 16 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Elif Mertoğlu 0000-0003-4490-723X

Rosa Maria Flores Rangel 0000-0002-0323-4043

Proje Numarası 116Y041
Yayımlanma Tarihi 31 Mart 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 33 Sayı: 2

Kaynak Göster

APA Mertoğlu, E., & Flores Rangel, R. M. (2021). Representation of the Diurnal Distribution of Organic Aerosol by Multidimensional Liquid Chromatography Systems. International Journal of Advances in Engineering and Pure Sciences, 33(2), 277-286. https://doi.org/10.7240/jeps.808964
AMA Mertoğlu E, Flores Rangel RM. Representation of the Diurnal Distribution of Organic Aerosol by Multidimensional Liquid Chromatography Systems. JEPS. Mart 2021;33(2):277-286. doi:10.7240/jeps.808964
Chicago Mertoğlu, Elif, ve Rosa Maria Flores Rangel. “Representation of the Diurnal Distribution of Organic Aerosol by Multidimensional Liquid Chromatography Systems”. International Journal of Advances in Engineering and Pure Sciences 33, sy. 2 (Mart 2021): 277-86. https://doi.org/10.7240/jeps.808964.
EndNote Mertoğlu E, Flores Rangel RM (01 Mart 2021) Representation of the Diurnal Distribution of Organic Aerosol by Multidimensional Liquid Chromatography Systems. International Journal of Advances in Engineering and Pure Sciences 33 2 277–286.
IEEE E. Mertoğlu ve R. M. Flores Rangel, “Representation of the Diurnal Distribution of Organic Aerosol by Multidimensional Liquid Chromatography Systems”, JEPS, c. 33, sy. 2, ss. 277–286, 2021, doi: 10.7240/jeps.808964.
ISNAD Mertoğlu, Elif - Flores Rangel, Rosa Maria. “Representation of the Diurnal Distribution of Organic Aerosol by Multidimensional Liquid Chromatography Systems”. International Journal of Advances in Engineering and Pure Sciences 33/2 (Mart 2021), 277-286. https://doi.org/10.7240/jeps.808964.
JAMA Mertoğlu E, Flores Rangel RM. Representation of the Diurnal Distribution of Organic Aerosol by Multidimensional Liquid Chromatography Systems. JEPS. 2021;33:277–286.
MLA Mertoğlu, Elif ve Rosa Maria Flores Rangel. “Representation of the Diurnal Distribution of Organic Aerosol by Multidimensional Liquid Chromatography Systems”. International Journal of Advances in Engineering and Pure Sciences, c. 33, sy. 2, 2021, ss. 277-86, doi:10.7240/jeps.808964.
Vancouver Mertoğlu E, Flores Rangel RM. Representation of the Diurnal Distribution of Organic Aerosol by Multidimensional Liquid Chromatography Systems. JEPS. 2021;33(2):277-86.