Distribution patterns and characterization of outdoor fine and coarse particles

Amaç & Hipotez:Simultaneous measurements of fine (PM2.5) and coarse (PM10) particles have been performed during three sampling period: summertime, early winter before the heating period, and during the winter heating.

📊

Temel Bulgular & Veri:The sampling was done at an urban site, Harbin City, China, to apportion pollution sources and to investigate the impacts of weather parameters and emission sources on distribution patterns of PM2.5 and PM10 and their chemical compositions.

💡

Sonuç & Katkı:HCA visualized the interrelations among the source markers, while the PMF5 modeling confirmed HCA findings

🔬Tür:Modelleme & Sayısal Simülasyon

📍Kapsam:Genişletilmiş Akademik Örneklem

⚙️Değişkenler:Alan Parametreleri

Özet

Simultaneous measurements of fine (PM2.5) and coarse (PM10) particles have been performed during three sampling period: summertime, early winter before the heating period, and during the winter heating. The sampling was done at an urban site, Harbin City, China, to apportion pollution sources and to investigate the impacts of weather parameters and emission sources on distribution patterns of PM2.5 and PM10 and their chemical compositions. Two chemometric techniques, hierarchical clustering analysis (HCA) and discriminant analysis (DA), and a receptor model (PMF5) were applied. The average mass concentrations of fine and coarse particles during the whole study period (i.e. annual averages), were 82.4 mg/m3 and 120.1 mg/m3 , with relative humidity and wind speed being the main factors influencing distribution patterns of PM2.5 and PM10, while sum of major ions (NO3 , NH4 þ, SO4 2), and organic carbon (OC) were the greater contributor to total mass concentrations. According to DA, the distribution patterns of PM2.5 and PM10 during the winter heating period were distinct but overlapping in summer and early winter before heating, implying that, PM2.5 and PM10 were originated from different sources during the winter heating period but from similar sources during the other two sampling periods. This claim was further supported by PMF results. Canonical discriminant function coefficients of DA have shown species such as OC, EC, SO4 2, NO3 , Cl, Ti, Sr, Ca2þ, Ni, and Ba to be good predictors/discriminants responsible for the differences between the three sampling periods. HCA visualized the interrelations among the source markers, while the PMF5 modeling confirmed HCA findings

🔖

Bu Makaleye Atıf Yap

MOHAMMED, M. O., SONG, W. W., LİU, L., MA, W. L., Lİ, Y. F., WANG, F. Y., IBRAHİM, M. A., Qİ, M. Y., ELZAKİ, A. A., LV, N. (2016). Distribution patterns and characterization of outdoor fine and coarse particles. Atmospheric Pollution Research, Cilt: 7 - Sayı: 5, 903–914. https://ajindex.com/articles/351542

Kaynakça (4)

Alberto, W.D., Marıa del Pilar, D.a., Marıa Valeria, A., Fabiana, P.S., Cecilia, H.A., Marıa de los Angeles, B., 2001. Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquıa River Basin (Cordoba eArgentina). Water Res. 35, 2881e2894. Aldabe, J., Elustondo, D., Santamaria, C., Lasheras, E., Pandolfi, M., Alastuey, A., Querol, X., Santamaria, J.M., 2011. Chemical characterisation and source apportionment of PM2.5 and PM10 at rural, urban and traffic sites in Navarra (North of Spain). Atmos. Res. 102, 191e205. Allen, R.W., Criqui, M.H., Diez Roux, A.V., Allison, M., Shea, S., Detrano, R., Sheppard, L., Wong, N.D., Stukovsky, K.H., Kaufman, J.D., 2009. Fine particulate matter air pollution, proximity to traffic, and aortic atherosclerosis. Epidemiology 20, 254e264. Almeida, S.M., Pio, C.A., Freitas, M.C., Reis, M.A., Trancoso, M.A., 2005. Source apportionment of fine and coarse particulate matter in a sub-urban area at the Western European Coast. Atmos. Environ. 39, 3127e3138. Bajo, S., 1991.
Dissolving Matrices. CRC. Cachon, B.F., Firmin, S., Verdin, A., Ayi-Fanou, L., Billet, S., Cazier, F., Martin, P.J., Aissi, F., Courcot, D., Sanni, A., Shirali, P., 2014. Proinflammatory effects and oxidative stress within human bronchial epithelial cells exposed to atmospheric particulate matter (PM2.5 and PM>2.5) collected from Cotonou, Benin. Environ. Pollut. 185, 340e351 (Barking, Essex: 1987).
Cheng, Y.-H., Li, Y.-S., 2010. Influences of traffic emissions and meteorological conditions on ambient PM10 and PM2.5 levels at a Highway Toll Station. Aerosol Air Qual. Res. 10, 456e462. Cheng, Y., Duan, F.-k., He, K.-b., Zheng, M., Du, Z.-y., Ma, Y.-l., Tan, J.-h, 2011. Intercomparison of thermaleoptical methods for the determination of organic and elemental carbon: influences of aerosol composition and implications. Environ. Sci. Technol. 45, 10117e10123. Chow, J.C., Watson, J.G., Robles, J., Wang, X., Chen, L.W.A., Trimble, D.L., Kohl, S.D., Tropp, R.J., Fung, K.K., 2011. Quality assurance and quality control for thermal/ optical analysis of aerosol samples for organic and elemental carbon. Anal. Bioanal. Chem. 401, 3141e3152. Contini, D., Belosi, F., Gambaro, A., Cesari, D., Stortini, A.M., Bove, M.C., 2012. Comparison of PM10 concentrations and metal content in three different sites of the Venice Lagoon: an analysis of possible aerosol sources. J. Environ. Sci. 24, 1954e1965.
Dabek-Zlotorzynska, E., Dan