Chemical mass balance source apportionment of PM10 and TSP in residential and industrial sites of an urban region of Kolkata, India

This paper describes results from a detailed source profile characterization study conducted in Raipur, India to prepare source profiles for traffic and dust-related sources. A companion paper has been published with results for a range of other combustion sources. PM 2.5 samples were analyzed for mass, elements (Al, As, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Mo, Na, Ni, Pb, S, Sb, Se, V, Zn), ions (Na + , K + , Mg 2+ , Ca 2+ , NH 4 + , Cl -, F -, NO 3 -, SO 4 2-) and carbonaceous fractions (OC and EC). All dust profiles were dominated by crustal elements (Al, Ca, Fe and Mg), while carbonaceous species (OC and EC) were most abundant in vehicular emission profiles. Trace element fraction was found to be significantly higher in vehicular exhaust compared to the resuspended dust. Remarkably, sulphur abundance was observed to be several-fold higher in vehicular emission profiles than resuspended dust profiles. Al and Ca were identified as reliable markers for resuspended dust while V, Pb and EC were identified as markers for vehicular exhaust.

Section: Source Profiles For Resuspended Dustsupporting

confidence: 87%

Characterization of PM2.5 Source Profiles for Traffic and Dust Sources in Raipur, India

Matawle

Pervez

Dewangan

et al. 2015

“…The mean concentrations for Fe, Zn, Pb, Mn, and Cr in Bozkoy that are mostly affected from Nemrut area activities were 2959, 2043, 379, 159, and 60 ng/m3, respectively. Compared these values with previous studies Carvalho and Freitas, 2011;Gupta et al, 2007;Kim et al, 2002), these levels were higher than those measured in several industrial areas. Table S3 compared selected elemental concentrations in PM10 samples collected from different industrial areas.…”

Section: Elemental Concentrationscontrasting

confidence: 49%

Characterization of PM Using Multiple Site Data in a Heavily Industrialized Region of Turkey

Kara

Hopke

Dumanoğlu

et al. 2015

Source apportionment has most often been applied to a time series of data collected at a single site. However, in a complex airshed where there are multiple sources, it may be helpful to collect samples from multiple sites to ensure that some of them have low contributions from specific sources such that edges can be properly defined. In this study, samples were collected at multiple sites in the Aliaga region (38°40′-38°54′N and 26°50′-27°03′E) located in western Turkey on the coast of the Aegean Sea. This area contains a number of significant air pollution sources including five scrap iron-steel processing plants with electric arc furnaces (EAFs), several steel rolling mills, a petroleum refinery, a petrochemical complex, a natural gas-fired power plant, a fertilizer plant, ship breaking yards, coal storage and packaging, scrap storage and classification sites, large slag and scrap piles, heavy road traffic, very intense transportation activities including ferrous scrap trucks and busy ports used for product and raw material transportation. A total of 456 samples of PM 10 at six sampling sites and 88 samples of PM 2.5 at one site were collected for four seasons and the elemental composition was determined for 43 elements. The newest version of EPA PMF (V5.0) that has the capability of handling multiple site data was used for source apportionment. Eight factors were identified as iron-steel production from scrap (23.4%), resuspended and road dust (23.3%), crustal (20.5%), marine aerosol (14.4%), biomass and wood combustion (7.2%), salvage activities (4.7%), coal combustion (3.7%) and residual oil combustion (2.8%). The pattern of source contributions and conditional probability function analysis were consistent with the locations of the known sources. Thus, the multiple site data allowed for a comprehensive identification of the primary sources of PM in this region.

“…Source apportionment using receptor models, one of the main methods used to quantify contributions of different sources to atmospheric particulate matter (PM), is well established and widely used (Watson et al, 2002;Samara et al, 2003;Gupta et al, 2007). It requires information about the chemical characteristics of the emission sources that affect pollutant concentrations at a receptor ).…”

Section: Introductionmentioning

confidence: 99%

Chemical Characterizations of PM10 Profiles for Major Emission Sources in Xining, Northwestern China

Han

et al. 2014

The chemical profiles of emission sources are indispensable for source apportionment using receptor models. To develop current knowledge of PM 10 profiles, the chemical composition of major emission sources were analyzed in Xining. Samples of geological sources (soil dust, road dust and construction derived dust) and industrial fly ash were collected from representative portions using a plastic dustpan and brush and sampled on filters through a re-suspension chamber. Samples of coal combustion source and vehicle exhaust were collected directly on the filters using a dilution stack sampler. Chemical analysis included inductively coupled plasma mass spectrometry for 19 elements (Na, P, K, As, Rb, Mo, Cd, Sn, Sb, La, V, Cr, Mn, Co, Ni, Cu, Zn, T1, and Pb), inductively coupled plasma optical emission spectrometry for 7 elements (Al, Sr, Mg, Ti, Ca, Fe, and Si), ion chromatography for water-soluble ions (Na + , K + , Mg 2+ , Ca 2+ , F -, Cl -, NO 3 -and SO 4 2-) and thermal/optical reflectance analysis for carbonaceous species. Crustal elements (Si, Al, Ca, Fe) predominated in geological sources, whereas trace elements (Pb, Cd, Cr, Zn and Ni) were predominant in industrial fly ash. An abundance of carbon and SO 4 2-was present in coal-combustion source and vehicle exhaust. The coal-combustion boilers were a source of trace elements (Ti, Co, Sr, Sb, Tl). High concentrations of Pb and OC in soil indicated the strong influence of agriculture activities in Xining. Comparison of vehicle exhaust profiles indicated that natural gas was high environmental friendliness in comparison with petroleum products used as motor vehicles fuel. Differences in EC, Cd and NO 3 -between natural gas-powered and gasoline-or diesel-powered vehicle exhaust can be used to differentiate the two types of vehicle emission sources. Differences in source profiles and indicator species between Xining and other cities suggest that source profiles should be developed locally and updated frequently.