Characterization of PM2.5 and identification of transported secondary and biomass burning contribution in Seoul, Korea.

The chemical and seasonal characteristics of fine particulates in Seoul, Korea, were investigated based on 24-h integrated PM2.5 measurements made over four 1-month periods in each season between October 2012 and September 2013. The four-season average concentration of PM2.5 was 37 µg m-3, and the major chemical components were secondary inorganic aerosol (SIA) species of sulfate, nitrate, and ammonium (49%), followed by organic matter (34%). The mass concentration and most of the chemical components of PM2.5 showed clear seasonal variation, with a winter-high and summer-low pattern. The winter-to-summer sulfate ratio and the winter organic carbon (OC)-to-elemental carbon (EC) ratio were unusually high compared with those in previous studies. Strong correlations of both the sulfate level and the sulfur oxidation ratio with relative humidity, and between water-soluble OC (WSOC) and SIA in winter, suggest the importance of aqueous phase chemistry for secondary aerosols. A strong correlation between non-sea salt sulfate and Na+ levels, a high Cl-/Na+ ratio, and an unusual positive correlation between the nitrogen oxidation ratio and temperature during the winter indicate the influence of transported secondary emission sources from upwind urban areas and from China across the Yellow Sea. Despite the absence of local forest fires and the regulation of wood burning, a high levoglucosan concentration and its correlations with OC and WSOC indicate that Seoul was affected by biomass burning sources in the winter. The unusually high water-insoluble OC (WIOC)-to-EC ratio in winter implies additional transported combustion sources of WIOC. The strong correlation between WIOC and levoglucosan suggests the likely influence of transported biomass burning sources on the high WIOC/EC ratio during the winter.

A multivariate receptor modeling study of air-borne particulate PAHs: Regional contributions in a roadside environment.

Understanding the geographic source contributions by particulate polycyclic aromatic hydrocarbons (PAHs) is important for the Korean peninsula due to its downwind location from source areas. Regional influence of particulate PAHs was previously identified using diagnostic ratios applied to mobile source dominated roadside sampling data (Kim et al., 2012b). However, no study has yet been conducted to quantify the regional source contributions. We applied a multivariate receptor modeling tool to identify and quantify the regional source contributions to particulate PAHs in Seoul. Sampling of roadside particulate PAHs was conducted in Seoul, Korea for four years between May 2005 and April 2009, and data analysis was performed with a new multivariate receptor model, Solver for Mixture Problem (SMP). The SMP model identified two sources, local mobile source and transported regional source, and quantified their source contributions. Analysis of the particulate PAHs data reveals three types of episodic periods: a high regional source contribution period with one case, a high mobile source contribution period with three cases, and a normal contribution period with eight cases. Four-year average particulate PAHs source contributions from the two sources are 4.6 ng m(-3) and 10.7 ng m(-3) for regional and mobile sources, respectively and equivalent to 30% and 70% of the total estimated contribution from each of these sources.

Characterization of PM25 and PM10 in the South Coast Air Basin of Southern California: Part 1-Spatial Variations.

In December 1994, the South Coast Air Quality Management District (SCAQMD) initiated a comprehensive program, the PM10 Technical Enhancement Program (PTEP), to characterize fine PM in the South Coast Air Basin (SCAB). A 1-year special particulate monitoring project was conducted from January 1995 to February 1996 as part of the PTEP. Under this enhanced monitoring, HNO3, NH3, and speciated PM10 and PM2.5 concentrations were measured at five stations (Anaheim, downtown Los Angeles, Diamond Bar, Fontana, and Rubidoux) in the SCAB and at one background station at San Nicolas Island. PM2.5 and PM10 mass and 43 individual species were analyzed for a full chemical speciation of the particle data. The PTEP data indicate that the most abundant chemical components of PM10 and PM25 in the SCAB are NH4+ (8-9% of PM10 and 14-17% of PM25), NO3- (23-26% of PM10 and 28-41% of PM25), SO4= (6-11% of PM10 and 9-18% of PM2 5), organic carbon (OC) (15-19% of PM10 and 18-26% of PM2.5), and elemental carbon (EC) (5-8% of PM10 and 8-13% of PM25). On an annual average basis, PM25 comprises 52-59% of the SCAB PM10. Annual average PM10 and PM2.5 concentrations showed strong spatial variations, low at coastal sites and high at inland sites. Annual average PM10 concentrations varied from 40.8 ug/m3 at Anaheim to 76.8 ug/m3 at Rubidoux, while annual average PM2.5 concentrations varied from 21.7 ug/m3 at Anaheim to 39.8 ug/m3 at Rubidoux. The chemical characterizations of the PM2.5 and PM10 concentrations, as well as their spatial variations, were examined; the important findings are summarized in this paper, and the temporal variations are discussed in the companion paper.1.

Characterization of PM25 and PM10 in the South Coast Air Basin of Southern California: Part 2-Temporal Variations.

The South Coast Air Quality Management District (SCAQMD) conducted a 1-year special particulate monitoring study from January 1995 to February 1996. This monitoring data indicates that high PM10 and PM2 5 concentrations were observed in the fall (October, November, and December), with November concentrations being the highest. During the rest of the year, PM2.5 and PM10 masses gradually increased from January to September. Monthly PM10 mass varied from 20 to 120 |ig/m3, and monthly PM25 mass varied from 13 to 63 |j.g/m3. The PM2.5-to-PM10 ratio varied daily and ranged between 22 and 96%. Two types of high-PM days were observed. The first type was observed under fall stagnation conditions, which lead to high secondary species concentrations. The second type was observed under high wind conditions, which lead to high primary coarse particles of crustal components. The highest 24-hr average PM10 concentration (226.3 |ig/m3) was observed at the Fontana station, while the highest PM25 concentration (129.3 |ig/m3) was observed at the Diamond Bar station.

Diagnostics for Determining Influential Species in the Chemical Mass Balance Receptor Model.

The chemical mass balance (CMB) model can be applied to estimate the amount of airborne particulate matter (PM) coming from various sources given the ambient chemical composition of the particles measured at the receptor and the chemical composition of the source emissions. Of considerable practical importance is the identification of those chemical species that have a large effect on either the source contributions or errors estimated by the CMB model. This paper details a study of a number of influential diagnostics for application of the CMB software. Some of the diagnostics studied are standard regression diagnostics based on single-row deletion diagnostics. A number of new diagnostics were developed specifically for the CMB application, based on the pseudo-inverse of the source composition matrix and called nondeletion diagnostics to distinguish them from the standard deletion diagnostics. Simulated data sets were generated to compare the diagnostics and their response to controlled amounts of random error. A particular diagnostic called a modified pseudoinverse matrix (MPIN), developed for this study, was found to be the best choice for CMB model application. The MPIN diagnostic contains virtually all the information present in both deletion and nondeletion diagnostics. Since the MPIN diagnostic requires only the source profiles, it can be used to identify influential species in advance without sampling the ambient data and to improve CMB results through possible remedial actions for the influential species. Specific recommendations are given for interpretation and use of the MPIN diagnostic with the CMB model software. Elements with normalized MPIN absolute values of 1 to 0.5 are associated with influential elements. Noninfluential elements have normalized MPIN absolute values of 0.3 or less. Elements with absolute values between 0.3 and 0.5 are ambiguous but should generally be considered noninfluential.

Nitrate Artifacts during PM25 Sampling in the South Coast Air Basin of California.

In February 1993, the South Coast Air Basin (SCAB) was redesignated as a "serious" nonattainment area for PM10. To improve the understanding and characterization of fine particulate matter in the SCAB, the South Coast Air Quality Management District (SCAQMD) initiated a comprehensive PM10 Technical Enhancement Program (PTEP). Using enhanced PTEP monitors (specially designed multichannel/multifilter samplers), a one-year fine particulate matter (PM) monitoring program was initiated in January 1995. As part of the special monitoring program, nitric acid, ammonia, and speciated PM10 and PM2.5 concentrations were measured at five locations in the SCAB (downtown Los Angeles, Anaheim, Diamond Bar, Fontana, and Rubidoux) and at one background station (San Nicolas Island). The PM2.5 data are the first spatially resolved speciated data collected in the SCAB on an annual basis. Within the SCAB, where nitrate is a major component of PM2.5, nitrate losses have been documented. The spatial and temporal variations of the nitrate losses during PM2.5 sampling and the uncertainties of the nitrate losses are discussed. Significant losses occur at a low mass range, between 10 and 50 ìg/m3. Significant gains occur at an even lower mass range of less than 30 ìg/m3. On an annual average basis, nitrate losses vary between 1.25 and 2.32 ìg/m3 and the SCAB-wide average value of nitrate loss is 1.8 ìg/m3 based on five PTEP stations in the SCAB. The maximum nitrate losses for each station vary from 6.4 ìg/m3 to 22.5 ìg/m 3. Theoretical prediction of the sampling efficiency of the nitrate during PM2.5 sam - pling was compared with the PTEP data. In general, theoretical prediction was in good agreement with measured values.