Molecular marker characterization and source appointment of particulate matter and its organic aerosols.

This study was carried out to identify possible sources and to estimate their contribution to total suspended particle (TSP) organic aerosol (OA) contents. A total of 120 TSP and PM2.5 samples were collected simultaneously every third day over a one-year period in urban area of Incheon, Korea. High concentration in particulate matters (PM) and its components (NO3(-), water soluble organic compounds (WSOCs), and n-alkanoic acids) were observed during the winter season. Among the organics, n-alkanes, n-alkanoic acids, levoglucosan, and phthalates were major components. Positive matrix factorization (PMF) analysis identified seven sources of organic aerosols including combustion 1 (low molecular weight (LMW)-polycyclic aromatic hydrocarbons (PAHs)), combustion 2 (high molecular weight (HMW)-PAHs), biomass burning, vegetative detritus (n-alkane), secondary organic aerosol 1 (SOA1), secondary organic aerosol 2 (SOA2), and motor vehicles. Vegetative detritus increased during the summer season through an increase in biogenic/photochemical activity, while most of the organic sources were prominent in the winter season due to the increases in air pollutant emissions and atmospheric stability. The correlation factors were high among the main components of the organic carbon (OC) in the TSP and PM2.5. The results showed that TSP OAs had very similar characteristics to the PM2.5 OAs. SOA, combustion (PAHs), and motor vehicle were found to be important sources of carbonaceous PM in this region. Our results imply that molecular markers (MMs)-PMF model can provide useful information on the source and characteristics of PM.

Source apportionment of PM2.5 at the coastal area in Korea.

In this study, we analyzed the chemical composition of fine particulate matter 2.5 µm or less (PM) collected at Incheon, the coastal area in Seoul, Korea every third day from June 2009 to May 2010. Based on the analyzed chemical species in the PM samples, the sources of PM were identified using a positive matrix factorization (PMF). Nine sources of PM were determined from PMF analysis. The major sources of PM were secondary nitrate (25.4%), secondary sulfate (19.0%), motor vehicle 1 (14.8%) with a lesser contribution from industry (8.5%), motor vehicle 2 (8.2%), biomass burning (6.1%), soil (6.1%), combustion and copper production emissions (6.1%), and sea salt (5.9%). From a paired t-test, it was found that yellow sand samples were characterized as having higher contribution from soil sources (p<0.05). Furthermore, the likely source areas of PM emissions were determined using the conditional probability function (CPF) and the potential source contribution function (PSCF). CPF analysis identified the likely local sources of PM as motor vehicles and sea salt. PSCF analysis indicated that the likely source areas for secondary particles (sulfate and nitrate) were the major industrial areas in China. Finally, using the source contribution of PM and associated organic composition data, principal component analysis (PCA) was conducted to evaluate the accuracy of the PM source apportionments by PMF. The PCA analysis confirmed eight of the nine PM sources. Our result implies that the chemical composition analysis of PM data and various modeling techniques can effectively identify the potential contributing sources.

Sulfur deposition simulations over China, Japan, and Korea: a model intercomparison study for abating sulfur emission.

In response to increasing trends in sulfur deposition in Northeast Asia, three countries in the region (China, Japan, and Korea) agreed to devise abatement strategies. The concepts of critical loads and source-receptor (S-R) relationships provide guidance for formulating such strategies. Based on the Long-range Transboundary Air Pollutants in Northeast Asia (LTP) project, this study analyzes sulfur deposition data in order to optimize acidic loads over the three countries. The three groups involved in this study carried out a full year (2002) of sulfur deposition modeling over the geographic region spanning the three countries, using three air quality models: MM5-CMAQ, MM5-RAQM, and RAMS-CADM, employed by Chinese, Japanese, and Korean modeling groups, respectively. Each model employed its own meteorological numerical model and model parameters. Only the emission rates for SO(2) and NO(x) obtained from the LTP project were the common parameter used in the three models. Three models revealed some bias from dry to wet deposition, particularly the latter because of the bias in annual precipitation. This finding points to the need for further sensitivity tests of the wet removal rates in association with underlying cloud-precipitation physics and parameterizations. Despite this bias, the annual total (dry plus wet) sulfur deposition predicted by the models were surprisingly very similar. The ensemble average annual total deposition was 7,203.6 ± 370 kt S with a minimal mean fractional error (MFE) of 8.95 ± 5.24 % and a pattern correlation (PC) of 0.89-0.93 between the models. This exercise revealed that despite rather poor error scores in comparison with observations, these consistent total deposition values across the three models, based on LTP group's input data assumptions, suggest a plausible S-R relationship that can be applied to the next task of designing cost-effective emission abatement strategies.