Bong Mann Kim

Characterization of PM2.5 and PM10 in the South Coast Air Basin of Southern California: Part 2 - Temporal variations

ABSTRACT 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.

Extension of self-modeling curve resolution to mixtures of more than three components: Part 2. Finding the complete solution

Abstract The previous paper [R.C. Henry, B.M. Kim, Extension of self-modeling curve resolution to mixtures of more than three components: Part 1. Finding the basic feasible region, Chemometrics and Intelligent Laboratory Systems 8 (1990) 205–216] explained an extension of self-modeling curve resolution for an arbitrary number of components. A method was described to determine the basic feasible region that is formed with certain natural physical constraints in eigenspace. This basic feasible region is not a complete solution. It is still necessary to find where components are located in the basic feasible region. For a complete solution, a priori information is required and this is incorporated into the natural physical constraints as additional physical constraints. In this paper, the use of additional physical constraints is described. Once the location of one component has been determined from the additional physical constraints in the basic feasible region, this information further restricts the possible locations for other components. To incorporate this restriction into the model, a method has been developed similar to that used to determine the inner boundary. This algorithm was implemented in Fortran 77, and a new model, Source Apportionment by Factors with Explicit Restrictions (SAFER), was developed. As a first attempt, the SAFER model was applied to an error-free four-component problem to examine the new models performance when there are no random measurement errors. The results of the simulation study show that the new model successfully resolves the feasible ranges of the source compositions and estimates source compositions with some bias. In addition, the feasible region of one unknown component was also resolved.

Extension of self-modeling curve resolution to mixtures of more than three components: Part 3. Atmospheric aerosol data simulation studies☆

Abstract In the previous two papers [R.C. Henry, B.M. Kim, Extension of self-modeling curve resolution to mixtures of more than three components: Part 1. Finding the basic feasible region, Chemom. Intell. Lab. Syst. 8 (1990) 205–216; B.M. Kim, R.C. Henry, Extension of self-modeling curve resolution to mixtures of more than three components: Part 2. Finding the complete solution, Chemom. Intell. Lab. Syst. 49 (1999) 67–77.], a method was described to extend the self-modeling curve resolution (SMCR) technique to mixtures of more than three components, one set of data was created without errors to examine the performance of the model when no random measurement errors exist. In this paper, simulation studies were conducted to examine the performance of the model and to determine the effects of random measurement errors, both in source compositions and in ambient concentrations, on the estimated source compositions. The bias and errors in the estimated source compositions were also determined. Five sources (roadway, marine, secondary, crustal, and residual oil) were assumed in the simulation. It was shown that the model estimates source compositions with acceptable error and bias. The maximum percentage uncertainties in the estimated compositions of the roadway and secondary sources were less than 10% for most of the major species, except elemental carbon in the roadway and organic carbon (OC) in the secondary sources, which are 20%. The maximum uncertainties in the estimated marine compositions were about 30% for major species.

Positive Organic Carbon Artifacts on Filter Medium During PM2.5 Sampling in the South Coast Air Basin

The South Coast Air Quality Management District conducted a special one year monitoring program in 1995 using particulate samplers designed to properly account for positive organic artifact formation during PM2.5 sampling. Positive organic carbon artifacts are quantified and characterized in this paper. The organic carbon concentrations vary from 1.42 to 20.66

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.