Philip K. Hopke

Atmospheric aerosol over Alaska: 2. Elemental composition and sources

The fine particle (<2.5 μm) composition data from seven National Park Service locations in Alaska for the period from 1986 to 1995 was performed using a new type of factor analysis, positive matrix factorization (PMF). This method uses the estimates of the error in the data to provide optimum data point scaling and permits a better treatment of missing and below detection limit values. Eight source components were obtained for data sets from the Northwest Alaska Areas and the Bering Land Bridge sites. Five to seven components were obtained for the other Alaskan sites. The solutions were normalized by using aerosol fine mass concentration data. Squared correlation coefficients between the reconstructed mass obtained from aerosol composition data for the sites and the measured mass were in the range of 0.74–0.95. Two factors identified as soils were obtained for all of the sites. Concentrations for these factors for most of the sites have maxima in the summer and minima in the winter. A sea-salt component was found at five locations. A factor with the highest concentrations of black carbon (BC), H+, and K identified as forest fire smoke was obtained for all data sets except at Katmai. Factors with high concentrations of S, BC-Na-S, and Zn-Cu were obtained at all sites. At three sites, the solutions also contained a factor with high Pb and Br values. The factors with the high S, Pb, and BC-Na-S values at most sites show an annual cycle with maxima during the winter-spring season and minima in the summer. The seasonal variations and elemental compositions of these factors suggest anthropogenic origins with the spatial pattern suggesting that the sources are distant from the receptor sites. The seasonal maxima/minima ratios of these factors were higher for more northerly locations. Four main sources contribute to the observed concentrations at these locations: long-range transported anthropogenic aerosol (Arctic haze aerosol), sea-salt aerosol, local soil dust, and aerosol with high BC concentrations from regional forest fires or local wood smoke. A northwest to southeast negative gradient suggesting long-range transport of air masses from regions north or northwest of Alaska dominated the spatial distribution of the high S factor concentrations.

Review of receptor model fundamentals

Abstract There are several broad classes of mathematical models used to apportion the aerosol measured at a receptor site to its likely sources. This paper surveys the two types applied in exercises for the Mathematical and Empirical Receptor Models Workshop (Quail Roost II): chemical mass balance models and multivariate models . The fundamental principles of each are reviewed. Also considered are the specific models available within each class. These include: tracer element, linear programming, ordinary linear least-squares, effective variance least-squares and ridge regression (all solutions to the chemical mass balance equation), and factor analysis, target transformation factor analysis, multiple linear regression and extended Q-mode factor analysis (all multivariate models). In practical application of chemical mass balance models, a frequent problem is the presence of two or more emission sources whose signatures are very similar. Several techniques to reduce the effects of such multicollinearity are discussed. The propagation of errors for source contribution estimates, another practical concern, also is given special attention.

Discarding or downweighting high-noise variables in factor analytic models

This work examines the factor analysis of matrices where the proportion of signal and noise is very different in different columns (variables). Such matrices often occur when measuring elemental concentrations in environmental samples. In the strongest variables, the error level may be a few percent. For the weakest variables, the data may consist almost entirely of noise. This paper demonstrates that the proper scaling of weak variables is critical. It is found that if a few weak variables are scaled to too high a weight in the analysis, the errors in computed factors would grow, possibly obscuring the weakest factor(s) by the increased noise level. The mathematical explanation of this phenomenon is explored by means of Givens rotations. It is shown that the customary form of principal component analysis (PCA), based on autoscaling the original data, is generally very ineffective because the scaling of weak variables becomes much too high. Practical advice is given for dealing with noisy data in both PCA and positive matrix factorization (PMF).

Understanding and controlling rotations in factor analytic models

Abstract Positive Matrix Factorization (PMF) is a least-squares approach for solving the factor analysis problem. It has been implemented in several forms. Initially, a program called PMF2 was used. Subsequently, a new, more flexible modeling tool, the Multilinear Engine, was developed. These programs can utilize different approaches to handle the problem of rotational indeterminacy. Although both utilize non-negativity constraints to reduce rotational freedom, such constraints are generally insufficient to wholly eliminate the rotational problem. Additional approaches to control rotations are discussed in this paper: (1) global imposition of additions among “scores” and subtractions among the corresponding “loadings” (or vice versa), (2) constraining individual factor elements, either scores and/or loadings, toward zero values, (3) prescribing values for ratios of certain key factor elements, or (4) specifying certain columns of the loadings matrix as known fixed values. It is emphasized that application of these techniques must be based on some external information about acceptable or desirable shapes of factors. If no such a priori information exists, then the full range of possible rotations can be explored, but there is no basis for choosing one of these rotations as the “best” result. Methods for estimating the rotational ambiguity in any specific result are discussed.

Source Identification of Atlanta Aerosol by Positive Matrix Factorization

Abstract Data characterizing daily integrated particulate matter (PM) samples collected at the Jefferson Street monitoring site in Atlanta, GA, were analyzed through the application of a bilinear positive matrix factorization (PMF) model. A total of 662 samples and 26 variables were used for fine particle (particles ≤2.5 µm in aerodynamic diameter) samples (PM2.5 ), and 685 samples and 15 variables were used for coarse particle (particles between 2.5 and 10 µm in aerodynamic diameter) samples (PM10–2.5 ). Measured PM mass concentrations and compositional data were used as independent variables. To obtain the quantitative contributions for each source, the factors were normalized using PMF-apportioned mass concentrations. For fine particle data, eight sources were identified: SO4 2−-rich secondary aerosol (56%), motor vehicle (22%), wood smoke (11%), NO3 −-rich secondary aerosol (7%), mixed source of cement kiln and organic carbon (OC) (2%), airborne soil (1%), metal recycling facility (0.5%), and mixed source of bus station and metal processing (0.3%). The SO4 2−-rich and NO3 −-rich secondary aerosols were associated with NH4 +. The SO4 2−-rich secondary aerosols also included OC. For the coarse particle data, five sources contributed to the observed mass: airborne soil (60%), NO3 −-rich secondary aerosol (16%), SO4 2−-rich secondary aerosol (12%), cement kiln (11%), and metal recycling facility (1%). Conditional probability functions were computed using surface wind data and identified mass contributions from each source. The results of this analysis agreed well with the locations of known local point sources.

Receptor modeling for air quality management

1. An Introduction to Receptor Modeling (P.K. Hopke). 2. Sampling and Analysis Methods for Ambient PM-10 Aerosol (T.G. Dzubay and R.K. Stevens). 3. Source Sampling for Receptor Modeling (J.E. Houck). 4. Chemical Mass Balance (J.G. Watson, J.C. Chow and T. G. Pace). 5. Multivariate Receptor Models (R.C. Henry). 6. Scanning Electron Microscopy (P.K. Hopke and G.S. Casuccio). 7. Receptor Modeling for Volatile Organic Compounds (P.A. Scheff and R.A. Wadden). 8. Receptor Modeling in the Context of Ambient Air Quality Standard for Particulate Matter (T.G. Pace). 9. Application of Receptor Modeling to Solving Local Air Quality Problems (J.E. Core). Index.

The use of multivariate analysis to identify sources of selected elements in the Boston urban aerosol

Abstract The concentrations of eighteen elements were determined by instrumental neutron activation analysis for samples of air particulates collected over a five month period in the Boston metropolitan area. This set of data is analyzed for underlying structure by the methods of common factor analysis and hierarchial aggregative cluster analysis. The data can be interpreted on the basis of six common factors accounting for 77.5% of the total variance in the system. These factors are attributed to various sources of particulate material by noting the dependence of the factors on the elements. The cluster analysis assists in the interpretation of the factors.

Characterization of the Gent Stacked Filter Unit PM10 Sampler

ABSTRACT An integral part of several International Atomic Energy Agency sponsored coordinated research programmes involving the sampling and analysis of ambient airborne particules was the development of a PM10 sampler. Each participant was provided with such a sampler so that comparable samples would be obtained by each of the participating groups. Thus, in order to understand the characteristics of this sampler, we undertoke several characterization studies in which we examined the aerodynamic collection characteristics of the impactor inlet and the reproducibility of the sample mass collection. One of the samplers machined in Belgium was compared with one built from the same design in the U.S. and comparable results were obtained. The sampler was operated side-by-side with a commercial PM10 beta gauge and an IMPROVE-design 2.5 μm cut-point cyclone. Although the sampler was not wind tunnel tested as required for certification as a reference sampler, it does provide a collection efficiency that generally...

Ambient Air Pollution Exposure Estimation for the Global Burden of Disease 2013.

Exposure to ambient air pollution is a major risk factor for global disease. Assessment of the impacts of air pollution on population health and evaluation of trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure estimates. We combined satellite-based estimates, chemical transport model simulations, and ground measurements from 79 different countries to produce global estimates of annual average fine particle (PM2.5) and ozone concentrations at 0.1° × 0.1° spatial resolution for five-year intervals from 1990 to 2010 and the year 2013. These estimates were applied to assess population-weighted mean concentrations for 1990-2013 for each of 188 countries. In 2013, 87% of the worlds population lived in areas exceeding the World Health Organization Air Quality Guideline of 10 μg/m(3) PM2.5 (annual average). Between 1990 and 2013, global population-weighted PM2.5 increased by 20.4% driven by trends in South Asia, Southeast Asia, and China. Decreases in population-weighted mean concentrations of PM2.5 were evident in most high income countries. Population-weighted mean concentrations of ozone increased globally by 8.9% from 1990-2013 with increases in most countries-except for modest decreases in North America, parts of Europe, and several countries in Southeast Asia.

Sources of fine particle composition in the northeastern US

Fine particle composition data obtained at three sampling sites in the northeastern US were studied using a relatively new type of factor analysis, positive matrix factorization (PMF). The three sites are Washington, DC, Brigantine, NJ and Underhill, VT. The PMF method uses the estimates of the error in the data to provide optimal point-by-point weighting and permits efficient treatment of missing and below detection limit values. It also imposes the non-negativity constraint on the factors. Eight, nine and 11 sources were resolved from the Washington, Brigantine and Underhill data, respectively. The factors were normalized by using aerosol fine mass concentration data through multiple linear regression so that the quantitative source contributions for each resolved factor were obtained. Among the sources resolved at the three sites, six are common. These six sources exhibit not only similar chemical compositions, but also similar seasonal variations at all three sites. They are secondary sulfate with a high concentration of S and strong seasonal variation trend peaking in summer time; coal combustion with the presence of S and Se and its seasonal variation peaking in winter time; oil combustion characterized by Ni and V; soil represented by Al, Ca, Fe, K, Si and Ti; incinerator with the presence of Pb and Zn; sea salt with the high concentrations of Na and S. Among the other sources, nitrate (dominated by NO3−) and motor vehicle (with high concentrations of organic carbon (OC) and elemental carbon (EC), and with the presence of some soil dust components) were obtained for the Washington data, while the three additional sources for the Brigantine data were nitrate, motor vehicle and wood smoke (OC, EC, K). At the Underhill site, five other sources were resolved. They are wood smoke, Canadian Mn, Canadian Cu smelter, Canadian Ni smelter, and another salt source with high concentrations of Cl and Na. A nitrate source similar to that found at the other sites could not be obtained at Underhill since NO3− was not measured at this site. Generally, most of the sources at the three sites showed similar chemical composition profiles and seasonal variation patterns. The study indicated that PMF was a powerful factor analysis method to extract sources from the ambient aerosol concentration data.