THE CHEMICAL MASS BALANCE METHOD FOR ESTIMATING ATMOSPHERIC PARTICLE SOURCES IN SOUTHERN CALIFORNIA

A Novel Robust Method for Solving CMB Receptor Model Based on Enhanced Sampling Monte Carlo Simulation

The traditional effective variance weighted least squares algorithms for solving CMB (Chemical Mass Balance) models have the following drawbacks: When there is collinearity among the sources or the number of species is less than the number of sources, then some negative value of contribution will appear in the results of the source apportionment or the algorithm does not converge to calculation. In this paper, a novel robust algorithm based on enhanced sampling Monte Carlo simulation and effective variance weighted least squares (ESMC-CMB) is proposed, which overcomes the above weaknesses. In the following practical instances for source apportionment, when nine species and nine sources, with no collinearity among them, are selected, EPA-CMB8.2 (U.S. Environmental Protection Agency-CMB8.2), NKCMB1.0 (NanKai University, China-CMB1.0) and ESMC-CMB can obtain similar results. When the source raise dust is added to the source profiles, or nine sources and eight species are selected, EPA-CMB8.2 and NKCMB1.0 cannot solve the model, but the proposed ESMC-CMB algorithm can achieve satisfactory results that fully verify the robustness and effectiveness of ESMC-CMB.

Source apportionment studies on particulate matter (PM10 and PM2.5) in ambient air of urban Mangalore, India

Particulate matter (PM₁₀ and PM_2.5) samples were collected from six sites in urban Mangalore and the mass concentrations for PM₁₀ and PM_2.5 were measured using gravimetric technique. The measurements were found to exceed the national ambient air quality standards (NAAQS) limits, with the highest concentration of 231.5 μg/m³ for PM₁₀ particles at Town hall and 120.3 μg/m³ for PM_2.5 particles at KMC Attavar. The elemental analysis using inductively coupled plasma optical emission spectrophotometer (ICPOES) revealed twelve different elements (As, Ba, Cd, Cr, Cu, Fe, Mg, Mn, Mo, Ni, Sr and Zn) for PM₁₀ particles and nine different elements (Ba, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Sr and Zn) for PM_2.5 particles. Similarly, ionic composition of these samples measured by ion chromatography (IC) divulged nine different ions (F^-, Cl^-, NO₃^-, PO₄^3-, SO₄^2-, Na⁺, K⁺, Mg²⁺ and Ca²⁺) for PM₁₀ particles and ten different ions (F^-, Cl^-, NO₃^-, PO₄^3-, SO₄^2-, Na⁺, NH₄⁺, K⁺, Mg²⁺ and Ca²⁺) for PM_2.5 particles. The source apportionment study of PM₁₀ and PM_2.5 for urban Mangalore in accordance with these six sample sites using chemical mass balance model (CMBv8.2) revealed nine and twelve predominant contributors for both PM₁₀ and PM_2.5, respectively. The highest contributor of PM₁₀ was found to be paved road dust followed by diesel and gasoline vehicle emissions. Correspondingly, PM_2.5 was found to be contributed mainly from two-wheeler vehicle emissions followed by four-wheeler and heavy vehicle emissions (diesel vehicles). The current study depicts that the PM₁₀ and PM_2.5 in ambient air of Mangalore region has 70% of its contribution from vehicular emissions (both exhaust and non-exhaust).

Identification and elucidation of anthropogenic source contribution in PM10 pollutant: Insight gain from dispersion and receptor models

Source apportionment study of PM₁₀ (Particulate Matter) in a critically polluted area of Jharia coalfield, India has been carried out using Dispersion model, Principle Component Analysis (PCA) and Chemical Mass Balance (CMB) techniques. Dispersion model Atmospheric Dispersion Model (AERMOD) was introduced to simplify the complexity of sources in Jharia coalfield. PCA and CMB analysis indicates that monitoring stations near the mining area were mainly affected by the emission from open coal mining and its associated activities such as coal transportation, loading and unloading of coal. Mine fire emission also contributed a considerable amount of particulate matters in monitoring stations. Locations in the city area were mostly affected by vehicular, Liquid Petroleum Gas (LPG) & Diesel Generator (DG) set emissions, residential, and commercial activities. The experimental data sampling and their analysis could aid understanding how dispersion based model technique along with receptor model based concept can be strategically used for quantitative analysis of Natural and Anthropogenic sources of PM₁₀.

Source apportionment of polycyclic aromatic hydrocarbons in sediments from polluted rivers

Over the past few decades, in response to growing concerns about the impact of polycyclic aromatic hydrocarbons (PAHs) on human health, a variety of environmental forensics and geochemical techniques have emerged for studying organic pollutants. These techniques include chemical fingerprinting, receptor modeling, and compound-specific stable isotope analysis (CSIA). Chemical fingerprinting methodology involves the use of diagnostic ratios. Receptor modeling techniques include the chemical mass balance (CMB) model and multivariate statistics. Multivariate techniques include factor analysis with multiple linear regression (FA/MLR), positive matrix factorization (PMF), and UNMIX. This article reviews applications of chemical fingerprinting, receptor modeling, and CSIA; comments on their uses; and contrasts the strengths and weaknesses of each methodology.

Quantitative source apportionment of PAHs in sediments of Little Menomonee River, Wisconsin: weathered creosote versus urban background

Polycyclic aromatic hydrocarbons (PAHs) in urban environments are often derived from point and nonpoint sources, the latter collectively considered as urban background. Quantifying the contributions of point sources and urban background is important for managing and remediating urban sediments. In this work, the sources of PAHs in 350 sediments from a 1.5-mile portion of the Little Menomonee River (Milwaukee, WI) were determined using principal component analysis (PCA), chemical fingerprinting, and positive matrix factorization (PMF), the combination of which mitigates weaknesses of any one method. At issue was quantifying the contributions of a creosote point-source formerly located 3.5 to 5.0 miles upstream versus urban background-derived PAHs in the sediments. In total, creosote and urban background contributed 27 and 73% (+/-14%) of eight carcinogenic PAHs (CPAHs), respectively, in this part of the River. The concentrations of CPAHs derived from urban background were highest in surface sediments (0-6 in.; 20 +/- 17 mg/kg), particularly near major roadway crossings, increased in the downstream direction, and (on average) exceeded the 15 mg/kg regulatory cleanup threshold. Weathered creosote-derived CPAHs were widespread at low concentrations (4.8 +/- 8.1 mg/kg) although some discrete sediments, mostly at depths below 6 in., contained elevated CPAHs derived from creosote. This work demonstrates the value of combining multiple techniques in source apportionment studies in urban sediments. It further demonstrates a means to determine the concentration of PAHs attributable to nonpoint sourced background in urban sediments without the need to identify, collect, and analyze (assumedly) "representative" background samples, which may not even exist in heterogeneous urban watersheds.

The impact of wood smoke on ambient PM2.5 in northern Rocky Mountain valley communities

During the winters of 2006/2007 and 2007/2008, PM2.5 source apportionment programs were carried out within five western Montana valley communities. Filter samples were analyzed for mass and chemical composition. Information was utilized in a Chemical Mass Balance (CMB) computer model to apportion the sources of PM2.5. Results showed that wood smoke (likely residential woodstoves) was the major source of PM2.5 in each of the communities, contributing from 56% to 77% of the measured wintertime PM2.5. Results of 14C analyses showed that between 44% and 76% of the measured PM2.5 came from a new carbon (wood smoke) source, confirming the results of the CMB modeling. In summary, the CMB model results, coupled with the 14C results, support that wood smoke is the major contributor to the overall PM2.5 mass in these rural, northern Rocky Mountain airsheds throughout the winter months.

Source attribution of black carbon in Arctic snow

Snow samples obtained at 36 sites in Alaska, Canada, Greenland, Russia, and the Arctic Ocean in early 2007 were analyzed for light-absorbing aerosol concentration together with a suite of associated chemical species. The light absorption data, interpreted as black carbon concentrations, and other chemical data were input into the EPA PMF 1.1 receptor model to explore the sources for black carbon in the snow. The analysis found four factors or sources: two distinct biomass burning sources, a pollution source, and a marine source. The first three of these were responsible for essentially all of the black carbon, with the two biomass sources (encompassing both open and closed combustion) together accounting for >90% of the black carbon.

Source apportionment of personal exposure of fine particulates among school communities in India

Source contribution estimates (SCE) of school community personal Respirable Particulate Matter (RPM) have been investigated. Reported relationships of personal RPM with Ambient-outdoors and indoor RPM levels have given the concept of defining the sources of personal exposure. Ambient-outdoors, indoors, soils and local road- traffic dusts were identified as main routes and principal sources of fine particulates at personal exposure levels. Fifteen subjects (05 from each of three schools) were selected from previous conducted study of interrelationships among classified atmospheric receptors in theses schools located in Bhilai-Durg, District Durg, India. Samples of RPM collected from identified receptors and sources were analyzed for selected chemical constituents and the chemical data has been utilized in preparation of source-receptor profiles. Chemical mass balance (CMB8) model has been used for source apportionment study. Major dominating source is ambient-outdoors in case of school located near to steel plant downwind. Indoors and road-traffic dusts have also played dominating role in case of school located near to National Highways. Indoor ventilation properties have played an important role in source contribution estimates.

Seasonal trends in coarse and fine particle sources in Delhi by the chemical mass balance receptor model

A study of the source contribution of atmospheric particulate matter and associated heavy metal concentrations using chemical mass balance model Version 8 (CMB8) in coarse and fine size mode has been carried out for the city of Delhi. Urban particles were collected using a five-stage impactor at six sites in three different seasons, viz. winter, summer and monsoon in the year 2001. Five samples from each site in each season were collected. The results obtained indicate the dominance of vehicular pollutants in fine size mode, whilst the contribution in coarse mode to some extent is site specific but largely due to vehicular pollution and, soil and crustal dust. Seasons also play an important role but in coarse size fraction only.

Receptor modeling application framework for particle source apportionment

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.