340 likes | 350 Views
This study applies Positive Matrix Factorization (PMF) along with Chemical Transport Model (CTM) to evaluate the effects of wildfires on air quality at two IMPROVE sites. The research compares different models and explores the implications of the findings. It discusses the concept of source apportionment, meteorology effects, and the importance of identifying emission sources accurately. The text highlights the significance of understanding the contributions of various pollutants in the atmosphere and the challenges faced in attributing them to specific sources. The advantages and disadvantages of PMF and CTM are analyzed, along with the introduction of a hybrid model to improve accuracy in analyzing source impacts. The findings shed light on the complexities of aerosol composition and emphasize the necessity of accurate modeling techniques in assessing air quality impacts from wildfires.
E N D
Coupling Chemical Transport Model Source Attributions with Positive Matrix Factorization: Application to two IMPROVE sites impacted by wildfires Sturtz et. al. 2014 ATMS 790 seminar Ashley Pierce
Outline • Background • Source Apportionment • Positive Matrix Factorization (PMF) • Chemical Transport Model (CTM) • Hybrid • The study • Model comparison and evaluation • Implications
Background • Particulate matter (PM2.5): mixture of small particles and liquid drops • Aerosol: PM suspended in a gas (e.g. air) • Volatile Organic Compounds (VOCs): variety of chemicals (benzene, isoprene) • Secondary Organic Aerosols (SOA) • Carbonaceous aerosols – major component of fine particulate mass • IMPROVE: Interagency Monitoring of Protected Visual Environments (1985) • National Ambient Air Quality Standards (NAAQS)
Source Apportionment Meteorology Primary & secondary pollutants Transport, transformation, & removal processes Transport, transformation, & removal processes CTM Primary & secondary pollutants Primary pollutants PMF Emission Source (Anthropogenic combustion, biomass burning, biogenic emissions from plants) Receptor Affected site/organism or measurement area • Source-Receptor relationship: determining the role of meteorology and physical/chemical effects linking source emissions to receptor concentrations • Source profiles: the experimentally determined unique proportion of species concentrations • (or speciated PM or speciated aerosol) from a source • Ex. Biomass burning – OC, EC, levoglucosan • Feature: A factor profile (source factor) unique proportion of speciated aerosols determined by the PMF analysis
Particulate carbon • Fossil carbon: coal, oil, gas fuels • Biogenic carbon: biomass burning, meat cooking, Secondary organic aerosols (SOAs) • Upper bound for biomass burning contribution
More volatile, lower light absorption Higher light absorption http://www.lucci.lu.se/wp1_projects.html
Importance • Adversely affect health, contribute to haze, affect radiation balance • Biogenic sources of carbonaceous aerosols • 80-100% of fine particulate carbon in rural areas • ~50% in some urban areas • Carbonaceous species often largest contributor to haze and PM2.5 • Smoke thought to be large contributor (W and SE U.S.) • Difficult to apportion smoke from other emissions or between smoke types • >50% of smoke particulate mass can be secondary organic aerosol (SOA) • Similar to SOA composition formed from gases emitted by plant respiration • Biomass burning emissions inventories likely overestimate PM emissions, underestimate VOC emissions from biomass combustion and biogenic release
Positive Matrix Factorization (PMF) • Multivariate factor analysis • Uses matrix of speciated sample data • Source contributions • Source profiles • Inputs: • Species concentrations () • uncertainties () • number of sources () • Interpret source types: • Source profile information • Wind direction analysis • Emission inventories
PMF disadvantages • True source profiles not known (no emission info) • Requires assumptions that are not always true • Can’t apportion secondary organic aerosols to source types • Factor profiles can have large errors and may correspond to a mixture of source types • Uncertainties in measurements are not always known or well-defined
PMF advantages • Each data point can be weighted individually • uncertainty value () • Data below detection limit • Variability in solution estimated by bootstrapping technique, “re-sampling” of data set • Based on observations • Don’t need source emissions • Less uncertainty than CTM
Chemical Transport Model (CTM) • CAPITA Monte Carlo Lagrangian CTM • Direct simulation of atmospheric pollutants • Each emitted quantum contains a fixed quantity of mass for various pollutants based on the source emission rate • Individual particles subjected to transport, transformation, and removal processes • 6-day back trajectories of air masses using meteorological data from the Eta Data Assimilation System (EDAS) • Non-fire emissions: Western Regional Air Partnership (WRAP) 2002 emissions inventory • Biomass burning: MODIS inventory • Source profiles compiled from burns
CTM disadvantages • Large information requirements • Chemical mechanisms are incomplete • Large errors and biases • Particularly with wildfires • Driven by emissions inventory • Overall higher root mean square error (RMSE) than PMF and Hybrid
CTM Advantages • Identification and separation of different source types based on emissions inventory • Primary and secondary carbonaceous fine particles can be identified from source types • biomass combustion, biogenic, mobile, area, oil, point, other
Hybrid • Source-oriented • Measured data used to constrain CTM • Direct incorporation of measured data into model • Post-processing of model results • Receptor-oriented • CTM results constrain receptor model (PMF) using Multilinear Engine-2 (ME-2)
mass balance • – speciated sample data matrix with dimensions by • : number of samples • : chemical species measured • - residual for each sample/species (model error) • Want to identify: • – amount of mass contributed by each source to each individual sample • – species profile of each source • – number of sources • No samples can have a negative source contribution • and > 0
The Study Goal: • Distinguish source contributions to total fine particle carbon • Biogenic sources • Biomass combustion due to wildfires • Using a receptor-oriented hybrid model
Sites • Speciated PM2.5 from Monture and Sula Peak Montana • Three year: 2006-2008 Monture Sula
Species • Species with 0.2 ≤ S/N < 2.0 were down weighted by factor of 3 • Removed species: • S/N ratio <0.2 • below detection limit • missing > 50% samples • Mass reconstruction outside IMPROVE limits • 8% samples from Monture • 25% samples from Sula • Looked at 23 species
Sources • Smallest value of where a change in the ratio of cross-validated to approaches zero • User judgment based on qualitative agreement between the species profiles () and prior knowledge of source profiles from known source types within the model region
Missoula paper mill & mining Gold, cobalt and Molybdenum mines Dry soils, Long-range Transport, Fires?
Seasons (CTM and Hybrid) • Winter (Dec Jan Feb) • Spring (Mar Apr May) • Summer (Jun Jul Aug) • Autumn (Sep Oct Nov)
Model evaluation • Root mean square error (RMSE) – measure of the differences between the value predicted by the model and the observed values • Sample standard deviation of the differences between predicted and observed values • Measure of accuracy • Correlation coefficient (R) – measure of strength and direction of linear relationship between two variables • The covariance of two variables divided by the product of their standard deviations
Model Evaluation • PMF γ = 0 • CTM γ = 1 • Montureγ= 0.83 • Sula γ = 0.67 0.83 0.67
Hybrid Disadvantages • Still unable to distinguish between primary and secondary biomass combustion impacts • CTM model predictions were highly correlated • Equation 2 should account for multiplicative bias but does not work with high correlation and no tracer species • Requires experts to run model in current form
Hybrid Advantages • Complementary attributes from PMF and CTM • Directly applying the CTM predictions to the PMF model allows for resolution of sources not identified by the PMF alone • Biogenic vs. biomass combustion • Theoretically primary and secondary features should be distinguishable
Implications • Accurate identification of relevant sources and impact on receptors will guide control policy • lower costs • better results • Ability to better distinguish sources to prove pollution events are due to exceptional events such as wildfires
References • Norris, G., & Vedantham, R. (2008). EPA Positive Matrix Factorization (PMF) 3.0 Fundamentals & user guide. • Paatero, P. (1999). The Multilinear Engine—A Table-Driven, Least Squares Program for Solving Multilinear Problems, Including the n-Way Parallel Factor Analysis Model. Journal of Computational and Graphical Statistics, 8(4), 854-888. doi: 10.1080/10618600.1999.10474853 • Polissar, A. V., Hopke, P. K., Paatero, P., Malm, W. C., & Sisler, J. F. (1998). Atmospheric aerosol over Alaska: 2. Elemental composition and sources. Journal of Geophysical Research: Atmospheres, 103(D15), 19045-19057. doi: 10.1029/98JD01212 • Ramadan, Z., Eickhout, B., Song, X.-H., Buydens, L. M. C., & Hopke, P. K. (2003). Comparison of Positive Matrix Factorization and Multilinear Engine for the source apportionment of particulate pollutants. Chemometrics and Intelligent Laboratory Systems, 66(1), 15-28. doi: http://dx.doi.org/10.1016/S0169-7439(02)00160-0 • Schichtel, B., Fox, D., Patterson, L., & Holden, A. Hybrid Source Apportionment Model: an operational tool to distinguish wildfire emissions from prescribed fire emissions in measurements of PM2.5 for use in visibility and PM regulatory programs. • Schichtel, B. A., & Husar, R. B. (1997). Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model. Journal of the Air & Waste Management Association, 47(3), 301-333. doi: 10.1080/10473289.1997.10464449 • Schichtel, B. A., & Husar, R. B. (1997). The Monte Carlo Model: PC-Implementation. Retrieved 02/16/15, 2015, from http://capita.wustl.edu/capita/CapitaReports/MonteCarloDescr/mc_pcim0.html • Schichtel, B. A., Malm, W. G., Collett, J. L., Sullivan, A. P., Holden, A. S., Patterson, L. A., . . . Barna, M. G. (2008). Estimating the contribution of smoke to fine particulate matter using a hybrid-receptor model. Paper presented at the Air and Waste Management aerosol and atmospheric optics. • Sturtz, T. M., Schichtel, B. A., & Larson, T. V. (2014). Coupling Chemical Transport Model Source Attributions with Positive Matrix Factorization: Application to Two IMPROVE Sites Impacted by Wildfires. Environmental Science & Technology, 48(19), 11389-11396. doi: 10.1021/es502749r
Multilinear Engine-2 (ME-2) • performs iterations via a preconditioned conjugate gradient algorithm until convergence to a minimum Q value • Conjugate gradient algorithm: algorithm for the numerical solution of particular systems of linear equations, usually a symmetric, positive-definite matrix
: species measurement uncertainty : CTM uncertainty : user-defined weighting parameter for CTM predictions relative to mass balance model Residuals from Equations 1-2: • Equation 1: Standard PMF chemical mass balance equation • Equation 2: CTM constraint: contributions to total fine particulate carbon predicted by the CTM model sources (total biomass combustion and biogenic emissions)
Prior source profile constraints: • Equation 3: Normalized thermal fractions of carbon for each source. Rescales such that in eq. 1 and 2 represents total fine particle carbon and = mass fraction of species in source relative to total carbon • Equation 4: Secondary feature profile constraint • Primary biogenic source consists only of VOCs • Sets all r=1 to c non-carbonaceous species near zero • Biomass source: carbon thermal fractions, potassium, nitrate, sulfate, hydrogen