About comparability of measured and modeled metrics Jean-Philippe Putaud Fabrizia Cavalli DG JRC

1. TFEIP workshop Uncertainties in emission inventories and atmospheric models October 22, 2007 About comparability of measured and modeled metrics Jean-Philippe Putaud Fabrizia Cavalli DG JRC Institute for Environment and Sustainability Climate Change Unit

2. rationale particulate mass in inventories, models and observations carbonaceous aerosol in inventories, models and observations uncertainties in atmospheric observations uncertainty in emission factor measurements problems in answering our case: point vs grid cell do we measure the same “thing” Suggestions Rationale

3. The vicious circle Emission inventories are based on emission factors obtained from measurements at sources Models calculate fields of atmospheric concentrations (PM, sulfate, EC, etc…) based on emission inventories Models output are compared to measurements in the atmosphere Comparability of modeled and measured metrics How well can point measurements represent grid cell averages ? Do model calculate the same “thing” as monitoring station measure ? PMx Carbonaceous aerosol Rationale

4. Emission factors measured at sources (primary PM) Size cut (aerodynamic diameter) depends on RH (hygroscopic growth) Positive and/or negative sampling artifacts Models calculate PM concentration fields (incl. “production” of secondary aerosol) In the atmosphere In various size bins:do models calculate the aerodynamic equivalent diameter of particles Monitoring stations measure PM (e.g. with the reference method) with filter-based methods Size cut (aerodynamic diameter) in ambient conditions (water included) Positive and/or negative sampling artifacts Measurement at 50% RH => Water content is not nil, but is different from atmospheric aerosol water content Particulate matter mass concentration (PMx)

5. At best, emission factors are measured at sources (filter based techniques) BC should relate to optical measurements (light absorption) EC should relate to thermal measurements, which are method-dependent Thermal protocol Charring correction protocol (or even no correction) OC is defined as TC – EC TC analyses are a quite robust data TC on filters is affected by positive and/or negative sampling artifacts Models calculate EC and OM atmospheric concentration fields (incl. “production” of SOA) could as well determine OC easily ? Monitoring stations measure OC+EC and/or BC with filter-based methods EC data are method-dependent BC derived from absorption measurement using valid cross sections ? TC (OC) affected by positive and/or negative sampling artifacts OC converted to OM with valid / specified conversion factors ? Carbonaceous aerosol (BC/EC, OC, OM….)

6. uncertainties (random errors) and biases (systematic errors). a) random errors: any measurement is made with a finite precision b) systematic errors: a method generally (or on average) lead to data that are different from the true value e.g. reference method for PM mass concentration determination leads to systematically low values (with respect to the actual PM mass concentration) in summer where NH4NO3 contributes a significant fraction to PM e.g. EC concentration, there is currently no “true” value, because EC is methodologically defined. However, it is know that thermal method that do not account for charring a systematically biased “high” compared to methods that do correct for charring. What are the main uncertainties in PM measurements ?

7. We cannot say that emission factors derived from measurements are wrong. They may be biased with respect to the truth or to reference methods, which means they could be corrected through an in depth literature survey of papers reporting emission factors. I’ve been told that emission factors for EC are just guesses, so they should be highly uncertain. Measurements, as already stated are always associated with random uncertainties, which are quantifiable. For sources, representativeness is perhaps a major source of uncertainties (e.g. for vehicle emission, due to fuel variability in power plants, without talking of uncontrolled sources like agriculture waste burning or wood stoves). are the emission factors derived from these measurements wrong or highly uncertain. What error is made, can it be quantified?

8. b) the old issue of point versus grid information? - will always exist, except if satellite-borne instrumentation is used. 1. PM often measured where pollution problems may occur, at point where concentrations are probably larger than the grid cell average. 2. Concentrations are often (at least in winter, with little convection) larger at the ground (where monitoring stations sit) than the average within the lowest model layer Point versus grid information

9. 1. Measurements lead to PMx mass concentrations where: a. x = aerodynamic diameter in ambient conditions (i.e. with particle-bound water included) b. When the reference method is used, PM is measured from a filter (i.e. with sampling artifacts) equilibrated at 50% RH (i.e. still containing water, but not in equal amount as in the atmospheric aerosol Do models and measurements deal with the same PM?

10. 2. Models calculate PMx a. Based on PMx emission factor measurements (same kind of error as atmospheric measurements) b. I guess calculating geometric diameter of particles, i.e. a model considers that a pure EC particle with a mass of 0.4 ng has a diameter of 11.5 µm (assuming EC density is 2). In contrast a PM10 inlet would not cut it away because its aerodynamic diameter is 8.1 µm. c. Models calculate the amount of particulate matter per meter cube of air, not the mass of PM per meter cube collected by a filter and exposed at 50% RH Do models and measurements deal with the same PM?

11. Check that emission, models, and monitoring station talk of the same thing. For PM, there’s the question of size cut, although it’s very probably a minor source of discrepancy. The size of a particle is not the same in a chimney as in the air (hygroscopic growth). Models do not always consider aerodynamic diameters whereas (emission and imission) measurements by principle do. Models calculate atmospheric concentrations, whereas measurements may be biased by sampling and analytical artifacts. Suggestion is to compare SO42-, NO3-, OC, EC, etc… rather than PM. Models and measurements of PM may agree as a result of compensating errors. There’s no way to understand why models and measurements PM values differ if single major PM components cannot be compared. - Regarding EC, the point is to check that what is called EC at emission sources (and transported, deposited, washed out by models) is the same as what is called EC at monitoring stations. It’s very probably not true, but both EC emissions and atmospheric concentrations can be translated to a common “reference EC” metric. What do we need to check in model, measurement and inventories to conclude on the research question.

12. PMx: address size cut issues (models) go for artifact-free methods (measurements) Carbonaceous aerosol 1- make EC data from emission sources comparable with EC data in the atmosphere i.e. convert to a common metric 2- aim at artifact-free methods (measurements) 3- calculate (also) OC (models) Reconciling model outputs and atmospheric measurements