Uncertainty evaluation in regional air quality management. Carlo Trozzi

Transcription

1 Task Force on Emission Inventory & Projection Workshop on Uncertainties & QA/QC

2 Abstract A lot of presentation were presented in the last years about uncertainty but little or no steps forward was been made in the direction to introduce a global approach to compute uncertainty in emission inventories This presentation would be a solid proposal to introduce an evaluation schema in emission inventories as a standard method to assess the uncertainty in the inventories and to monitor its evolution on time

3 Topics Uncertainty calculations: why? Uncertainty in the actual Guidebook Uncertainty and QA/QC The general methodology for uncertainties evaluation proposed by US EPA EIIP and referred as DARS as personalized for national and regional inventory The system (E 2 gov) developed for government of energy and environment, energy balances and pollutant emission inventory where methodology was integrated

4 Uncertainty calculations: why? In the history of emission inventories often uncertainty calculation was seen as a mathematical exercise to assign some range to emission estimates, mainly in EFs, based on statistical consideration such as Monte Carlo methods From another point of view uncertainty evaluation can be a powerful tool when combined with QA/QC to improve global quality of the inventory, evaluating uncertainty of the whole cycle of life of data (both activity and EFs)

5 LRTAP Guidelines & Guidebook Uncertainties Chapter Parties should quantify uncertainties in their emission estimates using the most appropriate methodologies available, taking into account guidance provided in the GB. Uncertainties should be described in the IIR (Guidelines for Reporting Emissions and Projections Data under the CLRTAP) The GB follows 2006 IPCC Guidelines to define uncertainty and was not essentially update from the previous releases The GB suggests a very simple approach to define uncertainty in activity levels (ALs)

6 Guidebook Uncertainties ALs approach

7 Uncertainty and QA/QC: Key Issues (1) The basic consideration is that the process of define indicators and the selection of Emission Factors (EFs) it s often more complex than simple GB approach The uncertainty in emission inventories is not simply due to the statistical uncertainty in the emission factor values or activity levels as it is often said The overall uncertainty of emissions should be expressed as weighted average of individual uncertainty of estimates

8 Uncertainty and QA/QC: Key Issues (2) The uncertainty is largely influenced by methods of collecting and evaluating indicators of activity The uncertainty of the indicators of activity is not limited to statistical error of the data but must also take account of the whole estimate process at the territorial level chosen The uncertainty analysis is a fundamental instrument of QA/QC and must be used to evaluate the global quality of the inventory and to individuate pollutants/activities where estimates needs improvement

9 Workshop on Uncertainties & QA/QC Methodology Inventory uncertainty in emission data is evaluated by adapting the methodology Data Attribute Rating System (DARS) of the EPA, the U.S. Agency responsible for environmental protection The method was originally described by Beck in 1994, and modified in the Emission Inventory Improvement Program (EIIP) The evaluation criteria, originally formulated for area sources, have been extended to the evaluation of point sources and mobile The original methodology was adapted for the structure of national, regional and local inventories

10 Single activity or single source Uncertainty Evaluation Scheme Four aspect of activity level (AL) and emission factors evaluation (EF) are selected: measurement/method of determination (MM) source specificity (SS) spatial congruity (SC) temporal congruity (TC) For each criterion is assigned a score from 1 to 10 for ALs and EFs (highest the score less the uncertainty) Score is more appropriate to assign than % as often % uncertainty is a numeric expression of score

11 Single activity total uncertainty The final score is then processed by calculating, for a set of criteria, the average score among the products on the emission factor and activity level scores

12 Measurement/method (MM) General criteria The score is based on the quality of the factor itself, not on how it has been used The best results are usually obtained by direct measurement of either emissions or of surrogate parameters that have a strong, statistically documented correlation with the pollutant of interest In the original US EPA work some decision diagrams are reported while in the following slide some examples of application of DARS criteria was presented

13 Measurement/method (MM) ALs score assignation criteria

14 Measurement/method (MM) EFs score assignation criteria AP-42 uncertainty codes and corresponding DARS scores

15 Measurement/method (MM) EFs score assignation criteria uncertainty codes assigned in GB

16 Source specificity (SS) General criteria The source specificity attribute concerns how specific the original factor or activity surrogate is to the source being estimated This attribute is easily confused with the previous one but the key point to ask is "was this emission factor (or activity parameter) specifically developed for this source category? The criteria can be specialized to differentiate the evaluation when Tier 1, Tier 2 or Tier 3 methodologies are used

17 Source specificity (SS) Example Tier1 EFs are Tier 2 EFs for conventional stoves so if we use for ALs total wood combustion we introduce an uncertainty due to source specificity other than uncertainty on EF and AL measurement values

18 Source specificity (SS) ALs score assignation criteria

19 Source specificity (SS) EFs score assignation criteria

20 Temporal congruity (TC) General criteria The temporal congruity attribute concerns how specific the original factor or activity surrogate is to the year of inventory For example EFs may be related to an older technology than the prevalent technology in operation at the year of inventory (also in this case the example of conventional stoves may be pertinent) ALs refer to a previous year, or are estimate from data of a previous year, as the data for current one are not available

21 Temporal congruity (TC) ALs score assignation criteria

22 Temporal congruity (TC) EFs score assignation criteria

23 Spatial congruity (SC) General criteria The spatial congruity attribute concerns how specific the original factor or activity surrogate is to the country (region) of inventory For example EFs may be an average of technologies/practices at European level not representative of country (region) of the inventory ALs are derived from larger region than the country (region) of the inventory (for example data at regional level are evaluated, with proxy variables, from national data)

24 Spatial congruity (SC) ALs score assignation criteria

25 Spatial congruity (SC) EFs score assignation criteria

26 Special case: point sources stacks measurement If emissions are measured at the stacks of a point source, the scores are directly assigned to the emissions, without compute it from indicators and emission factors continuous measurement: scores are 10 for MM, 10 for SS, 10 for SC and 10 for TC (unless reference is to a different year; in this case the assigned score will be smaller as discussed for other sources) periodical measurements: scores are 8 for MM, 10 for SS, 10 for SC and 10 for TC (unless reference is to a different year, see previous case)

27 Special case: point sources stacks measurement If emissions are measured at the stacks of a point source, the scores are directly assigned to the emissions, without compute it from indicators and emission factors continuous measurement: scores are 10 for MM, 10 for SS, 10 for SC and 10 for TC (unless reference is to a different year; in this case the assigned score will be smaller as discussed for other sources) periodical measurements: scores are 8 for MM, 10 for SS, 10 for SC and 10 for TC (unless reference is to a different year, see previous case)

28 Examples of evaluation from a case study Italy regional emission inventory Range from 0 (very poor) to 10 (very good)

29 The methodology was integrated in a new system (E 2 gov) developed for government of energy and environment, energy balances and pollutant emission inventory

30 Conclusions Systematic uncertainty evaluations must be introduced in emission inventory activities: to support quality assurance/quality control activities as a process to reduce uncertainty and move towards more affordable inventories to select activities more sensitive to better estimate methodologies and that have greater impact on whole inventory to evaluate cost/effectives of invenstment in inventory activities