Download
1 / 24
240 likes | 411 Views
PM emission data for the NIS: current state. Sergey Kakareka Anna Malchykhina. Institute for Problems of Natural Resources Use & Ecology Minsk, Belarus. 7 th JOINT UNECE TFEIP & EIONET Workshop on Emission Inventories and Projections 31 October - 2 November 2006, Thessaloniki, Greece.
E N D
PM emission data for the NIS: current state Sergey KakarekaAnna Malchykhina Institute for Problems of Natural Resources Use & Ecology Minsk, Belarus 7th JOINT UNECE TFEIP & EIONET Workshop on Emission Inventories and Projections 31October - 2 November 2006, Thessaloniki, Greece
Presentation contains draft results of analysis of PM emission data available now for some NIS countries made as a taskof national contribution “in-kind” of the Republic of Belarus into EMEP for 2006. • Purpose: • - assessment of the quality (completeness, consistency, transparency etc.) of PM emission data available for the NIS, prioritization of sectors and determination of ways for its improvement.
Data sets on emission analized: 1. Emission statistics (from National Reports on the State of Environment, Statistical Yearbooks and other official edition); 2. Official PM data reported to EMEP from EMEP database (Webdap); 3. Expert estimates of EMEP (Webdap); 4. RAINS model estimates; 5. CEPMEIP dataset; 6. MSC-W by country reports.
Countries analyzed: • Belarus, Moldova, the Ukraine, Russian Federation (the European part). • Years: • 2000, 2001, 2002, 2003, 2004 • Methodology of analysis: • А) Comparison of emission estimates: • by totals; • by key sectors (SNAP 1); • by years (trends);
B) Comparison with activity data • Data sources on activity: national statistics. • Compilation of analized data for PM national totals by countries is shown on the charts below. • Explanation of notations on the charts: • Statistics – statistical data on emissions (generally for stationary sources only; domestic sector and some others not accounted); • Official – data reported to EMEP (Webdap); • Expert – emission estimates by EMEP (Webdap); • RAINS – estimation by RAINS model (for 2000 and 2005).
By the data EMEP database contains scarce official information on PM10 or PM2.5. Only for Moldova there is a whole set of PM10 data. For other NIS it available for 1-3 last years. Statistics do not contain it at all. So mainly TSP emission data can be used for in-deep analysis. TSP official data available for most of years: 2000-2004 for Moldova, 2002-2004 for other countries. Generally it is rather smooth though some jumps present. Statistics available for all years except the European part of the Russian Federation for which mainly emission data for total Russia available. Emission on the European part can be estimated using known share of the European part of Russia in total emissions for 2003 (about 31%).
To compare expert and official estimates we can accept that roughly PM10 comprises 50-60% of TSP. Expert estimates and RAINS data are generally higher then official and statistical. Thus for Belarus expert estimates are higher than official (about 20%). For Moldova expert emissions for 2000-2001 are higher than for 2002-2003 7-8 times. For 2000-2001 they are closer to official and close to RAINS, for 2002-2003 – to statistics and differ from RAINS.
For the European part of Russia official emission values in 2004 – 1030 thous. tones (by statistics– 830.8 thous. tones). Expert estimates for 2003 – 1351.9 thous. tones PM10, close to RAINS values of emission. For testing of probable reasons of detected differences emission data by key sectors was analized and available production statistical data was used.
Differences of classificators should be taken into account. Thus, in the NIS statistics Power Industry emission includes not only emission from fuel combustion, but also technological emissions. Technological and combustion emissions can hardly be separated for other key sectors (Ferrous, Non-Ferrous, Building Materials, Communal etc.). Analysis of SNAP sectors emission data have revealed significant differences between estimates.
Thus expert estimates of SNAP1 emissions for Belarus are higher than statistical about 100 times while taking into account that Power Industry in Belarus practically do not utilize solid fuels. For the Ukraine SNAP1 emissions by expert estimates compared to statistics looks rather low taking into account that most of coal in the Ukraine is combusted in Power Industry. For the whole territory of the Russian Federation about 35% of total dust is emitted by power plants (965.6 thous. tones in 2004). It should be expected that in the European part of Russia 150-250 thous. tones dust maybe emitted by Energy sector; according to expert estimatesfor 200350.6 thous. tones PM10 was emitted in this sector in EMEP part of Russia.
Similar analysis was made for other key sectors. Conclusions: - progress was made last years in PM emission inventory improvement for NIS region; a few datasets on PM emission for the NIS are available now (statistics, official, expert), some of them include data on PM10 and PM2.5; - all datasets are obtained by different methodologies; - determination of the quality of dataset needs a clarification of requirements to emission data;
application of traditional EMEP criteria to all PM emission datasets (completeness, accuracy, consistency, transparency et al.) is useful; no one now can be considered as fully compliant with all these requirements; • even not fully compliant emission estimates are useful if based on a known methodology; - emission estimates should be validated by independent methods; - by the date for the NIS TSP emission data is of the best quality, thus it is important to collect TSP emission data in EMEP database in view of verification purposes of PM10 and PM2.5 data.
prioritization between totals and sectors estimates is necessary: estimates can coincide in total emission values but differ significantly in sector estimates sometime only because put the same activity into different classes; • estimates should be made for natural classes - for which a statistics can be collected; application of detailed artificial classification schemes do not led to the growth of the quality of estimates; • should be expected that uncertainty of estimates is growing from top to down; this is a specific feature of top-down approach while for bottom-up should be expected backward tendency;
key problem in inventory improvement in the NIS: lack of necessary statistical information especially for mobile sources; - emission factors are results of a certain emission inventory (emissions divided consumption), after that they are used in another inventory. So default emission factors which are planned for deriving national totals can be attributed from national emission inventory in other country.
Proposals for PM emission inventory improvement: - guidelines or courses on the sources of statistical data and its transformation before application in emission inventory will be useful; • emission factors can be derived from best quality emission inventory data and included in the Guidebook as default for certain cases; • sectors of priority improvement by regions taking into consideration input in total emissions and level of ucertainty; • regular intercomparison of emission estimates; • training courses in inventory tools like COPERT, RAINS, CEPMEIP etc.
