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Uncertainties in Heavy Metals Emission Inventories: Dimensions and Levels. S.Kakareka, T.Kukharchyk Institute for Nature Management National Academy of Sciences Minsk, Belarus. TFEIP/TFMM Workshop: 12 May 2010, Larnaca, Cyprus.

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uncertainties in heavy metals emission inventories dimensions and levels

Uncertainties in Heavy Metals Emission Inventories: Dimensions and Levels

S.Kakareka, T.Kukharchyk

Institute for Nature Management

National Academy of Sciences

Minsk, Belarus

TFEIP/TFMM Workshop: 12May 2010, Larnaca, Cyprus


Heavy metals emission uncertainty is a multidimensional and multi-aspect phenomena.

  • The presentation is limited to two aspects of uncertainty which can be described as:
    • - country dimension;
    • - process dimension:
    • - case study chlor-alkali production;
    • - case study cement.
    • Sources of uncertainties in HM emission inventories for both dimensions are described on an example of Belarus and to a certain extent other EECCA emission inventory experience.

1. Sources of uncertainties in HM emission inventories: country dimension

a) Emission inventory methodology issues

Most of the EECCA countries widely use statistical emission data for reporting to EMEPwhich is bottom-up. EMEP emission inventory methodology is based on top-down approach. So questions of comparability of results obtained using different methodologies, possibilities of combining of two approaches and arising uncertainties are facing. These issues were tested on an example of Belarus.

Statistical, calculated by EMEP methodology and combined final emission values by sectors and totals were compared for heavy metals.


Calculated total lead emission is about 16 times higher than statistical. In final results share of statistical data is about 6 % only. Similar picture is typical for emissions of the most of other heavy metals: statistics account only small share of total heavy metal emissions. For mercury there is no statistical data on emission at all.


Statistical versus calculated emission data for lead in EMEP emission inventory

Shares of statistical and calculated data


So depending on applied methodology emission inventory results will be different.

  • Other emission inventory methodology issues which affect uncertainties:
    • certain sources are not provided with inventory guidelines or provision is poor. Examples: gaps in the GB – sources with HM application; gaps in national guidelines – combustion sources…
    • certain sources are not clear how to inventory using the Guidebook (included into aggregated chapters, etc.). Example: combustion in manufacturing industries (1A2).

b) Not accounted sources:

  • Heavy metals are contained in any dust; so any source emitting dust will emit heavy metals. Accounting of all sources is far from current possibilities and will be done by step. Examples of not yet accounted sources::
      • mineral products – dolomite, lime, brick..;
      • agriculture - soil dust, livestock..;
    • storage and handling of mineral and other products, construction;
    • open fires; emission from wastes, reservoirs (mercury);
    • minor technological processes - in chemical, machine-building, electrothechnical industry..
    • Lack of statistics data also can lead to emission underestimation in certain sectors like domestic heating, minor non-ferrous metals production and reclamation.

c) time series inconsistencies:

    • accurate time series production is resource-consuming; estimations based on economic indicators trends without detection of changes in technologies/raw quality etc through EF modification can lead to significant errors in timely distributed estimates. This limitation is typical for top-down methodology.

d) short-term fluctuations in emissions:

    • emission levels are not homogenious in time; sources have certain regular (i.e. seasonality in stationary combustion) and irregular (for instance due to work with partial load) fluctuations. Significant fluctuations may cause shifts of means and result in short-term unexpected spikes in HM content in the air or precipitates.

e) Spatial distribution of sources:

    • application of population density as a proxy for emission spatial distribution not sound: generally large point sources emit significant part of HM emissions and their proper location is important. Thus in Belarus 6 facilities emit 83 % of total lead and 12 facilities - 86 % of lead. Similar proportion is for most of other metals.



Lead and mercury emission in Belarus in 2005, tons/50x50 grid cell


Differencies in spatial structures of emission depending on distribution algorithm:

Spatial structure of lead emission in East Europe by Webdab and expert estimates

a case study chlor alkali production mercury emission

2. Sources of uncertainties: process dimension

a) Case study chlor-alkali production (mercury emission)

A lot of studies were made last years on assessment of mercury emission from chlor-alkali production. But estimates are still uncertain; this reduce possibilities for detection of temporal and spatial trends of emission.

Chlor-alkali production is among key sources of mercury emission at global and regional scales. Its input estimated as 47-163 tons or 3-7% of global anthropogenic emission (Pirrone et al., 2009, 2010; AMAP/UNEP, 2008). For EECCA the input of chlor-alkali industry earlier was estimated as 26% of total mercury emission.


Our latest studies allowed to make following conclusions regarding mercury emissions from caustic soda and chlorine production since 1990 for EECCA region:

- descending trend of mercury emission is evident;

- absolute values of uncertainty are reducing;

- relative uncertainty remains high and not changed significantly;

- bias of emission level estimates is possible.


Trends of mercury emission in EECCA from caustic soda/chlorine production (low, high and ‘best’ estimates)


Reasons of emission reduction with relations to uncertainty:

a) Reduction of caustic soda production by mercury cell method

A few caustic soda production enterprises have been closed or changed technology. On the whole distribution of production by technology type is known, but information for a certain enterprise is not easy to get so this can lead to uncertainties on a regional level and at spatial distribution of emission.


b) Reduction of mercury technological losses.

Data available shows that mercury consumption at EECCA caustic soda enterprises are decreasing: from 0.5 - 2 kg/ton caustic for the period before 1990, to 0.1 - 0.6 kg/t – in 1990th and to 0.04 - 0.6 kg/ton – in the beginning of 2000th. Mercury losses are still very different from plant to plant.


c) Reduction of mercury air emission (air emission factors).During production mercury is lost to the air, waste waters, products, solid wastes. Material balance methodology is prevailing for emission estimates especially in EECCA region. Share of mercury losses to the air is still uncertain. In many cases also share of unaccounted losses of mercury is very high - up to 50-80% of total losses, i.e. it is not known what media most of mercury is emitted.

Generally there is a significant lack of mercury air emission measurements. There are no published data on content of mercury in hydrogen, waste gases and ventilation air of electrolysis shops of this region. So air emission factors can be treated as greatest source of Hg emission estimates uncertainty for EECCA now; level of their uncertainty can hardly been reduced without air emission measurements campaigns.


d)Mercury in wastes (as a source of secondary emission)

There are a huge amount of mercury content wastes on the territory of EECCA – more than 1 mln tons, and annually more then 10 thous. tons mercury-containing wastes are formed additionally. Mercury content in these wastes varies from 10 to 750000 mg/kg. The most amount of waste are accumulated on the territory of non-ferrous metal, as well as chemicals, pulp-and paper, gold mining, electrothechnical enterprises. Account of emission from this source is lack.

b case study cement

Cement is a key source of mercury and is among large sources of other HM emission in Belarus.

  • Sources of HM emission uncertainty from this sector:
  • regional and local variations of consumption and HM content in raw materials, fuels, additives;
  • fluctuation of HM distribution between product and emission;
  • fluctuations of PM emission levels.

b) Case study cement


Heavy metals emissions factors for cement production in Belarus for model facilities, g/t cement

Greatest contributor of HM into emission from cement production in Belarus is pyrite drosses. Their application is known on a facility basis; this allows to produce rather accurate spatially distributed HM emission estimates from this sector.

conclu ding remarks

Concluding remarks

numerous factors affects accuracy/uncertainty of HM emission estimates;

they are different depending on the aspect of emission inventory concerned: emission totals, spatial resolution (distribution), time resolution etc.

Methods of uncertainties reduction are also numerous. Among perspective ones - balance method, which demands collection on a regular basis data on HM contents in raw materials, wastes, products. This method can be applied at a country, sector or facility level. So tracking of waste flows from non-ferrous industry and sulfuric acid production will allow to improve estimates of HM emission from cement production.