2.L(a) - Handling of Bulk Products

Short description

Under category 2.L(a) - Handling of Bulk Products dust emissions from bulk material handling (loading and unloading) including agricultural bulk materials offsite the fields are reported. Emissions from quarrying and mining of minerals and from point source emissions are excluded.

Methodology

For 1990 to 1996, only simplified estimates without a differentiation of handled materials and products exist. For all following years, emissions are calculated using a tier1 method taking into account detailed data on handled materials and products.

Activity data

Official statistics are of limited use in determining handling of bulk products. There are only transport statistics available providing the amounts of several transported materials.

During a research project carried out by (Müller-BBM) 1), activity data was derived from primary statistical data from the Federal Statistical Office for Germany (Satistisches Bundesamt, Destatis) and the Federal Motor Transport Authority (Kraftfahrt-Bundesamt, KBA). Here, data on goods transported by railways and ships is gathered by Destatis whereas data for road transport is collected by the KBA.

Here, for all years until 2009, the collection of data for transported goods followed the official NST/R (1968) nomenclature and regulation (Eurostat, 2015a) 2).

As of 2010, statistical data following the newly implemented NST-2007 3), 4) nomenclature and regulation from Destatis and KBA is applied instead.

Table 1: Overview of primary activity data sources over time

1990-1996 simplified estimates without differentiation of handled materials
1997-2009 statistical data following NST/R nomenclature
as of 2010 statistical data following NST-2007 nomenclature

Here, NST/R allowed the distribution of a broad variety of goods and materials (e.g. barley, corn, oats, rice, rye, and wheat), whereas NST-2007 provides only a very condensed list of classes of goods (e.g. 'crops').

Due to these methodological breaks, activity data and emissions show inconsistencies (especially on the level of specific goods and materials) that cannot be eliminated at the moment. Nonetheless, on a aggregate level, these breaks are balanced out more or less automatically as the total amount of transported dry materials does not chnage too much with changing statistical approaches.

For estimating the amount of moved bulk materials as well as emissions from the loading and unloading of bulk materials, these primary activity data (PAD, including the amounts of imported and exported goods as well as goods transported within Germany) have to be calculated from the amounts of transported goods:

PADmaterial i = PADimport + PADexport + 2 * PADdomestic handling

with

  1. PADimport = amount of imported good or material,
  2. PADexport = amount of exported good or material and
  3. PADdomestic handling = amount of good or material transported only within Germany

As the basic statistics provide only total amounts of imported, exported and domestically transported dry goods without any distinction into bulk and packed goods, the shares of bulk goods had to be estimated via expert judgement during the workshop mentioned above.

During this workshop, experts, for comparable kinds of dry bulk material, discussed specific shares displaying which part of the total amount of dry material i loaded and/or unloaded within Germany might be transported as bulk material thus causing PM emissions.

So the activity data finally used for estimating specific particulate matter emissions for every bulk material is calculated as a specific share s of the amount of this material i loaded and/or unloaded within Germany:

ADbulk material i = PADbulk material i * s bulk share

Table 2: Amounts of dry, dusty bulk goods handled in Germany 2010-2019, in tonnes

transport mode 2010 2015 2016 2017 2018 2019 2020
other herbal products inland vessel 5.523.633 39,189,603 38,498,874 34,508,319 30,305,094 6,279,089 6,745,999
railways 1.242.916 470,000 547,545 532,253 445,547 613,000 588,000
heavy-duty vehicle 20.847.400 34,166,200 22,918,493 24,118,587 35,511,100 18,815,200 18,718,500
sea-going vessel 4.052.384 6,376,068 7,164,149 6,953,293 6,614,999 6,067,598 6,535,195
raw mineral chemicals inland vessel 6.794.922 2,366,579 2,573,770 2,696,029 11,798,872 11,909,168 11,706,333
railways 9.827.059 9,273,000 9,627,577 9,885,631 10,634,917 9,158,000 8,714,000
heavy-duty vehicle 78.928.400 82,363,000 10,043,513 11,351,314 63,713,624 11,315,500 9,742,600
sea-going vessel 5.550.621 7,905,516 7,888,208 8,131,408 7,386,700 4,839,421 5,150,665
raw organic chemicals inland vessel 6.299.350 57,126 114,803 175,726 6,667,823 6,528,823 6,025,705
railways 16.287.803 21,094,000 18,661,643 18,339,593 0 17,607,000 17,718,000
heavy-duty vehicle 11.345.600 4,570,800 0 828,916 12,601,908 0 0
sea-going vessel 3.638.264 2,478,579 2,341,016 2,413,459 2,463,615 2,623,994 2,370,156
iron ore inland vessel 25.728.177 25,203,179 25,755,504 25,193,580 22,796,286 21,531,669 18,676,735
railways 38.565.334 37,708,000 37,434,377 37,586,847 38,252,864 36,601,000 32,240,000
heavy-duty vehicle 203.800 0 1,764,223 534,846 1,680,885 731,400 0
sea-going vessel 13.922.885 13,967,430 13,365,447 14,810,135 14,761,129 14,521,110 12,666,336
crops inland vessel 9.816.233 11,243,918 10,046,500 9,546,963 7,715,977 8,128,252 9,593,182
railways 2.982.548 4,583,000 3,545,040 3,759,205 2,985,786 3,169,000 4,513,000
heavy-duty vehicle 65.464.800 70,614,200 58,304,413 61,639,154 58,957,570 56,315,100 55,307,700
sea-going vessel 9.319.143 12,142,981 10,735,948 8,851,781 7,672,262 7,985,888 9,630,445
potatoes inland vessel 1.383 0 0 1,056 0 49,119 46,427
railways 17.135 0 0 4,581,528 4,896,748 0 0
heavy-duty vehicle 10.627.000 9,956,800 4,683,480 5,039,904 9,621,800 4,789,300 5,227,200
sea-going vessel 29.296.456 21,170,067 20,406,870 22,490,149 20,701,636 25,168,423 22,127,609
coal products inland vessel 2.409.311 1,361,655 2,003,004 2,129,778 1,560,991 1,870,568 1,635,431
railways 22.499.503 6,721,000 6,610,955 6,456,917 8,421,754 6,743,000 4,712,000
heavy-duty vehicle 11.801.600 15,401,600 7,065,314 8,549,595 13,182,782 6,653,300 3,878,200
sea-going vessel 802.164 48,778 43,760 135,197 25,450 21,898 18,396
products from grinding and shelling mills inland vessel 1.782.712 4,133,053 5,180,094 5,368,877 5,275,005 5,506,351 5,509,211
railways 2.852 0 465,039 381,098 349,419 645,000 506,000
heavy-duty vehicle 97.539.400 99,568,200 75,685,582 69,634,714 99,763,916 69,628,300 74,421,000
sea-going vessel 3.104.125 3,525,359 3,586,612 3,747,650 3,788,108 4,001,310 4,365,473
mineral fertilisers inland vessel 760.174 305,202 281,603 255,398 197,705 202,054 138,138
railways 4.122.535 3,424,000 3,619,997 3,581,858 3,224,654 2,756,000 3,000,000
heavy-duty vehicle 7.923.200 4,322,000 1,338,908 1,006,750 1,814,964 1,423,800 2,364,000
sea-going vessel 117.224 409,515 256,924 323,622 311,822 392,516 406,221
natural sands, gravel and stones inland vessel 40.518.020 31,927,501 33,178,046 36,072,381 35,475,139 38,522,204 36,093,810
railways 56.517.180 43,958,000 43,837,499 39,960,787 41,345,431 43,057,000 44,816,000
heavy-duty vehicle 1.655.747.400 1,853,177,400 1,669,958,849 1,672,131,248 1,838,142,737 1,639,276,500 1,609,497,100
sea-going vessel 8.739.096 9,739,769 10,353,589 13,515,063 12,463,686 13,506,136 12,271,288
non-iron ores inland vessel 1.512.246 2,964,925 2,827,648 3,199,797 3,043,062 2,749,584 3,389,500
railways 29.742 8,000 6,642 16,877 61,486 22,000 250,000
heavy-duty vehicle 705.600 0 0 827,676 512,051 0 675,600
sea-going vessel 2.687.815 2,850,350 3,870,273 4,368,429 4,621,799 3,488,596 4,680,063
raw coals inland vessel 36.652.759 0 0 0 19,571 26,136,332 18,399,530
railways 58.433.815 67,749,000 61,034,978 51,142,196 48,277,288 41,538,000 32,449,000
heavy-duty vehicle 10.561.400 13,275,800 11,858,051 16,057,484 12,593,015 12,603,300 6,404,100
sea-going vessel 13.299.295 16,476,145 14,401,269 15,919,606 16,187,881 12,695,386 7,439,063
secondary raw materials inland vessel 15.691.876 11,521,886 11,212,165 12,089,358 15,101,718 16,441,457 16,504,345
railways 25.614.264 22,113,000 21,261,312 22,147,649 20,565,387 17,627,000 14,626,000
heavy-duty vehicle 422.570.000 490,299,000 161,493,436 171,462,235 502,448,809 175,973,100 164,093,200
sea-going vessel 5.047.097 5,810,444 5,057,435 4,173,386 3,427,249 3,502,952 3,465,765
rock & saline salt inland vessel 2.769.356 3,939,437 3,651,498 4,115,651 3,977,618 4,621,784 4,370,103
railways 3.067.187 2,575,000 2,362,886 2,603,115 3,017,352 2,673,000 2,078,000
heavy-duty vehicle 21.579.000 7,887,600 7,238,776 10,591,977 11,820,822 8,527,200 5,613,400
sea-going vessel 567.059 919,251 888,593 812,124 1,116,411 961,803 624,742
nitrogen fertilisers inland vessel 5.737.386 5,104,076 4,930,755 4,742,988 4,466,442 4,975,009 4,881,285
railways 15.708.472 14,091,000 13,614,102 14,066,445 12,318,493 11,774,000 11,651,000
heavy-duty vehicle 37.454.600 71,366,600 28,434,989 30,619,530 68,151,044 29,142,700 28,658,700
sea-going vessel 5.309.443 6,509,499 7,011,855 7,392,865 7,239,705 7,385,920 7,868,139
white cement, lime, cement inland vessel 3.273.975 2,479,720 2,532,347 2,776,593 2,978,726 2,896,408 3,398,570
railways 17.849.146 21,867,000 19,270,679 18,928,775 18,679,111 17,758,000 14,257,000
heavy-duty vehicle 69.407.200 86,441,400 76,251,684 77,289,169 99,899,785 82,985,300 82,091,500
sea-going vessel 1.544.488 2,757,516 2,470,814 2,552,567 2,172,344 1,972,384 1,483,783
sugar beet inland vessel 0 6,366,439 6,426,328 6,396,070 5,912,659 0 4,265
railways 123.598 24,000 64,094 37,555 0 2,000 2,000
heavy-duty vehicle 26.946.200 36,601,000 22,159,060 32,853,554 31,023,482 32,540,800 31,424,000
sea-going vessel 17 2,872 3,125 9,676 3,277 0 3,257

sources: annual data deliveries DESTATIS & KBA (for heavy-duty vehicles) to the inventory compiler

Emission factors

Emission factors are based on the methodology according VDI guidelines 3790. The values used here originate from a research project by (Müller-BBM, 2011) 5) taking into account information of an expert panel of industry and administration. For details see the [*https://www.umweltbundesamt.de/publikationen/konsistenzpruefung-verbesserungspotenzial project report] (German version only).

Within the study, PM emission factors are estimated for each material or good that might be transported as dry and unpacked bulk. These very specific EF are than assigned to the classes of materials/goods available from the different different statistics (NST/R, NST-2007) to form implied Ef for these class of bulk material.

As NST/R provided a wide variaty of goods and materials, whereas NST-2007 provides only a very condensed list of classes of goods, the very specific EF derived during the study and the joint expert workshop have been aggregated in order to match the classes of goods following NST-2007.

Table 2: specific EF for PM emissions from NST/R crop products, in [kg/t], as used for 2009 estimates

TSP PM10 PM2.5
for barley
inland ship 0.038 0.019 0.004
railway 0.038 0.019 0.004
maritime ship 0.038 0.019 0.0038
heavy-duty vehicle 0.038 0.019 0.004
for oats
inland ship 0.018 0.009 0.002
railway 0.018 0.009 0.002
maritime ship 0.018 0.009 0.00179
heavy-duty vehicle 0.018 0.009 0.002
for corn
inland ship 0.029 0.014 0.003
railway 0.029 0.014 0.003
maritime ship 0.029 0.014 0.00287
heavy-duty vehicle 0.029 0.014 0.003
for rice
inland ship 0.015 0.008 0.002
railway 0.015 0.008 0.002
maritime ship 0.015 0.008 0.00151
heavy-duty vehicle 0.015 0.008 0.002
for rye
inland ship 0.038 0.019 0.004
railway 0.038 0.019 0.004
maritime ship 0.038 0.019 0.0038
heavy-duty vehicle 0.038 0.019 0.004
for wheat
inland ship 0.038 0.019 0.004
railway 0.038 0.019 0.004
maritime ship 0.038 0.019 0.0038
heavy-duty vehicle 0.038 0.019 0.004

Here, in order to match the new NST-2007 classes for goods and materials, the very specific emission factors used in fomer submissions were converted to aggregated implied emission factors.

Table 3: IEFs used for emission estimates as of 2010, in [kg/t]

Heavy-duty vehicles Railways Inland vessels Sea vessels
TSP PM10 PM2.5 TSP PM10 PM2.5 TSP PM10 PM2.5 TSP PM10 PM2.5
Other herbal products 0.032000 0.016000 0.003200 0.024000 0.012000 0.002400 0.022000 0.011000 0.002200 0.028000 0.014000 0.002800
Chemische Grundstoffe. mineralisch 0.041000 0.020500 0.004100 0.031000 0.015500 0.003100 0.029000 0.014500 0.002900 0.036000 0.018000 0.003600
Raw organic chemicals 0.024000 0.012000 0.002400 0.018000 0.009000 0.001800 0.017000 0.008500 0.001700 0.021000 0.010500 0.002100
Iron ore 0.057000 0.028500 0.005700 0.042000 0.021000 0.004200 0.040000 0.020000 0.004000 0.050000 0.025000 0.005000
Crops 0.045000 0.022500 0.004500 0.034000 0.017000 0.003400 0.031000 0.015500 0.003100 0.039000 0.019500 0.003900
Potatoes 0.007000 0.003500 0.000700 0.005000 0.002500 0.000500 0.005000 0.002500 0.000500 0.006000 0.003000 0.000600
Coal products 0.019000 0.009500 0.001900 0.014000 0.007000 0.001400 0.013000 0.006500 0.001300 0.017000 0.008500 0.001700
Products from grinding and shelling mills 0.003000 0.001500 0.000300 0.003000 0.001500 0.000300 0.003000 0.001500 0.000300 0.003000 0.001500 0.000300
Mineral fertilisers 0.024000 0.012000 0.002400 0.018000 0.009000 0.001800 0.017000 0.008500 0.001700 0.021000 0.010500 0.002100
Natural sands. gravel. and stones 0.027000 0.013500 0.002700 0.020000 0.010000 0.002000 0.019000 0.009500 0.001900 0.023000 0.011500 0.002300
Non-iron ores 0.066000 0.033000 0.006600 0.049000 0.024500 0.004900 0.046000 0.023000 0.004600 0.058000 0.029000 0.005800
Raw coals 0.016000 0.008000 0.001600 0.016000 0.008000 0.001600 0.020000 0.010000 0.002000 0.028000 0.014000 0.002800
Secondary raw materials 0.027000 0.013500 0.002700 0.020000 0.010000 0.002000 0.019000 0.009500 0.001900 0.023000 0.011500 0.002300
Rock & saline salt 0.068000 0.034000 0.006800 0.051000 0.025500 0.005100 0.047000 0.023500 0.004700 0.059000 0.029500 0.005900
Nitrogen fertilisers 0.024000 0.012000 0.002400 0.018000 0.009000 0.001800 0.017000 0.008500 0.001700 0.021000 0.010500 0.002100
White cement. lime. cement 0.005000 0.002500 0.000500 0.004000 0.002000 0.000400 0.003000 0.001500 0.000300 0.004000 0.002000 0.000400
Sugar beet 0.000240 0.000120 0.000024 0.000180 0.000090 0.000018 0.000170 0.000085 0.000017 0.000210 0.000105 0.000021

Ratio TSP : PM10 : PM2.5

The shares of PM10 and PM2.5 of the entire amounts of emitted TSP have been set to fixed values used for the entire time series.

Assumptions:

  1. TSP = 100%,
  2. 50% of TSP are =< 10 µm. Therefore, the EF(PM10) are assumed as 1/2 of the corresponding EF(TSP), and
  3. 10% of TSP are =< 2.5 µm. Therefore, the EF(PM2.5) are assumed as 1/10 of the corresponding EF(TSP).

The ratios of TSP, PM10, and PM2.5 were also discussed in the research project mentioned above, but without generating any new data. Nonetheless, the ratios might be to low at the moment and will be checked furthermore.

Recalculations

As for submission 2021 activity data for 2019 could not be derived from national statistics, amounts of goods transported for 2018 were applied for 2019, too. With the current submission, these data are replaced with original 2019 activity data, resulting in correspondingly revised emission estimates.

table: Revised amounts of bulk goods transported in 2019, in tonnes

Submission 2021 Submission 2022 absolute change relative change
other herbal products 30,305,094 6,279,089 -24,026,005 -79%
445,547 613,000 167,453 38%
35,511,100 18,815,200 -16,695,900 -47%
6,614,999 6,067,598 -547,401 -8%
raw mineral chemicals 11,798,872 11,909,168 110,296 1%
10,634,917 9,158,000 -1,476,917 -14%
63,713,624 11,315,500 -52,398,124 -82%
7,386,700 4,839,421 -2,547,279 -34%
raw organic chemicals 6,667,823 6,528,823 -139,000 -2%
0 17,607,000 17,607,000
12,601,908 0 -12,601,908 -100%
2,463,615 2,623,994 160,379 7%
iron ore 22,796,286 21,531,669 -1,264,617 -6%
38,252,864 36,601,000 -1,651,864 -4%
1,680,885 731,400 -949,485 -56%
14,761,129 14,521,110 -240,019 -2%
crops 7,715,977 8,128,252 412,275 5%
2,985,786 3,169,000 183,214 6%
58,957,570 56,315,100 -2,642,470 -4%
7,672,262 7,985,888 313,626 4%
potatoes 0 49,119 49,119
4,896,748 0 -4,896,748 -100%
9,621,800 4,789,300 -4,832,500 -50%
20,701,636 25,168,423 4,466,787 22%
coal products 1,560,991 1,870,568 309,577 20%
8,421,754 6,743,000 -1,678,754 -20%
13,182,782 6,653,300 -6,529,482 -50%
25,450 21,898 -3,552 -14%
products from grinding and shelling mills 5,275,005 5,506,351 231,347 4%
349,419 645,000 295,581 85%
99,763,916 69,628,300 -30,135,616 -30%
3,788,108 4,001,310 213,202 6%
mineral fertilisers 197,705 202,054 4,349 2%
3,224,654 2,756,000 -468,654 -15%
1,814,964 1,423,800 -391,164 -22%
311,822 392,516 80,694 26%
natural sands, gravel and stones 35,475,139 38,522,204 3,047,065 9%
41,345,431 43,057,000 1,711,569 4%
1,838,142,737 1,639,276,500 -198,866,237 -11%
12,463,686 13,506,136 1,042,450 8%
non-iron ores 3,043,062 2,749,584 -293,478 -10%
61,486 22,000 -39,486 -64%
512,051 0 -512,051 -100%
4,621,799 3,488,596 -1,133,203 -25%
raw coals 19,571 26,136,332 26,116,761 133446%
48,277,288 41,538,000 -6,739,288 -14%
12,593,015 12,603,300 10,285 0%
16,187,881 12,695,386 -3,492,495 -22%
secondary raw materials 15,101,718 16,441,457 1,339,739 9%
20,565,387 17,627,000 -2,938,387 -14%
502,448,809 175,973,100 -326,475,709 -65%
3,427,249 3,502,952 75,703 2%
rock & saline salt 3,977,618 4,621,784 644,167 16%
3,017,352 2,673,000 -344,352 -11%
11,820,822 8,527,200 -3,293,622 -28%
1,116,411 961,803 -154,608 -14%
nitrogen fertilisers 4,466,442 4,975,009 508,567 11%
12,318,493 11,774,000 -544,493 -4%
68,151,044 29,142,700 -39,008,344 -57%
7,239,705 7,385,920 146,215 2%
white cement, lime, cement 2,978,726 2,896,408 -82,318 -3%
18,679,111 17,758,000 -921,111 -5%
99,899,785 82,985,300 -16,914,485 -17%
2,172,344 1,972,384 -199,960 -9%
sugar beet 5,912,659 0 -5,912,659 -100%
0 2,000 2,000
31,023,482 32,540,800 1,517,318 5%
3,277 0 -3,277 -100%

table: Revised particulate matter emissions from of bulk goods transported in 2019, in kilotonnes

PM2.5 PM10 TSP
current submission 6,62 33,1 66,2
previous submission 8,52 42,6 85,2
absolute change -1,90 -9,52 -19,0
relative change -22,3% -22,3% -22,3%

Planned improvements

Although no specific improvement is planned, additional effort will be necessary to further minimise the inconsistencies in the activity data time series resulting from the different approaches applied.


1), 5) Müller-BBM, 2011: Dr. Matthias Bender, Ludger Gronewäller, Detlef Langer: Konsistenzprüfung und Verbesserungspotenzial der Schüttgutemissionsberechnung - Umweltforschungsplan des Bundesministeriums für Umwelt, Naturschutz und Reaktorsicherheit, Förderkennzeichen3708 49 107 2 - FB 00 1453 UBA; Müller- BBM GmbH, Im Auftrag des Umweltbundesamtes, Planegg/Dessau-Roßlau, Februar 2011 - URL: https://www.umweltbundesamt.de/publikationen/konsistenzpruefung-verbesserungspotenzial
2) Eurostat, 2015a: Standard Goods Classification for Transport Statistics/Revised (1967) NST/R - URL: http://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_NOM_DTL&StrNom=NSTR_1967&StrLanguageCode=EN&IntPcKey=&StrLayoutCode=HIERARCHIC
4) Destatis, 2013: Statistisches Bundesamt, Verkehr, NST-2007: Einheitliches Güterverzeichnis für die Verkehrsstatistik – 2007 - URL: https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Transport-Verkehr/Gueterverkehr/Tabellen/nsz-2007.html