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:

PAD_(total material i) = PAD_(import material i) + 2 * PAD_(domestic handling material i) + PAD_(export material i)

where:

  • PAD_(import material i) = amount of imported good or material,
  • PAD_(domestic handling material i) = amount of good or material transported within Germany
  • PAD_(export material i) = amount of exported good or material

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:

AD_(bulk material i) = PAD_(total material i) * s_(bulk share)

where:

  • PAD_(total material i) = total amount of good or material imported to, transported within, and exported from Germany,
  • s_(bulk share) = percental share of a specific good/material that is considered to be handled as dry bulk good causing particulate matter emissions during handling

Table 2: Amounts of dry, dusty bulk goods handled in Germany 2010-2022, in tonnes

transport mode 2010 2015 2016 2017 2018 2019 2020 2021 2022
other herbal products inland vessel 3,273,975 2,479,720 2,532,347 2,776,593 2,978,726 2,896,408 3,398,570 2,775,621 3,185,121
railways 17,849,146 21,867,000 19,270,679 18,928,775 18,679,111 17,758,000 14,257,000 13,930,000 14,929,000
heavy-duty vehicle 1,544,488 2,757,516 2,470,814 2,552,567 2,172,344 1,972,384 1,483,783 1,522,528 1,298,793
sea-going vessel 69,407,200 86,441,400 76,251,684 77,289,169 99,899,785 82,985,300 82,091,500 73,636,700 80,938,771
raw mineral chemicals inland vessel 9,827,059 9,273,000 9,627,577 9,885,631 10,634,917 9,158,000 8,714,000 8,238,000 7,877,000
railways 6,794,922 2,366,579 2,573,770 2,696,029 11,798,872 11,909,168 11,706,333 11,957,508 11,484,122
heavy-duty vehicle 78,928,400 82,363,000 10,043,513 11,351,314 63,713,624 11,315,500 9,742,600 7,608,000 7,944,572
sea-going vessel 5,550,621 7,905,516 7,888,208 8,131,408 7,386,700 4,839,421 5,150,665 6,158,004 5,183,607
raw organic chemicals inland vessel 16,287,803 21,094,000 18,661,643 18,339,593 0 17,607,000 17,718,000 18,922,000 16,764,000
railways 6,299,350 57,126 114,803 175,726 6,667,823 6,528,823 6,025,705 6,242,593 5,843,685
heavy-duty vehicle 11,345,600 4,570,800 0 828,916 12,601,908 0 0 532,800 598,840
sea-going vessel 3,638,264 2,478,579 2,341,016 2,413,459 2,463,615 2,623,994 2,370,156 2,620,126 2,316,765
iron ore inland vessel 2,409,311 1,361,655 2,003,004 2,129,778 1,560,991 1,870,568 1,635,431 1,646,949 1,540,486
railways 22,499,503 6,721,000 6,610,955 6,456,917 8,421,754 6,743,000 4,712,000 3,633,000 3,456,000
heavy-duty vehicle 802,164 48,778 43,760 135,197 25,450 21,898 18,396 136,867 545,520
sea-going vessel 11,801,600 15,401,600 7,065,314 8,549,595 13,182,782 6,653,300 3,878,200 3,596,000 8,319,232
crops inland vessel 9,816,233 11,243,918 10,046,500 9,546,963 7,715,977 8,128,252 9,593,182 9,174,271 9,175,097
railways 2,982,548 4,583,000 3,545,040 3,759,205 2,985,786 3,169,000 4,513,000 5,223,000 5,415,000
heavy-duty vehicle 9,319,143 12,142,981 10,735,948 8,851,781 7,672,262 7,985,888 9,630,445 7,423,593 6,861,301
sea-going vessel 65,464,800 70,614,200 58,304,413 61,639,154 58,957,570 56,315,100 55,307,700 57,393,200 57,542,622
potatoes inland vessel 2,852 0 465,039 381,098 349,419 645,000 506,000 399,000 288,000
railways 1,782,712 4,133,053 5,180,094 5,368,877 5,275,005 5,506,351 5,509,211 5,289,677 4,708,901
heavy-duty vehicle 97,539,400 99,568,200 75,685,582 69,634,714 99,763,916 69,628,300 74,421,000 75,421,200 67,185,245
sea-going vessel 3,104,125 3,525,359 3,586,612 3,747,650 3,788,108 4,001,310 4,365,473 3,372,983 3,797,498
coal products inland vessel 25,728,177 25,203,179 25,755,504 25,193,580 22,796,286 21,531,669 18,676,735 20,936,706 20,416,412
railways 38,565,334 37,708,000 37,434,377 37,586,847 38,252,864 36,601,000 32,240,000 35,795,000 34,599,000
heavy-duty vehicle 13,922,885 13,967,430 13,365,447 14,810,135 14,761,129 14,521,110 12,666,336 14,496,550 13,755,726
sea-going vessel 203,800 0 1,764,223 534,846 1,680,885 731,400 0 1,035,800 0
products from grinding & shelling mills inland vessel 760,174 305,202 281,603 255,398 197,705 202,054 138,138 154,680 158,954
railways 4,122,535 3,424,000 3,619,997 3,581,858 3,224,654 2,756,000 3,000,000 4,297,000 4,969,000
heavy-duty vehicle 117,224 409,515 256,924 323,622 311,822 392,516 406,221 480,587 490,794
sea-going vessel 7,923,200 4,322,000 1,338,908 1,006,750 1,814,964 1,423,800 2,364,000 1,046,400 3,866,610
mineral fertilisers inland vessel 56,517,180 43,958,000 43,837,499 39,960,787 41,345,431 43,057,000 44,816,000 50,082,000 51,545,000
railways 40,518,020 31,927,501 33,178,046 36,072,381 35,475,139 38,522,204 36,093,810 33,268,472 34,596,242
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 1,633,052,300 1,605,183,983
sea-going vessel 8,739,096 9,739,769 10,353,589 13,515,063 12,463,686 13,506,136 12,271,288 13,020,839 13,113,287
natural sands, gravel & stones inland vessel 5,737,386 5,104,076 4,930,755 4,742,988 4,466,442 4,975,009 4,881,285 4,221,526 3,898,815
railways 15,708,472 14,091,000 13,614,102 14,066,445 12,318,493 11,774,000 11,651,000 11,850,000 11,180,000
heavy-duty vehicle 5,309,443 6,509,499 7,011,855 7,392,865 7,239,705 7,385,920 7,868,139 6,943,731 6,206,307
sea-going vessel 37,454,600 71,366,600 28,434,989 30,619,530 68,151,044 29,142,700 28,658,700 26,005,100 26,816,891
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 3,282,220 3,550,037
railways 29,742 8,000 6,642 16,877 61,486 22,000 250,000 28,000 385,000
heavy-duty vehicle 2,687,815 2,850,350 3,870,273 4,368,429 4,621,799 3,488,596 4,680,063 4,398,542 3,761,297
sea-going vessel 705,600 0 0 827,676 512,051 0 675,600 0 0
raw coals inland vessel 5,523,633 39,189,603 38,498,874 34,508,319 30,305,094 6,279,089 6,745,999 6,438,938 5,834,164
railways 1,242,916 470,000 547,545 532,253 445,547 613,000 588,000 743,000 731,000
heavy-duty vehicle 4,052,384 6,376,068 7,164,149 6,953,293 6,614,999 6,067,598 6,535,195 6,402,964 6,314,380
sea-going vessel 20,847,400 34,166,200 22,918,493 24,118,587 35,511,100 18,815,200 18,718,500 25,919,300 22,471,870
secondary raw materials inland vessel 1,383 0 0 1,056 0 49,119 46,427 0 597
railways 17,135 0 0 4,581,528 4,896,748 0 0 0 8,000
heavy-duty vehicle 29,296,456 21,170,067 20,406,870 22,490,149 20,701,636 25,168,423 22,127,609 18,406 17,061
sea-going vessel 10,627,000 9,956,800 4,683,480 5,039,904 9,621,800 4,789,300 5,227,200 5,502,200 5,013,672
rock & saline salt inland vessel 36,652,759 0 0 0 19,571 26,136,332 18,399,530 25,256,586 25,510,105
railways 58,433,815 67,749,000 61,034,978 51,142,196 48,277,288 41,538,000 32,449,000 33,689,000 40,216,000
heavy-duty vehicle 13,299,295 16,476,145 14,401,269 15,919,606 16,187,881 12,695,386 7,439,063 10,027,842 11,851,034
sea-going vessel 10,561,400 13,275,800 11,858,051 16,057,484 12,593,015 12,603,300 6,404,100 13,025,600 11,662,471
nitrogen fertilisers inland vessel 2,769,356 3,939,437 3,651,498 4,115,651 3,977,618 4,621,784 4,370,103 4,366,688 4,431,492
railways 3,067,187 2,575,000 2,362,886 2,603,115 3,017,352 2,673,000 2,078,000 2,723,000 2,751,000
heavy-duty vehicle 567,059 919,251 888,593 812,124 1,116,411 961,803 624,742 926,648 877,961
sea-going vessel 21,579,000 7,887,600 7,238,776 10,591,977 11,820,822 8,527,200 5,613,400 12,493,000 7,482,911
white cement, lime, cement inland vessel 5,047,097 5,810,444 5,057,435 4,173,386 3,427,249 3,502,952 3,465,765 3,552,873 3,328,845
railways 422,570,000 490,299,000 161,493,436 171,462,235 502,448,809 175,973,100 164,093,200 121,392,900 81,280,591
heavy-duty vehicle 25,614,264 22,113,000 21,261,312 22,147,649 20,565,387 17,627,000 14,626,000 16,501,000 17,558,000
sea-going vessel 15,691,876 11,521,886 11,212,165 12,089,358 15,101,718 16,441,457 16,504,345 15,622,479 15,264,191
sugar beet inland vessel 0 6,366,439 6,426,328 6,396,070 5,912,659 0 4,265 1,274 0
railways 123,598 24,000 64,094 37,555 0 2,000 2,000 127,000 2,000
heavy-duty vehicle 17 2,872 3,125 9,676 3,277 0 3,257 12,340 6,313
sea-going vessel 26,946,200 36,601,000 22,159,060 32,853,554 31,023,482 32,540,800 31,424,000 31,685,800 46,174,132

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 2022 estimates

TSP PM10 PM2.5
Other herbal products 0.032000 0.016000 0.003200
Chemische Grundstoffe. mineralisch 0.041000 0.020500 0.004100
Raw organic chemicals 0.024000 0.012000 0.002400
Iron ore 0.057000 0.028500 0.005700
Crops 0.045000 0.022500 0.004500
Potatoes 0.007000 0.003500 0.000700
Coal products 0.019000 0.009500 0.001900
Products from grinding and shelling mills 0.003000 0.001500 0.000300
Mineral fertilisers 0.024000 0.012000 0.002400
Natural sands. gravel. and stones 0.027000 0.013500 0.002700
Non-iron ores 0.066000 0.033000 0.006600
Raw coals 0.016000 0.008000 0.001600
Secondary raw materials 0.027000 0.013500 0.002700
Rock & saline salt 0.068000 0.034000 0.006800
Nitrogen fertilisers 0.024000 0.012000 0.002400
White cement. lime. cement 0.005000 0.002500 0.000500
Sugar beet 0.000240 0.000120 0.000024

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

With activity data and emission factors remaining unrevised, no recalculations were carried out compared to the previous submission.

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
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