Evaluating the Theoretical Background of STOFFENMANAGER® and the Advanced REACH Tool Article Swipe
YOU?
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· 2021
· Open Access
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· DOI: https://doi.org/10.1093/annweh/wxab057
STOFFENMANAGER® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER® and ART are based on a modified multiplicative model where an exposure base level (mg m−3) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m−3) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For example, for dusts the calibration data were pooled from exposure measurements sampled from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER® and ART. A literature review showed that the models have been validated only at the ‘operational analysis’ level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson’s correlations (r is <0.6) for both STOFFENMANAGER® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.
Related Topics
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1093/annweh/wxab057
- https://academic.oup.com/annweh/article-pdf/66/4/520/43410662/wxab057.pdf
- OA Status
- hybrid
- Cited By
- 15
- References
- 93
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3188779576
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3188779576Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/annweh/wxab057Digital Object Identifier
- Title
-
Evaluating the Theoretical Background of STOFFENMANAGER® and the Advanced REACH ToolWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-07-12Full publication date if available
- Authors
-
Antti Joonas Koivisto, Michael A. Jayjock, Kaarle Hämeri, Markku Kulmala, Patrick Van Sprang, Mingzhou Yu, Brandon E. Boor, Tareq Hussein, Ismo Kalevi Koponen, Jakob Löndahl, Lídia Morawska, John C. Little, Susan ArnoldList of authors in order
- Landing page
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https://doi.org/10.1093/annweh/wxab057Publisher landing page
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https://academic.oup.com/annweh/article-pdf/66/4/520/43410662/wxab057.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://academic.oup.com/annweh/article-pdf/66/4/520/43410662/wxab057.pdfDirect OA link when available
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Calibration, Statistics, Computer science, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
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2025: 7, 2024: 1, 2023: 4, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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93Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.based | 75, 108 |
| abstract_inverted_index.basic | 306 |
| abstract_inverted_index.dusts | 283 |
| abstract_inverted_index.first | 62 |
| abstract_inverted_index.given | 161 |
| abstract_inverted_index.group | 249 |
| abstract_inverted_index.level | 85, 203, 386 |
| abstract_inverted_index.local | 142 |
| abstract_inverted_index.mists | 253 |
| abstract_inverted_index.mixed | 236 |
| abstract_inverted_index.model | 80, 239, 305, 390 |
| abstract_inverted_index.often | 427 |
| abstract_inverted_index.range | 403 |
| abstract_inverted_index.score | 95, 226 |
| abstract_inverted_index.shown | 416 |
| abstract_inverted_index.solid | 256 |
| abstract_inverted_index.steps | 165 |
| abstract_inverted_index.study | 63 |
| abstract_inverted_index.their | 56, 317, 339, 474 |
| abstract_inverted_index.these | 442 |
| abstract_inverted_index.tools | 10, 334 |
| abstract_inverted_index.users | 39 |
| abstract_inverted_index.using | 116, 207, 259 |
| abstract_inverted_index.where | 81 |
| abstract_inverted_index.which | 196, 301 |
| abstract_inverted_index.~1000 | 260 |
| abstract_inverted_index.Agency | 15 |
| abstract_inverted_index.cannot | 322, 346 |
| abstract_inverted_index.dusts, | 251 |
| abstract_inverted_index.effect | 237 |
| abstract_inverted_index.expert | 175 |
| abstract_inverted_index.factor | 213 |
| abstract_inverted_index.levels | 222, 426 |
| abstract_inverted_index.mainly | 107, 158 |
| abstract_inverted_index.making | 467 |
| abstract_inverted_index.model. | 70 |
| abstract_inverted_index.models | 22, 190, 351, 377 |
| abstract_inverted_index.m−3) | 87, 205 |
| abstract_inverted_index.pooled | 288 |
| abstract_inverted_index.review | 373, 393 |
| abstract_inverted_index.safety | 19, 465 |
| abstract_inverted_index.score, | 195 |
| abstract_inverted_index.should | 468 |
| abstract_inverted_index.showed | 374 |
| abstract_inverted_index.values | 160, 337 |
| abstract_inverted_index.widely | 24 |
| abstract_inverted_index.within | 28 |
| abstract_inverted_index.Further | 144 |
| abstract_inverted_index.carried | 50 |
| abstract_inverted_index.concept | 363 |
| abstract_inverted_index.current | 475 |
| abstract_inverted_index.exhibit | 428 |
| abstract_inverted_index.factor. | 210 |
| abstract_inverted_index.factors | 100, 245 |
| abstract_inverted_index.groups. | 279 |
| abstract_inverted_index.models, | 453 |
| abstract_inverted_index.models. | 354 |
| abstract_inverted_index.natural | 163 |
| abstract_inverted_index.quality | 318 |
| abstract_inverted_index.reflect | 360 |
| abstract_inverted_index.sampled | 292 |
| abstract_inverted_index.studies | 414 |
| abstract_inverted_index.typical | 418 |
| abstract_inverted_index.vapors, | 252 |
| abstract_inverted_index.without | 141 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Advanced | 4 |
| abstract_inverted_index.Chemical | 14 |
| abstract_inverted_index.European | 13 |
| abstract_inverted_index.accepted | 27 |
| abstract_inverted_index.accuracy | 397 |
| abstract_inverted_index.assigned | 215 |
| abstract_inverted_index.chemical | 18, 464 |
| abstract_inverted_index.criteria | 449 |
| abstract_inverted_index.decision | 466 |
| abstract_inverted_index.discrete | 159 |
| abstract_inverted_index.emission | 94, 121, 130 |
| abstract_inverted_index.example, | 281 |
| abstract_inverted_index.exposure | 83, 98, 117, 194, 202, 221, 225, 233, 248, 278, 290, 421, 425 |
| abstract_inverted_index.globally | 40 |
| abstract_inverted_index.measured | 219, 424 |
| abstract_inverted_index.modeling | 356 |
| abstract_inverted_index.modified | 78 |
| abstract_inverted_index.outputs. | 340 |
| abstract_inverted_index.personal | 220 |
| abstract_inverted_index.physical | 184, 362 |
| abstract_inverted_index.publicly | 313 |
| abstract_inverted_index.replaced | 89, 102 |
| abstract_inverted_index.revealed | 394 |
| abstract_inverted_index.selected | 114 |
| abstract_inverted_index.violates | 302 |
| abstract_inverted_index.algorithm | 357 |
| abstract_inverted_index.allocated | 173 |
| abstract_inverted_index.available | 314 |
| abstract_inverted_index.bakeries, | 295 |
| abstract_inverted_index.calculate | 191, 243 |
| abstract_inverted_index.collected | 275 |
| abstract_inverted_index.comparing | 217 |
| abstract_inverted_index.databases | 310 |
| abstract_inverted_index.departure | 446 |
| abstract_inverted_index.describes | 388 |
| abstract_inverted_index.different | 277 |
| abstract_inverted_index.empirical | 304, 353 |
| abstract_inverted_index.generated | 136 |
| abstract_inverted_index.industry, | 297 |
| abstract_inverted_index.intrinsic | 93, 120 |
| abstract_inverted_index.logarithm | 164 |
| abstract_inverted_index.modifying | 99 |
| abstract_inverted_index.potential | 131 |
| abstract_inverted_index.predicted | 420 |
| abstract_inverted_index.presented | 365 |
| abstract_inverted_index.provided. | 154 |
| abstract_inverted_index.providing | 335 |
| abstract_inverted_index.qualified | 471 |
| abstract_inverted_index.reasoning | 180 |
| abstract_inverted_index.reported. | 188 |
| abstract_inverted_index.selection | 151 |
| abstract_inverted_index.substance | 129 |
| abstract_inverted_index.taxonomy. | 118 |
| abstract_inverted_index.validated | 380 |
| abstract_inverted_index.Regardless | 54 |
| abstract_inverted_index.acceptance | 462 |
| abstract_inverted_index.assessment | 47, 452 |
| abstract_inverted_index.calculated | 229 |
| abstract_inverted_index.categories | 111 |
| abstract_inverted_index.classified | 330, 348 |
| abstract_inverted_index.community. | 31 |
| abstract_inverted_index.evaluated. | 324 |
| abstract_inverted_index.evaluating | 64 |
| abstract_inverted_index.explicitly | 470 |
| abstract_inverted_index.literature | 372 |
| abstract_inverted_index.originally | 364 |
| abstract_inverted_index.quantities | 185 |
| abstract_inverted_index.regression | 238 |
| abstract_inverted_index.regulatory | 17, 448, 461 |
| abstract_inverted_index.represents | 133 |
| abstract_inverted_index.respective | 232 |
| abstract_inverted_index.scenarios. | 234 |
| abstract_inverted_index.scientific | 30, 147, 179 |
| abstract_inverted_index.selections | 321 |
| abstract_inverted_index.subjective | 110, 193, 320, 332 |
| abstract_inverted_index.translated | 199 |
| abstract_inverted_index.usability. | 391 |
| abstract_inverted_index.validation | 413 |
| abstract_inverted_index.widespread | 57 |
| abstract_inverted_index.Calibration | 411 |
| abstract_inverted_index.Pearson’s | 430 |
| abstract_inverted_index.analysis’ | 385 |
| abstract_inverted_index.assessment. | 20 |
| abstract_inverted_index.backgrounds | 67 |
| abstract_inverted_index.calibration | 209, 212, 273, 285, 309 |
| abstract_inverted_index.correlation | 244 |
| abstract_inverted_index.definition, | 342 |
| abstract_inverted_index.information | 145 |
| abstract_inverted_index.judgements. | 176 |
| abstract_inverted_index.limitations | 443 |
| abstract_inverted_index.measurement | 270 |
| abstract_inverted_index.mechanistic | 350 |
| abstract_inverted_index.multipliers | 104, 156, 178 |
| abstract_inverted_index.performance | 445 |
| abstract_inverted_index.pharmacies, | 294 |
| abstract_inverted_index.principles. | 307 |
| abstract_inverted_index.qualitative | 336 |
| abstract_inverted_index.recommended | 9, 456 |
| abstract_inverted_index.theoretical | 66 |
| abstract_inverted_index.&lt;0.6) | 434 |
| abstract_inverted_index.construction | 296 |
| abstract_inverted_index.correlations | 431 |
| abstract_inverted_index.measurements | 261, 266, 291 |
| abstract_inverted_index.standardized | 139 |
| abstract_inverted_index.ventilation. | 143 |
| abstract_inverted_index.concentration | 126, 135, 422 |
| abstract_inverted_index.deficiencies. | 476 |
| abstract_inverted_index.dimensionless | 92 |
| abstract_inverted_index.justification | 148 |
| abstract_inverted_index.categorization | 333 |
| abstract_inverted_index.multiplicative | 79 |
| abstract_inverted_index.‘operational | 384 |
| abstract_inverted_index.log-transformed | 419 |
| abstract_inverted_index.(low-volatiles), | 254 |
| abstract_inverted_index.STOFFENMANAGER® | 1, 32, 71, 263, 325, 343, 368, 399, 437, 458 |
| abstract_inverted_index.object/abrasion] | 257 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5040196554 |
| countries_distinct_count | 8 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I133731052, https://openalex.org/I4210132186 |
| citation_normalized_percentile.value | 0.96531792 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |