[1]

Jones KC. 2021. Persistent organic pollutants (POPs) and related chemicals in the global environment: some personal reflections. Environmental Science & Technology 55:9400−9412

doi: 10.1021/acs.est.0c08093
[2]

du Plessis A. 2022. Persistent degradation: global water quality challenges and required actions. One Earth 5:129−131

doi: 10.1016/j.oneear.2022.01.005
[3]

Wang Z, Walker GW, Muir DCG, Nagatani-Yoshida K. 2020. Toward a global understanding of chemical pollution: a first comprehensive analysis of national and regional chemical inventories. Environmental Science & Technology 54:2575−2584

doi: 10.1021/acs.est.9b06379
[4]

Kosnik MB, Hauschild MZ, Fantke P. 2022. Toward assessing absolute environmental sustainability of chemical pollution. Environmental Science & Technology 56:4776−4787

doi: 10.1021/acs.est.1c06098
[5]

Xu J, Liu J. 2023. Managing the risks of new pollutants in China: the perspective of policy integration. Environment & Health 1:360−366

doi: 10.1021/envhealth.3c00054
[6]

Hahladakis JN, Velis CA, Weber R, Iacovidou E, Purnell P. 2018. An overview of chemical additives present in plastics: migration, release, fate and environmental impact during their use, disposal and recycling. Journal of Hazardous Materials 344:179−199

doi: 10.1016/j.jhazmat.2017.10.014
[7]

Martin O, Scholze M, Ermler S, McPhie J, Bopp SK, et al. 2021. Ten years of research on synergisms and antagonisms in chemical mixtures: a systematic review and quantitative reappraisal of mixture studies. Environment International 146:106206

doi: 10.1016/j.envint.2020.106206
[8]

Landrigan PJ, Fuller R, Acosta NJR, Adeyi O, Arnold R, et al. 2018. The Lancet Commission on pollution and health. The Lancet 391:462−512

doi: 10.1016/S0140-6736(17)32345-0
[9]

The General Office of the State Council. 2022. China outlines plan to control new pollutants. https://english.www.gov.cn/policies/latestreleases/202205/24/content_WS628cd024c6d02e533532b3e1.html

[10]

Rodayan A, Majewsky M, Yargeau V. 2014. Impact of approach used to determine removal levels of drugs of abuse during wastewater treatment. Science of The Total Environment 487:731−739

doi: 10.1016/j.scitotenv.2014.03.080
[11]

Wu F, Cheng K, Cheng F, You J. 2023. Life cycle thinking supports 21st-century new pollutant and chemical management. Integrated Environmental Assessment and Management 19:859−860

doi: 10.1002/ieam.4758
[12]

He A, Liang Y, Li F, Lu Y, Liu C, et al. 2022. Vital environmental sources for multitudinous fluorinated chemicals: new evidence from industrial byproducts in multienvironmental matrices in a fluorochemical manufactory. Environmental Science & Technology 56:16789−16800

doi: 10.1021/acs.est.2c04372
[13]

Lopez G, Keiner D, Fasihi M, Koiranen T, Breyer C. 2023. From fossil to green chemicals: sustainable pathways and new carbon feedstocks for the global chemical industry. Energy & Environmental Science 16:2879−2909

doi: 10.1039/d3ee00478c
[14]

Zhou Z, Wu F, Tong Y, Zhang S, Li L, et al. 2024. Toxicity and chemical characterization of shale gas wastewater discharged to the receiving water: evidence from toxicity identification evaluation. Science of The Total Environment 912:169510

doi: 10.1016/j.scitotenv.2023.169510
[15]

Tran NH, Reinhard M, Gin KYH. 2018. Occurrence and fate of emerging contaminants in municipal wastewater treatment plants from different geographical regions-a review. Water Research 133:182−207

doi: 10.1016/j.watres.2017.12.029
[16]

Harrower J, McNaughtan M, Hunter C, Hough R, Zhang Z, et al. 2021. Chemical fate and partitioning behavior of antibiotics in the aquatic environment—a review. Environmental Toxicology and Chemistry 40:3275−3298

doi: 10.1002/etc.5191
[17]

Chibwe L, Titaley IA, Hoh E, Simonich SLM. 2017. Integrated framework for identifying toxic transformation products in complex environmental mixtures. Environmental Science & Technology letters 4:32−43

doi: 10.1021/acs.estlett.6b00455
[18]

Cheng F, Escher BI, Li H, König M, Tong Y, et al. 2024. Deep learning bridged bioactivity, structure, and GC-HRMS-readable evidence to decipher nontarget toxicants in sediments. Environmental Science & Technology 58:15415−15427

doi: 10.1021/acs.est.3c10814
[19]

Wu F, Cheng F, Li H, Chen S, Brooks BW, et al. 2025. Toward the "emiss-ome": multisystem coupling to advance life cycle ecological risk assessment of industrial chemicals. Environmental Science & Technology 59:8881−8884

doi: 10.1021/acs.est.5c04632
[20]

Brunner PH, Rechberger H. 2016. Handbook of material flow analysis: For environmental, resource, and waste engineers, 2nd edition. Boca Raton: CRC press. 456 pp doi: 10.1201/9781315313450

[21]

Xiang N, Li S, Shu C, Xu F. 2023. Dynamic material flow analysis of Chinese ethylene production processes and optimal pathway exploration with potential environmental-economic impacts. Journal of Cleaner Production 392:136282

doi: 10.1016/j.jclepro.2023.136282
[22]

Nakajima K, Ohno H, Kondo Y, Matsubae K, Takeda O, et al. 2013. Simultaneous material flow analysis of nickel, chromium, and molybdenum used in alloy steel by means of input–output analysis. Environmental Science & Technology 47:4653−4660

doi: 10.1021/es3043559
[23]

Liu B, Zhang Q, Liu J, Hao Y, Tang Y, et al. 2022. The impacts of critical metal shortage on China's electric vehicle industry development and countermeasure policies. Energy 248:123646

doi: 10.1016/j.energy.2022.123646
[24]

Miller RE, Blair PD. 2009. Input-output analysis: foundations and extensions, 2nd Edition. Cambridge: Cambridge university press. 784 pp. doi: 10.1017/CBO9780511626982

[25]

Giljum S, Hubacek K. 2009. Conceptual foundations and applications of physical input-output tables. In Handbook of Input-Output Economics in Industrial Ecology, ed. Sangwon S. Vol. 23. Dordrecht: Springer. pp. 61−75 doi: 10.1007/978-1-4020-5737-3_4

[26]

Owen A, Scott K, Barrett J. 2018. Identifying critical supply chains and final products: an input-output approach to exploring the energy-water-food nexus. Applied Energy 210:632−642

doi: 10.1016/j.apenergy.2017.09.069
[27]

Ren L, Cheng S, Tong Y, Zhang Y, Zhu F, et al. 2025. Study on carbon emission accounting method system and its application in the iron and steel industry. Sustainability 17:3829

doi: 10.3390/su17093829
[28]

Cimpan C, Bjelle EL, Strømman AH. 2021. Plastic packaging flows in Europe: a hybrid input-output approach. Journal of Industrial Ecology 25:1572−1587

doi: 10.1111/jiec.13175
[29]

Della Bella S, Sen B, Cimpan C, Rocco MV, Liu G. 2023. Exploring the impact of recycling on demand–supply balance of critical materials in green transition: a dynamic multi-regional waste input–output analysis. Environmental Science & Technology 57:10221−10230

doi: 10.1021/acs.est.2c09676
[30]

Meglin R, Kytzia S, Habert G. 2022. Regional circular economy of building materials: environmental and economic assessment combining Material Flow Analysis, Input-Output Analyses, and Life Cycle Assessment. Journal of Industrial Ecology 26:562−576

doi: 10.1111/jiec.13205
[31]

Chen Z, Lyu W, Wang R, Li Y, Xu C, et al. 2023. A molecular kinetic model incorporating catalyst acidity for hydrocarbon catalytic cracking. AIChE Journal 69:e18060

doi: 10.1002/aic.18060
[32]

Baerends EJ, Gritsenko OV. 1997. A quantum chemical view of density functional theory. The Journal of Physical Chemistry A 101:5383−5403

doi: 10.1021/jp9703768
[33]

Sutton JE, Vlachos DG. 2012. A theoretical and computational analysis of linear free energy relations for the estimation of activation energies. ACS Catalysis 2:1624−1634

doi: 10.1021/cs3003269
[34]

Fernández I, Bickelhaupt FM. 2014. The activation strain model and molecular orbital theory: understanding and designing chemical reactions. Chemical Society Reviews 43:4953−4967

doi: 10.1039/C4CS00055B
[35]

Xu W, Wang Y, Zhang D, Yang Z, Yuan Z, et al. 2024. Transparent AI-assisted chemical engineering process: machine learning modeling and multi-objective optimization for integrating process data and molecular-level reaction mechanisms. Journal of Cleaner Production 448:141412

doi: 10.1016/j.jclepro.2024.141412
[36]

Grajciar L, Heard CJ, Bondarenko AA, Polynski MV, Meeprasert J, et al. 2018. Towards operando computational modeling in heterogeneous catalysis. Chemical Society Reviews 47:8307−8348

doi: 10.1039/C8CS00398J
[37]

Fernandes NCP, Romanenko A, Reis MS. 2019. Mechanistic modeling and simulation for process data generation. Industrial & Engineering Chemistry Research 58:17871−17884

doi: 10.1021/acs.iecr.9b01752
[38]

Wei HL, Mukherjee T, Zhang W, Zuback JS, Knapp GL, et al. 2021. Mechanistic models for additive manufacturing of metallic components. Progress in Materials Science 116:100703

doi: 10.1016/j.pmatsci.2020.100703
[39]

Karimi Estahbanati MR, Feilizadeh M, Iliuta MC. 2019. An intrinsic kinetic model for liquid-phase photocatalytic hydrogen production. AIChE Journal 65:e16724

doi: 10.1002/aic.16724
[40]

Valverde JL, Ferro VR, Giroir-Fendler A. 2023. Automation in the simulation of processes with Aspen HYSYS: an academic approach. Computer Applications in Engineering Education 31:376−388

doi: 10.1002/cae.22589
[41]

Csendes VF, Egedy A, Leveneur S, Kummer A. 2023. Application of multi-software engineering: a review and a kinetic parameter identification case study. Processes 11:1503

doi: 10.3390/pr11051503
[42]

Tripodi A, Compagnoni M, Martinazzo R, Ramis G, Rossetti I. 2017. Process simulation for the design and scale up of heterogeneous catalytic process: kinetic modelling issues. Catalysts 7:159

doi: 10.3390/catal7050159
[43]

Liu Y, Li F, Li H, Tong Y, Li W, et al. 2022. Bioassay-based identification and removal of target and suspect toxicants in municipal wastewater: Impacts of chemical properties and transformation. Journal of Hazardous Materials 437:129426

doi: 10.1016/j.jhazmat.2022.129426
[44]

Xie P, Yan Q, Xiong J, Li H, Ma X, et al. 2022. Point or non-point source: toxicity evaluation using m-POCIS and zebrafish embryos in municipal sewage treatment plants and urban waterways. Environmental Pollution 292:118307

doi: 10.1016/j.envpol.2021.118307
[45]

Chen Y, Jiang C, Wang Y, Song R, Tan Y, et al. 2022. Sources, environmental fate, and ecological risks of antibiotics in sediments of Asia's longest river: a whole-basin investigation. Environmental Science & Technology 56:14439−14451

doi: 10.1021/acs.est.2c03413
[46]

Su C, Zhang H, Cridge C, Liang R. 2019. A review of multimedia transport and fate models for chemicals: principles, features and applicability. Science of The Total Environment 668:881−892

doi: 10.1016/j.scitotenv.2019.02.456
[47]

Li S, Zhu Y, Zhong G, Huang Y, Jones KC. 2024. Comprehensive assessment of environmental emissions, fate, and risks of veterinary antibiotics in China: an environmental fate modeling approach. Environmental Science & Technology 58:5534−5547

doi: 10.1021/acs.est.4c00993
[48]

MacLeod M, Scheringer M, McKone TE, Hungerbuhler K. 2010. The state of multimedia mass-balance modeling in environmental science and decision-making. Environmental Science & Technology 44:8360−8364

doi: 10.1021/es100968w
[49]

Mackay D. 1979. Finding fugacity feasible. Environmental Science & Technology 13:1218−1223

doi: 10.1021/es60158a003
[50]

Mackay D, Di Guardo A, Paterson S, Cowan CE. 1996. Evaluating the environmental fate of a variety of types of chemicals using the EQC model. Environmental Toxicology and Chemistry 15:1627−1637

doi: 10.1002/etc.5620150929
[51]

Han G, Song S, Huang Y, Xia H, Yu M. 2025. Advancing fugacity modeling of POPs: critical evaluation of multimedia frameworks and future pathways. Process Safety and Environmental Protection 202:107722

doi: 10.1016/j.psep.2025.107722
[52]

Mackay D, Joy M, Paterson S. 1983. A quantitative water, air, sediment interaction (QWASI) fugacity model for describing the fate of chemicals in lakes. Chemosphere 12:981−997

doi: 10.1016/0045-6535(83)90251-5
[53]

Diamond ML, Priemer DA, Law NL. 2001. Developing a multimedia model of chemical dynamics in an urban area. Chemosphere 44:1655−1667

doi: 10.1016/S0045-6535(00)00509-9
[54]

Mackay D, Celsie AKD, Powell DE, Parnis JM. 2018. Bioconcentration, bioaccumulation, biomagnification and trophic magnification: a modelling perspective. Environmental Science: Processes & Impacts 20:72−85

doi: 10.1039/C7EM00485K
[55]

Seth R, Webster E, Mackay D. 2008. Continued development of a mass balance model of chemical fate in a sewage treatment plant. Water Research 42:595−604

doi: 10.1016/j.watres.2007.08.004
[56]

Hollander A, Schoorl M, Van de Meent D. 2016. SimpleBox 4.0: Improving the model while keeping it simple. Chemosphere 148:99−107

doi: 10.1016/j.chemosphere.2016.01.006
[57]

Franco A, Trapp S. 2010. A multimedia activity model for ionizable compounds: validation study with 2, 4-dichlorophenoxyacetic acid, aniline, and trimethoprim. Environmental Toxicology and Chemistry 29:789−799

doi: 10.1002/etc.115
[58]

Zhu Y, Price OR, Tao S, Jones KC, Sweetman AJ. 2014. A new multimedia contaminant fate model for China: how important are environmental parameters in influencing chemical persistence and long-range transport potential? Environment International 69:18−27

doi: 10.1016/j.envint.2014.03.020
[59]

Escher BI, Fenner K. 2011. Recent advances in environmental risk assessment of transformation products. Environmental Science & Technology 45:3835−3847

doi: 10.1021/es1030799
[60]

Suman TY, Kim SY, Yeom DH, Jeon J. 2022. Transformation products of emerging pollutants explored using non-target screening: perspective in the transformation pathway and toxicity mechanism-a review. Toxics 10:54

doi: 10.3390/toxics10020054
[61]

Gao J, Ellis LB, Wackett LP. 2010. The University of Minnesota Biocatalysis/Biodegradation database: improving public access. Nucleic Acids Research 38:D488−D491

doi: 10.1093/nar/gkp771
[62]

Wicker J, Lorsbach T, Gütlein M, Schmid E, Latino D, et al. 2016. enviPath—The environmental contaminant biotransformation pathway resource. Nucleic Acids Research 44:502−508

doi: 10.1093/nar/gkv1229
[63]

Tam JYC, Lorsbach T, Schmidt S, Wicker JS. 2021. Holistic evaluation of biodegradation pathway prediction: assessing multi-step reactions and intermediate products. Journal of Cheminformatics 13:63

doi: 10.1186/s13321-021-00543-x
[64]

Moriya Y, Shigemizu D, Hattori M, Tokimatsu T, Kotera M, et al. 2010. PathPred: an enzyme-catalyzed metabolic pathway prediction server. Nucleic Acids Research 38:W138−W143

doi: 10.1093/nar/gkq318
[65]

Finley SD, Broadbelt LJ, Hatzimanikatis V. 2009. Computational framework for predictive biodegradation. Biotechnology and Bioengineering 104:1086−1097

doi: 10.1002/bit.22489
[66]

Tian S, Feunang YD, Oler E, Wang F, Greiner R, et al. 2025. BioTransformer 4.0 a comprehensive computational tool for small molecule metabolism prediction. bioRxiv: Preprint

doi: 10.1101/2025.07.28.667289
[67]

Tebes-Stevens C, Patel JM, Jones WJ, Weber EJ. 2017. Prediction of hydrolysis products of organic chemicals under environmental pH conditions. Environmental Science & Technology 51:5008−5016

doi: 10.1021/acs.est.6b05412
[68]

Dimitrov S, Pavlov T, Dimitrova N, Georgieva D, Nedelcheva D, et al. 2011. Simulation of chemical metabolism for fate and hazard assessment. II CATALOGIC simulation of abiotic and microbial degradation. SAR and QSAR in Environmental Research 22:719−755

doi: 10.1080/1062936X.2011.623322
[69]

Helmus R, van de Velde B, Brunner AM, ter Laak TL, van Wezel AP, et al. 2022. patRoon 2.0: improved non-target analysis workflows including automated transformation product screening. Journal of Open Source Software 7:4029

doi: 10.21105/joss.04029
[70]

Sprague JB. 1970. Measurement of pollutant toxicity to fish. II. Utilizing and applying bioassay results. Water Research 4:3−32

doi: 10.1016/0043-1354(70)90018-7
[71]

Weisner O, Frische T, Liebmann L, Reemtsma T, Roß-Nickoll M, et al. 2021. Risk from pesticide mixtures–The gap between risk assessment and reality. Science of The Total Environment 796:149017

doi: 10.1016/j.scitotenv.2021.149017
[72]

Junghans M, Backhaus T, Faust M, Scholze M, Grimme LH. 2006. Application and validation of approaches for the predictive hazard assessment of realistic pesticide mixtures. Aquatic Toxicology 76:93−110

doi: 10.1016/j.aquatox.2005.10.001
[73]

Liu Y, Jin X, Zhan A, Liao J, Johnson AC, et al. 2025. Beyond linear Thinking: redefining chemical pollution impacts on biodiversity. Environmental Science and Ecotechnology 26:100589

doi: 10.1016/j.ese.2025.100589
[74]

Yan Z, Jin X, Feng C, Leung KMY, Zhang X, et al. 2025. Beyond the single-contaminant paradigm: advancing mixture toxicity and cumulative risk assessment in environmental toxicology. Environmental Science & Technology 59:10711−10714

doi: 10.1021/acs.est.5c05712
[75]

Sigurnjak Bureš M, Cvetnić M, Miloloža M, Kučić Grgić D, Markić M, et al. 2021. Modeling the toxicity of pollutants mixtures for risk assessment: a review. Environmental Chemistry Letters 19:1629−1655

doi: 10.1007/s10311-020-01107-5
[76]

Ankley GT, Bennett RS, Erickson RJ, Hoff DJ, Hornung MW, et al. 2010. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environmental Toxicology and Chemistry 29:730−741

doi: 10.1007/s11356-023-30647-w
[77]

Knapen D, Angrish MM, Fortin MC, Katsiadaki I, Leonard M, et al. 2018. Adverse outcome pathway networks I: development and applications. Environmental Toxicology and Chemistry 37:1723−1733

doi: 10.1002/etc.4125
[78]

Wittwehr C, Aladjov H, Ankley G, Byrne HJ, de Knecht J, et al. 2017. How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology. Toxicological Sciences 155:326−336

doi: 10.1093/toxsci/kfw207
[79]

Conolly RB, Ankley GT, Cheng W, Mayo ML, Miller DH, et al. 2017. Quantitative adverse outcome pathways and their application to predictive toxicology. Environmental Science & Technology 51:4661−4672

doi: 10.1021/acs.est.6b06230
[80]

Moe SJ, Wolf R, Xie L, Landis WG, Kotamäki N, et al. 2021. Quantification of an adverse outcome pathway network by Bayesian regression and Bayesian network modeling. Integrated Environmental Assessment and Management 17:147−164

doi: 10.1002/ieam.4348
[81]

Perkins EJ, Gayen K, Shoemaker JE, Antczak P, Burgoon L, et al. 2019. Chemical hazard prediction and hypothesis testing using quantitative adverse outcome pathways. ALTEX 36:91−102

doi: 10.14573/ALTEX.1808241
[82]

Cao Y, Moe SJ, De Bin R, Tollefsen KE, Song Y. 2023. Comparison of piecewise structural equation modeling and Bayesian network for de novo construction of a quantitative adverse outcome pathway network. ALTEX 40:287−298

doi: 10.14573/altex.2207113
[83]

Cheng F, Li H, Brooks BW, You J. 2021. Signposts for aquatic toxicity evaluation in China: text mining using event-driven taxonomy within and among regions. Environmental Science & Technology 55:8977−8986

doi: 10.1021/acs.est.1c00152
[84]

Cheng F, Huang J, Li H, Escher BI, Tong Y, et al. 2023. Text mining-based suspect screening for aquatic risk assessment in the big data era: event-driven taxonomy links chemical exposures and hazards. Environmental Science & Technology Letters 10:1004−1010

doi: 10.1021/acs.estlett.3c00250
[85]

Shi H, Zhao Y. 2024. Integration of advanced large language models into the construction of adverse outcome pathways: opportunities and challenges. Environmental Science & Technology 58:15355−15358

doi: 10.1021/acs.est.4c07524