Fri Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Ashraf, Maliha, Siddiqui, Mohammad Tahir, Galodha, Abhinav, Anees, Sanya, Lall, Brejesh, Chakma, Sumedha, and Ahammad, Shaikh Ziauddin, 2024. Pharmaceuticals and personal care product modelling: Unleashing artificial intelligence and machine learning capabilities and impact on one health and sustainable development goals. Science of the Total Environment, 955:176999, doi:10.1016/j.scitotenv.2024.176999.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2024ScTEn.95576999A,
author = {{Ashraf}, Maliha and {Siddiqui}, Mohammad Tahir and {Galodha}, Abhinav and {Anees}, Sanya and {Lall}, Brejesh and {Chakma}, Sumedha and {Ahammad}, Shaikh Ziauddin},
title = "{Pharmaceuticals and personal care product modelling: Unleashing artificial intelligence and machine learning capabilities and impact on one health and sustainable development goals}",
journal = {Science of the Total Environment},
keywords = {PPCPs, Multimedia modelling, GIS, AI and ML, SDG, One health},
year = 2024,
month = dec,
volume = {955},
eid = {176999},
pages = {176999},
abstract = "{The presence of pharmaceutical and personal care products (PPCPs) in the
environment poses a significant threat to environmental
resources, given their potential risks to ecosystems and human
health, even in trace amounts. While mathematical modelling
offers a comprehensive approach to understanding the fate and
transport of PPCPs in the environment, such studies have
garnered less attention compared to field and laboratory
investigations. This review examines the current state of
modelling PPCPs, focusing on their sources, fate and transport
mechanisms, and interactions within the whole ecosystem.
Emphasis is placed on critically evaluating and discussing the
underlying principles, ongoing advancements, and applications of
diverse multimedia models across geographically distinct
regions. Furthermore, the review underscores the imperative of
ensuring data quality, strategically planning monitoring
initiatives, and leveraging cutting-edge modelling techniques in
the quest for a more holistic understanding of PPCP dynamics. It
also ventures into prospective developments, particularly the
integration of Artificial Intelligence (AI) and Machine Learning
(ML) methodologies, to enhance the precision and predictive
capabilities of PPCP models. In addition, the broader
implications of PPCP modelling on sustainability development
goals (SDG) and the One Health approach are also discussed. GIS-
based modelling offers a cost-effective approach for
incorporating time-variable parameters, enabling a spatially
explicit analysis of contaminant fate. Swin-Transformer model
enhanced with Normalization Attention Modules demonstrated
strong groundwater level estimation with an R<SUP
loc=``post''>2</SUP> of 82 \%. Meanwhile, integrating
Interferometric Synthetic Aperture Radar (InSAR) time-series
with gravity recovery and climate experiment (GRACE) data has
been pivotal for assessing water-mass changes in the Indo-
Gangetic basin, enhancing PPCP fate and transport modelling
accuracy, though ongoing refinement is necessary for a
comprehensive understanding of PPCP dynamics. The review aims to
establish a framework for the future development of a
comprehensive PPCP modelling approach, aiding researchers and
policymakers in effectively managing water resources impacted by
increasing PPCP levels.}",
doi = {10.1016/j.scitotenv.2024.176999},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024ScTEn.95576999A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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