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Spatial Heterogeneity in Modeling Environmental and Human Health Impacts of Chemicals



Spatial Heterogeneity in Modeling Environmental and Human Health Impacts of Chemicals


Mengya Tao

The number of chemicals that humanity is using for producing goods and services is rapidly increasing, while our understanding of their environmental and human health impacts improves slowly. Life cycle assessment (LCA) is one of the tools that evaluate environmental and human health impacts of chemicals. Traditional LCAs often rely on the models that represent broad spatial boundaries at regional, national, or global scales. However, the use, release, fate, and transport of chemicals, which are collectively referred to as biophysical processes, may vary substantially within those boundaries. This misalignment in spatial attributes between LCA models and the biophysical processes that determine environmental and human health impacts is one of the major sources of uncertainties in LCA. This dissertation is an attempt to close the gap between the spatial resolutions of the models used in LCA and the biophysical processes relevant for understanding the environmental and human health impacts of chemicals.

This dissertation covers three topics that collectively addresses the aforementioned objectives: (1) measuring spatial variabilities in LCI, (2) modeling the fate of chemicals in the environment at the spatial resolution that matches with the underlying biophysical processes, (3) building a systematic release framework to estimate chemical releases that supports the fate modeling.

First, I demonstrated that spatial disparities in state-specific LCI for four major crops in the USA can lead to two to fourfold differences in characterized results for most impact categories. The differences, however, increase to over an order of magnitude for freshwater ecotoxicity and human health non-cancer. Among the crops analyzed, winter wheat shows higher variability partly due to a larger difference in yield. As a result, the use of national average data derived from top corn and soybean producing states significantly underestimates the characterized impacts of corn and soybean in the states where land conversion from cotton to corn or soybean actually took place.

Secondly, I developed a spatially explicit and time-dependent multimedia fate modeling framework, ChemFate, that can be incorporated into regional LCIA. ChemFate consists of four multimedia fate models: (1) organoFate, a model for non-ionizable organic chemicals, (2) ionOFate, a model for ionizable organic chemicals, (3) metalFate, a model for metals, and (4) nanoFate, a model for nanomaterials. ChemFate is able to not only provide predictions for four different classes of chemicals, but also incorporate dynamic emissions and dynamic environmental conditions. The dynamic capability of ChemFate supports the model to simulate with real regional climatic data and produce better model performance.

Thirdly, I built a comprehensive release framework, OrganoRelease, to estimate the release of organic chemicals from the use and post-use of consumer products with limited information. OrganoRelease connects 19 unique functional uses and 14 product categories across 4 data sources and provides multiple pathways for chemical release estimation. The results can be used as input for methods estimating environmental fate and exposure.

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