Assessing regional intake fractions in North America

Pharmaceuticals and personal care product modelling: Unleashing artificial intelligence and machine learning capabilities and impact on one health and sustainable development goals

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 R2 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.

Exploring Regional Reduction Pathways for Human Exposure to Fine Particulate Matter (PM2.5) Using a Traffic Assignment Model

An exposure-based traffic assignment (TA) model is used to quantify primary and secondary fine particulate matter (PM2.5) exposure from on-road vehicle flow on the Chicago Metropolitan Area regional network. PM2.5 exposure due to emissions from light-duty vehicles, heavy-duty trucks, public transportation, and electricity generation for electric vehicle charging and light-rail transportation is considered. The model uses travel demand data disaggregated by time-of-day period and vehicle user class to compare the exposure impacts of two TA optimization scenarios: a baseline user equilibrium with respect to travel time (UET) and a system optimal with respect to pollutant intake (SOI). Estimated baseline PM2.5 exposure damages are $3.7B-$8.3B/year. The SOI uses exposure-based vehicle rerouting to reduce total damages by 8.2%, with high-impacted populations benefiting from 10% to 20% reductions. However, the SOI's rerouting principle leads to a 66% increase in travel time. The model is then used to quantify the mitigation potential of different exposure reduction strategies, including a bi-objective optimization formulation that minimizes travel time and PM2.5 exposure concurrently, adoption of a cleaner vehicle fleet, higher public transportation use, particle filtration, and exposure-based truck routing. Exposure reductions range between 1% and 40%, but collective adoption of all strategies would lead to reductions upward of 50%.

Exploring Regional Fine Particulate Matter (PM2.5) Exposure Reduction Pathways Using an Optimal Power Flow Model: The Case of the Illinois Power Grid

This work develops an exposure-based optimal power flow model (OPF) that accounts for fine particulate matter (PM_2.5) exposure from electricity generation unit (EGU) emissions. Advancing health-based dispatch models to an OPF with transmission constraints and reactive power flow is an essential development given its utility for short- and long-term planning by system operators. The model enables the assessment of the exposure mitigation potential and the feasibility of intervention strategies while still prioritizing system costs and network stability. A representation of the Illinois power grid is developed to demonstrate how the model can inform decision making. Three scenarios minimizing dispatch costs and/or exposure damages are simulated. Other interventions assessed include adopting best-available EGU emission control technologies, having higher renewable generation, and relocating high-polluting EGUs. Neglecting transmission constraints fails to account for 4% of exposure damages ($60 M/y) and dispatch costs ($240 M/y). Accounting for exposure in the OPF reduces damages by 70%, a reduction on the order of that achieved by high renewable integration. About 80% of all exposure is attributed to EGUs fulfilling only 25% of electricity demand. Siting these EGUs in low-exposure zones avoids 43% of all exposure. Operation and cost advantages inherent to each strategy beyond exposure reduction suggest their collective adoption for maximum benefits.

Spatially resolved environmental fate models: A review

Spatially resolved environmental models are important tools to introduce and highlight the spatial variability of the real world into modeling. Although various spatial models have been developed so far, yet the development and evaluation of these models remain a challenging task due to several difficulties related to model setup, computational cost, and obtaining high-resolution input data (e.g., monitoring and emission data). For example, atmospheric transport models can be used when high resolution predicted concentrations in atmospheric compartments are required, while spatial multimedia fate models may be preferred for regulatory risk assessment, life cycle impact assessment of chemicals, or when the partitioning of chemical substances in a multimedia environment is considered. The goal of this paper is to review and compare different spatially resolved environmental models, according to their spatial, temporal and chemical domains, with a closer insight into spatial multimedia fate models, to achieve a better understanding of their strengths and limitations. This review also points out several requirements for further improvement of existing models as well as for their integration.

Overview and recommendations for regionalized life cycle impact assessment

PURPOSE

Regionalized life cycle impact assessment (LCIA) has rapidly developed in the past decade, though its widespread application, robustness, and validity still faces multiple challenges. Under the umbrella of UNEP/SETAC Life Cycle Initiative, a dedicated cross-cutting working group on regionalized LCIA aims to provides an overview of the status of regionalization in LCIA methods. We give guidance and recommendations to harmonize and support regionalization in LCIA for developers of LCIA methods, LCI databases, and LCA software.

METHOD

A survey of current practice among regionalized LCIA method developers was conducted. The survey included questions on chosen method spatial resolution and scale, the spatial resolution of input parameters, choice of native spatial resolution and limitations, operationalization and alignment with life cycle inventory data, methods for spatial aggregation, the assessment of uncertainty from input parameters and model structure, and variability due to spatial aggregation. Recommendations are formulated based on the survey results and extensive discussion by the authors.

RESULTS AND DISCUSSION

Survey results indicate that majority of regionalized LCIA models have global coverage. Native spatial resolutions are generally chosen based on the availability of global input data. Annual modelled or measured elementary flow quantities are mostly used for aggregating characterization factors (CFs) to larger spatial scales, although some use proxies, such as population counts. Aggregated CFs are mostly available at the country level. Although uncertainty due to input parameter, model structure, and spatial aggregation are available for some LCIA methods, they are rarely implemented for LCA studies. So far, there is no agreement if a finer native spatial resolution is the best way to reduce overall uncertainty. When spatially differentiated models CFs are not easily available, archetype models are sometimes developed.

CONCLUSIONS

Regionalized LCIA methods should be provided as a transparent and consistent set of data and metadata using standardized data formats. Regionalized CFs should include both uncertainty and variability. In addition to the native-scale CFs, aggregated CFs should always be provided, and should be calculated as the weighted averages of constituent CFs using annual flow quantities as weights whenever available. This paper is an important step forward for increasing transparency, consistency and robustness in the development and application of regionalized LCIA methods.

Integrating site-specific dispersion modeling into life cycle assessment, with a focus on inhalation risks in chemical production

It has become increasingly important for environmental managers to evaluate the human health (HH) impact of chemicals in their supply chain. Current life cycle assessment (LCA) methods are limited because they often only address the HH impact at large geographical scales. This paper aims to develop a method that derives a regionalized life cycle inventory data set and site-specific air dispersion modeling to evaluate the HH impact of chemicals along the life cycle phases at finer geographical scales to improve decision-making, with focus on inhalation pathway. More specifically, cancer risk and noncancer hazard index (HI) are quantified at the county level to identify high-risk regions and at the census tract level to reveal the geographical pattern of health impacts. The results showed that along the cradle-to-gate life cycle stages of a widely used chemical, methylene diphenyl diisocyanate (MDI), the accumulative inhalation risk was 3 orders of magnitude below the U.S. Environmental Protection Agency (EPA) risk management thresholds for both cancer risk (2.16 × 10^-9) and noncancer HI (1.53 × 10^-3). However, the absolute value of inhalation risks caused by the case study chemicals varied significantly in different geographical areas, up to 4 orders of magnitude. This paper demonstrates a feasible approach to improve human health impact assessment (HHIA) by combining site-specific air dispersion modeling and LCA using publicly available inventory data. This proposed method complements existing life cycle impact assessment (LCIA) models to improve HHIA by employing both HH risk assessment and LCA techniques. One potential outcome is to prioritize pollution prevention and risk reduction measures based on the risk maps derived from this method. Implications: It has become increasingly important for environmental managers to evaluate the human health impacts of chemicals in their supply chain. Regionalized life cycle inventory data sets should be developed using publically available databases such as EPA's toxic release inventory. The combination of site-specific dispersion modeling and life cycle assessment modeling can improve human health impact assessment of chemicals by providing more regionalized results along their supply chain.

Intake Fraction of PM10 from Coal Mine Emissions in the North of Colombia

Intake fraction was determined in this study to provide insight into population exposures to PM₁₀ that is effectively inhaled due to emissions of an opencast coal mine. We applied the CALPUFF model to a coal mine in Northern Colombia, which has 6 active pits with an annual production of 33.7 million tons. We estimated the intake fractions for 7 towns through the integration of dispersion model results over the population data. The resulting average intake fractions were between 6.13 × 10⁻⁹ and 3.66 × 10⁻⁸ for PM₁₀. 62.4% of the intake fractions in the domain were calculated within a 23 km radius from the coal mine and coved 44.3% of the total population in this area. We calculated an estimate point for morbidity impacts using standard epidemiological assumptions. It is estimated that there were annually 105835 restricted activity days and 336832 respiratory symptom cases due to the direct impact of the opencast coal mining. These data also provide a framework for improved understanding of the effect of coal mining in Colombia.

Source-to-exposure assessment with the Pangea multi-scale framework - case study in Australia

Effective planning of airshed pollution mitigation is often constrained by a lack of integrative analysis able to relate the relevant emitters to the receptor populations at risk. Both emitter and receptor perspectives are therefore needed to consistently inform emission and exposure reduction measures. This paper aims to extend the Pangea spatial multi-scale multimedia framework to evaluate source-to-receptor relationships of industrial sources of organic pollutants in Australia. Pangea solves a large compartmental system in parallel by block to determine arrays of masses at steady-state for 100 000+ compartments and 4000+ emission scenarios, and further computes population exposure by inhalation and ingestion. From an emitter perspective, radial spatial distributions of population intakes show high spatial variation in intake fractions from 0.68 to 33 ppm for benzene, and from 0.006 to 9.5 ppm for formaldehyde, contrasting urban, rural, desert, and sea source locations. Extending analyses to the receptor perspective, population exposures from the combined emissions of 4101 Australian point sources are more extended for benzene that travels over longer distances, versus formaldehyde that has a more local impact. Decomposing exposure per industrial sector shows petroleum and steel industry as the highest contributing industrial sectors for benzene, whereas the electricity sector and petroleum refining contribute most to formaldehyde exposures. The source apportionment identifies the main sources contributing to exposure at five locations. Overall, this paper demonstrates high interest in addressing exposures from both an emitter perspective well-suited to inform product oriented approaches such as LCA, and from a receptor perspective for health risk mitigation.

Multiscale Spatial Modeling of Human Exposure from Local Sources to Global Intake

Exposure studies, used in human health risk and impact assessments of chemicals, are largely performed locally or regionally. It is usually not known how global impacts resulting from exposure to point source emissions compare to local impacts. To address this problem, we introduce Pangea, an innovative multiscale, spatial multimedia fate and exposure assessment model. We study local to global population exposure associated with emissions from 126 point sources matching locations of waste-to-energy plants across France. Results for three chemicals with distinct physicochemical properties are expressed as the evolution of the population intake fraction through inhalation and ingestion as a function of the distance from sources. For substances with atmospheric half-lives longer than a week, less than 20% of the global population intake through inhalation (median of 126 emission scenarios) can occur within a 100 km radius from the source. This suggests that, by neglecting distant low-level exposure, local assessments might only account for fractions of global cumulative intakes. We also study ∼10 000 emission locations covering France more densely to determine per chemical and exposure route which locations minimize global intakes. Maps of global intake fractions associated with each emission location show clear patterns associated with population and agriculture production densities.

Characterizing Aggregated Exposure to Primary Particulate Matter: Recommended Intake Fractions for Indoor and Outdoor Sources

Exposure to fine particulate matter (PM_2.5) from indoor and outdoor sources is a leading environmental contributor to global disease burden. In response, we established under the auspices of the UNEP/SETAC Life Cycle Initiative a coupled indoor-outdoor emission-to-exposure framework to provide a set of consistent primary PM_2.5 aggregated exposure factors. We followed a matrix-based mass balance approach for quantifying exposure from indoor and ground-level urban and rural outdoor sources using an effective indoor-outdoor population intake fraction and a system of archetypes to represent different levels of spatial detail. Emission-to-exposure archetypes range from global indoor and outdoor averages, via archetypal urban and indoor settings, to 3646 real-world cities in 16 parametrized subcontinental regions. Population intake fractions from urban and rural outdoor sources are lowest in Northern regions and Oceania and highest in Southeast Asia with population-weighted means across 3646 cities and 16 subcontinental regions of, respectively, 39 ppm (95% confidence interval: 4.3-160 ppm) and 2 ppm (95% confidence interval: 0.2-6.3 ppm). Intake fractions from residential and occupational indoor sources range from 470 ppm to 62 000 ppm, mainly as a function of air exchange rate and occupancy. Indoor exposure typically contributes 80-90% to overall exposure from outdoor sources. Our framework facilitates improvements in air pollution reduction strategies and life cycle impact assessments.

Toward refined environmental scenarios for ecological risk assessment of down-the-drain chemicals in freshwater environments

Current regulatory practice for chemical risk assessment suffers from the lack of realism in conventional frameworks. Despite significant advances in exposure and ecological effect modeling, the implementation of novel approaches as high-tier options for prospective regulatory risk assessment remains limited, particularly among general chemicals such as down-the-drain ingredients. While reviewing the current state of the art in environmental exposure and ecological effect modeling, we propose a scenario-based framework that enables a better integration of exposure and effect assessments in a tiered approach. Global- to catchment-scale spatially explicit exposure models can be used to identify areas of higher exposure and to generate ecologically relevant exposure information for input into effect models. Numerous examples of mechanistic ecological effect models demonstrate that it is technically feasible to extrapolate from individual-level effects to effects at higher levels of biological organization and from laboratory to environmental conditions. However, the data required to parameterize effect models that can embrace the complexity of ecosystems are large and require a targeted approach. Experimental efforts should, therefore, focus on vulnerable species and/or traits and ecological conditions of relevance. We outline key research needs to address the challenges that currently hinder the practical application of advanced model-based approaches to risk assessment of down-the-drain chemicals. Integr Environ Assess Manag 2017;13:233-248. © 2016 SETAC.

Life cycle assessment of capital goods in waste management systems

The environmental importance of capital goods (trucks, buildings, equipment, etc.) was quantified by LCA modelling 1 tonne of waste treated in five different waste management scenarios. The scenarios involved a 240L collection bin, a 16m(3) collection truck, a composting plant, an anaerobic digestion plant, an incinerator and a landfill site. The contribution of capital goods to the overall environmental aspects of managing the waste was significant but varied greatly depending on the technology and the impact category: Global Warming: 1-17%, Stratospheric Ozone Depletion: 2-90%, Ionising Radiation, Human Health: 2-91%, Photochemical Ozone Formation: 2-56%, Freshwater Eutrophication: 0.05-99%, Marine Eutrophication: 0.03-8%, Terrestrial Acidification: 2-13%, Terrestrial Eutrophication: 1-8%, Particulate Matter: 11-26%, Human Toxicity, Cancer Effect: 10-92%, Human Toxicity, non-Cancer Effect: 1-71%, Freshwater Ecotoxicity: 3-58%. Depletion of Abiotic Resources - Fossil: 1-31% and Depletion of Abiotic Resources - Elements (Reserve base): 74-99%. The single most important contribution by capital goods was made by the high use of steel. Environmental impacts from capital goods are more significant for treatment facilities than for the collection and transportation of waste and for the landfilling of waste. It is concluded that the environmental impacts of capital goods should always be included in the LCA modelling of waste management, unless the only impact category considered is Global Warming.

Limitations of toxicity characterization in life cycle assessment: Can adverse outcome pathways provide a new foundation?

Life cycle assessment (LCA) has considerable merit for holistic evaluation of product planning, development, production, and disposal, with the inherent benefit of providing a forecast of potential health and environmental impacts. However, a technical review of current life cycle impact assessment (LCIA) methods revealed limitations within the biological effects assessment protocols, including: simplistic assessment approaches and models; an inability to integrate emerging types of toxicity data; a reliance on linear impact assessment models; a lack of methods to mitigate uncertainty; and no explicit consideration of effects in species of concern. The purpose of the current study is to demonstrate that a new concept in toxicological and regulatory assessment, the adverse outcome pathway (AOP), has many useful attributes of potential use to ameliorate many of these problems, to expand data utility and model robustness, and to enable more accurate and defensible biological effects assessments within LCIA. Background, context, and examples have been provided to demonstrate these potential benefits. We additionally propose that these benefits can be most effectively realized through development of quantitative AOPs (qAOPs) crafted to meet the needs of the LCIA framework. As a means to stimulate qAOP research and development in support of LCIA, we propose 3 conceptual classes of qAOP, each with unique inherent attributes for supporting LCIA: 1) mechanistic, including computational toxicology models; 2) probabilistic, including Bayesian networks and supervised machine learning models; and 3) weight of evidence, including models built using decision-analytic methods. Overall, we have highlighted a number of potential applications of qAOPs that can refine and add value to LCIA. As the AOP concept and support framework matures, we see the potential for qAOPs to serve a foundational role for next-generation effects characterization within LCIA. Integr Environ Assess Manag 2016;12:580-590. Published 2015. This article is a US Government work and is in the public domain in the USA.

Human health benefits and burdens of a pharmaceutical treatment: Discussion of a conceptual integrated approach

The effects of a pharmaceutical treatment have until now been evaluated by the field of Health Economics on the patient health benefits, expressed in Quality-Adjusted Life Years (QALYs) versus the monetary costs. However, there is also a Human Health burden associated with this process, resulting from emissions that originate from the pharmaceutical production processes, Use Phase and End of Life (EoL) disposal of the medicine. This Human Health burden is evaluated by the research field of Life Cycle Assessment (LCA) and expressed in Disability-Adjusted Life Years (DALYs), a metric similar to the QALY. The need for a new framework presents itself in which both the positive and negative health effects of a pharmaceutical treatment are integrated into a net Human Health effect. To do so, this article reviews the methodologies of both Health Economics and the area of protection Human Health of the LCA methodology and proposes a conceptual framework on which to base an integration of both health effects. Methodological issues such as the inclusion of future costs and benefits, discounting and age weighting are discussed. It is suggested to use the structure of an LCA as a backbone to cover all methodological challenges involved in the integration. The possibility of monetizing both Human Health benefits and burdens is explored. The suggested approach covers the main methodological aspects that should be considered in an integrated assessment of the health effects of a pharmaceutical treatment.

Assessing burden of disease as disability adjusted life years in life cycle assessment

Disability adjusted life years (DALYs) have been used to quantify endpoint indicators of the human burden of disease in life cycle assessment (LCA). The purpose of this paper was to examine the current use of DALYs in LCA, and also to consider whether DALYs as used in LCA have the potential to be compatible with DALYs as used in quantitative risk assessment (QRA) to facilitate direct comparison of the results of the two approaches. A literature review of current usage of DALYs in LCA was undertaken. Two prominent methods were identified: ReCiPe 2008 and LIME2. The methods and assumptions used in their calculations were then critically reviewed. The assumptions used for the derivation of characterization factors in DALYs were found to be considerably different between LCA methods. In many cases, transparency of these calculations and assumptions is lacking. Furthermore, global average DALY values are often used in these calculations, but may not be applicable for impact categories where the local factors play a significant role. The concept of DALYs seems beneficial since it enables direct comparison and aggregation of different health impacts. However, given the different assumptions used in each LCA method, it is important that LCA practitioners are aware of the differences and select the appropriate method for the focus of their study. When applying DALYs as a common metric between LCA and QRA, understanding the background information on how DALYs were derived is crucial to ensure the consistency of DALYs used in LCA and QRA for resulting DALYs to be comparable and to minimize any double counting of effects.

A CFD modeling study in an urban street canyon for ultrafine particles and population exposure: The intake fraction approach

Air quality in street canyons is of major importance, since the highest pollution levels are often encountered in these microenvironments. The canyon effect (reduced natural ventilation) makes them "hot spots" for particulate pollution contributing to adverse health effects for the exposed population. In this study we tried to characterize the influence of UFP (ultrafine particle) emissions from traffic on population exposure in an urban street canyon, by applying the intake fraction (iF) approach. One month long measurements of UFP levels have been monitored and used for the need of this study. We applied a three dimensional computational fluid dynamic (CFD) model based on real measurements for the simulation of UFP levels. We used infiltration factors, evaluated on a daily basis for the under study area, to estimate the indoor UFP levels. As a result the intake fraction for the pedestrians, residents and office workers is in the range of (1E-5)-(1E-4). The street canyon is mostly residential justifying partially the higher value of intake fraction for residents (1E-4). The above iF value is on the same order of magnitude with the corresponding one evaluated in a relative street canyon study. The total iF value in this microenvironment is one order of magnitude higher than ours, explained partially by the different use and activities. Two specific applications of iF to assess prioritization among emission sources and environmental justice issues are also examined. We ran a scenario with diesel and gasoline cars and diesel fueled vehicle seems to be a target source to improve overall iF. Our application focus on a small residential area, typical of urban central Athens, in order to evaluate high resolution iF. The significance of source-exposure relationship study in a micro scale is emphasized by recent research.

Integration of life cycle assessment and regional emission information in agricultural systems

BACKGROUND

Life cycle assessment (LCA) is a compilation and evaluation of the input energy and materials, output emissions and the potential environmental impacts of a product, service or system throughout its life cycle. While methodological issues of LCA are still being developed, much research is being conducted worldwide in order to improve them. One of the important advances in LCA is a regionalised LCA, i.e. the development of regionalised databases, inventories, and impact assessment methods and models.

RESULTS

Regional emission information (REI) was developed and integrated with the characterisation results in LCA of an agricultural product in the study area. Comparison of outcomes obtained with LCA characterisation results that did not include REI shows that the characterisation results taking REI into account are much higher as regards human toxicity, from 0.02% to 0.18%, freshwater ecotoxicity from 89% to 99% and terrestrial ecotoxicity from 8.006% to 26.177%.

CONCLUSION

Results of current LCA studies on agricultural products and systems that do not include REI are under-estimating the life cycle environmental impact. For the LCA of agricultural products and systems, the REI as well as regionalised life cycle inventory data should be developed and integrated into the current LCA approach.

Data-Driven Method to Estimate Nonlinear Chemical Equivalence

There is great need to express the impacts of chemicals found in the environment in terms of effects from alternative chemicals of interest. Methods currently employed in fields such as life-cycle assessment, risk assessment, mixtures toxicology, and pharmacology rely mostly on heuristic arguments to justify the use of linear relationships in the construction of "equivalency factors," which aim to model these concentration-concentration correlations. However, the use of linear models, even at low concentrations, oversimplifies the nonlinear nature of the concentration-response curve, therefore introducing error into calculations involving these factors. We address this problem by reporting a method to determine a concentration-concentration relationship between two chemicals based on the full extent of experimentally derived concentration-response curves. Although this method can be easily generalized, we develop and illustrate it from the perspective of toxicology, in which we provide equations relating the sigmoid and non-monotone, or "biphasic," responses typical of the field. The resulting concentration-concentration relationships are manifestly nonlinear for nearly any chemical level, even at the very low concentrations common to environmental measurements. We demonstrate the method using real-world examples of toxicological data which may exhibit sigmoid and biphasic mortality curves. Finally, we use our models to calculate equivalency factors, and show that traditional results are recovered only when the concentration-response curves are "parallel," which has been noted before, but we make formal here by providing mathematical conditions on the validity of this approach.

Intake fraction variability between air pollution emission sources inside an urban area

The cost-effective mitigation of adverse health effects caused by air pollution requires information on the contribution of different emission sources to exposure. In urban areas the exposure potential of different sources may vary significantly depending on emission height, population density, and other factors. In this study, we quantified this intraurban variability by predicting intake fraction (iF) for 3,066 emission sources in Warsaw, Poland. iF describes the fraction of the pollutant that is inhaled by people in the study area. We considered the following seven pollutants: particulate matter (PM), nitrogen oxides (NOx), sulfur dioxide (SO2), benzo[a] pyrene (BaP), nickel (Ni), cadmium (Cd), and lead (Pb). Emissions for these pollutants were grouped into four emission source categories (Mobile, Area, High Point, and Other Point sources). The dispersion of the pollutants was predicted with the CALPUFF dispersion model using the year 2005 emission rate data and meteorological records. The resulting annual average concentrations were combined with population data to predict the contribution of each individual source to population exposure. The iFs for different pollutant-source category combinations varied between 51 per million (PM from Mobile sources) and 0.013 per million (sulfate PM from High Point sources). The intraurban iF variability for Mobile sources primary PM emission was from 4 per million to 100 per million with the emission-weighted iF of 44 per million. These results propose that exposure due to intraurban air pollution emissions could be decreased more effectively by specifically targeting sources with high exposure potency rather than all sources.

Uncertainty in multi-media fate and transport models: a case study for TNT life cycle assessment

Life cycle assessment (LCA) is an evaluation method used by decision-makers to help assess the relative environmental impacts of various industrial processes. Despite that many LCA methods remain sensitive to uncertain input data, which can reduce the utility of their results, uncertainty arising from constituent LCA models remains poorly understood. Here, we begin to address this problem by evaluating the extent to which parameter-value uncertainty affects the SimpleBox 2.0 fate and transport model, which serves as a backbone for many LCA ecotoxicological impact categories. Two Monte Carlo type sampling methods were used to evaluate dispersion in steady-state concentration values for three chemicals involved in grenade production: toluene, 2,4-dinitrotoluene (2,4-DNT), and 2,4,6-trinitrotoluene (TNT). Parameters were first sampled stochastically one-at-a-time, then by randomly exploring a local patch of the parameter space. We confirmed that global temperatures contribute primarily to the overall variance of model results, which at most spanned approximately 8 decades in magnitude. These results are consistent with previous results obtained for the whole of the LCA method. LCA methods carry out calculations iteratively; a reduction in the error of a single component, such as the fate and transport model, may therefore improve its performance and utility as a decision-making aid.

Spatial analysis of toxic emissions in LCA: a sub-continental nested USEtox model with freshwater archetypes

This paper develops continent-specific factors for the USEtox model and analyses the accuracy of different model architectures, spatial scales and archetypes in evaluating toxic impacts, with a focus on freshwater pathways. Inter-continental variation is analysed by comparing chemical fate and intake fractions between sub-continental zones of two life cycle impact assessment models: (1) the nested USEtox model parameterized with sub-continental zones and (2) the spatially differentiated IMPACTWorld model with 17 interconnected sub-continental regions. Substance residence time in water varies by up to two orders of magnitude among the 17 zones assessed with IMPACTWorld and USEtox, and intake fraction varies by up to three orders of magnitude. Despite this variation, the nested USEtox model succeeds in mimicking the results of the spatially differentiated model, with the exception of very persistent volatile pollutants that can be transported to polar regions. Intra-continental variation is analysed by comparing fate and intake fractions modelled with the a-spatial (one box) IMPACT Europe continental model vs. the spatially differentiated version of the same model. Results show that the one box model might overestimate chemical fate and characterisation factors for freshwater eco-toxicity of persistent pollutants by up to three orders of magnitude for point source emissions. Subdividing Europe into three archetypes, based on freshwater residence time (how long it takes water to reach the sea), improves the prediction of fate and intake fractions for point source emissions, bringing them within a factor five compared to the spatial model. We demonstrated that a sub-continental nested model such as USEtox, with continent-specific parameterization complemented with freshwater archetypes, can thus represent inter- and intra-continental spatial variations, whilst minimizing model complexity.

Regionalized LCA-based optimization of building energy supply: method and case study for a Swiss municipality

This paper presents a regionalized LCA-based multiobjective optimization model of building energy demand and supply for the case of a Swiss municipality for the minimization of greenhouse gas emissions and particulate matter formation. The results show that the environmental improvement potential is very large: in the optimal case, greenhouse gas emissions from energy supply could be reduced by more than 75% and particulate emissions by over 50% in the municipality. This scenario supposes a drastic shift of heat supply systems from a fossil fuel dominated portfolio to a portfolio consisting of mainly heat pump and woodchip incineration systems. In addition to a change in heat supply technologies, roofs, windows and walls would need to be refurbished in more than 65% of the municipality's buildings. The full potential of the environmental impact reductions will hardly be achieved in reality, particularly in the short term, for example, because of financial constraints and social acceptance, which were not taken into account in this study. Nevertheless, the results of the optimization model can help policy makers to identify the most effective measures for improvement at the decision making level, for example, at the building level for refurbishment and selection of heating systems or at the municipal level for designing district heating networks. Therefore, this work represents a starting point for designing effective incentives to reduce the environmental impact of buildings. While the results of the optimization model are specific to the municipality studied, the model could readily be adapted to other regions.

Measuring environmental sustainability of water in watersheds

Environmental sustainability assessment is a rapidly growing field where measures of sustainability are used within an assessment framework to evaluate and compare alternative actions. Here we argue for the importance of evaluating environmental sustainability of water at the watershed scale. We review existing frameworks in brief before reviewing watershed-relevant measures in more detail. While existing measures are diverse, overlapping, and interdependent, certain attributes that are important for watersheds are poorly represented, including spatial explicitness and the effect of natural watershed components, such as rivers. Most studies focus on one or a few measures, but a complete assessment will require use of many existing measures, as well as, perhaps, new ones. Increased awareness of the broad dimensions of environmental sustainability as applied to water management should encourage integration of existing approaches into a unified assessment framework appropriate for watersheds.

Evaluating the relevance of seasonal differentiation of human health intake fractions in life cycle assessment

The intake fraction (iF) is the fraction of an emitted mass of chemical that is ultimately taken in by an entire population, and it is used as an indicator of human health potential impacts related to environmental chemical persistence and bioaccumulation in the food chain. In chemical screening applications, the iF can be predicted using multimedia and multipathway fate and exposure models. One of the sources of iF uncertainty is the natural seasonal variability of the input parameters used in the models, i.e., the physicochemical properties of the pollutant and the landscape and exposure parameters. The objective of this article is to determine the relevance of including seasonal differentiation when assessing iFs in life cycle assessment. This was done by calculating and comparing seasonal iFs with each other and with iFs at 25° C, for both Canadian and global contexts. Two Canadian seasonal models based on the IMPACT 2002 predictive tool, and 2 models for the global context based on the USEtox consensus model were developed to calculate summer and winter iFs. Emissions into air and water and a set of 35 representative organic chemicals were considered. Partition coefficients for seasonal conditions were calculated using an integration of the van't Hoff equation. First-order degradation rate constants were calculated assuming that the rate constant doubles with each 10° C increase in temperature. For Canadian air emissions, results indicated that iFs for winter emissions could be up to 1 to 2 orders of magnitude higher than summer iFs or iFs calculated at 25° C. For Canadian water emissions, results showed that iFs for both summer and winter conditions were, in general, closer to each other with outliers within 1 order of magnitude to iFs calculated at 25° C. Results also indicated that seasonal variability was of lesser importance when assessing iFs within a global context. Because the ranking between chemicals was maintained, it can be concluded that seasonal variability is not relevant within a comparative context. However, this difference might be significant when comparing the magnitude of human toxicity impacts versus other impact categories contributing to human health damages.

GIS-based regionalized life cycle assessment: how big is small enough? Methodology and case study of electricity generation

We describe a new methodology for performing regionalized life cycle assessment and systematically choosing the spatial scale of regionalized impact assessment methods. We extend standard matrix-based calculations to include matrices that describe the mapping from inventory to impact assessment spatial supports. Uncertainty in inventory spatial data is modeled using a discrete spatial distribution function, which in a case study is derived from empirical data. The minimization of global spatial autocorrelation is used to choose the optimal spatial scale of impact assessment methods. We demonstrate these techniques on electricity production in the United States, using regionalized impact assessment methods for air emissions and freshwater consumption. Case study results show important differences between site-generic and regionalized calculations, and provide specific guidance for future improvements of inventory data sets and impact assessment methods.

Implications of geographic variability on Comparative Toxicity Potentials of Cu, Ni and Zn in freshwaters of Canadian ecoregions

Current methods of estimating potential environmental impacts of metals in hazard and Life Cycle Impact Assessment (LCIA) do not consider differences in chemistry and landscape properties between geographic sites. Here, we developed and applied a model for regional aquatic impact characterization of metals using an updated method for estimating environmental fate factor (FF), bioavailability factor (BF) and aquatic ecotoxicity factor (EF). We applied the model to analyze differences in Comparative Toxicity Potentials (CTPs) of Cu, Ni and Zn for 24 Canadian ecoregions. The combined impacts of regional variability in ambient chemistry (in particular DOC, pH and hardness) and landscape properties (water residence time) can change the CTPs of these metals for freshwater by up to three orders of magnitude and change the relative ranking of metal hazard between ecoregions. Variation among Canadian freshwater chemistries and landscape characteristics influence the FFs within two orders of magnitude, BFs within two orders of magnitude for Ni and Zn and four orders of magnitude for Cu, and EFs within one order of magnitude. Sensitivity of metal FFs to environmental parameters alone spans three orders of magnitude when a constant water chemistry was used for all ecoregions. These results indicate that application of regionalised metal CTPs can have a significant influence in the analysis of ecotoxicological impacts in the life cycle assessment of products and processes.

GLOBOX: A spatially differentiated global fate, intake and effect model for toxicity assessment in LCA

GLOBOX is a model for the calculation of spatially differentiated LCA toxicity characterisation factors on a global scale. It can also be used for human and environmental risk assessment. The GLOBOX model contains equations for the calculation of fate, intake and effect factors, and equations for the calculation of LCA characterisation factors for human toxicity and ecotoxicity. The model is differentiated on the level of 239 countries/territories and 50 seas/oceans. Each region has its own set of homogeneous compartments, and the regions are interconnected by atmospheric and aquatic flows. Multimedia transport and degradation calculations are largely based on the EUSES 2.0 multimedia model, and are supplemented by specific equations to account for the advective air and water transport between different countries and/or seas. Metal-specific equations are added to account for speciation in fresh and marine surface water. Distribution parameters for multimedia transport equations are differentiated per country or sea with respect to geographic features, hydrology, and climate. The model has been tested with nitrobenzene as a test chemical, for emissions to all countries in the world. Spatially differentiated characterisation factors turn out to show wide ranges of variation between countries, especially for releases to inland water and soil compartments. Geographic position, distribution of lakes and rivers and variations in environmental temperature and rain rate are decisive parameters for a number of different characterisation factors. Population density and dietary intake play central roles in the variation of characterisation factors for human toxicity. Among the countries that show substantial deviations from average values of the characterisation factors are not only small and remote islands, but also countries with a significant economic production rate, as indicated by their GDPs. It is concluded that spatial differentiation between countries is an important step forward with respect to the improvement of LCA toxicity characterisation factors.

Spatial variability of intake fractions for Canadian emission scenarios: a comparison between three resolution scales

Spatially differentiated intake fractions (iFs) linked to Canadian emissions of toxic organic chemicals were developed using the multimedia and multipathways fate and exposure model IMPACT 2002. The fate and exposure of chemicals released to the Canadian environment were modeled with a single regional mass-balance model and three models that provided multiple mass-balance regions within Canada. These three models were based on the Canadian subwatersheds (172 zones), ecozones (15 zones), and provinces (13 zones). Releases of 32 organic chemicals into water and air were considered. This was done in order to (i) assess and compare the spatial variability of iFs within and across the three levels of regionalization and (ii) compare the spatial iFs to nonspatial ones. Results showed that iFs calculated using the subwatershed resolution presented a higher spatial variability (up to 10 orders of magnitude for emissions into water) than the ones based on the ecozones and provinces, implying that higher spatial resolution could potentially reduce uncertainty in iFs and, therefore, increase the discriminating power when assessing and comparing toxic releases for known emission locations. Results also indicated that, for an unknown emission location, a model with high spatial resolution such as the subwatershed model could significantly improve the accuracy of a generic iF. Population weighted iFs span up to 3 orders of magnitude compared to nonspatial iFs calculated by the one-box model. Less significant differences were observed when comparing spatial versus nonspatial iFs from the ecozones and provinces, respectively.