Enhancing Life-Cycle Inventories via Reconciliation with the Laws of Thermodynamics

Abstract: Obtaining reliable results from life-cycle assessment studies is often quite difficult because life-cycle inventory (LCI) data are usually erroneous, incomplete, and even physically meaningless. The real data must satisfy the laws of thermodynamics, so the quality of LCI data may be enhanced by adjusting them to satisfy these laws. This is not a new idea, but a formal thermodynamically sound and statistically rigorous approach for accomplishing this task is not yet available. This article proposes such an approach based on methods for data rectification developed in process systems engineering. This approach exploits redundancy in the available data and models and solves a constrained optimization problem to remove random errors and estimate some missing values. The quality of the results and presence of gross errors are determined by statistical tests on the constraints and measurements. The accuracy of the rectified data is strongly dependent on the accuracy and completeness of the available models, which should capture information such as the life-cycle network, stream compositions, and reactions. Such models are often not provided in LCI databases, so the proposed approach tackles many new challenges that are not encountered in process data rectification. An iterative approach is developed that relies on increasingly detailed information about the life-cycle processes from the user. A comprehensive application of the method to the chlor-alkali inventory being compiled by the National Renewable Energy Laboratory demonstrates the benefits and challenges of this approach.     

Impact of photon cross section uncertainties on Monte Carlo-determined depth-dose distributions

This work studies the impact of systematic uncertainties associated to interaction cross sections on depth dose curves determined by Monte Carlo simulations. The corresponding sensitivity factors are quantified by changing cross sections by a given amount and determining the variation in the dose. The influence of total and partial photon cross sections is addressed. Partial cross sections for Compton and Rayleigh scattering, photo-electric effect, and pair production have been accounted for. The PENELOPE code was used in all simulations. It was found that photon cross section sensitivity factors depend on depth. In addition, they are positive and negative for depths below and above an equilibrium depth, respectively. At this depth, sensitivity factors are null. The equilibrium depths found in this work agree very well with the mean free path of the corresponding incident photon energy. Using the sensitivity factors reported here, it is possible to estimate the impact of photon cross section uncertainties on the uncertainty of Monte Carlo-determined depth dose curves.     

Uncertainty-based dynamic multimedia human health risk assessment for polycyclic aromatic hydrocarbons (PAHs) in a land oil exploitation area

With the increasing development of the petrochemical industry and the growing demand for oil, polycyclic aromatic hydrocarbons (PAHs) pollutions in the environment, especially in petroleum exploitation areas, are caused by the discharge of waste from the petroleum extraction process into an environmental system. This study aims to develop a new health risk assessment approach based on interval dynamic multimedia fugacity (IDMF) model and uncertainty analysis that could analyze the human exposure risk level for PAH contamination. The developed IDM health risk assessment (IDMHRA) approach is applied to assess previous, current, and future risks at a case study site in Daqing, Heilongjiang, China, from 1985 to 2020 for model validation. The human health risk assessment results show that 11 PAHs (NAP, ANT, FLA, PYR, BaA, CHR, BbF, BkF, BaP, IPY, and DBA) in the study site require further remediation efforts in terms of their unacceptable non-carcinogenic and carcinogenic risk. The results of risk source analysis reveal that soil media is the main risk pathway as compared with other exposure pathways. It can be seen that remediation process for soil contamination in the study site is urgently demanded. The assessment results demonstrate that the developed IDMHRA approach provides an effective tool for decision-makers and environmental managers to make remediation decisions in contaminated sites.     

Lessons From Risk Assessment

Risk assessment and uncertainty analysis are important tools for improving environmental decision making. However, their value is limited when the environmental endpoints assessed by scientists do not coincide with the publicly-meaningful attributes that are of concern to decision makers. Approaches for addressing this disconnect are presented using examples from water quality assessment and management. Recommendations to scientists for maximizing the usefulness of uncertainty analysis are given.     

Uncertainty Is Part of Making Decisions

Advances in computer technology and applied statistics have provided the opportunity for the non-statistician to investigate uncertainty in a quantitative manner. The following discussion argues, notwithstanding the possible misuse of uncertainty analysis, that uncertainty is always present and that decisions based on human or ecological risk assessment would benefit from disclosure of uncertainty in the estimated risks.     

Sources of Variability in Net Primary Production Predictions at a Regional Scale: A Comparison Using PnET-II and TEM 4.0 in Northeastern US Forests

Because model predictions at continental and global scales are necessarily based on broad characterizations of vegetation, soils, and climate, estimates of carbon stocks and fluxes made by global terrestrial biosphere models may not be accurate for every region. At the regional scale, we suggest that attention can be focused more clearly on understanding the relative strengths of predicted net primary productivity (NPP) limitation by energy, water, and nutrients. We evaluate the sources of variability among model predictions of NPP with a regional-scale comparison between estimates made by PnET-II (a forest ecosystem process model previously applied to the northeastern region) and TEM 4.0 (a terrestrial biosphere model typically applied to the globe) for the northeastern US. When the same climate, vegetation, and soil data sets were used to drive both models, regional average NPP predictions made by PnET-II and TEM were remarkably similar, and at the biome level, model predictions agreed fairly well with NPP estimates developed from field measurements. However, TEM 4.0 predictions were more sensitive to regional variations in temperature as a result of feedbacks between temperature and belowground N availability. In PnET-II, the direct link between transpiration and photosynthesis caused substantial water stress in hardwood and pine forest types with increases in solar radiation; predicted water stress was relieved substantially when soil water holding capacity (WHC) was increased. Increasing soil WHC had little effect on TEM 4.0 predictions because soil water storage was already sufficient to meet plant demand with baseline WHC values, and because predicted N availability under baseline conditions in this region was not limited by water. Because NPP predictions were closely keyed to forest cover type, the relative coverage of low- versus high-productivity forests at both fine and coarse resolutions was an important determinant of regional NPP predictions. Therefore, changes in grid cell size and differences in the methods used to aggregate from fine to coarse resolution were important to NPP predictions insofar as they changed the relative proportions of forest cover. We suggest that because the small patches of high-elevation spruce-fir forest in this region are substantially less productive than forests in the remainder of the region, more accurate NPP predictions will result if models applied to this region use land cover input data sets that retain as much fine-resolution forest type variability as possible. The differences among model responses to variations in climate and soil WHC data sets suggest that the models will respond quite differently to scenarios of future climate. A better understanding of the dynamic interactions between water stress, N availability, and forest productivity in this region will enable models to make more accurate predictions of future carbon stocks and fluxes. Received 19 June 1998; accepted 25 June 1999.