The influence on partial order ranking from input parameter uncertainty. Definition of a robustness parameter

The method of partial order ranking has been used within the environmental area for a variety of purposes as an attractive way of handling complex information. However, the environmental data are often associated with a significant degree of uncertainty. In this investigation the general nature of the influence from data uncertainty on the partial order ranking is analyzed. A Monte Carlo type analysis is performed in which a series of randomly formed data are used to test the influence of data uncertainty. The partial order ranking is interpreted, where the results are transferred to a one-dimensional ranking scale taking into account that not all elements are ranked with the same certainty. A simple general robustness parameter (E) in form of the expected number of comparisons for each ranking element is defined and correlated to the uncertainty analysis results. A simple equation relates E to the number of elements and the number of parameters, respectively. The magnitude of the ranking uncertainty is shown to increase rapidly when the E value decreases below 4-5 comparisons per element. When the E value exceeds 5 the ranking uncertainty becomes nearly constant and independent on the actual E value.     

Application of artificial neural networks to modeling and prediction of ambient ozone concentrations

The deterministic modeling of ambient O3 concentrations is difficult because of the complexity of the atmospheric system in terms of the number of chemical species; the availability of accurate, time-resolved emissions data; and the required rate constants. However, other complex systems have been successfully approximated using artificial neural networks (ANNs). In this paper, ANNs are used to model and predict ambient O3 concentrations based on a limited number of measured hydrocarbon species, NOx compounds, temperature, and radiant energy. In order to examine the utility of these approaches, data from the Coastal Oxidant Assessment for Southeast Texas (COAST) program in Houston, TX, have been used. In this study, 53 hydrocarbon compounds, along with O3, nitrogen oxides, and meteorological data were continuously measured during summer 1993. Steady-state ANN models were developed to examine the ability of these models to predict current O3 concentrations from measured VOC and NOx concentrations. To predict the future concentrations of O3, dynamic models were also explored and were used for extraction of chemical information such as reactivity estimations for the VOC species. The steady-state model produced an approximation of O3 data and demonstrated the functional relationship between O3 and VOC-NOx concentrations. The dynamic models were able to the adequately predict the O3 concentration and behavior of VOC-NOx-O3 system a number of hourly intervals into the future. For 3 hr into the future, O3 concentration could be predicted with a root-mean squared error (RMSE) of 8.21 ppb. Extending the models further in time led to an RMSE of 11.46 ppb for 5-hr-ahead values. This prediction capability could be useful in determining when control actions are needed to maintain measured concentrations within acceptable value ranges.     

Estimating separately personal exposure to ambient and nonambient particulate matter for epidemiology and risk assessment: why and how

This paper discusses the legal and scientific reasons for separating personal exposure to PM into ambient and nonambient components. It then demonstrates by several examples how well-established models and data typically obtained in exposure field studies can be used to estimate both individual and community average exposure to ambient-generated PM (ambient PM outdoors plus ambient PM that has infiltrated indoors), indoor-generated PM, and personal activity PM. Ambient concentrations are not highly correlated with personal exposure to nonambient PM or total PM but are highly correlated with personal exposure to ambient-generated PM. Therefore, ambient concentrations may be used in epidemiology as an appropriate surrogate for personal exposure to ambient-generated PM. Suggestions are offered as to how exposure to ambient-generated PM may be obtained and used in epidemiology and risk assessment.     

Development of a sample equilibration system for the TEOM continuous PM monitor

In recent years, scientific discussion has included the influence of thermodynamic conditions (e.g., temperature, relative humidity, and filter face velocity) on PM retention efficiency of filter-based samplers and monitors. Method-associated thermodynamic conditions can, in some instances, dramatically influence the presence of particle-bound water and other light-molecular-weight chemical components such as particulate nitrates and certain organic compounds. The measurement of fine particle mass presents a new challenge for all PM measurement methods, since a relatively greater fraction of the mass is semi-volatile. The tapered element oscillating microbalance (TEOM) continuous PM monitor is a U.S. Environmental Protection Agency (EPA) PM10 equivalent method (EQPM-1090079). Several hundred of these monitors are deployed throughout the United States. The TEOM monitor has the unique characteristic of providing direct PM mass measurement without the calibration uncertainty inherent in mass surrogate methods. In addition, it provides high-precision, near-real-time continuous data automatically. Much attention has been given to semi-volatile species retention of the TEOM method.     

Correlation between inorganic (heavy metals) and organic (PCBs and PAHs) micropollutant concentrations during sewage sludge composting processes

The nature and congener composition of PCBs and PAHs present in sewage sludge composting processes was investigated. These studies included analysis of the most significant process parameters (such as pH, temperature, weight percentage variation) and in addition heavy metals whose typical composting speciation and behaviour were also considered in order to better understand organic compound time profiles. The significant correlation found between Pb, Cd, Cu and PCBs and between PAHs and Hg implies that quite a strong adsorption of PCBs onto organic matter takes place and also provides evidence for the volatilisation of PAHs. Chemical characteristics of inorganic species and organic compounds are summarised to account for the observed correlation and time trend profiles. Moreover, single congener concentrations demonstrate that the number of Cl substituents for PCBs and condensed benzene rings for PAHs determine to what extent they can be broken down for biodegradation and removed through volatilisation respectively.     

Modeling traffic-related air pollution in street canyons of Beijing

It is important to develop a general model to accurately simulate the air pollution in urban street areas. In this paper, the Operational Street Pollution Model (OSPM) initially developed in Denmark is tested with measured data from a relatively wide and open street in Beijing. Major factors influencing the dispersion, such as emission factors, stationary source emissions, and solar radiation, are analyzed. Results show that the model can reflect the basic dispersion pattern in the street but gives systematically higher concentrations. After modifications to estimate street-level wind speed in the model, performance is obviously improved.     

A life-cycle comparison of alternative automobile fuels

We examine the life cycles of gasoline, diesel, compressed natural gas (CNG), and ethanol (C2H5OH)-fueled internal combustion engine (ICE) automobiles. Port and direct injection and spark and compression ignition engines are examined. We investigate diesel fuel from both petroleum and biosources as well as C2H5OH from corn, herbaceous bio-mass, and woody biomass. The baseline vehicle is a gasoline-fueled 1998 Ford Taurus. We optimize the other fuel/powertrain combinations for each specific fuel as a part of making the vehicles comparable to the baseline in terms of range, emissions level, and vehicle lifetime. Life-cycle calculations are done using the economic input-output life-cycle analysis (EIO-LCA) software; fuel cycles and vehicle end-of-life stages are based on published model results. We find that recent advances in gasoline vehicles, the low petroleum price, and the extensive gasoline infrastructure make it difficult for any alternative fuel to become commercially viable. The most attractive alternative fuel is compressed natural gas because it is less expensive than gasoline, has lower regulated pollutant and toxics emissions, produces less greenhouse gas (GHG) emissions, and is available in North America in large quantities. However, the bulk and weight of gas storage cylinders required for the vehicle to attain a range comparable to that of gasoline vehicles necessitates a redesign of the engine and chassis. Additional natural gas transportation and distribution infrastructure is required for large-scale use of natural gas for transportation. Diesel engines are extremely attractive in terms of energy efficiency, but expert judgment is divided on whether these engines will be able to meet strict emissions standards, even with reformulated fuel. The attractiveness of direct injection engines depends on their being able to meet strict emissions standards without losing their greater efficiency. Biofuels offer lower GHG emissions, are sustainable, and reduce the demand for imported fuels. Fuels from food sources, such as biodiesel from soybeans and C2H5OH from corn, can be attractive only if the co-products are in high demand and if the fuel production does not diminish the food supply. C2H5OH from herbaceous or woody biomass could replace the gasoline burned in the light-duty fleet while supplying electricity as a co-product. While it costs more than gasoline, bioethanol would be attractive if the price of gasoline doubled, if significant reductions in GHG emissions were required, or if fuel economy regulations for gasoline vehicles were tightened.     

Determination of Q1, an unknown organochlorine contaminant, in human milk, Antarctic air, and further environmental samples

Q1, an organochlorine component with the molecular formula C(9)H(3)Cl(7)N(2) and of unknown origin was recently identified in seal blubber samples from the Namibian coast (southwest of Africa) and the Antarctic. In these samples, Q1 was more abundant than PCBs and on the level of DDT residues. Furthermore, Q1 was more abundant in seals from the Antarctic than the Arctic. To prove this assumption, gas chromatography-electron-capture negative ion mass spectrometry (GC/ECNI-MS), which is sensitive and selective for Q1, allowed for screening of traces of Q1 even in samples with particularly high levels of other organochlorine contaminants. Q1 was isolated by high-performance liquid chromatography (HPLC) from a skua liver sample. A 1:1 mixture with trans-nonachlor in electron-capture detectors (ECDs) was used to determine the relative response factor with ECNI-MS. The ECNI-MS response of Q1 turned out to be 4.5 times higher than that of trans-nonachlor in an ECD. With GC/ECNI-MS in the selected ion-monitoring mode, four Antarctic and four Arctic air samples were investigated for the presence of Q1. In the Antarctic air samples, Q1 levels ranged from 0.7 to 0.9 fg/m(3). In Arctic air samples, however, Q1 was below the detection limit (<0.06 fg/m(3) or 60 ag/m(3)). We also report on high Q1 levels in selected human milk samples (12-230 microg/kg lipid) and, therefore, suggested that the unknown Q1 is an environmental compound whose origin and distribution should be investigated in detail. Our data confirm that Q1 is a bioaccumulative natural organochlorine product. Detection of a highly chlorinated natural organochlorine compound in air and human milk is novel.     

Suitability of process-based tree growth models for addressing tree response to climate change

Empirical models of tree growth have been used for many years to predict timber yields and other properties of trees. However, such models rely on measured relationships between tree growth and historic environmental conditions. As anthropogenic actions alter the environment, especially atmospheric composition, empirical models become less reliable and process-based models become more useful. Process-based models are challenged to simulate growth of structurally and physiologically complex organisms using explicit mathematical expressions to capture growth response to environmental conditions. In this review we summarize the physiological requirements of process-based models and examine the capabilities of six published models (CARBON, ECOPHYS, PGSM, TREE-BGC, TREGRO, W91) for simulating tree response to changes in environmental conditions (elevated temperature, increased CO(2) concentration, and enhanced concentrations of tropospheric ozone). These analyses indicate that current models are reliable integrators of environmental effects on individual processes (e.g. photosynthesis), but may be less reliable where physiological acclimation occurs or when extrapolated to growth of specific tree compartments.