Iowa Calibration of MEPDG Performance Prediction Models, 2013

This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 representative pavement sites across Iowa were selected. The selected pavement sites represent flexible, rigid, and composite pavement systems throughout Iowa. The required MEPDG inputs and the historical performance data for the selected sites were extracted from a variety of sources. The accuracy of the nationally-calibrated MEPDG prediction models for Iowa conditions was evaluated. The local calibration factors of MEPDG performance prediction models were identified to improve the accuracy of model predictions. The identified local calibration coefficients are presented with other significant findings and recommendations for use in MEPDG/DARWin-ME for Iowa pavement systems.

Iowa Calibration of MEPDG Performance Prediction Models: Tech Transfer Summary

This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 representative pavement sites across Iowa were selected. The selected pavement sites represent flexible, rigid, and composite pavement systems throughout Iowa. The required MEPDG inputs and the historical performance data for the selected sites were extracted from a variety of sources. The accuracy of the nationally-calibrated MEPDG prediction models for Iowa conditions was evaluated. The local calibration factors of MEPDG performance prediction models were identified to improve the accuracy of model predictions. The identified local calibration coefficients are presented with other significant findings and recommendations for use in MEPDG/DARWin-ME for Iowa pavement systems.

Accuracy of plant assemblage predictions from species distribution models varies along environmental gradients

Aim: Climatic niche modelling of species and community distributions implicitly assumes strong and constant climatic determinism across geographic space. This assumption had however never been tested so far. We tested it by assessing how stacked-species distribution models (S-SDMs) perform for predicting plant species assemblages along elevation. Location: Western Swiss Alps. Methods: Using robust presence-absence data, we first assessed the ability of topo-climatic S-SDMs to predict plant assemblages in a study area encompassing a 2800 m wide elevation gradient. We then assessed the relationships among several evaluation metrics and trait-based tests of community assembly rules. Results: The standard errors of individual SDMs decreased significantly towards higher elevations. Overall, the S-SDM overpredicted far more than they underpredicted richness and could not reproduce the humpback curve along elevation. Overprediction was greater at low and mid-range elevations in absolute values but greater at high elevations when standardised by the actual richness. Looking at species composition, the evaluation metrics accounting for both the presence and absence of species (overall prediction success and kappa) or focusing on correctly predicted absences (specificity) increased with increasing elevation, while the metrics focusing on correctly predicted presences (Jaccard index and sensitivity) decreased. The best overall evaluation - as driven by specificity - occurred at high elevation where species assemblages were shown to be under significant environmental filtering of small plants. In contrast, the decreased overall accuracy in the lowlands was associated with functional patterns representing any type of assembly rule (environmental filtering, limiting similarity or null assembly). Main Conclusions: Our study reveals interesting patterns of change in S-SDM errors with changes in assembly rules along elevation. Yet, significant levels of assemblage prediction errors occurred throughout the gradient, calling for further improvement of SDMs, e.g., by adding key environmental filters that act at fine scales and developing approaches to account for variations in the influence of predictors along environmental gradients.

The use of heterogeneous chemistry for the characterization of functional groups at the gas/particle interface of soot and TiO(2) nanoparticles

Six gases (N((CH3)3), NH2OH, CF3COOH, HCl, NO2, O3) were selected to probe the surface of seven combustion aerosol (amorphous carbon, flame soot) and three types of TiO2 nanoparticles using heterogeneous, that is gas-surface reactions. The gas uptake to saturation of the probes was measured under molecular flow conditions in a Knudsen flow reactor and expressed as a density of surface functional groups on a particular aerosol, namely acidic (carboxylic) and basic (conjugated oxides such as pyrones, N-heterocycles) sites, carbonyl (R1-C(O)-R2) and oxidizable (olefinic, -OH) groups. The limit of detection was generally well below 1% of a formal monolayer of adsorbed probe gas. With few exceptions most investigated aerosol samples interacted with all probe gases which points to the coexistence of different functional groups on the same aerosol surface such as acidic and basic groups. Generally, the carbonaceous particles displayed significant differences in surface group density: Printex 60 amorphous carbon had the lowest density of surface functional groups throughout, whereas Diesel soot recovered from a Diesel particulate filter had the largest. The presence of basic oxides on carbonaceous aerosol particles was inferred from the ratio of uptakes of CF3COOH and HCl owing to the larger stability of the acetate compared to the chloride counterion in the resulting pyrylium salt. Both soots generated from a rich and a lean hexane diffusion flame had a large density of oxidizable groups similar to amorphous carbon FS 101. TiO2 15 had the lowest density of functional groups among the three studied TiO2 nanoparticles for all probe gases despite the smallest size of its primary particles. The used technique enabled the measurement of the uptake probability of the probe gases on the various supported aerosol samples. The initial uptake probability, g0, of the probe gas onto the supported nanoparticles differed significantly among the various investigated aerosol samples but was roughly correlated with the density of surface groups, as expected. [Authors]