Characterizing the dispersive state of convective boundary layers for applied dispersion modeling

Estimates from semiempirical models that characterize surface heat flux, mixing depth, and profiles of temperature, wind, and turbulence are compared with observations from atmospheric field studies conducted in Colorado, Illinois, Indiana, and Minnesota. Sodar observations are compared with tower measurements at the Colorado site, for wind and turbulence profiles. The median surface heat flux, as calculated using surface-layer flux-profile relationships and an energy budget model, was consistently overestimated by 20 to 80%. Several mixing-depth models were evaluated: (1) integration of the hourly surface heat flux and friction velocity, (2) solving for the time rate of change of profiles of virtual potential temperature, and (3) an interpolation scheme used by the U.S. Environmental Protection Agency in regulatory dispersion models. For the late afternoon, 80 to 90% of the estimates from the first and third models were within 40% of the observed values. For the morning hours after sunrise, all were less accurate. Temperature estimates from surface-layer flux-profile relationships compared well with observations within the mixed layer, but were too low for the inversion layer aloft. Wind profiles were derived using surface-layer flux-profile relationships, a windprofile power-law based on Pasquill stability category, and sodar measurements. The sodar measurements were superior to both types of model estimates. Turbulence profiles were derived from sodar measurements and from semiempirical similarity relationships based on mixing depth and Obukhov length. The scatter in the comparisons with the sodar observations is twice that seen in the comparisons with empirical profile relationships. Overall, it appears that uncertainty of as low as 20 to 30% in the characterization of the diffusion meteorology is the exception rather than the rule.On assignment from the National Oceanic and Atmospheric Administration, U. S. Department of Commerce.Disclaimer: Although the research described in this article has been supported by the United States Environmental Protection Agency, it has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.     

Electric sector investments under technological and policy-related uncertainties: a stochastic programming approach

Although emerging technologies like carbon capture and storage and advanced nuclear are expected to play leading roles in greenhouse gas mitigation efforts, many engineering and policy-related uncertainties will influence their deployment. Capital-intensive infrastructure decisions depend on understanding the likelihoods and impacts of uncertainties such as the timing and stringency of climate policy as well as the technological availability of carbon capture systems. This paper demonstrates the utility of stochastic programming approaches to uncertainty analysis within a practical policy setting, using uncertainties in the US electric sector as motivating examples. We describe the potential utility of this framework for energy-environmental decision making and use a modeling example to reinforce these points and to stress the need for new tools to better exploit the full range of benefits the stochastic programming approach can provide. Model results illustrate how this framework can give important insights about hedging strategies to reduce risks associated with high compliance costs for tight CO₂ caps and low CCS availability. Metrics for evaluating uncertainties like the expected value of perfect information and the value of the stochastic solution quantify the importance of including uncertainties in capacity planning, of making precautionary low-carbon investments, and of conducting research and gathering information to reduce risk.     

Unique isoprene oxidation products demonstrate chlorine atom chemistry occurs in the Houston,Texas urban area

As part of the 2000 Texas Air Quality Study (TexAQS), we studied the isoprene oxidation process under ambient conditions to discern the presence of chlorine atom (Cl) chemistry in the Houston, Texas urban area. By measuring chloromethylbutenone (CMBO) and an isomer of chloromethylbutenal (CMBA), we clearly observed sixteen episodes of active Cl chemistry during the 24-day experiment. Estimated median Cl concentration during each of these episodes was between the detection limit of ~10² atoms cm⁻³ and 50 _{- 30} ^{+ 70} ×10⁴ 50_{ - 30}^{ + 70} \times {10^4} atoms cm⁻³. Cl concentration during all the episodes averaged 7.6 _{- 2.0} ^{+ 4.7} ×10⁴ 7.6_{ - 2.0}^{ + 4.7} \times {10^4} atoms cm⁻³ and thus amounted to less than 3% of the OH concentration during the same periods. During the episodes, the fraction of oxidation chemistry initiated by Cl ranged from 3–43% and was strongly dependent on the quantity and type of hydrocarbons present in the atmosphere. Because of its intermittent presence and low concentration, Cl is not a broadly influential oxidant in the Houston, Texas urban area.     

Review of Lagrangian stochastic models for trajectories in the turbulent atmosphere

We review the theoretical basis for, and the advantages of, random flight models for the trajectories of tracer particles in turbulence. We then survey their application to calculate dispersion in the principal types of atmospheric turbulence (stratified, vertically-inhomogeneous, Gaussian or non-Gaussian turbulence in the surface layer and above), and show that they are especially suitable for some problems (e.g., quantifying ground emissions).     

Black Holes and Loose Connections in a Global Urban Network

This paper introduces the concept of black holes and loose connections in a global urban hierarchy. Black holes are defined as large cities, with a population of over 3 million, that are not classified as world cities. The paper draws upon a classification that uses advanced producer services as an indicator of world city status. Large, nonworld cities are identified, and provisional ideas about explaining their position are outlined. Connectivity and population data are used in a simple regression analysis to identify loose connections: cities whose connectivity is less than that predicted by their population.     

Contents to Volume 47

Volume Contents

Contents to Volume 47     

3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling,Anhui, China

Natural Resources Research - In the past few decades, a variety of data-driven predictive modeling techniques has led to a dramatic advancement in mineral prospectivity mapping (MPM). The random...