A variant of cavity-enhanced Raman spectroscopy (CERS) is introduced, in which diode laser radiation at 635 nm is coupled into an external linear optical cavity composed of two highly reflective mirrors. Using optical feedback stabilisation, build-up of circulating laser power by 3 orders of magnitude occurs. Strong Raman signals are collected in forward scattering geometry. Gas phase CERS spectra of H(2), air, CH(4) and benzene are recorded to demonstrate the potential for analytical applications and fundamental molecular studies. Noise equivalent limits of detection in the ppm by volume range (1 bar sample) can be achieved with excellent linearity with a 10 mW excitation laser, with sensitivity increasing with laser power and integration time. The apparatus can be operated with battery powered components and can thus be very compact and portable. Possible applications include safety monitoring of hydrogen gas levels, isotope tracer studies (e.g., (14)N/(15)N ratios), observing isotopomers of hydrogen (e.g., radioactive tritium), and simultaneous multi-component gas analysis. CERS has the potential to become a standard method for sensitive gas phase Raman spectroscopy.
Reducing man-made greenhouse gas emissions depends on the effective detection and location of sources. We present a new method that remotely detects, locates, and quantifies gas emission rates by sequentially steering an optical beam between multiple retro-reflectors. The novel open-path laser gas sensor uses Laser Dispersion Spectroscopy (LDS), with seven beams up to 98 meters long deployed across open, flat terrain. LDS offers high precision (10-20 ppb), high dynamic range and linearity, enhanced immunity to atmospheric perturbations, with fast response to probe an area in 3 s. Simultaneous wind and concentration data were collected for four calibrated methane gas release schemes with emission rates of 1.3 kg/hr. The resulting data were processed using a Bayesian, Markov chain Monte-Carlo inverse solver to locate the sources and quantify their mass emission rates and uncertainty bounds. All the sources were located to within a few meters and mass emission rates established within the associated confidence bounds.Plain Language Summary The Earth's atmosphere contains 600 times as much CO 2 as methane (by mass), but the warming effect due to the small amount of methane is 58% of that due to all the CO 2 . Furthermore, methane's atmospheric lifetime is~10 yr whereas CO 2 's is~100 yr. So, reducing methane emissions not only provides much greater impact per unit mass but that reduction in atmospheric warming is realized in years not centuries. Many industrial activities produce methane emissions, but difficulties in remotely attributing and quantifying emission rates have severely impeded effective remedial action. We present a novel method to continuously detect, locate, and quantify methane emission sources distributed across extensive areas. We demonstrate its performance in simple controlled tests using a novel optical beam gas sensor to measure path-averaged gas concentrations. The data are analyzed using advanced statistical methods to locate and quantify the emission rates of the sources.
Provides a case study that describes the successful re‐engineering of the project management process of the Hydro‐Electric Corporation (HEC) through workflow technology. Documents the background leading to the development of a workflow project management system and its adoption, and concludes with a number of insights into the complementary relationship between business process redesign and workflow computing in facilitating corporate implementation of strategic information management.
The action to reduce anthropogenic greenhouse gas emissions is severely constrained by the difficulty of locating sources and quantifying their emission rates. Methane emissions by the energy sector are of particular concern. We report results achieved with a new area monitoring approach using laser dispersion spectroscopy to measure path-averaged concentrations along multiple beams. The method is generally applicable to greenhouse gases, but this work is focused on methane. Nineteen calibrated methane releases in four distinct configurations, including three separate blind trials, were made within a flat test area of 175 m by 175 m. Using a Gaussian plume gas dispersion model, driven by wind velocity data, we calculate the data anticipated for hundreds of automatically proposed candidate source configurations. The Markov-chain Monte Carlo analysis finds source locations and emission rates whose calculated path-averaged concentrations are consistent with those measured and associated uncertainties. This approach found the correct number of sources and located them to be within <9 m in more than 75% of the cases. The relative accuracy of the mass emission rate results was highly correlated to the localization accuracy and better than 30% in 70% of the cases. The discrepancies for mass emission rates were <2 kg/h for 95% of the cases.
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