The multispecies analysis of daily air samples collected at the NOAA Boulder Atmospheric Observatory (BAO) in Weld County in northeastern Colorado since 2007 shows highly correlated alkane enhancements caused by a regionally distributed mix of sources in the Denver‐Julesburg Basin. To further characterize the emissions of methane and non‐methane hydrocarbons (propane, n‐butane, i‐pentane, n‐pentane and benzene) around BAO, a pilot study involving automobile‐based surveys was carried out during the summer of 2008. A mix of venting emissions (leaks) of raw natural gas and flashing emissions from condensate storage tanks can explain the alkane ratios we observe in air masses impacted by oil and gas operations in northeastern Colorado. Using the WRAP Phase III inventory of total volatile organic compound (VOC) emissions from oil and gas exploration, production and processing, together with flashing and venting emission speciation profiles provided by State agencies or the oil and gas industry, we derive a range of bottom‐up speciated emissions for Weld County in 2008. We use the observed ambient molar ratios and flashing and venting emissions data to calculate top‐down scenarios for the amount of natural gas leaked to the atmosphere and the associated methane and non‐methane emissions. Our analysis suggests that the emissions of the species we measured are most likely underestimated in current inventories and that the uncertainties attached to these estimates can be as high as a factor of two.

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This paper is motivated by problems from biology involving estimation of concentration fields in a tissue sample using point measurements given by optical contactless biosensors. Due to biological constraints, the sensors may only be sparsely distributed and intermittently monitored. This paper proposes a nonbiological experimental platform, based on hydrogel and dye diffusion, for studying the problem of concentration field estimation using point sensors. A reduced order model for the diffusion system is derived using KarhunenLoéve-Galerkin method and a discrete time Kalman filter is used to estimate the concentration field. The estimator is then applied to experimental data consisting of subsampled images from a diffusion process. Performance is evaluated by comparing the state estimates to the original images. Future work will use the experimental platform to study the effects of spatial distribution and timing of sensor measurements, to examine the effects of additional dynamics such as advection and reaction, and to perform online identification of process parameters.