Detection and variability of combustion-derived vapor in an urban basin

Abstract

Abstract. Water emitted during combustion may comprise a significant portion of ambient humidity ( > 10 %) in urban areas, where combustion emissions are strongly focused in space and time. Stable water vapor isotopes can be used to apportion measured humidity values between atmospherically transported and combustion-derived water vapor, as combustion-derived vapor possesses an unusually negative deuterium excess value (d-excess, d = δ 2 H − 8δ 18 O). We investigated the relationship between the d-excess of atmospheric vapor, ambient CO 2 concentrations, and atmospheric stability across four winters in Salt Lake City, Utah. We found a robust inverse relationship between CO 2 excess above background and d-excess on sub-diurnal to seasonal timescales, which was most prominent during periods of strong atmospheric stability that occur during Salt Lake City winter. Using a Keeling-style mixing model approach, and assuming a molar ratio of H 2 O to CO 2 in emissions of 1.5, we estimated the d-excess of combustion-derived vapor in Salt Lake City to be −179 ± 17 ‰, consistent with the upper limit of theoretical estimates. Based on this estimate, we calculate that vapor from fossil fuel combustion often represents 5-10 % of total urban humidity, with a maximum estimate of 16.7 %, consistent with prior estimates for Salt Lake City. Moreover, our analysis highlights that changes in the observed d-excess during periods of high atmospheric stability cannot be explained without a vapor source possessing a strongly negative d-excess value. Further refinements in this humidity apportionment method, most notably empirical validation of the d-excess of combustion vapor or improvements in the estimation of the background d-excess value in the absence of combustion, can yield more certain estimates of the impacts of fossil fuel combustion on urban humidity and meteorology.

The Utah urban carbon dioxide (UUCON) and Uintah Basin greenhouse gas networks: instrumentation, data, and measurement uncertainty

“…The Uintah Basin GHG network has supported several recent projects including Foster et al (2017Foster et al ( , 2019, in which the data collected from this network were used to estimate and confirm basin-wide CH 4 emissions and examine CH 4 emissions during wintertime stagnation episodes respectively. In an effort to minimize differences between the two networks, measurement frequency, networking, calibration materials (Sect.…”

Stable Water Isotopes Reveal Effects of Intermediate Disturbance and Canopy Structure on Forest Water Cycling

“…We used Version 1.2 of the University of Utah vapor processing scripts (Fiorella, Bares, et al, 2018) to cali-brate~1-Hz isotope data and determine instrument precision. We found that isotope values varied with humidity.…”

Contributions of Atmospheric Transport and Rain–Vapor Exchange to Near-Surface Water Vapor in the Zhanjiang Mangrove Reserve, Southern China: An Isotopic Perspective

“…Our measurements of isotopes in water vapor (Figure 3a) trace a local atmospheric water line with a slightly shallower slope (6.8) and a smaller intercept (−5.9%) relative to the GMWL. Potential contributors to these differences include wind speeds during evaporation from the nearby sea surface, isotopic distillation during transport, partial evaporation of raindrops during rainfall, and the ratio of transpiration to evaporation in the local ecosystem, among others [26][27][28][29][30]. Figure 3b further shows that the value of d-excess is inversely related with δ 18 O.…”

“…is insensitive to (reversible) equilibrium fractionation, and is therefore often used to infer the location or meteorological conditions that characterize the vapor source region under the assumptions that kinetic effects and water loss during transport are small [24,25]. Several recent studies have suggested that the interpretation of d-excess is less straightforward than originally thought [26][27][28][29]; however, as a general rule, values of d-excess are positively correlated with temperature and negatively correlated with RH at the evaporative source. These signals may be confounded by other factors, such as strong winds at the evaporative source or the partial evaporation of falling rain [24,30].…”