Non-dispersive infrared (NDIR) sensors are a low-cost way to observe carbon dioxide concentrations in air, but their specified accuracy and precision are not sufficient for some scientific applications. An initial evaluation of six SenseAir K30 carbon dioxide NDIR sensors in a lab setting showed that without any calibration or correction, the sensors have an individual root mean square error (RMSE) between ~5 and 21 parts per million (ppm) compared to a research-grade greenhouse gas analyzer using cavity enhanced laser absorption spectroscopy. Through further evaluation, after correcting for environmental variables with coefficients determined through a multivariate linear regression analysis, the calculated difference between the each of six individual K30 NDIR sensors and the higher-precision instrument had an RMSE of between 1.7 and 4.3 ppm for 1 min data. The median RMSE improved from 9.6 for off-the-shelf sensors to 1.9 ppm after correction and calibration, demonstrating the potential to provide useful information for ambient air monitoring.
Urban areas are responsible for a substantial fraction of anthropogenic emissions of greenhouse gases (GHGs) including methane (CH4), with the second largest anthropogenic direct radiative forcing relative to carbon dioxide (CO2). Quantification of urban CH4 emissions is important for establishing GHG mitigation policies. Comparison of observation‐based and inventory‐based urban CH4 emissions suggests possible improvements in estimating CH4 source emissions in urban environments. In this study, we quantify CH4 emissions from the Baltimore‐Washington area based on the mass balance aircraft flight experiments conducted in Winters 2015 and 2016. The field measurement‐based mean winter CH4 emission rates from this area were 8.66 ± 4.17 kg/s in 2015 and 9.14 ± 4.49 kg/s in 2016, which are 2.8 times the 2012 average U.S. GHG Inventory‐based emission rate. The observed emission rate is 1.7 times that given in a population‐apportioned state of Maryland inventory. Methane emission rates inferred from carbon monoxide (CO) and CO2 emission inventories and observed CH4/CO and CH4/CO2 enhancement ratios are similar to those from the mass balance approach. The observed ethane‐to‐methane ratios, with a mean value of 3.3% in Winter 2015 and 4.3% in Winter 2016, indicate that the urban natural gas system could be responsible for ~40–60% of total CH4 emissions from this area. Landfills also appear to be a major contributor, providing 25 ± 15% of the total emissions for the region. Our study suggests there are grounds to reexamine the CH4 emissions estimates for the Baltimore‐Washington area and to conduct flights in other seasons.
There is increased interest in understanding urban greenhouse gas (GHG) emissions. To accurately estimate city emissions, the influence of extraurban fluxes must first be removed from urban greenhouse gas (GHG) observations. This is especially true for regions, such as the U.S. Northeastern Corridor‐Baltimore/Washington, DC (NEC‐B/W), downwind of large fluxes. To help site background towers for the NEC‐B/W, we use a coupled Bayesian Information Criteria and geostatistical regression approach to help site four background locations that best explain CO2 variability due to extraurban fluxes modeled at 12 urban towers. The synthetic experiment uses an atmospheric transport and dispersion model coupled with two different flux inventories to create modeled observations and evaluate 15 candidate towers located along the urban domain for February and July 2013. The analysis shows that the average ratios of extraurban inflow to total modeled enhancements at urban towers are 21% to 36% in February and 31% to 43% in July. In July, the incoming air dominates the total variability of synthetic enhancements at the urban towers (R2 = 0.58). Modeled observations from the selected background towers generally capture the variability in the synthetic CO2 enhancements at urban towers (R2 = 0.75, root‐mean‐square error (RMSE) = 3.64 ppm; R2 = 0.43, RMSE = 4.96 ppm for February and July). However, errors associated with representing background air can be up to 10 ppm for any given observation even with an optimal background tower configuration. More sophisticated methods may be necessary to represent background air to accurately estimate urban GHG emissions.
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