Atmospheric CO2 and 14CO2 observations at the northern foot of the Qinling Mountains in China: Temporal characteristics and source quantification

A two-year (March 2021 to February 2023) continuous atmospheric CO2 and a one-year regular atmospheric 14CO2 measurement records were measured at the northern foot of the Qinling Mountains in Xi'an, China, aiming to study the temporal characteristics of atmospheric CO2 and the contributions from the sources of fossil fuel CO2 (CO2ff) and biological CO2 (CO2bio) fluxes. The two-year mean CO2 mole fraction was 442.2 ± 16.3 ppm, with a yearly increase of 4.7 ppm (i.e., 1.1 %) during the two-year observations. Seasonal CO2 mole fractions were the highest in winter (452.1 ± 17.7 ppm) and the lowest in summer (433.5 ± 13.3 ppm), with the monthly CO2 levels peaking in January and troughing in June. Diurnal CO2 levels peaked at dawn (05:00-07:00) in spring, summer and autumn, and at 10:00 in winter. 14C analysis revealed that the excess CO2 (CO2ex, atmospheric CO2 minus background CO2) at this site was mainly from CO2ff emissions (67.0 ± 26.8 %), and CO2ff mole fractions were the highest in winter (20.6 ± 17.7 ppm). Local CO enhancement above the background mole fraction (ΔCO) was significantly (r = 0.74, p < 0.05) positively correlated with CO2ff in a one-year measurement, and ΔCO:CO2ff showed a ratio of 23 ± 6 ppb/ppm during summer and winter sampling days, much lower than previous measurements and suggesting an improvement in combustion efficiency over the last decade. CO2bio mole fractions also peaked in winter (14.2 ± 9.6 ppm), apparently due to biomass combustion and the lower and more stable wintertime atmospheric boundary layer. The negative CO2bio values in summer indicated that terrestrial vegetation of the Qinling Mountains had the potential to uptake atmospheric CO2 during the corresponding sampling days. This site is most sensitive to local emissions from Xi'an and to short distance transportation from the southern Qinling Mountains through the valleys.

Urban flask measurements of CO2ff and CO to identify emission sources at different site types in Auckland, New Zealand

As part of the CarbonWatch-NZ research programme, air samples were collected at 28 sites around Auckland, New Zealand, to determine the atmospheric ratio (RCO) of excess (local enhancement over background) carbon monoxide to fossil CO2 (CO2ff). Sites were categorized into seven types (background, forest, industrial, suburban, urban, downwind and motorway) to observe RCO around Auckland. Motorway flasks observed RCO of 14 ± 1 ppb ppm-1 and were used to evaluate traffic RCO. The similarity between suburban (14 ± 1 ppb ppm-1) and traffic RCO suggests that traffic dominates suburban CO2ff emissions during daytime hours, the period of flask collection. The lower urban RCO (11 ± 1 ppb ppm-1) suggests that urban CO2ff emissions are comprised of more than just traffic, with contributions from residential, commercial and industrial sources, all with a lower RCO than traffic. Finally, the downwind sites were believed to best represent RCO for Auckland City overall (11 ± 1 ppb ppm-1). We demonstrate that the initial discrepancy between the downwind RCO and Auckland's estimated daytime inventory RCO (15 ppb ppm-1) can be attributed to an overestimation in inventory traffic CO emissions. After revision based on our observed motorway RCO, the revised inventory RCO (12 ppb ppm-1) is consistent with our observations. This article is part of the Theo Murphy meeting issue 'Radiocarbon in the Anthropocene'.

Tracing fossil fuel CO2 by 14C in maize leaves in Guanzhong Basin of China

Quantifying fossil fuel CO₂ (CO_2ff) in the atmosphere provides a benchmark method to monitor anthropogenic carbon emissions. Radiocarbon (¹⁴C) in atmospheric CO_2ff has been widely studied using the ¹⁴C in plants to document regional CO_2ff patterns. However, annual CO_2ff variations, reflecting spatial distributions based on plant samples, are still scarce. In this paper, the spatial distribution and temporal CO_2ff changes in the Guanzhong Basin is established using Δ¹⁴C measurements from maize leaves collected in 2011 and 2012. With regard to spatial distribution, samples collected around Xi'an City showed lower Δ¹⁴C values (more CO_2ff), while sites located near the perimeter of the basin showed higher Δ¹⁴C values (less CO_2ff). This is likely due to the concentration of anthropogenic activities in the center of the Guanzhong Basin. The observed CO_2ff mole fractions generally matched with PKU CO₂ inventory and the ODIAC CO₂ inventory data in the spatial distribution trend. However, it seems that thermal power plants were not well captured by the PKU CO₂ inventory. Our results provide useful information for the improvement of the inventory and verification of regional carbon cycle models.

Characterization of Regional Combustion Efficiency using ΔXCO: ΔXCO2 Observed by a Portable Fourier-Transform Spectrometer at an Urban Site in Beijing

Measurements of column-averaged dry-air mole fractions of carbon dioxide and carbon monoxide, CO₂ (XCO₂) and CO (XCO), were performed throughout 2019 at an urban site in Beijing using a compact Fourier Transform Spectrometer (FTS) EM27/SUN. This data set is used to assess the characteristics of combustion-related CO₂ emissions of urban Beijing by analyzing the correlated daily anomalies of XCO and XCO₂ (e.g., ΔXCO and ΔXCO₂). The EM27/SUN measurements were calibrated to a 125HR-FTS at the Xianghe station by an extra EM27/SUN instrument transferred between two sites. The ratio of ΔXCO over ΔXCO₂ (ΔXCO:ΔXCO₂) is used to estimate the combustion efficiency in the Beijing region. A high correlation coefficient (0.86) between ΔXCO and ΔXCO₂ is observed. The CO:CO₂ emission ratio estimated from inventories is higher than the observed ΔXCO:ΔXCO₂ (10.46 ± 0.11 ppb ppm^-1) by 42.54%-101.15%, indicating an underestimation in combustion efficiency in the inventories. Daily ΔXCO:ΔXCO₂ are influenced by transportation governed by weather conditions, except for days in summer when the correlation is low due to the terrestrial biotic activity. By convolving the column footprint [ppm (µmol m^-2 s^-1)^-1] generated by the Weather Research and Forecasting-X-Stochastic Time-Inverted Lagrangian Transport models (WRF-X-STILT) with two fossil-fuel emission inventories (the Multi-resolution Emission Inventory for China (MEIC) and the Peking University (PKU) inventory), the observed enhancements of CO₂ and CO were used to evaluate the regional emissions. The CO₂ emissions appear to be underestimated by 11% and 49% for the MEIC and PKU inventories, respectively, while CO emissions were overestimated by MEIC (30%) and PKU (35%) in the Beijing area.

Isotopic biomonitoring of anthropic carbon emissions in a megalopolis

Atmospheric pollution has become a serious threat for human health and the environment. However, the deployment, operation and maintenance of monitoring networks can represent a high cost for local governments. In certain locations, the use of naturally occurring plants for monitoring pollution can be a useful supplement of existing monitoring networks, and even provide information when other types of monitoring are lacking. In this work, we (i) determined the tissue carbon content and the δ13C values for the epiphytic CAM bromeliad Tillandsia recurvata and the relationship of both parameters with the existing CO concentrations in the Valley of Mexico basin and (ii) mapped the spatial distribution of such elemental and isotopic composition for this plant within the basin, in order to assess its potential as an atmospheric biomonitor of carbon monoxide, a pollutant with important repercussions on public health. The CO concentrations in the basin ranged from 0.41 ppm at rural locations to 0.81 ppm at urban sites. The carbon content of T. recurvata, which averaged 42.9 ± 0.34% (dry weight), was not influenced by the surrounding CO concentration. In contrast, the δ13C depended on the sites where the plants were collected. For example, the values were -13.2‰ in rural areas and as low as -17.5‰ in an urban site. Indeed, the isotopic values had a positive linear relationship with the atmospheric CO concentrations. Given the close relationship observed between the isotopic composition of T. recurvata with the CO concentrations in the Valley of Mexico, the δ13C values can be useful for the detection of atmospheric carbonaceous emissions.

Atmospheric fossil fuel CO2 traced by 14CO2 and air quality index pollutant observations in Beijing and Xiamen, China

Radiocarbon (¹⁴C) is the most accurate tracer available for quantifying atmospheric CO₂ derived from fossil fuel (CO_2ff), but it is expensive and time-consuming to measure. Here, we used common hourly Air Quality Index (AQI) pollutants (AQI, PM_2.5, PM₁₀, and CO) to indirectly trace diurnal CO_2ff variations during certain days at the urban sites in Beijing and Xiamen, China, based on linear relationships between AQI pollutants and CO_2ff traced by ¹⁴C ([Formula: see text]) for semimonthly samples obtained in 2014. We validated these indirectly traced CO_2ff (CO_2ff-in) concentrations against [Formula: see text] concentrations traced by simultaneous diurnal ¹⁴CO₂ observations. Significant (p < 0.05) strong correlations were observed between each of the separate AQI pollutants and [Formula: see text] for the semimonthly samples. Diurnal variations in CO_2ff traced by each of the AQI pollutants generally showed similar trends to those of [Formula: see text], with high agreement at the sampling site in Beijing and relatively poor agreement at the sampling site in Xiamen. AQI pollutant tracers showed high normalized root-mean-square (NRMS) errors for the summer diurnal samples due to low [Formula: see text] concentrations. After the removal of these summer samples, the NRMS errors for AQI pollutant tracers were in the range of 31.6-64.2%. CO generally showed a high agreement and low NRMS errors among these indirect tracers. Based on these linear relationships, monthly CO_2ff averages at the sampling sites in Beijing and Xiamen were traced using CO concentration as a tracer. The monthly CO_2ff averages at the Beijing site showed a shallow U-type variation. These results indicate that CO can be used to trace CO_2ff variations in Chinese cities with CO_2ff concentrations above 5 ppm.

Observations of Atmospheric Δ14CO2 at the Global and Regional Background Sites in China: Implication for Fossil Fuel CO2 Inputs

Six months to more than one year of atmospheric Δ¹⁴CO₂ were measured in 2014-2015 at one global background site in Waliguan (WLG) and four regional background sites at Shangdianzi (SDZ), Lin'an (LAN), Longfengshan (LFS) and Luhuitou (LHT), China. The objectives of the study are to document the Δ¹⁴CO₂ levels at each site and to trace the variations in fossil fuel CO₂ (CO_2ff) inputs at regional background sites. Δ¹⁴CO₂ at WLG varied from 7.1 ± 2.9‰ to 32.0 ± 3.2‰ (average 17.1 ± 6.8‰) in 2015, with high values generally in autumn/summer and low values in winter/spring. During the same period, Δ¹⁴CO₂ values at the regional background sites were found to be significantly (p < 0.05) lower than those at WLG, indicating different levels of CO_2ff inputs at those sites. CO_2ff concentrations at LAN (12.7 ± 9.6 ppm) and SDZ (11.5 ± 8.2 ppm) were significantly (p < 0.05) higher than those at LHT (4.6 ± 4.3 ppm) in 2015. There were no significant (p > 0.05) seasonal differences in CO_2ff concentrations for the regional sites. Regional sources contributed in part to the CO_2ff inputs at LAN and SDZ, while local sources dominated the trend observed at LHT. These data provide a preliminary understanding of atmospheric Δ¹⁴CO₂ and CO_2ff inputs for a range of Chinese background sites.

Quantification of fossil fuel CO2 emissions on the building/street scale for a large U.S. city

In order to advance the scientific understanding of carbon exchange with the land surface, build an effective carbon monitoring system, and contribute to quantitatively based U.S. climate change policy interests, fine spatial and temporal quantification of fossil fuel CO(2) emissions, the primary greenhouse gas, is essential. Called the "Hestia Project", this research effort is the first to use bottom-up methods to quantify all fossil fuel CO(2) emissions down to the scale of individual buildings, road segments, and industrial/electricity production facilities on an hourly basis for an entire urban landscape. Here, we describe the methods used to quantify the on-site fossil fuel CO(2) emissions across the city of Indianapolis, IN. This effort combines a series of data sets and simulation tools such as a building energy simulation model, traffic data, power production reporting, and local air pollution reporting. The system is general enough to be applied to any large U.S. city and holds tremendous potential as a key component of a carbon-monitoring system in addition to enabling efficient greenhouse gas mitigation and planning. We compare the natural gas component of our fossil fuel CO(2) emissions estimate to consumption data provided by the local gas utility. At the zip code level, we achieve a bias-adjusted Pearson r correlation value of 0.92 (p < 0.001).