COVID-19 lockdowns cause global air pollution declines

Vertical measurements of atmospheric CO2 and 14CO2 at the northern foot of the Qinling Mountains in China

The CO2 and 14CO2 levels in air samples from the northern foot of the Qinling Mountains (Xi'an, China) were determined. In 2021, a hexacopter unmanned aerial vehicle sampled air at different heights, from near-ground to 2000 m. The objectives of this study were to determine vertical characteristics of CO2 and 14CO2, the sources of different-height CO2, and the influence of air mass transport. The CO2 concentrations mainly exhibited a slight decreasing trend with increasing height during summer observations, which was in contrast to the increasing trend that was followed by a subsequent gradual decreasing trend during early winter observations, with peak CO2 levels (443.4 ± 0.4-475.7 ± 0.5 ppm) at 100-500 m. The variation in vertical concentrations from 20 to 1000 m in early winter observations (21.6 ± 19.3 ppm) was greater than that in summer observations (14.6 ± 14.3 ppm), and the maximum vertical variation from 20 to ∼2000 m reached 61.1 ppm. Combining Δ14C and δ13C vertical measurements, the results showed that fossil fuel CO2 (CO2ff, 56.1 ± 15.2 %), which mainly come from coal combustion (81.2 ± 3.4 %), was the main contributor to CO2 levels in excess of the background level (CO2ex) during early winter observations. In contrast, biological CO2 (CO2bio) dominated CO2ex in summer observations. The vertical distributions of CO2ff in early winter observations and CO2bio in summer observations were consistent with those of CO2 during early winter and summer observations, respectively. The strong correlation between winter CO2bio and ΔCO (r = 0.81, p < 0.01) indicated that biomass burning was the main contributor to CO2bio during early winter observations. Approximately half of the air masses originated from the Guanzhong Basin during observations. The results provide insights into the vertical distribution of different-sources of atmospheric CO2 in scientific support of formulating carbon emission-reduction strategies.

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'.

Estimating regional fossil fuel CO2 concentrations from 14CO2 observations: challenges and uncertainties

The direct way to estimate the regional fossil fuel CO2 surplus (ΔffCO2) at a station is by measuring the Δ14CO2 depletion compared with a respective background. However, this approach has several challenges, which are (i) the choice of an appropriate Δ14CO2 background, (ii) potential contaminations through nuclear 14CO2 emissions and (iii) masking of ΔffCO2 by 14C-enriched biosphere respiration. Here we evaluate these challenges and estimate potential biases and typical uncertainties of 14C-based ΔffCO2 estimates in Europe. We show that Mace Head (MHD), Ireland, is a representative background station for the Integrated Carbon Observation System (ICOS) atmosphere station network. The mean ΔffCO2 representativeness bias when using the MHD Δ14CO2 background for the whole observation network is of order 0.1 ± 0.3 ppm. At ICOS sites, the median nuclear contamination leads to 25% low-biased ΔffCO2 estimates if not corrected for. The ΔffCO2 masking due to 14C-enriched heterotrophic CO2 respiration can lead to similar ΔffCO2 biases as the nuclear contaminations, especially in summer. Our evaluation of all components contributing to the uncertainty of ΔffCO2 estimates reveals that, due to the small ffCO2 signals at ICOS stations, almost half of the 14C-based ΔffCO2 estimates from integrated samples have an uncertainty that is larger than 50%. This article is part of the Theo Murphy meeting issue 'Radiocarbon in the Anthropocene'.

Spatial distribution of fossil fuel derived CO2 over India using radiocarbon measurements in crop plants

Examining the contribution of fossil fuel CO₂ to the total CO₂ changes in the atmosphere is of primary concern due to its alarming levels of fossil fuel emissions over the globe, specifically developing countries. Atmospheric radiocarbon represents an important observational constraint and utilized to trace fossil fuel derived CO₂ (CO_2ff) in the atmosphere. For the first time, we have presented a detailed analysis on the spatial distribution of fossil fuel derived CO₂ (CO_2ff) over India using radiocarbon (Δ¹⁴C) measurements during three-year period. Analysis shows that the Δ¹⁴C values are varying between 29.33‰ to -34.06‰ across India in the year 2017, where highest value belongs to a location from Gujarat while lowest value belongs to a location from Chhattisgarh. Based on the Δ¹⁴C patterns, spatial distributions of CO_2ff mole fractions have been determined over India and the calculated values of CO_2ff mole fractions are varying between 4.85 ppm to 26.59 ppm across India. It is also noticed that the highest CO_2ff mole fraction is observed as 26.59 ppm from a site in Chhattisgarh. CO_2ff mole fraction values from four high altitude sites are found to be varied between 4.85 ppm to 14.87 ppm. Effect of sampling different crop plants from the same growing season and different crop plant organs (grains, leaves, stems) on the Δ¹⁴C and CO_2ff have been studied. Annual and intra seasonal variations in the Δ¹⁴C and CO_2ff mole fractions have also been analyzed from a rural location (Dholpur, Rajasthan).

Dramatic decline of observed atmospheric CO2 and CH4 during the COVID-19 lockdown over the Yangtze River Delta of China

The temporal variation of greenhouse gas concentrations in China during the COVID-19 lockdown in China is analyzed in this work using high resolution measurements of near surface △CO₂, △CH₄ and △CO concentrations above the background conditions at Lin'an station (LAN), a regional background station in the Yangtze River Delta region. During the pre-lockdown observational period (IOP-1), both △CO₂ and △CH₄ exhibited a significant increasing trend relative to the 2011-2019 climatological mean. The reduction of △CO₂, △CH₄ and △CO during the lockdown observational period (IOP-2) (which also coincided with the Chinese New Year Holiday) reached up to 15.0 ppm, 14.2 ppb and 146.8 ppb, respectively, and a reduction of △CO₂/△CO probably due to a dramatic reduction from industrial emissions. △CO₂, △CH₄ and △CO were observed to keep declining during the post-lockdown easing phase (IOP-3), which is the synthetic result of lower than normal CO₂ emissions from rural regions around LAN coupled with strong uptake of the terrestrial ecosystem. Interestingly, the trend reversed to gradual increase for all species during the later easing phase (IOP-4), with △CO₂/△CO constantly increasing from IOP-2 to IOP-3 and finally IOP-4, consistent with recovery in industrial emissions associated with the staged resumption of economic activity. On average, △CO₂ declined sharply throughout the days during IOP-2 but increased gradually throughout the days during IOP-4. The findings showcase the significant role of emission reduction in accounting for the dramatic changes in measured atmospheric △CO₂ and △CH₄ associated with the COVID-19 lockdown and recovery.

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.

Dramatic Lockdown Fossil Fuel CO2 Decrease Detected by Citizen Science-Supported Atmospheric Radiocarbon Observations

COVID-19 lockdowns resulted in dramatic changes to fossil fuel CO₂ emissions around the world, most prominently in the transportation sector. Yet travel restrictions also hampered observational data collection, making it difficult to evaluate emission changes as they occurred. To overcome this, we used a novel citizen science campaign to detect emission changes during lockdown and engage youth in climate science. Citizen scientists collected grass samples from their garden or local park, from which we analyzed the radiocarbon content to infer the recently added atmospheric fossil fuel CO₂ mole fraction at each sampling location. The local fossil fuel CO₂ mole fractions during lockdown were compared with a "normal" nonlockdown period. Our results from 17 sites in five cities around New Zealand demonstrate dramatic reductions in traffic emissions of 75 ± 3% during the most severe lockdown restriction period. This is consistent with sparse local traffic count information and a much larger decrease in traffic emissions than reported in global aggregate estimates of emission changes. Our results demonstrate that despite nationally consistent rules on travel during lockdown, emission changes varied by location, with inner-city sites typically dominated by bus traffic showing smaller decreases in emissions than elsewhere.

Anatomy of the atmospheric emissions from the transport sector in Greece: trends and challenges

Emissions of atmospheric pollutants are well-known for their adverse effects on air quality and public health. Additionally, GHG emissions are responsible for the so called "Radiating Forcing" leading to climate change and degradation of ecosystem services. In this work, we analyze the annual emission trends of various air pollutants, including GHGs, from all 4 sectors of transport (roads, aviation, navigation, and railway) in Greece during the 28-year period between 1990 and 2017, in order to examine the confounding dynamics among external forces, such as the major fiscal recession of 2008, and the GHG/pollutant emissions in the country. The analysis is performed with a suite of statistical tools consisting of bivariate correlation analysis, Mann-Kendall test, Sen's slope estimation, and Joinpoint regression analysis, in order to thoroughly study the trends of emissions. It is found that all transport sectors (except for the railway) show a significant increase in their emissions, despite the fiscal recession of 2008 that temporarily decelerated all aspects of economic activity in the country. Given the major share of transport in GHG emissions (37%) and air pollution in urban centers, it is essential that the road sector adapts to the new challenges, by means of switching to low-emission technologies and electromobilization. The same applies for the navigation and aviation sectors, which are known pillars of the tourist industry in the country.

Novel quantification of regional fossil fuel CO2 reductions during COVID-19 lockdowns using atmospheric oxygen measurements

It is not currently possible to quantify regional-scale fossil fuel carbon dioxide (ffCO₂) emissions with high accuracy in near real time. Existing atmospheric methods for separating ffCO₂ from large natural carbon dioxide variations are constrained by sampling limitations, so that estimates of regional changes in ffCO₂ emissions, such as those occurring in response to coronavirus disease 2019 (COVID-19) lockdowns, rely on indirect activity data. We present a method for quantifying regional signals of ffCO₂ based on continuous atmospheric measurements of oxygen and carbon dioxide combined into the tracer "atmospheric potential oxygen" (APO). We detect and quantify ffCO₂ reductions during 2020-2021 caused by the two U.K. COVID-19 lockdowns individually using APO data from Weybourne Atmospheric Observatory in the United Kingdom and a machine learning algorithm. Our APO-based assessment has near-real-time potential and provides high-frequency information that is in good agreement with the spread of ffCO₂ emissions reductions from three independent lower-frequency U.K. estimates.

CO Fluxes in Western Europe during 2017–2020 Winter Seasons Inverted by WRF-Chem/Data Assimilation Research Testbed with MOPITT Observations

The study of anthropogenic carbon monoxide (CO) emissions is crucial to investigate anthropogenic activities. Assuming the anthropogenic CO emissions accounted for the super majority of the winter CO fluxes in western Europe, they could be roughly estimated by the inversion approach. The CO fluxes and concentrations of four consecutive winter seasons (i.e., December–February) in western Europe since 2017 were estimated by a regional CO flux inversion system based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Data Assimilation Research Testbed (DART). The CO retrievals from the Measurements Of Pollution In The Troposphere instrument (MOPITT) version 8 level 2 multi-spectral Thermal InfraRed (TIR)/Near-InfraRed (NIR) CO retrieval data products were assimilated by the inversion system. The analyses of the MOPITT data used by the inversion system indicated that the mean averaging kernel row sums of the surface level was about 0.25, and the difference percentage of the surface-level retrievals relative to a priori CO-mixing ratios was 14.79%, which was similar to that of the other levels. These results suggested the MOPITT’s surface-level observations contained roughly the same amount of information as the other levels. The inverted CO fluxes of the four winter seasons were 6198.15 kilotons, 4939.72 kilotons, 4697.80 kilotons, and 5456.19 kilotons, respectively. Based on the assumption, the United Nations Framework Convention on Climate Change (UNFCCC) inventories were used to evaluate the accuracy of the inverted CO fluxes. The evaluation results indicated that the differences between the inverted CO fluxes and UNFCCC inventories of the three winter seasons of 2017–2019 were 13.36%, −4.59%, and −4.76%, respectively. Detailed surface-CO concentrations and XCO comparative analyses between the experimental results and the external Community Atmosphere Model with Chemistry (CAM-Chem) results and the MOPITT data were conducted. The comparative analysis results indicated that the experimental results of the winter season of 2017 were obviously affected by high boundary conditions. The CO concentrations results of the experiments were also evaluated by the CO observation data from Integrated Carbon Observation System (ICOS), the average Mean Bias Error (MBE), and the Root Mean Square Error (RMSE) between the CO concentrations results of the inversion system, and the ICOS observations were −22.43 ppb and 57.59 ppb, respectively. The MBE and RMSE of the inversion system were 17.53-ppb and 4.17-ppb better than those of the simulation-only parallel experiments, respectively.

Stable carbon isotopic characteristics of fossil fuels in China

Good knowledge on the stable carbon isotopic composition (δ¹³C) of fossil fuels is critical for the estimation of atmospheric CO₂ sources. Here, we complied a comprehensive δ¹³C database including 336 coal, 580 oil, and 1160 natural gas data based on the extensive literature search, and conducted field measurements in two megacities, to characterize the δ¹³C signatures of Chinese fossil fuels. Results show that coal exhibits a narrow range and the most enriched in δ¹³C signature, oil displays intermediate variations both in the distribution and value of δ¹³C. By contrast, natural gas is strongly depleted but became more enriched in δ¹³C signature due to the shift in production from isotopically light oil-type gas to isotopically heavy coal-type gas. We found an obvious overlap between the δ¹³C distributions of oil and natural gas, and the carbon isotopic difference between oil and natural gas is minimized in Ordos Basin. Therefore, we suggested that the geographic origin is a certain factor that must be considered when δ¹³C of fossil fuels is used to estimate CO₂ source contributions, and the measurement of δ¹³CO₂ signatures of local end members is a better alternative in the absence of detailed information about the geographical origins of fossil fuels. This work is helpful in improving the ability to quantify CO₂ sources of fossil fuel emissions in China, and also make a contribute to the global carbon isotope database.

Strong Southern Ocean carbon uptake evident in airborne observations

[Figure: see text].

Radiocarbon bomb-peak signal in tree-rings from the tropical Andes register low latitude atmospheric dynamics in the Southern Hemisphere

South American tropical climate is strongly related to the tropical low-pressure belt associated with the South American monsoon system. Despite its central societal role as a modulating agent of rainfall in tropical South America, its long-term dynamical variability is still poorly understood. Here we combine a new (and world's highest) tree-ring ¹⁴C record from the Altiplano plateau in the central Andes with other ¹⁴C records from the Southern Hemisphere during the second half of the 20th century in order to elucidate the latitudinal gradients associated with the dissemination of the bomb ¹⁴C signal. Our tree-ring ¹⁴C record faithfully captured the bomb signal of the 1960's with an excellent match to atmospheric ¹⁴C measured in New Zealand but with significant differences with a recent record from Southeast Brazil located at almost equal latitude. These results imply that the spreading of the bomb signal throughout the Southern Hemisphere was a complex process that depended on atmospheric dynamics and surface topography generating reversals on the expected north-south gradient in certain years. We applied air-parcel modeling based on climate data to disentangle their different geographical provenances and their preformed (reservoir affected) radiocarbon content. We found that air parcel trajectories arriving at the Altiplano during the bomb period were sourced i) from the boundary layer in contact with the Pacific Ocean (41%), ii) from the upper troposphere (air above the boundary layer, with no contact with oceanic or continental carbon reservoirs) (38%) and iii) from the Amazon basin (21%). Based on these results we estimated the ∆¹⁴C endmember values for the different carbon reservoirs affecting our record which suggest that the Amazon basin biospheric ¹⁴C isoflux could have been reversed from negative to positive as early as the beginning of the 1970's. This would imply a much faster carbon turnover rate in the Amazon than previously modelled.

The impact of COVID-19 lockdown on atmospheric CO2 in Xi'an, China

Lockdown measures to control the spread of the novel coronavirus disease (COVID-19) sharply limited energy consumption and carbon emissions. The lockdown effect on carbon emissions has been studied by many researchers using statistical approaches. However, the lockdown effect on atmospheric carbon dioxide (CO₂) on an urban scale remains unclear. Here we present CO₂ concentration and carbon isotopic (δ¹³C) measurements to assess the impact of COVID-19 control measures on atmospheric CO₂ in Xi'an, China. We find that CO₂ concentrations during the lockdown period were 7.5% lower than during the normal period (prior to the Spring Festival, Jan 25 to Feb 4, 2020). The observed CO_2excess (total CO₂ minus background CO₂) during the lockdown period was 52.3% lower than that during the normal period, and 35.7% lower than the estimated CO_2excess with the effect of weather removed. A Keeling plot shows that in contrast CO₂ concentrations and δ¹³C were weakly correlated (R² = 0.18) during the lockdown period, reflecting a change in CO₂ sources imposed by the curtailment of traffic and industrial emissions. Our study also show that the sharp reduction in atmospheric CO₂ during lockdown were short-lived, and returned to normal levels within months after lockdown measures were lifted.

Investigating the Uncertainties Propagation Analysis of CO2 Emissions Gridded Maps at the Urban Scale: A Case Study of Jinjiang City, China

Gridded CO2 emission maps at the urban scale can aid the design of low-carbon development strategies. However, the large uncertainties associated with such maps increase policy-related risks. Therefore, an investigation of the uncertainties in gridded maps at the urban scale is essential. This study proposed an analytic workflow to assess uncertainty propagation during the gridding process. Gridded CO2 emission maps were produced using two resolutions of geospatial datasets (e.g., remote sensing satellite-derived products) for Jinjiang City, China, and a workflow was applied to analyze uncertainties. The workflow involved four submodules that can be used to evaluate the uncertainties of CO2 emissions in gridded maps, caused by the gridded model and input. Fine-resolution (30 m) maps have a larger spatial variation in CO2 emissions, which gives the fine-resolution maps a higher degree of uncertainty propagation. Furthermore, the uncertainties of gridded CO2 emission maps, caused by inserting a random error into spatial proxies, were found to decrease after the gridding process. This can be explained by the “compensation of error” phenomenon, which may be attributed to the cancellation of the overestimated and underestimated values among the different sectors at the same grid. This indicates a nonlinear change between the sum of the uncertainties for different sectors and the actual uncertainties in the gridded maps. In conclusion, the present workflow determined uncertainties were caused by the gridded model and input. These results may aid decision-makers in establishing emission reduction targets, and in developing both low-carbon cities and community policies.

Changes to Carbon Isotopes in Atmospheric CO2 Over the Industrial Era and Into the Future

In this "Grand Challenges" paper, we review how the carbon isotopic composition of atmospheric CO2 has changed since the Industrial Revolution due to human activities and their influence on the natural carbon cycle, and we provide new estimates of possible future changes for a range of scenarios. Emissions of CO2 from fossil fuel combustion and land use change reduce the ratio of 13C/12C in atmospheric CO2 (δ13CO2). This is because 12C is preferentially assimilated during photosynthesis and δ13C in plant-derived carbon in terrestrial ecosystems and fossil fuels is lower than atmospheric δ13CO2. Emissions of CO2 from fossil fuel combustion also reduce the ratio of 14C/C in atmospheric CO2 (Δ14CO2) because 14C is absent in million-year-old fossil fuels, which have been stored for much longer than the radioactive decay time of 14C. Atmospheric Δ14CO2 rapidly increased in the 1950s to 1960s because of 14C produced during nuclear bomb testing. The resulting trends in δ13C and Δ14C in atmospheric CO2 are influenced not only by these human emissions but also by natural carbon exchanges that mix carbon between the atmosphere and ocean and terrestrial ecosystems. This mixing caused Δ14CO2 to return toward preindustrial levels in the first few decades after the spike from nuclear testing. More recently, as the bomb 14C excess is now mostly well mixed with the decadally overturning carbon reservoirs, fossil fuel emissions have become the main factor driving further decreases in atmospheric Δ14CO2. For δ13CO2, in addition to exchanges between reservoirs, the extent to which 12C is preferentially assimilated during photosynthesis appears to have increased, slowing down the recent δ13CO2 trend slightly. A new compilation of ice core and flask δ13CO2 observations indicates that the decline in δ13CO2 since the preindustrial period is less than some prior estimates, which may have incorporated artifacts owing to offsets from different laboratories' measurements. Atmospheric observations of δ13CO2 have been used to investigate carbon fluxes and the functioning of plants, and they are used for comparison with δ13C in other materials such as tree rings. Atmospheric observations of Δ14CO2 have been used to quantify the rate of air-sea gas exchange and ocean circulation, and the rate of net primary production and the turnover time of carbon in plant material and soils. Atmospheric observations of Δ14CO2 are also used for comparison with Δ14C in other materials in many fields such as archaeology, forensics, and physiology. Another major application is the assessment of regional emissions of CO2 from fossil fuel combustion using Δ14CO2 observations and models. In the future, δ13CO2 and Δ14CO2 will continue to change. The sign and magnitude of the changes are mainly determined by global fossil fuel emissions. We present here simulations of future δ13CO2 and Δ14CO2 for six scenarios based on the shared socioeconomic pathways (SSPs) from the 6th Coupled Model Intercomparison Project (CMIP6). Applications using atmospheric δ13CO2 and Δ14CO2 observations in carbon cycle science and many other fields will be affected by these future changes. We recommend an increased effort toward making coordinated measurements of δ13C and Δ14C across the Earth System and for further development of isotopic modeling and model-data analysis tools.

Large and seasonally varying biospheric CO2 fluxes in the Los Angeles megacity revealed by atmospheric radiocarbon

Measurements of Δ¹⁴C and CO₂ can cleanly separate biogenic and fossil contributions to CO₂ enhancements above background. Our measurements of these tracers in air around Los Angeles in 2015 reveal high values of fossil CO₂ and a significant and seasonally varying contribution of CO₂ from the urban biosphere. The biogenic CO₂ is composed of sources such as biofuel combustion and human metabolism and an urban biospheric component likely originating from urban vegetation, including turf and trees. The urban biospheric component is a source in winter and a sink in summer, with an estimated amplitude of 4.3 parts per million (ppm), equivalent to 33% of the observed annual mean fossil fuel contribution of 13 ppm. While the timing of the net carbon sink is out of phase with wintertime rainfall and the sink seasonality of Southern California Mediterranean ecosystems (which show maximum uptake in spring), it is in phase with the seasonal cycle of urban water usage, suggesting that irrigated urban vegetation drives the biospheric signal we observe. Although 2015 was very dry, the biospheric seasonality we observe is similar to the 2006-2015 mean derived from an independent Δ¹⁴C record in the Los Angeles area, indicating that 2015 biospheric exchange was not highly anomalous. The presence of a large and seasonally varying biospheric signal even in the relatively dry climate of Los Angeles implies that atmospheric estimates of fossil fuel-CO₂ emissions in other, potentially wetter, urban areas will be biased in the absence of reliable methods to separate fossil and biogenic CO₂.

Fossil fuel CO2 traced by radiocarbon in fifteen Chinese cities

China is an important fossil fuel CO₂ (CO_2ff) emitter and the international community is thus concerned with quantifying reductions in Chinese carbon emissions in the recent past. Compared to traditional statistical method, radiocarbon (¹⁴C) offers a different approach to quantify atmospheric CO₂ derived from fossil fuel emissions. Here, we carry out a multi-year (2011-2016) CO_2ff tracing by ¹⁴C in Xi'an, and a three-year (2014-2016) CO_2ff tracing in 15 Chinese cities. The Xi'an results show that average CO_2ff concentrations fell 35.9 ± 6.6% from 2014- 2016, compared to 2011-2013, and the timing of this decrease coincides with the implementation of nationwide carbon reduction measures in China, known as the Action Plan on Prevention and Control of Air Pollution. A WRF-Chem forward modeling simulation reveals that the CO_2ff in Xi'an is mainly derived from local sources, and a source apportionment combined stable-carbon isotope showed that the CO_2ff in this city is dominated by coal combustion (72.6 ± 10.4%). Strong CO_2ff differences are found between January and July in most Chinese cities. High CO_2ff concentrations often correspond to severe haze episodes and there are generally positive correlations between CO_2ff and fine particulate (PM_2.5) concentrations. Our study provides scientific data to understand the effects of CO_2ff reduction strategies in China that can be applied to other countries as well.

Estimating US fossil fuel CO2 emissions from measurements of 14C in atmospheric CO2

We report national scale estimates of CO₂ emissions from fossil-fuel combustion and cement production in the United States based directly on atmospheric observations, using a dual-tracer inverse modeling framework and CO₂ and [Formula: see text] measurements obtained primarily from the North American portion of the National Oceanic and Atmospheric Administration's Global Greenhouse Gas Reference Network. The derived US national total for 2010 is 1,653 ± 30 TgC yr^-1 with an uncertainty ([Formula: see text]) that takes into account random errors associated with atmospheric transport, atmospheric measurements, and specified prior CO₂ and ¹⁴C fluxes. The atmosphere-derived estimate is significantly larger ([Formula: see text]) than US national emissions for 2010 from three global inventories widely used for CO₂ accounting, even after adjustments for emissions that might be sensed by the atmospheric network, but which are not included in inventory totals. It is also larger ([Formula: see text]) than a similarly adjusted total from the US Environmental Protection Agency (EPA), but overlaps EPA's reported upper 95% confidence limit. In contrast, the atmosphere-derived estimate is within [Formula: see text] of the adjusted 2010 annual total and nine of 12 adjusted monthly totals aggregated from the latest version of the high-resolution, US-specific "Vulcan" emission data product. Derived emissions appear to be robust to a range of assumed prior emissions and other parameters of the inversion framework. While we cannot rule out a possible bias from assumed prior Net Ecosystem Exchange over North America, we show that this can be overcome with additional [Formula: see text] measurements. These results indicate the strong potential for quantification of US emissions and their multiyear trends from atmospheric observations.

Determining diurnal fossil fuel CO2 and biological CO2 by Δ14CO2 observation on certain summer and winter days at Chinese background sites

Diurnal atmospheric Δ¹⁴CO₂ was measured on two consecutive days in summer and winter, 2016 at Shangdianzi, Lin'an and Luhuitou regional background sites, and at Waliguan global background site in China. The objectives of this study were to determine diurnal fossil fuel CO₂ (CO_2ff) and biological CO₂ (CO_2bio) concentrations and to ascertain the factors influencing them. Evident CO_2ff inputs (0-33.0 ± 1.4 ppm) were found, with some small morning and afternoon rush hour signals. Particularly, the long-range transport of air masses influenced the seasonal differences and rapid diurnal variations in CO_2ff. Diurnal CO_2bio showed violent variations (-20.9-113.3 ppm), with high values at night and low or negative values during the daytime. Diurnal CO_2bio variations resulted from the tradeoffs between photosynthetic CO₂ uptake and biological respiration CO₂ emission as well as atmospheric boundary layer heights variations. These results might help to understand the roles of fossil fuel sources and biological sources on atmospheric CO₂ diurnal variations at Chinese background sites.

14C-AMS measurements in modern tree rings to trace local fossil fuel-derived CO2 in the greater Xi'an area, China

Fossil fuel-derived CO₂ (CO_2ff) time series are critical to understanding urban carbon emissions, and to devise strategies to mitigate emission reduction. Using tree ring ¹⁴C archives, we reconstruct an historical CO_2ff time series from 1991 to 2015 in the greater Xi'an region, China. CO_2ff concentrations from the urban sites reached 22.5 ppm, with an average of 14.0 ppm, while average values from rural and mountain sites averaged about 6.0 ppm. These values provide a good measure of the distribution of anthropogenic CO₂ emissions in the region. We also observed CO_2ff concentration increases from both urban and rural sites during the study period, with more significant increases among urban sites. The persistent rise in CO_2ff was attributed to increasing energy consumption caused by regional socio-economic development, which are corroborated by strong correlations between CO_2ff and socioeconomic parameters.

Analysis of the Diurnal, Weekly, and Seasonal Cycles and Annual Trends in Atmospheric CO2 and CH4 at Tower Network in Siberia from 2005 to 2016

We analyzed 12 years (2005–2016) of continuous measurements of atmospheric CO 2 and CH 4 concentrations made at nine tower observation sites in the Japan–Russia Siberian Tall Tower Inland Observation Network (JR-STATION), located in Siberia. Since the data are very noisy and have a low temporal resolution due to gaps in instrument operation, we used the recently developed Prophet model, which was designed to handle the common features of time series (multiple strong seasonalities, trend changes, outliers) and has a robust performance in the presence of missing data and trend shifts. By decomposing each sampled time-series into its major components (i.e., annual trend and seasonal, weekly, and hourly variation), we observed periodically changing patterns of tracer concentrations. Specifically, we detected multi-year variability of tracers and identified high-concentration events. The frequency of such events was found to vary throughout the year, reaching up to 20% of days for some months, while the number of such events was found to be different for CO 2 and CH 4 . An analysis of weather conditions showed that, in most cases, high-concentration events were caused by a temperature inversion and low wind speed. Additionally, wind directions were found to be different for high- and low-concentration events. For some sites, the wind direction indicated the location of strong local sources of CO 2 and CH 4 . As well as elucidating the seasonality of greenhouse gas concentrations, this study confirmed the potential of the Prophet model for detecting periodicity in environmental phenomena.

Atmospheric observation-based estimation of fossil fuel CO2 emissions from regions of central and southern California

Combustion of fossil fuel is the dominant source of greenhouse gas emissions to the atmosphere in California. Here, we describe radiocarbon (¹⁴CO₂) measurements and atmospheric inverse modeling to estimate fossil fuel CO₂ (ffCO₂) emissions for 2009-2012 from a site in central California, and for June 2013-May 2014 from two sites in southern California. A priori predicted ffCO₂ mixing ratios are computed based on regional atmospheric transport model (WRF-STILT) footprints and an hourly ffCO₂ prior emission map (Vulcan 2.2). Regional inversions using observations from the central California site suggest that emissions from the San Francisco Bay Area (SFBA) are higher in winter and lower in summer. Taking all years together, the average of a total of fifteen 3-month inversions from 2009 to 2012 suggests ffCO₂ emissions from SFBA were within 6 ± 35% of the a priori estimate for that region, where posterior emission uncertainties are reported as 95% confidence intervals. Results for four 3-month inversions using measurements in Los Angeles South Coast Air Basin (SoCAB) during June 2013-May 2014 suggest that emissions in SoCAB are within 13 ± 28% of the a priori estimate for that region, with marginal detection of any seasonality. While emissions from the SFBA and SoCAB urban regions (containing ~50% of prior emissions from California) are constrained by the observations, emissions from the remaining regions are less constrained, suggesting that additional observations will be valuable to more accurately estimate total ffCO₂ emissions from California as a whole.

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.

Simulations of summertime fossil fuel CO2 in the Guanzhong basin, China

Recent studies on fossil fuel CO₂ simulation associated with Δ¹⁴CO₂ measurements is quite limited, particularly in China. In this study, the fossil fuel CO₂ recently added to the atmosphere (δCO₂ff) over the Guanzhong basin, central China, during summer 2012 is simulated using a modified WRF-CHEM model constrained by measured CO₂ mixing ratio and Δ¹⁴CO₂. The model well captures the temporal variation of observed CO₂ mixing ratio and Δ¹⁴CO₂, and reasonably reproduces the distribution of observed Δ¹⁴CO₂. The simulation shows a significant variation of δCO₂ff during summertime, ranging from <5ppmv to ~100ppmv and no remarkable trend of δCO₂ff is found for June, July, and August. The δCO₂ff level is closely associated with atmospheric diffusion conditions. The diurnal cycle of δCO₂ff presents a double-peak pattern, a nocturnal one and a rush-hour one, related to the development of planetary boundary layer and CO₂ emission from vehicles. The spatial distributions of summertime δCO₂ff within the basin is clearly higher than the outside, reaching up to 40ppmv in urban Xi'an and 15ppmv in its surrounding areas, indicative of large local fossil fuel emissions. Furthermore, we find that neglecting the influence of summer heterotrophic respiration in terrestrial biosphere would slightly underestimate the calculated δCO₂ff by about 0.38ppmv in the basin.

Δ14CO2 from dark respiration in plants and its impact on the estimation of atmospheric fossil fuel CO2

Radiocarbon (¹⁴C) has been widely used for quantification of fossil fuel CO₂ (CO_2ff) in the atmosphere and for ecosystem source partitioning studies. The strength of the technique lies in the intrinsic differences between the ¹⁴C signature of fossil fuels and other sources. In past studies, the ¹⁴C content of CO₂ derived from plants has been equated with the ¹⁴C content of the atmosphere. Carbon isotopic fractionation mechanisms vary among plants however, and experimental study on fractionation associated with dark respiration is lacking. Here we present accelerator mass spectrometry (AMS) radiocarbon results of CO₂ respired from 21 plants using a lab-incubation method and associated bulk organic matter. From the respired CO₂ we determine Δ¹⁴C_res values, and from the bulk organic matter we determine Δ¹⁴C_bom values. A significant difference between Δ¹⁴C_res and Δ¹⁴C_bom (P < 0.01) was observed for all investigated plants, ranging from -42.3‰ to 10.1‰. The results show that Δ¹⁴C_res values are in agreement with mean atmospheric Δ¹⁴CO₂ for several days leading up to the sampling date, but are significantly different from corresponding bulk organic Δ¹⁴C values. We find that although dark respiration is unlikely to significantly influence the estimation of CO_2ff, an additional bias associated with the respiration rate during a plant's growth period should be considered when using Δ¹⁴C in plants to quantify atmospheric CO_2ff.

The Indianapolis Flux Experiment (INFLUX): A test-bed for developing urban greenhouse gas emission measurements

The objective of the Indianapolis Flux Experiment (INFLUX) is to develop, evaluate and improve methods for measuring greenhouse gas (GHG) emissions from cities. INFLUX's scientific objectives are to quantify CO2 and CH4 emission rates at 1 km resolution with a 10% or better accuracy and precision, to determine whole-city emissions with similar skill, and to achieve high (weekly or finer) temporal resolution at both spatial resolutions. The experiment employs atmospheric GHG measurements from both towers and aircraft, atmospheric transport observations and models, and activity-based inventory products to quantify urban GHG emissions. Multiple, independent methods for estimating urban emissions are a central facet of our experimental design. INFLUX was initiated in 2010 and measurements and analyses are ongoing. To date we have quantified urban atmospheric GHG enhancements using aircraft and towers with measurements collected over multiple years, and have estimated whole-city CO2 and CH4 emissions using aircraft and tower GHG measurements, and inventory methods. Significant differences exist across methods; these differences have not yet been resolved; research to reduce uncertainties and reconcile these differences is underway. Sectorally- and spatially-resolved flux estimates, and detection of changes of fluxes over time, are also active research topics. Major challenges include developing methods for distinguishing anthropogenic from biogenic CO2 fluxes, improving our ability to interpret atmospheric GHG measurements close to urban GHG sources and across a broader range of atmospheric stability conditions, and quantifying uncertainties in inventory data products. INFLUX data and tools are intended to serve as an open resource and test bed for future investigations. Well-documented, public archival of data and methods is under development in support of this objective.

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.

Surrogate gas prediction model as a proxy for Δ14C-based measurements of fossil fuel-CO2

The measured 14C:12C isotopic ratio of atmospheric CO2 (and its associated derived Δ14C value) is an ideal tracer for determination of the fossil fuel derived CO2 enhancement contributing to any atmospheric CO2 measurement (Cff ). Given enough such measurements, independent top-down estimation of US fossil fuel-CO2 emissions should be possible. However, the number of Δ14C measurements is presently constrained by cost, available sample volume, and availability of mass spectrometer measurement facilities. Δ14C is therefore measured in just a small fraction of samples obtained by ask air sampling networks around the world. Here, we develop a Projection Pursuit Regression (PPR) model to predict Cff as a function of multiple surrogate gases acquired within the NOAA/ESRL Global Greenhouse Gas Reference Network (GGGRN). The surrogates consist of measured enhancements of various anthropogenic trace gases, including CO, SF6, and halo- and hydrocarbons acquired in vertical airborne sampling profiles near Cape May, NJ and Portsmouth, NH from 2005 through 2010. Model performance for these sites is quantified based on predicted values corresponding to test data excluded from the model building process. Chi-square hypothesis test analysis indicates that these predictions and corresponding observations are consistent given our uncertainty budget which accounts for random effects and one particular systematic effect. However, quantification of the combined uncertainty of the prediction due to all relevant systematic effects is difficult because of the limited range of the observations and their relatively high fractional uncertainties at the sampling sites considered here. To account for the possibility of additional systematic effects, we incorporate another component of uncertainty into our budget. Expanding the number of Δ14C measurements in the NOAA GGGRN and building new PPR models at additional sites would improve our understanding of uncertainties and potentially increase the number of Cff estimates by approximately a factor of three. Provided that these estimates are of comparable quality to Δ14C-based estimates, we expect an improved determination of fossil fuel-CO2 emissions.

Atmospheric Fossil Fuel CO2 Traced by Δ(14)C in Beijing and Xiamen, China: Temporal Variations, Inland/Coastal Differences and Influencing Factors

One year of atmospheric Δ(14)CO2 were observed in 2014 in the inland city of Beijing and coastal city of Xiamen, China, to trace temporal CO2ff variations and to determine the factors influencing them. The average CO2ff concentrations at the sampling sites in Beijing and Xiamen were 39.7 ± 36.1 ppm and 13.6 ± 12.3 ppm, respectively. These contributed 75.2 ± 14.6% and 59.1 ± 26.8% to their respective annual ΔCO2 offsets over background CO2 concentrations. Significantly (p < 0.05) high CO2ff values were observed in winter in Beijing. We did not find any significant differences in CO2ff values between weekdays and weekends. Diurnal CO2ff variations were plainly evident, with high values between midnight and 4:00, and during morning and afternoon rush hours. The sampling site in the inland city of Beijing displayed much higher CO2ff inputs and overall temporal variations than the site in the coastal city of Xiamen. The variations of CO2ff at both sites were controlled by a combination of emission sources, topography, and atmospheric dispersion. In particular, diurnal observations at the urban site in Beijing showed that CO2ff was easily accumulated under the southeast wind conditions.

The spatial distribution of fossil fuel CO2 traced by Δ(14)C in the leaves of gingko (Ginkgo biloba L.) in Beijing City, China

Atmospheric fossil fuel CO2 (CO2ff ) information is an important reference for local government to formulate energy-saving and emission reduction in China. The CO2ff spatial distribution in Beijing City was traced by Δ(14)C in the leaves of gingko (Ginkgo biloba L.) from late March to September in 2009. The Δ(14)C values were in the range of -35.2 ± 2.8∼15.5 ± 3.2 ‰ (average 3.4 ± 11.8 ‰), with high values found at suburban sites (average 12.8 ± 3.1 ‰) and low values at road sites (average -8.4 ± 18.1 ‰). The CO2ff concentrations varied from 11.6 ± 3.7 to 32.5 ± 9.0 ppm, with an average of 16.4 ± 4.9 ppm. The CO2ff distribution in Beijing City showed spatial heterogeneity. CO2ff hotspots were found at road sites resulted from the emission from vehicles, while low CO2ff concentrations were found at suburban sites because of the less usage of fossil fuels. Additionally, CO2ff concentrations in the northwest area were generally higher than those in the southeast area due to the disadvantageous topography.

Impact of fossil fuel emissions on atmospheric radiocarbon and various applications of radiocarbon over this century

Radiocarbon analyses are commonly used in a broad range of fields, including earth science, archaeology, forgery detection, isotope forensics, and physiology. Many applications are sensitive to the radiocarbon ((14)C) content of atmospheric CO2, which has varied since 1890 as a result of nuclear weapons testing, fossil fuel emissions, and CO2 cycling between atmospheric, oceanic, and terrestrial carbon reservoirs. Over this century, the ratio (14)C/C in atmospheric CO2 (Δ(14)CO2) will be determined by the amount of fossil fuel combustion, which decreases Δ(14)CO2 because fossil fuels have lost all (14)C from radioactive decay. Simulations of Δ(14)CO2 using the emission scenarios from the Intergovernmental Panel on Climate Change Fifth Assessment Report, the Representative Concentration Pathways, indicate that ambitious emission reductions could sustain Δ(14)CO2 near the preindustrial level of 0‰ through 2100, whereas "business-as-usual" emissions will reduce Δ(14)CO2 to -250‰, equivalent to the depletion expected from over 2,000 y of radioactive decay. Given current emissions trends, fossil fuel emission-driven artificial "aging" of the atmosphere is likely to occur much faster and with a larger magnitude than previously expected. This finding has strong and as yet unrecognized implications for many applications of radiocarbon in various fields, and it implies that radiocarbon dating may no longer provide definitive ages for samples up to 2,000 y old.

Detecting fossil fuel emissions patterns from subcontinental regions using North American in situ CO2 measurements

The ability to monitor fossil fuel carbon dioxide (FFCO₂) emissions from subcontinental regions using atmospheric CO₂ observations remains an important but unrealized goal. Here we explore a necessary but not sufficient component of this goal, namely, the basic question of the detectability of FFCO₂ emissions from subcontinental regions. Detectability is evaluated by examining the degree to which FFCO₂ emissions patterns from specific regions are needed to explain the variability observed in high-frequency atmospheric CO₂ observations. Analyses using a CO₂ monitoring network of 35 continuous measurement towers over North America show that FFCO₂ emissions are difficult to detect during nonwinter months. We find that the compounding effects of the seasonality of atmospheric transport patterns and the biospheric CO₂ flux signal dramatically hamper the detectability of FFCO₂ emissions. Results from several synthetic data case studies highlight the need for advancements in data coverage and transport model accuracy if the goal of atmospheric measurement-based FFCO₂ emissions detection and estimation is to be achieved beyond urban scales.

KEY POINTS

Poor detectability of fossil fuel CO₂ emissions from subcontinental regionsDetectability assessed via attribution of emissions patterns in atmospheric dataLoss in detectability due to transport modeling errors and biospheric signal.

Toward verifying fossil fuel CO2 emissions with the CMAQ model: motivation, model description and initial simulation

Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO2 spatiotemporal variability and verify CO2 fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO2. This paper presents methods, input data, and initial results for CO2 simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere-atmosphere exchange, and meteorology in regulating the spatial distribution of CO2 near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO2 concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver CO, the CMAQ model using hourly varying, high-resolution CO2 fossil-fuel emissions from the Vulcan inventory and Carbon Tracker optimized NEE reproduced the observed diurnal profile of CO2 reasonably well but with a low bias in the early morning. The spatial distribution of CO2 was found to correlate with NO(x), SO2, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO2 observations and understand CO2 variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO2 source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO2 spatiotemporal variability and various uncertainties in the future.

IMPLICATIONS

Atmospheric CO2 has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO2 variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO2 emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO2 as an air pollutant.

Network environ perspective for urban metabolism and carbon emissions: a case study of Vienna, Austria

Cities are considered major contributors to global warming, where carbon emissions are highly embedded in the overall urban metabolism. To examine urban metabolic processes and emission trajectories we developed a carbon flux model based on Network Environ Analysis (NEA). The mutual interactions and control situation within the urban ecosystem of Vienna were examined, and the system-level properties of the city's carbon metabolism were assessed. Regulatory strategies to minimize carbon emissions were identified through the tracking of the possible pathways that affect these emission trajectories. Our findings suggest that indirect flows have a strong bearing on the mutual and control relationships between urban sectors. The metabolism of a city is considered self-mutualistic and sustainable only when the local and distal environments are embraced. Energy production and construction were found to be two factors with a major impact on carbon emissions, and whose regulation is only effective via ad-hoc pathways. In comparison with the original life-cycle tracking, the application of NEA was better at revealing details from a mechanistic aspect, which is crucial for informed sustainable urban management.