Assessment of ground-based atmospheric observations for verification of greenhouse gas emissions from an urban region

A high-resolution monitoring approach of urban CO2 fluxes. Part 2 - surface flux optimisation using eddy covariance observations

Achieving climate neutrality by 2050 requires ground-breaking technological and methodological advancements in climate change mitigation planning and actions from local to regional scales. Monitoring the cities' CO2 emissions with sufficient detail and accuracy is crucial for guiding sustainable urban transformation. Current methodologies for CO2 emission inventories rely on bottom-up (BU) approaches which do not usually offer information on the spatial or temporal variability of the emissions and present substantial uncertainties. This study develops a novel approach which assimilates direct CO2 flux observations from urban eddy covariance (EC) towers with very high spatiotemporal resolution information from an advanced urban BU surface flux model (Part 1 of this study, Stagakis et al., 2023) within a Bayesian inversion framework. The methodology is applied to the city centre of Basel, Switzerland (3 × 3 km domain), taking advantage of two long-term urban EC sites located 1.6 km apart. The data assimilation provides optimised gridded CO2 flux information individually for each urban surface flux component (i.e. building heating emissions, commercial/industrial emissions, traffic emissions, human respiration emissions, biogenic net exchange) at 20 m resolution and weekly time-step. The results demonstrate that urban EC observations can be consistently used to improve high-resolution BU surface CO2 flux model estimations, providing realistic seasonal variabilities of each flux component. Traffic emissions are determined with the greatest confidence among the five flux components during the inversions. The optimised annual anthropogenic emissions are 14.7 % lower than the prior estimate, the human respiration emissions have decreased by 12.1 %, while the biogenic components transformed from a weak sink to a weak source. The root-mean-square errors (RMSEs) of the weekly comparisons between EC observations and model outputs are consistently reduced. However, a slight underestimation of the total flux, especially in locations with complex CO2 source/sink mixture, is still evident in the optimised fluxes.

Spatiotemporal Geostatistical Analysis and Global Mapping of CH4 Columns from GOSAT Observations

Methane (CH4) is one of the most important greenhouse gases causing the global warming effect. The mapping data of atmospheric CH4 concentrations in space and time can help us better to understand the characteristics and driving factors of CH4 variation as to support the actions of CH4 emission reduction for preventing the continuous increase of atmospheric CH4 concentrations. In this study, we applied a spatiotemporal geostatistical analysis and prediction to develop an approach to generate the mapping CH4 dataset (Mapping-XCH4) in 1° grid and three days globally using column averaged dry air mole fraction of CH4 (XCH4) data derived from observations of the Greenhouse Gases Observing Satellite (GOSAT) from April 2009 to April 2020. Cross-validation for the spatiotemporal geostatistical predictions showed better correlation coefficient of 0.97 and a mean absolute prediction error of 7.66 ppb. The standard deviation is 11.42 ppb when comparing the Mapping-XCH4 data with the ground measurements from the total carbon column observing network (TCCON). Moreover, we assessed the performance of this Mapping-XCH4 dataset by comparing with the XCH4 simulations from the CarbonTracker model and primarily investigating the variations of XCH4 from April 2009 to April 2020. The results showed that the mean annual increase in XCH4 was 7.5 ppb/yr derived from Mapping-XCH4, which was slightly greater than 7.3 ppb/yr from the ground observational network during the past 10 years from 2010. XCH4 is larger in South Asia and eastern China than in the other regions, which agrees with the XCH4 simulations. The Mapping-XCH4 shows a significant linear relationship and a correlation coefficient of determination (R2) of 0.66, with EDGAR emission inventories over Monsoon Asia. Moreover, we found that Mapping-XCH4 could detect the reduction of XCH4 in the period of lockdown from January to April 2020 in China, likely due to the COVID-19 pandemic. In conclusion, we can apply GOSAT observations over a long period from 2009 to 2020 to generate a spatiotemporally continuous dataset globally using geostatistical analysis. This long-term Mpping-XCH4 dataset has great potential for understanding the spatiotemporal variations of CH4 concentrations induced by natural processes and anthropogenic emissions at a global and regional scale.

Atmospheric mercury sources in a coastal-urban environment: a case study in Boston, Massachusetts, USA

Mercury (Hg) is an environmental toxicant dangerous to human health and the environment. Its anthropogenic emissions are regulated by global, regional, and local policies. Here, we investigate Hg sources in the coastal city of Boston, the third largest metropolitan area in the Northeastern United States. With a median of 1.37 ng m^-3, atmospheric Hg concentrations measured from August 2017 to April 2019 were at the low end of the range reported in the Northern Hemisphere and in the range reported at North American rural sites. Despite relatively low ambient Hg concentrations, we estimate anthropogenic emissions to be 3-7 times higher than in current emission inventories using a measurement-model framework, suggesting an underestimation of small point and/or nonpoint emissions. We also test the hypothesis that a legacy Hg source from the ocean contributes to atmospheric Hg concentrations in the study area; legacy emissions (recycling of previously deposited Hg) account for ∼60% of Hg emitted annually worldwide (and much of this recycling takes place through the oceans). We find that elevated concentrations observed during easterly oceanic winds can be fully explained by low wind speeds and recirculating air allowing for accumulation of land-based emissions. This study suggests that the influence of nonpoint land-based emissions may be comparable in size to point sources in some regions and highlights the benefits of further top-down studies in other areas.

Evaluating national and subnational CO2 mitigation goals in China's thirteenth five-year plan from satellite observations

Good-quality emission data are essential to validate national climate commitments and implement domestic policies. However, conventional bottom-up data collection is costly and subject to potential manipulation by stakeholders when data pass through their hands, which could pose challenges on the efficiency and effectiveness of compliance monitoring especially in developing countries. Satellite observations, specifically OCO-2 XCO₂ measurements, are utilized in this study to develop a relatively independent and timely dataset for screening the attainment statuses of national and subnational CO₂ mitigation goals in China's 13th Five-Year Plan (FYP, 2016-2020). We establish CO₂ emission estimation models at both pixel and provincial levels. As interpolated with the pixel-level model, CO₂ emissions of prefecture-level municipalities indicate that approximately three fifth of them had accomplished their individualized FYP mitigation goals by 2019, while our provincial-level estimation suggests that three quarters of provinces had attained theirs. More resources for compliance monitoring could thus be directed to other presumably-unattaining local governments. National aggregate absolute emissions showed 8.0-18.3% reduction in 2019 across three provincial models from the 2015 level, while national CO₂ intensity dropped by 28.8-36.8% to imply attaining the 18% reduction goals.

Reduction in urban atmospheric CO2 enhancement in Seoul, South Korea, resulting from social distancing policies during the COVID-19 pandemic

With the spread of the COVID-19 virus globally, cities worldwide have implemented unprecedented social distancing policies to mitigate infection rates. Many studies have demonstrated that improved air quality and reduced carbon emissions have resulted from the COVID-19 pandemic. Yet, questions remain regarding changes in atmospheric CO₂ concentrations because of the complex cycles involving the interaction of CO₂ with the natural environment. In this study, we compared the changes in urban CO₂ enhancement (△CO₂) reflecting the contribution of local CO₂ emissions to the atmospheric CO₂ in urban areas, according to the intensity of social distancing policies implemented during the COVID-19 pandemic in Seoul, South Korea. We used data from three CO₂ ground observation sites in the central area of Seoul and outside the urban area of Seoul. By comparing the urban CO₂ concentration in Seoul with that of the background area using two different methods, considering both vertical and horizontal differences in CO₂ concentration, we quantified the △CO₂ of the pre-COVID-19 period and two COVID-19 periods, during which intensive social distancing policies with different intensities were implemented (Level 1, Level 2.5). During the pre-COVID-19 period, the average △CO₂ calculated using the two methods was 24.82 ppm, and it decreased significantly to 16.42 and 14.36 ppm during the Level 1 and Level 2.5 periods, respectively. In addition, the urban contribution of Seoul to atmospheric CO₂ concentration decreased from 5.27% during the pre-COVID-19 period to 3.54% and 3.19% during the Level 1 and Level 2.5 periods, respectively. The results indicate that the social distancing policies implemented in Seoul resulted in reduced local CO₂ emissions, leading to a reduction in atmospheric CO₂ concentration. Interestingly, it also shows that the extent of atmospheric CO₂ concentration reduction can be greatly affected by the intensity of policies. Our study suggests that changes in human activity could reduce the urban direct contribution to the background CO₂ concentration helping to further mitigate climate change.

Comparative Evaluation of Top-Down GOSAT XCO2 vs. Bottom-Up National Reports in the European Countries

Submitting national inventory reports (NIRs) on emissions of greenhouse gases (GHGs) is obligatory for parties of the United Nations Framework Convention on Climate Change (UNFCCC). The NIR forms the basis for monitoring individual countries’ progress on mitigating climate change. Countries prepare NIRs using the default bottom–up methodology of the Intergovernmental Panel on Climate Change (IPCC), as approved by the Kyoto protocol. We provide tangible evidence of the discrepancy between official bottom–up NIR reporting (unit: tons) versus top–down XCO2 reporting (unit: ppm) within the European continent, as measured by the Greenhouse Gases Observing Satellite (GOSAT). Bottom–up NIR (annual growth rate of CO2 emission from 2010 to 2016: −1.55%) does not show meaningful correlation (geographically weighted regression coefficient = −0.001, R2 = 0.024) to top–down GOSAT XCO2 (annual growth rate: 0.59%) in the European countries. The top five countries within the European continent on carbon emissions in NIR do not match the top five countries on GOSAT XCO2 concentrations. NIR exhibits anthropogenic carbon-generating activity within country boundaries, whereas satellite signals reveal the trans-boundary movement of natural and anthropogenic carbon. Although bottom–up NIR reporting has already gained worldwide recognition as a method to track national follow-up for treaty obligations, the single approach based on bottom–up did not present background atmospheric CO2 density derived from the air mass movement between the countries. In conclusion, we suggest an integrated measuring, reporting, and verification (MRV) approach using top–down observation in combination with bottom–up NIR that can provide sufficient countrywide objective evidence for national follow-up activities.

The assessment of two different pollutants dispersion from a coal-fired power plant for various thermal regimes

In this study, numerical simulations of the movement and emissions dispersion of two pollutants (sulfur dioxide(SO₂) and carbon dioxide(CO₂)) into the atmospheric boundary layer were considered under natural atmospheric conditions. To test the numerical algorithm and to select the optimal turbulent model, the test problem was solved numerically. The obtained computational data were compared with measurement data and values from the computation of other authors and the SST k-omega model illustrated the closest values to the data from the experiment, this is achieved by modifying the boundary condition for turbulent kinetic energy. The tested computational algorithm was used to characterize the emissions process of two pollutants from two chimneys of the Ekibastuz SDPP and the distribution of CO₂ and SO₂ in the air flow field in natural air condition. For this task, four various velocity variations were considered, as well as several various thermal variations (temperature inversion, constant temperature and decreasing temperature by the height). From the obtained computational results, it should be noticed that different environmental temperature conditions extremely impact the distribution of pollutants CO₂ and SO₂ in the atmospheric surface layer, so at constant temperature conditions, the species for all velocity variations have nearly identical species profile.

Sub-Daily Natural CO2 Flux Simulation Based on Satellite Data: Diurnal and Seasonal Pattern Comparisons to Anthropogenic CO2 Emissions in the Greater Tokyo Area

During the last decade, advances in the remote sensing of greenhouse gas (GHG) concentrations by the Greenhouse Gases Observing SATellite-1 (GOSAT-1), GOSAT-2, and Orbiting Carbon Observatory-2 (OCO-2) have produced finer-resolution atmospheric carbon dioxide (CO2) datasets. These data are applicable for a top-down approach towards the verification of anthropogenic CO2 emissions from megacities and updating of the inventory. However, great uncertainties regarding natural CO2 flux estimates remain when back-casting CO2 emissions from concentration data, making accurate disaggregation of urban CO2 sources difficult. For this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) land products, meso-scale meteorological data, SoilGrids250 m soil profile data, and sub-daily soil moisture datasets to calculate hourly photosynthetic CO2 uptake and biogenic CO2 emissions with 500 m resolution for the Kantō Plain, Japan, at the center of which is the Tokyo metropolis. Our hourly integrated modeling results obtained for the period 2010–2018 suggest that, collectively, the vegetated land within the Greater Tokyo Area served as a daytime carbon sink year-round, where the hourly integrated net atmospheric CO2 removal was up to 14.15 ± 4.24% of hourly integrated anthropogenic emissions in winter and up to 55.42 ± 10.39% in summer. At night, plants and soil in the Greater Tokyo Area were natural carbon sources, with hourly integrated biogenic CO2 emissions equivalent to 2.27 ± 0.11%–4.97 ± 1.17% of the anthropogenic emissions in winter and 13.71 ± 2.44%–23.62 ± 3.13% in summer. Between January and July, the hourly integrated biogenic CO2 emissions of the Greater Tokyo Area increased sixfold, whereas the amplitude of the midday hourly integrated photosynthetic CO2 uptake was enhanced by nearly five times and could offset up to 79.04 ± 12.31% of the hourly integrated anthropogenic CO2 emissions in summer. The gridded hourly photosynthetic CO2 uptake and biogenic respiration estimates not only provide reference data for the estimation of total natural CO2 removal in our study area, but also supply prior input values for the disaggregation of anthropogenic CO2 emissions and biogenic CO2 fluxes when applying top-down approaches to update the megacity’s CO2 emissions inventory. The latter contribution allows unprecedented amounts of GOSAT and ground measurement data regarding CO2 concentration to be analyzed in inverse modeling of anthropogenic CO2 emissions from Tokyo and the Kantō Plain.

Wintertime Nitrous Oxide Emissions in the San Joaquin Valley of California Estimated from Aircraft Observations

Nitrous oxide (N₂O) is a long-lived greenhouse gas that also destroys stratospheric ozone. N₂O emissions are uncertain and characterized by high spatiotemporal variability, making individual observations difficult to upscale, especially in mixed land use source regions like the San Joaquin Valley (SJV) of California. Here, we calculate spatially integrated N₂O emission rates using nocturnal and convective boundary-layer budgeting methods. We utilize vertical profile measurements from the NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) campaign, which took place January-February, 2013. For empirical constraints on N₂O source identity, we analyze N₂O enhancement ratios with methane, ammonia, carbon dioxide, and carbon monoxide separately in the nocturnal boundary layer, nocturnal residual layer, and convective boundary layer. We find that an established inventory (EDGAR v4.3.2) underestimates N₂O emissions by at least a factor of 2.5, that wintertime emissions from animal agriculture are important to annual totals, and that there is evidence for higher N₂O emissions during the daytime than at night.

Trends in funding research and international collaboration on greenhouse gas emissions: a bibliometric approach

The Web of Science Core Collection platform was used to withdraw the papers included in this study. The studied period comprised from inception till 2018. Trends in research, journals of publication, subject areas of research, keywords most frequently used, countries of publication, international collaboration, and trends of funding research were also analyzed. A total of 3902 articles were published, most of them (52.5%) during the five-year period 2014-2018. The area with the highest number of papers was environmental sciences (41%), followed by energy fuels (16.6%) and engineering environmental (15.7%). "Nitrous oxide emissions" was the most frequent word, followed by "Carbon dioxide emissions" and "Methane emissions". Other words that stood out were "Life cycle assessment", "Climate change" and "Environmental impacts". The United States was the country with the highest productivity (27.9%), followed by China (12.8%) and the United Kingdom (9.6%). There was a concentration of research in recent years, as more than 80% of the papers were published in the last 10 years. The journals that published the largest number of publications were devoted mainly to environmental studies (sciences and engineering), sustainable and green science and technology, energy and fuels, economics, and agriculture. Half of the works were published in Europe and the other half between North America and Asia. Two thirds of the works (67%) were financed compared to a third that were not financed. The percentage of funded works has been increasing over the last decade, which is seen as an indication of the importance of GHGE.

The influence of near-field fluxes on seasonal carbon dioxide enhancements: results from the Indianapolis Flux Experiment (INFLUX)

BACKGROUND

Networks of tower-based CO₂ mole fraction sensors have been deployed by various groups in and around cities across the world to quantify anthropogenic CO₂ emissions from metropolitan areas. A critical aspect in these approaches is the separation of atmospheric signatures from distant sources and sinks (i.e., the background) from local emissions and biogenic fluxes. We examined CO₂ enhancements compared to forested and agricultural background towers in Indianapolis, Indiana, USA, as a function of season and compared them to modeled results, as a part of the Indianapolis Flux (INFLUX) project.

RESULTS

At the INFLUX urban tower sites, daytime growing season enhancement on a monthly timescale was up to 4.3-6.5 ppm, 2.6 times as large as those in the dormant season, on average. The enhancement differed significantly depending on choice of background and time of year, being 2.8 ppm higher in June and 1.8 ppm lower in August using a forested background tower compared to an agricultural background tower. A prediction based on land cover and observed CO₂ fluxes showed that differences in phenology and drawdown intensities drove measured differences in enhancements. Forward modelled CO₂ enhancements using fossil fuel and biogenic fluxes indicated growing season model-data mismatch of 1.1 ± 1.7 ppm for the agricultural background and 2.1 ± 0.5 ppm for the forested background, corresponding to 25-29% of the modelled CO₂ enhancements. The model-data total CO₂ mismatch during the dormant season was low, - 0.1 ± 0.5 ppm.

CONCLUSIONS

Because growing season biogenic fluxes at the background towers are large, the urban enhancements must be disentangled from the biogenic signal, and growing season increases in CO₂ enhancement could be misinterpreted as increased anthropogenic fluxes if the background ecosystem CO₂ drawdown is not considered. The magnitude and timing of enhancements depend on the land cover type and net fluxes surrounding each background tower, so a simple box model is not appropriate for interpretation of these data. Quantification of the seasonality and magnitude of the biological fluxes in the study region using high-resolution and detailed biogenic models is necessary for the interpretation of tower-based urban CO₂ networks for cities with significant vegetation.

The potential of CO2 satellite monitoring for climate governance: A review

Good-quality CO₂ emission data are fundamental for effective climate policy and governance. Data manipulation should be deterred, while developing countries are generally weaker than developed countries in compiling bottom-up CO₂ emission inventories due to less adequate data collection capacity. This paper assesses the capabilities of CO₂ satellites as objective, independent, potentially low-cost and external data sources for monitoring energy-related anthropogenic CO₂ emissions at regional/national, megacity and point-source geographical scales. After overviewing all major CO₂ satellites, SCIAMACHY, GOSAT and OCO-2 are focused on due to their wider research applications and higher CO₂ sensitivity in total column measurements that include near surface emissions. Nighttime light satellite data for proxy CO₂ monitoring are also brought into comparison to distinguish the importance of direct CO₂ satellite monitoring. Studies are reviewed from the perspectives of spatial and temporal capability and accuracy to comprehend the current statuses of applications, assess the strengths and weaknesses of research methods, investigate major challenges and propose suggestions for future progress. We conclude that CO₂ satellite monitoring can strengthen the data foundation for implementing international climate treaties and domestic climate policies.

Constraining Urban CO2 Emissions Using Mobile Observations from a Light Rail Public Transit Platform

Urban environments are characterized by pronounced spatiotemporal heterogeneity, which can present sampling challenges when utilizing conventional greenhouse gas (GHG) measurement systems. In Salt Lake City, Utah, a GHG instrument was deployed on a light rail train car that continuously traverses the Salt Lake Valley (SLV) through a range of urban typologies. CO₂ measurements from a light rail train car were used within a Bayesian inverse modeling framework to constrain urban emissions across the SLV during the fall of 2015. The primary objectives of this study were to (1) evaluate whether ground-based mobile measurements could be used to constrain urban emissions using an inverse modeling framework and (2) quantify the information that mobile observations provided relative to conventional GHG monitoring networks. Preliminary results suggest that ingesting mobile measurements into an inverse modeling framework generated a posterior emission estimate that more closely aligned with observations, reduced posterior emission uncertainties, and extends the geographical extent of emission adjustments.

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.

Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements.

Fluxes of Atmospheric Greenhouse-Gases in Maryland (FLAGG-MD): Emissions of Carbon Dioxide in the Baltimore, MD-Washington, D.C. area

To study emissions of CO2 in the Baltimore, MD-Washington, D.C. (Balt-Wash) area, an aircraft campaign was conducted in February 2015, as part of the FLAGG-MD (Fluxes of Atmospheric Greenhouse-Gases in Maryland) project. During the campaign, elevated mole fractions of CO2 were observed downwind of the urban center and local power plants. Upwind flight data and HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model analyses help account for the impact of emissions outside the Balt-Wash area. The accuracy, precision, and sensitivity of CO2 emissions estimates based on the mass balance approach were assessed for both power plants and cities. Our estimates of CO2 emissions from two local power plants agree well with their CEMS (Continuous Emissions Monitoring Systems) records. For the 16 power plant plumes captured by the aircraft, the mean percentage difference of CO2 emissions was -0.3 %. For the Balt-Wash area as a whole, the 1σ CO2 emission rate uncertainty for any individual aircraft-based mass balance approach experiment was ±38 %. Treating the mass balance experiments, which were repeated seven times within nine days, as individual quantifications of the Balt-Wash CO2 emissions, the estimation uncertainty was ±16 % (standard error of the mean at 95% CL). Our aircraft-based estimate was compared to various bottom-up fossil fuel CO2 (FFCO2) emission inventories. Based on the FLAGG-MD aircraft observations, we estimate 1.9±0.3 MtC of FFCO2 from the Balt-Wash area during the month of February 2015. The mean estimate of FFCO2 from the four bottom-up models was 2.2±0.3 MtC.

Assessing Lagrangian inverse modelling of urban anthropogenic CO2 fluxes using in situ aircraft and ground-based measurements in the Tokyo area

BACKGROUND

In order to use in situ measurements to constrain urban anthropogenic emissions of carbon dioxide (CO2), we use a Lagrangian methodology based on diffusive backward trajectory tracer reconstructions and Bayesian inversion. The observations of atmospheric CO2 were collected within the Tokyo Bay Area during the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) flights, from the Tsukuba tall tower of the Meteorological Research Institute (MRI) of the Japan Meteorological Agency and at two surface sites (Dodaira and Kisai) from the World Data Center for Greenhouse Gases (WDCGG).

RESULTS

We produce gridded estimates of the CO2 emissions and calculate the averages for different areas within the Kanto plain where Tokyo is located. Using these inversions as reference we investigate the impact of perturbing different elements in the inversion system. We modified the observations amount and location (surface only sparse vs. including aircraft CO2 observations), the background representation, the wind data used to drive the transport model, the prior emissions magnitude and time resolution and error parameters of the inverse model.

CONCLUSIONS

Optimized fluxes were consistent with other estimates for the unperturbed simulations. Inclusion of CONTRAIL measurements resulted in significant differences in the magnitude of the retrieved fluxes, 13% on average for the whole domain and of up to 21% for the spatiotemporal cells with the highest fluxes. Changes in the background yielded differences in the retrieved fluxes of up to 50% and more. Simulated biases in the modelled transport cause differences in the retrieved fluxes of up to 30% similar to those obtained using different meteorological winds to advect the Lagrangian trajectories. Perturbations to the prior inventory can impact the fluxes by ~ 10% or more depending on the assumptions on the error covariances. All of these factors can cause significant differences in the estimated flux, and highlight the challenges in estimating regional CO2 fluxes from atmospheric observations.

Synthesis of Urban CO2 Emission Estimates from Multiple Methods from the Indianapolis Flux Project (INFLUX)

Urban areas contribute approximately three-quarters of fossil fuel derived CO₂ emissions, and many cities have enacted emissions mitigation plans. Evaluation of the effectiveness of mitigation efforts will require measurement of both the emission rate and its change over space and time. The relative performance of different emission estimation methods is a critical requirement to support mitigation efforts. Here we compare results of CO₂ emissions estimation methods including an inventory-based method and two different top-down atmospheric measurement approaches implemented for the Indianapolis, Indiana, U.S.A. urban area in winter. By accounting for differences in spatial and temporal coverage, as well as trace gas species measured, we find agreement among the wintertime whole-city fossil fuel CO₂ emission rate estimates to within 7%. This finding represents a major improvement over previous comparisons of urban-scale emissions, making urban CO₂ flux estimates from this study consistent with local and global emission mitigation strategy needs. The complementary application of multiple scientifically driven emissions quantification methods enables and establishes this high level of confidence and demonstrates the strength of the joint implementation of rigorous inventory and atmospheric emissions monitoring approaches.

Estimating vehicle carbon dioxide emissions from Boulder, Colorado, using horizontal path-integrated column measurements

We performed 7.5 weeks of path-integrated concentration measurements of CO2, CH4, H2O, and HDO over the city of Boulder, Colorado. An open-path dual-comb spectrometer simultaneously measured time-resolved data across a reference path, located near the mountains to the west of the city, and across an over-city path that intersected two-thirds of the city, including two major commuter arteries. By comparing the measured concentrations over the two paths when the wind is primarily out of the west, we observe daytime CO2 enhancements over the city. Given the warm weather and the measurement footprint, the dominant contribution to the CO2 enhancement is from city vehicle traffic. We use a Gaussian plume model combined with reported city traffic patterns to estimate city emissions of on-road CO2 as (6.2 ± 2.2) × 105 metric tons (t) CO2 yr-1 after correcting for non-traffic sources. Within the uncertainty, this value agrees with the city's bottom-up greenhouse gas inventory for the on-road vehicle sector of 4.5 × 105 t CO2 yr-1. Finally, we discuss experimental modifications that could lead to improved estimates from our path-integrated measurements.

Anthropogenic CO2 emissions from a megacity in the Yangtze River Delta of China

Anthropogenic CO₂ emissions from cities represent a major source contributing to the global atmospheric CO₂ burden. Here, we examined the enhancement of atmospheric CO₂ mixing ratios by anthropogenic emissions within the Yangtze River Delta (YRD), China, one of the world's most densely populated regions (population greater than 150 million). Tower measurements of CO₂ mixing ratios were conducted from March 2013 to August 2015 and were combined with numerical source footprint modeling to help constrain the anthropogenic CO₂ emissions. We simulated the CO₂ enhancements (i.e., fluctuations superimposed on background values) for winter season (December, January, and February). Overall, we observed mean diurnal variation of CO₂ enhancement of 23.5~49.7 μmol mol^-1, 21.4~52.4 μmol mol^-1, 28.1~55.4 μmol mol^-1, and 29.5~42.4 μmol mol^-1 in spring, summer, autumn, and winter, respectively. These enhancements were much larger than previously reported values for other countries. The diurnal CO₂ enhancements reported here showed strong similarity for all 3 years of the study. Results from source footprint modeling indicated that our tower observations adequately represent emissions from the broader YRD area. Here, the east of Anhui and the west of Jiangsu province contributed significantly more to the anthropogenic CO₂ enhancement compared to the other sectors of YRD. The average anthropogenic CO₂ emission in 2014 was 0.162 (± 0.005) mg m^-2 s^-1 and was 7 ± 3% higher than 2010 for the YRD. Overall, our emission estimates were significantly smaller (9.5%) than those estimated (0.179 mg m^-2 s^-1) from the EDGAR emission database.

Anthropogenic and biogenic CO2 fluxes in the Boston urban region

With the pending withdrawal of the United States from the Paris Climate Accord, cities are now leading US actions toward reducing greenhouse gas emissions. Implementing effective mitigation strategies requires the ability to measure and track emissions over time and at various scales. We report CO2 emissions in the Boston, MA, urban region from September 2013 to December 2014 based on atmospheric observations in an inverse model framework. Continuous atmospheric measurements of CO2 from five sites in and around Boston were combined with a high-resolution bottom-up CO2 emission inventory and a Lagrangian particle dispersion model to determine regional emissions. Our model-measurement framework incorporates emissions estimates from submodels for both anthropogenic and biological CO2 fluxes, and development of a CO2 concentration curtain at the boundary of the study region based on a combination of tower measurements and modeled vertical concentration gradients. We demonstrate that an emission inventory with high spatial and temporal resolution and the inclusion of urban biological fluxes are both essential to accurately modeling annual CO2 fluxes using surface measurement networks. We calculated annual average emissions in the Boston region of 0.92 kg C·m-2·y-1 (95% confidence interval: 0.79 to 1.06), which is 14% higher than the Anthropogenic Carbon Emissions System inventory. Based on the capability of the model-measurement approach demonstrated here, our framework should be able to detect changes in CO2 emissions of greater than 18%, providing stakeholders with critical information to assess mitigation efforts in Boston and surrounding areas.

Emission characteristics of atmospheric carbon dioxide in Xi'an, China based on the measurements of CO2 concentration, △14C and δ13C

Given that cities contributed most of China's CO₂ emissions, understanding the emission characteristics of urban atmospheric CO₂ is critical for regulating CO₂ emissions. Regular observations of atmospheric CO₂ concentration, △¹⁴C and δ¹³C values were performed at four different sites in Xi'an, China in 2016 to illustrate the temporal and spatial variations of CO₂ emissions and recognize their sources and sinks in urban carbon cycles. We found seasonal variations in CO₂ concentration and δ¹³C values, the peak to peak amplitude of which was 80.8ppm for CO₂ concentration and 4.0‰ for its δ¹³C. With regard to the spatial variations, the urban CO₂ "dome" effect was the most pronounced during the winter season. The use of △¹⁴C combines with δ¹³C measurements aid in understanding the emission patterns. The results show that in the winter season, emissions from fossil fuel derived CO₂ (CO_2ff) contributed 61.8±10.6% and 57.4±9.7% of the excess CO₂ (CO_2ex) in urban and suburban areas respectively. Combining with the result of estimated δ¹³C value of fossil fuel (δ¹³C_ff=-24‰), which suggest coal burning was the dominant source of fossil fuel emissions. In contrast, the proportions of CO_2ff in CO_2ex varied more in the summer season than that in the winter season, ranging from 42.3% to >100% with the average contributions of 82.5±23.8% and 90.0±24.8%. Given the estimation of δ¹³C value of local sources (δ¹³C_s) was -21.9‰ indicates that the intensively biogenic activities, such as soil respiration and corn growth have significantly impacted urban carbon cycles, and occasionally played a role of carbon sink.

Long-term urban carbon dioxide observations reveal spatial and temporal dynamics related to urban characteristics and growth

Cities are concentrated areas of CO2 emissions and have become the foci of policies for mitigation actions. However, atmospheric measurement networks suitable for evaluating urban emissions over time are scarce. Here we present a unique long-term (decadal) record of CO2 mole fractions from five sites across Utah's metropolitan Salt Lake Valley. We examine "excess" CO2 above background conditions resulting from local emissions and meteorological conditions. We ascribe CO2 trends to changes in emissions, since we did not find long-term trends in atmospheric mixing proxies. Three contrasting CO2 trends emerged across urban types: negative trends at a residential-industrial site, positive trends at a site surrounded by rapid suburban growth, and relatively constant CO2 over time at multiple sites in the established, residential, and commercial urban core. Analysis of population within the atmospheric footprints of the different sites reveals approximately equal increases in population influencing the observed CO2, implying a nonlinear relationship with CO2 emissions: Population growth in rural areas that experienced suburban development was associated with increasing emissions while population growth in the developed urban core was associated with stable emissions. Four state-of-the-art global-scale emission inventories also have a nonlinear relationship with population density across the city; however, in contrast to our observations, they all have nearly constant emissions over time. Our results indicate that decadal scale changes in urban CO2 emissions are detectable through monitoring networks and constitute a valuable approach to evaluate emission inventories and studies of urban carbon cycles.

Elliptic Cylinder Airborne Sampling and Geostatistical Mass Balance Approach for Quantifying Local Greenhouse Gas Emissions

In this study, we explore observational, experimental, methodological, and practical aspects of the flux quantification of greenhouse gases from local point sources by using in situ airborne observations, and suggest a series of conceptual changes to improve flux estimates. We address the major sources of uncertainty reported in previous studies by modifying (1) the shape of the typical flight path, (2) the modeling of covariance and anisotropy, and (3) the type of interpolation tools used. We show that a cylindrical flight profile offers considerable advantages compared to traditional profiles collected as curtains, although this new approach brings with it the need for a more comprehensive subsequent analysis. The proposed flight pattern design does not require prior knowledge of wind direction and allows for the derivation of an ad hoc empirical correction factor to partially alleviate errors resulting from interpolation and measurement inaccuracies. The modified approach is applied to a use-case for quantifying CH₄ emission from an oil field south of San Ardo, CA, and compared to a bottom-up CH₄ emission estimate.

Tower-Based Greenhouse Gas Measurement Network Design---The National Institute of Standards and Technology North East Corridor Testbed

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

Accounting for urban biogenic fluxes in regional carbon budgets

Many ecosystem models incorrectly treat urban areas as devoid of vegetation and biogenic carbon (C) fluxes. We sought to improve estimates of urban biomass and biogenic C fluxes using existing, nationally available data products. We characterized biogenic influence on urban C cycling throughout Massachusetts, USA using an ecosystem model that integrates improved representation of urban vegetation, growing conditions associated with urban heat island (UHI), and altered urban phenology. Boston's biomass density is 1/4 that of rural forests, however 87% of Massachusetts' urban landscape is vegetated. Model results suggest that, kilogram-for-kilogram, urban vegetation cycles C twice as fast as rural forests. Urban vegetation releases (R_E) and absorbs (GEE) the equivalent of 11 and 14%, respectively, of anthropogenic emissions in the most urban portions of the state. While urban vegetation in Massachusetts fully sequesters anthropogenic emissions from smaller cities in the region, Boston's UHI reduces annual C storage by >20% such that vegetation offsets only 2% of anthropogenic emissions. Asynchrony between temporal patterns of biogenic and anthropogenic C fluxes further constrains the emissions mitigation potential of urban vegetation. However, neglecting to account for biogenic C fluxes in cities can impair efforts to accurately monitor, report, verify, and reduce anthropogenic emissions.

Changes in land use driven by urbanization impact nitrogen cycling and the microbial community composition in soils

Transition of populations from rural to urban living causes landscape changes and alters the functionality of soil ecosystems. It is unclear how this urbanization disturbs the microbial ecology of soils and how the disruption influences nitrogen cycling. In this study, microbial communities in turfgrass-grown soils from urban and suburban areas around Xiamen City were compared to microbial communities in the soils from rural farmlands. The potential N₂O emissions, potential denitrification activity, and abundances of denitrifiers were higher in the rural farmland soils compared with the turfgrass soils. Ammonia oxidizing archaea (AOA) were more abundant than ammonia oxidizing bacteria (AOB) in turfgrass soils. Within turfgrass soils, the potential nitrification activities and AOA abundances were higher in the urban than in the suburban soils. These results indicate a more pivotal role of AOA in nitrification, especially in urban soils. Microbial community composition was distinctly grouped along urbanization categories (urban, suburban, and rural) classified according to the population density, which can in part be attributed to the differences in soil properties. These observed changes could potentially have a broader impact on soil nutrient availability and greenhouse gas emissions.

Inter-seasonal and spatial distribution of ground-level greenhouse gases (CO2, CH4, N2O) over Nagpur in India and their management roadmap

Ground-level concentrations of carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) were monitored over three seasons, i.e., post-monsoon (September-October), winter (January-February), and summer (May-June) for 1 year during 2013-2014 in Nagpur City in India. The selected gases had moderate to high variation both spatially (residential, commercial, traffic intersections, residential cum commercial sites) and temporally (at 7:00, 13:00, 18:00, and 23:00 hours in all three seasons). Concentrations of gases were randomly distributed diurnally over city in all seasons, and there was no specific increasing or decreasing trend with time in a day. Average CO₂ and N₂O concentrations in winter were higher over post-monsoon and summer while CH₄ had highest average concentration in summer. Observed concentrations of CO₂ were predominantly above global average of 400 ppmv while N₂O and CH₄ concentrations frequently dropped down below global average of 327 ppbv and 1.8 ppmv, respectively. Two-tailed Student's t test indicated that post-monsoon CO₂ concentrations were statistically different from summer but not so from winter, while difference between summer and winter concentrations was statistically significant (P < 0.05). CH₄ concentrations in all seasons were statistically at par to each other. In case of N₂O, concentrations in post-monsoon were statistically different from summer but not so from winter, while difference between summer and winter concentrations was statistically significant (P < 0.05). Average ground-level concentrations of the gases calculated for three seasons together were higher in commercial areas. Environmental management priorities vis a vis greenhouse gas emissions in the city are also discussed.

Evaluation and environmental correction of ambient CO2 measurements from a low-cost NDIR sensor

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.

Carbon dioxide and methane measurements from the Los Angeles Megacity Carbon Project - Part 1: calibration, urban enhancements, and uncertainty estimates

We report continuous surface observations of carbon dioxide (CO₂) and methane (CH₄) from the Los Angeles (LA) Megacity Carbon Project during 2015. We devised a calibration strategy, methods for selection of background air masses, calculation of urban enhancements, and a detailed algorithm for estimating uncertainties in urban-scale CO₂ and CH₄ measurements. These methods are essential for understanding carbon fluxes from the LA megacity and other complex urban environments globally. We estimate background mole fractions entering LA using observations from four "extra-urban" sites including two "marine" sites located south of LA in La Jolla (LJO) and offshore on San Clemente Island (SCI), one "continental" site located in Victorville (VIC), in the high desert northeast of LA, and one "continental/mid-troposphere" site located on Mount Wilson (MWO) in the San Gabriel Mountains. We find that a local marine background can be established to within ~1 ppm CO₂ and ~10 ppb CH₄ using these local measurement sites. Overall, atmospheric carbon dioxide and methane levels are highly variable across Los Angeles. "Urban" and "suburban" sites show moderate to large CO₂ and CH₄ enhancements relative to a marine background estimate. The USC (University of Southern California) site near downtown LA exhibits median hourly enhancements of ~20 ppm CO₂ and ~150 ppb CH₄ during 2015 as well as ~15 ppm CO₂ and ~80 ppb CH₄ during mid-afternoon hours (12:00-16:00 LT, local time), which is the typical period of focus for flux inversions. The estimated measurement uncertainty is typically better than 0.1 ppm CO₂ and 1 ppb CH₄ based on the repeated standard gas measurements from the LA sites during the last 2 years, similar to Andrews et al. (2014). The largest component of the measurement uncertainty is due to the single-point calibration method; however, the uncertainty in the background mole fraction is much larger than the measurement uncertainty. The background uncertainty for the marine background estimate is ~10 and ~15 % of the median mid-afternoon enhancement near downtown LA for CO₂ and CH₄, respectively. Overall, analytical and background uncertainties are small relative to the local CO₂ and CH₄ enhancements; however, our results suggest that reducing the uncertainty to less than 5 % of the median mid-afternoon enhancement will require detailed assessment of the impact of meteorology on background conditions.

Carbon dioxide and methane measurements from the Los Angeles Megacity Carbon Project - Part 1: calibration, urban enhancements, and uncertainty estimates

We report continuous surface observations of carbon dioxide (CO2) and methane (CH4) from the Los Angeles (LA) Megacity Carbon Project during 2015. We devised a calibration strategy, methods for selection of background air masses, calculation of urban enhancements, and a detailed algorithm for estimating uncertainties in urban-scale CO2 and CH4 measurements. These methods are essential for understanding carbon fluxes from the LA megacity and other complex urban environments globally. We estimate background mole fractions entering LA using observations from four "extra-urban" sites including two "marine" sites located south of LA in La Jolla (LJO) and offshore on San Clemente Island (SCI), one "continental" site located in Victorville (VIC), in the high desert northeast of LA, and one "continental/mid-troposphere" site located on Mount Wilson (MWO) in the San Gabriel Mountains. We find that a local marine background can be established to within ~1 ppm CO2 and ~10 ppb CH4 using these local measurement sites. Overall, atmospheric carbon dioxide and methane levels are highly variable across Los Angeles. "Urban" and "suburban" sites show moderate to large CO2 and CH4 enhancements relative to a marine background estimate. The USC (University of Southern California) site near downtown LA exhibits median hourly enhancements of ~20 ppm CO2 and ~150 ppb CH4 during 2015 as well as ~15 ppm CO2 and ~80 ppb CH4 during mid-afternoon hours (12:00-16:00 LT, local time), which is the typical period of focus for flux inversions. The estimated measurement uncertainty is typically better than 0.1 ppm CO2 and 1 ppb CH4 based on the repeated standard gas measurements from the LA sites during the last 2 years, similar to Andrews et al. (2014). The largest component of the measurement uncertainty is due to the single-point calibration method; however, the uncertainty in the background mole fraction is much larger than the measurement uncertainty. The background uncertainty for the marine background estimate is ~10 and ~15 % of the median mid-afternoon enhancement near downtown LA for CO2 and CH4, respectively. Overall, analytical and background uncertainties are small relative to the local CO2 and CH4 enhancements; however, our results suggest that reducing the uncertainty to less than 5 % of the median mid-afternoon enhancement will require detailed assessment of the impact of meteorology on background conditions.

Independent evaluation of point source fossil fuel CO2 emissions to better than 10%

Independent estimates of fossil fuel CO2 (CO2ff) emissions are key to ensuring that emission reductions and regulations are effective and provide needed transparency and trust. Point source emissions are a key target because a small number of power plants represent a large portion of total global emissions. Currently, emission rates are known only from self-reported data. Atmospheric observations have the potential to meet the need for independent evaluation, but useful results from this method have been elusive, due to challenges in distinguishing CO2ff emissions from the large and varying CO2 background and in relating atmospheric observations to emission flux rates with high accuracy. Here we use time-integrated observations of the radiocarbon content of CO2 ((14)CO2) to quantify the recently added CO2ff mole fraction at surface sites surrounding a point source. We demonstrate that both fast-growing plant material (grass) and CO2 collected by absorption into sodium hydroxide solution provide excellent time-integrated records of atmospheric (14)CO2 These time-integrated samples allow us to evaluate emissions over a period of days to weeks with only a modest number of measurements. Applying the same time integration in an atmospheric transport model eliminates the need to resolve highly variable short-term turbulence. Together these techniques allow us to independently evaluate point source CO2ff emission rates from atmospheric observations with uncertainties of better than 10%. This uncertainty represents an improvement by a factor of 2 over current bottom-up inventory estimates and previous atmospheric observation estimates and allows reliable independent evaluation of emissions.

Aerosol lidar observations of atmospheric mixing in Los Angeles: Climatology and implications for greenhouse gas observations

Atmospheric observations of greenhouse gases provide essential information on sources and sinks of these key atmospheric constituents. To quantify fluxes from atmospheric observations, representation of transport-especially vertical mixing-is a necessity and often a source of error. We report on remotely sensed profiles of vertical aerosol distribution taken over a 2 year period in Pasadena, California. Using an automated analysis system, we estimate daytime mixing layer depth, achieving high confidence in the afternoon maximum on 51% of days with profiles from a Sigma Space Mini Micropulse LiDAR (MiniMPL) and on 36% of days with a Vaisala CL51 ceilometer. We note that considering ceilometer data on a logarithmic scale, a standard method, introduces, an offset in mixing height retrievals. The mean afternoon maximum mixing height is 770 m Above Ground Level in summer and 670 m in winter, with significant day-to-day variance (within season σ = 220m≈30%). Taking advantage of the MiniMPL's portability, we demonstrate the feasibility of measuring the detailed horizontal structure of the mixing layer by automobile. We compare our observations to planetary boundary layer (PBL) heights from sonde launches, North American regional reanalysis (NARR), and a custom Weather Research and Forecasting (WRF) model developed for greenhouse gas (GHG) monitoring in Los Angeles. NARR and WRF PBL heights at Pasadena are both systematically higher than measured, NARR by 2.5 times; these biases will cause proportional errors in GHG flux estimates using modeled transport. We discuss how sustained lidar observations can be used to reduce flux inversion error by selecting suitable analysis periods, calibrating models, or characterizing bias for correction in post processing.

Greenhouse Gas Molecule CO2 Detection Using a Capacitive Micromachined Ultrasound Transducer

We manufactured and tested a capacitive micromachined ultrasound transducer (CMUT)-based sensor for CO2 detection at environmentally relevant concentrations using polyethylenimine as a CO2 binding material. The assembly of a sensing chip was 10 × 20 mm, and up to 5 gases can potentially be detected simultaneously using a masking technique and different sensing materials. The limit of detection was calculated to be 0.033 CO2 vol % while the limit of quantification was calculated to be 0.102%. The sensor exhibited a linear response between 0.06% and 0.30% CO2 while concentrations close to those in flue gas can also be measured using dilution with inert gas.

High-resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX)

Based on a uniquely dense network of surface towers measuring continuously the atmospheric concentrations of greenhouse gases (GHGs), we developed the first comprehensive monitoring systems of CO₂ emissions at high resolution over the city of Indianapolis. The urban inversion evaluated over the 2012-2013 dormant season showed a statistically significant increase of about 20% (from 4.5 to 5.7 MtC ± 0.23 MtC) compared to the Hestia CO₂ emission estimate, a state-of-the-art building-level emission product. Spatial structures in prior emission errors, mostly undetermined, appeared to affect the spatial pattern in the inverse solution and the total carbon budget over the entire area by up to 15%, while the inverse solution remains fairly insensitive to the CO₂ boundary inflow and to the different prior emissions (i.e., ODIAC). Preceding the surface emission optimization, we improved the atmospheric simulations using a meteorological data assimilation system also informing our Bayesian inversion system through updated observations error variances. Finally, we estimated the uncertainties associated with undetermined parameters using an ensemble of inversions. The total CO₂ emissions based on the ensemble mean and quartiles (5.26-5.91 MtC) were statistically different compared to the prior total emissions (4.1 to 4.5 MtC). Considering the relatively small sensitivity to the different parameters, we conclude that atmospheric inversions are potentially able to constrain the carbon budget of the city, assuming sufficient data to measure the inflow of GHG over the city, but additional information on prior emission error structures are required to determine the spatial structures of urban emissions at high resolution.

Soil respiration contributes substantially to urban carbon fluxes in the greater Boston area

Urban areas are the dominant source of U.S. fossil fuel carbon dioxide (FFCO2) emissions. In the absence of binding international treaties or decisive U.S. federal policy for greenhouse gas regulation, cities have also become leaders in greenhouse gas reduction efforts through climate action plans. These plans focus on anthropogenic carbon flows only, however, ignoring a potentially substantial contribution to atmospheric carbon dioxide (CO2) concentrations from biological respiration. Our aim was to measure the contribution of CO2 efflux from soil respiration to atmospheric CO2 fluxes using an automated CO2 efflux system and to use these measurements to model urban soil CO2 efflux across an urban area. We find that growing season soil respiration is dramatically enhanced in urban areas and represents levels of CO2 efflux of up to 72% of FFCO2 within greater Boston's residential areas, and that soils in urban forests, lawns, and landscaped cover types emit 2.62 ± 0.15, 4.49 ± 0.14, and 6.73 ± 0.26 μmolCO2 m(-2) s(-1), respectively, during the growing season. These rates represent up to 2.2 times greater soil respiration than rates found in nearby rural ecosystems in central Massachusetts (MA), a potential consequence of imported carbon amendments, such as mulch, within a general regime of landowner management. As the scientific community moves rapidly towards monitoring, reporting, and verification of CO2 emissions using ground based approaches and remotely-sensed observations to measure CO2 concentrations, our results show that measurement and modeling of biogenic urban CO2 fluxes will be a critical component for verification of urban climate action plans.

Carbon fluxes from an urban tropical grassland

Turfgrass covers a large fraction of the urbanized landscape, but the carbon exchange of urban lawns is poorly understood. We used eddy covariance and flux chambers in a grassland field manipulative experiment to quantify the carbon mass balance in a Singapore tropical turfgrass. We also assessed how management and variations in environmental factors influenced CO2 respiration. Standing aboveground turfgrass biomass was 80 gC m(-2), with a mean ecosystem respiration of 7.9 ± 1.1 μmol m(-2) s(-1). The contribution of autotrophic respiration was 49-76% of total ecosystem respiration. Both chamber and eddy covariance measurements suggest the system was in approximate carbon balance. While we did not observe a significant relationship between the respiration rates and soil temperature or moisture, daytime fluxes increased during the rainy interval, indicating strong overall moisture sensitivity. Turfgrass biomass is small, but given its abundance across the urban landscape, it significantly influences diurnal CO2 concentrations.

Cities, traffic, and CO2: A multidecadal assessment of trends, drivers, and scaling relationships

Emissions of CO2 from road vehicles were 1.57 billion metric tons in 2012, accounting for 28% of US fossil fuel CO2 emissions, but the spatial distributions of these emissions are highly uncertain. We develop a new emissions inventory, the Database of Road Transportation Emissions (DARTE), which estimates CO2 emitted by US road transport at a resolution of 1 km annually for 1980-2012. DARTE reveals that urban areas are responsible for 80% of on-road emissions growth since 1980 and for 63% of total 2012 emissions. We observe nonlinearities between CO2 emissions and population density at broad spatial/temporal scales, with total on-road CO2 increasing nonlinearly with population density, rapidly up to 1,650 persons per square kilometer and slowly thereafter. Per capita emissions decline as density rises, but at markedly varying rates depending on existing densities. We make use of DARTE's bottom-up construction to highlight the biases associated with the common practice of using population as a linear proxy for disaggregating national- or state-scale emissions. Comparing DARTE with existing downscaled inventories, we find biases of 100% or more in the spatial distribution of urban and rural emissions, largely driven by mismatches between inventory downscaling proxies and the actual spatial patterns of vehicle activity at urban scales. Given cities' dual importance as sources of CO2 and an emerging nexus of climate mitigation initiatives, high-resolution estimates such as DARTE are critical both for accurately quantifying surface carbon fluxes and for verifying the effectiveness of emissions mitigation efforts at urban scales.

Spatial variability of methane: attributing atmospheric concentrations to emissions

Atmospheric methane concentrations were quantified along transects in Switzerland, using a mobile laser spectrometer combined with a GPS, to identify their spatio-temporal patterns and their controlling factors. Based on these measurements in complex terrain dominated by agriculture, three main factors were found to be responsible for the diurnal and regional patterns of atmospheric methane: (1) magnitude and distribution of methane sources within the region, (2) efficiency of vertical exchange, and (3) local wind patterns within the complex topography. An autocorrelation analysis of measured methane concentrations showed that nighttime measurements close to the ground provide information about regional sources (up to 8.3 km), while daytime measurements only carry information about sources located up to 240 m away in the upwind fetch. Compared to daytime concentrations, nighttime methane concentrations do also better reflect emissions obtained from a spatially explicit methane emission inventory and allowed the investigation of inconsistencies in this emission inventory.

Multiscale observations of CO2, 13CO2, and pollutants at Four Corners for emission verification and attribution

There is a pressing need to verify air pollutant and greenhouse gas emissions from anthropogenic fossil energy sources to enforce current and future regulations. We demonstrate the feasibility of using simultaneous remote sensing observations of column abundances of CO2, CO, and NO2 to inform and verify emission inventories. We report, to our knowledge, the first ever simultaneous column enhancements in CO2 (3-10 ppm) and NO2 (1-3 Dobson Units), and evidence of δ(13)CO2 depletion in an urban region with two large coal-fired power plants with distinct scrubbing technologies that have resulted in ∆NOx/∆CO2 emission ratios that differ by a factor of two. Ground-based total atmospheric column trace gas abundances change synchronously and correlate well with simultaneous in situ point measurements during plume interceptions. Emission ratios of ∆NOx/∆CO2 and ∆SO2/∆CO2 derived from in situ atmospheric observations agree with those reported by in-stack monitors. Forward simulations using in-stack emissions agree with remote column CO2 and NO2 plume observations after fine scale adjustments. Both observed and simulated column ∆NO2/∆CO2 ratios indicate that a large fraction (70-75%) of the region is polluted. We demonstrate that the column emission ratios of ∆NO2/∆CO2 can resolve changes from day-to-day variation in sources with distinct emission factors (clean and dirty power plants, urban, and fires). We apportion these sources by using NO2, SO2, and CO as signatures. Our high-frequency remote sensing observations of CO2 and coemitted pollutants offer promise for the verification of power plant emission factors and abatement technologies from ground and space.

AIRS retrieved CO2 and its association with climatic parameters over India during 2004-2011

Atmospheric Infrared Sounder (AIRS) retrieved mid-tropospheric Carbon Dioxide (CO2) have been used to study the variability and its association with the climatic parameters over India during 2004 to 2011. The study also aims in understanding transport of CO2 from surface to mid-troposphere over India. The annual cycle of mid-tropospheric CO2 shows gradual increase in concentration from January till the month of May at the rate ~0.6 ppm/month. It decreases continuously in summer monsoon (JJAS) at the same rate during which strong westerlies persists over the region. A slight increase is seen during winter monsoon (DJF). Being a greenhouse gas, annual cycle of CO2 show good resemblance with annual cycle of surface air temperature with correlation coefficient (CC) of +0.8. Annual cycle of vertical velocity indicate inverse pattern compared to annual cycle of CO2. High values of mid-tropospheric CO2 correspond to upward wind, while low values of mid-tropospheric CO2 correspond to downward wind. In addition to vertical motion, zonal winds are also contributing towards the transport of CO2 from surface to mid-troposphere. Vegetation as it absorbs CO2 at surface level, show inverse annual cycle to that of annual cycle of CO2 (CC-0.64). Seasonal variation of rainfall-CO2 shows similarities with seasonal variation of NDVI-CO2. However, the use of long period data sets for CO2 at the surface and at the mid-troposphere will be an advantage to confirm these results.

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.

A bottom up approach to on-road CO2 emissions estimates: improved spatial accuracy and applications for regional planning

On-road transportation is responsible for 28% of all U.S. fossil-fuel CO2 emissions. Mapping vehicle emissions at regional scales is challenging due to data limitations. Existing emission inventories use spatial proxies such as population and road density to downscale national or state-level data. Such procedures introduce errors where the proxy variables and actual emissions are weakly correlated, and limit analysis of the relationship between emissions and demographic trends at local scales. We develop an on-road emission inventory product for Massachusetts-based on roadway-level traffic data obtained from the Highway Performance Monitoring System (HPMS). We provide annual estimates of on-road CO2 emissions at a 1 × 1 km grid scale for the years 1980 through 2008. We compared our results with on-road emissions estimates from the Emissions Database for Global Atmospheric Research (EDGAR), with the Vulcan Product, and with estimates derived from state fuel consumption statistics reported by the Federal Highway Administration (FHWA). Our model differs from FHWA estimates by less than 8.5% on average, and is within 5% of Vulcan estimates. We found that EDGAR estimates systematically exceed FHWA by an average of 22.8%. Panel regression analysis of per-mile CO2 emissions on population density at the town scale shows a statistically significant correlation that varies systematically in sign and magnitude as population density increases. Population density has a positive correlation with per-mile CO2 emissions for densities below 2000 persons km(-2), above which increasing density correlates negatively with per-mile emissions.

Modeling and validation of on-road CO2 emissions inventories at the urban regional scale

On-road emissions are a major contributor to rising concentrations of atmospheric greenhouse gases. In this study, we applied a downscaling methodology based on commonly available spatial parameters to model on-road CO(2) emissions at the 1 × 1 km scale for the Boston, MA region and tested our approach with surface-level CO(2) observations. Using two previously constructed emissions inventories with differing spatial patterns and underlying data sources, we developed regression models based on impervious surface area and volume-weighted road density that could be scaled to any resolution. We found that the models accurately reflected the inventories at their original scales (R(2) = 0.63 for both models) and exhibited a strong relationship with observed CO(2) mixing ratios when downscaled across the region. Moreover, the improved spatial agreement of the models over the original inventories confirmed that either product represents a viable basis for downscaling in other metropolitan regions, even with limited data.