Anthropogenic and biogenic CO2 fluxes in the Boston urban region

Sustained Reductions of Bay Area CO2 Emissions 2018-2022

Cities represent a significant and growing portion of global carbon dioxide (CO2) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO2 emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO2 Network (BEACO2N), in combination with Bayesian inversions, result in a synthesis of measured CO2 concentrations and meteorology to yield an improved estimate of CO2 emissions and provide a cost-effective and accurate assessment of CO2 emissions trends over time. We describe nearly 5 years of continuous CO2 observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO2 emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.

Mid-infrared trace detection with parts-per-quadrillion quantitation accuracy: Expanding frontiers of radiocarbon sensing

Detection sensitivity is a critical characteristic to consider during selection of spectroscopic techniques. However, high sensitivity alone is insufficient for spectroscopic measurements in spectrally congested regions. Two-color cavity ringdown spectroscopy (2C-CRDS), based on intra-cavity pump-probe detection, simultaneously achieves high detection sensitivity and selectivity. This combination enables mid-infrared detection of radiocarbon dioxide ([Formula: see text]CO[Formula: see text]) molecules in room-temperature CO[Formula: see text] samples, with 1.4 parts-per-quadrillion (ppq, 10[Formula: see text]) sensitivity (average measurement precision) and 4.6-ppq quantitation accuracy (average calibrated measurement error for 21 samples from four separate trials) demonstrated on samples with [Formula: see text]C/C up to [Formula: see text]1.5[Formula: see text] natural abundance ([Formula: see text]1,800 ppq). These highly reproducible measurements, which are the most sensitive and quantitatively accurate in the mid-infrared, are accomplished despite the presence of orders-of-magnitude stronger, one-photon signals from other CO[Formula: see text] isotopologues. This is a major achievement in laser spectroscopy. A room-temperature-operated, compact, and low-cost 2C-CRDS sensor for [Formula: see text]CO[Formula: see text] benefits a wide range of scientific fields that utilize [Formula: see text]C for dating and isotope tracing, most notably atmospheric [Formula: see text]CO[Formula: see text] monitoring to track CO[Formula: see text] emissions from fossil fuels. The 2C-CRDS technique significantly enhances the general utility of high-resolution mid-infrared detection for analytical measurements and fundamental chemical dynamics studies.

The potential of urban irrigation for counteracting carbon-climate feedback

Global climate changes, especially the rise of global mean temperature due to the increased carbon dioxide (CO2) concentration, can, in turn, result in higher anthropogenic and biogenic greenhouse gas emissions. This potentially leads to a positive loop of climate-carbon feedback in the Earth's climate system, which calls for sustainable environmental strategies that can mitigate both heat and carbon emissions, such as urban greening. In this study, we investigate the impact of urban irrigation over green spaces on ambient temperatures and CO2 exchange across major cities in the contiguous United States. Our modeling results indicate that the carbon release from urban ecosystem respiration is reduced by evaporative cooling in humid climate, but promoted in arid/semi-arid regions due to increased soil moisture. The irrigation-induced environmental co-benefit in heat and carbon mitigation is, in general, positively correlated with urban greening fraction and has the potential to help counteract climate-carbon feedback in the built environment.

The impact of urban irrigation on the temperature-carbon feedback in U.S. cities

Urban areas experience numerous environmental challenges, among which the anthropogenic emissions of heat and carbon are two major contributors, the former is responsible for the notorious urban heat effect, the latter longterm climate changes. Moreover, the exchange of heat and carbon dioxide are closely interlinked in the built environment, and can form positive feedback loops that accelerate the degradation of urban environmental quality. Among a handful countermeasures for heat and carbon mitigation, urban irrigation is believed to be effective in cooling, yet the understanding of its impact on the co-evolution of heat and carbon emission remains obscure. In this study, we conducted multiphysics urban climate modeling for all urban areas in the contiguous United States, and evaluated the irrigation-induced cooling and carbon mitigation. Furthermore, we assessed the impact of urban irrigation on the potential heat-carbon feedback loop, with their strength of coupling quantified by an advanced causal inference method using the convergent cross mapping algorithms. It is found that the impact of urban irrigation varies vastly in geographically different cities, with its local and non-local effect unraveling distinct pathways of heat-carbon feedback mechanism.

Anthropogenic CO2 emission reduction during the COVID-19 pandemic in Nanchang City, China

China is the largest CO₂ emitting country on Earth. During the COVID-19 pandemic, China implemented strict government control measures on both outdoor activity and industrial production. These control measures, therefore, were expected to significantly reduce anthropogenic CO₂ emissions. However, large discrepancies still exist in the estimated anthropogenic CO₂ emission reduction rate caused by COVID-19 restrictions, with values ranging from 10% to 40% among different approaches. Here, we selected Nanchang city, located in eastern China, to examine the impact of COVID-19 on CO₂ emissions. Continuous atmospheric CO₂ and ground-level CO observations from January 1st to April 30th, 2019 to 2021 were used with the WRF-STILT atmospheric transport model and a priori emissions. And a multiplicative scaling factor and Bayesian inversion method were applied to constrain anthropogenic CO₂ emissions before, during, and after the COVID-19 pandemic. We found a 37.1-40.2% emission reduction when compared to the COVID-19 pandemic in 2020 with the same period in 2019. Carbon dioxide emissions from the power industry and manufacturing industry decreased by 54.5% and 18.9% during the pandemic period. The power industry accounted for 73.9% of total CO₂ reductions during COVID-19. Further, emissions in 2021 were 14.3-14.9% larger than in 2019, indicating that economic activity quickly recovered to pre-pandemic conditions.

Lagrangian dynamics of contaminant particles released from a point source in New York City

In this study, we investigated the transport of contaminants in the southern tip of Manhattan, New York City, under prevailing wind conditions. We considered a hypothetical contaminant particle release on the East side of the New York Stock Exchange at 50 m above the ground level. The transport of individual particles due to the wind flow in the city was simulated by coupling large-eddy simulations (Eulerian) with a Lagrangian model. The simulation results of our coupled Eulerian and Lagrangian approach showed that immediately after the contaminant particles are released, they propagate downwind and expand in the spanwise direction by ∼0.5 km. Specifically, approximately 15 min after the release, the contaminant particles reach the end of the 2.5-km-long study area with a mean velocity of 1.8 m/s, which is approximately 50% of the dominant wind velocity. With the cessation of the particle release, the contaminant particles start to recede from the urban area, mainly owing to their outflux from the study area and the settling of some particles on solid surfaces in the metropolitan area. More specifically, the study area becomes clear of particles in approximately 48.5 min. It was observed that some particles propagate with a mean velocity of 0.6 m/s, i.e., ∼17% of the dominant wind velocity. We also conducted a detailed investigation of the nature of particle transport patterns using finite-time Lyapunov exponents, which showed that dynamically rich Lagrangian coherent structures are formed around the buildings and off the tops of the skyscrapers.

A decade of CO2 flux measured by the eddy covariance method including the COVID-19 pandemic period in an urban center in Sakai, Japan

Cities constitute an important source of greenhouse gases, but few results originating from long-term, direct CO₂ emission monitoring efforts have been reported. In this study, CO₂ emissions were quasi-continuously measured in an urban center in Sakai, Osaka, Japan by the eddy covariance method from 2010 to 2021. Long-term CO₂ emissions reached 22.2 ± 2.0 kg CO₂ m^-2 yr^-1 from 2010 to 2019 (± denotes the standard deviation) in the western sector from the tower representing the densely built-up area. Throughout the decade, the annual CO₂ emissions remained stable. According to an emission inventory, traffic emissions represented the major source of CO₂ emissions within the flux footprint. The interannual variations in the annual CO₂ flux were positively correlated with the mean annual traffic counts at two highway entrances and exits. The CO₂ emissions decreased suddenly, by 32% ± 3.1%, in April and May 2020 during the period in which the first state of emergency associated with COVID-19 was declared. The annual CO₂ emissions also decreased by 25% ± 3.1% in 2020. Direct long-term observations of CO₂ emissions comprise a useful tool to monitor future emission reductions and sudden disruptions in emissions, such as those beginning in 2020 during the COVID-19 pandemic.

A multi-city urban atmospheric greenhouse gas measurement data synthesis

Urban regions emit a large fraction of anthropogenic emissions of greenhouse gases (GHG) such as carbon dioxide (CO₂) and methane (CH₄) that contribute to modern-day climate change. As such, a growing number of urban policymakers and stakeholders are adopting emission reduction targets and implementing policies to reach those targets. Over the past two decades research teams have established urban GHG monitoring networks to determine how much, where, and why a particular city emits GHGs, and to track changes in emissions over time. Coordination among these efforts has been limited, restricting the scope of analyses and insights. Here we present a harmonized data set synthesizing urban GHG observations from cities with monitoring networks across North America that will facilitate cross-city analyses and address scientific questions that are difficult to address in isolation.

Measurement(s)	carbon dioxide • methane • carbon monoxide
Technology Type(s)	spectroscopy
Sample Characteristic - Environment	city
Sample Characteristic - Location	North America

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

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

Observing Annual Trends in Vehicular CO2 Emissions

Transportation emissions are the largest individual sector of greenhouse gas (GHG) emissions. As such, reducing transportation-related emissions is a primary element of every policy plan to reduce GHG emissions. The Berkeley Environmental Air-quality and CO₂ Observation Network (BEACO₂N) was designed and deployed with the goal of tracking changes in urban CO₂ emissions with high spatial (∼1 km) and temporal (∼1 hr) resolutions while allowing the identification of trends in individual emission sectors. Here, we describe an approach to inferring vehicular CO₂ emissions with sufficient precision to constrain annual trends. Measurements from 26 individual BEACO₂N sites are combined and synthesized within the framework of a Gaussian plume model. After removing signals from biogenic emissions, we are able to report normalized annual emissions for 2018-2020. A reduction of 7.6 ± 3.5% in vehicular CO₂ emissions is inferred for the San Francisco Bay Area over this 2 year period. This result overlaps with, but is slightly larger than, estimates from the 2017 version of the California Air Resources Board EMFAC emissions model, which predicts a 4.7% decrease over these 2 years. This demonstrates the feasibility of independently and rapidly verifying policy-driven reductions in GHG emissions from transportation with atmospheric observations in cities.

Assessing the Effectiveness of an Urban CO2 Monitoring Network over the Paris Region through the COVID-19 Lockdown Natural Experiment

The Paris metropolitan area, the largest urban region in the European Union, has experienced two national COVID-19 confinements in 2020 with different levels of restrictions on mobility and economic activity, which caused reductions in CO₂ emissions. To quantify the timing and magnitude of daily emission reductions during the two lockdowns, we used continuous atmospheric CO₂ monitoring, a new high-resolution near-real-time emission inventory, and an atmospheric Bayesian inverse model. The atmospheric inversion estimated the changes in fossil fuel CO₂ emissions over the Greater Paris region during the two lockdowns, in comparison with the same periods in 2018 and 2019. It shows decreases by 42-53% during the first lockdown with stringent measures and by only 20% during the second lockdown when traffic reduction was weaker. Both lockdown emission reductions are mainly due to decreases in traffic. These results are consistent with independent estimates based on activity data made by the city environmental agency. We also show that unusual persistent anticyclonic weather patterns with north-easterly winds that prevailed at the start of the first lockdown period contributed a substantial drop in measured CO₂ concentration enhancements over Paris, superimposed on the reduction of urban CO₂ emissions. We conclude that atmospheric CO₂ monitoring makes it possible to identify significant emission changes (>20%) at subannual time scales over an urban region.

Monthly direct and indirect greenhouse gases emissions from household consumption in the major Japanese cities

Urban household consumption contributes substantially to global greenhouse gases (GHGs) emissions. Urban household emissions encompass both direct and indirect emissions, with the former associated with the direct use of fossil fuels and the latter with the emissions embodied in the consumed goods and services. However, there is a lack of consistent and comprehensive datasets outlining in great detail emissions from urban household consumption. To bridge this data gap, we construct an emission inventory of urban household emissions for 52 major cities in Japan that covers around 500 emission categories. The dataset spans from January 2011 to December 2015 and contains 12,384 data records for direct emissions and 1,543,128 records for indirect emissions. Direct emission intensity is provided in g-CO2/JPY to facilitate both future studies of household emission in Japan, as well as act as a reference for the development of detailed household emission inventories in other countries.

Majority of US urban natural gas emissions unaccounted for in inventories

Across many cities, estimates of methane emissions from natural gas (NG) distribution and end use based on atmospheric measurements have generally been more than double bottom-up estimates. We present a top-down study of NG methane emissions from the Boston urban region spanning 8 y (2012 to 2020) to assess total emissions, their seasonality, and trends. We used methane and ethane observations from five sites in and around Boston, combined with a high-resolution transport model, to calculate methane emissions of 76 ± 18 Gg/yr, with 49 ± 9 Gg/yr attributed to NG losses. We found no significant trend in the NG loss rate over 8 y, despite efforts from the city and state to increase the rate of repairing NG pipeline leaks. We estimate that 2.5 ± 0.5% of the gas entering the urban region is lost, approximately three times higher than bottom-up estimates. We saw a strong correlation between top-down NG emissions and NG consumed on a seasonal basis. This suggests that consumption-driven losses, such as in transmission or end-use, may be a large component of emissions that is missing from inventories, and require future policy action. We also compared top-down NG emission estimates from six US cities, all of which indicate significant missing sources in bottom-up inventories. Across these cities, we estimate NG losses from distribution and end use amount to 20 to 36% of all losses from the US NG supply chain, with a total loss rate of 3.3 to 4.7% of NG from well pad to urban consumer, notably larger than the current Environmental Protection Agency estimate of 1.4% [R. A. Alvarez et al., Science 361, 186-188 (2018)].

Environmental co-benefits of urban greening for mitigating heat and carbon emissions

Urban greening has been as a popular and effective strategy for ameliorating urban thermal environment and air quality. Nevertheless, it remains an outstanding challenge for numerical urban models to disentangle and quantify the complex interplay between heat and carbon dynamics. In this study, we used a newly developed coupled urban canopy-carbon dynamics model to investigate the environmental co-benefits for mitigating urban heat stress as well as the reduction of carbon dioxide (CO₂) emission. In particular, we evaluated the impact of specific components of urban greening, viz. fraction of the urban lawn, bare soil, tree coverage, and irrigation on heat and carbon fluxes in the built environment. The results of numerical simulations show that the expansion of urban green space, in general, leads to environmental cooling and reduced CO₂ emission, albeit the efficacy varies for different vegetation types. In addition, adequate irrigation is essential to effect plant physiological functions for cooling and CO₂ uptake, whereas further improvement becomes marginal with excessive irrigation. The findings of this study, along with its implications on environmental management, will help to promote sustainable urban development strategies for achieving desirable environmental co-benefits for urban residents and practitioners.

A Satellite-Based Model for Estimating Latent Heat Flux From Urban Vegetation

The impacts of extreme heat events are amplified in cities due to unique urban thermal properties. Urban greenspace mitigates high temperatures through evapotranspiration and shading; however, quantification of vegetative cooling potential in cities is often limited to simple remote sensing greenness indices or sparse, in situ measurements. Here, we develop a spatially explicit, high-resolution model of urban latent heat flux from vegetation. The model iterates through three core equations that consider urban climatological and physiological characteristics, producing estimates of latent heat flux at 30-m spatial resolution and hourly temporal resolution. We find strong agreement between field observations and model estimates of latent heat flux across a range of ecosystem types, including cities. This model introduces a valuable tool to quantify the spatial heterogeneity of vegetation cooling benefits across the complex landscape of cities at an adequate resolution to inform policies addressing the effects of extreme heat events.

Transboundary sources dominated PM2.5 in Thimphu, Bhutan

This study estimates the potential source regions contributing to PM_2.5 in the capital city of Thimphu, Bhutan, during the years 2018-2020 using the ground-based data, followed by the HYSPLIT back trajectory analysis. The average PM_2.5 concentration in the entire study period was 32.47 µg/m³ which is three times of the World Health Organization recommended limit of 10 µg/m³. Less than half of the days in pre-monsoon (43.47%) and post-monsoon (46.41%), and no days in winter were within the 24-h average WHO guideline of 25 μg/m³. During the COVID-19 lockdown imposed from August 11 to September 21 in Bhutan, only a marginal reduction of 4% in the PM_2.5 concentrations was observed, indicating that nonlocal emissions dominate the PM_2.5 concentrations in Thimphu, Bhutan. Most back trajectories in the analysis period were allocated to south or south-west sector. India was the major contributor (~ 44%), followed by Bangladesh (~ 19%), Bhutan itself (~ 19%) and China (~ 16%). This study confirms that there are significant contributions from transboundary sources to PM_2.5 concentrations in Thimphu, Bhutan, and the elevated PM_2.5 concentrations need to be tackled with appropriate action plans and interventions.

Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories

The concentration of atmospheric carbon dioxide (CO2) has increased rapidly worldwide, aggravating the global greenhouse effect, and coal-fired power plants are one of the biggest contributors of greenhouse gas emissions in China. However, efficient methods that can quantify CO2 emissions from individual coal-fired power plants with high accuracy are needed. In this study, we estimated the CO2 emissions of large-scale coal-fired power plants using Orbiting Carbon Observatory-2 (OCO-2) satellite data based on remote sensing inversions and bottom-up methods. First, we mapped the distribution of coal-fired power plants, displaying the total installed capacity, and identified two appropriate targets, the Waigaoqiao and Qinbei power plants in Shanghai and Henan, respectively. Then, an improved Gaussian plume model method was applied for CO2 emission estimations, with input parameters including the geographic coordinates of point sources, wind vectors from the atmospheric reanalysis of the global climate, and OCO-2 observations. The application of the Gaussian model was improved by using wind data with higher temporal and spatial resolutions, employing the physically based unit conversion method, and interpolating OCO-2 observations into different resolutions. Consequently, CO2 emissions were estimated to be 23.06 ± 2.82 (95% CI) Mt/yr using the Gaussian model and 16.28 Mt/yr using the bottom-up method for the Waigaoqiao Power Plant, and 14.58 ± 3.37 (95% CI) and 14.08 Mt/yr for the Qinbei Power Plant, respectively. These estimates were compared with three standard databases for validation: the Carbon Monitoring for Action database, the China coal-fired Power Plant Emissions Database, and the Carbon Brief database. The comparison found that previous emission inventories spanning different time frames might have overestimated the CO2 emissions of one of two Chinese power plants on the two days that the measurements were made. Our study contributes to quantifying CO2 emissions from point sources and helps in advancing satellite-based monitoring techniques of emission sources in the future; this helps in reducing errors due to human intervention in bottom-up statistical methods.

The Impact of COVID-19 on CO2 Emissions in the Los Angeles and Washington DC/Baltimore Metropolitan Areas

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course-correct CO2 reduction activities efficiently.

Declining Oxygen Level as an Emerging Concern to Global Cities

Rising CO₂ concentration and temperatures in urban areas are now well-known, but the potential of an emerging oxygen crisis in the world's large cities has so far attracted little attention from the science community. Here, we investigated the oxygen balance and its related risks in 391 global large cities (with a population of more than 1 million people) using the oxygen index (O_I), which is the ratio of oxygen consumption to oxygen production. Our results show that the global urban areas, occupying only 3.8% of the global land surface, accounted for 39% (14.3 ± 1.5 Gt/yr) of the global terrestrial oxygen consumption during 2001-2015. We estimated that 75% of cities with a population more than 5 million had an O_I of greater than 100. Also, cities with larger O_I values were correlated with more frequent heatwaves and severe water withdrawals. In addition, cities with excessively large O_I values would likely experience severe hypoxia in extremely calm weather. Thus, mitigation measures should be adopted to reduce the urban O_I in order to build healthier and more sustainable cities.

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.

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.

Influence of landscape management practices on urban greenhouse gas budgets

BACKGROUND

With a lack of United States federal policy to address climate change, cities, the private sector, and universities have shouldered much of the work to reduce carbon dioxide (CO₂) and other greenhouse gas emissions. This study aims to determine how landcover characteristics influence the amount of carbon (C) sequestered and respired via biological processes, evaluating the role of land management on the overall C budget of an urban university. Boston University published a comprehensive Climate Action Plan in 2017 with the goal of achieving C neutrality by 2040. In this study, we digitized and discretized each of Boston University's three urban campuses into landcover types, with C sequestration and respiration rates measured and scaled to provide a University-wide estimate of biogenic C fluxes within the broader context of total University emissions.

RESULTS

Each of Boston University's three highly urban campuses were net sources of biogenic C to the atmosphere. While trees were estimated to sequester 0.6 ± 0.2 kg C m^-2 canopy cover year^-1, mulch and lawn areas in 2018 emitted C at rates of 1.7 ± 0.4 kg C m^-2 year^-1 and 1.4 ± 0.4 kg C m^-2 year^-1, respectively. C uptake by tree canopy cover, which can spatially overlap lawn and mulched landcovers, was not large enough to offset biogenic emissions. The proportion of biogenic emissions to Scope 1 anthropogenic emissions on each campus varied from 0.5% to 2%, and depended primarily on the total anthropogenic emissions on each campus.

CONCLUSIONS

Our study quantifies the role of urban landcover in local C budgets, offering insights on how landscaping management strategies-such as decreasing mulch application rates and expanding tree canopy extent-can assist universities in minimizing biogenic C emissions and even potentially creating a small biogenic C sink. Although biogenic C fluxes represent a small fraction of overall anthropogenic emissions on urban university campuses, these biogenic fluxes are under active management by the university and should be included in climate action plans.

Background conditions for an urban greenhouse gas network in the Washington, D.C. and Baltimore metropolitan region

As city governments take steps towards establishing emissions reduction targets, the atmospheric research community is increasingly able to assist in tracking emissions reductions. Researchers have established systems for observing atmospheric greenhouse gases in urban areas with the aim of attributing greenhouse gas concentration enhancements (and thus, emissions) to the region in question. However, to attribute enhancements to a particular region, one must isolate the component of the observed concentration attributable to fluxes inside the region by removing the background, which is the component due to fluxes outside. In this study, we demonstrate methods to construct several versions of a background for our carbon dioxide and methane observing network in the Washington, DC and Baltimore, MD metropolitan region. Some of these versions rely on transport and flux models, while others are based on observations upwind of the domain. First, we evaluate the backgrounds in a synthetic data framework, then we evaluate against real observations from our urban network. We find that backgrounds based on upwind observations capture the variability better than model-based backgrounds, although care must be taken to avoid bias from biospheric carbon dioxide fluxes near background stations in summer. Model-based backgrounds also perform well when upwind fluxes can be modeled accurately. Our study evaluates different background methods and provides guidance determining background methodology that can impact the design of urban monitoring networks.

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.

Policy-Relevant Assessment of Urban CO2 Emissions

Global fossil fuel carbon dioxide (FFCO₂) emissions will be dictated to a great degree by the trajectory of emissions from urban areas. Conventional methods to quantify urban FFCO₂ emissions typically rely on self-reported economic/energy activity data transformed into emissions via standard emission factors. However, uncertainties in these traditional methods pose a roadblock to implementation of effective mitigation strategies, independently monitor long-term trends, and assess policy outcomes. Here, we demonstrate the applicability of the integration of a dense network of greenhouse gas sensors with a science-driven building and street-scale FFCO₂ emissions estimation through the atmospheric CO₂ inversion process. Whole-city FFCO₂ emissions agree within 3% annually. Current self-reported inventory emissions for the city of Indianapolis are 35% lower than our optimal estimate, with significant differences across activity sectors. Differences remain, however, regarding the spatial distribution of sectoral FFCO₂ emissions, underconstrained despite the inclusion of coemitted species information.

Toward Accurate, Policy-Relevant Fossil Fuel CO2 Emission Landscapes

The bottom-up (BU) approach has been used to develop spatiotemporally resolved, sectorally disaggregated fossil fuel CO₂ (FFCO₂) emission data products. These efforts are critical constraints to atmospheric assessment of anthropogenic fluxes in addition to offering the climate change policymaking community usable information to guide mitigation. In the United States, there are two high-resolution FFCO₂ emission data products, Vulcan and the Anthropogenic Carbon Emissions System (ACES). As a step toward developing improved, accurate, and detailed FFCO₂ emission landscapes, we perform a comparison of the two data products. We find that while agreeing on total FFCO₂ emissions at the aggregate scale (relative difference = 1.7%), larger differences occur at smaller spatial scales and in individual sectors. Differences in the smaller-emitting sectors are likely errors in ACES input data or emission factors. ACES advances the approach for estimating emissions in the gas and oil sector, while Vulcan shows better geocoordinate correction in the electricity production sector. Differences in the subcounty residential and commercial building sectors are driven by different spatial proxies and suggest a task for future investigation. The gridcell absolute median relative difference, a measure of the average gridcell-scale relative difference, indicates a 53.5% difference. The recommendation for improved BU granular FFCO₂ emission estimation includes review, assessment, and archive of point source geolocations, CO emission input data, CO and CO₂ emission factors, and uncertainty approaches including those due to spatial errors. Finally, intensives where local utility data are publicly available could test the spatial proxies used in estimating residential and commercial building emissions. These steps toward best practices will lead to more accurate, granular emissions, enabling optimal emission mitigation policy choices.

Japan prefectural emission accounts and socioeconomic data 2007 to 2015

In the wake of the Fukushima nuclear disaster, Japan largely moved away from nuclear power generation and turned back towards an energy sector dominated by fossil fuels. As a result, the pace towards reaching emission reduction targets has largely slowed down. This situation indicates that higher emissions will continue to be generated if there is no appropriate and efficient measurement implemented to bridge the energy demand gap. To contribute adequate mitigation policies, a detailed inventory of both CO₂ emissions and socioeconomic factors, both at the national and regional level, should be issued. Thereby, this work contributes to a time-series emission with a record of 47 prefectures in Japan as well as their associated socioeconomic features. The compiled emission inventory is based on three major fossil fuels and 26 sectors with careful emission allocations for regional electricity generation. This dataset is uniformly formatted and can be expected to provide vital information to set regional reduction allowances and sectoral reduction priorities.

Enhanced regional terrestrial carbon uptake over Korea revealed by atmospheric CO2 measurements from 1999 to 2017

Understanding changes in terrestrial carbon balance is important to improve our knowledge of the regional carbon cycle and climate change. However, evaluating regional changes in the terrestrial carbon balance is challenging due to the lack of surface flux measurements. This study reveals that the terrestrial carbon uptake over the Republic of Korea has been enhanced from 1999 to 2017 by analyzing long-term atmospheric CO₂ concentration measurements at the Anmyeondo Station (36.53°N, 126.32°E) located in the western coast. The influence of terrestrial carbon flux on atmospheric CO₂ concentrations (ΔCO₂ ) is estimated from the difference of CO₂ concentrations that were influenced by the land sector (through easterly winds) and the Yellow Sea sector (through westerly winds). We find a significant trend in ΔCO₂ of -4.75 ppm per decade (p < .05) during the vegetation growing season (May through October), suggesting that the regional terrestrial carbon uptake has increased relative to the surrounding ocean areas. Combined analysis with satellite measured normalized difference vegetation index and gross primary production shows that the enhanced carbon uptake is associated with significant nationwide increases in vegetation and its production. Process-based terrestrial model and inverse model simulations estimate that regional terrestrial carbon uptake increases by up to 18.9 and 8.0 Tg C for the study period, accounting for 13.4% and 5.7% of the average annual domestic carbon emissions, respectively. Atmospheric chemical transport model simulations indicate that the enhanced terrestrial carbon sink is the primary reason for the observed ΔCO₂ trend rather than anthropogenic emissions and atmospheric circulation changes. Our results highlight the fact that atmospheric CO₂ measurements could open up the possibility of detecting regional changes in the terrestrial carbon cycle even where anthropogenic emissions are not negligible.

Current and future biomass carbon uptake in Boston's urban forest

Ecosystem services provided by urban forests are increasingly included in municipal-level responses to climate change. However, the ecosystem functions that generate these services, such as biomass carbon (C) uptake, can differ substantially from nearby rural forest. In particular, the scaled effect of canopy spatial configuration on tree growth in cities is uncertain, as is the scope for medium-term policy intervention. This study integrates high spatial resolution data on tree canopy and biomass in the city of Boston, Massachusetts, with local measurements of tree growth rates to estimate the magnitude and distribution of annual biomass C uptake. We further project C uptake, biomass, and canopy cover change to 2040 under alternative policy scenarios affecting the planting and preservation of urban trees. Our analysis shows that 85% of tree canopy area was within 10 m of an edge, indicating essentially open growing conditions. Using growth models accounting for canopy edge effects and growth context, Boston's current biomass C uptake may be approximately double (median 10.9 GgC yr^-1, 0.5 MgC ha^-1 yr^-1) the estimates based on rural forest growth, much of it occurring in high-density residential areas. Total annual C uptake to long-term biomass storage was equivalent to <1% of estimated annual fossil CO₂ emissions for the city. In built-up areas, reducing mortality in larger trees resulted in the highest predicted increase in canopy cover (+25%) and biomass C stocks (236 GgC) by 2040, while planting trees in available road margins resulted in the greatest predicted annual C uptake (7.1 GgC yr^-1). This study highlights the importance of accounting for the altered ecosystem structure and function in urban areas in evaluating ecosystem services. Effective municipal climate responses should consider the substantial fraction of total services performed by trees in developed areas, which may produce strong but localized atmospheric C sinks.

Wintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC-Baltimore Metropolitan Area Using an Inverse Modeling Technique

Since greenhouse gas mitigation efforts are mostly being implemented in cities, the ability to quantify emission trends for urban environments is of paramount importance. However, previous aircraft work has indicated large daily variability in the results. Here we use measurements of CO2, CH4, and CO from aircraft over 5 days within an inverse model to estimate emissions from the DC-Baltimore region. Results show good agreement with previous estimates in the area for all three gases. However, aliasing caused by irregular spatiotemporal sampling of emissions is shown to significantly impact both the emissions estimates and their variability. Extensive sensitivity tests allow us to quantify the contributions of different sources of variability and indicate that daily variability in posterior emissions estimates is larger than the uncertainty attributed to the method itself (i.e., 17% for CO2, 24% for CH4, and 13% for CO). Analysis of hourly reported emissions from power plants and traffic counts shows that 97% of the daily variability in posterior emissions estimates is explained by accounting for the sampling in time and space of sources that have large hourly variability and, thus, caution must be taken in properly interpreting variability that is caused by irregular spatiotemporal sampling conditions.

Urban land cover type determines the sensitivity of carbon dioxide fluxes to precipitation in Phoenix, Arizona

Urbanization modifies land surface characteristics with consequent impacts on local energy, water, and carbon dioxide (CO2) fluxes. Despite the disproportionate impact of cities on CO2 emissions, few studies have directly quantified CO2 conditions for different urban land cover patches, in particular for arid and semiarid regions. Here, we present a comparison of eddy covariance measurements of CO2 fluxes (FC) and CO2 concentrations ([CO2]) in four distinct urban patches in Phoenix, Arizona: a xeric landscaping, a parking lot, a mesic landscaping, and a suburban neighborhood. Analyses of diurnal, daily, and seasonal variations of FC and [CO2] were related to vegetation activity, vehicular traffic counts, and precipitation events to quantify differences among sites in relation to their urban land cover characteristics. We found that the mesic landscaping with irrigated turf grass was primarily controlled by plant photosynthetic activity, while the parking lot in close proximity to roads mainly exhibited the signature of vehicular emissions. The other two sites that had mixtures of irrigated vegetation and urban surfaces displayed an intermediate behavior in terms of CO2 fluxes. Precipitation events only impacted FC in urban patches without outdoor water use, indicating that urban irrigation decouples CO2 fluxes from the effects of infrequent storms in an arid climate. These findings suggest that the proportion of irrigated vegetation and urban surfaces fractions within urban patches could be used to scale up CO2 fluxes to a broader city footprint.

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.

Greenhouse gas observations from the Northeast Corridor tower network

We present the organization, structure, instrumentation, and measurements of the Northeast Corridor greenhouse gas observation network. This network of tower-based in situ carbon dioxide and methane observation stations was established in 2015 with the goal of quantifying emissions of these gases in urban areas in the northeastern United States. A specific focus of the network is the cities of Baltimore, MD, and Washington, DC, USA, with a high density of observation stations in these two urban areas. Additional observation stations are scattered throughout the northeastern US, established to complement other existing urban and regional networks and to investigate emissions throughout this complex region with a high population density and multiple metropolitan areas. Data described in this paper are archived at the National Institute of Standards and Technology and can be found at https://doi.org/10.18434/M32126 (Karion et al., 2019).

Greenhouse gas observations from the Northeast Corridor tower network

We present the organization, structure, instrumentation, and measurements of the Northeast Corridor greenhouse gas observation network. This network of tower-based in situ carbon dioxide and methane observation stations was established in 2015 with the goal of quantifying emissions of these gases in urban areas in the northeastern United States. A specific focus of the network is the cities of Baltimore, MD, and Washington, DC, USA, with a high density of observation stations in these two urban areas. Additional observation stations are scattered throughout the northeastern US, established to complement other existing urban and regional networks and to investigate emissions throughout this complex region with a high population density and multiple metropolitan areas. Data described in this paper are archived at the National Institute of Standards and Technology and can be found at https://doi.org/10.18434/M32126 (Karion et al., 2019).

Exploiting OMI NO2 satellite observations to infer fossil-fuel CO2 emissions from U.S. megacities

Fossil-fuel CO₂ emissions and their trends in eight U.S. megacities during 2006-2017 are inferred by combining satellite-derived NO_X emissions with bottom-up city-specific NO_X-to-CO₂ emission ratios. A statistical model is fit to a collection NO₂ plumes observed from the Ozone Monitoring Instrument (OMI), and is used to calculate top-down NO_X emissions. Decreases in OMI-derived NO_X emissions are observed across the eight cities from 2006 to 2017 (-17% in Miami to -58% in Los Angeles), and are generally consistent with long-term trends of bottom-up inventories (-25% in Miami to -49% in Los Angeles), but there are some interannual discrepancies. City-specific NO_X-to-CO₂ emission ratios, used to calculate inferred CO₂, are estimated through annual bottom-up inventories of NO_X and CO₂ emissions disaggregated to 1 × 1 km² resolution. Over the study period, NO_X-to-CO₂ emission ratios have decreased by ~40% nationwide (-24% to -51% for our studied cities), which is attributed to a faster reduction in NO_X when compared to CO₂ due to policy regulations and fuel type shifts. Combining top-down NO_X emissions and bottom-up NO_X-to-CO₂ emission ratios, annual fossil-fuel CO₂ emissions are derived. Inferred OMI-based top-down CO₂ emissions trends vary between +7% in Dallas to -31% in Phoenix. For 2017, we report annual fossil-fuel CO₂ emissions to be: Los Angeles 113 ± 49 Tg/yr; New York City 144 ± 62 Tg/yr; and Chicago 55 ± 24 Tg/yr. A study in the Los Angeles area, using independent methods, reported a 2013-2016 average CO₂ emissions rate of 104 Tg/yr and 120 Tg/yr, which suggests that the CO₂ emissions from our method are in good agreement with other studies' top-down estimates. We anticipate future remote sensing instruments - with better spatial and temporal resolution - will better constrain the NO_X-to-CO₂ ratio and reduce the uncertainty in our method.

Using Lidar Technology To Assess Urban Air Pollution and Improve Estimates of Greenhouse Gas Emissions in Boston

Simulation of the planetary boundary layer (PBL) is key for forecasting air quality and estimating greenhouse gas (GHG) emissions in cities. Here we conducted the first long-term and continuous study of PBL heights (PBLHs) in Boston, MA, using a compact lidar instrument. We developed an image recognition algorithm to estimate PBLHs from the lidar measurements and evaluated simulations of the PBL from seven numerical weather prediction (NWP) model versions, which showed different systematic errors and variability in simulating the PBLHs (discrepancies from -2.5 to 4.0 km). The NWP model with the best overall agreement for the fully developed PBL had R² = 0.72 and a bias of only 0.128 km. However, this model predicted a notable number of anomalously high carbon dioxide concentrations at ground stations, because it occasionally significantly underestimated the PBLH. We also developed a novel method that combines lidar data with footprints from a Lagrangian particle dispersion model to identify long-range transport of air pollution in the nocturnal residual layer. Our framework was powerful in evaluating the performance of models used to estimate air pollution and GHG emissions in cities, which is critical to track progress on emission reduction targets and guide effective policies.

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.