Urban high-resolution fossil fuel CO2 emissions quantification and exploration of emission drivers for potential policy applications

A Long-Term Comparison between the AethLabs MA350 and Aerosol Magee Scientific AE33 Black Carbon Monitors in the Greater Salt Lake City Metropolitan Area

Black carbon (BC) or soot contains ultrafine combustion particles that are associated with a wide range of health impacts, leading to respiratory and cardiovascular diseases. Both long-term and short-term health impacts of BC have been documented, with even low-level exposures to BC resulting in negative health outcomes for vulnerable groups. Two aethalometers-AethLabs MA350 and Aerosol Magee Scientific AE33-were co-located at a Utah Division of Air Quality site in Bountiful, Utah for just under a year. The aethalometer comparison showed a close relationship between instruments for IR BC, Blue BC, and fossil fuel source-specific BC estimates. The biomass source-specific BC estimates were markedly different between instruments at the minute and hour scale but became more similar and perhaps less-affected by high-leverage outliers at the daily time scale. The greater inter-device difference for biomass BC may have been confounded by very low biomass-specific BC concentrations during the study period. These findings at a mountainous, high-elevation, Greater Salt Lake City Area site support previous study results and broaden the body of evidence validating the performance of the MA350.

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

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

Indoor environmental quality in naturally ventilated schools of a dusty region: Excess health risks and effect of heating and desert dust transport

Indoor air quality (IAQ) is impacted by polluted outdoor air in naturally ventilated schools, especially in places where both anthropogenic and natural sources of ambient air pollution exist. CO₂ , PM_2.5 , PM₁₀ , temperature, relative humidity (RH), and noise were measured in five naturally ventilated primary schools in City of Sanliurfa, in a dusty region of Turkey, Southeast Anatolia. Excess risk levels were estimated for particulate matter. Investigation was conducted through an educational year including two seasons in terms of anthropogenic effect, that is, heating/non-heating, and natural effect, that is, desert dust transport/non-dust transport. The median CO₂ concentration was measured to be >1000 ppm in all seasons/schools. Temperature and RH fell out of the comfort zone in October-December, during which pollutant concentrations were considerably increased, specifically in November, that heating and dust transport periods coincide. The overall mean indoor PM₁₀ and PM_2.5 levels were 58 and 31.8 μg/m³ , respectively. Risk assessment indicate that both short (incidence of asthma symptoms in asthmatic children) and long-term (prevalence of bronchitis) effects are considerable with 10.9 (2.4-19.6)% and 19.5 (2.2-38.8)%, respectively. The findings suggest that mechanical ventilation retrofitting with particle filtration is needed to mitigate potential negative health consequences on children.

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

Finding successful strategies in a complex urban sustainability game

The adverse effects of unsustainable behaviors on human society are leading to an increasingly urgent and critical need to change policies and practices worldwide. This requires that citizens become informed and engaged in participatory governance and measures leading to sustainable futures. Citizens’ understanding of the inherent complexity of sustainable systems is a necessary (though generally not sufficient) ingredient for them to understand controversial public policies and maintain the core principles of democratic societies. In this work, we present a novel, open-ended experiment where individuals had the opportunity to solve model urban sustainability problems in a purposeful game. Participants were challenged to interact with familiar LEGO blocks representing elements in a complex generative urban economic indicators model. Players seeks to find a specific urban configuration satisfying particular sustainability requirements. We show that, despite the intrinsic complexity and non-linearity of the problems, participants’ ability to make counter-intuitive actions helps them find suitable solutions. Moreover, we show that through successive iterations of the experiment, participants can overcome the difficulties linked to non-linearity and increase the probability of finding the correct solution to the problem. We contend that this kind of what-if platforms could have a crucial role in future approaches to sustainable developments goals.

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.

The Vulcan Version 3.0 High-Resolution Fossil Fuel CO2 Emissions for the United States

Estimates of high-resolution greenhouse gas (GHG) emissions have become a critical component of climate change research and an aid to decision makers considering GHG mitigation opportunities. The "Vulcan Project" is an effort to estimate bottom-up carbon dioxide emissions from fossil fuel combustion and cement production (FFCO₂) for the U.S. landscape at space and time scales that satisfy both scientific and policy needs. Here, we report on the Vulcan version 3.0 which quantifies emissions at a resolution of 1 km²/hr for the 2010-2015 time period. We estimate 2011 FFCO₂ emissions of 1,589.9 TgC with a 95% confidence interval of 1,367/1,853 TgC (-14.0%/+16.6%), implying a one-sigma uncertainty of ~ ±8%. Per capita emissions are larger in states dominated by electricity production and industrial activity and smaller where onroad and building emissions dominate. The U.S. FFCO₂ emissions center of mass (CoM) is located in the state of Missouri with mean seasonality that moves on a near-elliptical NE/SW path. Comparison to ODIAC, a global gridded FFCO₂ emissions estimate, shows large total emissions differences (100.4 TgC for year 2011), a spatial correlation of 0.68 (R²), and a mean absolute relative difference at the 1 km² scale of 104.3%. The Vulcan data product offers a high-resolution estimate of FFCO₂ emissions in every U.S. city, obviating costly development of self-reported urban inventories. The Vulcan v3.0 annual gridded emissions data product can be downloaded from the Oak Ridge National Laboratory Distributed Active Archive Center (Gurney, Liang, et al., 2019, https://doi.org/10.3334/ORNLDAAC/1741).

The Vulcan Version 3.0 High-Resolution Fossil Fuel CO2 Emissions for the United States

Estimates of high-resolution greenhouse gas (GHG) emissions have become a critical component of climate change research and an aid to decision makers considering GHG mitigation opportunities. The "Vulcan Project" is an effort to estimate bottom-up carbon dioxide emissions from fossil fuel combustion and cement production (FFCO2) for the U.S. landscape at space and time scales that satisfy both scientific and policy needs. Here, we report on the Vulcan version 3.0 which quantifies emissions at a resolution of 1 km2/hr for the 2010-2015 time period. We estimate 2011 FFCO2 emissions of 1,589.9 TgC with a 95% confidence interval of 1,367/1,853 TgC (-14.0%/+16.6%), implying a one-sigma uncertainty of ~ ±8%. Per capita emissions are larger in states dominated by electricity production and industrial activity and smaller where onroad and building emissions dominate. The U.S. FFCO2 emissions center of mass (CoM) is located in the state of Missouri with mean seasonality that moves on a near-elliptical NE/SW path. Comparison to ODIAC, a global gridded FFCO2 emissions estimate, shows large total emissions differences (100.4 TgC for year 2011), a spatial correlation of 0.68 (R2), and a mean absolute relative difference at the 1 km2 scale of 104.3%. The Vulcan data product offers a high-resolution estimate of FFCO2 emissions in every U.S. city, obviating costly development of self-reported urban inventories. The Vulcan v3.0 annual gridded emissions data product can be downloaded from the Oak Ridge National Laboratory Distributed Active Archive Center (Gurney, Liang, et al., 2019, https://doi.org/10.3334/ORNLDAAC/1741).

Informing urban climate planning with high resolution data: the Hestia fossil fuel CO2 emissions for Baltimore, Maryland

BACKGROUND

Cities contribute more than 70% of global anthropogenic carbon dioxide (CO2) emissions and are leading the effort to reduce greenhouse gas (GHG) emissions through sustainable planning and development. However, urban greenhouse gas mitigation often relies on self-reported emissions estimates that may be incomplete and unverifiable via atmospheric monitoring of GHGs. We present the Hestia Scope 1 fossil fuel CO2 (FFCO2) emissions for the city of Baltimore, Maryland-a gridded annual and hourly emissions data product for 2010 through 2015 (Hestia-Baltimore v1.6). We also compare the Hestia-Baltimore emissions to overlapping Scope 1 FFCO2 emissions in Baltimore's self-reported inventory for 2014.

RESULTS

The Hestia-Baltimore emissions in 2014 totaled 1487.3 kt C (95% confidence interval of 1158.9-1944.9 kt C), with the largest emissions coming from onroad (34.2% of total city emissions), commercial (19.9%), residential (19.0%), and industrial (11.8%) sectors. Scope 1 electricity production and marine shipping were each generally less than 10% of the city's total emissions. Baltimore's self-reported Scope 1 FFCO2 emissions included onroad, natural gas consumption in buildings, and some electricity generating facilities within city limits. The self-reported Scope 1 FFCO2 total of 1182.6 kt C was similar to the sum of matching emission sectors and fuels in Hestia-Baltimore v1.6. However, 20.5% of Hestia-Baltimore's emissions were in sectors and fuels that were not included in the self-reported inventory. Petroleum use in buildings were omitted and all Scope 1 emissions from industrial point sources, marine shipping, nonroad vehicles, rail, and aircraft were categorically excluded.

CONCLUSIONS

The omission of petroleum combustion in buildings and categorical exclusions of several sectors resulted in an underestimate of total Scope 1 FFCO2 emissions in Baltimore's self-reported inventory. Accurate Scope 1 FFCO2 emissions, along with Scope 2 and 3 emissions, are needed to inform effective urban policymaking for system-wide GHG mitigation. We emphasize the need for comprehensive Scope 1 emissions estimates for emissions verification and measuring progress towards Scope 1 GHG mitigation goals using atmospheric monitoring.

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.

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.

The TRAX Light-Rail Train Air Quality Observation Project

Observing air quality from sensors onboard light rail cars in Salt Lake County, Utah began as a pilot study in 2014 and has now evolved into a five-year, state-funded program. This metropolitan region suffers from both elevated ozone levels during summer and high PM2.5 events during winter. Pollution episodes result predominantly from local anthropogenic emissions but are also impacted by regional transport of dust, chemical precursors to ozone, and wildfire smoke, as well as being exacerbated by the topographical features surrounding the city. Two electric light-rail train cars from the Utah Transit Authority light-rail Transit Express (“TRAX”) system were outfitted with PM2.5 and ozone sensors to measure air quality at high spatial and temporal resolutions in this region. Pollutant concentration data underwent quality control procedures to determine whether the train motion affected the readings and how the sensors compared against regulatory sensors. Quality assurance results from data obtained over the past year show that TRAX Observation Project sensors are reliable, which corroborates earlier preliminary validation work. Three case studies from summer 2019 are presented to illustrate the strength of the finely-resolved air quality observations: (1) an elevated ozone event, (2) elevated particulate pollution resulting from 4th of July fireworks, and (3) elevated particle pollution during a winter time inversion event. The mobile observations were able to capture spatial gradients, as well as pollutant hotspots, during both of these episodes. Sensors have been recently added to a third light rail train car, which travels on a north–south oriented rail line, where air quality was unable to be monitored previously. The TRAX Observation Project is currently being used to provide reliable pollutant data for health studies and inform urban planning efforts. Links to real-time data displays and updated information on the quality-controlled data from this study are available on the webpage for the Department of Atmospheric Sciences at the University of Utah.

Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data

In this study, we create a high-resolution (1 km x 1 km) carbon emission spatially gridded dataset in Shanghai for 2010 to 2015 to help researchers understand the spatial pattern of urban CO2 emissions and facilitate exploration of their driving forces. First, we conclude that high spatial agglomeration, CO2 emissions centralized along the river and coastline, and a structure with three circular layers are the three notable temporal–spatial characteristics of Shanghai fossil fuel CO2 emissions. Second, we find that large point sources are the leading factors that shaped the temporal–spatial characteristics of Shanghai CO2 emission distributions. The changes of CO2 emissions in each grid during 2010–2015 indicate that the energy-controlling policies of large point emission sources have had positive effects on CO2 reduction since 2012. The changes suggest that targeted policies can have a disproportionate impact on urban emissions. Third, area sources bring more uncertainties to the forecasting of carbon emissions. We use the Geographical Detector method to identify these leading factors that influence CO2 emissions emitted from area sources. We find that Shanghai’s circular layer structure, population density, and population activity intensity are the leading factors. This result implied that urban planning has a large impact on the distribution of urban CO2 emissions. At last, we find that unbalanced development within the city will lead to different leading impact factors for each circular layer. Factors such as urban development intensity, traffic land, and industrial land have stronger power to determine CO2 emissions in the areas outside the Outer Ring, while factors such as population density and population activity intensity have stronger impacts in the other two inner areas. This research demonstrates the potential utility of high-resolution carbon emission data to advance the integration of urban planning for the reduction of urban CO2 emissions and provide information for policymakers to make targeted policies across different areas within the city.

Findings from a Pilot Light-Emitting Diode (LED) Bulb Exchange Program at a Neighborhood Scale

In the United States, 44% of low-income households struggle to pay their utility bills, affecting their ability to afford necessities such as food and health expenses. There is a high demand for, but low availability of, energy efficiency services in underserved neighborhoods, creating an opportunity for community-based programs to fill this inherent gap. This pilot project aims to bring energy savings and education to a uniquely targeted portion of Salt Lake City, UT, through the exchange of light-emitting diode bulbs and examines its feasibility in addressing energy insecurity at larger scales. Through the 8-month project duration, 1432 bulbs were exchanged at 23 events reaching 181 households in low-income areas that, through a year of use, were estimated to save residents approximately $18,219 in electricity bills, reducing CO2 emissions from power plants by 122 metric tons. Since this pilot reached less than 1% of households, we extrapolated a reach of 2%, 5%, and 7.5%, and found substantial potential decreases in power plant emissions and financial savings. Ongoing expansion efforts include more direct engagement with trusted members of the targeted communities and stronger attempts to engage participants in energy efficiency education as our project encountered some difficulties in reaching the intended population.

Extended STIRPAT model-based driving factor analysis of energy-related CO2 emissions in Kazakhstan

Extended stochastic impact by regression on population, affluence, and technology model incorporating ridge regression was used to analyze the driving mechanism of energy-related CO₂ emissions in Kazakhstan during 1992-2014. The research period was divided into two stages based on GDP of Kazakhstan in 1991 (85.70 × 10⁹ dollars), the first stage (1992-2002), GDP < 85.70 × 10⁹ dollars, the stage of economic recovery; the second stage (2003-2014), GDP > 85.70 × 10⁹ dollars, the stable economic development stage. The results demonstrated that (1) population scale and the technological improvement were the dominant contributors to promote the growth of the CO₂ emissions during 1992-2014 in Kazakhstan. (2) Economic growth and industrialization played more positive effect on the increase of the CO₂ emissions in the stable economic development stage (2003-2014) than those in the stage of economic recovery (1992-2002). The proportion of the tertiary industry, the trade openness, and foreign direct investment were transformed from negative factors into positive factors in the stable economic development stage (2003-2014). (3) Due to the over-urbanization of Kazakhstan before the independence, the level of urbanization continued to decline, urbanization was the first factor to curb CO₂ emissions during 1992-2014. Finally, some policy recommendations are put forward to reduce energy-related carbon emissions.

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.

On the Impact of Granularity of Space-based Urban CO2 Emissions in Urban Atmospheric Inversions: A Case Study for Indianapolis, IN

Quantifying greenhouse gas (GHG) emissions from cities is a key challenge towards effective emissions management. An inversion analysis from the INdianapolis FLUX experiment (INFLUX) project, as the first of its kind, has achieved a top-down emission estimate for a single city using CO2 data collected by the dense tower network deployed across the city. However, city-level emission data, used as a priori emissions, are also a key component in the atmospheric inversion framework. Currently, fine-grained emission inventories (EIs) able to resolve GHG city emissions at high spatial resolution, are only available for few major cities across the globe. Following the INFLUX inversion case with a global 1×1 km ODIAC fossil fuel CO2 emission dataset, we further improved the ODIAC emission field and examined its utility as a prior for the city scale inversion. We disaggregated the 1×1 km ODIAC non-point source emissions using geospatial datasets such as the global road network data and satellite-data driven surface imperviousness data to a 30×30 m resolution. We assessed the impact of the improved emission field on the inversion result, relative to priors in previous studies (Hestia and ODIAC). The posterior total emission estimate (5.1 MtC/yr) remains statistically similar to the previous estimate with ODIAC (5.3 MtC/yr). However, the distribution of the flux corrections was very close to those of Hestia inversion and the model-observation mismatches were significantly reduced both in forward and inverse runs, even without hourly temporal changes in emissions. EIs reported by cities often do not have estimates of spatial extents. Thus, emission disaggregation is a required step when verifying those reported emissions using atmospheric models. Our approach offers gridded emission estimates for global cities that could serves as a prior for inversion, even without locally reported EIs in a systematic way to support city-level Measuring, Reporting and Verification (MRV) practice implementation.