Articles | Volume 22, issue 6
https://doi.org/10.5194/acp-22-3891-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-3891-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing vehicle fuel efficiency using a dense network of CO2 observations
Helen L. Fitzmaurice
Department of Earth and Planetary Science, University of California
Berkeley, Berkeley, CA 94720, USA
Alexander J. Turner
Department of Atmospheric Sciences, University of Washington, Seattle,
WA 98195, USA
Jinsol Kim
Department of Earth and Planetary Science, University of California
Berkeley, Berkeley, CA 94720, USA
Katherine Chan
Program Coordination Division, Sacramento Metro Air Quality Management District, Sacramento, CA 95814,
USA
Erin R. Delaria
Department of Chemistry, University of California Berkeley, Berkeley, CA
94720, USA
Catherine Newman
Department of Chemistry, University of California Berkeley, Berkeley, CA
94720, USA
Paul Wooldridge
Department of Chemistry, University of California Berkeley, Berkeley, CA
94720, USA
Department of Earth and Planetary Science, University of California
Berkeley, Berkeley, CA 94720, USA
Department of Chemistry, University of California Berkeley, Berkeley, CA
94720, USA
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Atmos. Chem. Phys., 22, 15403–15411, https://doi.org/10.5194/acp-22-15403-2022, https://doi.org/10.5194/acp-22-15403-2022, 2022
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We develop a novel method for finding heavy-duty vehicle (HDV) emission factors (g PM kg fuel) using regulatory sensor networks and publicly available traffic data. We find that particulate matter emission factors have decreased by a factor of ~ 9 in the past decade in the San Francisco Bay area. Because of the wide availability of similar data sets across the USA and globally, this method could be applied to other settings to understand long-term trends and regional differences in HDV emissions.
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The use of a dense network of low-cost CO2 sensors is an attractive option for measuring CO2 emissions in cities. However, these low-cost sensors are also subject to uncertainties. Here, we describe a novel method of field calibration for correcting temperature-related errors in the CO2 sensors deployed in the BEACO2N network. We show that with this temperature correction, we can achieve a sufficiently low network error to allow for the evaluation of CO2 emissions at a neighborhood scale.
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This study shows large chemical and radiative effects of smoke aerosols from fires on near-surface ozone production. Aerosol loading and NOx levels are identified as the primary factors influencing these effects. Furthermore, we show that the ratio of surface PM2.5 to NO2 tropospheric column can be used as an indicator for identifying aerosol-dominated regimes, facilitating the assessment of aerosol impacts on ozone formation through satellite observations.
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Atmos. Chem. Phys., 25, 8475–8492, https://doi.org/10.5194/acp-25-8475-2025, https://doi.org/10.5194/acp-25-8475-2025, 2025
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Satellites, such as NASA's Orbiting Carbon Observatory-2 and -3 (OCO-2 and OCO-3, respectively), retrieve carbon dioxide (CO2) concentrations, which provide vital information for estimating surface CO2 emissions. Here, we investigate the ability of OCO-2/3 retrievals to constrain CO2 emissions for the state of California for the major emission sectors (i.e., fossil fuels, net ecosystem exchange, and wildfire).
Nikhil Dadheech and Alexander J. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3441, https://doi.org/10.5194/egusphere-2025-3441, 2025
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We developed a generalized emulator of atmospheric transport (FootNet v3) trained over the United States, enabling the emulation of both surface & column-averaged footprints at kilometer-scale resolution. We demonstrate that FootNet v3 generalizes to previously unseen regions and meteorological conditions, enabling accurate out-of-sample simulation of atmospheric transport. Flux inversion case studies show that FootNet matches or exceeds the performance of full-physics models in unseen regions.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-3258, https://doi.org/10.5194/egusphere-2025-3258, 2025
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Chemistry-climate models are used to investigate how physical climate influences the composition of the atmosphere but are slow and expensive to run. We train a linear inverse model that can replicate the behavior of chemistry-climate models at low computational cost. It captures how large-scale climate features like El Niño affect atmospheric composition and can make accurate forecasts up to a year ahead. This model enables fast hypothesis testing and estimates of past atmospheric composition.
Nikhil Dadheech, Tai-Long He, and Alexander J. Turner
Atmos. Chem. Phys., 25, 5159–5174, https://doi.org/10.5194/acp-25-5159-2025, https://doi.org/10.5194/acp-25-5159-2025, 2025
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We developed an efficient GHG (greenhouse gas) flux inversion framework using a machine-learning emulator (FootNet) as a surrogate for an atmospheric transport model, resulting in a 650 × speedup. Paradoxically, the flux inversion using the ML (machine-learning) model outperforms the full-physics model in our case study. We attribute this to the ML model mitigating transport errors in the GHG flux inversion.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Deepangsu Chatterjee, Randall V. Martin, Chi Li, Dandan Zhang, Haihui Zhu, Daven K. Henze, James H. Crawford, Ronald C. Cohen, Lok N. Lamsal, and Alexander M. Cede
Atmos. Chem. Phys., 24, 12687–12706, https://doi.org/10.5194/acp-24-12687-2024, https://doi.org/10.5194/acp-24-12687-2024, 2024
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Benjamin A. Nault, Katherine R. Travis, James H. Crawford, Donald R. Blake, Pedro Campuzano-Jost, Ronald C. Cohen, Joshua P. DiGangi, Glenn S. Diskin, Samuel R. Hall, L. Gregory Huey, Jose L. Jimenez, Kyung-Eun Min, Young Ro Lee, Isobel J. Simpson, Kirk Ullmann, and Armin Wisthaler
Atmos. Chem. Phys., 24, 9573–9595, https://doi.org/10.5194/acp-24-9573-2024, https://doi.org/10.5194/acp-24-9573-2024, 2024
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Ozone (O3) is a pollutant formed from the reactions of gases emitted from various sources. In urban areas, the density of human activities can increase the O3 formation rate (P(O3)), thus impacting air quality and health. Observations collected over Seoul, South Korea, are used to constrain P(O3). A high local P(O3) was found; however, local P(O3) was partly reduced due to compounds typically ignored. These observations also provide constraints for unmeasured compounds that will impact P(O3).
Katherine R. Travis, Benjamin A. Nault, James H. Crawford, Kelvin H. Bates, Donald R. Blake, Ronald C. Cohen, Alan Fried, Samuel R. Hall, L. Gregory Huey, Young Ro Lee, Simone Meinardi, Kyung-Eun Min, Isobel J. Simpson, and Kirk Ullman
Atmos. Chem. Phys., 24, 9555–9572, https://doi.org/10.5194/acp-24-9555-2024, https://doi.org/10.5194/acp-24-9555-2024, 2024
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Qindan Zhu, Rebecca H. Schwantes, Matthew Coggon, Colin Harkins, Jordan Schnell, Jian He, Havala O. T. Pye, Meng Li, Barry Baker, Zachary Moon, Ravan Ahmadov, Eva Y. Pfannerstill, Bryan Place, Paul Wooldridge, Benjamin C. Schulze, Caleb Arata, Anthony Bucholtz, John H. Seinfeld, Carsten Warneke, Chelsea E. Stockwell, Lu Xu, Kristen Zuraski, Michael A. Robinson, J. Andrew Neuman, Patrick R. Veres, Jeff Peischl, Steven S. Brown, Allen H. Goldstein, Ronald C. Cohen, and Brian C. McDonald
Atmos. Chem. Phys., 24, 5265–5286, https://doi.org/10.5194/acp-24-5265-2024, https://doi.org/10.5194/acp-24-5265-2024, 2024
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Atmos. Meas. Tech., 17, 1051–1060, https://doi.org/10.5194/amt-17-1051-2024, https://doi.org/10.5194/amt-17-1051-2024, 2024
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Jinsol Kim, John B. Miller, Charles E. Miller, Scott J. Lehman, Sylvia E. Michel, Vineet Yadav, Nick E. Rollins, and William M. Berelson
Atmos. Chem. Phys., 23, 14425–14436, https://doi.org/10.5194/acp-23-14425-2023, https://doi.org/10.5194/acp-23-14425-2023, 2023
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In this study, we present the partitioning of CO2 signals from biogenic, petroleum and natural gas sources by combining CO, 13CO2 and 14CO2 measurements. Using measurements from flask air samples at three sites in the greater Los Angeles region, we find larger and positive contributions of biogenic signals in winter and smaller and negative contributions in summer. The largest contribution of natural gas combustion generally occurs in summer.
Clara M. Nussbaumer, Bryan K. Place, Qindan Zhu, Eva Y. Pfannerstill, Paul Wooldridge, Benjamin C. Schulze, Caleb Arata, Ryan Ward, Anthony Bucholtz, John H. Seinfeld, Allen H. Goldstein, and Ronald C. Cohen
Atmos. Chem. Phys., 23, 13015–13028, https://doi.org/10.5194/acp-23-13015-2023, https://doi.org/10.5194/acp-23-13015-2023, 2023
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NOx is a precursor to hazardous tropospheric ozone and can be emitted from various anthropogenic sources. It is important to quantify NOx emissions in urban environments to improve the local air quality, which still remains a challenge, as sources are heterogeneous in space and time. In this study, we calculate NOx emissions over Los Angeles, based on aircraft measurements in June 2021, and compare them to a local emission inventory, which we find mostly overpredicts the measured values.
Eva Y. Pfannerstill, Caleb Arata, Qindan Zhu, Benjamin C. Schulze, Roy Woods, John H. Seinfeld, Anthony Bucholtz, Ronald C. Cohen, and Allen H. Goldstein
Atmos. Chem. Phys., 23, 12753–12780, https://doi.org/10.5194/acp-23-12753-2023, https://doi.org/10.5194/acp-23-12753-2023, 2023
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The San Joaquin Valley is an agricultural area with poor air quality. Organic gases drive the formation of hazardous air pollutants. Agricultural emissions of these gases are not well understood and have rarely been quantified at landscape scale. By combining aircraft-based emission measurements with land cover information, we found mis- or unrepresented emission sources. Our results help in understanding of pollution sources and in improving predictions of air quality in agricultural regions.
Qindan Zhu, Bryan Place, Eva Y. Pfannerstill, Sha Tong, Huanxin Zhang, Jun Wang, Clara M. Nussbaumer, Paul Wooldridge, Benjamin C. Schulze, Caleb Arata, Anthony Bucholtz, John H. Seinfeld, Allen H. Goldstein, and Ronald C. Cohen
Atmos. Chem. Phys., 23, 9669–9683, https://doi.org/10.5194/acp-23-9669-2023, https://doi.org/10.5194/acp-23-9669-2023, 2023
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Nitrogen oxide (NOx) is a hazardous air pollutant, and it is the precursor of short-lived climate forcers like tropospheric ozone and aerosol particles. While NOx emissions from transportation has been strictly regulated, soil NOx emissions are overlooked. We use the airborne flux measurements to observe NOx emissions from highways and urban and cultivated soil land cover types. We show non-negligible soil NOx emissions, which are significantly underestimated in current model simulations.
Xueying Yu, Dylan B. Millet, Daven K. Henze, Alexander J. Turner, Alba Lorente Delgado, A. Anthony Bloom, and Jianxiong Sheng
Atmos. Chem. Phys., 23, 3325–3346, https://doi.org/10.5194/acp-23-3325-2023, https://doi.org/10.5194/acp-23-3325-2023, 2023
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We combine satellite measurements with a novel downscaling method to map global methane emissions at 0.1°×0.1° resolution. These fine-scale emission estimates reveal unreported emission hotspots and shed light on the roles of agriculture, wetlands, and fossil fuels for regional methane budgets. The satellite-derived emissions point in particular to missing fossil fuel emissions in the Middle East and to a large emission underestimate in South Asia that appears to be tied to monsoon rainfall.
Chi Li, Randall V. Martin, Ronald C. Cohen, Liam Bindle, Dandan Zhang, Deepangsu Chatterjee, Hongjian Weng, and Jintai Lin
Atmos. Chem. Phys., 23, 3031–3049, https://doi.org/10.5194/acp-23-3031-2023, https://doi.org/10.5194/acp-23-3031-2023, 2023
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Models are essential to diagnose the significant effects of nitrogen oxides (NOx) on air pollution. We use an air quality model to illustrate the variability of NOx resolution-dependent simulation biases; how these biases depend on specific chemical environments, driving mechanisms, and vertical variabilities; and how these biases affect the interpretation of satellite observations. High-resolution simulations are thus critical to accurately interpret NOx and its relevance to air quality.
Amir H. Souri, Matthew S. Johnson, Glenn M. Wolfe, James H. Crawford, Alan Fried, Armin Wisthaler, William H. Brune, Donald R. Blake, Andrew J. Weinheimer, Tijl Verhoelst, Steven Compernolle, Gaia Pinardi, Corinne Vigouroux, Bavo Langerock, Sungyeon Choi, Lok Lamsal, Lei Zhu, Shuai Sun, Ronald C. Cohen, Kyung-Eun Min, Changmin Cho, Sajeev Philip, Xiong Liu, and Kelly Chance
Atmos. Chem. Phys., 23, 1963–1986, https://doi.org/10.5194/acp-23-1963-2023, https://doi.org/10.5194/acp-23-1963-2023, 2023
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We have rigorously characterized different sources of error in satellite-based HCHO / NO2 tropospheric columns, a widely used metric for diagnosing near-surface ozone sensitivity. Specifically, the errors were categorized/quantified into (i) an inherent chemistry error, (ii) the decoupled relationship between columns and the near-surface concentration, (iii) the spatial representativeness error of ground satellite pixels, and (iv) the satellite retrieval errors.
Viral Shah, Daniel J. Jacob, Ruijun Dang, Lok N. Lamsal, Sarah A. Strode, Stephen D. Steenrod, K. Folkert Boersma, Sebastian D. Eastham, Thibaud M. Fritz, Chelsea Thompson, Jeff Peischl, Ilann Bourgeois, Ilana B. Pollack, Benjamin A. Nault, Ronald C. Cohen, Pedro Campuzano-Jost, Jose L. Jimenez, Simone T. Andersen, Lucy J. Carpenter, Tomás Sherwen, and Mat J. Evans
Atmos. Chem. Phys., 23, 1227–1257, https://doi.org/10.5194/acp-23-1227-2023, https://doi.org/10.5194/acp-23-1227-2023, 2023
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NOx in the free troposphere (above 2 km) affects global tropospheric chemistry and the retrieval and interpretation of satellite NO2 measurements. We evaluate free tropospheric NOx in global atmospheric chemistry models and find that recycling NOx from its reservoirs over the oceans is faster than that simulated in the models, resulting in increases in simulated tropospheric ozone and OH. Over the U.S., free tropospheric NO2 contributes the majority of the tropospheric NO2 column in summer.
Helen L. Fitzmaurice and Ronald C. Cohen
Atmos. Chem. Phys., 22, 15403–15411, https://doi.org/10.5194/acp-22-15403-2022, https://doi.org/10.5194/acp-22-15403-2022, 2022
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We develop a novel method for finding heavy-duty vehicle (HDV) emission factors (g PM kg fuel) using regulatory sensor networks and publicly available traffic data. We find that particulate matter emission factors have decreased by a factor of ~ 9 in the past decade in the San Francisco Bay area. Because of the wide availability of similar data sets across the USA and globally, this method could be applied to other settings to understand long-term trends and regional differences in HDV emissions.
Glenn M. Wolfe, Thomas F. Hanisco, Heather L. Arkinson, Donald R. Blake, Armin Wisthaler, Tomas Mikoviny, Thomas B. Ryerson, Ilana Pollack, Jeff Peischl, Paul O. Wennberg, John D. Crounse, Jason M. St. Clair, Alex Teng, L. Gregory Huey, Xiaoxi Liu, Alan Fried, Petter Weibring, Dirk Richter, James Walega, Samuel R. Hall, Kirk Ullmann, Jose L. Jimenez, Pedro Campuzano-Jost, T. Paul Bui, Glenn Diskin, James R. Podolske, Glen Sachse, and Ronald C. Cohen
Atmos. Chem. Phys., 22, 4253–4275, https://doi.org/10.5194/acp-22-4253-2022, https://doi.org/10.5194/acp-22-4253-2022, 2022
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Smoke plumes are chemically complex. This work combines airborne observations of smoke plume composition with a photochemical model to probe the production of ozone and the fate of reactive gases in the outflow of a large wildfire. Model–measurement comparisons illustrate how uncertain emissions and chemical processes propagate into simulated chemical evolution. Results provide insight into how this system responds to perturbations, which can help guide future observation and modeling efforts.
Johannes Gensheimer, Alexander J. Turner, Philipp Köhler, Christian Frankenberg, and Jia Chen
Biogeosciences, 19, 1777–1793, https://doi.org/10.5194/bg-19-1777-2022, https://doi.org/10.5194/bg-19-1777-2022, 2022
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We develop a convolutional neural network, named SIFnet, that increases the spatial resolution of SIF from TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations, together with a broad range of high-resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.
Douglas A. Day, Pedro Campuzano-Jost, Benjamin A. Nault, Brett B. Palm, Weiwei Hu, Hongyu Guo, Paul J. Wooldridge, Ronald C. Cohen, Kenneth S. Docherty, J. Alex Huffman, Suzane S. de Sá, Scot T. Martin, and Jose L. Jimenez
Atmos. Meas. Tech., 15, 459–483, https://doi.org/10.5194/amt-15-459-2022, https://doi.org/10.5194/amt-15-459-2022, 2022
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Particle-phase nitrates are an important component of atmospheric aerosols and chemistry. In this paper, we systematically explore the application of aerosol mass spectrometry (AMS) to quantify the organic and inorganic nitrate fractions of aerosols in the atmosphere. While AMS has been used for a decade to quantify nitrates, methods are not standardized. We make recommendations for a more universal approach based on this analysis of a large range of field and laboratory observations.
Alexander J. Turner, Philipp Köhler, Troy S. Magney, Christian Frankenberg, Inez Fung, and Ronald C. Cohen
Biogeosciences, 18, 6579–6588, https://doi.org/10.5194/bg-18-6579-2021, https://doi.org/10.5194/bg-18-6579-2021, 2021
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This work builds a high-resolution estimate (500 m) of gross primary productivity (GPP) over the US using satellite measurements of solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) between 2018 and 2020. We identify ecosystem-specific scaling factors for estimating gross primary productivity (GPP) from TROPOMI SIF. Extreme precipitation events drive four regional GPP anomalies that account for 28 % of year-to-year GPP differences across the US.
Xiaomeng Jin, Qindan Zhu, and Ronald C. Cohen
Atmos. Chem. Phys., 21, 15569–15587, https://doi.org/10.5194/acp-21-15569-2021, https://doi.org/10.5194/acp-21-15569-2021, 2021
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We describe direct estimates of NOx emissions and lifetimes for biomass burning plumes using daily TROPOMI retrievals of NO2. Satellite-derived NOx emission factors are consistent with those from in situ measurements. We observe decreasing NOx lifetime with fire intensity, which is due to the increase in NOx abundance and radical production. Our findings suggest promise for applying space-based observations to track the emissions and chemical evolution of reactive nitrogen from wildfires.
Erin R. Delaria, Jinsol Kim, Helen L. Fitzmaurice, Catherine Newman, Paul J. Wooldridge, Kevin Worthington, and Ronald C. Cohen
Atmos. Meas. Tech., 14, 5487–5500, https://doi.org/10.5194/amt-14-5487-2021, https://doi.org/10.5194/amt-14-5487-2021, 2021
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The use of a dense network of low-cost CO2 sensors is an attractive option for measuring CO2 emissions in cities. However, these low-cost sensors are also subject to uncertainties. Here, we describe a novel method of field calibration for correcting temperature-related errors in the CO2 sensors deployed in the BEACO2N network. We show that with this temperature correction, we can achieve a sufficiently low network error to allow for the evaluation of CO2 emissions at a neighborhood scale.
Xueling Liu, Arthur P. Mizzi, Jeffrey L. Anderson, Inez Fung, and Ronald C. Cohen
Atmos. Chem. Phys., 21, 9573–9583, https://doi.org/10.5194/acp-21-9573-2021, https://doi.org/10.5194/acp-21-9573-2021, 2021
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Observations of winds in the planetary boundary layer remain sparse, making it challenging to simulate and predict the atmospheric conditions that are most important for describing and predicting urban air quality. Here we investigate the application of data assimilation of NO2 columns as will be observed from geostationary orbit to improve predictions and retrospective analysis of wind fields in the boundary layer.
Erin R. Delaria, Bryan K. Place, Amy X. Liu, and Ronald C. Cohen
Atmos. Chem. Phys., 20, 14023–14041, https://doi.org/10.5194/acp-20-14023-2020, https://doi.org/10.5194/acp-20-14023-2020, 2020
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Observations of NO2 deposition to vegetation have been widely reported, but the magnitude and mechanism remain uncertain. We use laboratory measurements to study NO2 deposition to leaves of 10 native California tree species. We report important differences in the uptake rates between species and find that this process is primarily diffusion-regulated. We suggest that processes within leaves at a cellular level represent a negligible limitation to NO2 deposition at the canopy level.
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Short summary
On-road emissions are thought to vary widely from existing predictions, as the effects of the age of the vehicle fleet, the performance of emission control systems, and variations in speed are difficult to assess under ambient driving conditions. We present an observational approach to characterize on-road emissions and show that the method is consistent with other approaches to within ~ 3 %.
On-road emissions are thought to vary widely from existing predictions, as the effects of the...
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