Daytime evolution of oxidized reactive nitrogen in western U.S. wildfire smoke plumes: in situ and satellite observations

2020 Fall.Includes bibliographical references.The Western Wildfire Experiment for Cloud Chemistry, Aerosol Absorption, and Nitrogen (WE-CAN) deployed the NSF/NCAR C-130 aircraft in summer 2018 across the western U.S. to sample wildfire smoke during its first day of atmospheric evolution. We present a summary of a subset of oxidized nitrogen species (NOy) in plumes sampled in a pseudo-lagrangian fashion. Emissions of nitrogen oxides (NOx = NO + NO2) and nitrous acid (HONO) are rapidly converted to more oxidized forms. Within 4 hours, ∼86% of the measured NOy (∑ NOy) is in the form of peroxy acyl nitrates (PANs) (∼37%), particulate nitrate (pNO3) (∼26%) and gas-phase organic nitrates (∼23%). The average e-folding time and distance for NOx are ∼90 minutes and ∼40 km, respectively. Nearly no enhancements in nitric acid (HNO3) were observed in plumes sampled in a pseudo-lagrangian fashion, implying HNO3-limited ammonium nitrate (NH4NO3) formation, with one notable exception that we highlight as a case study. We also summarize the observed partitioning of ∑ NOy in all the smoke-impacted samples intercepted during WE-CAN. In the smoke-impacted samples intercepted below 3 km above sea level (ASL), HNO3 is the dominant form of ∑ NOy and its relative contribution increases with smoke age. Above 3 km ASL, the contributions of PANs and pNO3 to ∑ NOy increase with altitude. WE-CAN also sampled smoke from multiple fires mixed with anthropogenic emissions over the California Central Valley. We distinguish samples where anthropogenic NOx emissions appear to lead to an increase in NOx abundances by a factor of 4 and contribute to additional PAN formation. We utilize data from the Cross-Track Infrared Sounder (CrIS) on the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite, which continues the thermal infrared peroxyacetyl nitrate (PAN) satellite record established by the Tropospheric Emission Spectrometer (TES) onboard the Aura satellite. CrIS provides improved spatial resolution, allowing for improved analysis opportunities. Here we present an analysis of CrIS PAN retrievals over the western US during the summer 2018 wildfire season. The analysis period coincides with WE-CAN. CrIS is capable of detecting PAN and CO enhancements from smoke plumes sampled during WE-CAN, especially those that became active before the satellite overpass or burned for several days (e.g., Carr Fire, Mendocino Complex Fire). The analysis show that ∼40 - 70% of PAN over the western U.S. can be attributed to smoke from wildfires. The contribution of smoke from wildfires to free tropospheric PAN generally increases with latitude. We calculate peroxyacetyl nitrate (PAN) excess mixing ratios normalized by CO (NEMRs) in fresh smoke plumes from fires and follow the evolution as these plumes are transported several hours to days downwind. This analysis shows that elevated PAN within smoke plumes can be detected several states downwind from the fire source. The combination of high CrIS spatial resolution and favorable background conditions on 13 September 2018 permits detecting chemical changes within the Pole Creek smoke plume in Utah. In this plume, CrIS PAN NEMRs increase from < 1% to 3.5% within 3 - 4 hours of physical aging. These results are within the range observed in fresh plumes sampled during WE-CAN, where PAN NEMRs increased from 1.5% to 4% within 4 hours of physical aging

Evaluación del modelo WRF (weather research and forecasting) entre la superficie y 30 km sobre Quito casos de estudio entre abril y septiembre de 2015

The evaluation of meteorological models that simulate physical variables in a region—which it is accomplishes through the comparison with experimental data—is useful to understand the atmospheric behavior revealed in the parametric configuration which better fits the reality. In Ecuador, meteorological model evaluation, as a function of altitude or pressure, is a field that is yet to be explored and, for that reason, we have simulated meteorological variables during four stages, which coincide with a radiosounding campaign performed from the Atmospheric Measurement Station at Universidad San Francisco de Quito (EMA-USFQ, by its Spanish acronym). This study incorporates data from four dates, which are: April 29th, 2015 (07h00, local time), June 30th, 2015 (13h00), August 27th, 2015 (09h00) and, September 30th, 2015 (07h00). The results from WRF (V3.2) model (performed by R. Parra) were used for this evaluation. These results were generated with a master domain and two nested subdomains. The second subdomain comprehends Ecuador’s continental territory, and it is made up of a 199x199 grid (with 4x4 km cells). The top of the model is 10 hPa with 44 altitude layers. The simulations were performed under 2 configurations called FNL1 and FNL2, which combined physical options for Surface Layer, Planetary Boundary Layer and Cumulus Parameterization. The data for the simulation were extracted from the WRF model’s tridimensional results matrix, as a function of the eta level. These data was used to calculate air’s temperature, water vapor mixing ratio, wind speed and wind direction for each level of pressure. These variables are compared to experimental data collected in 2015 at EMA-USFQ (M. Cazorla). The vertical profiles from the model as well as from the experimental data were plotted together to make the comparison. A scatter plot and several fit statistics were done and calculated, such as lineal correlation R2, root mean square error (RMSE), mean bias error (MBE) and index of agreement (IA) in order to determine the goodness of fit. The vertical profiles showed that temperature was correctly validated along the trajectory, overestimating the value of the cold point tropopause (CPT) in 3 ºC. For the FNL1 (FNL2) configuration, the correlation coefficient was 0.99 (0.99), RMSE was 1.90 ºC (1.97 ºC), MBE was 0.08 ºC (-0.12 ºC) and IA was 0.99 (0.99). For the water vapor mixing ratio, the model does not simulate the values near to the surface very well; however, it improves with altitude. The correlation coefficient was 0.91 (0.86), RMSE was 0.82 g kg-1 (1.17 g kg-1), MBE was 0.09 gkg-1 (0.25 g kg-1), and IA was 0.97 (0.95). With regard to wind speed and wind direction, the model simulates the variables’ patterns along the trajectory. For wind speed, the correlation coefficient was 0.47 (0.49), RMSE was 4.6 m/s (4.6 m/s), MBE was -0.7 m/s (-0.8 m/s), and IA was 0.82 (0.83). For wind direction, the correlation coefficient was 0.57 (0.52), RMSE was 69º (70 º), MBE was 39 º (40 º), and IA was 0.84 (0.85).La evaluación de resultados de modelos meteorológicos que simulan variables físicas en una región – realizada mediante comparación con datos experimentales – sirve para entender el comportamiento atmosférico revelado en la configuración paramétrica que mejor se ajusta a la realidad. En el Ecuador, la evaluación de resultados de modelos meteorológicos, como función de la altitud o de la presión, es un campo todavía inexplorado y, por ello, constituye una prioridad de investigación. Con el objetivo de explorar este campo, se simularon variables meteorológicas durante cuatro periodos que coinciden con una campaña de radiosondeos realizados desde la Estación de Mediciones Atmosféricas de la Universidad San Francisco de Quito (EMA-USFQ). Los casos de estudio seleccionados son: el 29-abril (07h00, hora local), 30-junio (13h00), 27-agosto (09h00) y 30-septiembre (07h00) de 2015. Se utilizaron los resultados del modelo WRF (V3.2) (obtenidos por R. Parra), generados con un dominio maestro y dos subdominios anidados. El segundo subdominio abarca al territorio continental del Ecuador, y se conforma de una malla de 199 filas y 199 columnas (celdas de 4 km), y 44 capas en altura, hasta una presión de 10 hPa. Las simulaciones se efectuaron bajo 2 configuraciones, llamadas FNL1 y FNL2, que combinan opciones físicas para la Capa Superficial (CS), Capa Límite Planetaria (CLP) y esquema Cúmulo Convectivo (CCU). De la matriz tridimensional de resultados del modelo WRF se extrajeron los datos de la simulación, como función del nivel eta, y con ellos se determinó la temperatura del aire, la fracción másica del vapor de agua y la velocidad y dirección del viento en cada nivel de presión. Estas variables se compararon con datos experimentales de los sondeos realizados en 2015 desde la EMA (M. Cazorla). La comparación se realizó graficando en forma superpuesta los perfiles verticales, tanto del modelo como de los sondeos. También se realizó un gráfico 1:1 de los resultados del modelo contra los datos del sondeo y se halló la correlación lineal, el Root Mean Square Error (RMSE), Mean Bias Error (MBE) e Índice de Aceptación (IA) del modelo. Los perfiles verticales evidencian que la temperatura se validó correctamente a lo largo de toda la trayectoria, sobreestimando el CPT en 3 ºC en promedio. Para la configuración FNL1 (FNL2) el coeficiente de correlación es 0.99 (0.99), el RMSE es 1.90 ºC (1.97 ºC), el MBE es 0.08 ºC (-0.12 ºC) y el IA es 0.99 (0.99). Para la fracción másica del vapor de agua, se observa que el modelo no captura bien los valores cercanos a la superficie, y mejora en altura. El coeficiente de correlación es 0.91 (0.86), el RMSE es 0.82 g kg-1 (1.18 g kg-1), el MBE es 0.09 g kg-1 aire (0.25 g kg-1) y el IA es 0.97 (0.95). En cuanto a la velocidad y dirección del viento, el modelo captura el patrón de estas variables a lo largo de la trayectoria. Para la velocidad del viento, el coeficiente de correlación es 0.47 (0.49), el RMSE es 4.6 m/s (4.6 m/s), el MBE es -0.7 m/s (-0.8 m/s) y es IA es 0.82 (0.83). Para la dirección del viento, el coeficiente de correlación para es 0.57 (0.53), el RMSE es 69 º (70º), el MBE es 39º (40º) y el IA es 0.87 (0.86)

Satellite measurements of peroxyacetyl nitrate from the Cross-Track Infrared Sounder: comparison with ATom aircraft measurements

We present an overview of an optimal estimation algorithm to retrieve peroxyacetyl nitrate (PAN) from single-field-of-view Level 1B radiances measured by the Cross-Track Infrared Sounder (CrIS). CrIS PAN retrievals show peak sensitivity in the mid-troposphere, with degrees of freedom for signal less than or equal to 1.0. We show comparisons with two sets of aircraft measurements from the Atmospheric Tomography Mission (ATom), the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) and the Georgia Tech chemical ionization mass spectrometer (GT-CIMS). We find a systematic difference between the two aircraft datasets, with vertically averaged mid-tropospheric values from the GT-CIMS around 14 % lower than equivalent values from PANTHER. However, the two sets of aircraft measurements are strongly correlated (R2 value of 0.92) and do provide a consistent view of the large-scale variation of PAN. We demonstrate that the retrievals of PAN from CrIS show skill in measurement of these large-scale PAN distributions in the remote mid-troposphere compared to the retrieval prior. The standard deviation of individual CrIS–aircraft differences is 0.08 ppbv, which we take as an estimate of the uncertainty of the CrIS mid-tropospheric PAN for a single satellite field of view. The standard deviation of the CrIS–aircraft comparisons for averaged CrIS retrievals (median of 20 satellite coincidences with each aircraft profile) is lower at 0.05 ppbv. This would suggest that the retrieval error is reduced with averaging, although not with the square root of the number of observations. We find a negative bias of the order of 0.1 ppbv in the CrIS PAN results with respect to the aircraft measurements. This bias shows a dependence on column water vapor. We provide a water-vapor-dependent bias correction for use with the CrIS PAN data.</p

Cross-Track Infrared Sounder (CrIS) Peroxyacetyl Nitrate (PAN) and Carbon Monoxide (CO) retrievals for the 2018 wildfire season over the western U.S.

This dataset includes processed data from Cross-Track Infrared Sounder (CrIS) Peroxyacetyl Nitrate (PAN) and Carbon Monoxide (CO) retrievals from 24 July 2018 – 13 September 2018 coinciding with the wildfire season of 2018 and the Western wildfire Experiment for Cloud chemistry, Aerosol absorption, and Nitrogen (WE-CAN) for the western U.S. (35º N - 50º N lat, 127º W – 101º W lon). There are 50 days of data represented in 50 different .csv files (there is not August 22 and September 3 in the files). Not a Number (Nan) values are left blank.We use new peroxyacetyl nitrate (PAN) observations from the Cross-Track Infrared Sounder (CrIS) on the Suomi National Polar-orbiting Partnership satellite to investigate PAN over the western U.S. during the summer 2018 North American wildfire season. This period coincides with the Western Wildfire Experiment for Cloud Chemistry, Aerosol Absorption, and Nitrogen (WE-CAN). When combined with favorable background conditions, the resolution and sensitivity of CrIS is sufficient to observe PAN production in plumes. CrIS PAN normalized excess mixing ratios (NEMRs) in the Pole Creek Fire increase from 0.2% to 0.4% within 3-4 hours of physical aging, consistent with NEMRs calculated from WE-CAN observations. CrIS is also able to detect PAN and CO enhancements in plumes that have been transported hours to days downwind. On average for the study period, 24-56% of PAN in the free troposphere during the afternoon over the western U.S. can be attributed to fires.NASA Award Number NNH17ZDA001N-TASNP

Interactions between air pollution and terrestrial ecosystems: perspectives on challenges and future directions

Interactions between air pollution and terrestrial ecosystems play an important role in the Earth system. However, process-based knowledge of air pollution–terrestrial ecosystem interactions is limited, hindering accurate quantification of how changes in tropospheric chemistry, biogeochemical cycling, and climate affect air quality and its impact on humans and ecosystems. Here we summarize current challenges and future directions for advancing the understanding of air pollution–ecosystem interactions by synthesizing discussions from a multidisciplinary group of scientists at a recent Integrated Land Ecosystem–Atmosphere Processes Study (iLEAPS) early-career workshop. Specifically, we discuss the important elements of air pollution–terrestrial ecosystem interactions, including vegetation and soil uptake and emissions of air pollutants and precursors, in-canopy chemistry, and the roles of human activities, fires, and meteorology. We highlight the need for a coordinated network of measurements of long-term chemical fluxes and related meteorological and ecological quantities with expanded geographic and ecosystem representation, data standardization and curation to reduce uncertainty and enhance observational syntheses, integrated multiscale observational and modeling capabilities, collaboration across scientific disciplines and geographic regions, and active involvement by stakeholders and policymakers. Such an enhanced network will continue to facilitate the process-level understanding and thus predictive ability of interactions between air pollution and terrestrial ecosystems and impacts on local-to-global climate and human health