Results for “Particulates” — Page 52

DOE OSTI2025

The sources and diurnal variations of submicron aerosols in a coastal–rural environment near Houston, US

Aerosol properties were characterized at a rural site southwest of Houston from May to September 2022 during the intensive operation periods (IOPs) of the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). Backward trajectory analysis reveals three major air mass types: marine air mass from the Gulf, urban air mass influenced by urban emissions, and regional air mass. Marine aerosols typically show a bimodal size distribution and have the lowest particle number and mass concentrations of PM 1 (particulate matter with an aerodynamic diameter of less than 1 µm), while aerosols from air masses strongly influenced by urban emissions exhibit the highest concentrations. Organic aerosol (OA) accounts for more than 50 % of PM 1 for urban and regional air masses, whereas sulfate is comparable to OA in marine air masses. Positive matrix factorization (PMF) analysis of aerosol mass spectra identifies 6 OA factors: hydrocarbon-like OA (HOA), OA from the oxidation of monoterpenes (MT-SOA), OA from the reactive uptake of isoprene epoxydiols by acidic sulfate particles (isoprene-SOA), oxygenated OA arising from shipping emissions (shipping-OOA), and two oxygenated OA factors with high O : C ratios (OOA1 and OOA2). OOA2 has the highest O : C ratio and exhibits elevated mass concentration in the afternoon. Similar diurnal variation of highly oxidized OA factors was commonly observed in the Houston area during previous studies and attributed to the SOA formation by photochemistry and mixing from aloft. Here, using air mass backward trajectories and a 1-D box model, we show the diurnal trend of OOA2 mass concentration is instead driven by changes in air mass arriving at the rural site. The air mass changes are likely caused by the shift between land breezes and sea/bay breezes. Within the same air mass type (e.g., either urban or marine air mass), OOA2 mass concentration is largely independent of wind direction and shows essentially no diurnal variation, suggesting OOA2 is related to aged OA with minimal influence by local emissions. This study helps identify the major sources of OA in the Houston region and highlights the impacts of both atmospheric chemistry and meteorology on aerosol properties in the coastal–rural environment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

AERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition

Abstract. Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers.

Mahowald, Natalie M. (ORCID:000000022873997X)↗

DOE OSTI2022

Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles

The space-borne active sounders have been contributing invaluable vertically resolved information of atmospheric optical properties since the launch of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in 2006. To build long-term records from space-borne lidars useful for climate studies, one has to understand the differences between successive space lidars operating at different wavelengths, flying on different orbits, and using different viewing geometries, receiving paths, and detectors. In this article, we compare the results of Atmospheric Laser Doppler INstrument (ALADIN) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidars for the period from 28 June to 31 December 2019. First, we build a dataset of ALADIN–CALIOP collocated profiles (Δdist<1°; Δtime<6 h). Then we convert ALADIN's 355 nm particulate backscatter and extinction profiles into the scattering ratio vertical profiles SR(z) at 532 nm using molecular density profiles from Goddard Earth Observing System Data Assimilation System, version 5 (GEOS-5 DAS). And finally, we build the CALIOP and ALADIN globally gridded cloud fraction profiles CF(z) by applying the same cloud detection threshold to the SR(z) profiles of both lidars at the same spatial resolution. Before comparing the SR(z) and CF(z) profiles retrieved from the two analyzed lidar missions, we performed a numerical experiment to estimate the best achievable cloud detection agreement CDA norm (z) considering the differences between the instruments. We define CDA norm (z) in each latitude–altitude bin as the occurrence frequency of cloud layers detected by both lidars, divided by a cloud fraction value for the same latitude–altitude bin. We simulated the SR(z) and CF(z) profiles that would be observed by these two lidars if they were flying over the same atmosphere predicted by a global model. By analyzing these simulations, we show that the theoretical limit for CDA$_{norm}^{theor}$(z) for a combination of ALADIN and CALIOP instruments is equal to 0.81±0.07 at all altitudes. In other words, 19 % of the clouds cannot be detected simultaneously by two instruments due to said differences. The analyses of the actual observed CALIOP–ALADIN collocated dataset containing ~78 000 pairs of nighttime SR(z) profiles revealed the following points: (a) the values of SR(z) agree well up to ~3 km height. (b) The CF(z) profiles show agreement below ~3 km, where ~80 % of the clouds detected by CALIOP are detected by ALADIN as expected from the numerical experiment. (c) Above this height, the CDA$_{norm}^{obs}$(z) reduces to ~50 %. (d) On average, better sensitivity to lower clouds skews ALADIN's cloud peak height in pairs of ALADIN–CALIOP profiles by ~0.5±0.6 km downwards, but this effect does not alter the heights of polar stratospheric clouds and high tropical clouds thanks to their strong backscatter signals. (e) The temporal evolution of the observed CDA$_{norm}^{obs}$(z) does not reveal any statistically significant change during the considered period. This indicates that the instrument-related issues in ALADIN L0/L1 have been mitigated, at least down to the uncertainties of the following CDA$_{norm}^{obs}$(z) values: 68±12 %, 55±14 %, 34±14 %, 39±13 %, and 42±14 % estimated at 0.75, 2.25, 6.75, 8.75, and 10.25 km, respectively.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically defined MEMS 2.0 model

Abstract. For decades, predominant soil biogeochemical models have used conceptual soil organic matter (SOM) pools and only simulated them to a shallow depth in soil. Efforts to overcome these limitations have prompted the development of the new generation SOM models, including MEMS 1.0, which represents measurable biophysical SOM fractions, over the entire root zone, and embodies recent understanding of the processes that govern SOM dynamics. Here we present the result of continued development of the MEMS model, version 2.0. MEMS 2.0 is a full ecosystem model with modules simulating plant growth with above- and belowground inputs, soil water and temperature by layer, decomposition of plant inputs and SOM, and mineralization and immobilization of nitrogen (N). The model simulates two commonly measured SOM pools – particulate and mineral-associated organic matter (POM and MAOM, respectively). We present results of calibration and validation of the model with several grassland sites in the US. MEMS 2.0 generally captured the soil carbon (C) stocks (R2 of 0.89 and 0.6 for calibration and validation, respectively) and their distributions between POM and MAOM throughout the entire soil profile. The simulated soil N matches measurements but with lower accuracy (R2 of 0.73 and 0.31 for calibration and validation of total N in SOM, respectively) than for soil C. Simulated soil water and temperature were compared with measurements, and the accuracy is comparable to the other commonly used models. The seasonal variation in gross primary production (GPP; R2 = 0.83), ecosystem respiration (ER; R2 = 0.89), net ecosystem exchange (NEE; R2 = 0.67), and evapotranspiration (ET; R2 = 0.71) was well captured by the model. We will further develop the model to represent forest and agricultural systems and improve it to incorporate new understanding of SOM decomposition.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A comparative study of two-way and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US: performance evaluation and impacts of chemistry–meteorology feedbacks on air quality

The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry–meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US. Long-term (5 years from 2008 to 2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, precipitation (except for against the National Climatic Data Center data), and shortwave and longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O3) and fine particulate matter (PM 2.5 ). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O 3 , sulfate, nitrate, ammonium, elemental carbon, tropospheric O 3 residual, and column nitrogen dioxide (NO 2 ). For example, the mean biases have been reduced by more than 10 W m –2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8 h O 3 . However, relatively large biases still exist for cloud predictions, some PM 2.5 species, and PM 10 that warrant follow-up studies to better understand those issues. The impacts of chemistry–meteorological feedbacks are found to play important roles in affecting regional air quality in the US by reducing domain-average concentrations of carbon monoxide (CO), O 3 , nitrogen oxide (NO x ), volatile organic compounds (VOCs), and PM 2.5 by 3.1 % (up to 27.8 %), 4.2 % (up to 16.2 %), 6.6 % (up to 50.9 %), 5.8 % (up to 46.6 %), and 8.6 % (up to 49.1 %), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.

58 GEOSCIENCES↗

DOE OSTI2021

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Gas Adsorption Analyzer

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

3Flex,BET specific surface area and pore volume, A↗

DOE OSTI2021

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) X-Ray Diffraction

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Cryogenic Refrigerator Applied to Freezing Test

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Aerodynamic Particle Sizer, Condensation Particle Counter, and Meteorological Instrument Data Analysis

Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - Tracegases

Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - APS

Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - SP2

Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - SPAMS

Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - SMPS

Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - CAPS-SSA

Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - PASS

Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER-CAT-LANL Aerosol Optics and Chemical Speciation - CCN

Aerosol size distribution, optical properties, and speciation data collected in La Port TX, during TRACER-CAT-LANL, July 2022, with basic trace gas measurements. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by Los Alamos National Laboratory (TRACER-CAT-LANL, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-LANL is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol and trace gas data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments Include: Picarro GHG Photoacoustic Soot Spectrometer (PASS-3) Humidified-cavity attenuated phase shift-single scattering albedo particulate matter monitor (H-CAPS-PMSSA) Soot Particle Aerosol Mass Spectrometer (SPAMS) Single-Particle Soot Photometer (SP2) Cloud Condensation Nuclei Counter (CCNc) Aerodynamic Particle Sizer (APS) Scanning Mobility Particle Sizer (SMPS)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

EPCAPE-PT-LANL Measurements: Humidified Cavity Attenuated Phase Shift Spectroscopy

Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on cloud properties. As part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted. Our campaign focused on measuring the optical and chemical properties of aerosols and their interactions within marine stratocumulus clouds in La Jolla, California. EPCAPE-PT-LANL enhances the primary goals of EPCAPE through innovative observations of vapor-phase transitions between aerosols and cloud droplets, the impact of black carbon on aerosol-cloud dynamics, and the effects of cloud processing on aerosol optical properties. Instrument: Humidified Cavity Attenuated Phase Shift Particulate Matter Single Scattering Albedo (H-CAPS-PMSSA, Aerodyne Inc) Data Notes: The scattering truncation correction was not applied to Bsca. The Bext needs no correction. Files: data_10sec_CAPS.csv, data_10min_CAPS.csv Header: - Bext_wet_CAPS_450nm[1/Mm]: Wet aerosol extinction coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Bsca_wet_CAPS_450nm[1/Mm]: Wet aerosol scattering coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Temp_wet_CAPS[K]: Temperature inside the wet CAPS measurement chamber, in Kelvin. - Bext_dry_CAPS_450nm[1/Mm]: Dry aerosol extinction coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Bsca_dry_CAPS_450nm[1/Mm]: Dry aerosol scattering coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Temp_dry_CAPS[K]: Temperature inside the dry CAPS measurement chamber, in Kelvin. - Wet_RH_preCAPS[%]: Relative humidity before entering the wet CAPS, in percent. - Wet_RH_postCAPS[%]: Relative humidity after exiting the wet CAPS, in percent. - Humidifier_RH_CAPS[%]: Relative humidity inside the humidifier used with the CAPS, in percent. - dualCAPS_inlet_RH[%]: Relative humidity at the inlet of the dual (wet/dry) CAPS setup, in percent. - Wet_Temp_preCAPS[C]: Temperature before entering the wet CAPS, in degrees Celsius. - Wet_Temp_postCAPS[C]: Temperature after exiting the wet CAPS, in degrees Celsius. - Humidifier_Temp[C]: Temperature inside the humidifier used with the CAPS, in degrees Celsius. - dualCAPS_inlet_Temp[C]: Temperature at the inlet of the dual (wet/dry) CAPS setup, in degrees Celsius. - Zero_dry_CAPS: 1 is a calibration check for the dry CAPS to ensure zero reading under filter air conditions. - Zero_wet_CAPS: 1 is a calibration check for the wet CAPS to ensure zero reading under filter air conditions. - CVI_Flag[bool]: A boolean flag indicating whether the Counterflow Virtual Impactor (CVI) was active (true) or inactive (false) during the measurement.

54 ENVIRONMENTAL SCIENCES↗