Atmospheric Radiation Measurement Climate Research Facility US Department of Energy

acsm > Aerosol Chemical Speciation MonitorInstrument Type(s) > Baseline • Guest

The aerosol chemical speciation monitor is a thermal vaporization, electron impact, ionization mass spectrometer that measures bulk chemical composition of the rapidly evaporating component of sub-micron aerosol particles in real time. Standard measurements include mass concentrations of organics, sulfate, nitrate, ammonium, and chloride.


Data are used to determine the chemical composition of the aerosol and help identify the source.


  • Fixed
  • AMF1
  • AMF2
  • AMF3

Data Details

Developed By Thomas Watson
Contact Thomas Watson
Resource(s) Data Directory
Data format The data are in IGOR text files, *.itx and CSV files.
Site SGP
Content time range 18 November 2010 - 29 November 2022
Attribute accuracy No formal attribute accuracy tests were conducted.
Positional accuracy No formal positional accuracy tests were conducted.
Data Consistency and Completeness Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
Access Restriction No access constraints are associated with this data.
Use Restriction No use constraints are associated with this data.
File naming convention time_series_20190402_10_29_19_20200105_22_22_30.itx
Citations Watson, T., Aiken, A., Zhang, Q., Croteau, P., Onasch, T., Williams, L., and Flynn, C. First ARM Aerosol Chemical Speciation Monitor Users Meeting Report (DOE/SC-ARM-TR-215). Informal Report, BNL-209030-2018-INRE, June 22, 2018.


Barrett P, S Abel, H Coe, I Crawford, A Dobracki, J Haywood, S Howell, A Jones, J Langridge, G McFarquhar, G Nott, H Price, J Redemann, Y Shinozuka, K Szpek, J Taylor, R Wood, H Wu, P Zuidema, S Bauguitte, R Bennett, K Bower, H Chen, S Cochrane, M Cotterell, N Davies, D Delene, C Flynn, A Freedman, S Freitag, S Gupta, D Noone, T Onasch, J Podolske, M Poellot, S Schmidt, S Springston, A Sedlacek III, J Trembath, A Vance, M Zawadowicz, and J Zhang. 2022. "Intercomparison of airborne and surface-based measurements during the CLARIFY, ORACLES and LASIC field experiments." Atmospheric Measurement Techniques, 15(21), 10.5194/amt-15-6329-2022.

Giangrande S, J Comstock, S Collis, J Shilling, K Gaustad, K Kehoe, S Xie, and D Zhang. 2022. Translator Plan: A Coordinated Vision for Fiscal Years 2023-2025. Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-22-003. 10.2172/1893730.

Kumar J, T Paik, N Shetty, P Sheridan, A Aiken, M Dubey, and R Chakrabarty. 2022. "Correcting for filter-based aerosol light absorption biases at the Atmospheric Radiation Measurement program's Southern Great Plains site using photoacoustic measurements and machine learning." Atmospheric Measurement Techniques, 15(15), 10.5194/amt-15-4569-2022.

May A and H Li. 2022. "Application of machine learning approaches in the analysis of mass absorption cross-section of black carbon aerosols: Aerosol composition dependencies and sensitivity analyses." Aerosol Science and Technology, 56(11), 10.1080/02786826.2022.2114312.

Dedrick J, G Saliba, A Williams, L Russell, and D Lubin. 2022. "Retrieval of the sea spray aerosol mode from submicron particle size distributions and supermicron scattering during LASIC." Atmospheric Measurement Techniques, 15(14), 10.5194/amt-15-4171-2022.

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