Assessment of the relationship between Biomass Burning typology and the Aerosol intensive optical properties from LIDAR measurements during smoke transport (ABBA)

PN-III-P2-2.1-PED-2019-1816

Implementation period: 15/10/2020-14/10/2022

Objective: To develop a methodology which quantifies the relationship between the biomass burning typology and the smoke measurements acquired by lidar in remote locations. Biomass burning typology (or smoke typology) will refer to the vegetation type (land cover).

The smoke measurements by lidar (observations) are quantified in terms of IOPs. The analysis will be based on the long timeseries (2008-2018) of lidar measurements at INOE (Magurele, Ilfov) and exiting state-of-the-art algorithms that INOE developed in the framework of the European infrastructure ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure - https://www.actris.eu/). Additional synergetic information from satellites’ and model will be used to identify the fires (BB source). Thus, in order to identify the smoke origin, we use the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model (Rolph et al., 2017), which computes the backtrajectories of the air masses from a certain altitude at our location (based on certain meteorological fields) and the fire database FIRMS (Fire Information for Resource Management System), which contains all the fires detected around the globe. After fires’ identification (in time and space), we extract the vegetation type in those locations. The fire radiative power (FRP) provided by FIRMS will be taken into account as well in order to assess the injection height and further assess the impact of transported BB smoke in Magurele. The cluster analysis will be used in order to group the fires aiming to obtain homogeneous areas (clusters) from vegetation type point of view. Next, statistical tools will be employed to quantify the link between vegetation type and the lidar measurements (IOPs). We expect the results will bring insights about how different fires, based on the vegetation type, affect Magurele site. The direct application of this investigation will be the integration of the results in the NATALI neural network aerosol typing algorithm built at INOE. Thus, the algorithm will provide more information on smoke category, being able to identify BB more specifically (e.g. grass BB or forest BB). NATALI is described in Nicolae et al. (2018) and it has free access for various users (http://natali.inoe.ro/).

Estimated results:

  • Report on assessment of the lidar data and land cover data over 2008-2018,
  • Website development;
  • Report on webpage based interactive tool;
  • Report on assessment of the relationships between the BB typology and intensive parameters as retrieved from lidar measurements;
  • Report on dissemination and communication (workshops, conferences, 1 article journal high IF)