RepOD

Back trajectories of the passing air masses and the areas they passed over before arriving at the receptor site (Vilnius, 54.72° N, 25.32° E, 161 m above sea level) were evaluated using the geographic information system (GIS) based software, TrajStat (Wang et al. 2009).

Statistical analysis of air mass backward trajectories was combined with average hourly mass concentration (μg/m³) of black carbon related to biomass burning (BC_bb). The BC was measured in-situ using an online 7-wavelength (370, 450, 520, 590, 660, 880, 950 nm) aethalometer (Magee Scientific; model AE-33) during the measurement campaign of the project “Importance of long range transport of BIOmass burning emissions to local Smog events in Urban Environments (BIOSURE)“ in June, 2022. The source apportionment of BC_bb was done as in Minderytė et al. 2022.

The aim of the analysis was to identify the 72-h backward trajectories of the air masses with high concentration of BC and geographical source regions of BC, applying three different analysis methods: clustering analysis (Sirois and Bottenheim, 1995), potential source contribution function (PSCF) (Ashbaugh et al. 1985) and concentration weighted trajectory (CWT) (Hsu et al. 2003). Detailed calculation methods for PSCF and CWT models were described by Wang et al. (2006). Note that both PSCF and CWT values were additionally weighted to reduce the effect of the small values.

The grid covered a study domain between 40–70° north latitude and -20–40° east longitude with a 0.4° × 0.4° resolution.

The study period covered from May 31 to July 1, 2022, and it was divided into three episodes: Episode 1 (May 31 – June11), Episode 2 (June 12–23), and Episode 3 (June 25 – July 1).

The NCEP/NCAR Reanalysis Archive for global scale analysis provided meteorological data for the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler, Hess, 1998), which is integrated into TrajStat program, in order to calculate the air masses backward trajectories arriving every hour (00-23 UTC). The arrival height of back trajectories is 20 m above ground level.

The dataset is comprising following components:

1. Trajectories and clusters_3 episodes.jpg

Figures illustrating the 72-hour backward trajectories with comparable transport tracks which were grouped into clusters for the three measurement episodes. For calculations of air mass back trajectories starting time (date) and location (54.72 N, 25.32 E, 20 m agl), run time (-72 hours), top of model (10000 m agl), vertical (0:data), meteorological (.gbl) files, start day, end day and output path were set regarding to the episode in the TrajStat program. Then backward trajectories were classified by clusters. For cluster calculations the Euclidean distance clustering method was used; the endpoint interval: 1; max cluster number: 30; an actual cluster number for each episode was chosen according to total spatial variation (TSV) results: the TSV percent change versus cluster numbers indicates the suitable cluster number before sudden increase of TSV percent.

2. WPSCF_3 episodes.jpg

Figures illustrating the proportion of pollution trajectories in a study area which was used to analyse the contribution of BC sources. The colours in the weighted PSCF (WPSCF) plot reflect the conditional probability that a cell is a source of BC – the higher the value of WPSCF, the greater the potential impact on BC concentration (Guo et al., 2021). For the BC concentration criterion the value of 0 µg/m³ was chosen; the missing value was denoted with -9999.0.

3. WCWT_3 episodes.jpg

Figures illustrating the trajectories with certain pollution levels. In WCWT method (Hsu et al., 2003), each grid cell, that had associated trajectories which crossed this grid cell, was assigned a weighted average concentration of BC (μg/m³). For WCWT layer its field width and precision were set 10 and 2, respectively. The missing value was denoted with -9999.0.

4. gbl meteo files.zip

Compressed global gridded meteorological data for each day from 31^st of May till 1^st of July, 2022, provided by the NCEP/NCAR Reanalysis Project.

More detailed descriptions on methodology can be found in following references:

Ashbaugh et al. 1985, https://doi.org/10.1016/0004-6981(85)90256-2 

Sirois and Bottenheim 1995, https://doi.org/10.1029/94JD02951

Draxler and Hess 1998, An overview of the HYSPLIT_4 modelling system for trajectories, dispersion, and deposition. Australian Meteorological Magazine, 47, 295-308

Hsu et al. 2003, https://doi.org/10.1016/S1352-2310(02)00886-5

Wang et al. 2006, https://doi.org/10.1016/j.scitotenv.2006.03.040

Wang et al. 2009, https://doi.org/10.1016/j.envsoft.2009.01.004

Guo et al. 2021, https://doi.org/10.1007/s10661-021-09597-8

Minderytė et al. 2022, https://doi.org/10.1016/j.atmosenv.2022.119043

ATTENTION:

We offer a free access to this dataset. The user is however encouraged to share the information on the data use with the Remote Sensing Laboratory by sending an e-mail to rslab@fuw.edu.pl.

In the case this dataset is used for a scientific communication (publication, conference contribution, thesis) we would kindly ask to acknowledge this data provision by adding the following statement in Acknowledgments: "We acknowledge the data originators A.Kalinauskaitė, S.Byčenkienė, and I.S.Stachlewska for the quality-assurance, evaluation, and provision of data sets of the Center for Physical Sciences and Technology (FTMC) in Vilnius and the Remote Sensing Laboratory at the Faculty of Physics of the University of Warsaw (RS-Lab UW)."