Advection of Biomass Burning Aerosols towards the Southern Hemispheric Mid-Latitude Station of Punta Arenas as Observed with Multiwavelength Polarization Raman Lidar

Abstract

In this paper, we present long-term observations of the multiwavelength Raman lidar PollyXT conducted in the framework of the DACAPO-PESO campaign. Regardless of the relatively clean atmosphere in the southern mid-latitude oceans region, we regularly observed events of long-range transported smoke, originating either from regional sources in South America or from Australia. Two case studies will be discussed, both identified as smoke events that occurred on 5 February 2019 and 11 March 2019. For the first case considered, the lofted smoke layer was located at an altitude between 1.0 and 4.2 km, and apart from the predominance of smoke particles, particle linear depolarization values indicated the presence of dust particles. Mean lidar ratio values at 355 and 532 nm were 49 ± 12 and 24 ± 18 sr respectively, while the mean particle linear depolarization was 7.6 ± 3.6% at 532 nm. The advection of smoke and dust particles above Punta Arenas affected significantly the available cloud condensation nuclei (CCN) and ice nucleating particles (INP) in the lower troposphere, and effectively triggered the ice crystal formation processes. Regarding the second case, the thin smoke layers were observed at altitudes 5.5–7.0, 9.0 and 11.0 km. The particle linear depolarization ratio at 532 nm increased rapidly with height, starting from 2% for the lowest two layers and increasing up to 9.5% for the highest layer, indicating the possible presence of non-spherical coated soot aggregates. INP activation was effectively facilitated. The long-term analysis of the one year of observations showed that tropospheric smoke advection over Punta Arenas occurred 16 times (lasting from 1 to 17 h), regularly distributed over the period and with high potential to influence cloud formation in the otherwise pristine environment of the region.

Automated time–height-resolved air mass source attribution for profiling remote sensing applications

“…Punta Arenas is located in a region where the atmosphere is known to be clean and one of the least affected by anthropogenic influences (Hamilton et al, 2014). Nevertheless, events of aerosol long-range transport occur occasionally (Foth et al, 2019;Floutsi et al, 2021). Due to the large distance between Punta Arenas and the aerosol source regions, an attribution of observed aerosol events is, in general, rather complicated.…”

“…A comprehensive, easy-to-use software package is also provided. Earlier versions were already used in Haarig et al (2017), Foth et al (2019) and Floutsi et al (2021). Afterwards, two applications illustrate the potential use cases.…”

Impact of exposure to smoke from biomass burning in the Amazon rain forest on human health

“…(32) Algumas partículas podem permanecer na atmosfera por dias a semanas e percorrer longas distâncias, às vezes centenas de quilômetros, (23,24) afetando a concentração de poluentes em regiões distantes da fonte. Como exemplos, podemos citar a presença de partículas provenientes dos incêndios florestais australianos na cidade de Porto Alegre no início de 2020 (33) e a presença de partículas provenientes da Bacia Amazônica e da Bolívia na cidade de São Paulo em agosto de 2019, (34) bem como a presença de partículas na neve e geleiras andinas mostrada em imagens de satélite, levantando a hipótese de que parte do carbono negro encontrado naquela região possivelmente tenha origem em incêndios na Amazônia. (35)…”

Fire-Net: A Deep Learning Framework for Active Forest Fire Detection

“…In recent years, advanced remote sensing (RS) technologies have been adopted to monitor and observe the earth and land covers, which can be on a large scale [12][13][14][15]. Availability of high spatio-temporal resolution data and multispectral imagery allow tremendous applications of forest monitoring such as burned area mapping [7], active fire detection [11,16], burning biomass detection [17,18], and forest disturbance monitoring [19,20]. It is worth noting that one widely and freely available RS data source can be used for all the tasks, which are imagery form Landsat satellites.…”