Search

The dispersion of particulate matter (PM10) in complex topographic areas is a scientific problem that poses significant challenges to human health, environmental scientists, and policymakers. To date, misused terrain elevation data in air quality modelling studies influence the over/under predictions results. The study evaluates the AERMOD model for modelling the dispersion of particulate matter (PM10) in the complex topography of Kigali, Rwanda. It compares model outputs for four digital elevation models (DEMs), namely SRTM1 (30 m), SRTM3 (90 m), SRTM30 (900 m), and GTOPO30 (900 m). It evaluates the model performance using in-situ monitoring data and statistical metrics counting the fraction of predictions within a factor of two (FAC2), fractional bias (FB), mean bias (MB), geometric mean bias (MG), geometric variance (GV), normalized mean bias (NMB), and normalized mean squared error (NMSE). The annual averages of in-situ monitoring covering 12 months of 2021 at stations A, B, C, D, E, and F were 43.73 µg m-3, 53.64 µg m-3, 53.25 µg m-3, 42.02 µg m-3, 44.04 µg m-3, and 56.01 µg m-3, respectively. The 24-hour average for modelling results was 22 µg m-3 for SRTM1 and 23 µg m-3 for other DEMs, while the annual average was 10 µg m-3 for all DEMs. The FB metric suggests that the choice of DEM significantly impacts the accuracy of the model predictions, with SRTM3 and GTOPO30 providing the best performance at stations A and F, whereas FAC2, MB, MG, GV, NMB, and NMSE provided a meaningful model performance at stations B, C, D, and E. The study provides valuable insights into the dispersion of PM10 in complex topographic areas and can help inform air quality models and policymaking in similar regions. [ABSTRACT FROM AUTHOR]

Aerosols play an important role in Earth's climate system, and thus long-time ground- based measurements of aerosol optical properties are useful in understanding this role. Ten years of quality-assured measurements between 2010 and 2020 are used to investigate the aerosol climatology in the Cluj-Napoca area, in North-Western Romania. In this study, we analyze the aerosol optical depth (AOD), single scattering albedo (SSA) and angstrom exponent obtained by the CIMEL sun photometer, part of the aerosol robotic network (AERONET), to extract the seasonality of aerosols in the region and investigate the aerosol climatology of the area. Higher aerosol loads are found during July and August. The angstrom exponent has the lowest values in April and May, and the highest in August. The classification of aerosols using AERONET data is performed to separate dust, biomass burning, polluted urban, marine and continental-dominant aerosol mixtures. In addition, the study presents the validation efforts of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) dataset against AERONET AOD over a 10-year period. [ABSTRACT FROM AUTHOR]

Black carbon aerosols are the second largest contributor to global warming while also being linked to respiratory and cardiovascular disease. These particles are generally found in smoke plumes originating from biomass burning and fossil fuel combustion. They are also heavily concentrated in smoke plumes originating from oil fires exhibiting the largest ratio of black carbon to organic carbon. In this study, we identified and analyzed oil smoke plumes derived from 30 major industrial events within a 12-year timeframe. To our knowledge, this is the first study of its kind that utilized a synergetic approach based on satellite remote sensing techniques. One objective of this study is to highlight the importance of satellite remote sensing techniques in identifying these types of events. As opposed to ground stations, satellite data offers access to remote areas all over the globe which would otherwise be very difficult to reach. Satellite data offers access to these events which, as seen in this study, are mainly located in war prone or hazardous areas. This study focuses on the use of MODIS (Moderate Resolution Imaging Spectroradiometer) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) products regarding these types of aerosol while also highlighting their intrinsic limitations. By using data from both MODIS instruments onboard Terra and Aqua satellites we addressed the temporal evolution of the smoke plume while assessing Lidar specific properties and plume elevation using CALIPSO data. We present several aerosol properties in the form of plume specific averaged values. The MODIS ocean algorithms were successful in retrieving aerosol properties which, on average, ranged from -0.06 to 0.16 for plume specific AOD, - 0.18 to 1.25 for Ångström exponent and 0.29 to 1.73 μm for the effective radius. CALIPSO measurements showed values of plume AOD ranging from 0 to 0.14 (532 nm) and 0 to 0.13 (1064 nm) except for one event where AOD values showed 1.52 (532 nm) and 1.42 (1064 nm). AE values ranged from 0.11 to 0.33 which were in agreeance with MODIS values. A large discrepancy can be found in one event where CALIPSO measured AOD values 5 times higher than MODIS. This event also produced the largest lidar ratio at 109 sr (532 nm) and 86 (1064 nm). Other lidar ratio values ranged from 37 to 55 sr however these unconstrained solutions were obtained for the entire layer of which the plumes were a part of and thus did not reflect specific plume conditions. Particulate backscatter values ranged from 0.002 to 0.0017km-1 sr-1 while extinction coefficient values ranged from 0.10 to 1.65 km-1. On average backscatter and extinction coefficient values were 2 to 9 times higher than local background values. Particulate depolarization ratios ranged from 0.11 to 0.15 in 4 out of 6 cases while the remaining two ranged from 0.27 to 0.32 where dust was highly dominant. The values represented in this study are in good agreement with similar studies that used ground based and flight measurements. We believe that MODIS values are a conservative estimation of plume AOD since MODIS algorithms rely on general aerosol models and various atmospheric conditions within the look-up tables which do not reflect the highly absorbing nature of these smoke plumes. CALIPSO measurements are heavily dependent on lidar ratios which are not directly measured if plumes within the planetary boundary layer. We also believe that AOD values based on CALIPSO measurements are conservative in nature since heavy absorbing smoke would yield larger lidar ratios and AOD values. Based on this study we conclude that the MODIS land algorithms are not yet suited for retrieving aerosol properties for these types of smoke plumes due to the strong absorbing properties of these aerosols. We believe that these types of studies are a strong indicator for the need of improved aerosol models and retrieval algorithms. [ABSTRACT FROM AUTHOR]

In this paper we analyse the optimization of lidar systems in order to investigate Clear Air Turbulence (CAT), which is a dangerous weather phenomenon affecting especially the commercial aircrafts. It is assumed that aircrafts can cause air turbulence that can be detected by lidar, since pressure gradients could cause slight variations of the molecular depolarization values. This study will assess the capability of the lidar systems to highlight air turbulence from depolarization profiles. In order to do this, lidar measurements were performed at two sites in Romania: one in Magurele, near Bucharest and one in Cluj-Napoca. The measurements were accompanied by continuous flight path monitoring to mark the incidence events above the measuring locations. [ABSTRACT FROM AUTHOR]