Assessment of mineralogical and chemical properties of airborne dust in Iraq

Airborne desert dust in the Northern Adriatic area (Croatia): Different sources

During the implementation of the INTERREG IT-HR project ECOMOBILITY, whose one of the goals was to estimate the impact of ship emissions on air quality in the port city of Rijeka (Croatia) and Venice (Italy), two particular weekly samples were collected in Rijeka, during the first and the thirteen weeks of sampling, i.e. S01 (16.10.-23.10.2018) and S13 (24.04.-30.04.2019.), respectively. Both samples have similarities regarding species characteristic for desert dust contribution, but HYSPLIT analyses excluded Saharan desert to be the source of the S01 sample. Unlike Saharan dust, this sample had a high contribution of fine and ultrafine particles (>50 % and 9.8 %, respectively), as well as secondary inorganic (sulfates, ammonium) and organic (water soluble organic compounds - WSOC) aerosols. Detailed synoptic situation and HYSPLIT backward trajectories pointed out the Syrian Desert as the source of this collected sample. The same source was proved by MERRA-2 reanalysis of the desert dust emission. Although the Saharan dust episodes, mostly in precipitation, are well known in the Northern Adriatic area, this is the first time to indicate Syrian Desert as a source of airborne particulates. This assumption was confirmed with chemical species characteristic for the Syrian Desert, i.e. higher content of potassium from K- feldspar and phosphates.

Agricultural abandoned lands as emission sources of dust containing metals and pesticides in the Sonora-Arizona Desert

This investigation examines the transport of metal- and pesticide-polluted dust emitted by one of the most relevant agricultural areas of Northwestern Mexico. In the contaminated area, an excessive water extraction of the aquifer and seawater intrusion caused the abandonment of fields, which are pollutant-loaded dust emitters. We used air mass forward trajectories (HYSPLIT) model to obtain particle trajectories in the wind and the use of banned pesticides as geochemical tracers for dust transported by wind. Fifty dust samples from 10 agriculture fields and 26 roof dust of a city close to the agricultural area were analyzed for their contents of zirconium, lead, arsenic, zinc, copper, iron, manganese, vanadium, and titanium, by portable X-ray fluorescence. Nine pesticides were analyzed in the roof dust and agricultural soil samples by gas chromatography. Results show that the distribution of metals was significantly different between active and abandoned fields. Arsenic-lead-copper was mainly concentrated in abandoned fields, while zinc-iron-manganese-titanium was dominant in active fields. Two potential sources of metal contamination were found by principal component analysis (PCA): (I) a mixture of traffic and agricultural sources and (II) a group related to agricultural activities. The occurrence of banned pesticides in dust deposited on roofs collected at nearby cities confirms the atmospheric transport from the agricultural area. The HYSPLIT results indicated that the dust emitted from agricultural fields can reach up to the neighboring states of Sonora, Mexico, and the USA. The impacts that these emissions can have on human health should be studied in future research.

Dust detection and susceptibility mapping by aiding satellite imagery time series and integration of ensemble machine learning with evolutionary algorithms

To mitigate the impact of dust on human health and the environment, it is crucial to create a model and map that identifies the areas susceptible to dust. The present study focused on identifying dust occurrences in the Bushehr province of Iran between 2002 and 2022 using moderate-resolution imaging spectroradiometer (MODIS) imagery. Subsequently, an ensemble machine learning model was improved to prepare a dust susceptibility map (DSM). The study employed differential evolution (DE), genetic algorithm (GA), and flower pollination algorithm (FPA) - three evolutionary algorithms - to enhance the random forest (RF) ensemble model. A spatial database was created for modeling, including 519 dust occurrence points (extracted from MODIS imagery) and 15 factors affecting dust (Slope, bulk density, aspect, clay, altitude, sand, rainfall, lithology, soil order, distance to river, soil texture, normalized difference vegetation index (NDVI), soil water content, land cover, and wind speed). By utilizing the differential evolution (DE) algorithm, we determined the significance of these factors in impacting dust occurrences. The results indicated that altitude, wind speed, and land cover were the most influential factors, while the distance to the river, bulk density, and soil texture had less impact on dust occurrence. Data were preprocessed using multicollinearity analysis and the frequency ratio (FR) approach. For this research, three RF-based meta-heuristic optimization algorithms, namely RF-FPA, RF-GA, and RF-DE, were created for DSM. The effectiveness prediction of the constructed models by indexes of root-mean-square-error (RMSE), the area under the receiver operating characteristic (AUC-ROC), and coefficient of determination (R2) from best to worst were RF-DE (RMSE = 0.131, AUC-ROC = 0.988, and R2 = 0.93), RF-GA (RMSE = 0.141, AUC-ROC = 0.986, and R2 = 0.919), RF-FPA (RMSE = 0.157, AUC-ROC = 0.981, and R2 = 0.9), and RF (RMSE = 0.173, AUC-ROC = 0.964, and R2 = 0.878). The results showed that combining evolutionary algorithms with an RF model improves the accuracy of dust susceptibility modeling.

Spatio-Temporal Evolution of a Typical Sandstorm Event in an Arid Area of Northwest China in April 2018 Based on Remote Sensing Data

Northwest China is significantly affected by sandstorm disasters. To mitigate the negative impacts of sandstorm events, it is critical to understand the spatio-temporal variations in typical sand and dust storms and their influencing factors. In this work, using ground-based measurements of particulate matter and remote sensing data such as MODIS, OMI, and CALIPSO data, the sources of aerosol pollution and aerosol optical properties of a typical sandstorm event that occurred in Northwest China in 2018 was studied. In addition, the HYSPLIT model was used to explore the air mass trajectories in order to analyze the sand and dust migration process during the sandstorm event. Furthermore, the wind erosion sensitivity of Northwest China was analyzed via single factor analysis and multi-factor superposition of wind field intensity, soil drought index, vegetation coverage, and relief amplitude. Finally, the region of the study area having a high comprehensive wind erosion sensitivity was identified. The results showed that the PM10 concentrations exceeded 400 µg/m3 and the PM2.5/PM10 ratio did not exceeded 0.6 during the sandstorm event, indicating that natural particulate matter was dominant in the ambient air. At the epicenter of pollution, the aerosol optical depth (AOD) at 550 nm was 0.75–1. By combining AOD data with wind speed and direction data from field observation stations, it was found that the sandstorm event in 2018 mainly occurred between 1 April and 3 April, and affected all of Northwest China on 2 April and 3 April. The absorbed aerosol index (AAI) ranged between 2.5 and 4, indicating that the Taklimakan Desert was the main source of sandstorm events in Northwest China. The CALIPSO total attenuated backscatter coefficient at 532 nm indicated that the main component of tropospheric aerosol in this region was distributed in the range of 0–12.5 km. The simulated airflow track showed that it had the same dust source regions as AAI index studies. Moreover, investigation of wind erosion sensitivity in the study areas indicated that the Taklimakan Desert and other desert regions were the main ecologically sensitive areas. These conclusions can provide references and suggestions for the mitigation of damage caused by sandstorm events, in addition to the enhancement of ecological governance.

Mineralogical, geochemical, and textural characteristics of soil and airborne samples during dust storms in Khuzestan, southwest Iran

Dust storms are frequent phenomena in Khuzestan Province, southwest Iran, leading to environmental hazards and deleterious impacts on human health. This work analyzes mineralogic and geochemical characteristics of dust sediments at the source regions and in deposition areas in southwest Iran during three dust-storm events in winter and spring 2018. Twenty soil and airborne dust samples were collected and analyzed for compositions of dust at different distances from the source regions in Iraq and southwest Iran, aiming to assess the source characterization and possible mixing processes in the atmosphere. The grain size distributions were also analyzed at specific sites. The results show that about 50 % of the volume size distribution corresponds to particle sizes of above 20 μm, indicating local/regional dust storms of coarse to giant particles. XRD analysis indicates that calcite is the dominant mineral in all the samples, with a high quartz and dolomite fraction. The most abundant major compounds are SiO₂ and CaO, while Cl, Ba, Sr, Pb, Ni, Zn, Cr, V are the main trace elements. The enrichment factor (EF) analysis showed that apart from Th, Nb, Ce, and V, all the other elements (Pb, Zn, Cr, etc.) have an anthropogenic origin or represent high amounts of pollutant contamination. High levels of elemental enrichment are attributable to intensive pollution in Khuzestan Province and at sampling sites due to fossil-fuel combustion, gas and petroleum drilling activities. Moreover, based on the geo-accumulation index (Igeo), all samples are found to be contaminated by heavy metals due to prior war-related materiel, oil and gas extraction, and emissions from polluting industries.

Atmospheric Aerosols: Some Highlights and Highlighters, Past to Recent Years

The severe harmful impact of atmospheric aerosols over the environment leads to create the diverse human interests and concerns. Various progressive steps were taken by researchers and scientists to understand the fundamentals, such as nucleation and growth mechanisms, formalization of particle dynamics, characterization of the mechanisms for the particle-size dispensation, detection of chemical processes for atmospheric particle sources. The increase in population growth and different manmade activities have led to change in the environmental conditions causes to pollute the distinct vicinities. Different changes in the environment such as land use pattern, increased concentration of various greenhouse gases, and Industrial pollutants change the energy balance in our climatic conditions and affect the radiation budget of earth’ atmosphere. Such changes in climate and polluted environment leads to many health-related ailments to mankind. The present study outlines the recent research perspectives of atmospheric aerosols, their estimation through different modes, effects, and an overview of the current situations that need to be addressed before they become completely incorporated.

Dust Criteria Derived from Long-Term Filter and Online Observations at Gosan in South Korea

Dust and pollution are frequently mixed together in East Asia, causing large uncertainties in assessing climate change and environmental influence and in relevant policymaking. To discern the dust effect on particle mass, we carried out long-term measurements of the mass and key chemical compositions of PM10, PM2.5, and PM1 from August 2007 to February 2012 and collected hourly data of PM10 and PM2.5 concentrations from January 2012 to October 2020 at Gosan, South Korea. The principal component analysis of measured species reveals two dominant factors, pollution and dust, accounting for 46% and 16% of the total variance, respectively. The mode distribution of PM10, PM2.5, and PM1 mass in addition to the dust events helps to provide a robust criterion of the dust impact. Dust can be identified by the mean + standard deviation (σ) of PM10, while the threshold is down to the mean concentration when dust particles experience precipitation. High PM2.5 concentration also presents dust impact; however, the criterion decreases from mean + σ in 2007–2012 to mean in 2012–2020. It indicates that dust is no longer a high-concentration event of PM2.5, but its influence gradually appears in low-concentration particles. Therefore, the dust criterion obtained from long-term PM10 concentration data is robust; however, the standard is based on PM2.5 changes over time and still needs to be determined by follow-up long-term observations.