Potential Source Contribution Function Analysis of High Latitude Dust Sources over the Arctic: Preliminary Results and Prospects

A comprehensive approach to quantify the source identification and human health risk assessment of toxic elements in park dust

In this research, enrichment factor (EF) and pollution load index were utilized to explore the contamination characteristics of toxic elements (TEs) in park dust. The results exhibited that park dust in the study area was mainly moderately polluted, and the EF values of dust Cd, Zn, Pb, Cu and Sb were all > 1. The concentrations of Cr, Cu, Zn and Pb increased with the decrease of dust particle size. The investigation results of chemical speciation and bioavailability of TEs showed that Zn had the highest bioavailability. Three sources of TEs were determined by positive matrix factorization model, Pearson correlation analysis and geostatistical analysis, comprising factor 1 mixed sources of industrial and transportation activities (46.62%), factor 2 natural source (25.56%) and factor 3 mixed source of agricultural activities and the aging of park infrastructures (27.82%). Potential ecological risk (PER) and human health risk (HHR) models based on source apportionment were exploited to estimate PER and HHR of TEs from different sources. The mean PER value of TEs in the park dust was 114, indicating that ecological risk in the study area was relatively high. Factor 1 contributed the most to PER, and the pollution of Cd was the most serious. There were no significant carcinogenic and non-carcinogenic risks for children and adults in the study area. And factor 3 was the biggest source of non-carcinogenic risk, and As, Cr and Pb were the chief contributor to non-carcinogenic risk. The primary source of carcinogenic risk was factor 2, and Cr was the cardinal cancer risk element.

Rainwater chemistry composition in Bellsund: Sources of elements and deposition discrepancies in the coastal area (SW Spitsbergen, Svalbard)

Discrepancies in rainfall chemistry in Bellsund were found to be influenced by the orographic barrier and related to the variability in the inflow of air masses as well as to the distance of sampling sites from the sea and thus the extent of sea spray impact. This study covers measurements of rainfall (P) and air temperature (T), physicochemical parameters (pH, specific electrolytic conductivity (SEC), major ions (Cl^-, NO₃^-, SO₄^2-) and elements (Na, Ca, Mg,K), as well as trace elements (i.a. As, Cd, Cr, Fe, Co, Pb, Ni, Zn) and dissolved organic carbon (DOC) in 22 rainfall samples collected in August on the Calypsostranda marine terrace and in the forefield of a land-terminating glacier (NW Wedel Jarlsberg Land). The comparison of chemical parameters in the samples revealed major discrepancies, including statistically significant higher rainwater pH and SEC, and the levels of Ag, As, Bi, Ca, Co, Fe, Li, Mn, Mo, Ni, Pb, Sb, and V, deposited near the seashore (Calypsostranda) than in the glacier forefield. Cluster analysis (CA) showed that elements deposited in lower concentrations at the glacier forefield site came from predominately anthropogenic sources. Conversely, CA results of metals and metalloids deposited on the Calypsostranda marine terrace indicate both natural and anthropogenic sources. A correlation matrix and principal component analysis (PCA) permitted identifying two primary factors affecting rainfall chemistry at each of the study sites. In Calypsostranda, these were the inflow of relatively unpolluted cold air (F1 = 35.1%) and sea spray (F2 = 27.6%), while in the glacier forefield the factors were an orographic barrier (F1 = 37.3%) and the inflow of polluted warm air (F2 = 25.2%).

Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning

Atmospheric aerosols are important drivers of Arctic climate change through aerosol-cloud-climate interactions. However, large uncertainties remain on the sources and processes controlling particle numbers in both fine and coarse modes. Here, we applied a receptor model and an explainable machine learning technique to understand the sources and drivers of particle numbers from 10 nm to 20 μm in Svalbard. Nucleation, biogenic, secondary, anthropogenic, mineral dust, sea salt and blowing snow aerosols and their major environmental drivers were identified. Our results show that the monthly variations in particles are highly size/source dependent and regulated by meteorology. Secondary and nucleation aerosols are the largest contributors to potential cloud condensation nuclei (CCN, particle number with a diameter larger than 40 nm as a proxy) in the Arctic. Nonlinear responses to temperature were found for biogenic, local dust particles and potential CCN, highlighting the importance of melting sea ice and snow. These results indicate that the aerosol factors will respond to rapid Arctic warming differently and in a nonlinear fashion.

A new application of multi-criteria decision making in identifying critical dust sources and comparing three common receptor-based models

Dust storms are a common phenomenon in arid and semi-arid regions in West Asia, which has led to high levels of PM₁₀ in local and remote area. The Yazd city in Iran with a high PM₁₀ level located downstream of dust sources in the Middle East and Central Asia. In this study, based on meteorological and PM₁₀ monitoring data, backward trajectory modeling of air parcels related to dust events at Yazd station was performed using the HYSPLIT model in 2012-2019. The trajectory cluster analysis was used to identify the main dust transport pathways and wind systems. Three methods of Cross-referencing Backward Trajectory (CBT), Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) were used to identify the most critical dust sources. Multi-Criteria Decision Making (MCDM) methods were also used to integrate the results. Nine dust sources affecting central Iran were determined, and six criteria from different aspects were considered. To prioritize the dust sources affecting central Iran from four new MCDM methods, including WASPAS, EDAS, ARAS and TOPSIS were used. The results showed that the Levar wind system (51%), the Shamal wind system (32%) and the Prefrontal wind system (18%) were the most important wind systems to cause dust events in central Iran. The MCDM approach to identify dust sources also showed that Dasht-e-Kavir in central Iran was the most critical dust source. The results also showed that in hot seasons (spring and summer), local and Central Asia dust sources and cold seasons (autumn and winter), Middle East dust sources have the greatest impact on dust events in central Iran. Also, a comparison of common receptor-based methods for identifying dust sources showed that CBT, CWT and PSCF were the most appropriate methods for identifying dust sources, respectively.