Water-soluble ions in dust particles depending on meteorological conditions in urban environment

Particulate Oxalate‐To‐Sulfate Ratio as an Aqueous Processing Marker: Similarity Across Field Campaigns and Limitations

“…To more deeply characterize the high-OXL population, we compared several key variables between the CAMP 2 Ex high-OXL and high-SO 4 2− populations (Table S3 in Supporting Information S1), all of which showed statistically significant differences based on the Mann-Whitney U-test (99% confidence level; p < 0.01). The following characteristics hint to gas-particle partitioning of OXL and/or its precursors onto dust aloft as has been documented in other studies (e.g., Stahl et al, 2020b;Sullivan & Prather, 2007): (a) dust species such as Ca 2+ (Kchih et al, 2015) had approximately double the mass concentration in high-OXL air as compared to high-SO 4 2− air (Table S3 in Supporting Information S1), (b) high-OXL air was mostly sampled in the free troposphere (Figure S5 in Supporting Information S1), (c) ionic crustal ratios in the free troposphere (>5 km) were more similar to dust values than those for sea salt based on literature (Park et al, 2004;Švédová et al, 2019;Wang et al, 2018) (Figure 2), and (d) a prominent coarse mode peak is observed for high-OXL samples (Figure S6 in Supporting Information S1). Among the other two campaigns sampling PM 4 , elevated OXL:SO 4 2− values at higher altitudes were also observed during AToM (Figure S4 in Supporting Information S1); during ACTIVATE, dust was not prevalent at the altitudes sampled (<5 km).…”

“… Altitude‐resolved linear regressions of dust species collected by the particle‐into‐liquid sampler during the Cloud, Aerosol, and Monsoon Processes‐Philippines Experiment (BB samples excluded) colored by OXL:SO 4 2− . Red and blue dashed lines denote literature‐based ratios for dust (Park et al., 2004; Švédová et al., 2019; Wang et al., 2018) and sea salt (Chesselet et al., 1972), respectively. …”

“…Regardless of BB differences between campaigns, such a pronounced impact on the OXL:SO 4 2− ratio in those respective datasets demonstrates the importance of accounting for BB when exploiting the OXL:SO 4 2− ratio for analysis and modeling purposes relevant to secondary aerosol formation processes. (Park et al, 2004;Švédová et al, 2019;Wang et al, 2018) and sea salt (Chesselet et al, 1972), respectively.…”

An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species

“…In this study, MLP was employed because of its comparatively powerful training steps combined with the requirement of optimising the weights of the artificial layers of neurones [41,42]. In spite of the fact that several potential contributors, such as vehicle exhausts, factory emissions, open burnings, and domestic heating, are responsible for the altering contents of WSIS, OC, and EC ( [1,21,22]), weather conditions can dramatically affect the fluctuations of these chemical constituents [43][44][45].…”

A Novel Stacking Ensemble Learning Approach for Predicting PM2.5 Levels in Dense Urban Environments Using Meteorological Variables: A Case Study in Macau

“…Additionally, fine PM pollution adversely affects the respiratory health of humans and animals. Concentrations of PM, comprising carbonaceous material, elemental carbon, sulphates, nitrates, ammonia, and resuspended particles, are linked to a variety of clinical manifestations of pulmonary and cardiovascular diseases, as well as to the morbidity and mortality associated with respiratory diseases in humans and animals [3][4][5].…”