Abstract

Assessment of Sea Salt (SS) and Non-Sea Salt (NSS) aerosols in rainwater is important to understand the characterization of marine and continental aerosols and their source pathways. Sea salt quantification based on standard seawater ratios are primarily constrained with high uncertainty with its own limitations. Here, by the novelty of k-mean clustering and Positive Matrix Factorization (PMF) analysis, we segregate the air masses into two distinct clusters (oceanic and continental) during summer monsoon period signifying the complex intermingle of sources that act concomitantly. The rainwater composition during strong south-westerly wind regimes (cluster 2-oceanic) was profoundly linked with high sea salt and dust, whereas north-westerly low wind regimes (cluster 1-continental) showed an increase in SO42− and NO3−. However, SO42− abundance over NO3− in rain-water depicted its importance as a major acidifying ion at the region. The satellite-based observations indicate the presence of mid-tropospheric dust at the top (3–5 km) and marine sea salt at bottom acts as a “sandwich effect” for maritime clouds that leads to elevated Ca2+, Na+, Mg2+, and Cl− in rainwater. This characteristic feature is unique as sea spray generation due to high surface winds and dust aloft is only seen during this period. Furthermore, four source factors (secondary inorganic aerosol, mixed dust & sea salt, biomass burning & fertilizer use, and calcium neutralization) derived from PMF analysis showed contribution from local activities as well as long-range transport as dominant sources for the rainwater species.

Highlights

  • Assessment of Sea Salt (SS) and Non-Sea Salt (NSS) aerosols in rainwater is important to understand the characterization of marine and continental aerosols and their source pathways

  • The present study utilizes rainwater composition data from June to October 2016 at Mahabaleshwar, a high altitude site situated in Western Ghats mountain region in peninsular India along with various other available satellite and reanalysis datasets to assess the impact of different sources on the presence of major inorganic water-soluble constituents of the rainwater. k-mean clustering algorithm was primarily applied to air mass back trajectories to segregate the point observation as well as satellite and model reanalysis datasets

  • The identical scenario was visualized from the rainwater composition data with higher volume-weighted average concentrations of Na+, Cl−, and Ca2+ in cluster 2 as compared to cluster 1

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Summary

Introduction

Assessment of Sea Salt (SS) and Non-Sea Salt (NSS) aerosols in rainwater is important to understand the characterization of marine and continental aerosols and their source pathways. We utilize k-mean clustering algorithm[19,20,21] imposed over hourly HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) backward (5 days) trajectories to separate discrete air masses originated during SW-monsoon period over the Arabian Sea reaching at the receptor site, Mahabaleshwar (High Altitude Cloud Physics Laboratory-HACPL 17.92° N, 73.66° E) at an altitude of 1375 m above mean sea level definite into two clusters to segregate as NSS and SS spells respectively This cluster classification is applied to daily 113 rainwater samples during 2016 monsoon rainfall that is further based on greater than 60 percent threshold trajectories grouped into a specific cluster for a day

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