The Interrelated Pollution Characteristics of Atmospheric Speciated Mercury and Water-Soluble Inorganic Ions in Ningbo, China

Abstract

Atmospheric mercury and water-soluble inorganic ions (WSIIs) are commonly observable airborne pollutants in the atmosphere that may have similar emission sources. In this study, the interrelated pollution characteristics of atmospheric speciated mercury and WSIIs were studied using a Piper diagram, correlation analysis, pollution episode analysis and potential source contribution function (PSCF) techniques. Also, an empirical regression equation for predicting the temporal variation in gaseous elemental mercury (GEM) was constructed. The results showed that the concentrations of GEM and particle-bound mercury (PBM) roughly increased with the increasing percentage values of NH4+ in cationic normality, and exponentially increased with the decreasing percentage values of Na+ + Mg2+ in cationic normality. Correlation analysis revealed that the atmospheric speciated mercury was positively (p < 0.01) correlated with most water-soluble inorganic ions, especially for GEM, which was closely correlated with NO2, NOx, CO, PM2.5, NO3− SO42−, NH4+ and K+ (r > 0.5, p < 0.01), indicating that the emission sources of GEM were related to fossil fuel and biomass combustion, industrial activities, and traffic exhausts. Pollution episode analysis showed that PM2.5, WSIIs (including SO42−, NO3−, NH4+, K+ and Cl−), SO2 and NO2 generally exhibited synchronous variations with GEM and PBM, and positive correlations were observed between GEM and PM2.5, SO42−, NO3−, NH4+, K+, Cl−, SO2 and NO2 (r = 0.35–0.74, p-value < 0.01). In addition, the potential source region of GEM was similar to that of PM2.5, SO42−, NO3−, NH4+, K+ and Ca2+. Based on the above findings, a satisfactory empirical regression equation, with PM2.5, NOx, CO and the percentage value of Na+ + Mg2+ in cationic normality as independent variables for GEM simulation, was constructed. The result showed that the variation in GEM concentrations could be predicted well by these variables. This model could serve as a potential substitute tool for GEM measurement in the future.