Combining Raman microspectrometry and chemometrics for determining quantitative molecular composition and mixing state of atmospheric aerosol particles

Raman spectroscopy for profiling physical and chemical properties of atmospheric aerosol particles: A review

Atmosphere aerosols have significant impact on human health and the environment. Aerosol particles have a number of characteristics that influence their health and environmental effects, including their size, shape, and chemical composition. A great deal of difficulty is associated with quantifying and identifying atmospheric aerosols because these parameters are highly variable on a spatial and temporal scale. An important component of understanding aerosol fate is Raman Spectroscopy (RS), which is capable of resolving chemical compositions of individual particles. This review presented strategic techniques, especially RS methods for characterizing atmospheric aerosols. The nature and properties of atmospheric aerosols and their influence on environment and human health were briefly described. Analytical methodologies that offer insight into the chemistry and multidimensional properties of aerosols were discussed. In addition, perspectives for practical applications of atmospheric aerosols using RS are featured.

Pollution sources of atmospheric fine particles and secondary aerosol characteristics in Beijing

To investigate the secondary formation and pollution sources of atmospheric particles in urban Beijing, PM_2.5 and its chemical components were collected and determined by URG-9000D ambient ion monitor (AIM) from March 2016 to January 2017. Among water-soluble ions (WSIs), NO₃^-, SO₄^2- and NH₄⁺ (SNA) had the largest proportion (77.8%) with the total concentration of 23.8 μg/m³. Moreover, as fine particle pollution worsened, the NO₃^-, SO₄^2- and NH₄⁺ concentrations increased basically, which revealed that secondary aerosols were the main cause of particle pollution in Beijing. Furthermore, the particle neutralization ratio (1.1), the ammonia to sulfate molar ratio (3.4) and the nitrate to sulfate molar ratio (2.2) showed that secondary aerosols are under ammonium-rich conditions with the main chemical forms of NH₄NO₃ and (NH₄)₂SO₄, and vehicle emission could be the main anthropogenic source of secondary aerosols in Beijing. Source analysis further indicated that secondary aerosols, solid fuel combustion, dust and marine aerosol were the principal pollution sources of PM_2.5, accounting for about 46.1%, 22.4% and 13.0%, respectively, and Inner Mongolia and Hebei Provinces could be considered as the main potential sources of PM_2.5 in urban Beijing. In addition, secondary formation process was closely related with gaseous precursor emission amounts (SO₂, NO₂, NH₃ and HONO), atmospheric ozone concentration (O₃), meteorological conditions (temperature and relative humidity) and particle components. Sensitive analysis of the thermodynamic equilibrium model (ISORROPIA II) revealed that controlling total nitrate (TN) is the effective measure to mitigate fine particle pollution in Beijing.

Application of multivariate analysis on naphthalene adsorption in aqueous solutions

Naphthalene (NAP) is found as a pollutant in water, soil, and air, and adsorption is the most prominent removal process of this compound, among the methods studied. A study concerning the types of adsorbents and the parameters with the greatest influence on the adsorption process is interesting to direct future works on new adsorbents. The use of multivariate data analysis tools becomes an appealing way to compile data obtained from bibliographic reviews and to establish a behavior in NAP adsorption. This work aims to evaluate the parameters with greater influence on NAP adsorption process regarding adsorption capacity (qe_exp) with the principal component analysis (PCA), and to group common NAP adsorbents by chemical characteristics through hierarchical cluster analysis (HCA). The variables qe_exp, S, [NAP]₀, T, CT, and [Ads] were used to perform PCA with correlation matrix. For the HCA, the variables S, [NAP]₀, T, CT, and [Ads] with average linkage method (UPGMA) and Euclidean distance were used. Through PCA, it is possible to infer that S and [NAP]₀ are the factors with greater influence in qe_exp of NAP, while T, CT, and [Ads] have little correlation. PCA also shows that activated charcoal is the adsorbent with higher qe_exp. HCA grouped the adsorbents into four groups by their chemical classes, except group A. Both PCA and HCA methods show themselves as potential tools to evaluate a data set of NAP adsorption processes.