Temporal profile of ionic species and n-alkanes composition of PM10 in a rural environment of Western Himalaya

The temporal profile of PM10 and its composition comprising water-soluble ionic species and n-alkanes studied over the 14 months was segregated into three seasons: winter, summer, and monsoon. The average PM10 con-centration exceeds the prescribed National PM10 limits in summer and winter. The PM10 concentration was highest in summer, followed by winter, and lowest in monsoon. Water-soluble inorganic ionic species- major cations (Ca2+, Mg2+, Na+, K+, and NH4+) and anions (F-, Cl-, NO3- and SO42-) contributed an average 30.7% to PM10. The ionic species displayed significant variation, with the highest concentration in winter and the lowest in the monsoon. The secondary inorganic ions, SO42-, NO3-, and NH4+, contributed 84% to the total ionic mass. The ion balance study revealed a strong correlation between anion and cation charge equivalents, suggesting their main contribution to PM10. The neutralization of NO3- + SO42-with NH4+ suggested, NH4+ being the main neutralizing species. n-alkanes concentration in PM10 was significant and showed seasonal variation, highest in the winters and lowest in monsoon. The source profiling of PM10 components, using statistical correlation, regression, and principal component analysis (PCA), revealed solid-fuel biomass, soil dust, and brick kilns and transported materials as major sources.

Automated summary

This study investigated the temporal profile and composition of PM10 over a 14-month period, and found significant variations between different seasons. The highest concentrations of PM10 were observed in summer and winter, exceeding the national limits. Water-soluble ionic species and n-alkanes contributed to the PM10 mass, with the highest concentration in winter and the lowest in the monsoon season. The ion balance study revealed a strong correlation between anion and cation charge equivalents, indicating their main contribution to PM10. The main sources of PM10 components were identified using statistical correlation, regression, and principal component analysis.