Source profile of PM2.5 emissions from different primary sources in the coal capital city Dhanbad, India

Abstract

The rise of fine particulate matter (PM_2.5) levels in urban areas, driven by traffic, construction, and combustion emissions, has prompted urgent air quality concerns. Understanding source-specific PM_2.5 chemical characteristics and developing associated source markers is essential for knowing their accurate contribution to atmospheric PM_2.5. This study focuses on developing PM_2.5 chemical source profiles from nine different emissions, primarily categorized under traffic and combustion sources. The chemical characterization included the carbonaceous thermal fractions, inorganic ions, and elemental composition. Heterogeneity in chemical composition across emission sources was examined using the coefficient of divergence and diagnostic ratio, and finally, source-specific chemical fingerprints were developed using the ratio normalization approach. The finding revealed significant inter and intra-variation in the chemical composition among traffic and combustion emission sources. Organic matter is observed significantly higher in combustion sources (84%–92%) than in traffic sources (22%–45%). Both OC/EC and char-EC/soot-EC values showed much higher values for combustion sources than traffic emissions, with cow dung cake burning emissions displaying the largest values. Also, char-EC/soot-EC values showed a similar trend with OC/EC values and thus can be used as an additional marker for deciphering emission sources. The ion-balance ratio revealed particle emissions from coal, cow dung, and garbage burning to be highly acidic, while traffic and construction sources were alkaline. Source marker results provide new insights into differences in the chemical fingerprint of specific emission sources. A new set of source markers was seen for garbage burning while coal-burning emissions showed varying chemical fingerprints and were found to be dependent on coal processing. Among elements, bromine and chlorine are found to be the unique markers for cow dung cake-burning emissions. Receptor models can use the database developed from the current work to demarcate the emission sources accurately and benefit the regulatory bodies in developing efficient control measures.