Machine learning based quantification of VOC contribution in surface ozone prediction.

The prediction of surface ozone is essential attributing to its impact on human and environmental health. Volatile organic compounds (VOCs) are crucial in driving ozone concentration; particularly in urban areas where VOC limited regimes are prominent. The limited measurements of VOCs, however, hinder assessing the VOC-ozone relationship.

Tracer-based characterization of source variations of ambient isoprene mixing ratios in a hillocky megacity, India, influenced by the local meteorology.

The ambient biogenic volatile organic compounds (BVOCs), mainly isoprene, are potentially involved in the formation of secondary pollutants, hence, they are significant in terms of air quality and climate. Although the largest sources of BVOCs are tropical regions, the measurements of isoprene in the Indian subcontinent are limited. We conducted the measurements of isoprene, benzene, and toluene at an urban site in a hillocky megacity of India using a high-sensitivity proton transfer reaction quadrupole mass spectrometer (PTR-QMS).