Advanced Data Analytics of VOC Measurements in Diverse Terrains

My research is centered on the advanced analysis of Volatile Organic Compounds (VOCs) using both in situ and airborne measurement techniques across complex and ecologically significant terrains, including the Indian subcontinent and the Amazon rainforest.

Field Campaigns & Data Acquisition

In India, I participated in multiple ground-based field campaigns across varied terrains such as urban areas, forested zones, and semi-arid regions. These measurements were conducted using high-resolution instruments like Proton Transfer Reaction – Time-of-Flight – Mass Spectrometer (PTR-TOF-MS) and Gas Chromatography (GC) based VOC analyzers. The aim was to capture the spatial and temporal variability of VOCs in different emission regimes influenced by anthropogenic and biogenic activities.

Over the Amazon rainforest, I was involved in flight-based VOC measurements using research aircraft equipped with advanced mass spectrometric instrumentation. These missions provided vertical profiles and large-scale distribution patterns of VOCs in pristine tropical environments, offering key insights into their role in atmospheric chemistry and climate feedback mechanisms.

Data Preprocessing

Given the complexity and volume of raw data collected, extensive preprocessing was crucial. I worked with high-frequency mass spectrometric data stored in HDF5 (.hdf) format, developing robust Python pipelines to clean, calibrate, and convert the data into usable time series.

For GC-based data, I used Peak Viewer software to extract chromatograms and identify VOC peaks. The integration of multiple datasets from PTR-TOF-MS and GC analyzers required careful temporal alignment and resolution matching.

Advanced Analytical Techniques

I employed a suite of software tools and programming environments to perform high-level data analytics:

  • Python: For scripting large-scale data operations, applying statistical filters, and developing visualization pipelines using libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
  • MATLAB: Used for signal processing, chemometric analysis, and validation of VOC identification techniques.
  • Igor Pro: Utilized for detailed analysis of PTR-MS data, especially in tracking mass-to-charge (m/z) signatures of key VOC species.
  • SigmaPlot and MS Excel: Employed for trend analysis, statistical plotting, and report preparation.

These tools enabled me to identify emission patterns, quantify trace species concentrations, and correlate VOC behavior with meteorological and boundary layer dynamics.

Research Applications

The processed VOC datasets were foundational to my research on:

  • Atmospheric chemistry modeling
  • Source attribution of VOC emissions
  • Air quality assessments
  • Secondary organic aerosol (SOA) formation

Through this work, I have contributed to a deeper understanding of VOC dynamics in different environmental contexts and provided data-driven insights critical to regional and global atmospheric science.