AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
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Aditya Sinha, Graduate Student Researcher, Industry
ADITYA SINHA, North Carolina State University
Abstract Number: 684 Working Group: Meet the Job Seekers
Abstract The research question my work addresses is how atmospheric pollutants and their associated properties transform in the atmosphere. Secondary organic aerosol (SOA) are air pollutants produced through complex interactions of sunlight and volatile organic compounds (VOCs). SOA form a major component of particulate matter and so understanding its properties are important. I have focused on SOA from cookstove emissions, which approximately 3 billion people use for their daily needs. To study SOA, I employed the use of an oxidation flow reactor (OFR) – which simulates days of atmospheric aging on the timescale of minutes. Upon conducting these experiments in conjunction with various supervised and unsupervised statistical learning techniques, I gained insight into how cookstoves of different efficiencies and fuel types affect the physical and chemical properties of SOA. Through this, I built on my analytical skills developed during my Master’s at Carnegie Mellon University on working on mathematical models of kinetic parameters.
In addition to conducting laboratory controlled experiments, I have also worked on characterizing SOA in a real-world setting. Field monitoring efforts have consistently shown a clear distinction between in-field performance of cookstoves and that measured in standardized lab tests. To address this disparity, in the context of SOA from cookstoves, I conducted an experimental campaign in a ‘quasi-field’ setting in Mexico to observe differences in SOA in the two environments. During this campaign, I focused on the application of low-cost sensors to characterize SOA in favor of complex and immobile instrumentation.
I am currently working on the application of deconvolution algorithms in identifying organic species from chromatograms in collaboration with the EPA. In addressing these research questions I have picked up strong transferable data analytical skills (in R, Python, Igor) along with significant experimentation experience. Upon graduation in April 2020, I hope to use these data analysis skills in an industry based setting (no geographical preference).