Advances and application of positive matrix factorization for source attribution of air pollution in megacities
Creators: name: Bhandari, Sahil
name: 0000-0002-9822-420X
Publisher: name: The University of Texas at Austin
Year: 2021
Subjects: India
Delhi
Indo-Gangetic Plains
Air pollution
Submicron aerosol
Fine particulate matter
Particulate matter
Aerosol chemical speciation monitor
ACSM
Mass spectrometry
Source apportionment
PMF
ME-2
Time-of-day
Machine learning
Supervised learning
Unsupervised learning

description: Air pollution is considered the greatest current environmental health threat to humanity, with an estimated mortality burden of 7 million per year. More than half the world’s population is exposed to increasing air pollution. Reduction of air pollution is essential to global health and can be expected to generate long-term societal benefits. Receptor models are efficient mathematical tools for identification of sources of air pollution. A popular receptor modeling technique is Positive Matrix Factorization (PMF). However, PMF is limited by the assumption of constant source profiles throughout the modeling period—while the contribution of each source is modeled to change over time, its profile (e.g., mass spectrum, when PMF is applied to mass spectrometer data) stays constant. PMF is frequently applied to data on air pollution from fine particulate matter (PM), particularly in megacities. Megacities are centers of economic activity, harbor very large populations, and have high PM levels, especially in the developing world, posing acute challenges to public health. One such city is Delhi, India. Delhi is the second most populated city in the world and routinely experiences some of the highest particulate matter concentrations of any megacity on the planet. However, the current understanding of the sources and dynamics of PM pollution in Delhi is limited. Measurements at the Delhi Aerosol Supersite (DAS) provide long-term chemical characterization of ambient submicron aerosol in Delhi, with near-continuous online measurements of aerosol composition. In this dissertation, I apply PMF on data collected in the DAS study to characterize sources and atmospheric dynamics of submicron aerosols in Delhi. In study 1 (chapter 2), I report on source apportionment based on unsupervised (unconstrained) positive matrix factorization (PMF), conducted on 15 months of highly time-resolved speciated submicron non-refractory PM₁ (NR-PM₁) between January 2017 and March 2018. This dataset was collected in the DAS study. I report on seasonal variability across four seasons of 2017 and interannual variability using data from the two winters and springs of 2017 and 2018. I also show that a modified tracer-based organic component analysis provides an opportunity for a real-time source apportionment approach for organics in Delhi. Phase equilibrium modeling of aerosols using the extended aerosol inorganics model (E-AIM) predicts equilibrium gas-phase concentrations and allows evaluation of the importance of the ventilation coefficient (VC) and temperature in controlling primary and secondary organic aerosol. I also find that primary aerosol dominates severe air pollution episodes, and secondary aerosol dominates seasonal averages. An edited version of this chapter has been published in Atmospheric Chemistry and Physics. In study 2 (chapter 3), we develop the approach of conducting supervised (constrained) PMF on long-term datasets separated into 4 hour periods with limited variability in emissions and meteorology and statistically demonstrate its viability. I apply this time-of-day PMF approach on two seasons of highly time-resolved NR-PM₁ organics. This approach improves upon the seasonal source apportionment previously employed in Delhi by capturing the diurnal variability in source mass spectral profiles and retaining low computational intensity. Use of the EPA PMF tool allows application of constraints and quantifies random errors and rotational ambiguity in PMF solutions. Results in this study demonstrate that time-of-day PMF approach gives a greater number of more appropriate PMF factors compared to the traditional seasonal PMF approach. The time-of-day PMF approach fits data better, improving fits at specific time points, and at key m/zs. Portions of this chapter will be submitted to Atmospheric Measurement Techniques. Previous receptor modeling studies have identified vehicular emissions and fossil fuel combustion as prevalent factors contributing to fine PM pollution in Delhi. However, cooking and biomass burning have not been consistently identified in ambient studies. Bottom-up (source-oriented) studies have recognized the high exposure to residential energy emissions from cooking and heating and associated biomass burning emissions. In study 3 (chapter 4), I address these limitations of receptor modeling studies by applying PMF on two seasons of highly time-resolved NR-PM₁ organics. I utilize the time-of-day PMF approach (chapter 3) to separate primary organics into component primary factors. Hydrocarbon-like organic aerosol, or HOA, the fuel combustion and traffic primary organic aerosol surrogate, occurs in every season, and shows strong diurnal patterns. Biomass burning organic aerosol, or BBOA, separates only in winter, and exhibits time series peaks associated with space heating and solid-fuel combustion. Cooking organic aerosol, or COA, separates only in monsoon and reports stable diurnal patterns, suggesting the presence of cooking sources all-day. Equilibrium modeling of organic aerosols using volatility basis sets (VBS) suggests that differences in ventilation coefficient and temperature can explain the differences in factor separation between winter and monsoon. Overall, I show that traffic, and cooking and biomass burning contribute almost equally to the primary organic aerosol burden in Delhi, in broad agreement with several bottom-up studies. Portions of this chapter will be submitted to Atmospheric Chemistry and Physics