This thesis analyzes the effectiveness of spatially averaged Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements at regular azimuth angle intervals on an hourly basis to validate satellite based DOAS measurements. Off-Axis MAX-DOAS Measurements taken in Blacksburg, Virginia, between November 2021 and April 2022 with an evenly distributed set of measurements were averaged every hour and compared to Direct Sun measurements, also averaged every hour. Comparisons of the difference in average measurement from both measuring strategies, as well as the distribution standard deviations of hourly measurements suggests that the NO₂ distribution around Blacksburg is homogeneous. In order to test the effectiveness of this sampling strategy,in an inhomogeneous location, the LOTOS-EUROS high resolution (1kmx1km) chemical transport model was used to simulate profiles and vertical column densities of real measurements taken during the TROLIX'19 Field Campaign. The LOTOs-EUROS model was used to simulate vertical profiles as well as Vertical Column Densities based on real MAX-DOAS measurements as well as TROPOMI viewing geometry. While the individual ground measurements were not equal to the TROPOMI profile, the TROPOMI profile is approximately the average of the profiles of measurements made within the hour of TROPOMI overpass. / M.S. / This thesis analyzes the effectiveness of spatially averaged Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements at regular intervals of angles offset from due North on an hourly basis to validate satellite based DOAS measurements. MAX-DOAS Measurements taken relative to the position of the sun in Blacksburg, Virginia, a low NO₂ location, between November 2021 and April 2022 to determine the effectiveness of a generalized measuring strategy for satellite validation in low pollution environments. An evenly distributed set of measurements were averaged every hour and compared to measurements taken in the direction of the sun, also averaged every hour, to determine if the variability of NO₂ around Blacksburg is high enough to require a generalized sampling strategy, or if the NO₂ distribution is homogeneous enough to be accurately validated with Direct Sun measurements only.. Comparisons of the difference in average measurement from both measuring strategies, as well as the distribution of standard deviations of hourly measurements suggests that the NO₂ distribution around Blacksburg is low. In order to test the effectiveness of this sampling strategy in a higher pollution location with many sources and sinks of NO₂, the data from the LOTOS-EUROS high resolution (1kmx1km) chemical transport model run by the Royal Dutch Meteorological Institute for the TROLIX'19 Field Campaign was used to simulate vertical distributions of NO₂ and vertical column densities of measurements taken during the field campaign. The LOTOS-EUROS model was used to simulate vertical distributions of NO₂ as well as Vertical Column Densities based on real MAX-DOAS measurements as well as viewing geometry seen by the TROPOspheric Monitoring Instrument (TROPOMI) satellite-based instrument. While the individual ground measurements were not equal to the vertical distribution seen by TROPOMI, the TROPOMI vertical distribution is approximately the average of the vertical distributions of measurements made within an hour of TROPOMI passing over Rotterdam.

Differential Optical Absorption Spectroscopy (DOAS) is a remote sensing technique to detect different trace gas concentrations in the atmosphere. The Multi Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements by the Pandora instrument scan the sky at different elevation angles and main data products include near surface concentration, tropospheric column and vertical profile for different trace gases. It addresses an important gap in near surface air quality measurements that is difficult for in-situ, satellite and other remote sensing measurements to address. Different applications of the MAX-DOAS technique have been presented in this study for improving our understanding of tropospheric chemistry and near surface air quality monitoring. Formaldehyde (HCHO) concentration retrieved from the DOAS technique exhibits significant variation depending on the fitting parameters used. This systematic variation stems from different factors such as uncertainty in molecular absorption cross section measurement, temperature dependence of trace gas absorption, correlation between trace gases and combination of absorbers used in the DOAS fitting. To investigate the sensitivity and systematic uncertainty of HCHO retrieval, different fitting scenarios were created where fitting parameters like wavelength range, polynomial order, offset order and molecular absorption cross section were varied. To minimize systematic uncertainty and provide steady variability, the fitting scenario that most closely resembles the median of the range is selected and recommended as base fitting scenario. In addition, a real time analytical method to calculate HCHO near surface volume mixing ratio is presented where radiative transfer modelling is not required. The HCHO near surface volume mixing ratio calculated by MAX-DOAS is compared with surface HCHO measured by a ground in-situ instrument. The Pandora MAX-DOAS agrees very well with the ground in-situ instrument for the whole campaign (R²= 0.83, slope= 0.92) and provides excellent agreement for clear days (R²= 0.83= 0.88, slope=0.95). Additionally, a methodology is presented for detecting the mixing layer height (MLH) by using Pandora MAX-DOAS vertical water vapor distribution measurements. The wavelet method is applied to detect sharp gradients in the water vapor vertical profiles for estimation of mixing layer height. The Pandora derived mixing layer depth is compared to the estimations from the collocated Ceilometer (Vaisala CL51, EPA) measurements. Pandora MAX-DOAS agrees well with Ceilometer measurements for different time intervals during the day with a correlation coefficient of 0.68 to 0.76. Nitrogen Dioxide (NO₂) and Formaldehyde (HCHO) tropospheric columns and vertical profiles measured at the Hartsfield-Jackson Atlanta International Airport are also presented. Even though anthropogenic emissions decreased severely all over the United States due to Covid lockdown restrictions in 2020, trace gas levels at airports remained relatively same due to continuing air traffic. MAX-DOAS measurements are performed at different azimuth angles which gives a three dimensional representation of NO₂ and HCHO vertical profiles and enables to observe and distinguish air pollution at different directions. These measurements further show the potential of MAX-DOAS measurements for near surface air quality monitoring. / Doctor of Philosophy / MAX-DOAS is a ground based spectroscopic technique which can measure near surface concentration, tropospheric column and vertical distribution of different trace gases. Even though Satellite measurements can provide worldwide coverage, they generally measure only one time per day and have limited knowledge of near surface conditions. MAX-DOAS measurements performed by the NASA Pandora spectrometer systems can be used to provide near surface diurnal variation of different trace gas properties. In this work, different real-time applications of the MAX-DOAS technique are presented. At first, near surface concentration of Pandora MAX-DOAS Formaldehyde (HCHO) observations are validated by comparing with an in-situ instrument. Next, a methodology is presented for detecting the mixing layer height (MLH) by using Pandora MAX-DOAS vertical water vapor distribution measurements. Finally, MAX-DOAS measurements of Nitrogen Dioxide (NO₂) and Formaldehyde (HCHO) concentrations during the COVID-19 pandemic at The Hartsfield-Jackson Atlanta International Airport is presented. The measurements are done at different azimuth angles which produces three dimensional representations of NO₂ and HCHO vertical profiles. All these results prove that the NASA Pandora spectrometer systems have great potential for improving our understanding of tropospheric chemistry and air quality monitoring.

Multi Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) is a passive remote sensing technique that has been widely used to derive aerosol extinction coefficient profiles and trace gas concentrations. The ill-posed nature of the MAX-DOAS inversion problem makes it almost impossible to design an inversion algorithm providing a definite solution. A possible way to find a low-error inversion algorithm is incorporating the machine learning (ML) technique into the MAX-DOAS retrieval. This dissertation serves as the author's exploration of designing such an ML-based inversion algorithm. The inversion problem is formulated as a supervised learning problem and the ML models are trained on synthetic datasets simulated by radiative transfer models.newline By starting with a feasibility study, it is first shown that a ML model with appropriate architecture (CNN+LSTM) is capable of extracting aerosol extinction coefficient profile, single scattering albedo and asymmetry factor from one MAX-DOAS scan. Then more realistic atmosphere states were used for generating the training set. Due to the high time cost of radiative transfer simulations, a data augmentation strategy was put forward to increase the number of samples in the training set. A physics-guided machine learning (PGML) algorithm was designed to retrieve aerosol information and trace gas concentrations simultaneously. The model is named as PGML model because: (1) its prediction is based on the physical laws it has learnt from the radiative transfer simulations and (2) introduction of the physical constraints and the pseudo-inverse layer. The PGML model was tested on both a synthetic test set and real MAX-DOAS measurements from Pandora instruments. Evaluation on the synthetic dataset suggests that with similar data distribution, the PGML model is capable of retrieving aerosol extinction coefficient profile, trace gas concentration profile and the box-AMFs with good accuracy. Validation on real data was done via comparisons with inversion results given by other algorithms. Generally, moderate linear correlation were found between the inversion results. Limitation of current version of the PGML model and factors might lead to the discrepancies between inversion results given by the PGML model and other algorithms were discussed. / Doctor of Philosophy / Multi Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) is a passive remote sensing technique for deriving aerosol and trace gas information in the lower atmosphere. A MAX-DOAS instrument is a ground-based system consists of a scanning telescope, a stepping motor and a spectrometer. It collects scattered solar photons at multiple elevation angles. And from spectrum analysis and inversion algorithms, aerosol properties such as aerosol extinction coefficient profile (a vertical profile describing how much the solar radiation is weakened by the atmosphere), single scattering albedo (the ratio of scattered light to incoming light) and trace gas concentrations can be retrieved. The ill-posed nature of the MAX-DOAS inversion problem makes it almost impossible to design an inversion algorithm providing a definite solution. A possible way to find a low-error inversion algorithm is incorporating the machine learning (ML) technique into the MAX-DOAS retrieval. This dissertation serves as the author's exploration of designing such an ML-based inversion algorithm. The inversion problem is formulated as a supervised learning problem. In supervised learning, a training set is used to teach the ML model to yield the desired output. And the ML models are trained on synthetic datasets simulated by radiative transfer models for two reasons: (1) There is no reliable dataset combining real MAX-DOAS measurements and observations of aerosol properties (macrophysical properties and aerosol extinction coefficient profiles) and trace gas concentrations. (2) Most of the existing algorithms somewhat rely on empirical knowledge (e.g.: a priori information (optimal estimation methods), introduction of parameters for representing the state vector (parameterized retrieval algorithms)). However, the method purely relies on the rules it has learned from the training set. By using simulated data, it is expected that the ML model to capture the radiative transfer theory and give predictions based on the physical laws.newline By starting with a feasibility study, it is first shown that by applying a machine learning model with appropriate architecture (combination of convolutional layers and long short-term memory layer), it is possible to extract aerosol extinction coefficient profile, single scattering albedo and asymmetry factor from one MAX-DOAS scan. And this architecture is capable of retrieving elevated layers of aerosol extinction coefficient profiles. Then more realistic atmosphere states were used for generating the training set and designed a physics-guided machine learning (PGML) model to retrieve aerosol information and trace gas concentrations simultaneously. The model is named as PGML model because: (1) its prediction is based on the physical laws it has learnt from the radiative transfer simulations and (2) introduction of the physical constraints and the pseudo-inverse layer. Due to the high time cost of running radiative transfer simulations, a data augmentation strategy was put forward to increase the number of samples in the training set. The PGML model was tested on both a synthetic test set and real MAX-DOAS measurements from Pandora instruments. Evaluation on the synthetic dataset suggests that with similar data distribution, the PGML model is capable of retrieving aerosol extinction coefficient profile, trace gas concentration profile and the box-AMFs with good accuracy. Validation on real data was done via comparisons with inversion results given by other algorithms. Generally, moderate linear correlation were found between the inversion results. Limitation of current version of the PGML model and factors might lead to the discrepancies between inversion results given by the PGML model and other algorithms were discussed.

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Multi-axis differential optical absorption spectroscopy (MAX-DOAS) was applied to retrieve tropospheric NO2 and aerosol vertical profiles from downtown Toronto, and King City, Ontario during select periods in 2006 – 2010. Linear regression of MAX-DOAS NO2 vertical column density (VCD) versus OMI (satellite) VCD yielded a good correlation (R = 0.88) and MAX-DOAS negative bias of 20%, which was within the reported uncertainty of the MAX-DOAS and OMI VCD. The average regional Toronto VCD (remotely-sensed via MAX-DOAS and OMI) was half of the near-road VCD obtained in-situ (2.4 x 1016 ± 1.2 x 1016 molec/cm2). MAX-DOAS measurements of O4 were coupled with radiative transfer modeling to obtain vertical aerosol extinction profiles and aerosol optical depth (AOD). A strong linear agreement was observed between PM2.5 concentration and aerosol extinction coefficient (R = 0.92), and MAX-DOAS versus sun photometer AOD (slope = 0.94; R= 0.90).

Multi-axis differential optical absorption spectroscopy (MAX-DOAS) was applied to retrieve tropospheric NO2 and aerosol vertical profiles from downtown Toronto, and King City, Ontario during select periods in 2006 – 2010. Linear regression of MAX-DOAS NO2 vertical column density (VCD) versus OMI (satellite) VCD yielded a good correlation (R = 0.88) and MAX-DOAS negative bias of 20%, which was within the reported uncertainty of the MAX-DOAS and OMI VCD. The average regional Toronto VCD (remotely-sensed via MAX-DOAS and OMI) was half of the near-road VCD obtained in-situ (2.4 x 1016 ± 1.2 x 1016 molec/cm2). MAX-DOAS measurements of O4 were coupled with radiative transfer modeling to obtain vertical aerosol extinction profiles and aerosol optical depth (AOD). A strong linear agreement was observed between PM2.5 concentration and aerosol extinction coefficient (R = 0.92), and MAX-DOAS versus sun photometer AOD (slope = 0.94; R= 0.90).

Reactive halogen species (RHS) are responsible for ozone depletion and oxidation of gaseous elemental mercury and dimethyl sulphide in the polar boundary layer, but the sources and mechanisms controlling their catalytic reaction cycles are still not completely understood. To further investigate these processes, ground– based Multi–Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of boundary layer BrO and IO were made from a portable instrument platform in McMurdo Sound during the Antarctic spring of 2006 and 2007. Measurements of surface ozone, temperature, pressure, humidity, and wind speed and direction were also made, along with fourteen tethersonde soundings and the collection of snow samples for mercury analysis. A spherical multiple scattering Monte Carlo radiative transfer model (RTM) was developed for the simulation of box-air-mass-factors (box-AMFs), which are used to determine the weighting functions and forward model differential slant column densities (DSCDs) required for optimal estimation. The RTM employed the backward adjoint simulation technique for the fast calculation of box-AMFs for specific solar zenith angles (SZA) and MAX-DOAS measurement geometries. Rayleigh and Henyey-Greenstein scattering, ground topography and reflection, refraction, and molecular absorption by multiple species were included. Radiance and box-AMF simulations for MAX-DOAS measurements were compared with nine other RTMs and showed good agreement. A maximum a posteriori (MAP) optimal estimation algorithm was developed to retrieve trace gas concentration profiles from the DSCDs derived from the DOAS analysis of the measured absorption spectra. The retrieval algorithm was validated by performing an inversion of artificial DSCDs, simulated from known NO2 profiles. Profiles with a maximum concentration near the ground were generally well reproduced, but the retrieval of elevated layers was less accurate. Retrieved partial vertical column densities (VCDs) were similar to the known values, and investigation of the averaging kernels indicated that these were the most reliable retrieval product. NO₂ profiles were also retrieved from measurements made at an NO₂ measurement and profiling intercomparison campaign in Cabauw, Netherlands in July 2009. Boundary layer BrO was observed on several days throughout both measurement periods in McMurdo Sound, with a maximum retrieved surface mixing ratio of 14.4±0.3 ppt. The median partial VCDs up to 3km were 9.7±0.07 x 10¹² molec cm ⁻ in 2007, with a maximum of 2.3±0.07 x 10¹³ molec cm⁻², and 7.4±0.06 x 10¹² molec cm⁻² in 2006, with a maximum of 1.05 ± 0.07 x 1013 molec cm⁻². The median mixing ratio of 7.5±0.5 ppt for 2007 was significantly higher than the median of 5.2±0.5 ppt observed in 2006, which may be related to the more extensive first year sea ice in 2007. These values are consistent with, though lower than estimated boundary layer BrO concentrations at other polar coastal sites. Four out of five observed partial ozone depletion events (ODEs) occurred during strong winds and blowing snow, while BrO was present in the boundary layer in both stormy and calm conditions, consistent with the activation of RHS in these two weather extremes. Air mass back trajectories, modelled using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, indicated that the events were locally produced rather than transported from other sea ice zones. Boundary layer IO mixing ratios of 0.5–2.5±0.2 ppt were observed on several days. These values are low compared to measurements at Halley and Neumayer Stations, as well as mid-latitudes. Significantly higher total mercury concentrations observed in 2007 may be related to the higher boundary layer BrO concentrations, but further measurements are required to verify this.