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Formaldehyde column density (omega HCHO) showed a potentially useful correlation with surface ozone during the LISTOS campaign on Long Island Sound and the KORUS-AQ campaign in Seoul, South Korea. This builds on previous work that identified this relationship from in situ aircraft observations with similar findings for ground-based and airborne remote sensing of omega HCHO. In the Long Island Sound region, omega HCHO and surface ozone exhibited strong temporal (r(2) = 0.66) and spatial (r(2) = 0.73) correlation. The temporal variability in omega HCHO (similar to 1 Dobson units [DU]) was larger than the range in the spatial average (similar to 0.1 DU). The spatial average is most useful for informing ozone monitoring strategies, demonstrating the challenge in using omega HCHO satellite data sets for this purpose. In Seoul, high levels of NO2 resulted in O-x better correlating with omega HCHO than surface ozone due to titration effects. The omega HCHO-O-x relationship may therefore reflect the sum of surface ozone and related photochemical oxidants, relevant to air quality standards set to regulate this quantity such as the U.S. EPA National Ambient Air Quality Standard (NAAQS). The relationship of omega HCHO to O-x shifted in Seoul during the campaign demonstrating the need to evaluate this relationship over longer time periods. With sufficient precision in future satellite retrievals, omega HCHO observations could be useful for evaluating the adequacy of surface air quality monitoring strategies. NASA Geostationary Coastal and Air Pollution Events (GEO-CAPE); NASA Earth Science Division (ESD) Tropospheric Composition Program Published version The LISTOS airborne measurements would not have been possible without the support of the NASA Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission study as well as the NASA Earth Science Division (ESD) Tropospheric Composition Program. We express gratitude to the entire LISTOS and KORUS-AQ science teams for their expertise, research, and measurement contributions toward the successful collaborative field study. We acknowledge Paul Wennberg for the use of his HNO3 data from the CIT-CIMS, Greg Huey for the use of his PAN data from the GT-CIMS during KORUS-AQ, Sam Hall for the use of his photolysis frequency data from the CAFS instrument, and Bill Brune for the use of his OH data from the ATHOS instrument. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency. Any mention of trade names, manufacturers or products does not imply an endorsement by the United States Government or the U.S. Environmental Protection Agency. EPA and its employees do not endorse any commercial products, services, or enterprises.

In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single-scattering albedo and asymmetry parameter at 360 nm from a single multi-axis differential optical absorption spectroscopy (MAX-DOAS) sky scan. Our method relies on a multi-output sequence-to-sequence model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory networks (LSTMs) for profile prediction. The model was trained and evaluated using data simulated by Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) v2.7, which contains 1 459 200 unique mappings. From the simulations, 75 % were randomly selected for training and the remaining 25 % for validation. The overall error of estimated aerosol properties (1) for total aerosol optical depth (AOD) is -1.4±10.1 %, (2) for the single-scattering albedo is 0.1±3.6 %, and (3) for the asymmetry factor is -0.1±2.1 %. The resulting model is capable of retrieving aerosol extinction coefficient profiles with degrading accuracy as a function of height. The uncertainty due to the randomness in ML training is also discussed.

In September 2016, 36 spectrometers from 24 institutes measured a number of key atmospheric pollutants for a period of 17 d during the Second Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) that took place at Cabauw, the Netherlands (51.97∘ N, 4.93∘ E). We report on the outcome of the formal semi-blind intercomparison exercise, which was held under the umbrella of the Network for the Detection of Atmospheric Composition Change (NDACC) and the European Space Agency (ESA). The three major goals of CINDI-2 were (1) to characterise and better understand the differences between a large number of multi-axis differential optical absorption spectroscopy (MAX-DOAS) and zenith-sky DOAS instruments and analysis methods, (2) to define a robust methodology for performance assessment of all participating instruments, and (3) to contribute to a harmonisation of the measurement settings and retrieval methods. This, in turn, creates the capability to produce consistent high-quality ground-based data sets, which are an essential requirement to generate reliable long-term measurement time series suitable for trend analysis and satellite data validation. The data products investigated during the semi-blind intercomparison are slant columns of nitrogen dioxide (NO2), the oxygen collision complex (O4) and ozone (O3) measured in the UV and visible wavelength region, formaldehyde (HCHO) in the UV spectral region, and NO2 in an additional (smaller) wavelength range in the visible region. The campaign design and implementation processes are discussed in detail including the measurement protocol, calibration procedures and slant column retrieval settings. Strong emphasis was put on the careful alignment and synchronisation of the measurement systems, resulting in a unique set of measurements made under highly comparable air mass conditions. The CINDI-2 data sets were investigated using a regression analysis of the slant columns measured by each instrument and for each of the target data products. The slope and intercept of the regression analysis respectively quantify the mean systematic bias and offset of the individual data sets against the selected reference (which is obtained from the median of either all data sets or a subset), and the rms error provides an estimate of the measurement noise or dispersion. These three criteria are examined and for each of the parameters and each of the data products, performance thresholds are set and applied to all the measurements. The approach presented here has been developed based on heritage from previous intercomparison exercises. It introduces a quantitative assessment of the consistency between all the participating instruments for the MAX-DOAS and zenith-sky DOAS techniques.

A ground-based tropospheric O3 lidar with unique vertical near-range capabilities was deployed in support of the larger OWLETS 2017 campaign on the Chesapeake Bay Bridge Tunnel, at the mouth of the Chesapeake Bay. It was sited in close proximity to a shipping channel with an ensemble of additional instrumentation including Pandora spectrometer systems, ozonesonde launches, and in-situ trace gas monitors – one flying on a drone. This unique combination enabled successful observation of a near-surface maritime ship plume emission event on August 01, 2017. The observations demonstrate an NO2 enhancement coincident with O3 depletion in the low altitude range of lidar data, allowing for quantification of ship plume height behavior as well as the evolution of trace-gas concentrations. The technological improvements enabling the observation are presented and discussed, demonstrating that a single observation platform would not have been able to fully capture and contextualize the emission event. This synergistic ground-based sampling approach shows great promise for future verification and validation of satellite air quality and atmospheric composition measurements.

Analysis of formaldehyde measurements by the Pandora spectrometer systems between 2016 and 2019 suggested that there was a temperature dependent process inside Pandora head sensor that emitted formaldehyde. Some parts in the head sensor were manufactured from thermal plastic polyoxymethylene homopolimer (E.I. Du Pont de Nemour & Co., USA: POM-H Delrin®) and were responsible for formaldehyde production. Laboratory analysis of the four Pandora head sensors showed that internal formaldehyde production had exponential temperature dependence with a damping coefficient of 0.0911 ± 0.0024 °C−1 and the exponential function amplitude ranging from 0.0041 DU to 0.049 DU. No apparent dependency on the head sensor age and heating/cooling rates was detected. The total amount of formaldehyde internally generated by the POM-H components and contributing to the direct sun measurements were estimated based on the head sensor temperature and solar zenith angle of the measurements. Measurements in winter, during cold days in general and at high solar zenith angles (> 75 °) were minimally impacted. Measurements during hot days and small solar zenith angles had up to 1 DU contribution from POM-H parts. Multi-axis differential slant column densities were minimally impacted ( Three new POM-free Pandora head sensors (manufactured in summer 2019) were evaluated for temperature dependent attenuation across the entire spectral range (300 to 530 nm). No formaldehyde or any other absorption above the instrumental noise was observed across the entire spectral range.

As part of the TROLIX'19 (TROpomi vaLIdation eXperiment) campaign (2 September to 4 October 2019), measurements of tropospheric NO2 columns and surface concentrations were made using the MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) technique. To characterise any TROPOMI sub pixel (less than 3.5 km x 7 km) heterogeneity, four MAX-DOAS instruments were deployed at rural locations close to Cabauw (51.97°N, 4.93°E) and further six instruments were operated within the highly industrialized area of Rotterdam (51.92°N, 4.48°E) in the Netherlands. All instruments performed sky scanning from the horizon (from approximately 1°) to the zenith. In addition, two of the MAX-DOAS instruments (Pandoras) also measured total NO2 columns in direct sun mode.Here we present first results focusing on the measurements of NO2 spatial gradients made at sites within approximately 3-10 km distance in a rural and an urban environment. The data analysis was done in two steps. Differential slant column densities were calculated using the data processing procedures established during the CINDI-2 intercomparison campaign (Kreher et al., in review, 2019) in UV and VIS spectral ranges. Tropospheric columns, near surface concentrations and profiles were then calculated using the Pandora real time algorithm as well as the NDACC UV-Vis Central Processing system developed in the ESA FRM4DOAS project. Local heterogeneity at the surface level was evaluated using in-situ NO2 measurements available from several routine monitoring stations within the area of interest. The local NO2 heterogeneity effect on TROPOMI validation is also discussed.Reference:Kreher, K. et al.: Intercomparison of NO2, O4, O3 and HCHO slant column measurements by MAX-DOAS and zenith-sky UV-Visible spectrometers during the CINDI-2 campaign, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-157, in review, 2019.

Increasing urbanization worldwide raise serious concerns about urban air quality and its effects on large human populations. This study presents application of the Differential Optical Absorption Spectroscopy technique to multi scale urban air quality (NO2) monitoring. A Pandora spectroscopic instrument (SciGlob Inc) has been deployed on top of a 30 m building in Boston, MA, USA, since September 2019. It performs over the roof sky scans (local to meso scales), into the street canyon "target" (micro-scale), and direct sun measurements. In situ NO2 measurements are also being conducted on top of the roof (co-located with Pandora) and at the street level near the "target" building. NO2 spatial and temporal heterogeneity within different scales is discussed.

The GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) was developed in support of NASA's decadal survey GEO-CAPE geostationary satellite mission. GCAS is an airborne push-broom remote-sensing instrument, consisting of two channels which make hyperspectral measurements in the ultraviolet/visible (optimized for air quality observations) and the visible–near infrared (optimized for ocean color observations). The GCAS instrument participated in its first intensive field campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign in Texas in September 2013. During this campaign, the instrument flew on a King Air B-200 aircraft during 21 flights on 11 days to make air quality observations over Houston, Texas. We present GCAS trace gas retrievals of nitrogen dioxide (NO2) and formaldehyde (CH2O), and compare these results with trace gas columns derived from coincident in situ profile measurements of NO2 and CH2O made by instruments on a P-3B aircraft, and with NO2 observations from ground-based Pandora spectrometers operating in direct-sun and scattered light modes. GCAS tropospheric column measurements correlate well spatially and temporally with columns estimated from the P-3B measurements for both NO2 (r2 = 0.89) and CH2O (r2 = 0.54) and with Pandora direct-sun (r2 = 0.85) and scattered light (r2 = 0.94) observed NO2 columns. Coincident GCAS columns agree in magnitude with NO2 and CH2O P-3B-observed columns to within 10 % but are larger than scattered light Pandora tropospheric NO2 columns by 33 % and direct-sun Pandora NO2 columns by 50 %.

In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single scattering albedo and asymmetry parameter at 360 nm from a single MAX-DOAS sky scan. Our method relies on a multi-output sequence-to-sequence model combining Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory networks (LSTM) for profile prediction. The model was trained and evaluated using data simulated by VLIDORT v2.7, which contains 1 459 200 unique mappings. 75 % randomly selected simulations were used for training and the remaining 25 % for validation. The overall error of estimated aerosol properties for (1) total AOD is −1.4 ± 10.1 %, (2) for single scattering albedo is 0.1 ± 3.6 %; and (3) asymmetry factor is −0.1 ± 2.1 %. The resulting model is capable of retrieving aerosol extinction coefficient profiles with degrading accuracy as a function of height. The uncertainty due to the randomness in ML training is also discussed.

The NASA/Smithsonian Tropospheric Emissions: Monitoring of Pollution (TEMPO; tempo.si.edu) satellite instrument will measure atmospheric pollution and much more over Greater North America at high temporal resolution (hourly or better in daylight, with selected observations at 10 minute or better sampling) and high spatial resolution (10 km2 at the center of the field of regard). It will measure ozone (O3) profiles (including boundary layer O3), and columns of nitrogen dioxide (NO2), nitrous acid (HNO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), water vapor (H2O), bromine oxide (BrO), iodine oxide (IO), chlorine dioxide (OClO), as well as clouds and aerosols, foliage properties, and ultraviolet B (UVB) radiation. The instrument has been delivered and is awaiting spacecraft integration and launch in 2022. This talk describes a selection of TEMPO applications based on the TEMPO Green Paper living document (http://tempo.si.edu/publications.html). Applications to air quality and health will be summarized. Other applications presented include: biomass burning and O3 production; aerosol products including synergy with GOES infrared measurements; lightning NOx; soil NOx and fertilizer application; crop and forest damage from O3; chlorophyll and primary productivity; foliage studies; halogens in coastal and lake regions; ship tracks and drilling platform plumes; water vapor studies including atmospheric rivers, hurricanes, and corn sweat; volcanic emissions; air pollution and economic evolution; high-resolution pollution versus traffic patterns; tidal effects on estuarine circulation and outflow plumes; air quality response to power blackouts and other exceptional events.

In September 2016, 36 spectrometers from 24 institutes measured a number of key atmospheric pollutants for a period of 17 days during the Second Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) that took place at Cabauw, The Netherlands (51.97° N, 4.93° E). We report on the outcome of the formal semi-blind intercomparison exercise, which was held under the umbrella of the Network for the Detection of Atmospheric Composition Change (NDACC) and the European Space Agency (ESA). The three major goals of CINDI-2 were to characterise and better understand the differences between a large number of Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) and zenith-sky DOAS instruments and analysis methods, to discuss the performance of the various types of instruments and to contribute to a harmonisation of the measurement settings and retrieval methods. This, in turn, creates the capability to produce consistent high-quality ground-based data sets, which are an essential requirement to generate reliable long-term measurement time series suitable for trend analysis and satellite data validation. The data products investigated during the semi-blind intercomparison are slant columns of nitrogen dioxide (NO2), the oxygen dimer (O4) and ozone (O3) measured in the UV and visible wavelength region, formaldehyde (HCHO) in the UV spectral region and NO2 in an additional (smaller) wavelength range in the visible. The campaign design and implementation processes are discussed in detail including the measurement protocol, calibration procedures and slant column retrieval settings. Strong emphasis was put on the careful alignment and synchronisation of the measurement systems, resulting in an unprecedented set of measurements made under highly comparable air mass conditions. The CINDI-2 data sets were investigated using a regression analysis of the slant columns measured by each instrument and for each of the target data products. The slope and intercept of the regression analysis respectively quantify the mean systematic bias and offset of the individual data sets against the reference, and the RMS error provides an estimate of the measurement noise or dispersion. These three criteria are examined and for each of the parameters and each of the data products, performance thresholds are set and applied to all the measurements. The approach presented here has been developed based on heritage from previous intercomparison exercises. It introduces a quantitative assessment of the measurement performance of all the participating instruments for the MAX-DOAS and zenith-sky DOAS techniques.

Several ground-based remote sensing and in-situ instruments were used to investigate the development of the sea-breeze front and its effect on both the optical and physical aerosol properties, the Particulate Matter (PM) content and the tropospheric and near-surface NO2 concentrations. Most of the instruments belongs to the Boundary-layer Air Quality-analysis Using Network of Instruments (BAQUNIN) supersite, in the urban area of Rome (Italy). Two characteristic sea-breeze patterns were identified: the front days, in which the sea-breeze front develops in a few minutes, and the gentle breeze days, in which the onset of the front is gradual (more than 20 min). In the case of front days, Aerosol Optical Depth (AOD) increases during the onset of the breeze. The angstrom ngstro center dot m Exponent (AE), tropospheric and near surface NO2 amounts are almost constant during the day, while the aerosols volume size distribution follows a trimodal distribution. PM2.5 and PM10 concentrations decrease before the development of the sea-breeze front and then increase. Conversely, during gentle breeze days, AOD and AE do not change significantly. The tropospheric and near surface amount of NO2, PM2.5 and PM10 concentrations decrease, suggesting a significant dispersion of pollutants, while the aerosol size distribution shows a trimodal predominance.

Multi-axis differential optical absorption spectroscopy (MAX-DOAS) is a widely used measurement technique for the detection of a variety of atmospheric trace gases. Using inverse modelling, the observation of trace gas column densities along different lines of sight enables the retrieval of aerosol and trace gas vertical profiles in the atmospheric boundary layer using appropriate retrieval algorithms. In this study, the ability of eight profile retrieval algorithms to reconstruct vertical profiles is assessed on the basis of synthetic measurements. Five of the algorithms are based on the optimal estimation method, two on parametrised approaches, and one using an analytical approach without involving any radiative transfer modelling. The synthetic measurements consist of the median of simulated slant column densities of O4 at 360 and 477 nm, as well as of HCHO at 343 nm and NO2 at 477 nm, from seven datasets simulated by five different radiative transfer models. Simulations are performed for a combination of 10 trace gas and 11 aerosol profiles, as well as 11 elevation angles, three solar zenith, and three relative azimuth angles. Overall, the results from the different algorithms show moderate to good performance for the retrieval of vertical profiles, surface concentrations, and total columns. Except for some outliers, the root-mean-square difference between the true and retrieved state ranges between (0.05–0.1) km−1 for aerosol extinction and (2.5–5.0) ×1010 molec cm−3 for HCHO and NO2 concentrations.

The GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) was developed in support of NASA's decadal survey GEO-CAPE geostationary satellite mission. GCAS is an airborne pushbroom remote sensing instrument, consisting of two channels which make hyperspectral measurements in the ultraviolet/visible (optimized for air quality observations) and the visible/near-infrared (optimized for ocean color observations). The GCAS instrument participated in its first intensive field campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) in Texas in September 2013. During this campaign, the instrument flew on a King Air B-200 aircraft during 21 flights on 11 days to make air quality observations over Houston, Texas. We present GCAS trace gas retrievals of nitrogen dioxide (NO2) and formaldehyde (CH2O), and compare these results with trace gas columns derived from coincident in situ profile measurements of NO2 and CH2O made by instruments on a P-3B aircraft, and with NO2 observations from ground-based Pandora spectrometers operating in direct sun and scattered light modes. GCAS tropospheric column measurements correlate well spatially and temporally with columns estimated from the P-3B measurements for both NO2 (r2 = 0.89) and CH2O (r2 = 0.54) and with Pandora direct sun (r2 = 0.85) and scattered light (r2 = 0.94) observed NO2 columns. Coincident GCAS columns agree in magnitude with NO2 and CH2O P-3B-observed columns to within 10 %, but are larger than scattered light Pandora tropospheric NO2 columns by 33 % and direct sun Pandora NO2 columns by 50 %.

The KORUS-AQ field study conducted during May–June 2016 offered the first opportunity to evaluate direct-sun observations of formaldehyde (HCHO) total column densities with improved Pandora spectrometer instruments. The measurements highlighted in this work were conducted both in the Seoul megacity area at the Olympic Park site (latitude: 37.5232° N, longitude: 127.1260° E, 26 m a.s.l.) and at a nearby rural site downwind of the city at the Mount Taehwa Research Forest site (latitude: 37.3123° N, longitude: 127.3106° E, 160 m a.s.l.). Evaluation of these measurements was made possible by concurrent ground-based in situ observations of HCHO at both sites as well as overflight by the NASA DC-8 research aircraft. The flights provided in situ measurements of HCHO to characterize its vertical distribution in the lower troposphere (0–5 km). Diurnal variation in HCHO total column densities followed the same pattern at both sites, with the minimum daily values typically observed between 6:00–7:00 local time, gradually increasing to a maximum between 13:00 and 17:00 before decreasing into the evening. Pandora vertical column densities were compared with those derived from the DC-8 HCHO in-situ measured profiles augmented with in-situ surface concentrations below the lowest altitude of the DC-8 in proximity to the ground sites. A comparison between 49 column densities measured by Pandora versus aircraft integrated in-situ data showed that Pandora values were larger by 16 % (intercept = 0.22 DU, R2 = 0.68). Pandora HCHO columns were also compared with columns calculated from the surface in-situ measurements over Olympic Park by assuming a well-mixed lower atmosphere up to a ceilometer measured mixed-layer height (MLH) and various assumptions about the small residual HCHO amounts in the free troposphere up to the tropopause. The best comparison (slope = 1.03 ± 0.03, intercept = 0.29 ± 0.02 DU and R2 of 0.78 ± 0.02) was achieved assuming equal mixing within ceilometer measured MLH combined with an exponential profile shape. These results suggest that diurnal changes in HCHO surface concentrations can be reasonably estimated from the Pandora total column and information on the mixed-layer height. More work is needed to understand the bias in the intercept and the slope relative to columns derived from the in-situ aircraft and surface measurements.

Quantifying the aerosol absorption at ultraviolet (UV) wavelengths is important for monitoring air pollution using current (e.g., Aura/OMI) and future (e.g., TROPOMI, TEMPO, GEMS, and Sentinel-4) satellite measurements. Measurements of column atmospheric aerosol absorption (i.e., column effective imaginary refractive index (k), single scattering albedo (SSA), and aerosol absorption optical depth (AAOD)) are performed on the ground by the NASA AERONET in the visible (VIS) and near-infrared (NIR) wavelengths and in the UV-VIS-NIR by the SKYNET networks. Previous comparison studies have focused on visible and NIR wavelengths due to the lack of co-incident measurements of aerosol and gaseous absorption properties in the UV. This study compares the SKYNET-retrieved SSA in the UV with the SSA derived from a combination of AERONET, MFRSR, and Pandora (AMP) retrievals in Seoul, South Korea in spring and summer of 2016. The results show that the spectrally invariant surface albedo assumed in the SKYNET SSA retrievals leads to underestimated SSA compared to AMP values at near UV wavelengths. Re-processed SKYNET inversions using spectrally varying surface albedo, consistent with the AERONET retrieval improves agreement with AMP SSA. The combined AMP inversions allow for separating aerosol and gaseous (NO2 and O3) absorption and provides aerosol retrievals from the shortest UVB (305 nm) through visible to NIR wavelengths (870 nm).

We present intercomparison results for formaldehyde (HCHO) slant column measurements performed during the Cabauw Intercomparison campaign of Nitrogen Dioxide measuring Instruments (CINDI) that took place in Cabauw, the Netherlands, in summer 2009. During two months, nine atmospheric research groups simultaneously operated MAX-DOAS (MultiAXis Differential Optical Absorption Spectroscopy) instruments of various designs to record UV-visible spectra of scattered sunlight at different elevation angles that were analysed using common retrieval settings. The resulting HCHO data set was found to be highly consistent, the mean difference between instruments generally not exceeding 15% or 7.5 × 1015 molec cm−2, for all viewing elevation angles. Furthermore, a sensitivity analysis was performed to investigate the uncertainties in the HCHO slant column retrieval when varying key input parameters such as the molecular absorption cross sections, correction terms for the Ring effect or the width and position of the fitting interval. This study led to the identification of potentially important sources of errors associated with cross-correlation effects involving the Ring effect, O4, HCHO and BrO cross sections and the DOAS closure polynomial. As a result, a set of updated recommendations was formulated for HCHO slant column retrieval in the 336.5–359 nm wavelength range. To conclude, an error budget is proposed which distinguishes between systematic and random uncertainties. The total systematic error is estimated to be of the order of 20% and is dominated by uncertainties in absorption cross sections and related spectral cross-correlation effects. For a typical integration time of one minute, random uncertainties range between 5 and 30%, depending on the noise level of individual instruments.

In June 2009, 22 spectrometers from 14 institutes measured tropospheric and stratospheric NO2 from the ground for more than 11 days during the Cabauw Intercomparison Campaign of Nitrogen Dioxide measuring Instruments (CINDI), at Cabauw, NL (51.97° N, 4.93° E). All visible instruments used a common wavelength range and set of cross sections for the spectral analysis. Most of the instruments were of the multi-axis design with analysis by differential spectroscopy software (MAX-DOAS), whose non-zenith slant columns were compared by examining slopes of their least-squares straight line fits to mean values of a selection of instruments, after taking 30-min averages. Zenith slant columns near twilight were compared by fits to interpolated values of a reference instrument, then normalised by the mean of the slopes of the best instruments. For visible MAX-DOAS instruments, the means of the fitted slopes for NO2 and O4 of all except one instrument were within 10% of unity at almost all non-zenith elevations, and most were within 5%. Values for UV MAX-DOAS instruments were almost as good, being 12% and 7%, respectively. For visible instruments at zenith near twilight, the means of the fitted slopes of all instruments were within 5% of unity. This level of agreement is as good as that of previous intercomparisons, despite the site not being ideal for zenith twilight measurements. It bodes well for the future of measurements of tropospheric NO2, as previous intercomparisons were only for zenith instruments focussing on stratospheric NO2, with their longer heritage.

Sulfur dioxide (SO2) is a major atmospheric pollutant with a strong anthropogenic component mostly produced by the combustion of fossil fuel and other industrial activities. As a precursor of sulfate aerosols that affect climate, air quality, and human health, this gas needs to be monitored on a global scale. Global climate and chemistry models including aerosol processes along with their radiative effects are important tools for climate and air quality research. Validation of these models against in-situ and satellite measurements is essential to ascertain the credibility of these models and to guide model improvements. In this study, the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) module running on-line inside the Goddard Earth Observing System version 5 (GEOS-5) model is used to simulate aerosol and SO2 concentrations. Data taken in November 2010 over Frostburg, Maryland during an SO2 field campaign involving ground instrumentation and aircraft are used to evaluate GEOS-5 simulated SO2 concentrations. Preliminary data analysis indicated the model overestimated surface SO2 concentration, which motivated the examination of the specification of SO2 anthropogenic emission rates. As a result of this analysis, a revision of anthropogenic emission inventories in GEOS-5 was implemented, and the vertical placement of SO2 sources was updated. Results show that these revisions improve the model agreement with observations locally and in regions outside the area of this field campaign. In particular, we use the ground-based measurements collected by the United States Environmental Protection Agency (US EPA) for the year 2010 to evaluate the revised model simulations over North America.

From June to July 2009 more than thirty different in-situ and remote sensing instruments from all over the world participated in the Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI). The campaign took place at KNMI's Cabauw Experimental Site for Atmospheric Research (CESAR) in the Netherlands. Its main objectives were to determine the accuracy of state-of-the-art ground-based measurement techniques for the detection of atmospheric nitrogen dioxide (both in-situ and remote sensing), and to investigate their usability in satellite data validation. The expected outcomes are recommendations regarding the operation and calibration of such instruments, retrieval settings, and observation strategies for the use in ground-based networks for air quality monitoring and satellite data validation. Twenty-four optical spectrometers participated in the campaign, of which twenty-one had the capability to scan different elevation angles consecutively, the so-called Multi-axis DOAS systems, thereby collecting vertical profile information, in particular for nitrogen dioxide and aerosol. Various in-situ samplers and lidar instruments simultaneously characterized the variability of atmospheric trace gases and the physical properties of aerosol particles. A large data set of continuous measurements of these atmospheric constituents has been collected under various meteorological conditions and air pollution levels. Together with the permanent measurement capability at the CESAR site characterizing the meteorological state of the atmosphere, the CINDI campaign provided a comprehensive observational data set of atmospheric constituents in a highly polluted region of the world during summertime. First detailed comparisons performed with the CINDI data show that slant column measurements of NO2, O4 and HCHO with MAX-DOAS agree within 5 to 15%, vertical profiles of NO2 derived from several independent instruments agree within 25% of one another, and MAX-DOAS aerosol optical thickness agrees within 20–30% with AERONET data. For the in-situ NO2 instrument using a molybdenum converter, a bias was found as large as 5 ppbv during day time, when compared to the other in-situ instruments using photolytic converters.

[1] The ozone monitoring instrument (OMI), launched on the EOS/Aura satellite in July 2004, makes daily global observations of natural and anthropogenic SO2 emissions with unprecedented spatial resolution. Here we present the first robust comparison of OMI volcanic SO2 retrievals with ground−based instrumentation, using direct Sun observations of the Okmok volcanic cloud from Washington State University (WSU) in Pullman, WA on 18–20 July 2008. These measurements were made by the multifunction differential optical absorption spectroscopy (MFDOAS) instrument developed at WSU, as the Okmok cloud drifted over Pullman in the upper troposphere and lower stratosphere (UTLS). Observation conditions were favorable with cloud-free skies and a relatively homogeneous volcanic cloud distribution on OMI ground pixel scales (∼20–50 km). Movement of the Okmok cloud north and south of Pullman over a period of several days permitted comparison with three OMI overpasses with SO2 column amounts above the SO2 background level. The total SO2 columns measured by MFDOAS during OMI overpasses were 3.11 ± 0.23 Dobson units (DU), 1.75 ± 0.16 DU and 1.22 ± 0.18 DU (1 DU = 2.69 × 1016 molecules/cm2 = 0.029 g/m2). Comparison of ground-based direct Sun and operational and off-line OMI retrievals show an excellent agreement, providing the first validation of OMI measurements of volcanic SO2 in the UTLS.

The NO_2 total column abundance, C_(NO_2) was measured with a direct Sun viewing technique using three different instruments at NASA Jet Propulsion Laboratory's (JPL) Table Mountain Facility in California during an instrument intercomparison campaign in July 2007. The instruments are a high‐resolution (∼0.001 nm) Fourier transform ultraviolet spectrometer (FTUVS) from JPL and two moderate‐resolution grating spectrometers, multifunction differential optical absorption spectroscopy (MF‐DOAS) (∼0.8 nm) from Washington State University and Pandora (∼0.4 nm) from NASA Goddard Space Flight Center. FTUVS uses high spectral resolution to determine the absolute NO_2 column abundance independently from the exoatmospheric solar irradiance using rovibrational NO_2 absorption lines. The NO_2 total column is retrieved after removing the solar background using Doppler‐shifted spectra from the east and west limbs of the Sun. The FTUVS measurements were used to validate the independently calibrated measurements of multifunction differential optical absorption spectroscopy (MF‐DOAS) and Pandora. The latter two instruments start with measured high‐Sun spectra as solar references to retrieve relative NO_2 columns and then apply modified Langley or “bootstrap” methods to determine the amounts of NO_2 in the references to obtain the absolute NO_2 columns. The calibration offset derived from the FTUVS measurements is consistent with the values derived from Langley and bootstrap calibration plots of the NO_2 slant column measured by the grating spectrometers. The calibrated total vertical column abundances of NO_2, C_(NO_2) from all three instruments are compared showing that MF‐DOAS and Pandora data agree well with each other, and both data sets agree with FTUVS data to within (1.5 ± 4.1)% and (6.0 ± 6.0)%, respectively.

[1] Vertical column amounts of nitrogen dioxide, C(NO 2 ), are derived from ground-based direct solar irradiance measurements using two new and independently developed spectrometer systems, Pandora (Goddard Space Flight Center) and MFDOAS (Washington State University). We discuss the advantages of C(NO 2 ) retrievals based on Direct Sun - Differential Optical Absorption Spectroscopy (DS-DOAS). The C(NO 2 ) data are presented from field campaigns using Pandora at Aristotle University (AUTH), Thessaloniki, Greece; a second field campaign involving both new instruments at Goddard Space Flight Center (GSFC), Greenbelt, Maryland; a Pandora time series from December 2006 to October 2008 at GSFC; and a MFDOAS time series for spring 2008 at Pacific Northwest National Laboratory (PNNL), Richland, Washington. Pandora and MFDOAS were compared at GFSC and found to closely agree, with both instruments having a clear-sky precision of 0.01 DU (1 DU = 2.67 × 10 16 molecules/cm 2 ) and a nominal accuracy of 0.1 DU. The high precision is obtained from careful laboratory characterization of the spectrometers (temperature sensitivity, slit function, pixel to pixel radiometric calibration, and wavelength calibration), and from sufficient measurement averaging to reduce instrument noise. The accuracy achieved depends on laboratory-measured absorption cross sections and on spectrometer laboratory and field calibration techniques used at each measurement site. The 0.01 DU precision is sufficient to track minute-by-minute changes in C(NO 2 ) throughout each day with typical daytime values ranging from 0.2 to 2 DU. The MFDOAS instrument has better noise characteristics for a single measurement, which permits MFDOAS to operate at higher time resolution than Pandora for the same precision. Because Pandora and MFDOAS direct-sun measurements can be made in the presence of light to moderate clouds, but with reduced precision (~0.2 DU for moderate cloud cover), a nearly continuous record can be obtained, which is important when matching OMI overpass times for satellite data validation. Comparisons between Pandora and MFDOAS with OMI are discussed for the moderately polluted GSFC site, between Pandora and OMI at the AUTH site, and between MFDOAS and OMI at the PNNL site. Validation of OMI measured C(NO 2 ) is essential for the scientific use of the satellite data for air quality, for atmospheric photolysis and chemistry, and for retrieval of other quantities (e.g., accurate atmospheric correction for satellite estimates of ocean reflectance and bio-optical properties). Changes in the diurnal variability of C(NO 2 ) with season and day of the week are presented based on the 2-year time series at GSFC measured by the Pandora instrument.

We review the standard nitrogen dioxide (NO2) data product (Version 1.0.), which is based on measurements made in the spectral region 415–465 nm by the Ozone Monitoring Instrument (OMI) on the NASA Earth Observing System-Aura satellite. A number of ground- and aircraft-based measurements have been used to validate the data product's three principal quantities: stratospheric, tropospheric, and total NO2 column densities under nearly or completely cloud-free conditions. The validation of OMI NO2 is complicated by a number of factors, the greatest of which is that the OMI observations effectively average the NO2 over its field of view (minimum 340 km2), while a ground-based instrument samples at a single point. The tropospheric NO2 field is often very inhomogeneous, varying significantly over tens to hundreds of meters, and ranges from 1016 cm−2 over urban and industrial areas. Because of OMI's areal averaging, when validation measurements are made near NO2 sources the OMI measurements are expected to underestimate the ground-based, and this is indeed seen. Further, we use several different instruments, both new and mature, which might give inconsistent NO2 amounts; the correlations between nearby instruments is 0.8–0.9. Finally, many of the validation data sets are quite small and span a very short length of time; this limits the statistical conclusions that can be drawn from them. Despite these factors, good agreement is generally seen between the OMI and ground-based measurements, with OMI stratospheric NO2 underestimated by about 14% and total and tropospheric columns underestimated by 15–30%. Typical correlations between OMI NO2 and ground-based measurements are generally >0.6.

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.

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.

With the ongoing COVID-19 pandemic being spread all over the world, lockdown measures are being implemented making air pollution levels go down in several countries. In this context, the air quality changes in the highly populated and trafficked Brazilian states of Sao Paulo (SP) and Rio de Janeiro (RJ) are hereby going to be addressed using a combination of satellite and ground-based data analysis. We explored nitrogen dioxide (NO2) and particulate matter (PM2.5) daily levels for the month of May during different years within 2015-2020. Daily measurements of NO2 column concentrations from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite were also gathered and averaged decreases of 42% and 49.6% were found for the year of 2020 compared to previous averaged 2015-2019 years. In parallel to the NO2 column retrieval, the ground-based data, measured by the Brazilian States Environmental Institutions, is analyzed, and correlated with satellite retrievals. Correlation coefficients between column and ground-based concentrations were 77% and 53% in SP and RJ, respectively. It was found a 13.3% (p-value = 0.099) and 18.8% (p-value = 0.077) decrease in NO2 levels for SP and RJ, respectively, in 2020 compared to 2019. For PM2.5, no significant change was observed for the same time period in the SP region, although the high number of fire burnings in the Southeast region seemed to be affecting PM2.5 levels. In addition to natural emissions (fire burnings), the combined data was also evaluated taking meteorological parameters, such as temperature and wind speed, into account. No interference of weather or fire was found in 2020 NO2 ground levels compared to previous years, This integrated analysis is innovative and has yet to be more explored in Brazilian studies. This is true specifically because the ground-based stations are spatially and temporally sparse in Brazil. Master of Science This study aims to explore satellite data applied to the lockdown context resultant from the COVID-19 pandemic in Brazil. Satellite data usage in air quality management is yet to be explored to its full potential. Two highly populated states were chosen: Sao Paulo (SP) and Rio de Janeiro (RJ). Local governments have been imposing limitations on private and public vehicle circulation, inducing a decrease in atmospheric pollutant levels, specifically nitrogen dioxide (NO2), which is directly emitted to the air by fuel combustion. NO2 is also short-lived in the atmosphere, so its variation within days can be easily captured. PM2.5, a category of fine inhalable particles, can be produced by wildfires, in addition to fuel burning and mechanical processes such as resuspension by cars. Here we retrieved daily NO2 vertical column densities for the month of May within the 2015-2020 years from the OMI instrument onboard of NASA's Aura satellite. Ground daily NO2 and PM2.5 measurements were also collected from local environmental agencies. Results showed an average 42% decrease of the NO2 column values in SP in 2020 compared to 2015-2019. The decrease was 49.6% in RJ for the same timeframe. Correspondent surface data showed a decrease of 13.3% (p-value = 0.099) and 18.8% (p-value = 0.077) during 2020 compared to 2019 in SP and RJ stations, respectively. No significant divergence in PM2.5 values was found between 2019 and 2020. Finally, weather data was added to the pollutant analysis. PM2.5 concentrations were associated with wildfires, while the NO2 levels found in 2020 for SP and RJ were attributed to local lockdown decrees. Satellite retrievals showed significant potential in filling out ground datasets, correlating with the SP and RJ surface data in 77% and 53%, respectively.

Formaldehyde (HCHO), plays an important role in atmospheric chemistry and is an indicator of atmospheric oxidation capacity and surface ozone photo chemistry. The Pandora Spectrometer Instruments are deployed within the NASA/ESA sponsored Pandonia Global Network designed for satellite validation of various gases in atmosphere (e.g. ozone, nitrogen dioxide and formaldehyde). In addition, Pandoras are extensively used during national (e.g. DISCOVER-AQ, OWLETS, LISTOS) and international (CINDI, KORUS-AQ) field campaigns organized to better characterise air pollution and its distribution. Recently it was discovered and shown in prior research conducted by (Spinei et al. 2020), that Pandora measurements of atmospheric HCHO are impacted by HCHO produced within the telescope assembly due to temperature dependent off-gassing from the Delrin® plastic components. The purpose of the research covered in this thesis is to provide a methodology to correct total HCHO vertical column densities measured during the past field campaigns. The methodology developed through the course of this thesis is first tested on the Pandora simulated measurements derived from the surface concentration HCHO observations during KORUS-AQ (2016) field campaign. The derived correction using synthetic data shows that the proposed methodology is accurate within 30%. The second part of the thesis characterizes heat transfer processes within the telescope assembly to estimate internal temperature as a function of ambient meteorological conditions. Considering that the Pandora instruments have mostly identical design of their telescope assemblies heat transfer coefficients derived from one pandora are expected to be applicable to all Pandoras. Convective heat transfer coefficients were derived at VT wind tunnel as a function of wind speed and telescope assembly position. Internally generated power was measured for several different instruments and averaged at $2.15 pm 0.38$ W. Total long wave emissivity was calculated at 0.63. Surface absorptivities were estimated from the material properties. Semi-empirically derived model is proposed to estimate the internal temperature based on the heat transfer parameters, ambient temperature, relative humidity, solar flux, wind speed and wind direction. The correlation between the estimated and measured internal temperatures is 0.93 R^2. Finally, the methodology is applied to the actual HCHO data collected during the KORUS-AQ campaign and the results are compared to concurrent in-situ measurements made aboard DC-8 aircraft for eight days in the months of May and June 2016. Master of Science Formaldehyde (HCHO), is a key indicator of atmospheric health and because of this, it is an important topic for study. The Pandora Sun Photometer is a low cost instrument developed at NASA Goddard Space Flight center. It has been used in the study of HCHO in various field campaigns. During the Korea-United States Air Quality Study (KORUS-AQ), the Long Island Sound Tropospheric Ozone Study (LISTOS) and the Ozone Water-Land Environmental Transition Study (OWLETS) the Pandora instrument design included a component manufactured from Delrin® plastic. It has recently been found to produce HCHO relative to the change in temperature. Due to the location of this component inside the telescope assembly of the Pandora instrument, the HCHO produced by the plastic was incorporated into the data invalidating the results. The purpose of this thesis is to provide a methodology for analyzing this issue through quantification of the HCHO produced by the plastic. An analysis is conducted to provide the ability to quantify the temperature internal to the telescope assembly. In addition, three methods are discussed for applying this to then quantify the proportion of HCHO that had been added to the measurements. Finally, the methods are applied to data collected during the KORUS-AQ campaign and the results are compared to a reliable set of data performed by a different instrument showing the improved agreement on eight days in the months of May and June.

Previous studies have identified longitudinal structures associated with non-migrating tides in observations of the upper neutral atmosphere of Mars. MAVEN’s Imaging Ultraviolet Spectrometer (IUVS) observations of the upper atmosphere reveal variations in density with longitude at altitudes of 130 – 200 km, and can be used to identify non-migrating tides. These observations cover higher latitudes and allow for studying the local time variations of tides. The analysis presented here shows that the longitudinal structure attributed to non-migrating tides is dominated by wavenumber 2 and wavenumber 3 harmonics during the periods studied. Comparison with the Neutral Ion and Gas Mass spectrometer (NGIMS) shows a good agreement in wave amplitudes observed for the first two cases studied. The temperatures and 𝑂/𝐶𝑂# ratios from the IUVS L2 data files revealed an anti-correlation with the densities which confirms the theoretical interpretation from the linear wave theory. Master of Science There are waves internal to all fluids in our surroundings and daily lives, such as sound waves. Waves in the atmosphere are also fluid in nature. In planetary atmospheres, the scale sizes of some of these waves become comparable to the size of the planet itself. The waves interact with the structure of the surface of Mars to form a certain type of wave called “Non-migrating tides”. These waves have been observed in multiple previous studies in the upper atmosphere of Mars (~130 km and above). These waves cause the atomic and molecular content of the upper atmosphere to be displaced in a particular manner to form a unique structure. The structures formed are observed on a scale that covers the entire planet. It is by studying these structures in the upper atmosphere that it is possible to characterize the waves that control them and thereby understand their nature and impact. Understanding how these waves vary helps spacecraft to gain better control over mechanisms required to swing them into the desired orbit (location). This study uses the observations from an instrument aboard the MAVEN mission and compares it to the observations from another instrument aboard the same mission. The results of this study demonstrate that these “Non-migrating” tides play a vital role in controlling the behavior of the upper atmosphere.