University, Diss., 2005--Heidelberg.

University, Diss., 2005--Heidelberg.

The focus of this research was the examination of the emission and transformation of nitrogen oxides emitted from vehicles. Measured data for this experiment were collected from May 1 thru May 31, 2002, and were compared to values modeled with CALINE4. CALINE4 is a photochemical and dispersive model used to predict concentrations of NOX (NO+NO2) from line sources. The measurement campaign was coincident with the Bay Regional Atmospheric Chemistry Experiment (BRACE). An ambient air quality monitoring site was constructed adjacent to Gandy Boulevard, in Tampa, FL. When comparisons of measured and modeled NO and NO2 values were made it was found that CALINE4 underpredicted NO2; i.e., underpredicted the conversion of NO, for both daytime and nighttime conditions. Possible causes of this bias were investigated and it was found that the simple kinetic mechanism present in CALINE4 was not sufficient to account for all of the reactions occurring. A simulation was run with a more comprehensive NO conversion mechanism and it was found that the reactions containing peroxy radicals affected the conversion rate but were not present in the simple CALINE4 mechanism. The simulation runs suggested that the ratio of radicals to O3 remained nearly constant during the course of the reaction. This pointed to an improved mechanism where the photolytic rate constant in CALINE4 could be replaced with a new constant, keff. This brought theday time calculations within reasonable agreement of the measured values, including an unexpected improvement in nighttime concentrations. Specifically, this modification eliminated the negative fractional bias in calculated daytime NO2 concentrations, moving it from -0.16 to 0.043. The fractional bias in nighttime calculations was improved from -0.17 to -0.036. Average hourly traffic counts were then used as inputs to the model to compare to the entire month of May 2002 data and it was found that the daytime fractional bias was improved from -0.27 to -0.06 and the nighttime from -0.35 to -0.24

Heidelberg, Univ., Diss., 2007. / Online publiziert: 2008.

University, Diss., 2002--Heidelberg.

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.

Este trabalho apresenta os resultados da construção de um espectrômetro utilizando a espectroscopia ótica por absorção diferencial (DOAS) para determinação de poluentes na atmosfera. A determinação e a quantificação de gases-traço contaminantes na atmosfera são possíveis pelo registro da transmitância, e posterior avaliação das estruturas de absorção características de cada espécie, em um caminho ótico aberto conhecido na atmosfera. As partes óticas e eletrônicas foram caracterizadas e o software de comando e processamento espectral foi desenvolvido. O sistema construído foi testado em laboratório e medidas de emissões veiculares de quatro automóveis foram realizadas. Os resultados das emissões veiculares apresentaram uma sensível diferença entre os veículos no regime de operação (motor frio e quente) por um fator que varia entre 5 e 8. / This work presents the results of the construction of a spectrometer using the differential optical absorption spectroscopy (DOAS) for determination of pollutants in the atmosphere. The determination and quantification of trace gas contaminations in atmosphere is possible by recording and later evaluation of characteristic absorption structures in a known path length in open atmosphere. The parts optics and electronic had been characterized and the software of command and spectral processing was developed. The system was built and tested in laboratory and vehicle emissions measures of four cars were performed. The results in vehicle emissions showed a noticeable difference between vehicles in the system of operation (hot and cold engine) by a factor ranging between 5 and 8.

Este trabalho apresenta os resultados da construção de um espectrômetro utilizando a espectroscopia ótica por absorção diferencial (DOAS) para determinação de poluentes na atmosfera. A determinação e a quantificação de gases-traço contaminantes na atmosfera são possíveis pelo registro da transmitância, e posterior avaliação das estruturas de absorção características de cada espécie, em um caminho ótico aberto conhecido na atmosfera. As partes óticas e eletrônicas foram caracterizadas e o software de comando e processamento espectral foi desenvolvido. O sistema construído foi testado em laboratório e medidas de emissões veiculares de quatro automóveis foram realizadas. Os resultados das emissões veiculares apresentaram uma sensível diferença entre os veículos no regime de operação (motor frio e quente) por um fator que varia entre 5 e 8. / This work presents the results of the construction of a spectrometer using the differential optical absorption spectroscopy (DOAS) for determination of pollutants in the atmosphere. The determination and quantification of trace gas contaminations in atmosphere is possible by recording and later evaluation of characteristic absorption structures in a known path length in open atmosphere. The parts optics and electronic had been characterized and the software of command and spectral processing was developed. The system was built and tested in laboratory and vehicle emissions measures of four cars were performed. The results in vehicle emissions showed a noticeable difference between vehicles in the system of operation (hot and cold engine) by a factor ranging between 5 and 8.

In atmospheric environment, synchronized survey of criteria air pollutants and volatile organic compounds (VOCs) are important for ambient air quality measurement. UV-DOAS (Ultra-violet Differential Optical Absorption Spectroscopy) in this research is an optics open-optical monitor based on the ultraviolet and visible-light absorption of gaseous pollutants. This measurement could be employed to examine the spatial average concentration of optical path in the open space, and to monitor criteria air pollutants (including O3, SO2, NO and NO2) as well as VOCs (including benzene, toluene, p-xylene). Aircraft takeoff and landing has serious impacts on the neighboring envvironment of Kaohsiung International Airport. Aircrafts generate two major pollution problems: chemical pollution (leaking flue from engine and fume with particulate and gaseous pollutants from incomplete combustion) and physical pollution (aircraft noise). Most air pollutant emissions from aero engine are generated during aircraft takeoff and landing. The distribution of gaseous pollutants along the runway is a linear pollution source. Restricted by flight safety, it is difficult to monitor and investigate aero engine pollution with traditional stationary monitors. Therefore, open-path monitoring has become a novel tool for research. First, this study investigated the spatial average concentration of air pollutants around the runway of Kaohsiung International Airport and compared with monitoring data from nearby air quality monitoring station to evaluate the performance of the open-path monitoring system. Comparing the stationary monitors with the open-path monitoring showed relatively better correlations on O3 and NO2 than on SO2 and NO, which might be caused by the differences of local emissions. Overall, these two monitoring systems are identical on monitoring regulated pollutants. Furthermore, the open-path monitoring can also examine volatile organic compounds (ex: benzene, p-xylene, and toluene). The open-path monitoring is a practical and reliable monitoring system. It could be considered as one of the ambient air quality standard monitors in the future. Secondly, this study chose the airspace of approach lane, located at west side of the runway 09 of Kaohsiung International Airport, as survey area. The open-path monitoring (OP-SIS) utilized UV-DOAS for sampling and monitoring the air quality of the survey area. It examined, recorded, and analyzed the criteria air pollutants (O3, SO2, NO and NO2) as well as VOCs (including benzene, toluene, p-xylene) to ascertain the impact of aircraft emissions on atmospheric environment. The results suggested that aircraft emissions influenced ambient air quality with high correlation to NO concentration. Additionally, the wind directions of land-sea breeze and monsoon also have influences on the concentration of air pollutants in the study area. In daytime, when wind direction was N or NNE, the variance of NO concentration was consistent with takeoff and landing time points with correlation coefficient of 0.60. After 5-6 minutes of an aircraft passed through the study airspace, the variance of instantaneous concentration of NO climbed up to 30 ppb. The results were contributed to understand the characteristics and sources of aircraft emitted air pollutants. It could provide the authority to have better judgment on reducing air pollutants as well as to assist the maintence and management of ambient air quality surrounding the airports.

We derive tropospheric column BrO during the ARCTAS and ARCPAC field campaigns in spring 2008 using retrievals of total column BrO from the satellite UV nadir sensors OMI and GOME-2 using a radiative transfer model and stratospheric column BrO from a photochemical simulation. We conduct a comprehensive comparison of satellite-derived tropospheric BrO column to aircraft in-situ observations of BrO and related species. The aircraft profiles reveal that tropospheric BrO, when present during April 2008, was distributed over a broad range of altitudes rather than being confined to the planetary boundary layer (PBL). Perturbations to the total column resulting from tropospheric BrO are the same magnitude as perturbations due to longitudinal variations in the stratospheric component, so proper accounting of the stratospheric signal is essential for accurate determination of satellite-derived tropospheric BrO. We find reasonably good agreement between satellite-derived tropospheric BrO and columns found using aircraft in-situ BrO profiles, particularly when satellite radiances were obtained over bright surfaces (albedo >0.7), for solar zenith angle <80 degree and clear sky conditions. The rapid activation of BrO due to surface processes (the bromine explosion) is apparent in both the OMI and GOME-2 based tropospheric columns. The wide orbital swath of OMI allows examination of the evolution of tropospheric BrO on about hourly time intervals near the pole. Low surface pressure, strong wind, and high PBL height are associated with an observed BrO activation event, supporting the notion of bromine activation by high winds over snow. We also provide monthly climatological maps of free tropospheric BrO volume mixing ratio (VMR) derived using the so-called cloud slicing technique. In this approach, the derived slope of the total column BrO versus cloud pressure is proportional to free tropospheric BrO VMR. Estimated BrO VMR shows a minimum in the tropics and greater values at higher latitudes in both hemispheres. High tropospheric BrO VMR at high latitudes in spring could be influenced by near-surface bromine activation.