STATISTICAL STUDY OF MODIS ALGORITHMS IN ESTIMATING AEROSOL OPTICAL DEPTH OVER THE CZECH REPUBLIC

As a result of the rapid development of remote sensing techniques and accurate satellite observations, it has become customary to use these technologies in ecological and aerosols studies on a regional and global level. In this paper, we analyse the performance of three Moderate Resolution Imaging Spectroradiometer (MODIS) algorithms in estimating Aerosol Optical Depth (AOD) in the Czech Republic to gain knowledge about their accuracy and uncertainty. The Dark Target (DT), the Deep Blue (DB), and the merged algorithm (DTB) of the MODIS latest collection 6.1 Level 2 aerosol products (MOD04_L2) were tested by comparing its results with the measurements of Aerosol Robotic Network (AERONET) Level 3 Version 2.0 ground station at Brno airport. The DT algorithm is compatible the best with AERONET observations with a correlation coefficient (R = 0.823), retrievals falling within the EE envelope (EE% = 82.67%), root mean square error (RMSE = 0.059), and mean absolute error (MAE = 0.044). The DTB algorithm provided close results of the DT algorithm but with less accuracy, on the other hand the DB algorithm has the lowest accuracy between all, but this algorithm was able to provide a bigger sample size than the other two algorithms

Satellite-based PM2.5 Exposure Estimation and Health Impacts over China

Exposure to suspended fine particulate matter (PM2.5) has been proven to adversely impact public health through increased risk of cardiovascular and respiratory mortality. Assessing health impacts of PM2.5 and its long-term variations requires accurate estimates of large-scale exposure data. Such data include mass concentration and particle size, the latter of which may be an effect modifier on PM2.5 attributable health risks. The availability of these exposure data, however, is limited by sparse ground-level monitoring networks. In this dissertation, an optical-mass relationship was first developed based on aerosol microphysical characteristics for ground-level PM2.5 retrieval. This method quantifies PM2.5 mass concentrations with a theoretical basis, which can simultaneously estimate large-scale particle size. The results demonstrate the effectiveness and applicability of the proposed method and reveal the spatiotemporal distribution of PM2.5 over China. To explore the spatial variability and population exposure, particle radii of PM2.5 are then derived using the developed theoretical relationship along with a statistical model for a better performance. The findings reveal the prevalence of exposure to small particles (i.e. PM1), identify the need for in-situ measurements of particle size, and motivate further research to investigate the effects of particle size on health outcomes. Finally, the long-term impacts of PM2.5 on health and environmental inequality are assessed by using the satellite-retrieved PM2.5 estimates over China during 2005-2017. Premature mortality attributable to PM2.5 exposure increased by 31% from 2005 to 2017. For some causes of death, the burden fell disproportionately on provinces with low-to-middle GDP per capita. As a whole, this work contributes to bridging satellite remote sensing and long-term exposure studies and sheds light on an ongoing need to understand the effects of PM2.5, including both concentrations and other particle characteristics, on human health

Air Quality Research Using Remote Sensing

Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

Application of Earth observations and chemical transport modelling to investigate air quality and health from the city to the global scale

Ambient air pollution is responsible for 4-9 million premature deaths worldwide each year. Routine ground-based monitoring of air quality in cities is sparse and expensive and only includes a handful of pollutants. Most health risk assessment models are derived with limited health outcomes and cover a narrow range (2.4-35 µg m$^{-3}$) of fine particulate (PM$_{2.5}$) concentrations. Satellites provide daily global coverage of a dynamic range of pollutants for more than a decade and there are updated health risk assessment models that account for the increasing number of health outcomes that have been associated with air pollution and that cover a wider exposure range than previous models. In this work, the skill of satellite observations at reproducing variability in surface air quality in the UK and Indian cities was assessed. Temporal consistency (R>0.5) occurred between space-based and surface observations of nitrogen dioxide (NO$_2$) and ammonia (NH$_2$), whereas measurements of aerosol optical depth (AOD) have weak month-to-month variability (R<0.4) with surface PM$_{2.5}$, but do replicate long term trends in PM$_{2.5}$. This provided the confidence to use satellite observations to determine recent (2000s 2010s) long-term trends in NO$_2$, NH$_3$, formaldehyde (HCHO) as a marker for reactive non-methane volatile organic compounds (NMVOCs), and AOD as a marker for PM$_{2.5}$ in London and Birmingham in the UK, and Delhi and Kanpur in India. Trends in most pollutants declined in UK cities because of successful control on vehicular emissions but increased in Indian cities despite recent pollution control measures. These validated satellite observations were then used to quantify long-term trends in air quality over 46 tropical cities which are growing at an unprecedented pace (1-10 % a$^{-1}$) and that lack routine, reliable and accessible ground-based air quality measurements. Most pollutants in almost all tropical cities increased, driven almost exclusively by increase in anthropogenic activity rather than traditional biomass burning. Population exposure to hazardous pollutants PM$_{2.5}$ and NO$_2$ increased by up to 23 % a$^{-1}$ for NO$_2$ and 18 % a$^{-1}$ for PM$_{2.5}$ due to the combined increase in emerging anthropogenic air pollution and population. This suggests an impending health crisis that demands further analysis to determine the increase in health burden from increased exposure to these hazardous pollutants. This was followed by examining the health burden from exposure to PM$_{2.5}$ produced exclusively from fossil fuel combustion, a dominant and controllable anthropogenic source of PM$_{2.5}$. The health burden was estimated using the chemical transport model GEOS-Chem, validated with satellite and surface observations, and a recent meta-analysis that accounted for a wider exposure range than previous approaches. 10.2 million adult premature deaths were estimated to be from fossil fuel related PM$_{2.5}$ in 2012 with 62 % of these in China and India. These estimates are more than double than those obtained from the Global Burden of Disease and other studies because of the updated health risk assessment model and a finer spatial resolution chemical transport model. These estimates decline to 8.7 million in 2018 due to substantial decline in fossil fuel emissions in China, demonstrating the efficacy of air quality policies that target fossil fuel sources. Fossil fuel combustion can be more readily controlled than other primary and secondary sources of PM$_{2.5}$ and transitioning towards cleaner sources of energy can mitigate these premature deaths. These results highlight the immediate health crisis due to ongoing reliance on fossil fuels to complement the longer term and potentially more severe effects these will have on climate. The thesis demonstrates the application of satellite observations, ground-based measurements, chemical transport models, emission inventories and health risk assessment models and statistical techniques to determine trends and drivers of these trends in air quality in cities and estimate the health burden at different spatial scales. This is crucial information that policymakers and stakeholders require to make informed decisions and develop prescient policies

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics

Observing the distributions and chemistry of major air pollutants (O3 and PM2.5) from space: trends, uncertainties, and health implications

Ambient exposure to fine particulate matter (PM2.5) and ground-level ozone (O3) is identified as a leading risk factor for global disease burden. A major limitation to advancing our understanding of the cause and impacts of air pollution is the lack of observations with the spatial and temporal resolution needed to observe variability in emission, chemistry and population exposure. Satellite remote sensing, which fills a spatial gap in ground-based networks, is playing an increasingly important role in atmospheric chemistry. This thesis exploits satellite remote sensing observations to: (1) estimate human exposure to PM2.5 from remotely sensed aerosol optical properties; (2) identify the chemical regimes of surface O3 formation using satellite observations of O3 precursors. In the first part, we use a forward geophysical approach to derive PM2.5 distributions from satellite AOD at 1 km2 resolution over the northeastern US by applying relationships between PM2.5 and AOD simulated from a regional air quality model (CMAQ). We use multi-platform ground, airborne and radiosonde measurements to quantify multiple sources of uncertainties in the satellite-derived PM2.5. We find that uncertainties in satellite-derived PM2.5 are largely attributed to the varying relationship between PM2.5 and AOD that depends on the aerosol vertical distribution, speciation, aerosol optical properties and ambient relative humidity. To assess the value of remote sensing to improve PM2.5 exposure estimate, we compile multiple PM2.5 products that include information from remote sensing, ground-based observations and models. Evaluating these products using independent observations, we find the inclusion of satellite remote sensing improves the representativeness of surface PM2.5 mostly in the remote areas with sparse monitors. Due to the success of emission control, PM2.5-related mortality burden over NYS decreased by 67% from 8410 (95% confidence interval (CI): 4, 570 – 12, 400) deaths in 2002 to 2750 (95% CI: 700 – 5790) deaths in 2012. We estimate a 28% uncertainty in the state-level PM2.5 mortality burden due to the choice of PM2.5 products, but such uncertainty is much smaller than the uncertainty (130%) associated with the exposure-response function. The second part of the thesis focuses on ground-level O3. O3 production over urban areas is non-linearly dependent on the availability of its precursors: nitrogen oxides (NOx) and volatile organic compounds (VOCs). A major challenge in lowering ground-level O3 in urban areas is to determine the limiting species for O3 production (NOx-limited or VOC-limited). We use satellite observations of NO2 and HCHO to infer the relative abundance of NOx versus VOCs, thus to identify the O3 chemical regime. We first use a global chemical transport model (GEOS-Chem) to evaluate the uncertainties of using satellite-based HCHO/NO2 to infer O3 sensitivity to precursor emissions. Next, we directly connect this space-based indicator, retrieved consistently from three satellite instruments, to spatiotemporal variations in O3 recorded by on-the-ground monitors from 1996 to 2016. The nationwide emission reduction has led the O3 formation over U.S. urban areas to shift from VOC-limited to NOx-limited regime. Urban O3 monitors reveal trends consistent with this regime transition. Nonetheless, it is a major challenge for these retrievals to accurately depict day-to-day variability within urban cores. TROPOspheric Monitoring Instrument (TROPOMI) which launched in 2017, offers an unprecedented view to infer O3 chemistry at fine spatial and temporal scales. As an example, we use TROPOMI HCHO/NO2 to identify short-term changes in O3 sensitivity during the California Camp Fire. We find that the emissions from wildfires lead to NOx-saturated ozone formation near the fire source but NOx -limited conditions downwind. This thesis bridges basic research in atmospheric chemistry, which advances the state-of-science related to O3 and PM2.5 pollution from urban to global scales, and applied research in air quality management and public health, by quantifying the health benefits of emission control, and informs policymakers on which emission reductions to focus so as to maximize the cost-effectiveness of pollution controls. We show how space-based measurements can complement in situ networks and model simulations by providing information on the spatial heterogeneity and temporal evolution of PM2.5 exposure and O3 chemical regimes, which will lay the scientific foundation for interpreting future products retrieved from upcoming geostationary platforms

Global Modeling and Analysis of Anthropogenic Combustion and Associated Emissions

Anthropogenic combustion and associated emissions have significant impacts on air quality and climate. However, current estimates of emissions from anthropogenic combustion are still subject to large uncertainties, especially in rapidly-developing regions. This hinders accurate assessments of their regional and global impacts on air quality and climate, which presents an urgent need to understand, assess, monitor, and predict anthropogenic combustion and associated emissions particularly at city-to-national scales. Combustion products co-emitted to the atmosphere and their relationships are typically related to characteristics of combustion processes. Thus, in order to understand anthropogenic combustion and associated emissions, my PhD study seeks to answer three major scientific questions: (1) To what extent could current observations of trace gases co-emitted from combustion be used to understand anthropogenic combustion, emissions, and related driving factors? (2) How well do present global climate-chemistry models simulate trace gases from combustion activities and could those models be used to study anthropogenic emissions? (3) To what extent could the current understanding of anthropogenic combustion and emissions be improved by jointly analyzing satellite, ground-based, aircraft measurements, and model simulations of trace gases co-emitted from combustion? To address the first scientific question, I combine air pollution measurements from multiple satellite instruments across 2005-2014 to characterize emergent features of the ratios of carbon monoxide (CO) and sulfate dioxide (SO2) to nitrogen dioxide (NO2) enhancements from anthropogenic emissions over 36 cities in China. The resulting emission pattern is well-correlated with economic development and traces a common emission pathway that resembles the evolution of air pollution in more developed cities. The absence of this progression in the current IPCC Representative Concentration Pathway emission inventory is most likely due to its deficient representation of the shift towards cleaner combustion in more developed cities. The results highlight the usefulness of augmenting observational capabilities by exploiting relationships of combustion tracers in constraining the temporal variation of emissions for gaseous pollutants. In addition, it is also desired to monitor and assess anthropogenic combustion and its impacts through modeling. Thus, to address the second scientific question, I evaluate simulations of two important anthropogenic combustion products (carbon dioxide (CO2) and CO) from a state-of-the-art high-resolution global prediction system, the Copernicus Atmosphere Monitoring Service (CAMS), by comparing with the Korea-United States Air Quality (KORUS-AQ) field measurements (May to June 2016) that aims to understand the factors controlling air quality over East Asia. The results show a slight overestimation for CAMS CO2 and a moderate underestimation for CAMS CO. CAMS also captures the observed more efficient combustion over Seoul compared to China outflows. Furthermore, to address both the second and third scientific questions, I combine observations and model simulations to uncover important combustion sources over East Asia, using the Community Atmosphere Model with chemistry (CAM-chem) with a CO tagging mechanism, where artificial CO tracers (i.e., tags) from specific sources are tracked as standard CO. With 17 CAM-chem tagged CO simulations using various model configurations, I quantify key regional sources of CO during KORUS-AQ. The results show that emissions from middle East Asia dominate continental outflows to Korea, while Korean emissions play an overall more important role for ground sites and plumes within the boundary layer in Korea. The CAM-chem tagging results are generally consistent with other source contribution approaches. Following the CO modeling, together with newly developed CO2 modeling and tagging mechanism in CAM-chem, I demonstrate the use of joint analysis of CO and CO2 towards a multi-species inversion. I simulate atmospheric CO2 as well as CO in CAM-chem using optimized carbon fluxes for CO2. The model results generally agree with observations from satellite, aircraft, and ground-based observations during KORUS-AQ. Then, I implement a CO2 tagging mechanism into the model. The modeled fossil fuel CO2 tags agree well with fossil fuel CO2 derived from radiocarbon samples during the field campaign. I also show that signatures of plume transport and sectoral emissions of CO2 are enhanced in CO analyses. Overall, this work elucidates the use of jointly analyzing CO2 and CO in tracking fossil fuel CO2, quantifying regional sources, and understanding combustion efficiency of sources. In my future work, I will (1) combine observations and model simulations of atmospheric gases to obtain improved estimates of their emissions from anthropogenic combustion based on inverse modeling techniques, and (2) use the improved emission estimates to quantify the impact of trace gases on air quality and climate

Development of a Toolbox to Compare Atmospheric Composition Datasets: Long-term trends in urban NO2 concentrations in Spain derived from CAMS reanalysis and GOME-2 data

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesSatellite and model atmospheric composition data are stored in different platforms, using heterogeneous file formats, varying spatiotemporal resolutions and noncompatible metadata. Comparing these datasets is not a trivial task, but required in data assimilation, validation and mutual coverage studies. This thesis investigates the prevailing methods used to compare sensor observations with data from the forecast and reanalysis system developed by the Copernicus Atmospheric Monitoring Service (CAMS). These are implemented in the development of the first prototype of the Atmospheric Datasets Comparison (ADC) Toolbox. This toolbox, which is the core part of the project, contains a set of tools that facilitate the file interoperability, binning and regridding, computation of levels pressure, conversion of units, application of the averaging kernels, datasets merge, geostatistical comparison and trend analysis. The contribution of this work is twofold: a toolbox is developed to merge and compare atmospheric composition datasets systematic and automatically for any region and time, and its applicability is shown in a case study, where the NO2 emissions in Spain in the last decade are analyzed using satellite and model data

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity