A novel fusion framework embedded with zero-shot super-resolution and multivariate autoregression for precipitable water vapor across the continental Europe

Precipitable water vapor (PWV), as the most abundant greenhouse gas, significantly impacts the evapotranspiration process and thus the global climate. However, the applicability of mainstream satellite PWV products is limited by the tradeoff between spatial and temporal resolutions, as well as some external factors such as cloud contamination. In this study, we proposed a novel PWV spatio-temporal fusion framework based on the zero-shot super-resolution and the multivariate autoregression models (ZSSR-ARF) to improve the accuracy and continuity of PWV. The framework is implemented in a way that the satellite-derived observations (MOD05) are fused with the reanalysis data (ERA5) to generate accurate and seamless PWV of high spatio-temporal resolution (0.01°, daily) across the European continent from 2001 to 2021. Firstly, the ZSSR approach is used to enhance the spatial resolution of ERA5 PWV based on the internal recurrence of image information. Secondly, the optimal ERA5-MOD05 image pairs are selected based on the image similarity as inputs to improve the fusion accuracy. Thirdly, the framework develops a multivariate autoregressive fusion approach to allocate weights adaptively for the high-resolution image prediction, which primely addresses the non-stationarity and autocorrelation of PWV. The results reveal that the accuracies of fused PWV are consistent with those of the GPS retrievals (r = 0.82–0.95 and RMSE = 2.21–4.01 mm), showing an enhancement in the accuracy and continuity compared to the original MODIS PWV. The ZSSR-ARF fusion framework outperforms the other methods with R$^2$ improved by over 24% and RMSE reduced by over 0.61 mm. Furthermore, the fused PWV exhibits similar temporal consistency (mean difference of 0.40 mm and DSTD of 3.22 mm) to the reliable ERA5 products, and substantial increasing trends (mean of 0.057 mm/year and over 0.1 mm/year near the southern and western coasts) are observed over the European continent. As the accuracy and continuity of PWV are improved, the outcome of this paper has potential for climatic analyses during the land-atmosphere cycle process

A Review on Different Modeling Techniques

In this study, the importance of air temperature from different aspects (e.g., human and plant health, ecological and environmental processes, urban planning, and modelling) is presented in detail, and the major factors affecting air temperature in urban areas are introduced. Given the importance of air temperature, and the necessity of developing high-resolution spatio- temporal air-temperature maps, this paper categorizes the existing approaches for air temperature estimation into three categories (interpolation, regression and simulation approaches) and reviews them. This paper focuses on high-resolution air temperature mapping in urban areas, which is difficult due to strong spatio-temporal variations. Different air temperature mapping approaches have been applied to an urban area (Berlin, Germany) and the results are presented and discussed. This review paper presents the advantages, limitations and shortcomings of each approach in its original form. In addition, the feasibility of utilizing each approach for air temperature modelling in urban areas was investigated. Studies into the elimination of the limitations and shortcomings of each approach are presented, and the potential of developed techniques to address each limitation is discussed. Based upon previous studies and developments, the interpolation, regression and coupled simulation techniques show potential for spatio-temporal modelling of air temperature in urban areas. However, some of the shortcomings and limitations for development of high-resolution spatio- temporal maps in urban areas have not been properly addressed yet. Hence, some further studies into the elimination of remaining limitations, and improvement of current approaches to high-resolution spatio-temporal mapping of air temperature, are introduced as future research opportunities

The use of satellite data, meteorology and land use data to define high resolution temperature exposure for the estimation of health effects in Italy

Introduction. Despite the mounting evidence on heat-related health risks, there is limited evidence in suburban and rural areas. The limited spatial resolution of temperature data also hinders the evidence of the differential heat effect within cities due to individual and area-based characteristics. Methods. Satellite land surface temperature (LST), observed meteorological and spatial and spatio-temporal land use data were combined in mixed-effects regression models to estimate daily mean air temperature with a 1x1km resolution for the period 2000-2010. For each day, random intercepts and slopes for LST were estimated to capture the day-to-day temporal variability of the Ta–LST relationship. The models were also nested by climate zones to better capture local climates and daily weather patterns across Italy. The daily exposure data was used to estimate the effects and impacts of heat on cause-specific mortality and hospital admissions in the Lazio region at municipal level in a time series framework. Furthermore, to address the differential effect of heat within an urban area and account for potential effect modifiers a case cross-over study was conducted in Rome. Mean temperature was attributed at the individual level to the Rome Population Cohort and the urban heat island (UHI) intensity using air temperature data was calculated for Rome. Results. Exposure model performance was very good: in the stage 1 model (only on grid cells with both LST and observed data) a mean R2 value of 0.96 and RMSPE of 1.1°C and R2 of 0.89 and 0.97 for the spatial and temporal domains respectively. The model was also validated with regional weather forecasting model data and gave excellent results (R2=0.95 RMSPE=1.8°C. The time series study showed significant effects and impacts on cause-specific mortality in suburban and rural areas of the Lazio region, with risk estimates comparable to those found in urban areas. High temperatures also had an effect on respiratory hospital admissions. Age, gender, pre-existing cardiovascular disease, marital status, education and occupation were found to be effect modifiers of the temperature-mortality association. No risk gradient was found by socio-economic position (SEP) in Rome. Considering the urban heat island (UHI) and SEP combined, differential effects of heat were observed by UHI among same SEP groupings. Impervious surfaces and high urban development were also effect modifiers of the heat-related mortality risk. Finally, the study found that high resolution gridded data provided more accurate effect estimates especially for extreme temperature intervals. Conclusions. Results will help improve heat adaptation and response measures and can be used predict the future heat-related burden under different climate change scenarios.Open Acces

Downscaling Aerosol Optical Thickness from Satellite Observations: Physics and Machine Learning Approaches

In recent years, the satellite observation of aerosol properties has been greatly improved. As a result, the derivation of Aerosol Optical Thickness (AOT), one of the most popular atmospheric parameters used in air pollution monitoring, over ocean and continents from satellite observations shows comparable quality to ground-based measurements. Satellite AOT products is often applied for monitoring at global scale because of its coarse spatial resolution. However, monitoring at local scale such as over cities requires more detailed AOT information. The increase spatial resolution to suitable level has potential for applications of air pollution monitoring at global-to-local scale, detecting emission sources, deciding pollution management strategies, localizing aerosol estimation, etc. In this thesis, we investigated, proposed, implemented and validated algorithms to derive AOT maps with spatial resolution increased up to 1×1 km2 from MODerate resolution Imaging Spectrometer (MODIS) observations provided by National Aeronautics and Space Administration (NASA), while MODIS standard aerosol products provide maps at 10×10 km2 of spatial resolution. The solutions are considered on two perspectives: dynamical downscaling by improving the algorithm for remote sensing of tropospheric aerosol from MODIS and statistical downscaling using Support Vector Regression

Spatio-temporal association of physic characteristics and chemical composition of the atmosphere with human mortality data

Air pollution is an environmental challenge that has an important influence on the life of human beings. Therefore, the development, implementation, and evaluation of new statistical approaches will improve the numerical modeling of the spatial distribution of air pollutants and their socio-economic impact. Demography statistically evaluates the change in human populations over time (temporal models). Mortality is a factor that influences the human population, and its definition in the short, medium and long term is of utmost importance for government health and economic plans. Air pollution directly influences human mortality, and it should be incorporated into the structure of demographic mortality models. Air pollution data is collected from satellite information or ground-level monitoring, which needs statistical models to obtain pollution levels in places with no monitoring stations. Air pollution data description uses the aggregate form (mean values over a large geographical level) and the spatially-structured form (values at local territories). In addition, air pollution data could be statistically treated using both traditional and compositional approaches. This thesis assesses the addition of air pollution data using both forms of descriptions separately under both statistical treatment approaches on the useful demographic Farrington-Like model. For this purpose, a generalized linear modelling framework was proposed assuming that the human mortality data has a negative binomial distribution. The mortality data used both total and disaggregated forms. The disaggregation used three demographic aspects sex, age, and location. Air pollutants were modelled using Dynamic Linear Models (DLM) and spatially extended with Gaussian and Gaussian-Mattern Fields under traditional and compositional approaches. For instance, the spatial distribution of concentration of PM2.5 in wildfires event with a limited number of monitoring stations was featured with a Gaussian-Mattern Field; and the spatial distribution of concentrations of SO2, CO, O3, NO2, and PM2.5 was featured using a Gaussian Field. The results obtained in each stage of this doctoral thesis presented adequate quality-model indexes (NSE = 0.5, RMSE ˜0, and Pearson correlation coefficients ˜ 1)La contaminación del aire es un desafío ambiental que tiene una influencia en la vida de los seres humanos. Por lo tanto, el desarrollo, implementación y evaluación de nuevos enfoques estadísticos mejorará el modelado numérico de la distribución espacial de los contaminantes del aire y su impacto socioeconómico. En el ámbito de la demografía, es común evaluar estadísticamente el cambio en las poblaciones humanas a lo largo del tiempo (modelos temporales). La mortalidad es un factor que influye en la población humana, y su definición en el corto, mediano y largo plazo es de suma importancia para los planes de salud y económicos del gobierno. La contaminación del aire influye directamente en la mortalidad humana y debe incorporarse a la estructura de los modelos demográficos de mortalidad. Los datos de contaminación del aire se recopilan a partir de información satelital o monitoreo a nivel del suelo que necesita modelos estadísticos para obtener los niveles de contaminación en lugares sin estaciones. La descripción de los datos de contaminación del aire se realiza de forma agregada (valores medios en una gran escala geográfica) y de forma espacialmente estructurada (territorios locales). Además, los datos de contaminación del aire se tratan estadísticamente utilizando enfoques tanto tradicionales como de composición. Esta tesis evalúa la adición de datos de contaminación del aire utilizando ambas formas de descripciones por separado bajo ambos enfoques de tratamiento estadístico en el modelo demográfico Farrington-Like. Para este propósito, se propuso un marco de modelado lineal generalizado asumiendo que los datos de mortalidad humana tienen una distribución binomial negativa. Los datos de mortalidad se usaron como totales y desagregados. La desagregación utilizó tres aspectos demográficos: sexo, edad y ubicación. Los contaminantes del aire se modelaron utilizando modelos lineales dinámicos (DLM) y se ampliaron espacialmente con los campos Gaussiano-Mattern y Gaussiano bajo enfoques tradicionales y de composición. Por ejemplo, la distribución espacial de la concentración de PM2.5 en un evento de incendios forestales con un número limitado de estaciones de monitoreo se presentó con un campo Gaussian-Mattern; y la distribución espacial de las concentraciones de SO2, CO, O3, NO2 y PM2.5 se presentó utilizando un campo gaussiano. Los resultados obtenidos en cada etapa de esta tesis doctoral presentaron índices de calidad de modelado adecuados (NSE = 0,5, RMSE ≈0 y coeficientes de correlación de Pearson ≈1).Postprint (published version

APPLICATIONS OF MODERATE-RESOLUTION REMOTE SENSING TECHNOLOGIES FOR SURFACE AIR POLLUTION MONITORING IN SOUTHEAST ASIA

Retrievals from Earth observation satellites are widely used for many applications, including analyzing dynamic lands and measuring atmospheric components. This research aims to evaluate appropriateness of using satellite retrievals to facilitate understanding characteristics of Southeast Asian (SEA) surface air pollution, attributed to regional biomass burnings and urban activities. The studies in this dissertation focused on using satellite retrievals for 1) mapping potential SEA air pollution sources; which are forests, rice paddies, and urban areas, 2) understanding dynamic optical characteristics of SEA biomass-burning aerosols, and 3) inferring surface ozone level. Data used in this study were from three NASA\u27s Earth Observing System (EOS) satellites, which are Terra, Aqua, and Aura. These retrievals have spatial resolution ranging from hundred meters to ten kilometers. Algorithms used for the SEA land cover classification were developed using time-series analyses of surface reflectance in multiple wavelength bands from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite. Comparing the results to national statistical databases, good agreement was obtained for spatial estimation of forest areas after correction with plantation areas. For estimation of rice paddies areas, the agreement depended on the rice ecosystems. It was good for rainfed rice and poor for deepwater rice. Models for irrigated and upland rice areas showed overall high coefficients of determination, suggesting that they effectively simulated the spatial distribution of those rice paddies; but were prone to underestimate and overestimate, respectively. Estimated SEA regional rice area was 42×106 ha, which agrees with previous published values. Analysis of the satellite retrieval could identify large urban areas. However, the satellite-derived urban areas also incorrectly included large sandy beaches. Optical properties of SEA background aerosols were investigated through the multivariate analyses of long-term ground-based aerosol measurements acquired from Aerosol Robotic Network (AERONET). The results in this study showed that from mid-September to December, the aerosol had both fine size and high light scattering efficiency. It was assumed to be largely urban/industrial aerosols, possibly coming from eastern China. From January to April, the aerosol had fine size and had single scattering albedo (SSA at 440 nm) of approximately 0.9. It was assumed to be smoke from local biomass burning. From October to January, when seasonal winds are strongest, more SEA urban aerosol was observed. This aerosol had coarser size and had SSA of ~0.9 or less. The appropriateness of using Ozone Monitoring Instrument (OMI) aerosol retrieval to facilitate understanding SEA biomass-burning aerosol properties was evaluated through three lines of evidence. These are 1) comparisons between the results obtained from multivariate analyses of the OMI aerosol retrieval and those obtained from the ground-measured AERONET data, 2) from Atmospheric Infrared Sounder (AIRS) total column CO product, and 3) from MODIS active fire detections. The results showed that the OMI retrieval used for large-scale SEA biomass-burning aerosol characterization was consistent with these alternative measures only when 1 \u3c OMI aerosol optical depth (442 nm) \u3c 3. The OMI aerosol retrieval was then used for the study on dynamic characteristics of biomass burning aerosol. This study considered the aerosols from two forest-fire episodes, 2007 SEA continent and 2008 Indonesian fires. Dependence of the aerosol optical properties on four variables was investigated. These variables were 1) wind speed/direction, 2) relative humidity (RH), 3) land use/cover as a surrogate of fuel type estimated from time-series analysis of MODIS surface reflectance, and 4) age of aerosol estimated from spatial-temporal analysis of MODIS active fire and the wind characteristics. Results from Pearson Chi-square test for independence showed that the dependence between aerosol group memberships with different optical properties and the limiting variables was significant for most cases, except for Indonesian aerosol age factor. These results agree with prior knowledge on regional burning conditions (types of fuel and relative humidity) and aerosol chemical/physical properties (chemical composition related to aerosol optical properties and hygroscopicity). Using EOS-Aura tropospheric column ozone (TCO) to infer surface ozone level was evaluated through analyses of linear relationships between TCO estimated from OMI and Microwave Limb Sounder (MLS) retrievals and coincident TCO from balloon-based ozonesonde measurements. This evaluation was for different tropospheric ozone profile shapes and for different geographical regions (for low, mid, and high latitudes and for Pacific and Atlantic regions). Results indicate that inference on ozone level derived from the satellite-based TCO requires corresponding information about tropospheric ozone profile shape. The use of satellite-based TCO was more appropriate for polluted low-latitude locations where upper troposphere ozone is rare and surface enhanced ozone is high

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

Water Quality Modelling Using Multivariate Statistical Analysis and Remote Sensing in South Florida

The overall objective of this dissertation research is to understand the spatiotemporal dynamics of water quality parameters in different water bodies of South Florida. Two major approaches (multivariate statistical techniques and remote sensing) were used in this study. Multivariate statistical techniques include cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), discriminant analysis (DA), absolute principal component score-multiple linear regression (APCS-MLR) and PMF receptor modeling techniques were used to assess the water quality and identify and quantify the potential pollution sources affecting the water quality of three major rivers of South Florida. For this purpose, a 15-year (2000–2014) data set of 12 water quality variables, and about 35,000 observations were used. Agglomerative hierarchical CA grouped 16 monitoring sites into three groups (low pollution, moderate pollution, and high pollution) based on their similarity of water quality characteristics. DA, as an important data reduction method, was used to assess the water pollution status and analysis of its spatiotemporal variation. PCA/FA identified potential pollution sources in wet and dry seasons, respectively, and the effective mechanisms, rules, and causes were explained. The APCS-MLR and PMF models apportioned their contributions to each water quality variable. Also, the bio-physical parameters associated with the water quality of the two important water bodies of Lake Okeechobee and Florida Bay were investigated based on remotely sensed data. The principal objective of this part of the study is to monitor and assess the spatial and temporal changes of water quality using the application of integrated remote sensing, GIS data, and statistical techniques. The optical bands in the region from blue to near infrared and all the possible band ratios were used to explore the relation between the reflectance of a waterbody and observed data. The developed MLR models appeared to be promising for monitoring and predicting the spatiotemporal dynamics of optically active and inactive water quality characteristics in Lake Okeechobee and Florida Bay. It is believed that the results of this study could be very useful to local authorities for the control and management of pollution and better protection of water quality in the most important water bodies of South Florida

Geostatistical modelling of PM10 mass concentrations with satellite imagery from MODIS sensor

Several epidemiological studies suggested that there is an association between incidence and exacerbation of adverse respiratory and cardiovascular health effects and air pollution. Accurate, high resolution maps of ground-level Particulate Matter (PM) are highly awaited for environmental policies and future monitoring stations design. Though the measurements made by the ground stations can ensure a high level of reliability, still they cannot provide full spatial coverage over an area, giving rise among other things to misclassified epidemiological studies. Fine particles are usually categorized by size distribution, known as fractions: PM10 represents the particles with aerodynamic diameter smaller than 10 µm and comprises the thoracic (or coarse) fraction – with diameter in the range 2.5-10 µm – and the smaller inhalable (or fine) fraction. Although including the less dangerous thoracic particles, PM10 measurements are usually far more available and hence lend themselves better for modelling. Spaceborne aerosols products like the ones offered by the polar-orbiting MODerate resolution Imaging Spectrometer (MODIS) are successfully finding practical applications for scientific research studies and, though not previously intended, the Aerosol Optical Thickness (AOT, or simply τ ) from MODIS revealed to have a leading role in the evaluation of surface air quality due to its full spatial (clear-sky constrained) coverage and daily overpasses almost throughout the globe. Despite the “promised land” has not been reached yet, researchers have verified an existing correlation between aerosols and particulate concentrations, rising expectation of air quality models for high-scale environmental characterization. Air quality modelling is generally a challenging application, due to the wide range of sources affecting this variable and the high spatial and temporal variability of the particles, especially over high populated areas with rugged topography and complex meteorological profiles. In this thesis, different variogram-based geostatistical techniques are evaluated to predict the concentrations of PM10, with a focus on the effective advantages brought by AOT from satellites. This work is meant as a guide for students and researchers who are taking their first steps in this specific application, as well as to experts of the field who want to overview geostatistical filling of PM concentrations, and weigh up the usefulness of MODIS imagery. Different areas of study and temporal resolutions will be considered, so as to propose directions and outline conclusions on how this task – still far from being definitively ruled out – should be approached. Aside from modelling, the interactive visualization, extraction and analysis of the model-based predicted maps are also covered, cutting-edge Web-based software architectures based on the Open Geospatial Consortium (OGC) standard services are proposed, giving rise to increased capabilities in the spatio-temporal elaboration of the model results. The availability of spaceborne maps of AOT at an increased nominal resolution of 1×1 km2 has been a unique occasion to experiment their role for air quality issues; the latest algorithmics from leading FOSS-like (Free and Open Source Software) modelling software where learned and used, resulting in several new testing results in a field where variogram-based geostatistics were lacking. Solutions for novel online analysis and visualization capabilities were explored, in order to approach an open and interconnected uncertainty-enabled Web