Integration Of Satellite Remote Sensing And Ground Based Measurement For Modelling The Spatiotemporal Distribution Of Fine Particulate Matter At A Regional Scale
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Integration Of Satellite Remote Sensing And Ground Based Measurement For Modelling The Spatiotemporal Distribution Of Fine Particulate Matter At A Regional Scale
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Author : Jie Tian
language : en
Publisher:
Release Date : 2008
Integration Of Satellite Remote Sensing And Ground Based Measurement For Modelling The Spatiotemporal Distribution Of Fine Particulate Matter At A Regional Scale written by Jie Tian and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Accurate information on the spatial-temporal distributions of air pollution at a regional scale is crucial for effective air quality control, as well as to impact studies on local climate and public health. The current practice of mapping air quality relies heavily on data from monitoring stations, which are often quite sparse and irregularly spaced. The research presented in this dissertation seeks to advance the methodologies involved in spatiotemporal analysis of air quality that integrates remotely-sensed data and in situ measurement. Aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) is analyzed to estimate fine particulate matter (PM2.5) concentrations as the target air pollutant. The spatial-temporal distribution of columnar aerosol loading is investigated through mapping MODIS AOD in southern Ontario, Canada throughout 2004. Clear distribution patterns and strong seasonality are found for the study area. There is a detectable relationship between an AOD level and underlying land use structure and topography on the ground. MODIS AOD was correlated with the ground-level PM2.5 concentration (GL-[PM2.5]) at various wavelengths. The AOD-PM2.5 correlation is found to be sensitive to spatial-temporal scale changes. Further, a semi-empirical model has been developed for a more accurate prediction of GL-[PM2.5]. The model employs MODIS AOD data, assimilated meteorological fields, and ground-based meteorological measurements and is able to explain 65% of the variability in GL-[PM2.5]. To achieve a more accurate and informative spatiotemporal modelling of GL-[PM2.5], a method is proposed that integrates the model-predictions and in situ measurements in the framework of Bayesian Maximum Entropy (BME) analysis. A case study of southern Ontario demonstrates the procedures of the method and support for its advantages by comparison with conventional geostatistical approaches. The BME estimation, coupled with BME posterior variance, can be used to depict GL-[PM2.5] distribution in a stochastic context. The methodologies covered in this work are expected to be applicable to the modelling or analysis of other types of air pollutant concentrations.
Estimating Ground Level Pm2 5 In Texas From Remote Sensing Satellite Data With Interpolation And Regression Methods
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Author : Xiaoyan Jiang
language : en
Publisher:
Release Date : 2009
Estimating Ground Level Pm2 5 In Texas From Remote Sensing Satellite Data With Interpolation And Regression Methods written by Xiaoyan Jiang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
The integration of remote sensing satellite data in air quality monitoring system at a regional scale is an important method to provide high spatial / temporal resolution information. This work focuses on estimating high spatial / temporal resolution ground-level information about particulate matter with aerodynamic diameters less than 2.5 um (PM2.5), with the utilization of MODIS aerosol optical thickness (AOT) data and meteorological data. Several missing data reconstruction techniques including Bayesian inversion, regularization and prediction-error filter are employed to estimate PM2.5 from satellite data. The results show that several direct missing data interpolation methods have the capability to estimate some distinctive features on the basis of available ground-based measurements, while the PEF method tends to generate more information with the aid of satellite AOT information. In addition to interpolation methods, general linear regression methods are used to predict ground-level PM2.5 with the consideration of other factors that have been shown to play an important role in predictions. Ordinary Least Square (OLS) method, when natural log taken on dependent and independent variables, is able to reduce the violation of homoscedasticity. The scatterplot of predicted and measured PM2.5 shows a strong correlation over the validation region, indicating the ability of the regression model to predict PM2.5. Weighted Least Square (WLS) method also has advantage in improving homoscedasticity. The predicted and measured PM2.5 has a relatively high correlation.
Integrating In Situ Measurements Land Surface Models And Satellite Remote Sensing To Understand Impacts Of Environmental Changes On Terrestrial Ecosystem Processes At Multiple Scales
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Author : Wenting Fu
language : en
Publisher:
Release Date : 2017
Integrating In Situ Measurements Land Surface Models And Satellite Remote Sensing To Understand Impacts Of Environmental Changes On Terrestrial Ecosystem Processes At Multiple Scales written by Wenting Fu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
How terrestrial ecosystems respond to environmental changes affects the well-being of human society. Thus, extreme climate events, increasing the atmospheric concentration of CO2, and drastic changes in temperature are sources of major concern. However, our current capacity to understand and predict these responses is still limited because a myriad of physical, chemical, and biological processes are involved. While many mechanistic-based land surface models have been developed, their performances remain relatively poor and require continuous improvement. While ground-based and space-based observational datasets of the surface of the Earth have been available for a long time, their linkages to the functional aspects of the processes in terrestrial ecosystems often are weak. In this study, I used the approach of integrating in-situ measurements, land surface models, and remote sensing by satellites. I hypothesized that, through such integration, the impacts of environmental changes on terrestrial processes at multiple scales could be better understood and even predicted, especially the impacts related to the functions of important ecosystems. I tested this hypothesis at the local, regional, and global scales. At the local scale, i.e., at a Midwest forest site known as the isoprene volcano of the world, I examined the effects of droughts on the emissions of isoprene, which is the most abundant, non-methane, biogenic volatile organic compound. I compared flux tower observations with simulations performed by a state-of-the-art land model (CLM) coupled with the model of emissions of gases and aerosols from Nature version 2.1 (MEGAN2.1), and I used these observations to develop an understanding of how the amount of moisture in the soil and the ambient temperature affect the prediction of isoprene emissions during droughts. I found that temperature had a delaying effect on isoprene emissions, which are sensitive to variations in the moisture content of the soil. Thus, during drought conditions, both the delaying effect and the sensitivity to moisture are overlooked by the model. A better model that does not have these two shortcomings is required for realistic predictions of isoprene emissions. At the regional scale, I investigated the potential of using sun-induced chlorophyll fluorescence (SIF) retrieved from a satellite to monitor vegetation activities in an arid region and a semi-arid region in Australia. I chose these two types of regions for this investigation because the ecosystems in such regions have important effects on the global carbon cycle, while their contributions are poorly constrained in global carbon budgets. I found that SIF was synchronized better with the activity of vegetation than other indices that are commonly used for this purpose. I quantified the relationships between the various activities of plants and the amount and frequency of precipitation, and I was able to demonstrate that, over the region being studied, SIF represented the activity of vegetation in response to the availability of water better than other, remotely-sensed variables. At the global scale, I used multiple model ensembles to determine the climatic and anthropogenic controls on the terrestrial evapotranspiration trends from 1982 to 2010. After climatic influences, increases in CO2 were found to be the second-most dominant factor that affected the trend of ET. CO2 causes a decreasing trend in the canopy’s transpiration and ET, and this is especially of concern for tropical forests and high-latitude shrub lands. The increased deposition of nitrogen amplifies the global ET slightly due to enhanced growth of plants. On a global scale, land-use-induced ET responses are minor, but they can be significant locally, particularly over regions with intensive changes in the land-cover. The results of my studies demonstrated that integrating in-situ measurements, models of the surface on the land, and remote sensing using satellites can provide insights regarding the impacts of environmental changes on terrestrial processes at multiple scales. This approach is particularly important when models are imperfect and observations are lacking. My findings indicated ways that future models can be improved and identified key observational needs for the functions of terrestrial ecosystems.
Integration Of Satellite Remote Sensing Measurements With Numerical Models To Estimate Particulate Matter Emissions From Forest Fires
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Author : Sanjeeb Bhoi
language : en
Publisher:
Release Date : 2009
Integration Of Satellite Remote Sensing Measurements With Numerical Models To Estimate Particulate Matter Emissions From Forest Fires written by Sanjeeb Bhoi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Artificial satellites in forestry categories.
Interpretation Of Ground Based Measurements From The Surface Particulate Matter Network To Understand The Global Distribution Of Fine Particulate Matter
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Author : Crystal Weagle
language : en
Publisher:
Release Date : 2020
Interpretation Of Ground Based Measurements From The Surface Particulate Matter Network To Understand The Global Distribution Of Fine Particulate Matter written by Crystal Weagle and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Exposure to ambient fine particulate matter (PM2.5¬) is increasingly recognized as the leading environmental risk factor for global burden of disease. This thesis develops the Surface PARTiculate mAtter Network (SPARTAN) to provide long-term measurements of PM2.5 mass and chemical composition, collocated with existing aerosol optical depth (AOD) observations in highly populated, globally diverse regions. Three projects are presented that interpret SPARTAN measurements to provide insight into the spatial variation in ground-based PM2.5 chemical composition, into the sources contributing to PM2.5, and into the relationship between AOD and PM2.5 used in satellite-based estimates of PM2.5. Analysis of SPARTAN filter samples collected across multiple continents for PM2.5 chemical composition show that absolute concentrations of several major components vary by more than an order of magnitude across sites, and exhibit consistency with available, collocated studies. Elevated Zn:Al ratios reveal an enhanced anthropogenic dust fraction relative to natural sources, signifying the need to include this PM2.5 source in global models and emission inventories. The developed compositional dataset provides much needed long-term chemical data for investigation of sources leading to the spatial variation of PM2.5 mass and chemical composition. Evaluation of the GEOS-Chem model, constrained by satellite-based estimates of PM2.5 and informed by SPARTAN compositional measurements, shows significant spatial consistency for major chemical components. Measured PM2.5 composition corroborate source attribution from sensitivity simulations, providing confidence in utilizing sensitivity simulations to explore the influence of source categories to global population-weighted PM2.5. This approach of coupling observational datasets with modelling at the global scale allows for insight into the main sources determining PM2.5 global variation, but also identification of modelled processes that require development to represent the wide range of PM2.5 and composition observed globally. An initial comparison between empirical and simulated relationships of PM2.5 and columnar AOD ( ) was conducted using the GEOS-Chem global chemical transport model. This comparison is the first to develop empirical, ground-based and provide an evaluation of modelled values widely used in satellite-based estimates. Collocated, modelled values generally fall within a factor of two of measured values and have a mean fractional bias that is an order of magnitude lower than for either PM2.5 or AOD alone. This lower bias in indicates that satellite-derived PM2.5 inferred using is likely to have lower bias than purely simulated PM2.5¬.
Using Remote Sensing To Understand Urban Air Quality Exposures And Inequities
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Author : Matthew Bechle
language : en
Publisher:
Release Date : 2021
Using Remote Sensing To Understand Urban Air Quality Exposures And Inequities written by Matthew Bechle and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Air categories.
Outdoor air pollution is one of the leading causes of morbidity and mortality in the United States and around the world, but these impacts are not distributed equally. Countries, communities, and households that are socially and economically deprived often experience higher levels of air pollution. Yet too often these locations remain unmonitored or insufficiently monitored by traditional ground-based measurements. In this dissertation I employ satellite-based remote sensing of nitrogen dioxide (NO2), a major contributor to urban air pollution and a proxy for a toxic mix of pollutants associated with traffic and combustion emissions, to explore air pollution levels globally and within the US. Within the last two decades, satellite air pollution measurements have considerably expanded the capability to measure air pollution in previously unmonitored locations and across administrative boundaries. Cities serve as focal points, concentrating social and economic opportunities, but may also concentrate hazards, including air pollution. Strategic, compact urban design may be a way to improve a cities air quality, yet global empirical evidence has historically been limited by data availability and consistency. Here I use satellite-based measurements of NO2 and built-up land area to explore the relationship between city-wide NO2 levels and urban form characteristics (i.e., contiguity, circularity, percent impervious surfaces, percent vegetation coverage) for a global sample of 1,274 cities. Three of the urban form metrics (contiguity, circularity, and vegetation) have a small, but statistically significant relationship with city NO2 levels; however, the combined effect of these three attributes could be sizeable. For example, a city at the 75th percentile for all three metrics could accommodate, on average, twice the population as a city at the 25th percentile, while maintaining similar air quality. This work also shows that country level factors such as economic conditions and environmental policies may impact the urban form - air pollution relationships. Moreover, the impact of urban form on air quality may be larger for small cities, an important finding given the large portion of current and projected future population that lives in small cities. Satellite air pollution measurements are limited by their spatial resolution. For example, they are well suited for exploring NO2 levels between cities, as described above; however, alone they typically cannot capture the fine-scale spatial variability needed to characterize population exposure to air pollution. Satellite-based empirical models combine the regional concentrations from satellite measurements with ground-based measurements and local land use and land cover information to predict air pollution concentrations with high spatial resolution (typically 1 km or less). These models have become ubiquitous, yet few studies have investigated how satellite and other regional air pollution covariates impact these models. In this dissertation, I address this gap by exploring the effect of several regional NO2 covariates in an empirical model for annual average NO2 over the contiguous US and find that inclusion of a regional covariate improves model predictive power, yet choice of covariate has limited impact. Additionally, empirical models can be data and computationally intensive, and are often limited to long-term averages and a small number of years. Here, I address these issues by developing a straightforward and easy to implement spatiotemporal scaling technique to extend the temporal coverage of a year-2006 annual NO2 model to over a decade (2000-2010) of monthly NO2 estimates. The resulting estimates are data publicly available online. The spatiotemporal scaling technique and these data have since been used in several publications exploring health effects and residential exposure disparities associated with outdoor NO2 levels. Residential air pollution disparities in the contiguous US have become a topic of recent interest. Children are a particularly vulnerable population and disparities in their air pollution exposure could have lasting impacts. Despite this, little has been done to track outdoor air pollution levels at schools throughout the US. In this dissertation, I add to this body of work by exploring a criteria pollutant, NO2, and by considering home and school locations to better understand the role of public schools in students' total exposure. I find that, on average, racial and ethnic minority students live in and attend schools in areas with higher NO2 levels than their non-Hispanic, white peers, and that impoverished students (defined here as those eligible for school lunch programs) attend, on average, schools with higher NO2 levels than their non-impoverished peers. Minority students are much more likely than their white peers to live in areas above the World Health Organization's annual outdoor NO2 guideline, and this likelihood is larger at schools than at home locations, particularly when comparing predominately minority schools to predominately white schools. This finding -- that public schools may exacerbate disparities -- has important implications for addressing childhood inequities. Notably, strategies that do not address school exposure inequities may fail to address overall exposure inequities. Moreover, strategies to reduce school segregation or to identify and mitigate NO2 levels at the most at-risk schools could have a significant impact on children's overall NO2 inequities. This work also shows that race and income are intertwined; independently, more impoverished schools and schools with more minority students tend to be in areas with higher NO2 levels than more well-off schools and schools with fewer minority students. Schools in large urban areas exhibit disparities by race/ethnicity alone, even when controlling for school-level income. This work highlights NO2 disparities at public schools throughout the contiguous US. Those national disparities are driven largely by disparities in the 50 largest urban areas, which provides motivation for additional exploration and tracking of air pollution levels at these locations. In summary, in this dissertation I have demonstrated how satellite measurements and empirical models that incorporate satellite measurements vastly improve the capability of uncovering and monitoring air pollution exposure disparities for a global and US-wide analysis. Recently launched and soon to be launched satellite-borne sensors promise higher spatial and temporal resolution air pollution measurements. Those measurements will allow for better understanding of concentrations and emission sources, as well as improve satellite-based empirical models, facilitating further tracking and characterization of exposures and exposure disparities from global to local scales.
Biomass Burning In South And Southeast Asia
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Author : Krishna Prasad Vadrevu
language : en
Publisher: CRC Press
Release Date : 2021-06-23
Biomass Burning In South And Southeast Asia written by Krishna Prasad Vadrevu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-23 with Nature categories.
Unique in its discussion of the sources and the causes of biomass burning and atmospheric research in South and Southeast Asia. Explains the latest tools and techniques, in particular, the use of Satellite Remote Sensing and Geospatial technologies for fire mapping, monitoring, and Land Cover/Land Use. Focuses on large spatial scales integrating top-down and bottom-up methodologies. Addresses the pressing issues of environmental pollution that are rampant in South and Southeast Asia. Includes contributions from global experts actually working on biomass burning projects in the US, Japan, South/Southeast Asia, and Europe.
Aerosol Remote Sensing
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Author : Jacqueline Lenoble
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-02-11
Aerosol Remote Sensing written by Jacqueline Lenoble and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-11 with Technology & Engineering categories.
This book gives a much needed explanation of the basic physical principles of radiative transfer and remote sensing, and presents all the instruments and retrieval algorithms in a homogenous manner. The editors provide, for the first time, an easy path from theory to practical algorithms in one easily accessible volume, making the connection between theoretical radiative transfer and individual practical solutions to retrieve aerosol information from remote sensing, and providing the specifics and intercomparison of all current and historical retrieval methods.
Insight Into Global Ground Level Air Quality Using Satellites Modeling And In Situ Measurements
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Author : Sajeev Philip
language : en
Publisher:
Release Date : 2015
Insight Into Global Ground Level Air Quality Using Satellites Modeling And In Situ Measurements written by Sajeev Philip and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Ground-level air quality depends on the ambient concentration of atmospheric aerosols and trace gases. We applied information on aerosols and trace gases gathered from satellite remote sensing, in situ observations, and atmospheric chemistry modelling to improve estimates of air quality. We inferred fine particulate matter (PM2.5) chemical composition at 0.1 degree x 0.1 degree spatial resolution for 2004-2008 by combining aerosol optical depth retrieved from the MODIS and MISR satellite instruments, with coincident profile and composition information from the GEOS-Chem global chemical transport model. Evaluation of the satellite-model PM2.5 composition dataset with North American in situ measurements indicated significant spatial agreement. We found that global population-weighted PM2.5 concentrations were dominated by particulate organic mass (11.9 ± 7.3 microgram per cubic meter), secondary inorganic aerosol (11.1 ± 5.0 microgram per cubic meter), and mineral dust (11.1 ± 7.9 microgram per cubic meter). Secondary inorganic PM2.5 concentrations exceeded 30 microgram per cubic meter over East China. Sensitivity simulations suggested that population-weighted ambient PM2.5 from biofuel burning (11 microgram per cubic meter) could be almost as large as from fossil fuel combustion sources (17 microgram per cubic meter). We developed a simple method to derive an estimate of the spatially and seasonally resolved global, lower tropospheric, ratio between organic mass (OM) and organic carbon (OC). We used the Aerosol Mass Spectrometer-measured organic aerosol data, and the ground-level nitrogen dioxide concentrations derived from the OMI satellite instrument, to develop the OM/OC estimate. The global OM/OC ratio ranged from 1.3 to 2.1 microgram/microgram Carbon, with distinct spatial variation between urban and rural regions. The seasonal OM/OC ratio had a summer maximum and a winter minimum over regions dominated by combustion emissions. We assessed the sensitivity of chemical transport models to the duration of the chemical and transport operators used to calculate the mass continuity equation. Increasing the transport timestep increased the concentrations of emitted species, and the production of ozone. Increasing the chemical timestep increased hydroxyl radical and chemical feedbacks. The simulation error from changing spatial resolution exceeds that from changing temporal resolution.
Space Based Remote Sensing Of The Earth A Report To The Congress
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Author : United States. National Oceanic and Atmospheric Administration
language : en
Publisher:
Release Date : 1987
Space Based Remote Sensing Of The Earth A Report To The Congress written by United States. National Oceanic and Atmospheric Administration and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with Landsat satellites categories.