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Incorporating Long Term Satellite Based Aerosol Optical Depth Localized Land Use Data And Meteorological Variables To Estimate Ground Level Pm25 Concentrations In Taiwan From 2005 To 2015
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Book Synopsis Incorporating Long-term Satellite-based Aerosol Optical Depth, Localized Land Use Data, and Meteorological Variables to Estimate Ground-level PM2.5 Concentrations in Taiwan from 2005 to 2015 by :
Download or read book Incorporating Long-term Satellite-based Aerosol Optical Depth, Localized Land Use Data, and Meteorological Variables to Estimate Ground-level PM2.5 Concentrations in Taiwan from 2005 to 2015 written by and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Airborne Particulate Matter by : Saurabh Sonwani
Download or read book Airborne Particulate Matter written by Saurabh Sonwani and published by Springer Nature. This book was released on 2022-05-25 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about airborne particulate matter, sources, chemistry and health and contained a complete information about their emission source, transport, atmospheric chemistry, distribution at local, regional and global levels, and their level in indoor and outdoor settings. Primary and secondary particulate matters in the ambient atmosphere also describe in detail. Analytical techniques, statistical tools and mathematical models used in airborne particulate research is also described. This book also covers the important aspects of the particulate matter chemistry in atmosphere, and their adverse impact on plant and human health. A detailed insight about the harmful impact of airborne particulate matter (biogenic and anthropogenic both) on different human system is described in detail. The toxicological significance of particulate matter on human body was also mentioned. The mitigation, management and regulatory policies to control ambient particulate matter is also provided. This book is also written in simple language with helpful photographs, diagrams, tables and flowcharts which will make the reader comfortable in understanding the concepts a more relatively easier way. Overall, the present book is a valuable tool for students working in the fields of Atmospheric Science, Environmental Science, Biological Sciences, Epidemiology and Agriculture Science. This book also a unique resource for environmental consultants, researchers, policymakers and other professionals involved in air quality, plant and human health.
Book Synopsis Atmospheric Air Pollution and Its Environmental and Health Effects by : Qiyuan Wang
Download or read book Atmospheric Air Pollution and Its Environmental and Health Effects written by Qiyuan Wang and published by Frontiers Media SA. This book was released on 2022-11-04 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multi-disciplinary Trends in Artificial Intelligence by : Olarik Surinta
Download or read book Multi-disciplinary Trends in Artificial Intelligence written by Olarik Surinta and published by Springer Nature. This book was released on 2022-11-10 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 15th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2022, held online on November 17–19, 2022. The 14 full papers and 5 short papers presented were carefully reviewed and selected from 42 submissions.
Book Synopsis Integration of Satellite Remote Sensing and Ground-based Measurement for Modelling the Spatiotemporal Distribution of Fine Particulate Matter at a Regional Scale by : Jie Tian
Download or read book 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 published by . This book was released on 2008 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Site-Specific PM2.5 Estimation at Three Urban Scales by : Yogita Yashawant Karale
Download or read book Site-Specific PM2.5 Estimation at Three Urban Scales written by Yogita Yashawant Karale and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Fine particulate matter, also known as PM2.5, is one of the major risk factors to human health. Because of their small size, these particles travel deep within human lungs and pose a variety of health problems. A primary source of acquiring PM2.5 exposure is based on the nearest groundlevel air quality monitoring station. However, these stations are often few and sparsely located due to their high costs for installation and maintenance. This study addresses three challenges related to PM2.5. First, the number of air-quality monitoring sites is insufficient to acquire the complex spatial variability of PM2.5. Therefore, in-situ ground observations fail to characterize PM2.5 distribution, and hence exposure, adequately. The shortfall calls for models capable of estimating PM2.5 at unmonitored locations. Satellite-based Aerosol Optical Depth (AOD) serves as a proxy to estimate PM2.5. Second, although satellite data can supplement PM2.5 estimates at unmonitored locations, the spatial resolutions of satellite-based estimates of PM2.5 are in the order of kilometers. These spatial grains are too coarse to capture PM2.50́9s spatial variation caused by contextual geographic factors such as buildings, and subsequently the estimates0́9 applicabilities to support environmental exposome on health effects. Third, the current standards measure PM2.5 in terms of mass per volume, but findings from some recent studies suggest that alternative measures of PM2.5 are also strongly associated with adverse health outcomes. However, observations in terms of these measures are not available. The dissertation research aimed to address the three challenges in three studies. The first study evaluated the potential of the Convolutional Neural Network (CNN) approach to downscale PM2.5 using satellite-based AOD and meteorological data using Dallas-Fort Worth as a case study. The study developed a model capable of estimating PM2.5 corresponding to the hour of satellite overpass time and examined environmental predictors commonly available for all monitored or non-monitored locations. In particular, the study investigated the effect of the spatial extent to which predictors from the surrounding area influenced the PM2.5 estimates at a location. The results showed that the proposed CNN model effectively estimates PM2.5 concentration with correlation coefficient (R) of 0.87 and root mean squared error (RMSE) of 2.57 Îơg/m3 . Moreover, spatially lagged variables from a wider area around an estimation location improved the model performance. As most monitoring stations were in open areas, data from these stations could not be used to examine the effect of contextual factors, such as the building on PM2.5. The second study evaluated the effects of contextual geographic factors on PM2.5 in mass per volume (i.e., standard measures) in pedestrian-friendly areas on the University of Texas at Dallas campus. The study used a mobile sensor to collect spatial and temporal fineresolution PM2.5 data on the campus. The study found very low spatial variation in the study area less than 1km2 . Furthermore, weather-related variables played a dominant role in PM2.5 distribution as temporal variation over-powered spatial variation in PM2.5 data. The study employed a fixed effect model to assess the effect of time-invariant building morphological characteristics on PM2.5 and found that building0́9s morphological characteristics explained 33.22% variation in the fixed effects in the model. Furthermore, openness in the direction of wind elevated the PM2.5 concentration. The third study investigated the potential of AOD to downscale Particle Number (PN) concentration, an alternative measure of PM2.5, and the effect of building morphology on PN concentration using PN measurements collected across the streets of San Francisco by the Google streetcar. The study showed that AOD remained useful to estimate street-level PN concentration across five different particle sizes. The subsequent analysis of variable importance revealed that AOD and AOD-related variables were more important than building morphology but less important than meteorological variables in the estimation of PN concentration.
Book Synopsis Assessment and Statistical Modeling of the Relationship Between Remotely Sensed Aerosol Optical Depth and PM2.5 in the Eastern United States by : Christopher J. Paciorek
Download or read book Assessment and Statistical Modeling of the Relationship Between Remotely Sensed Aerosol Optical Depth and PM2.5 in the Eastern United States written by Christopher J. Paciorek and published by . This book was released on 2012 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This report describes a study to assess the ability of satellite-based measurements from National Aeronautics and Space Administration (NASA) satellites to fill spatial and temporal gaps in existing monitoring networks in the eastern United States. Dr. Paciorek and colleagues developed statistical models for integrating monitoring, satellite, and geographic information system (GIS) data to estimate monthly ambient PM2.5 concentrations and used those models to estimate monthly average PM2.5 concentrations across the eastern United States. They then developed and applied statistical methods to quantify how uncertainties in exposure estimates based on ground-level monitoring data might be reduced."--Publisher's website.
Book Synopsis Cloud Detection and PM2.5 Estimation Using Machine Learning by : Xiaohe Yu
Download or read book Cloud Detection and PM2.5 Estimation Using Machine Learning written by Xiaohe Yu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Earth observation (EO) is the gathering of information about the physical, chemical, and biological systems of the planet via remote-sensing technologies, supplemented by Earthsurveying techniques, which encompasses the collection, analysis, and presentation of data. Research on exploring effective methods for earth observation data analysis has increased over the years because of the increasing amount of data generated by earth observation systems, such as remote sensing imagery and weather radars. Researchers have therefore taken an interest in machine learning, a technique that allows computer algorithms to learn from samples. In general, the more comprehensive our training samples are, the better the machine learning performance will be. This feature makes machine learning an ideal approach for analyzing earth observation data. Particulate matter of fine size, such as particulate matter 2.5 (PM2.5), poses a severe health risk to humans and is associated with many different health problems. PM2.5 concentrations are influenced by factors such as meteorological conditions, local population density, and the geographic context. As a result of the large quantity of information provided by Earth observation, they become a valuable tool for studying PM2.5. They are huge and come from different platforms, with different spatial and temporal resolutions, and in different formats, which challenge the approaches for PM2.5 studies. This dissertation shows how machine learning methods can be used to address these challenges in three subtopics connected to modeling and estimation for PM2.5. Satellite-based remote sensing products provide important variables that can be used to study regional and global PM2.5, such as the Aerosol Optical Depth (AOD). Nevertheless, AOD products in cloudy areas cannot be retrieved, and the quality of AOD data in nearby cloud areas cannot be guaranteed. Accordingly, the first study aims to detect cloud pixels based on remote sensing images. This study investigates the cloud detection with a set of machine learning models on four subsets of 88 Landsat8 images that have been carefully labelled by analysts. Four subsets of training data are used to train 16 machine learning models with different input feature selections. The performance of these models is then compared with that of the Fmask algorithm, which is widely used for cloud detection. When testing on the 88 annotated images, the best performance was observed with a model that incorporates unsupervised self-organizing map (SOM) classification results among the input features. In comparison with Fmask4.0, the model improves the correctness by 10.11% and reduces the cloud omission error by 6.39%. Focusing on the other 8 independent validation images that were never sampled as part of the model training, the model trained on the second largest training subset with additional 5 input features has the best overall performance. Compared with Fmask4.0, this model improves the overall correctness by 3.26% and reduces the cloud omission error by 1.28%. In the second study, high temporal resolution PM2.5 models are developed based on data from weather radar systems and the meteorological data from the European Centre for MediumRange Weather Forecasts (ECMWF). A dataset covering the period from July 2019 to June 2021 was collected for model training, which included the Next Generation Weather Radar (NEXRAD) retrieved from a repository on Amazon Web Services (AWS), meteorological data from ECMWF, and the PM2.5 ground observations from 31 sensors deployed across Dallas county, Collin county, and Tarrant county. The models are classified in groups to demonstrate the effectiveness of NEXRAD in high temporal PM2.5 modeling. The model utilizing NEXRAD data achieves an 0.855 score of the correlation of determination (R2 ), while the model without NEXRAD has a 0.7 R2 for PM2.5. The third study establishes a nationwide PM2.5 estimation model by using high temporal resolution AOD data from the GOES-16 geostationary satellite, meteorological variables from ECMWF and a set of ancillary data from a variety of sources, which achieves 3.0μg/m3 and 5.8 μg/m3 as the value of mean absolute error (MAE) and root mean square error (RMSE). The model performances are then further evaluated by time, elevation, soil order, population density, and lithology. The historical PM2.5 estimation surfaces are then reconstructed and the PM2.5 surfaces during the period of California Santa Clara Unite (SCU) Lightning Complex fires are demonstrated.
Book Synopsis Estimating Ground-level PM2.5 in Texas from Remote Sensing Satellite Data with Interpolation and Regression Methods by : Xiaoyan Jiang
Download or read book Estimating Ground-level PM2.5 in Texas from Remote Sensing Satellite Data with Interpolation and Regression Methods written by Xiaoyan Jiang and published by . This book was released on 2009 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Getting Down to Earth by : World Bank
Download or read book Getting Down to Earth written by World Bank and published by World Bank Publications. This book was released on 2022-01-14 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Outdoor air pollution accounts for an estimated 4.2 million deaths worldwide, caused predominantly by exposure to fine aerosols. This report investigates the performance of satellites for predicting outdoor concentrations of PM2.5, the most harmful air pollutant to human health, in low- and middle-income countries.
Book Synopsis Interpretation of Ground-Based Measurements from the Surface Particulate Matter Network to Understand the Global Distribution of Fine Particulate Matter by : Crystal Weagle
Download or read book 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 published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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¬.
Book Synopsis INSIGHT INTO GLOBAL GROUND-LEVEL AIR QUALITY USING SATELLITES, MODELING AND IN SITU MEASUREMENTS. by : Sajeev Philip
Download or read book INSIGHT INTO GLOBAL GROUND-LEVEL AIR QUALITY USING SATELLITES, MODELING AND IN SITU MEASUREMENTS. written by Sajeev Philip and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Modeling Spatiotemporal Patterns of PM2.5 at the Sub-Neighborhood Scale Using Low-Cost Sensor Networks by :
Download or read book Modeling Spatiotemporal Patterns of PM2.5 at the Sub-Neighborhood Scale Using Low-Cost Sensor Networks written by and published by . This book was released on 2019 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Epidemiological research has demonstrated an adverse relationship between fine particulate matter (PM2.5) and human health. While PM2.5 continues to pose a significant global health risk, there is still the need to further characterize exposures at the intra-urban scale. Land use regression is a statistical modeling technique which is used to predict air pollution concentrations at high resolution from a limited number of monitoring sites. However, the existing regulatory monitoring networks are typically not dense enough to apply these techniques. We explored the potential of using low-cost PM2.5 sensor networks to overcome the limitations of the existing regulatory monitoring infrastructure, and identified the need to determine sensor-specific correction factors based on the local PM2.5 source profile. Once calibrated, a land use regression model (R2 = 0.89) was developed using the low-cost sensor network (n ≈ 20), alongside several land use and meteorological variables, to predict daily particulate matter concentrations at a 50 m spatial resolution during a two year period within Portland, Oregon. From this model, we assessed the relative strengths of expected sources and sinks of fine particulate matter, focusing specifically on the role that the urban canopy may play in mitigating PM2.5 exposure. This model showed a modest but observable spatial pattern in PM2.5, but attributed the majority of PM2.5 variation to temporal predictors (e.g. ambient background PM2.5, wind speed, temperature). Neither proxies for traffic-related sources, or vegetation-related sinks were identified as significant predictors of PM2.5. Our research also demonstrated the importance of sensor placement, as a considerably different set of predictors was selected after the inclusion of four additional monitoring sites. Future work will apply this method to four cities with a varying degree of canopy cover to assess differences in intra-urban gradients of PM2.5 and to further characterize the influence of vegetation.
Book Synopsis Spatio-Temporal Modelling of Particulate Matter and Its Application to Assessing Mortality Effects of Long-Term Exposure by : Qishi Zheng
Download or read book Spatio-Temporal Modelling of Particulate Matter and Its Application to Assessing Mortality Effects of Long-Term Exposure written by Qishi Zheng and published by Open Dissertation Press. This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Spatio-temporal Modelling of Particulate Matter and Its Application to Assessing Mortality Effects of Long-term Exposure" by Qishi, Zheng, 鄭奇士, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: In Hong Kong, no studies have evaluated methodologies to estimate concentrations of particulate matter (PM) in small areas with complex urban morphology. Directly estimating long-term PM exposures from small number of monitoring stations alone provides little spatial variations and may lead to measurement errors. Therefore, traffic density and land-use types should be taken into consideration when determining individual-level exposures in a cohort study. This study proposed a novel method which incorporated remote sensing, meteorological and geographical data to estimate long-term PM exposures for assessing health effects. Therefore, this thesis aims to cover two objectives: 1) to develop a spatio-temporal approach to estimate PM10 and PM2.5 concentrations in small areas from 2000 to 2011 in Hong Kong; 2) to apply this approach to determine the extent to which long-term exposure to PM was associated with mortality using the data from an elderly cohort. For Objective 1, PM10 concentrations were estimated by twelve yearly generalized additive models. For each model, monthly PM10 averages from thirteen monitoring stations were regressed against surface extinction coefficient (SEC) derived from remote sensors, meteorological covariates, traffic counts, building density and distance to the nearest road. To reduce temporal fluctuations, each model used the data from a window of three consecutive years with the target prediction year in the centre of the window. To estimate PM2.5, because of small number of available stations, only one spatio-temporal model covering the whole study period was developed. This model included the estimated PM10, month of year and spatial covariates. R DEGREES2 and root-mean-square error (RMSE) were calculated to assess the predictive performance. For Objective 2, residential-level PM exposures were estimated by the above models based on the residence address of each cohort subject. The association between long-term PM exposures and mortality was analysed by Cox proportional hazard model adjusting for individual- and area-level confounders. As additional analyses, the PM exposures estimated by inverse distance weighting (IDW) method were used to show the need for the proposed modelling approach. The spatio-temporal models had high predicting power with adjusted R2 of 0.91 for PM10 and 0.87 for PM2.5, and high accuracy indicated by RMSE of 5.88μg/m3 and 4.98μg/m3, respectively. Among 61,586 subjects, the median follow-up time was 11.5 years (SD: 2.82) until the end of 2011, and there were 17,453 deaths (28.3% of the subjects). Exposure to a 10 μg/m3 increase was associated with 5% (95%CI: 4%-7%) for PM10, and 12% (10%-14%) for PM2.5 increase in death from all-natural causes; 7% (4%-10%) and 14% (10%-18%) from cardiovascular diseases; 9% (5%-12%) and 14% (10%-19%) from respiratory diseases. Females, non-smokers and subjects with high BMI were found at higher susceptibility of exposure. In the additional analyses, health effect estimates using IDW method yielded high excess risks for most mortality outcomes, including accidental mortality. This proposed modelling approach provided a reliable and robust estimation of PM concentrations and captured both temporal and spatial variations well in small areas. The magnitudes of the mortality effects associated with long-term PM exposures were comparable
Book Synopsis Using Remote Sensing to Understand Urban Air Quality Exposures and Inequities by : Matthew Bechle
Download or read book Using Remote Sensing to Understand Urban Air Quality Exposures and Inequities written by Matthew Bechle and published by . This book was released on 2021 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Geospatial Analysis of Environmental Health by : Juliana A. Maantay
Download or read book Geospatial Analysis of Environmental Health written by Juliana A. Maantay and published by Springer Science & Business Media. This book was released on 2011-03-18 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on a range of geospatial applications for environmental health research, including environmental justice issues, environmental health disparities, air and water contamination, and infectious diseases. Environmental health research is at an exciting point in its use of geotechnologies, and many researchers are working on innovative approaches. This book is a timely scholarly contribution in updating the key concepts and applications of using GIS and other geospatial methods for environmental health research. Each chapter contains original research which utilizes a geotechnical tool (Geographic Information Systems (GIS), remote sensing, GPS, etc.) to address an environmental health problem. The book is divided into three sections organized around the following themes: issues in GIS and environmental health research; using GIS to assess environmental health impacts; and geospatial methods for environmental health. Representing diverse case studies and geospatial methods, the book is likely to be of interest to researchers, practitioners and students across the geographic and environmental health sciences. The authors are leading researchers and practitioners in the field of GIS and environmental health.
Book Synopsis Influence of Model Spatial Resolution on Simulated Aerosol Surface Concentration by : Jessica Morena
Download or read book Influence of Model Spatial Resolution on Simulated Aerosol Surface Concentration written by Jessica Morena and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
