The Geography of Long Term Exposure to Particulate Matter 2.5 and COVID-19 Mortality; An Assessment of the Fragility and Spatial Sensitivity of a Significant Finding PDF Download

Author: Jennifer Badger
Publisher:
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Category :
Languages : en
Pages : 0

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Air pollution is directly linked to death. In December 2020, a UK coroner ruled that air pollution was the cause of a fatal asthma attack that led to the 2013 death of nine-year-old Ella Adoo-Kissi Debrah who lived adjacent to a busy motorway (BBC News, 2022). The assignment of air pollution as the official cause of death on a death certificate was the first of its kind in the world (Reynolds, 2020). Though this was the first official assignment of air pollution as a cause of death, there are numerous studies linking air pollution exposure with mortality all over the world. Before the COVID-19 pandemic, the air pollutant PM 2.5 was identified as the "largest environmental risk factor in the United States" (Goodkind et al. 2019, p. 8780) and the cause of more annual premature deaths than traffic accidents and homicides combined (Goodkind et al. 2019). With the onset of the COVID-19 pandemic, researchers began assessing the impact of air pollution exposure on COVID-19 incidence and death. In a widely received, nationwide study linking air pollution exposure to COVID-19 mortality, Harvard T.H. Chan School of Public Health researchers, Wu et al., produced significant findings linking the impact of long term exposure to PM 2.5 to COVID-19 mortality across the contiguous United States. This 2020 study, published in ScienceAdvances, has been cited over 600 times, covered by 131 news outlets and downloaded over 15,000 times. Georeferenced data is routinely used in public health research such as this, however, the substantive influence of geography in the relationship between the treatment and outcome variable is often not considered in the model specifications, research design, nor the sampling strategy (Goldhagen et al., 2005; Matisziw, Grubesic, and Wei 2008). Additionally, the mechanism of data aggregation to an administrative unit may spatially misrepresent the data (Delmelle et al., 2022). As air pollution is a local, regional, and transboundary phenomenon (Nordenstam et. al, 1998; Goodkind, 2019), spatial autocorrelation, or spatially similar values, in the long term exposure to PM 2.5 among U.S. counties is likely. Despite the inclusion of maps indicating strong spatial trends in the long term exposure to PM 2.5 and COVID-19 mortality, the possible presence of spatial autocorrelation at the local level or spatial heterogeneity at the regional level was not investigated by the authors. Epidemiological studies invoking large, areal units may misrepresent the underlying, spatial processes of environmental health-hazards and produce unreliable treatment effect estimates when relating air pollution exposure to disease (Fotheringham and Wong, 1991; Kolak and Anselin, 2019). In this thesis, the fragility of the Wu et al. treatment effect estimate to unobserved confounding is assessed utilizing an alternative sensitivity analysis framework. This framework revealed that the estimate derived by Wu et al. (2020) is much more fragile to confounding than reported by the authors. Spatial analysis was then applied to investigate the possibility of spatial regimes (e.g. hotspots) in the treatment and outcome variables which may contribute to biased or inefficient treatment effect estimates. Strong levels of spatial autocorrelation and regional spatial heterogeneity in the long term exposure to PM 2.5, and to a lesser extent in the COVID-19 mortality rate, were confirmed by both computational and exploratory spatial data analysis. The highly variable associations between long term exposure to PM 2.5 and COVID-19 Mortality per U.S. Census Region or EPA Climatically Consistent Region delivered the expected result that the relationship between the treatment and outcome variable changes with changes in the sub-National definition of place. An understanding of the geography of the ubiquitous, locally variable and far-reaching PM 2.5, and its related health-hazard risks can contribute to an uncovering of the politics, power relations, and socioenvironments that coproduce differential access to clean air and the resulting uneven health burdens experienced by Black, LatinX, Asian-American, and immigrant communities. This is an essential step towards disentangling the relationships rendering clean air no longer an "open-access good" (V ron, 2006).

Author: Eric Coker
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Long-term exposure to air pollutant concentrations is known to cause chronic lung inflammation, a condition that may promote increased severity of COVID-19 syndrome caused by the novel coronavirus. In this paper, we empirically investigate the ecologic association between longterm exposure to fine particulate matter (PM) concentration and excess deaths in the first quarter of 2020 in municipalities of Northern Italian. The study accounts for potentially spatial confounding factors related to urbanization that may have influenced the spreading of the novel coronavirus. Our epidemiological analysis uses geographical information (e.g., municipalities) and Poisson regression to assess whether both ambient PM concentration and excess mortality have a similar spatial distribution. Preliminary evidence confirms the hypothesis and suggests a positive association of ambient PM on excess mortality in Northern Italy.

Author: Takahiro Yamada
Publisher:
ISBN:
Category :
Languages : en
Pages : 38

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This study investigates the causal effects of long-term particulate matter 2.5 exposure on COVID-19 deaths, fatality rates, and cases in India by using an instrumental variables approach based on thermal inversion episodes. The estimation results indicate that a 1 percent increase in long-term exposure to particulate matter 2.5 leads to an increase in COVID-19 deaths by 5.7 percentage points and an increase in the COVID-19 fatality rate by 0.027 percentage point, but this exposure is not necessarily correlated with COVID-19 cases. People with underlying health conditions such as respiratory illness caused by exposure to air pollution might have a higher risk of death following SARS-CoV-2 infection. This finding might also apply to other countries where high levels of air pollution are a critical issue for development and public health.

Author: Qishi Zheng
Publisher: Open Dissertation Press
ISBN: 9781361024362
Category :
Languages : en
Pages :

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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

Author: Ingo Eduard Isphording
Publisher:
ISBN:
Category :
Languages : en
Pages :

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We study the impact of short-term exposure to ambient air pollution on the spread and severity of COVID-19 in Germany. We combine data on county-by-day level on confirmed cases and deaths with information on local air quality and weather conditions and exploit short-term variation in the concentration of particulate matter (PM10) and ozone (O3). We apply fixed effects regressions controlling for global time-varying confounding factors and regional time-invariant confounding factors on the county level, as well as potentially confounding weather conditions and the regional stage of the pandemic. We find significant positive effects of PM10 concentration after the onset of the illness on COVID-19 deaths specifically for elderly patients (80+ years): higher levels of air pollution by one standard deviation 3 to 12 days after developing symptoms increase deaths by 30 percent (males) and 35 percent (females) of the mean. In addition, air pollution raises the number of confirmed cases of COVID-19. The timing of results supports mechanisms of air pollution affecting the severity of already realized infections. Air pollution appears not to affect the probability of infection itself.

Author: Magali Nohemy Blanco
Publisher:
ISBN:
Category :
Languages : en
Pages : 240

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Dementia has been considered a major global public health priority. It is very common in older adults and characterized by the progressive and irreversible loss of memory and mental abilities. Those affected often experience other comorbidities, disability and early death. No cure currently exists for progressive dementias, and the associated healthcare costs exceed those of other age-related conditions. Recently, animal and human studies have begun reporting on air pollution neurotoxicity, including dementia. Traffic-related air pollutants (TRAP) such as nitrogen dioxide (NO2), ultrafine particulates (UFP) and black carbon (BC) are important components of community air pollution that can vary substantially over space and time. TRAP exposure has been shown to be associated with neurotoxicity and pathologies such as Alzheimer's Disease (AD) in animals as well as cognitive deficits, including late-life dementia, though the evidence has been stronger for some pollutants than others. In particular, research indicates that UFPs may play an important role in the adverse health effects associated with particulate matter. Still, epidemiologic studies investigating dementia and long-term TRAP exposure are limited due to the absence of models that appropriately capture long-term human exposure to TRAP. This study addresses this gap in the literature through three specific aims: In Aim 1, we use fine-scale, long-term NO2 exposure as well as road proximity to assess the association between TRAP and late-life all-cause and AD dementia incidence in a community-based prospective cohort study. This study was conducted using the Adult Changes in Thought (ACT) cohort, a well-characterized, Seattle-based, prospective cohort study of aging and the brain among elderly individuals (65+ years) that has been ongoing since 1994 (Kukull et al., 2002; L. Wang et al., 2006). Participants were assigned long-term NO2 exposure based on a spatiotemporal model that incorporates decades of local air quality monitoring data based on residential history. Our primary analyses indicated that for every additional 5 ppb increase in 10-year average NO2 exposure, the hazard of all-cause and AD dementia is estimated to be 1% (HR: 1.01, 95% CI: 0.91, 1.11) and 2% (HR: 1.02, 95% CI: 0.91, 1.13) greater, respectively, after adjusting for important potential confounders. Sensitivity and secondary analyses investigating the impact of different exposure windows, model adjustments, exposure quality and more were in agreement, supporting the robustness of our results. These findings are in line with the literature and a recent meta-analysis indicating that there is no evidence of an association between NO2 and dementia incidence. In Aim 2, we leverage a highly innovative mobile monitoring campaign specifically designed to assess spatially-granular, long-term TRAP exposure for the ACT cohort (Blanco et al., 2019; Stanley, 2019) to characterize otherwise unavailable annual-average UFP and BC exposure. We calculate weighted UFP and BC averages from repeated short-term monitoring samples and use these to build universal kriging models with partial least squares regression to summarize hundreds of geographic covariate predictors. The hold-out model validation results indicated low model bias and high precision (RMSE: 933 pt UFP/cm3, 58 ng BC/m3; R2: 0.87 for UFP, 0.85 for BC). Predicted annual average UFP and BC exposure for ACT cohort locations had a median (IQR) of 6,782 (1,788) pt/cm3 and 525 (134) ng/m3, respectively. Similar to past studies, predicted concentration were highest near the downtown, industrial and airport areas as well as along major highways. Sensitivity analyses taking different approaches for dealing with extreme observations, calculating annual averages and building models all resulted in very similar results, strengthening the robustness of these exposure models. These findings support the use of these prediction models for future epidemiologic investigations of TRAP exposure in the ACT cohort. Aim 3 extends the exposure surfaces developed in Aim 2 for 2019 back to 1995 in order to characterize otherwise unavailable, spatially granular, long-term BC and UFP exposure for the ACT cohort. We use time-varying values of emission indicators (highway emissions) and surrogates (population density and green space; hereafter referred to jointly as "indicators") known to be strongly associated with TRAP along with observations of air pollution trends over time to extrapolate model predictions back in time. We validate models against historical observations at air monitoring sites. Results from these models showed that annual average BC and UFP exposure estimates for the ACT cohort were generally higher and more variable for earlier years. Locations near Seattle and along major roadways saw the sharpest drops in BC levels, while locations near the Sea-Tac Airport saw the sharpest drops in UFP levels over time. Models captured overall spatial and temporal pollution trends, though they were conservative and underpredicted observed concentrations at AQS sites. These models provide an understanding of how these otherwise poorly characterized pollutants may have changed over time in the Puget Sound, an important gap in the field. Until now, investigations of TRAP exposure have been largely limited to short-term human exposure and animal studies despite the growing body of evidence linking some TRAPs to brain health. In one of the first truly long-term epidemiologic studies of TRAP exposure, we found no evidence that elevated levels of long-term NO2 exposure is associated with an increased risk of late-life dementia incidence. Furthermore, we are one of the first to build annual-average UFP and BC exposure models from a novel and extensive mobile monitoring campaign specifically designed to assess exposure in a long-standing, community-based, prospective cohort study of aging and the brain. These models can be used to further advance the field and support epidemiologic investigations of dementia incidence and long-term TRAP exposure, including UFPs and pollutant mixtures.

Author: Melinda Laituri
Publisher: Springer
ISBN: 9783031117749
Category : Computers
Languages : en
Pages : 0

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This volume of case studies focuses on the geographies of COVID-19 around the world. These geographies are located in both time and space concentrating on both first- and second-order impacts of the COVID-19 pandemic. First-order impacts are those associated with the immediate response to the pandemic that include tracking number of deaths and cases, testing, access to hospitals, impacts on essential workers, searching for the origins of the virus and preventive treatments such as vaccines and contact tracing. Second-order impacts are the result of actions, practices, and policies in response to the spread of the virus, with longer-term effects on food security, access to health services, loss of livelihoods, evictions, and migration. Further, the COVID-19 pandemic will be prolonged due to the onset of variants as well as setting the stage for similar future events. This volume provides a synopsis of how geography and geospatial approaches are used to understand this event and the emerging “new normal.” The volume's approach is necessarily selective due to the global reach of the pandemic and the broad sweep of second-order impacts where important issues may be left out. However, the book is envisioned as the prelude to an extended conversation about adaptation to complex circumstances using geospatial tools. Using case studies and examples of geospatial analyses, this volume adopts a geographic lens to highlight the differences and commonalities across space and time where fundamental inequities are exposed, the governmental response is varied, and outcomes remain uncertain. This moment of global collective experience starkly reveals how inequality is ubiquitous and vulnerable populations – those unable to access basic needs – are increasing. This place-based approach identifies how geospatial analyses and resulting maps depict the pandemic as it ebbs and flows across the globe. Data-driven decision making is needed as we navigate the pandemic and determine ways to address future such events to enable local and regional governments in prioritizing limited resources to mitigate the long-term consequences of COVID-19.

Author: Kanan Patel
Publisher:
ISBN:
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Languages : en
Pages : 0

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Long term exposure to particulate matter (PM) has been linked to an increase in mortality and cardiorespiratory diseases. In addition, PM affects Earth’s radiative balance, and is one of the main sources of uncertainty in climate change predictions. Hence, it is imperative to understand PM composition and concentrations and the factors contributing to their variability. Different parts of the world experience different levels of air pollution, due to an interplay between various factors including sources, meteorological factors, and chemical transformations. PM can either be directly emitted into the atmosphere (primary) or can be generated as result of oxidation of gas-phase precursors leading to the formation and partitioning of low volatility products to the particle phase (secondary). The nature, sources and dynamics of PM can be estimated by combining ambient field measurements with receptor modeling, machine learning and statistical analysis tools. The objective of my thesis is to understand the factors influencing PM concentration and composition in different environments. In chapter 2, I have reported the results of the measurements in Austin, Texas, one of the fastest growing metropolitan cities in the U.S. I used several modeling and data analysis tools to understand the sources and formation of particulate matter in Austin including positive matrix factorization (PMF), the Extended Aerosol Thermodynamics Model (E-AIM) and air back trajectory analysis using HYSPLIT. Through my analysis, I demonstrated that photochemistry is an important factor in governing PM composition in Austin. We observed rapid photochemical processing of traffic emissions, H2SO4-driven new particle formation (NPF) events, production of organic nitrate, and daytime peaks in the locally formed oxidized organic aerosol during the summer period. My analysis also suggested that SO2 emissions from cement kilns may be the main source of particulate sulfate observed at this receptor site, pointing toward the need for measurements at the source to investigate this further. This chapter has been published in ACS Earth and Space Chemistry. Meanwhile, Delhi (India) is the most polluted megacity in the world and routinely experiences extreme pollution episodes. Our group is one of the first in the world to measure long term PM composition at high time resolution in the city. As part of the Delhi Aerosol Supersite (DAS) study, we have recorded over five years of near-continuous PM composition to understand inter-seasonal as well as inter-annual variability in the PM concentrations and the factors influencing them. I have studied specific “special” events which have implications for policy decisions. In chapter 3, I have investigated the factors influencing high PM concentrations observed during the autumn (~Sep – Nov) season which experiences some of the most extreme pollution episodes observed anywhere in the world. I combined our measurements with data obtained from regulatory monitoring sites (CO, NOx, PM2.5) to gain insights from the temporal trends of the pollutants and to demonstrate the differences between autumn and winter, which also experiences high concentrations. I incorporated receptor models and non-parametric wind regression to understand the nature and sources of PM during this period. Further, I used meteorological data such as temperature, planetary boundary layer height, wind speed/direction and relative humidity to understand their impact on PM using statistical hypothesis testing. Using these tools, I demonstrated the influence of regional agricultural burning (from the neighboring states) and fireworks during the festival of Diwali on PM during this season. Overall, my analysis provided detailed insights into the sources and dynamics of PM during one of the most polluted seasons in Delhi (and in the world) and provided a direction for future studies in the region. This chapter has also been published in ACS Earth and Space Chemistry. In chapter 4, I have investigated the impact of COVID-19 lock-down on Delhi's air quality by combining PM and gas phase data of over four years with robust statistical analysis, including the method of “robust differences” to account for seasonal variability in the pollutant concentrations. My analysis suggests that future large-scale modification of activity restrictions in Delhi may impact the primary pollutants (NOx, CO, black carbon) more than the secondary pollutants, emphasizing the fundamental importance of secondary or regional pollutants on air quality in Delhi. I showed that overall, future strict activity reductions may lead to only a moderate reduction in PM1, reflective of complex PM1 chemistry and the need for integrative, multiscale, and multisectoral policies to address the major air pollution challenge in Delhi. This chapter has been published in ACS Environmental Science & Technology Letters. Because of the interplay between sources and meteorology in Delhi, in chapter 5 I have developed machine learning models incorporating random forest regression that estimate the concentrations of PM1 and its constituents by using meteorology and emission proxies. I have demonstrated the applicability of these models to capture temporal variability of the PM1 species, to understand the influence of individual factors via sensitivity analyses, and to separate impacts of the COVID-19 lockdowns and associated activity restrictions from impacts of other factors. Overall, these models provide new insights into the factors influencing ambient PM1 in New Delhi, India, demonstrating the power of machine learning models in atmospheric science applications. This chapter will be submitted to Aerosol Science and Technology. My research has advanced our understanding about PM formation and processing in different environments. These novel measurements and analyses will help guide future studies aimed at understanding and improving ambient air quality in these regions. Furthermore, the results of my scientific analyses may help guide policy decisions aimed at reducing PM levels in the atmosphere, thus helping improve the lives of millions of people

Author: National Intelligence Council
Publisher: Cosimo Reports
ISBN: 9781646794973
Category :
Languages : en
Pages : 158

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"The ongoing COVID-19 pandemic marks the most significant, singular global disruption since World War II, with health, economic, political, and security implications that will ripple for years to come." -Global Trends 2040 (2021) Global Trends 2040-A More Contested World (2021), released by the US National Intelligence Council, is the latest report in its series of reports starting in 1997 about megatrends and the world's future. This report, strongly influenced by the COVID-19 pandemic, paints a bleak picture of the future and describes a contested, fragmented and turbulent world. It specifically discusses the four main trends that will shape tomorrow's world: - Demographics-by 2040, 1.4 billion people will be added mostly in Africa and South Asia. - Economics-increased government debt and concentrated economic power will escalate problems for the poor and middleclass. - Climate-a hotter world will increase water, food, and health insecurity. - Technology-the emergence of new technologies could both solve and cause problems for human life. Students of trends, policymakers, entrepreneurs, academics, journalists and anyone eager for a glimpse into the next decades, will find this report, with colored graphs, essential reading.

Author: Michael Beenstock
Publisher: Springer
ISBN: 3030036146
Category : Business & Economics
Languages : en
Pages : 280

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This monograph deals with spatially dependent nonstationary time series in a way accessible to both time series econometricians wanting to understand spatial econometics, and spatial econometricians lacking a grounding in time series analysis. After charting key concepts in both time series and spatial econometrics, the book discusses how the spatial connectivity matrix can be estimated using spatial panel data instead of assuming it to be exogenously fixed. This is followed by a discussion of spatial nonstationarity in spatial cross-section data, and a full exposition of non-stationarity in both single and multi-equation contexts, including the estimation and simulation of spatial vector autoregression (VAR) models and spatial error correction (ECM) models. The book reviews the literature on panel unit root tests and panel cointegration tests for spatially independent data, and for data that are strongly spatially dependent. It provides for the first time critical values for panel unit root tests and panel cointegration tests when the spatial panel data are weakly or spatially dependent. The volume concludes with a discussion of incorporating strong and weak spatial dependence in non-stationary panel data models. All discussions are accompanied by empirical testing based on a spatial panel data of house prices in Israel.