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To investigate the possible relationship between airborne particulate matter and mortality, we developed regression models of daily mortality counts using meteorological covariates and measures of outdoor PM10. Our analyses included data from Cook County, Illinois, and Salt Lake County, Utah. We found no evidence that particulate matter < or = 10 m...

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... We compared these results with those of studies outside Korea. Styer et al. (1995) employed data for Chicago, USA, between 1985 and1990, and found that all-cause mortality increased by 0.3% with statistical significance, except for extrinsic mortality. Laden et al. (2000) found a 3.4% increase in daily mortality when PM 2.5 increased by 10 μg/m 3 ; these results are similar to those of this study. ...
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Analyzing the economic value of the damage to human health caused by environmental risks has become an essential research focus, given the increasing necessity for effective decision-making. Since logical and rational analyses such as cost–benefit and cost–utility analyses will likely gain importance in future policymaking, the evaluation of economic costs becomes necessary. Among the various types of air pollutants, fine particulate matter (PM) is reported as closely related to mortality. To reduce result uncertainty by improving the methodology of risk assessment or the economic evaluation of fine PM, risk control measures are required for high-priority areas. This study addresses this issue by estimating the relative risk of PM2.5 while calculating the economic loss cost arising from acute death due to fine PM exposure in Seoul, Korea. The value of statistical life of one person’s willingness to pay for mortality risk reduction is calculated to estimate the economic loss cost at each current level of exposure. The estimated economic loss cost due to all-cause mortality during 2016–2018 totaled approximately USD 1307.9 million per year; the costs of loss from respiratory and cardiovascular mortalities were USD 128.1 million per year and USD 426.9 million, respectively. Based on these results, this study concludes that the standards for PM2.5 are more effective than the ones established for PM10 in terms of economic value.
... From localized air-pollution caused by fireworks [1], to seasonal changes in pollution caused by cars [2], to planetary-scale dust transport from earth's deserts [3], particulate and gaseous hazardous matter can be dispersed throughout the environment from numerous natural and anthropogenic processes. One event which is important to public health and national security is the release of hazardous materials from nuclear weapons explosions, nuclear reactor breaches (such as Chernobyl or Fukushima), chemical spills, industrial accidents, and other toxic releases. ...
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In the event of an accidental or intentional hazardous material release in the atmosphere, researchers often run physics-based atmospheric transport and dispersion models to predict the extent and variation of the contaminant spread. These predictions are imperfect due to propagated uncertainty from atmospheric model physics (or parameterizations) and weather data initial conditions. Ensembles of simulations can be used to estimate uncertainty, but running large ensembles is often very time consuming and resource intensive, even using large supercomputers. In this paper, we present a machine-learning-based method which can be used to quickly emulate spatial deposition patterns from a multi-physics ensemble of dispersion simulations. We use a hybrid linear and logistic regression method that can predict deposition in more than 100,000 grid cells with as few as fifty training examples. Logistic regression provides probabilistic predictions of the presence or absence of hazardous materials, while linear regression predicts the quantity of hazardous materials. The coefficients of the linear regressions also open avenues of exploration regarding interpretability—the presented model can be used to find which physics schemes are most important over different spatial areas. A single regression prediction is on the order of 10,000 times faster than running a weather and dispersion simulation. However, considering the number of weather and dispersion simulations needed to train the regressions, the speed-up achieved when considering the whole ensemble is about 24 times. Ultimately, this work will allow atmospheric researchers to produce potential contamination scenarios with uncertainty estimates faster than previously possible, aiding public servants and first responders.
... Particularly, the effect of particulate matter influxes to cities from pollutants originating outside the cities [2] and the effect of pollutants from China, such as yellow smog [3], are factors that may amplify particulate matter concentrations in South Korea [4]. Previous studies have reported that particulate matter can have fatal impacts on vulnerable groups, including elderly people, pregnant women, and children, and that it has a close relationship with mortality rates; for instance, in the case of particulate matter with an aerodynamic diameter < 10 µm (PM10), mortality rates from disease increase by 0.3% as the concentration increases by 10 µg/m 3 [5][6][7][8][9][10]. ...
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This study analyzed the changes in particulate matter concentrations according to land-use over time and the spatial characteristics of the distribution of particulate matter concentrations using big data of particulate matter in Daejeon, Korea, measured by Private Air Quality Monitoring Smart Sensors (PAQMSSs). Land-uses were classified into residential, commercial, industrial, and green groups according to the primary land-use around the 650-m sensor radius. Data on particulate matter with an aerodynamic diameter <10 µm (PM10) and <2.5 µm (PM2.5) were captured by PAQMSSs from September‒October (i.e., fall) in 2019. Differences and variation characteristics of particulate matter concentrations between time periods and land-uses were analyzed and spatial mobility characteristics of the particulate matter concentrations over time were analyzed. The results indicate that the particulate matter concentrations in Daejeon decreased in the order of industrial, housing, commercial and green groups overall; however, the concentrations of the commercial group were higher than those of the residential group during 21:00–23:00, which reflected the vital nighttime lifestyle in the commercial group in Korea. Second, the green group showed the lowest particulate matter concentration and the industrial group showed the highest concentration. Third, the highest particulate matter concentrations were in urban areas where commercial and business functions were centered and in the vicinity of industrial complexes. Finally, over time, the PM10 concentrations were clearly high at noon and low at night, whereas the PM2.5 concentrations were similar at certain areas.
... A daily time-series analysis of mortality counts and air pollution in a neighboring Wasatch Front county (Salt Lake County) did not find evidence of an association between mortality and PM 10 [20]. A more comprehensive population-based daily time-series mortality study was conducted using the populations from all three primary metropolitan areas of the Wasatch Front including the following: the Ogden area (Weber County), the Salt Lake City area (Salt Lake and Davis Counties), and the Provo/Orem area (Utah County) [18]. ...
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Utah’s low-smoking population and high population density concentrated in mountain valleys, with intermittent industrial activity and frequent temperature inversions, have yielded unique opportunities to study air pollution. These studies have contributed to the understanding of the human health impacts of air pollution. The populated mountain valleys of Utah experience considerable variability in concentrations of ambient air pollution because of local emission sources that change over time and episodic atmospheric conditions that result in elevated concentrations of air pollution. Evidence from Utah studies indicates that air pollution, especially combustion-related fine particulate matter air pollution and ozone, contributes to various adverse health outcomes, including respiratory and cardiovascular morbidity and mortality and increased risk of lung cancer. The evidence suggests that air pollution may also contribute to risk of pre-term birth, pregnancy loss, school absences, and other adverse health outcomes.
... Figure 2 illustrates those of Hu et al. [12] who also investigated the short-term effect and lag effect of air environment on disease risk. Styer et al. [20] analyzed data from Cook County, Illinois, and Salt Lake County, Utah, and found (for Cook County) that the effect of PM 10 was higher in the spring and fall. The estimation of the short-term impact of air pollution on a single city's daily confirmed case is hindered by the high variability inherent in the impact estimates. ...
Preprint
The impact of the outbreak of COVID-19 on health has been widely concerned. Disease risk assessment, prediction, and early warning have become a significant research field. Previous research suggests that there is a relationship between air quality and the disease. This paper investigated the impact of the atmospheric environment on the basic reproduction number (R$_0$) in Australia, South Korea, and Italy by using atmospheric environment data, confirmed case data, and the distributed lag non-linear model (DLNM) model based on Quasi-Poisson regression. The results show that the air temperature and humidity have lag and persistence on short-term R$_0$, and seasonal factors have an apparent decorating effect on R$_0$. PM$_{10}$ is the primary pollutant that affects the excess morbidity rate. Moreover, O$_3$, PM$_{2.5}$, and SO$_2$ as perturbation factors have an apparent cumulative effect. These results present beneficial knowledge for correlation between environment and COVID-19, which guiding prospective analyses of disease data.
... Giannadaki et al. (2014) assess global mortality rates from long-term exposure to airborne mineral dusts. More studies include Styer et al. (1995), Ostro et al. (1999Ostro et al. ( , 2000, Lipsett et al. (2006), Cheng et al. (2008), and Goudie (2014). Whether in Kuwait (Thalib and Al-Taiar 2012), Lanzhou (Yan et al. 2012), Seoul (Hong et al. 2010), Sydney (Merrifield et al. 2013), Taipei (Chan and Ng 2011) or El Paso (Grineski et al. 2011), piece-by-piece the harm exacted by windblown dust is being documented. ...
Chapter
Arid regions, the source of most airborne mineral dusts, comprise a third of the Earth’s land surface, where some two billion people are exposed daily to the fine particles raised by wind. Crossing political borders and travelling on air currents around the world, these particles not only affect the health of local communities, but also put many other populations extant at risk for cardiovascular and respiratory illnesses and a host of other health problems. Risks of exposure are affected by climatic conditions and their local and regional weather characteristics. And today, because of advancements in science and technology we are at the threshold of significantly reducing these health problems. Examples of meningitis, asthma and Valley fever are used to illustrate how risks may be lowered through a Dust-Health Early Warning System. A little more than a half-century of dedicated measurements of particulate air quality and of environmental science enhanced by Earth-orbiting satellites reveal the truth of airborne dust extent, and much of its variability in time and space. These truths have been essential in advancing numerical, dynamical models of the atmosphere that mimic and predict weather systems that loft the airborne dusts that medical sciences and epidemiology are proving harmful. This union of scientific disciplines and services makes possible today a means to improve public health around the world through a Global Dust-Health Early Warning System.
... This changes the computation of R and r T (x) in the conditional prediction (2.4), which no longer interpolates the training data. For data from physical experimentation or observation, augmenting a GaSP model in this way is natural to reflect random errors (e.g., Gao, Sacks and Welch, 1996;McMillan et al., 1999;Styer et al., 1995). ...
Article
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Statistical methods based on a regression model plus a zero-mean Gaussian process (GP) have been widely used for predicting the output of a deterministic computer code. There are many suggestions in the literature for how to choose the regression component and how to model the correlation structure of the GP. This article argues that comprehensive, evidence-based assessment strategies are needed when comparing such modeling options. Otherwise, one is easily misled. Applying the strategies to several computer codes shows that a regression model more complex than a constant mean either has little impact on prediction accuracy or is an impediment. The choice of correlation function has modest effect, but there is little to separate two common choices, the power exponential and the Maté rn, if the latter is optimized with respect to its smoothness. The applications presented here also provide no evidence that a composite of GPs provides practical improvement in prediction accuracy. A limited comparison of Bayesian and empirical Bayes methods is similarly inconclusive. In contrast, we find that the effect of experimental design is surprisingly large, even for designs of the same type with the same theoretical properties.
... Giannadaki et al. (2014) assess global mortality rates from long-term exposure to airborne mineral dusts. More studies include Styer et al. (1995), Ostro et al. (1999Ostro et al. ( , 2000, Lipsett et al. (2006), Cheng et al. (2008), and Goudie (2014). Whether in Kuwait (Thalib and Al-Taiar 2012), Lanzhou (Yan et al. 2012), Seoul (Hong et al. 2010), Sydney (Merrifield et al. 2013), Taipei (Chan and Ng 2011) or El Paso (Grineski et al. 2011), piece-by-piece the harm exacted by windblown dust is being documented. ...
Chapter
Arid regions, the source of most airborne mineral dusts, comprise a third of the Earth’s land surface, where some two billion people are exposed daily to the fine particles raised by wind. Crossing political borders and travelling on air currents around the world, these particles not only affect the health of local communities, but also put many other populations extant at risk for cardiovascular and respiratory illnesses and a host of other health problems. Risks of exposure are affected by climatic conditions and their local and regional weather characteristics. And today, because of advancements in science and technology we are at the threshold of significantly reducing these health problems. Examples of meningitis, asthma and Valley fever are used to illustrate how risks may be lowered through a Dust-Health Early Warning System. A little more than a half-century of dedicated measurements of particulate air quality and of environmental science enhanced by Earth-orbiting satellites reveal the truth of airborne dust extent, and much of its variability in time and space. These truths have been essential in advancing numerical, dynamical models of the atmosphere that mimic and predict weather systems that loft the airborne dusts that medical sciences and epidemiology are proving harmful. This union of scientific disciplines and services makes possible today a means to improve public health around the world through a Global Dust-Health Early Warning System.
... Particulate matter (PM) refers to a complex mixture of pollutants consisting of smoke, dust, and all kinds of solid and liquid material generated by many different sources and that is in suspension in the atmosphere. Many epidemiological studies have provided evidence of adverse health effects of PM, including particles ≤2.5 μm in aerodynamic diameter (PM 2.5 ) and particles ≤10 μm in aerodynamic diameter (PM 10 ) (Dockery et al. 1993;Garrett and Casimiro 2011;Katsouyanni et al. 1997;Peng et al. 2005;Peters et al. 1997;Pope and Dockery 2006;Schwartz et al. 1996;Styer et al. 1995). However, most of these studies and the regulations designed to protect public health from airborne particles have focused on the risk associated with the total mass of particles, without regard to the characteristics of its components and sources. ...
Article
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Recent time series studies have indicated that daily mortality and morbidity are associated with particulate matters. However, about the relative effects and its seasonal patterns of fine particulate matter constituents is particularly limited in developing Asian countries. In this study, we examined the role of particulate matters and its key chemical components of fine particles on both mortality and morbidity in Beijing. We applied several overdispersed Poisson generalized nonlinear models, adjusting for time, day of week, holiday, temperature, and relative humidity, to investigate the association between risk of mortality or morbidity and particulate matters and its constituents in Beijing, China, for January 2005 through December 2009. Particles and several constituents were associated with multiple mortality or morbidity categories, especially on respiratory health. For a 3-day lag, the nonaccident mortality increased by 1.52, 0.19, 1.03, 0.56, 0.42, and 0.32 % for particulate matter (PM)2.5, PM10, K+, SO4 2−, Ca2+, and NO3 − based on interquartile ranges of 36.00, 64.00, 0.41, 8.75, 1.43, and 2.24 μg/m3, respectively. The estimates of short-term effects for PM2.5 and its components in the cold season were 1 ~ 6 times higher than that in the full year on these health outcomes. Most of components had stronger adverse effects on human health in the heavy PM2.5 mass concentrations, especially for K+, NO3 −, and SO4 2−. This analysis added to the growing body of evidence linking PM2.5 with mortality or morbidity and indicated that excess risks may vary among specific PM2.5 components. Combustion-related products, traffic sources, vegetative burning, and crustal component and resuspended road dust may play a key role in the associations between air pollution and public health in Beijing.
... This background influence might be affected by behavior and eating habits, even without changes in temperature or pressure. In addition to diurnal rhythms, heart rate variability might also be affected by other factors such as particulate matter (Styer et al. 1995;Creason et al. 2001). Third, we examined physiological changes only up to 1 h after eating, so we are unable to comment on whether there might be longer term influences. ...
Article
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Previous studies of autonomic nervous system responses before and after eating when controlling patient conditions and room temperature have provided inconsistent results. We hypothesized that several physiological parameters reflecting autonomic activity are affected by outdoor temperature before and after a meal. We measured the following physiological variables before and after a fixed meal in 53 healthy Japanese women: skin temperature, systolic and diastolic blood pressure, salivary amylase, blood glucose, heart rate, and heart rate variability. We assessed satiety before and after lunch using a visual analog scale (100 mm). We recorded outdoor temperature, atmospheric pressure, and relative humidity. Skin temperature rose significantly 1 h after eating (greater in cold weather) (P = 0.008). Cold weather markedly influenced changes in diastolic blood pressure before (P = 0.017) and after lunch (P = 0.013). Fasting salivary amylase activity increased significantly in cold weather but fell significantly after lunch (significantly greater in cold weather) (P = 0.007). Salivary amylase was significantly associated with cold weather, low atmospheric pressure, and low relative humidity 30 min after lunch (P < 0.05). Cold weather significantly influenced heart rate variability (P = 0.001). The decreased low frequency (LF)/high frequency (HF) ratio, increased Δ LF/HF ratio, and increased Δ salivary amylase activity imply that cold outdoor temperature is associated with dominant parasympathetic activity after lunch. Our results clarify the relationship between environmental factors, food intake, and autonomic system and physiological variables, which helps our understanding of homeostasis and metabolism. Electronic supplementary material The online version of this article (doi:10.1007/s00484-014-0800-1) contains supplementary material, which is available to authorized users.