Dataset Information

Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model.

ABSTRACT: Recently, the population of Seoul has been affected by particulate matter in the atmosphere. This problem can be addressed by developing an elaborate forecasting model to estimate the concentration of fine dust in the metropolitan area. We present a forecasting model of the fine dust concentration with an extended range of input variables, compared to existing models. The model takes inputs from holistic perspectives such as topographical features on the surface, chemical sources of the fine dusts, traffic and the human activities in sub-areas, and meteorological data such as wind, temperature, and humidity, of fine dust. Our model was evaluated by the index-of-agreement (IOA) and the root mean-squared error (RMSE) in predicting PM2.5 and PM10 over three subsequent days. Our model variations consist of linear regressions, ARIMA, and Gaussian process regressions (GPR). The GPR showed the best performance in terms of IOA that is over 0.6 in the three-day predictions.

SUBMITTER: Jang J

PROVIDER: S-EPMC7412375 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model.

Jang JoonHo J Shin Seungjae S Lee Hyunjin H Moon Il-Chul IC

Sensors (Basel, Switzerland) 20200709 14

Recently, the population of Seoul has been affected by particulate matter in the atmosphere. This problem can be addressed by developing an elaborate forecasting model to estimate the concentration of fine dust in the metropolitan area. We present a forecasting model of the fine dust concentration with an extended range of input variables, compared to existing models. The model takes inputs from holistic perspectives such as topographical features on the surface, chemical sources of the fine dus ...[more]

PMID: 32660163

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Project description:BackgroundNumerous studies have linked fine particles [? 2.5 µm in aerodynamic diameter (PM(2.5))] and health. Most studies focused on the total mass of the particles, although the chemical composition of the particles varies substantially. Which chemical components of fine particles that are the most harmful is not well understood, and research on the chemical composition of PM(2.5) and the components that are the most harmful is particularly limited in Asia.ObjectivesWe characterized PM(2.5) chemical composition and estimated the effects of cause-specific mortality of PM(2.5) mass and constituents in Seoul, Korea. We compared the chemical composition of particles to those of the eastern and western United States.MethodsWe examined temporal variability of PM(2.5) mass and its composition using hourly data. We applied an overdispersed Poisson generalized linear model, adjusting for time, day of week, temperature, and relative humidity to investigate the association between risk of mortality and PM(2.5) mass and its constituents in Seoul, Korea, for August 2008 through October 2009.ResultsPM(2.5) and chemical components exhibited temporal patterns by time of day and season. The chemical characteristics of Seoul's PM(2.5) were more similar to PM(2.5) found in the western United States than in the eastern United States. Seoul's PM(2.5) had lower sulfate (SO(4)) contributions and higher nitrate (NO(3)) contributions than that of the eastern United States, although overall PM(2.5) levels in Seoul were higher than in the United States. An interquartile range (IQR) increase in magnesium (Mg) (0.05 ?g/m³) was associated with a 1.4% increase (95% confidence interval: 0.2%, 2.6%) in total mortality on the following day. Several components that were among the largest contributors to PM(2.5) total mass--NO(3), SO(4), and ammonium (NH(4))--were moderately associated with same-day cardiovascular mortality at the p < 0.10 level. Other components with smaller mass contributions [Mg and chlorine (Cl)] exhibited moderate associations with respiratory mortality on the following day (p < 0.10).ConclusionsOur findings link PM(2.5) constituents with mortality and have implications for policy making on sources of PM(2.5) and on the relevance of PM(2.5) health studies from other areas to this region.

Project description:BackgroundGiven that fine particulate matter (≤ 2.5 μm; PM2.5) is a mixture of multiple components, it has been of high interest to identify its specific health-relevant physical and/or chemical features.ObjectivesWe conducted a time-series study of PM2.5 and cardiorespiratory emergency department (ED) visits in the St. Louis, Missouri-Illinois metropolitan area, using 2 years of daily PM2.5 and PM2.5 component measurements (including ions, carbon, particle-phase organic compounds, and elements) made at the St. Louis-Midwest Supersite, a monitoring site of the U.S. Environmental Protection Agency Supersites ambient air monitoring research program.MethodsUsing Poisson generalized linear models, we assessed short-term associations between daily cardiorespiratory ED visit counts and daily levels of 24 selected pollutants. Associations were estimated for interquartile range changes in each pollutant. To allow comparison of relationships among multiple pollutants and outcomes with potentially different lag structures, we used 3-day unconstrained distributed lag models controlling for time trends and meteorology.ResultsConsidering results of our primary models, as well as sensitivity analyses and models assessing co-pollutant confounding, we observed robust associations of cardiovascular disease visits with 17α(H),21β(H)-hopane and congestive heart failure visits with elemental carbon. We also observed a robust association of respiratory disease visits with ozone. For asthma/wheeze, associations were strongest with ozone and nitrogen dioxide; observed associations of asthma/wheeze with PM2.5 and its components were attenuated in two-pollutant models with these gases. Differential measurement error due to differential patterns of spatiotemporal variability may have influenced patterns of observed associations across pollutants.ConclusionsOur findings add to the growing field examining the health effects of PM2.5 components. Combustion-related components of the pollutant mix showed particularly strong associations with cardiorespiratory ED visit outcomes.

Dataset Information

Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model.

Publications

Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model.

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