Prediction of monthly mean surface air temperature in a region of China

Lee, Jeong-Hyeong; Sohn, Keon-Tae

doi:10.1007/s00376-007-0503-1

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…A seasonal-trend decomposed method was used to detect a potential long-term trend and seasonality of the daily concentration of PM (PM 2.5 and PM 10 ), daily hospitalizations and case-average hospitalization costs. The time-series was split into three additive components, including a long-term trend during the study period, seasonal variations within years and random variation [ 14 , 15 ]; the fundamental statistical model was as shown:

where

is the linear trend;

is the seasonal effect;

is the r random noise and t = 1, 2, …., N.…”

Section: Methodsmentioning

confidence: 99%

Attributable Risk and Economic Cost of Cardiovascular Hospital Admissions Due to Ambient Particulate Matter in Wuhan, China

Wang

Zhang

et al. 2020

IJERPH

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Although the adverse effects of ambient particulate matter (PM) on cardiovascular disease (CVD) have been previously documented, information about their economic consequence was insufficient. This study aimed to evaluate the attributable risk and economic cost of cardiovascular hospitalizations due to ambient PM. Data of CVD hospitalizations and PM concentrations from 1 January 2015 to 31 December 2017 were collected in Wuhan, China. A generalized additive model was applied to quantify the PM-attributable CVD hospitalizations, and total attributable hospitalization costs were calculated via multiplying the total attributable cases by the case-average hospitalization costs. A total of 45,714 CVD hospitalizations were included in this study. The results showed that a 10 µg/m3 increase in PM2.5 and PM10 concentrations at lag7 day, respectively, contributed to a 1.01% (95% confidence interval: 0.67–1.34) and 0.48% (0.26–0.70) increase in CVD hospitalizations. During the study period, 1487 and 983 CVD hospitalizations were attributable to PM2.5 and PM10, equaling an economic cost of 29.27 and 19.34 million RMB (1 RMB = 0.1424 USD), respectively, and significant differences in PM-attributable hospitalizations and economic burden were found between gender and age groups. Our study added evidence in heavily polluted megacities regarding the increased health risk and economic cost of CVD hospitalizations associated with ambient particulate pollution.

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where

is the linear trend;

is the seasonal effect;

is the r random noise and t = 1, 2, …., N.…”

Section: Methodsmentioning

confidence: 99%

Attributable Risk and Economic Cost of Cardiovascular Hospital Admissions Due to Ambient Particulate Matter in Wuhan, China

Wang

Zhang

et al. 2020

IJERPH

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show abstract

“…Among them, the univariate time-series models have gained relative popularity in recent years, partly due to the complexity of mainstream climate models, which are strongly constrained by the current knowledge of the physical climate system (IPCC, 2001). One subcategory of the univariate models, namely the structural time-series models (e.g., Lee and Sohn, 2007), has become quite popular due to its trend-detecting capability. In general, a structural time-series model comprises a deterministic trend plus random residuals about the trend, where the residuals are assumed to represent natural variability and can be viewed as a realization of an autoregressive integrated moving average (ARIMA) process (Romilly, 2005).…”

Section: Introductionmentioning

confidence: 99%

Time-series modeling and prediction of global monthly absolute temperature for environmental decision making

Yang

Ranst

et al. 2013

Adv. Atmos. Sci.

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A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined (DSC) approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average (SARIMA) models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term (∼10-year) environmental planning and decision making.

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“…Time-series decomposition was applied to preliminary analyse the numbers of admissions to explore changes in the number of hospitalized patients and the trend of admissions. Analysis of time series data are usually performed to identify long-term trends, seasonal variations and irregular fluctuations [30][31][32][33]. Long-term trend refers to the tendency for a phenomenon to continue to develop and change over a prolonged period of time.…”

Section: Discussionmentioning

confidence: 99%

Ambient air pollution, temperature and hospital admissions due to respiratory diseases in a cold, industrial city

Jia

Ning

et al. 2022

J Glob Health

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Background The influences of air pollution exposure and temperature on respiratory diseases have become major global health concerns. This study investigated the relationship between ambient air pollutant concentrations and temperature in cold industrial cities that have the risk of hospitalization for respiratory diseases. Methods A time-series study was conducted in Changchun, China, from 2015 to 2019 to analyse the number of daily admissions for respiratory diseases, air pollutant concentrations, and meteorological factors. Time-series decomposition was applied to analyse the trend and characteristics of the number of admissions. Generalized additive models and distributed lag nonlinear models were constructed to explore the effects of air pollutant concentrations and temperature on the number of admissions. Results The number of daily admissions showed an increasing trend, and the seasonal fluctuation was obvious, with more daily admissions in winter and spring than in summer and autumn. There were positive and gradually decreasing lag effects of PM10, PM2.5, NO 2 , and CO concentrations on the number of admissions, whereas O 3 showed a J-shaped trend. The results showed that within the 7-day lag period, 0.5°C was the temperature associated with the lowest relative risk of admission due to respiratory disease, and extremely low and high temperatures (<-18°C, >27°C, respectively) increased the risk of hospitalization for respiratory diseases by 8.3% and 12.1%, respectively. Conclusions From 2015 to 2019, respiratory diseases in Changchun showed an increasing trend with obvious seasonality. The increased concentrations of SO 2 , NO 2 , CO, PM2.5, O 3 and PM10 lead to an increased risk of hospitalization for respiratory diseases, with a significant lag effect. Both extreme heat and cold could lead to increases in the risk of admission due to respiratory disease.

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Prediction of monthly mean surface air temperature in a region of China

Cited by 11 publications

References 14 publications

Attributable Risk and Economic Cost of Cardiovascular Hospital Admissions Due to Ambient Particulate Matter in Wuhan, China

Attributable Risk and Economic Cost of Cardiovascular Hospital Admissions Due to Ambient Particulate Matter in Wuhan, China

Time-series modeling and prediction of global monthly absolute temperature for environmental decision making

Ambient air pollution, temperature and hospital admissions due to respiratory diseases in a cold, industrial city

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