Dataset Information

CO and CO₂ daily time series and time series of wind speed.

ABSTRACT: The global CO₂ has increased rapidly because of the vast use of fossil fuels over past 40 years. CO, co-emitted with CO₂, also increased during this period. To understand the CO₂ and CO regional emission, it is necessary to monitor the atmospheric CO₂ and CO. Ground-based high-resolution Fourier transform infrared spectroscopy (FTIR), an important technique to observe atmospheric trace gases, is used to measure the column-averaged dry-air mole fractions (DMF) of CO2 and CO [1]. The DMF of CO and CO₂ are not only insensitive to vertical diffusion, but also insensitive to the variation of surface CO₂ and CO concentrations. Therefore, high-resolution Fourier transform spectrometer (FTS) is used to measure atmospheric CO₂ and CO and obtain daily variation of CO₂ and CO in Hefei site. A weather station was installed near the FTS to record the weather data. And the wind speed is related to turbulence. So the wind speed time series and Cumulative Distribution Function (CDF) are also shown in data article.

SUBMITTER: Shan C

PROVIDER: S-EPMC6525326 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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CO and CO2 daily time series and time series of wind speed.

Shan Changgong C Wang Wei W

Data in brief 20190506

The global CO2 has increased rapidly because of the vast use of fossil fuels over past 40 years. CO, co-emitted with CO2, also increased during this period. To understand the CO2 and CO regional emission, it is necessary to monitor the atmospheric CO2 and CO. Ground-based high-resolution Fourier transform infrared spectroscopy (FTIR), an important technique to observe atmospheric trace gases, is used to measure the column-averaged dry-air mole fraction ...[more]

PMID: 31193343

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Project description:In recent years, PM2.5 and O3 have been the two main pollutants affecting public health in China, but the interaction of the two pollutants on human health remains unclear. A two-stage analytical approach was used to investigate the relationships of PM2.5-O3 co-pollution with nonaccidental, cardiovascular, and respiratory mortality levels across 14 cities in China. We first utilized a generalized additive model (GAM) to determine the city-specific associations of PM2.5 and O3 with daily mortality. The associations were then combined at the national and regional levels using meta-analysis. To investigate the potential interactions between the two pollutants and cause-specific mortality, we performed stratified analyses by co-pollutant exposure levels and the synergy index (SI) (SI > 1 indicates a synergistic interaction). The effect of changes in the two pollutants' concentrations (in 10 μg/m3 increases) on mortality was assessed. The stratification analysis results suggested that each 10 μg/m3 increase in PM2.5 at lag0-1 (lag01) in the low, moderate, and high strata of the O3 concentrations increased nonaccidental mortality by 0.07% (95% confidence interval: -0.03%, 0.17%), 0.33% (0.13%, 0.53%), and 0.68% (0.30%, 1.06%), respectively, with significant between-group differences (p < 0.001). Moreover, each 10 μg/m3 increase in O3 (lag01) in the low, moderate, and high strata of the PM2.5 concentrations increased nonaccidental mortality by 0.15% (-0.06%, 0.36%), 0.53% (0.19%, 0.87%), and 0.75% (0.14%, 1.36%), respectively, with significant between-group differences (p < 0.001). We also found substantial synergistic interactions between the two pollutants and nonaccidental, cardiovascular, and respiratory mortality levels, with SI values of 1.48, 1.51, and 1.33, respectively. Additionally, a subgroup analysis revealed that the interaction of these two pollutants on nonaccidental mortality were greater in South China compared to elsewhere, and during the warm season compared to during the cold season. Our findings suggested that the simultaneous control of PM2.5 and O3 within the context of combined air pollution could significantly decrease the disease risk, especially in southern China and during the warm season.

Dataset Information

CO and CO₂ daily time series and time series of wind speed.

Publications

CO and CO<sub>2</sub> daily time series and time series of wind speed.

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