A dataset of spatial distribution of bioclimatic variables in China at 1 km resolution

WEI, Linfeng; Hu, Xing−Biao; Cheng, Qi; WU, Xingqi; Jian, Ni

doi:10.11922/11-6035.csd.2022.0003.zh

Cited by 6 publications

(3 citation statements)

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“…The climate data of each sampling site were extracted from the climate and bioclimate datasets of China (Cheng et al, 2022;Wei et al, 2022). China's climate dataset consists of three variables (monthly temperature, precipitation and sunshine percentage) that were averaged from long-term records from 1981 to 2010 at 2152 meteorological stations across China (China Meteorological Data Service Centre, http:// Y. Jin et al: TiP-Leaf trait dataset data.cma.cn, last access: 1 June 2021).…”

Section: Climate Datamentioning

confidence: 99%

TiP-Leaf: a dataset of leaf traits across vegetation types on the Tibetan Plateau

Yili

Wang

XIA

et al. 2023

Earth Syst. Sci. Data

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Abstract. Functional trait databases are emerging as a crucial tool for a wide range of ecological studies, including next-generation vegetation modelling across the world. However, few large-scale studies have been reported on plant traits in the Tibetan Plateau (TP), the cradle of East Asian flora and fauna with specific alpine ecosystems, and no report on plant trait databases could be found. In this work, an extensive dataset of 11 leaf functional traits (TiP-Leaf), mainly for herbs and shrubs and a few trees on the TP, was compiled through field surveys. The TiP-Leaf dataset, which was compiled from 336 sites distributed mainly on the plateau surface and the northern margin of the TP across alpine and temperate vegetation regions and sampled from 2018 to 2021, contained 1692 morphological trait measurements of leaf thickness, leaf fresh weight, leaf dry weight, leaf dry-matter content, leaf water content, leaf area, specific leaf area and leaf mass per area and 1645 chemical element trait measurements of leaf carbon, nitrogen and phosphorus contents. Thus, 468 species that belong to 184 genera and 51 families were obtained and measured. In addition to leaf trait measurements, the geographic coordinates, bioclimate variables, disturbance intensities and vegetation types of each site were also recorded. The dataset could provide solid data support to effectively quantify the modern ecological features of alpine ecosystems, thereby further evaluating the response of alpine ecosystems to climate change and human disturbances and improving the next-generation vegetation model. The dataset, which is available from the National Tibetan Plateau Data Center (TPDC; Jin et al., 2022a; https://doi.org/10.11888/Terre.tpdc.272516), can make a great contribution to the regional and global plant trait databases.

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Section: Climate Datamentioning

confidence: 99%

TiP-Leaf: a dataset of leaf traits across vegetation types on the Tibetan Plateau

Yili

Wang

XIA

et al. 2023

Earth Syst. Sci. Data

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“…The raw/processed patient data are not available to the public due to the Information Center of Beijing Municipal Health Commission's data policy. Air pollution data from the CHAP data set including PM 1 , PM 2.5 , PM 10 , and O 3 can be found online at https://weijing-rs.github.io/product.html (Wei & Li, 2019, 2022. Air pollutant data of NO 2 , SO 2 , and CO data, and meteorological data are available at https://quotsoft.net/air/ and http://data.cma.cn, which acquirement steps for downloading can be found in the supplementary "Text S3 in Supporting Information S1.…”

Section: Conflict Of Interestmentioning

confidence: 99%

Causal Associations of Air Pollution With Cardiovascular Disease and Respiratory Diseases Among Elder Diabetic Patients

Liu

et al. 2023

GeoHealth

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Extensive researches have linked air pollutants with cardiovascular disease (CVD) and respiratory diseases (RD), however, there is limited evidence on causal effects of air pollutants on morbidity of CVD or RD with comorbidities, particularly diabetes mellitus in elder patients. We included hospital admissions for CVD or RD among elder (≥65 years) diabetic patients between 2014 and 2019 in Beijing. A time‐stratified case‐crossover design based on negative‐control exposure was used to assess causal associations of short‐term exposure to air pollutants with CVD and RD among diabetic patients with the maximum lag of 7 days. A random forest regression model was used to calculate the contribution magnitude of air pollutants. A total of 493,046 hospital admissions were recorded. Per 10 μg/m3 uptick in PM1, PM2.5, PM10, SO2, NO2, O3, and 1 mg/m3 in CO was associated with 0.29 (0.05, 0.53), 0.14 (0.02, 0.26), 0.06 (0.00, 0.12), 0.36 (0.01, 0.70), 0.21 (0.02, 0.40), −0.08 (−0.25, 0.09), and 4.59 (0.56, 8.61) causal effect estimator for admission of CVD among diabetic patients, corresponding to 0.12 (0.05, 0.18), 0.09 (0.05, 0.13), 0.05, 0.23 (0.06, 0.41), 0.10 (0.02, 0.19), −0.04 (−0.06, −0.01), and 3.91(1.81, 6.01) causal effect estimator for RD among diabetic patients. The effect of gaseous pollutants was higher than particulate pollutants in random forest model. Short‐term exposure to air pollution was causally associated with increased admission of CVD and RD among elder diabetic patients. Gaseous pollutants had a greater contribution to CVD and RD among elder diabetic patients.

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“…CN05.1 data is available at Wu and Gao (2013a); CHAP data is available at Wei and Li (2019). ERA5 data is available at Hersbach et al (2017); CERES_EBAF_Ed4.1 data is available at NASA/LARC/SD/ASDC (2019); MIX data is available at .…”

Section: Conflict Of Interestmentioning

confidence: 99%

Anthropogenic Aerosols Weaken Land–Atmosphere Coupling Over North China

Wei,

Lu,

Song

et al. 2023

Geophysical Research Letters

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North China, characterized by its strong land–atmosphere coupling, also has a high concentration of atmospheric aerosols from anthropogenic emissions. However, the impact of aerosols on land–atmosphere coupling in this region remains partially unclear. Here, we use Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) experiments to show that the aerosol radiative effect weakens land–atmosphere coupling in North China. This weakening occurs across all five indexes used to measure different aspects of land–atmosphere coupling. Notably, the weakening is more pronounced for indexes that describe surface coupling compared to the index that characterizes the coupling between the planetary boundary layer (PBL) and clouds. The mechanisms underlying the aerosol influence can be primarily attributed to the reduction of land surface fluxes and their sensitivities to soil moisture, while the weakened entrainment of moisture at the upper boundary of the PBL may also contribute to the effects.

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A dataset of spatial distribution of bioclimatic variables in China at 1 km resolution

Cited by 6 publications

References 0 publications

TiP-Leaf: a dataset of leaf traits across vegetation types on the Tibetan Plateau

TiP-Leaf: a dataset of leaf traits across vegetation types on the Tibetan Plateau

Causal Associations of Air Pollution With Cardiovascular Disease and Respiratory Diseases Among Elder Diabetic Patients

Anthropogenic Aerosols Weaken Land–Atmosphere Coupling Over North China

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