Automated Author Profile

Cui, Qiang

Current S-Index

30.6

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.5

Average Dataset Index per dataset

Total Datasets

66

Total datasets for this author

Average FAIR Score

74.3%

Average FAIR Score per dataset

Total Citations

19

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Data for "<b>Point source capture with carbon storage outperforms direct air capture for aviation climate and health benefits</b>"

Dataset S4 shows the concentrations, effective radiative forcing, and temperature differences at each altitude from 2025 to 2050. Dataset S5 presents the mortality rates for various diseases by airport, age group, and gender from 2025 to 2050. Dataset S6 summarizes the total costs and benefits under each scenario from 2025 to 2050.

Authors

  • Cui, Qiang
0 Citations0 Mentions85% FAIR0.3 Dataset Index
10.6084/m9.figshare.29590619.v12025

Data for "<b>Point source capture with carbon storage outperforms direct air capture for aviation climate and health benefits</b>"

Dataset S4 shows the concentrations, effective radiative forcing, and temperature differences at each altitude from 2025 to 2050. Dataset S5 presents the mortality rates for various diseases by airport, age group, and gender from 2025 to 2050. Dataset S6 summarizes the total costs and benefits under each scenario from 2025 to 2050.

Authors

  • Cui, Qiang
0 Citations0 Mentions85% FAIR0.3 Dataset Index
10.6084/m9.figshare.295906192025

Data for "Can NO<sub>x</sub> be reduced independently? Exploring the expandable reduction ratios of different bad outputs in airline environmental regulation"

The data of the airlines are shown in the table

Authors

  • Cui, Qiang
0 Citations0 Mentions85% FAIR0.3 Dataset Index
10.6084/m9.figshare.29147837.v12025

Data for "Can NO<sub>x</sub> be reduced independently? Exploring the expandable reduction ratios of different bad outputs in airline environmental regulation"

The data of the airlines are shown in the table

Authors

  • Cui, Qiang
0 Citations0 Mentions85% FAIR0.3 Dataset Index
10.6084/m9.figshare.291478372025

Data for "Contrasting gender and age vulnerability to aircraft noise and emissions exposure in global airports"

The results of noise DALYs and emission public health are available in Data S1-S2

Authors

  • Cui, Qiang
0 Citations0 Mentions85% FAIR0.3 Dataset Index
10.6084/m9.figshare.29040659.v12025

Data for "Contrasting gender and age vulnerability to aircraft noise and emissions exposure in global airports"

The results of noise DALYs and emission public health are available in Data S1-S2

Authors

  • Cui, Qiang
0 Citations0 Mentions85% FAIR0.3 Dataset Index
10.6084/m9.figshare.290406592025

Data for "Full deployment of SAF by 2030 could significantly reduce aircraft’s public health impact in China"

The excess deaths of different diseases at different ages due to CO, NO2 and SO2 emissions from aviation under different scenarios are available in Data S1-1--Data S3-7.

Authors

  • Cui, Qiang
0 Citations0 Mentions85% FAIR0.3 Dataset Index
10.6084/m9.figshare.28522337.v12025

Data for "Full deployment of SAF by 2030 could significantly reduce aircraft’s public health impact in China"

The excess deaths of different diseases at different ages due to CO, NO2 and SO2 emissions from aviation under different scenarios are available in Data S1-1--Data S3-7.

Authors

  • Cui, Qiang
0 Citations0 Mentions85% FAIR0.3 Dataset Index
10.6084/m9.figshare.285223372025

<b>Data for "Path analysis of using hydrogen energy to reduce greenhouse gas emissions in global aviation"</b>

Data S1 shows the emissions of global aviation from 2015 to 2022. Data S2 shows annual CO2 emissions from 2023 to 2080; Data S3 shows annual CH4 emissions from 2023 to 2080; Data S4 shows annual N2O emissions from 2023 to 2080; Data S5 shows annual CO2-equivalent emissions from 2023 to 2080.

Authors

  • Cui, Qiang
0 Citations0 Mentions85% FAIR0.1 Dataset Index
10.6084/m9.figshare.26240597.v12024

Data for "24-hour average PM2.5 concentration caused by aircraft in Chinese airports from Jan. 2006 to Dec. 2023"

Our computed results have been meticulously documented in two files. The first, "Supplementary Table 1-Monthly PM2.5 flow.xlsx," meticulously records the PM2.5 flow induced by aircraft at Chinese airports during the specified timeframe. The first column is the year and the second column of this document enumerates the corresponding three-character codes for each Chinese airport, as defined by the International Air Transport Association (IATA). The subsequent columns, spanning from the second to the thirteenth, encapsulate each airport's monthly PM2.5 flow data. Similarly, the second file, titled "Supplementary Table 2-Monthly PM2.5 concentration.xlsx," is a comprehensive summary of PM2.5 concentration values resulting from aircraft activities at Chinese airports within the mentioned timeframe. Mirroring the structure of the first document, the primary column designates the three-character codes for each airport, while the ensuing columns (second to thirteenth) meticulously outline the monthly PM2.5 concentration data produced by each airport.

Authors

  • Cui, Qiang
1 Citation0 Mentions85% FAIR0.7 Dataset Index
10.6084/m9.figshare.253212972024