Ling Jin

50 papers receiving 908 citations

Peers

Ling Jin
Comparison fields: 5 of 118
  • Transportation 116
  • Industrial and Manufacturing Engineering 121
  • Automotive Engineering 147
  • Health, Toxicology and Mutagenesis 150
  • Building and Construction 141
Replace J. S. Pandey with:
J. S. Pandey India
Yoshikuni Yoshida Japan
Shan Guo China
Pi‐Cheng Chen Taiwan
G. Banias Greece
Katherine von Stackelberg United States
Viktor Pocajt Serbia
Davor Antanasijević Serbia
Xueyan Li China
Ling Jin relative to J. S. Pandey India J. S. Pandey's profile →
Citations per field
00.5×10×14×
J. S. Pandey · 1×
Citations per year

Countries citing papers authored by Ling Jin

Since Specialization
Citations

This map shows the geographic impact of Ling Jin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ling Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ling Jin more than expected).

Fields of papers citing papers by Ling Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ling Jin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ling Jin. The network helps show where Ling Jin may publish in the future.

Co-authors

The 25 scholars most cited alongside Ling Jin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ling Jin Line = papers co-authored together Ling Jin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020133
2 2019119
3 201966
4 201862
5 201757
6 200832
7
Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data
201732
8 201731
9 202130
10 201429
11 202126
12 201126
13 202124
14 201821
15
Seasonal versus Episodic Performance Evaluation for an Eulerian Photochemical\nAir Quality Model
201018
16 201817
17 201317
18 202016
19 200816
20 201814

About Ling Jin

Ling Jin is a scholar working on Atmospheric Science, Health, Toxicology and Mutagenesis, Transportation, Environmental Engineering and Global and Planetary Change, having authored 54 papers that have together received 929 indexed citations. Recurring topics across this work include Atmospheric chemistry and aerosols (15 papers), Air Quality and Health Impacts (10 papers), Urban Transport and Accessibility (8 papers), Transportation Planning and Optimization (7 papers), Atmospheric Ozone and Climate (6 papers), Air Quality Monitoring and Forecasting (6 papers), Smart Grid Energy Management (5 papers) and Energy, Environment, and Transportation Policies (5 papers). The work is most often cited by research in Transportation (116 citations), Industrial and Manufacturing Engineering (121 citations), Automotive Engineering (147 citations), Health, Toxicology and Mutagenesis (150 citations) and Building and Construction (141 citations). Ling Jin has collaborated with scholars based in United States, China and France. Frequent co-authors include Nancy J. Brown, Corinne D. Scown, C. Anna Spurlock, Hanna Breunig, Annika Todd, Chelsea V. Preble, Andrew Satchwell, Thomas W. Kirchstetter, Sarah Smith and Robert A. Harley. Their work appears in journals such as Environmental Science & Technology, Journal of Geophysical Research Atmospheres, Atmospheric Environment, Transportation Research Part D Transport and Environment and Journal of Geophysical Research Atmospheres.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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