Ta‐Chien Chan

5.0k citations
168 papers · 3.6k indexed · h-index 33

Impact in

Papers in

Ta‐Chien Chan

153 papers receiving 3.6k citations

Peers

Ta‐Chien Chan
Comparison fields: 5 of 177
  • Health, Toxicology and Mutagenesis 1.6k
  • Modeling and Simulation 199
  • Environmental Engineering 535
  • Speech and Hearing 254
  • Transportation 212
Replace Hualiang Lin with:
Hualiang Lin China
Xiuhua Guo China
John S. Ji China
TQ Thach Hong Kong
Cui Guo China
Hao Xiang China
Antônio Ponce de León Brazil
Emily Ying Yang Chan Hong Kong
Shao Lin United States
Marta Blangiardo United Kingdom
Ta‐Chien Chan relative to Hualiang Lin China Hualiang Lin's profile →
Citations per field
00.5×2.9×
Hualiang Lin · 1×
Citations per year

Countries citing papers authored by Ta‐Chien Chan

Since Specialization
Citations

This map shows the geographic impact of Ta‐Chien Chan'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 Ta‐Chien Chan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ta‐Chien Chan more than expected).

Fields of papers citing papers by Ta‐Chien Chan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ta‐Chien Chan. 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 Ta‐Chien Chan. The network helps show where Ta‐Chien Chan may publish in the future.

Co-authors

The 25 scholars most cited alongside Ta‐Chien Chan, 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 Ta‐Chien Chan Line = papers co-authored together Ta‐Chien Chan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20260
2 20250
3 20250
4 20240
5 20247
6 20240
7 20231
8 20233
9 20223
10 20222
11 202122
12 20213
13 20219
14 202119
15 20218
16 201839
17 201749
18 201727
19 201614
20 201634

About Ta‐Chien Chan

Ta‐Chien Chan is a scholar working on Modeling and Simulation, Health, Toxicology and Mutagenesis, Health, Speech and Hearing and Infectious Diseases, having authored 168 papers that have together received 3.6k indexed citations. Recurring topics across this work include Air Quality and Health Impacts (42 papers), Climate Change and Health Impacts (34 papers), COVID-19 epidemiological studies (24 papers), Health disparities and outcomes (18 papers), Influenza Virus Research Studies (17 papers), Noise Effects and Management (15 papers), Viral Infections and Vectors (13 papers) and Global Health Care Issues (13 papers). The work is most often cited by research in Health, Toxicology and Mutagenesis (1.6k citations), Modeling and Simulation (199 citations), Environmental Engineering (535 citations), Speech and Hearing (254 citations) and Transportation (212 citations). Ta‐Chien Chan has collaborated with scholars based in Taiwan, China and Hong Kong. Frequent co-authors include Xiang Qian Lao, Ly-yun Chang, Tony Tam, Changqing Lin, Alexis K.H. Lau, Eng‐Kiong Yeoh, Zilong Zhang, Cui Guo, Wun Kai Jiang and Yuan Chieh Chuang. Their work appears in journals such as PLoS ONE, International Journal of Environmental Research and Public Health, Scientific Reports, International Journal of Health Geographics and International Journal of Infectious Diseases.

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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