Muanfun Inerb

418 citations
12 papers · 287 · h-index 9

Impact in

Papers in

Muanfun Inerb

12 papers receiving 285 citations

Peers

Muanfun Inerb
Comparison fields: 5 of 52
  • Health, Toxicology and Mutagenesis 204
  • Atmospheric Science 146
  • Environmental Engineering 92
  • Automotive Engineering 64
  • Pollution 32
Replace Panwadee Suwattiga with:
Panwadee Suwattiga Thailand
Shuhan Liu China
Massimo Berico Italy
Katarzyna Maciejewska Poland
Milad Pirhadi United States
C. Dutta India
Christian Mark Salvador Sweden
Francesco Manarini Italy
Ehsan Soleimanian United States
Günter Baumbach Germany
Muanfun Inerb relative to Panwadee Suwattiga Thailand Panwadee Suwattiga's profile →
Citations per field
00.5×1.5×
Panwadee Suwattiga · 1×
Citations per year

Countries citing papers authored by Muanfun Inerb

Since Specialization
Citations

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

Fields of papers citing papers by Muanfun Inerb

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Muanfun Inerb. 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 Muanfun Inerb. The network helps show where Muanfun Inerb may publish in the future.

Co-authors

The 16 scholars most cited alongside Muanfun Inerb, 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 Muanfun Inerb Line = papers co-authored together Muanfun Inerb links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 202073
2 202358
3 202133
4 202232
5 202226
6 202124
7 202218
8 202310
9 20238
10 20213
11 20231
12 20221

About Muanfun Inerb

Muanfun Inerb is a scholar working on Health, Toxicology and Mutagenesis, Atmospheric Science, Global and Planetary Change, Environmental Engineering and Automotive Engineering, having authored 12 papers that have together received 287 indexed citations. Recurring topics across this work include Air Quality and Health Impacts (9 papers), Atmospheric chemistry and aerosols (8 papers), Air Quality Monitoring and Forecasting (3 papers), Atmospheric aerosols and clouds (3 papers), Vehicle emissions and performance (2 papers), COVID-19 impact on air quality (2 papers), Radioactivity and Radon Measurements (1 paper) and Toxic Organic Pollutants Impact (1 paper). The work is most often cited by research in Health, Toxicology and Mutagenesis (204 citations), Atmospheric Science (146 citations), Environmental Engineering (92 citations), Automotive Engineering (64 citations) and Pollution (32 citations). Muanfun Inerb has collaborated with scholars based in Thailand, Japan and Indonesia. Frequent co-authors include Mitsuhiko Hata, Worradorn Phairuang, Masami Furuuchi, Perapong Tekasakul, Surajit Tekasakul, Muhammad Amin, Santi Chuetor, Panwadee Suwattiga, Racha Dejchanchaiwong and Prasit Wangpakapattanawong. Their work appears in journals such as Atmosphere, Environmental Pollution, International Journal of Environmental Research and Public Health, Atmospheric Environment X and Heliyon.

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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