Xiaohui Bi

4.3k citations
87 papers · 3.3k · 1 hit paper · h-index 33

Impact in

Papers in

Xiaohui Bi

82 papers receiving 3.3k citations

Xiaohui Bi's Hit Papers

Revealing Drivers of Haze Pollution by Explainable Machine Learning 2022 · 175 citations
1750+1+2Years since publication50100150

Peers

Xiaohui Bi
Comparison fields: 5 of 115
  • Health, Toxicology and Mutagenesis 2.5k
  • Atmospheric Science 2.1k
  • Environmental Engineering 1.2k
  • Automotive Engineering 643
  • Pollution 351
Replace Mauro Masiol with:
Mauro Masiol Italy
Qili Dai China
María de Fátima Andrade Brazil
Begoña Artı́ñano Spain
María Cruz Minguillón Spain
Angeliki Karanasiou Spain
Baoshuang Liu China
Tarun Gupta India
Huang Zheng China
Cristina Reche Spain
Xiaohui Bi relative to Mauro Masiol Italy Mauro Masiol's profile →
Citations per field
00.5×1.5×
Mauro Masiol · 1×
Citations per year

Countries citing papers authored by Xiaohui Bi

Since Specialization
Citations

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

Fields of papers citing papers by Xiaohui Bi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 87 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016214
2 2017198
3 2020180
4
Revealing Drivers of Haze Pollution by Explainable Machine Learning
Hit paper breakdown →
2022175
5 2006170
6 2014159
7 2015143
8 2014123
9 2018122
10 2018116
11 201193
12 201989
13 201585
14 201675
15 202061
16 201056
17 202053
18 201551
19 201742
20 201941

About Xiaohui Bi

Xiaohui Bi is a scholar working on Health, Toxicology and Mutagenesis, Atmospheric Science, Environmental Engineering, Automotive Engineering and Global and Planetary Change, having authored 87 papers that have together received 3.3k indexed citations. Recurring topics across this work include Air Quality and Health Impacts (64 papers), Atmospheric chemistry and aerosols (59 papers), Air Quality Monitoring and Forecasting (28 papers), Vehicle emissions and performance (20 papers), Atmospheric aerosols and clouds (5 papers), Aeolian processes and effects (5 papers), Atmospheric Ozone and Climate (4 papers) and Climate Change and Health Impacts (4 papers). The work is most often cited by research in Health, Toxicology and Mutagenesis (2.5k citations), Atmospheric Science (2.1k citations), Environmental Engineering (1.2k citations), Automotive Engineering (643 citations) and Pollution (351 citations). Xiaohui Bi has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Yinchang Feng, Jianhui Wu, Yufen Zhang, Qili Dai, Baoshuang Liu, Danni Liang, Philip K. Hopke, Zhimei Xiao, Jiamei Yang and Congbo Song. Their work appears in journals such as Environmental Pollution, The Science of The Total Environment, Atmospheric Environment, Atmospheric Research and Aerosol and Air Quality Research.

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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