Xiaohui Bi

4.4k citations
86 papers · 3.4k · 1 hit paper · h-index 33

Impact in

Papers in

Xiaohui Bi

80 papers receiving 3.4k citations

Xiaohui Bi's Hit Papers

Revealing Drivers of Haze Pollution by Explainable Machine Learning 2022 · 191 citations
1910+1+2Years since publication50100150

Peers

Xiaohui Bi
Comparison fields: 5 of 119
  • Health, Toxicology and Mutagenesis 2.5k
  • Atmospheric Science 2.1k
  • Environmental Engineering 1.3k
  • Automotive Engineering 655
  • Pollution 363
Replace Jianhui Wu with:
Jianhui Wu China
Qili Dai China
Mauro Masiol Italy
Gary A. Norris United States
Evangelia Diapouli Greece
Mohammad H. Sowlat United States
Yongliang Ma China
Bin Han China
Baoshuang Liu China
Hongmei Xu China
Xiaohui Bi relative to Jianhui Wu China Jianhui Wu's profile →
Citations per field
00.5×1.5×
Jianhui Wu · 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 86 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016213
2 2017203
3
Revealing Drivers of Haze Pollution by Explainable Machine Learning
Hit paper breakdown →
2022191
4 2020191
5 2006168
6 2014166
7 2015146
8 2014128
9 2018127
10 2018118
11 201993
12 201192
13 201589
14 201677
15 202065
16 201056
17 202053
18 201551
19 201947
20 202043

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 86 papers that have together received 3.4k 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), Heavy metals in environment (4 papers), Climate Change and Health Impacts (4 papers) and COVID-19 impact on air quality (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 (1.3k citations), Automotive Engineering (655 citations) and Pollution (363 citations). Xiaohui Bi has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Yinchang Feng, Yufen Zhang, Jianhui Wu, Qili Dai, Baoshuang Liu, Danni Liang, Philip Karl Hopke, Zhimei Xiao, Congbo Song and Jiamei Yang. Their work appears in journals such as The Science of The Total Environment, Environmental Pollution, 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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