Congbo Song

4.8k citations
47 papers · 3.4k indexed · 5 hit papers · h-index 24

Impact in

Papers in

Congbo Song

46 papers receiving 3.4k citations

Hit Papers

Revealing Drivers of Haze Pollution by Explainable Machine Learning 2022 · 156 citations
1562017202620202023100200300400500

Peers

Congbo Song
Comparison fields: 5 of 116
  • Health, Toxicology and Mutagenesis 2.7k
  • Environmental Engineering 1.5k
  • Atmospheric Science 1.8k
  • Automotive Engineering 594
  • Global and Planetary Change 887
Replace Qili Dai with:
Qili Dai China
Fumo Yang China
Lin Wu China
Mauro Masiol Italy
Baoshuang Liu China
Xiaohui Bi China
María de Fátima Andrade Brazil
Monica Crippa Italy
Liuju Zhong China
Congbo Song relative to Qili Dai China Qili Dai's profile →
Citations per field
00.5×1.5×
Qili Dai · 1×
Citations per year

Countries citing papers authored by Congbo Song

Since Specialization
Citations

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

Fields of papers citing papers by Congbo Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20243
3 20241
4 20238
5 202316
6 20235
7
Revealing Drivers of Haze Pollution by Explainable Machine Learning
Hit paper breakdown →
2022156
8 202232
9 202118
10 20216
11 202112
12 202137
13
Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns
Hit paper breakdown →
2021275
14 202161
15 202117
16 202090
17 202072
18 202079
19 202056
20
Air pollution in China: Status and spatiotemporal variations
Hit paper breakdown →
2017538

About Congbo Song

Congbo Song is a scholar working on Health, Toxicology and Mutagenesis, Atmospheric Science, Environmental Engineering, Automotive Engineering and Global and Planetary Change, having authored 47 papers that have together received 3.4k indexed citations. Recurring topics across this work include Air Quality and Health Impacts (35 papers), Atmospheric chemistry and aerosols (33 papers), Air Quality Monitoring and Forecasting (18 papers), Vehicle emissions and performance (12 papers), COVID-19 impact on air quality (8 papers), Climate Change and Health Impacts (6 papers), Atmospheric Ozone and Climate (5 papers) and Atmospheric aerosols and clouds (4 papers). The work is most often cited by research in Health, Toxicology and Mutagenesis (2.7k citations), Environmental Engineering (1.5k citations), Atmospheric Science (1.8k citations), Automotive Engineering (594 citations) and Global and Planetary Change (887 citations). Congbo Song has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Hongjun Mao, Jianjun He, Lin Wu, Qili Dai, Baoshuang Liu, Ruipeng Li, Taosheng Jin, Yinchang Feng, Yufen Zhang and Zongbo Shi. Their work appears in journals such as Environmental Pollution, Atmospheric Environment, Atmospheric chemistry and physics, Geophysical Research Letters and Chemosphere.

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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