Sangar Khan

742 total citations
44 papers, 512 citations indexed

About

Sangar Khan is a scholar working on Industrial and Manufacturing Engineering, Environmental Chemistry and Pollution. According to data from OpenAlex, Sangar Khan has authored 44 papers receiving a total of 512 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Industrial and Manufacturing Engineering, 19 papers in Environmental Chemistry and 12 papers in Pollution. Recurrent topics in Sangar Khan's work include Soil and Water Nutrient Dynamics (17 papers), Phosphorus and nutrient management (13 papers) and Soil Carbon and Nitrogen Dynamics (8 papers). Sangar Khan is often cited by papers focused on Soil and Water Nutrient Dynamics (17 papers), Phosphorus and nutrient management (13 papers) and Soil Carbon and Nitrogen Dynamics (8 papers). Sangar Khan collaborates with scholars based in China, Egypt and Australia. Sangar Khan's co-authors include Xinqiang Liang, Fayong Li, Kamel Mohamed Eltohamy, Junwei Jin, Chunlong Liu, Shuang He, Paul J. Milham, Christophe Niyungeko, Miaomiao He and Naicheng Wu and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and Water Research.

In The Last Decade

Sangar Khan

39 papers receiving 507 citations

Peers

Sangar Khan

IME

WST

POLLU

Kamel Mohamed Eltohamy China

Haiying Lu China

Xiaoyan Yang China

Bingqian Fan China

Ewa Szara Poland

Gourango Kar Canada

Worachart Wisawapipat Thailand

Jiuyu Li China

Miaomiao He China

Yuki Audette Canada

Kamel Mohamed Eltohamy China

Sangar Khan

141 ×0.7

149 ×0.9

IME

137 ×0.9

95 ×0.8

WST

72 ×0.9

POLLU

Citations per year, relative to Sangar Khan Sangar Khan (= 1×) peers Kamel Mohamed Eltohamy

Countries citing papers authored by Sangar Khan

Since Specialization

Citations

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

Fields of papers citing papers by Sangar Khan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sangar Khan

This figure shows the co-authorship network connecting the top 25 collaborators of Sangar Khan. A scholar is included among the top collaborators of Sangar Khan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sangar Khan. Sangar Khan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Wang, Yuting, Sangar Khan, Xinxin Qi, et al.. (2025). Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models. Ecological Informatics. 85. 102990–102990. 3 indexed citations

Zheng, Yuan, et al.. (2025). Small hydropower plants alter the assembly mechanisms and environmental drivers of macroinvertebrate communities in different sections. Global Ecology and Conservation. 62. e03718–e03718. 1 indexed citations

Qi, Xinxin, et al.. (2025). Impact of small hydropower plants on microbial community structure and functions in benthic biofilms. Journal of Environmental Management. 392. 126869–126869.

Ullah, Habib, Sangar Khan, Xiaoying Zhu, et al.. (2025). Machine learning-aided biochar design for the adsorptive removal of emerging inorganic pollutants in water. Separation and Purification Technology. 362. 131421–131421. 9 indexed citations

Wang, Yuting, Sangar Khan, Paul J. Milham, et al.. (2025). Sediment-associated ammonium-N increased benthic chlorophyll-a in streams: Evidence from SHAP integrated machine learning analysis. Ecological Indicators. 178. 113933–113933.

Dalu, Tatenda, et al.. (2025). Meta-analysis reveals temperature increase exacerbates microplastic toxicity in freshwater invertebrates. Environmental Sciences Europe. 37(1).

Eltohamy, Kamel Mohamed, et al.. (2025). Machine learning-assisted model for predicting biochar efficiency in colloidal phosphorus immobilisation in agricultural soils. Biochar. 7(1). 1 indexed citations

Qi, Xinxin, Yixia Wang, Yaochun Wang, et al.. (2024). Uncovering patterns and drivers of macroinvertebrate ecological uniqueness for conservation planning in riverine tributaries of Thousand Islands Lake, China. Ecological Indicators. 167. 112652–112652. 2 indexed citations

Khan, Sangar, Yasir Hamid, Abdul Ghaffar Khan, et al.. (2024). Seed nano-priming with multiple nanoparticles enhanced the growth parameters of lettuce and mitigated cadmium (Cd) bio-toxicity: An advanced technique for remediation of Cd contaminated environments. Environmental Pollution. 344. 123300–123300. 10 indexed citations

10.

Qi, Xinxin, Sangar Khan, Xiaodong Qu, et al.. (2024). Comparison of different macroinvertebrates bioassessment indices in a large near‐natural watershed under the context of metacommunity theory. Ecology and Evolution. 14(2). e10896–e10896. 8 indexed citations

11.

Wu, Naicheng, Xinxin Qi, Yixia Wang, et al.. (2024). Different facets of alpha and beta diversity of benthic diatoms along stream watercourse in a large near‐natural catchment. Ecology and Evolution. 14(6). e11577–e11577. 7 indexed citations

12.

Khan, Sangar, Hongli Mu, Meixiang Gao, et al.. (2024). Predicting the governing factors for the release of colloidal phosphorus using machine learning. Chemosphere. 362. 142699–142699. 9 indexed citations

13.

Eltohamy, Kamel Mohamed, Sangar Khan, Shuang He, et al.. (2023). Prediction of nano, fine, and medium colloidal phosphorus in agricultural soils with machine learning. Environmental Research. 220. 115222–115222. 14 indexed citations

14.

Ullah, Habib, Baoliang Chen, Audil Rashid, et al.. (2023). A critical review on selenium removal capacity from water using emerging non-conventional biosorbents. Environmental Pollution. 339. 122644–122644. 8 indexed citations

15.

Ullah, Habib, Sangar Khan, Baoliang Chen, et al.. (2023). Machine learning approach to predict adsorption capacity of Fe-modified biochar for selenium. SHILAP Revista de lepidopterología. 2(1). 27 indexed citations

16.

Eltohamy, Kamel Mohamed, Paul J. Milham, Mostafa Gouda, et al.. (2023). Size and composition of colloidal phosphorus across agricultural soils amended with biochar, manure and biogas slurry. Carbon Research. 2(1). 20 indexed citations

17.

Jin, Junwei, Sangar Khan, Kamel Mohamed Eltohamy, et al.. (2023). Biochar–coupled organic fertilizer reduced soil water-dispersible colloidal phosphorus contents in agricultural fields. Chemosphere. 333. 138963–138963. 11 indexed citations

18.

Hashmi, Muhammad Laeeq ur Rehman, Yasir Hamid, Muhammad Usman, et al.. (2023). Assessing the effectiveness of 3, 4-dimethylpyrazole phosphate (DMPP) inhibitor in mitigating N2O emissions from contrasting Cd-contaminated soils. The Science of The Total Environment. 912. 169105–169105. 1 indexed citations

19.

Khan, Sangar, Paul J. Milham, Kamel Mohamed Eltohamy, et al.. (2021). Pteris vittata plantation decrease colloidal phosphorus contents by reducing degree of phosphorus saturation in manure amended soils. Journal of Environmental Management. 304. 114214–114214. 20 indexed citations

20.

Niyungeko, Christophe, Xinqiang Liang, Shengdao Shan, et al.. (2019). Synergistic effects of anionic polyacrylamide and gypsum to control phosphorus losses from biogas slurry applied soils. Chemosphere. 234. 953–961. 14 indexed citations

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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