Peishi Gu

863 total citations
18 papers, 585 citations indexed

About

Peishi Gu is a scholar working on Health, Toxicology and Mutagenesis, Atmospheric Science and Environmental Engineering. According to data from OpenAlex, Peishi Gu has authored 18 papers receiving a total of 585 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Health, Toxicology and Mutagenesis, 13 papers in Atmospheric Science and 10 papers in Environmental Engineering. Recurrent topics in Peishi Gu's work include Air Quality and Health Impacts (18 papers), Atmospheric chemistry and aerosols (13 papers) and Air Quality Monitoring and Forecasting (10 papers). Peishi Gu is often cited by papers focused on Air Quality and Health Impacts (18 papers), Atmospheric chemistry and aerosols (13 papers) and Air Quality Monitoring and Forecasting (10 papers). Peishi Gu collaborates with scholars based in United States, Canada and Singapore. Peishi Gu's co-authors include Albert A. Presto, Zhongju Li, Allen L. Robinson, Joshua S. Apte, Ellis S. Robinson, Qing Ye, Timothy R. Dallmann, Rishabh U. Shah, Naomi Zimmerman and R. Subramanian and has published in prestigious journals such as Environmental Science & Technology, Environmental Health Perspectives and Atmospheric Environment.

In The Last Decade

Peishi Gu

18 papers receiving 581 citations

Peers

Peishi Gu

HTM

GPC

Ram Vedantham United States

Provat K. Saha United States

Rosalía Fernández-Patier Spain

Thiago Nogueira Brazil

Karoline K. Barkjohn United States

Susann Henschel France

Rishabh U. Shah United States

Hilary R. Hafner United States

Dennis Herod Canada

Ricardo Morales Colombia

Ram Vedantham United States

Peishi Gu

547 ×1.1

HTM

262 ×0.9

302 ×1.0

216 ×1.6

97 ×0.7

GPC

Citations per year, relative to Peishi Gu Peishi Gu (= 1×) peers Ram Vedantham

Countries citing papers authored by Peishi Gu

Since Specialization

Citations

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

Fields of papers citing papers by Peishi Gu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peishi Gu

This figure shows the co-authorship network connecting the top 25 collaborators of Peishi Gu. A scholar is included among the top collaborators of Peishi Gu 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 Peishi Gu. Peishi Gu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Chen, Yanju, Peishi Gu, Xiaochi Zhou, et al.. (2022). A new mobile monitoring approach to characterize community-scale air pollution patterns and identify local high pollution zones. Atmospheric Environment. 272. 118936–118936. 37 indexed citations

Shah, Rishabh U., Ellis S. Robinson, Peishi Gu, et al.. (2020). Socio-economic disparities in exposure to urban restaurant emissions are larger than for traffic. Environmental Research Letters. 15(11). 114039–114039. 23 indexed citations

Ye, Qing, Zhongju Li, Peishi Gu, et al.. (2020). Moving beyond Fine Particle Mass: High-Spatial Resolution Exposure to Source-Resolved Atmospheric Particle Number and Chemical Mixing State. Environmental Health Perspectives. 128(1). 17009–17009. 21 indexed citations

Robinson, Ellis S., Rishabh U. Shah, Kyle P. Messier, et al.. (2019). Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling. Environmental Science & Technology. 53(15). 8925–8937. 36 indexed citations

Li, Zhongju, Peishi Gu, Qing Ye, et al.. (2019). Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors. Atmospheric Environment X. 2. 100012–100012. 83 indexed citations

Zimmerman, Naomi, Zhongju Li, Aja Ellis, et al.. (2019). Improving Correlations between Land Use and Air Pollutant Concentrations Using Wavelet Analysis: Insights from a Low-cost Sensor Network. Aerosol and Air Quality Research. 20(2). 314–328. 26 indexed citations

Gu, Peishi, Timothy R. Dallmann, Zhongju Li, Yi Tan, & Albert A. Presto. (2019). Quantifying Urban Spatial Variations of Anthropogenic VOC Concentrations and Source Contributions with a Mobile Sampling Platform. International Journal of Environmental Research and Public Health. 16(9). 1632–1632. 10 indexed citations

Ye, Qing, Peishi Gu, Zhongju Li, et al.. (2018). Spatial Variability of Sources and Mixing State of Atmospheric Particles in a Metropolitan Area. Environmental Science & Technology. 52(12). 6807–6815. 51 indexed citations

Robinson, Ellis S., Peishi Gu, Qing Ye, et al.. (2018). Restaurant Impacts on Outdoor Air Quality: Elevated Organic Aerosol Mass from Restaurant Cooking with Neighborhood-Scale Plume Extents. Environmental Science & Technology. 52(16). 9285–9294. 70 indexed citations

10.

Shah, Rishabh U., Ellis S. Robinson, Peishi Gu, et al.. (2018). High-spatial-resolution mapping and source apportionment of aerosol composition in Oakland, California, using mobile aerosol mass spectrometry. Atmospheric chemistry and physics. 18(22). 16325–16344. 47 indexed citations

11.

Shah, Rishabh U., Ellis S. Robinson, Peishi Gu, et al.. (2018). High spatial resolution mapping of aerosol composition and sourcesin Oakland, California using mobile aerosol mass spectrometry. Biogeosciences (European Geosciences Union). 3 indexed citations

12.

Zimmerman, Naomi, Zhongju Li, Aja Ellis, et al.. (2018). Integrating Spatiotemporal Variability and Modifiable Factors into Air Pollution Estimates. ISEE Conference Abstracts. 2018(1). 2 indexed citations

13.

Gu, Peishi, Zhongju Li, Qing Ye, et al.. (2018). Intra-city variability of PM exposure is driven by carbonaceous sources and correlated with land use variables. Environmental Science & Technology. 52(20). 11545–11554. 40 indexed citations

14.

Tasoglou, Antonios, et al.. (2017). Cloud condensation nuclei activity and hygroscopicity of fresh and aged cooking organic aerosol. Atmospheric Environment. 176. 103–109. 15 indexed citations

15.

Li, Zhongju, Timothy R. Dallmann, Xiang Li, Peishi Gu, & Albert A. Presto. (2017). Urban Organic Aerosol Exposure: Spatial Variations in Composition and Source Impacts. Environmental Science & Technology. 52(2). 415–426. 40 indexed citations

16.

Presto, Albert A., Timothy R. Dallmann, Peishi Gu, & Unnati Rao. (2016). BTEX exposures in an area impacted by industrial and mobile sources: Source attribution and impact of averaging time. Journal of the Air & Waste Management Association. 66(4). 387–401. 19 indexed citations

17.

Li, Xiang, et al.. (2016). Estimating ambient particulate organic carbon concentrations and partitioning using thermal optical measurements and the volatility basis set. Aerosol Science and Technology. 50(6). 638–651. 18 indexed citations

18.

Li, Zhongju, Timothy R. Dallmann, Peishi Gu, & Albert A. Presto. (2016). Application of mobile sampling to investigate spatial variation in fine particle composition. Atmospheric Environment. 142. 71–82. 44 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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