Yannic Lops

1.2k total citations
26 papers, 841 citations indexed

About

Yannic Lops is a scholar working on Environmental Engineering, Health, Toxicology and Mutagenesis and Atmospheric Science. According to data from OpenAlex, Yannic Lops has authored 26 papers receiving a total of 841 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Environmental Engineering, 17 papers in Health, Toxicology and Mutagenesis and 15 papers in Atmospheric Science. Recurrent topics in Yannic Lops's work include Air Quality Monitoring and Forecasting (19 papers), Air Quality and Health Impacts (17 papers) and Atmospheric chemistry and aerosols (10 papers). Yannic Lops is often cited by papers focused on Air Quality Monitoring and Forecasting (19 papers), Air Quality and Health Impacts (17 papers) and Atmospheric chemistry and aerosols (10 papers). Yannic Lops collaborates with scholars based in United States and Iran. Yannic Lops's co-authors include Yunsoo Choi, Alqamah Sayeed, Ebrahim Eslami, Masoud Ghahremanloo, Jia Jung, Seyedali Mousavinezhad, Ahmed Khan Salman, Anirban Roy, Arman Pouyaei and Bijan Yeganeh and has published in prestigious journals such as The Science of The Total Environment, Geophysical Research Letters and Environmental Pollution.

In The Last Decade

Yannic Lops

24 papers receiving 826 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yannic Lops United States 15 591 494 415 284 72 26 841
Xu Feng China 17 328 0.6× 434 0.9× 462 1.1× 446 1.6× 93 1.3× 52 920
Suli Zhao China 12 419 0.7× 522 1.1× 245 0.6× 181 0.6× 141 2.0× 18 804
Kirti Soni India 14 247 0.4× 192 0.4× 416 1.0× 429 1.5× 34 0.5× 45 762
Shuaiyi Shi China 16 261 0.4× 405 0.8× 425 1.0× 310 1.1× 61 0.8× 46 805
Alqamah Sayeed United States 14 491 0.8× 376 0.8× 340 0.8× 163 0.6× 73 1.0× 22 657
Akshara Kaginalkar India 12 294 0.5× 251 0.5× 476 1.1× 453 1.6× 59 0.8× 27 787
Fangwen Bao China 13 216 0.4× 348 0.7× 419 1.0× 258 0.9× 48 0.7× 27 698
Siqi Ye China 12 372 0.6× 604 1.2× 603 1.5× 193 0.7× 153 2.1× 19 858
Zhanyong Wang China 21 760 1.3× 906 1.8× 382 0.9× 219 0.8× 416 5.8× 51 1.2k
Armando Pelliccioni Italy 14 269 0.5× 309 0.6× 178 0.4× 104 0.4× 90 1.3× 54 609

Countries citing papers authored by Yannic Lops

Since Specialization
Citations

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

Fields of papers citing papers by Yannic Lops

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yannic Lops

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

All Works

20 of 20 papers shown
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Ghahremanloo, Masoud, Yunsoo Choi, & Yannic Lops. (2023). Deep learning mapping of surface MDA8 ozone: The impact of predictor variables on ozone levels over the contiguous United States. Environmental Pollution. 326. 121508–121508. 20 indexed citations
4.
Ghahremanloo, Masoud, Yunsoo Choi, & Yannic Lops. (2023). Deep Learning Mapping of Surface Mda8 Ozone: The Impact of Predictor Variables on Ozone Levels Over the Contiguous United States. SSRN Electronic Journal. 1 indexed citations
5.
Ghahremanloo, Masoud, Yannic Lops, Yunsoo Choi, Seyedali Mousavinezhad, & Jia Jung. (2023). A Coupled Deep Learning Model for Estimating Surface NO2 Levels From Remote Sensing Data: 15‐Year Study Over the Contiguous United States. Journal of Geophysical Research Atmospheres. 128(2). 12 indexed citations
6.
Lops, Yannic, et al.. (2023). Development of Deep Convolutional Neural Network Ensemble Models for 36-Month ENSO Forecasts. Asia-Pacific Journal of Atmospheric Sciences. 59(5). 597–605. 6 indexed citations
7.
Lops, Yannic, Masoud Ghahremanloo, Arman Pouyaei, et al.. (2023). Spatiotemporal estimation of TROPOMI NO2 column with depthwise partial convolutional neural network. Neural Computing and Applications. 35(21). 15667–15678. 6 indexed citations
8.
Salman, Ahmed Khan, Arman Pouyaei, Yunsoo Choi, Yannic Lops, & Alqamah Sayeed. (2022). Deep learning solver for solving advection–diffusion equation in comparison to finite difference methods. Communications in Nonlinear Science and Numerical Simulation. 115. 106780–106780. 10 indexed citations
9.
Ghahremanloo, Masoud, et al.. (2022). Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter. Environmental Pollution. 310. 119863–119863. 34 indexed citations
10.
Ghahremanloo, Masoud, Yannic Lops, Yunsoo Choi, et al.. (2022). A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach. Atmospheric Environment. 272. 118944–118944. 35 indexed citations
11.
Sayeed, Alqamah, Ebrahim Eslami, Yannic Lops, & Yunsoo Choi. (2022). CMAQ-CNN: A new-generation of post-processing techniques for chemical transport models using deep neural networks. Atmospheric Environment. 273. 118961–118961. 28 indexed citations
12.
Sayeed, Alqamah, Yunsoo Choi, Arman Pouyaei, et al.. (2022). CNN-based model for the spatial imputation (CMSI version 1.0) of in-situ ozone and PM2.5 measurements. Atmospheric Environment. 289. 119348–119348. 19 indexed citations
13.
Lops, Yannic, Arman Pouyaei, Yunsoo Choi, et al.. (2021). Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data. Geophysical Research Letters. 48(15). 33 indexed citations
14.
Sayeed, Alqamah, Yunsoo Choi, Jia Jung, et al.. (2021). A Deep Convolutional Neural Network Model for Improving WRF Simulations. IEEE Transactions on Neural Networks and Learning Systems. 34(2). 750–760. 45 indexed citations
15.
Choi, Yunsoo, et al.. (2021). Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms. Neural Computing and Applications. 33(22). 15073–15089. 44 indexed citations
16.
Ghahremanloo, Masoud, Yannic Lops, Yunsoo Choi, & Seyedali Mousavinezhad. (2020). Impact of the COVID-19 outbreak on air pollution levels in East Asia. The Science of The Total Environment. 754. 142226–142226. 128 indexed citations
17.
Eslami, Ebrahim, Yunsoo Choi, Yannic Lops, Alqamah Sayeed, & Ahmed Khan Salman. (2020). Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system. Geoscientific model development. 13(12). 6237–6251. 11 indexed citations
18.
Choi, Yong‐Sang, Ebrahim Eslami, Alqamah Sayeed, & Yannic Lops. (2019). CAMQ-AI: A computationally efficient deep learning model to improve CMAQ performance over the United States. AGU Fall Meeting Abstracts. 2019. 1 indexed citations
19.
Sayeed, Alqamah, Yunsoo Choi, Ebrahim Eslami, et al.. (2019). Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance. Neural Networks. 121. 396–408. 121 indexed citations
20.
Eslami, Ebrahim, Yunsoo Choi, Yannic Lops, & Alqamah Sayeed. (2019). A real-time hourly ozone prediction system using deep convolutional neural network. Neural Computing and Applications. 32(13). 8783–8797. 81 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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