Spencer Snowling

744 total citations
30 papers, 525 citations indexed

About

Spencer Snowling is a scholar working on Pollution, Environmental Engineering and Water Science and Technology. According to data from OpenAlex, Spencer Snowling has authored 30 papers receiving a total of 525 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Pollution, 6 papers in Environmental Engineering and 6 papers in Water Science and Technology. Recurrent topics in Spencer Snowling's work include Wastewater Treatment and Nitrogen Removal (10 papers), Hydrological Forecasting Using AI (5 papers) and Water Systems and Optimization (3 papers). Spencer Snowling is often cited by papers focused on Wastewater Treatment and Nitrogen Removal (10 papers), Hydrological Forecasting Using AI (5 papers) and Water Systems and Optimization (3 papers). Spencer Snowling collaborates with scholars based in Canada, United States and China. Spencer Snowling's co-authors include Zhong Li, James R. Kramer, Pengxiao Zhou, Qianqian Zhang, Brian W. Baetz, Prashant Mhaskar, Debanjan Ghosh, Rajeev K. Goel, Ahmad Siam and Wael El‐Dakhakhni and has published in prestigious journals such as Industrial & Engineering Chemistry Research, Sustainability and Ecological Modelling.

In The Last Decade

Spencer Snowling

26 papers receiving 507 citations

Peers

Spencer Snowling

WST

IME

POLLU

CSE

David J. Dürrenmatt Switzerland

C. Díaz Muñiz Spain

Sina Borzooei Italy

Sunil Kumar Singal India

Mona M. Galal Egypt

Bartosz Kaźmierczak Poland

Saleh M. Al-Alawi Oman

Cong Dong Canada

Maged Mahmoud Hamed United States

David J. Dürrenmatt Switzerland

Spencer Snowling

172 ×0.7

WST

136 ×0.9

81 ×0.8

IME

47 ×0.5

POLLU

57 ×0.9

CSE

Citations per year, relative to Spencer Snowling Spencer Snowling (= 1×) peers David J. Dürrenmatt

Countries citing papers authored by Spencer Snowling

Since Specialization

Citations

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

Fields of papers citing papers by Spencer Snowling

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Spencer Snowling

This figure shows the co-authorship network connecting the top 25 collaborators of Spencer Snowling. A scholar is included among the top collaborators of Spencer Snowling 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 Spencer Snowling. Spencer Snowling 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

Zhou, Pengxiao, et al.. (2023). Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies. Frontiers of Environmental Science & Engineering. 17(12). 8 indexed citations

Zhou, Pengxiao, Zhong Li, Spencer Snowling, Rajeev Goel, & Qianqian Zhang. (2022). Multi-step ahead prediction of hourly influent characteristics for wastewater treatment plants: a case study from North America. Environmental Monitoring and Assessment. 194(5). 389–389. 14 indexed citations

Torfs, Elena, Saba Daneshgar, John B. Copp, et al.. (2022). The transition of WRRF models to digital twin applications. Water Science & Technology. 85(10). 2840–2853. 32 indexed citations

Goel, Rajeev, et al.. (2021). Modeling the anaerobic digestion of wastewater sludge under sulfate‐rich conditions. Water Environment Research. 93(10). 2084–2096. 5 indexed citations

Ghosh, Debanjan, et al.. (2019). Hybrid Modeling Approach Integrating First-Principles Models with Subspace Identification. Industrial & Engineering Chemistry Research. 58(30). 13533–13543. 61 indexed citations

Li, Zhong, et al.. (2019). Influent Forecasting for Wastewater Treatment Plants in North America. Sustainability. 11(6). 1764–1764. 49 indexed citations

Zhou, Pengxiao, et al.. (2019). A random forest model for inflow prediction at wastewater treatment plants. Stochastic Environmental Research and Risk Assessment. 33(10). 1781–1792. 94 indexed citations

Zhou, Peilin, et al.. (2019). Short-Term Wastewater Influent Prediction Based on Random Forests and Multi-Layer Perceptron. 10 indexed citations

Shoener, Brian D., Fabrice Béline, Olivier Bernard, et al.. (2018). Microalgae and cyanobacteria modeling in water resource recovery facilities: A critical review. Water Research X. 2. 100024–100024. 65 indexed citations

10.

Houweling, Dwight, et al.. (2018). Nitrifying below the “Washout” SRT: Experimental and Modelling Results for a Hybrid MABR / Activated Sludge Process. Proceedings of the Water Environment Federation. 2018(16). 1250–1263. 9 indexed citations

11.

Martin, Kelly J., et al.. (2017). A Sensitivity Analysis of Model Parameters Influencing the Biofilm Nitrification Rate: Comparison between the Aerated Biofilm Reactor (MABR) and Integrated Fixed Film Activated Sludge (IFAS) Process. Proceedings of the Water Environment Federation. 2017(16). 257–265. 7 indexed citations

12.

Houweling, Dwight, et al.. (2017). Nutrient Removal Intensification with MABR – Developing a Process Model Supported by Piloting. Proceedings of the Water Environment Federation. 2017(3). 657–669. 10 indexed citations

13.

Goel, Rajeev, et al.. (2016). Optimization of ABMet Biological Selenium Removal through Advanced Process Modelling. Proceedings of the Water Environment Federation. 2016(11). 949–961. 2 indexed citations

14.

Snowling, Spencer, et al.. (2012). Creating New Revenue With Existing Processes: Evaluating Grid Balance™ With Demand-side Loads At Wastewater Treatment Plants Using Dynamic Modeling. Proceedings of the Water Environment Federation. 2012(12). 4301–4322. 3 indexed citations

15.

Goel, Rajeev, et al.. (2010). Modeling pH Dynamics in the Nutrient Removal Activated Sludge Process. Proceedings of the Water Environment Federation. 2010(17). 733–741. 1 indexed citations

16.

Takács, I., et al.. (2007). A Closer Look at the Dangers of Uncalibrated Simulators. Proceedings of the Water Environment Federation. 2007(17). 1476–1486. 3 indexed citations

17.

Schraa, Oliver, et al.. (2007). A New Activated Sludge Model for Industrial Wastewater Treatment Facilities. Proceedings of the Water Environment Federation. 2007(7). 65–93. 1 indexed citations

18.

Hu, Zhirong, et al.. (2006). Model-Based Optimum Design of Sequencing Batch Reactors for COD and Nitrogen Removal from a Slaughterhouse Wastewater. Proceedings of the Water Environment Federation. 2006(7). 5100–5107. 2 indexed citations

19.

Copp, John B., Evangelia Belia, Spencer Snowling, & Oliver Schraa. (2005). Anaerobic digestion: a new model for plant-wide wastewater treatment process modelling. Water Science & Technology. 52(10-11). 1–11. 6 indexed citations

20.

Copp, John B., et al.. (2004). INTEGRATING ANAEROBIC DIGESTION INTO PLANT-WIDE WASTEWATER TREATMENT MODELING. Proceedings of the Water Environment Federation. 2004(8). 38–47. 1 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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