Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA
Jianzhao Bi, Jennifer Stowell, Edmund Seto, et al.
Environmental Research (2019) Vol. 180, pp. 108810-108810
Open Access | Times Cited: 77

Showing 1-25 of 77 citing articles:

Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale
Jianzhao Bi, Avani Wildani, Howard H. Chang, et al.
Environmental Science & Technology (2020) Vol. 54, Iss. 4, pp. 2152-2162
Open Access | Times Cited: 179
PM2.5 volatility prediction by XGBoost-MLP based on GARCH models
Hongbin Dai, Guangqiu Huang, Huibin Zeng, et al.
Journal of Cleaner Production (2022) Vol. 356, pp. 131898-131898
Closed Access | Times Cited: 78
Estimating hourly PM2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study
Yougeng Lu, Geneviève Giuliano, Rima Habre
Environmental Research (2021) Vol. 195, pp. 110653-110653
Closed Access | Times Cited: 83
Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network
Ellen M. Considine, Colleen E. Reid, Michael Ogletree, et al.
Environmental Pollution (2020) Vol. 268, pp. 115833-115833
Open Access | Times Cited: 78
Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network: Comparison of Linear, Machine Learning, and Hybrid Land Use Models
Sakshi Jain, Albert A. Presto, Naomi Zimmerman
Environmental Science & Technology (2021) Vol. 55, Iss. 13, pp. 8631-8641
Closed Access | Times Cited: 69
Satellite Monitoring for Air Quality and Health
Tracey Holloway, Daegan Miller, Susan C. Anenberg, et al.
Annual Review of Biomedical Data Science (2021) Vol. 4, Iss. 1, pp. 417-447
Open Access | Times Cited: 69
Characterizing outdoor infiltration and indoor contribution of PM2.5 with citizen-based low-cost monitoring data
Jianzhao Bi, Lance Wallace, Jeremy A. Sarnat, et al.
Environmental Pollution (2021) Vol. 276, pp. 116763-116763
Open Access | Times Cited: 62
Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast
Jianzhao Bi, K. Emma Knowland, Christoph A. Keller, et al.
Environmental Science & Technology (2022) Vol. 56, Iss. 3, pp. 1544-1556
Closed Access | Times Cited: 50
Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection
Jianzhao Bi, Nancy Carmona, Magali N. Blanco, et al.
Environment International (2021) Vol. 158, pp. 106897-106897
Open Access | Times Cited: 46
Leveraging Citizen Science and Low-Cost Sensors to Characterize Air Pollution Exposure of Disadvantaged Communities in Southern California
Tianjun Lu, Yisi Liu, Armando García, et al.
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 14, pp. 8777-8777
Open Access | Times Cited: 37
Socioeconomic Disparities of Low-Cost Air Quality Sensors in California, 2017–2020
Yi Sun, Amirhosein Mousavi, Shahir Masri, et al.
American Journal of Public Health (2022) Vol. 112, Iss. 3, pp. 434-442
Open Access | Times Cited: 28
Artificial intelligence-based prediction of indoor bioaerosol concentrations from indoor air quality sensor data
Justin Y. Y. Lee, Yanhao Miao, Ricky L.T. Chau, et al.
Environment International (2023) Vol. 174, pp. 107900-107900
Open Access | Times Cited: 17
Low‐cost air quality monitoring networks for long‐term field campaigns: A review
Federico Carotenuto, Andrea Bisignano, Lorenzo Brilli, et al.
Meteorological Applications (2023) Vol. 30, Iss. 6
Open Access | Times Cited: 17
High spatio-temporal resolution predictions of PM2.5 using low-cost sensor data
Armita Kar, Mohammed Ahmed, Andrew A. May, et al.
Atmospheric Environment (2024) Vol. 326, pp. 120486-120486
Open Access | Times Cited: 6
A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation
Tongwen Li, Huanfeng Shen, Chao Zeng, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2020) Vol. 13, pp. 1312-1321
Open Access | Times Cited: 46
Beyond air pollution at home: Assessment of personal exposure to PM2.5 using activity-based travel demand model and low-cost air sensor network data
Yougeng Lu
Environmental Research (2021) Vol. 201, pp. 111549-111549
Closed Access | Times Cited: 35
Prediction of Air Pollutant Concentration Based on One-Dimensional Multi-Scale CNN-LSTM Considering Spatial-Temporal Characteristics: A Case Study of Xi’an, China
Hongbin Dai, Guangqiu Huang, Jingjing Wang, et al.
Atmosphere (2021) Vol. 12, Iss. 12, pp. 1626-1626
Open Access | Times Cited: 33
Evaluating uncertainty in sensor networks for urban air pollution insights
Daniel R. Peters, Olalekan Popoola, R. L. Jones, et al.
Atmospheric measurement techniques (2022) Vol. 15, Iss. 2, pp. 321-334
Open Access | Times Cited: 24
Communities of color are disproportionately exposed to long-term and short-term PM2.5 in metropolitan America
Timothy W. Collins, Sara E. Grineski, Yasamin Shaker, et al.
Environmental Research (2022) Vol. 214, pp. 114038-114038
Closed Access | Times Cited: 23
On Memory-Based Precise Calibration of Cost-Efficient NO2 Sensor Using Artificial Intelligence and Global Response Correction
Sławomir Kozieł, Anna Pietrenko‐Dabrowska, W. Marek, et al.
Knowledge-Based Systems (2024) Vol. 290, pp. 111564-111564
Closed Access | Times Cited: 5
Harmonizing low-cost and regulatory air quality monitoring networks with interpretable semi-supervised learning: Reducing exposure misclassification in underrepresented communities
Dié Tang, Tan Mi, Xi Zheng, et al.
Journal of Hazardous Materials (2025) Vol. 491, pp. 137893-137893
Closed Access
Nitrogen Dioxide Monitoring by Means of a Low-Cost Autonomous Platform and Sensor Calibration via Machine Learning with Global Data Correlation Enhancement
Sławomir Kozieł, Anna Pietrenko‐Dabrowska, W. Marek, et al.
Sensors (2025) Vol. 25, Iss. 8, pp. 2352-2352
Open Access
Examining PM2.5 concentrations and exposure using multiple models
James T. Kelly, Cholsoon Jang, Brian Timin, et al.
Environmental Research (2020) Vol. 196, pp. 110432-110432
Open Access | Times Cited: 33
PM2.5 Concentration Prediction Based on Spatiotemporal Feature Selection Using XGBoost-MSCNN-GA-LSTM
Hongbin Dai, Guangqiu Huang, Huibin Zeng, et al.
Sustainability (2021) Vol. 13, Iss. 21, pp. 12071-12071
Open Access | Times Cited: 29
Differences in Ozone and Particulate Matter Between Ground Level and 20 m Aloft are Frequent During Wintertime Surface‐Based Temperature Inversions in Fairbanks, Alaska
Meeta Cesler‐Maloney, William R. Simpson, Tate Miles, et al.
Journal of Geophysical Research Atmospheres (2022) Vol. 127, Iss. 10
Open Access | Times Cited: 21
Page 1 - Next Page

Scroll to top