13 citations as recorded by crossref.

1. Harmonizing low-cost and regulatory air quality monitoring networks with interpretable semi-supervised learning: Reducing exposure misclassification in underrepresented communities D. Tang et al. 10.1016/j.jhazmat.2025.137893
2. Evaluating PurpleAir Sensors: Do They Accurately Reflect Ambient Air Temperature? J. Tse & L. Liang 10.3390/s25103044
3. A Scalable Calibration Method for Enhanced Accuracy in Dense Air Quality Monitoring Networks A. Winter et al. 10.1021/acs.est.4c08855
4. Observational Inferences of NOx and CO Emission Factors for Vehicles and Homes in the San Francisco Bay Area Y. Zhu et al. 10.1021/acsestair.5c00004
5. Calibration of PurpleAir low-cost particulate matter sensors: model development for air quality under high relative humidity conditions M. Mathieu-Campbell et al. 10.5194/amt-17-6735-2024
6. Sustained Performance of Low-Cost Air Quality Sensors in Long-Term Deployments A. Winter et al. 10.1021/acssensors.5c00566
7. A BIM-Oriented Framework for Integrating IoT-Based Air Quality Monitoring Systems Using the AllBIMclass Classification E. Renard-Julián et al. 10.3390/app151910409
8. Reliability Assessment of Low-Cost PM Sensors Under High Humidity and High PM Level Outdoor Conditions G. Kumar et al. 10.1109/JSEN.2025.3592796
9. Enhancing accuracy of air quality sensors with machine learning to augment large-scale monitoring networks K. Ravindra et al. 10.1038/s41612-024-00833-9
10. Plume Detection and Emissions Quantification Potential Using a Dense Sensor Network M. Patel et al. 10.1021/acsestair.5c00069
11. Low-Cost Source Apportionment (LoCoSA) of air pollution - literature review of the state of the art D. Bousiotis et al. 10.1016/j.scitotenv.2025.180257
12. Exploration of a practical approach to providing RH corrections to low cost sensor networks S. Lekamge & H. Oswin 10.1038/s41612-025-01115-8
13. Observing Anthropogenic and Biogenic CO2 Emissions in Los Angeles Using a Dense Sensor Network J. Kim et al. 10.1021/acs.est.4c11392