Land use regression modelling of PM2.5 and NO2 using low-cost sensor data
| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Hoek, Gerard | |
| dc.contributor.author | Aarts, Daan | |
| dc.date.accessioned | 2024-08-30T00:01:19Z | |
| dc.date.available | 2024-08-30T00:01:19Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/47502 | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | This research focuses on developing Land Use Regression (LUR) models to estimate concentrations of PM2.5 and NO2¬ using low-cost sensor data in the Netherlands. The study involved two measurement periods: October 2021 to March 2022 and July 2022 to February 2023, employing sensors deployed across 99 residential locations. The primary aim was to assess the performance of these low-cost sensors. Three algorithms—Simple Linear Regression (SLR), Random Forest (RF), and Least Absolute Shrinkage and S | |
| dc.title | Land use regression modelling of PM2.5 and NO2 using low-cost sensor data | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Health and Environment | |
| dc.thesis.id | 38413 |