{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Le S"],"funding":["NHLBI NIH HHS","NIOSH CDC HHS"],"pagination":["102580"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12671985"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(9)"],"pubmed_abstract":["Air pollution is a leading risk factor for the global burden of disease. There are limited data from Sub-Saharan Africa (SSA). This study addresses significant data gaps in air quality monitoring in South-Central Uganda. Using a near-reference grade monitor and a network of 27 low-cost air quality monitors in the rural Rakai region and urban cities of Masaka and Kampala, we developed a locally tailored calibration model for ambient PM<sub>2.5</sub> concentrations. Using calibrated data across all low-cost monitors, we then examined spatiotemporal trends in ambient PM<sub>2.5</sub> concentrations. The calibration model, which adjusts for relative humidity, demonstrated robust performance with an R<sup>2</sup> of 0.92, a root mean squared error (RMSE) of 3.83 µg/m<sup>3</sup> and bias of -0."],"journal":["Atmospheric pollution research"],"pubmed_title":["Calibration of low-cost sensor data for ambient PM&lt;sub&gt;2.5&lt;/sub&gt; monitoring across urban and rural settings in South Central Uganda."],"pmcid":["PMC12671985"],"funding_grant_id":["T42 OH008428","K12 HL143957","R01 HL152813"],"pubmed_authors":["Koehler K","Chang L","Ssuuna J","Le S","Bainomugisha E","Checkley W","Sserunjogi R","Okedi D","Dudley L","Okure D","Ssematimba J","Kagaayi J","Nicolaou L","Ndyanabo A"],"additional_accession":[]},"is_claimable":false,"name":"Calibration of low-cost sensor data for ambient PM&lt;sub&gt;2.5&lt;/sub&gt; monitoring across urban and rural settings in South Central Uganda.","description":"Air pollution is a leading risk factor for the global burden of disease. There are limited data from Sub-Saharan Africa (SSA). This study addresses significant data gaps in air quality monitoring in South-Central Uganda. Using a near-reference grade monitor and a network of 27 low-cost air quality monitors in the rural Rakai region and urban cities of Masaka and Kampala, we developed a locally tailored calibration model for ambient PM<sub>2.5</sub> concentrations. Using calibrated data across all low-cost monitors, we then examined spatiotemporal trends in ambient PM<sub>2.5</sub> concentrations. The calibration model, which adjusts for relative humidity, demonstrated robust performance with an R<sup>2</sup> of 0.92, a root mean squared error (RMSE) of 3.83 µg/m<sup>3</sup> and bias of -0.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-06T15:34:12.254Z","creation":"2026-06-02T03:08:49.049Z"},"accession":"S-EPMC12671985","cross_references":{"pubmed":["41341275"],"doi":["10.1016/j.apr.2025.102580"]}}