2026-10-13T08:00:47Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34036596 2026-09-30T18:12:51Z category_7 category_21 category_272 category_734 category_931 category_811 category_64 category_133 portal_63 item_type_6 month_year_09_2026

Integrating Fragmented Mobile Monitoring Data into a High-Resolution Spatiotemporal Exposure Model for Black Carbon Jia Xu (111777) Liyao Guo (25145538) Ziqi Liu (9261868) Yisi Liu (4042337) Tianjun Lu (11654296) Lijing Wang (290763) Guiqian Tang (9426417) Wen Yang (235745) Bin Han (45760) Zhipeng Bai (6489494) Medicine Biotechnology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Cancer Plant Biology urban residential areas time data set three seasonal campaigns temporally fragmented real subsequent epidemiological studies health risk assessment fill temporal gaps extreme gradient boosting external data sets equivalent black carbon adverse health effects integrated mobile measurements high spatial resolution unmonitored cohort locations 2 < sup r </ vehicle platform susceptible populations subsequently used study provides study developed spatially rich routinely monitored resulting site regulatory networks practical framework model achieved measured using measured site hourly level geographic covariates fold cross critical traffic applied directly >< sub Particulate black carbon (BC) is a critical traffic-related air pollutant associated with adverse health effects in susceptible populations, but exposure modeling remains challenging because BC is not routinely monitored in regulatory networks. This study developed a mobile monitoring-based spatiotemporal exposure modeling framework for estimating ambient BC exposure in a pregnancy cohort in Beijing, China. Equivalent black carbon (eBC) was measured using a vehicle platform during three seasonal campaigns from 2023 to 2024 in urban residential areas, generating a spatially rich but temporally fragmented real-time data set that was aggregated to the hourly level. To fill temporal gaps at locations with mobile BC measurements, we integrated mobile measurements with external temporal predictors using tree-based machine learning models and interpreted feature contributions using SHapley Additive exPlanations (SHAP) values. The resulting site-level daily BC estimates were subsequently used to develop a hierarchical geostatistical model based on geographic covariates, which was applied directly to estimate daily BC exposure at unmonitored cohort locations. The eXtreme Gradient Boosting (XGBoost) model achieved a 10-fold cross-validation mean squared error-based R2 (Rmse2) of 0.69 against measured site-hour eBC, while the subsequent spatiotemporal model showed strong internal interpolation performance for the XGBoost-derived daily BC estimates at the mobile monitoring locations. This study provides a practical framework for integrating temporally fragmented mobile monitoring data with external data sets to support BC exposure assessment at high spatial resolution in subsequent epidemiological studies and health risk assessment. 2026-09-30T00:00:00Z Text Journal contribution 10.1021/acs.est.6c08120.s001 https://figshare.com/articles/journal_contribution/Integrating_Fragmented_Mobile_Monitoring_Data_into_a_High-Resolution_Spatiotemporal_Exposure_Model_for_Black_Carbon/34036596 CC BY-NC 4.0