A two-stage modeling approach to estimate indoor NO2 exposure: a Barcelona case study.

BACKGROUND

Indoor air pollution is a major contributor to personal exposure to air pollution, yet accurately modeling indoor concentrations remains a major challenge in epidemiological studies. This issue is especially relevant since we spend a great portion of our time indoors.

OBJECTIVE

To develop a two-stage model to estimate indoor nitrogen dioxide (NO2) concentrations.

METHODS

The study was conducted in the Barcelona Life Study Cohort (2018-2021), a cohort of pregnant individuals in Barcelona, Spain. Indoor and outdoor NO2 concentrations were measured using passive NO2 samplers placed in participants' homes at weeks 12 and 32 of pregnancy. A total of 1695 indoor measurements and 1577 outdoor measurements were collected. In the first stage, we modeled the indoor-outdoor (I/O) NO2 ratio as a proxy for infiltration using a linear mixed-effects model with repeated measures using 1528 1-week integrated I/O NO2 measurements. In the second stage, we used a random forest algorithm to estimate weekly indoor NO2 concentrations covering the full pregnancy period, incorporating the predicted I/O ratios, a previously developed hybrid-model outdoor NO2 estimate, meteorological data, and home characteristics.

RESULTS

The I/O NO2 ratio model showed moderate performance, with a cross-validated R2 of 0.27 under leave-one-subject-out (generalized model) validation and 0.70 under leave-one-observation-out validation (cohort performance). The indoor NO2 model achieved a 10-fold cross-validated R2 of 0.53 and RMSE of 1.28 µg/m3. Finally, we estimated indoor NO2 concentrations for each week of pregnancy using a quantile random forest (QRF) algorithm incorporating uncertainty estimation through prediction intervals.

SIGNIFICANCE: The findings demonstrate that accounting for dynamic infiltration processes and individual-level behaviors improves the estimation of indoor NO2 exposure.

IMPACT: Integrating indoor and outdoor NO2 measurements in a two-stage modeling framework and combining it with key indoor determinants improves indoor NO2 estimation.