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.

Understanding the factors that shape daily mobility during pregnancy is essential for inclusive transportation planning that promotes active travel for all. Using smartphone-based Global Positioning System data from 860 pregnant women in Barcelona, Spain, we evaluated the correlates of active and passive travel in early and late pregnancy. We identified 33 correlates from 48 candidate variables including personal characteristics, the residential physical environment, the social environment, and temporal factors. The most important correlate across pregnancy was non-European ethnic origin, being associated with 10–15 min less daily active travel. In early pregnancy, commuting distance was the most important correlate, being positively associated with passive travel, while the COVID-19 pandemic was associated with less passive travel. In late pregnancy, residential walkability and having a university degree were positively associated with active travel. The neighbourhood education level was associated with more active travel, particularly during weekends. We discuss key priorities for supporting active travel during pregnancy.

Amid escalating chronic stress in urbanising societies, understanding nature’s role in recovery from stress is critical. Here, we relate daily variation in nature exposure to nighttime activation of the autonomic nervous system, a key stress regulatory system, using 10 months of Global Position Systems and heart rate data from 45 individuals in Gävle, Sweden (3224 person-days). We examined within-person associations of (1) time in nature, (2) active and passive movement, and (3) stationary time or active movement in nature vs. non-natural environments with resting heart rate (RHR) and heart rate variability (HRV), and their moderation by sex. We further explored cumulative exposure associations over different timescales (day, week, and month). Active movement in nature was associated with lower-than-usual RHR and higher-than-usual HRV in the full sample and in females (but not males). We provide within-person real-world evidence that active movement in nature may support nighttime cardiac regulation, which may be beneficial for health.

The Fourth Symposium on Social-Ecological Urbanism was hosted by the Urban Studio research programme at University of Gävle, and held 2–3 December 2025 at Gävle Concert Hall, Gävle, Sweden. It examined how multisensory experiences of urban environments and nature relate to human well-being, and how such perspectives can inform social-ecological approaches to urban planning and design.

Available modelling frameworks for estimating indoor temperature (T) and relative humidity (RH) for epidemiological studies remain scarce. We developed a modelling framework to assess the daily mean indoor T and RH. We monitored indoor T and RH at 1,029 homes of 978 participants from the Barcelona Life Study Cohort (BiSC), Spain (2018-2021), for one week each during the first and third trimesters of pregnancy. We applied a Deep Ensemble Machine Learning (DEML) approach to predict the daily mean indoor T and RH throughout pregnancy, which integrated predictions from three base models: Random Forest, eXtreme Gradient Boosting, and Gradient Boosting Machine. The models incorporated a comprehensive set of 56 predictor variables, including meteorological conditions, building and neighborhood characteristics, and occupants’ sociodemographic and behavioral characteristics. We applied a long-term validation to assess model performance across pregnancy and a short-term validation to evaluate daily fluctuation capture. The DEML model achieved excellent performance in the short-term validation (T: R² = 0.978, MAD = 0.312°C; RH: R² = 0.894, MAD = 1.666%), with a good performance for indoor T (R² = 0.891, MAD = 0.717°C) and a moderate performance for RH (R² = 0.499, MAD = 3.591%) in the long-term validation. Feature importance analysis indicated that the previous one-day mean outdoor T and the same-day outdoor RH were the most influential predictors for indoor T and RH, respectively. The model reliably predicted indoor T and RH, highlighting its utility for future epidemiological studies on health impacts of indoor exposure.

Health implications of mobility during pregnancy entail a need to understand pregnant women's activity spaces. We present ActMAP, a framework for quantifying multiple aspects of activity spaces from distinct trips and stays derived from GPS data. We applied ActMAP to data from 238 pregnant women in Barcelona, Spain (2018–2020) and explored weekday, weekend and intraday associations between pregnancy trimester and activity spaces. Activities were more centred around the home later in pregnancy. However, the number of visited places and daily trips remained largely constant throughout pregnancy. By constructing activity spaces from individual trips and stays, ActMAP could provide a framework for GPS-based holistic assessments of mobility.

Greenspace can promote health via diverse pathways. A common approach to assessing greenspace exposure is to estimate vegetation availability within buffers surrounding locations where people reside or spend time. However, no clear framework for informed buffer selection exists, and choices made show considerable heterogeneity, impeding evidence synthesis and causal inference. In this Personal View conducted by an interdisciplinary panel of experts, we aimed to establish a framework for informed buffer selection for epidemiological studies on greenspace. We began by reviewing available approaches for the selection of buffer types, which range from single fixed-location approaches to high-resolution mobility-based activity-space approaches, as well as different buffer sizes. We then summarised the determinants of buffer type and size selection including health outcomes and underlying mechanisms, study population, contextual factors, and data characteristics. Finally, based on these determinants, we developed recommendations for future research. Buffer type and size selection should be hypothesis driven, reflecting presumed greenspace-health mechanisms. Buffer selection should target activity-based approaches where feasible, and multiple buffer sizes should be tested. Overall, the assessment of greenspace exposure should shift from ad-hoc approaches to personalised, multiscale, and context-specific methods. We call for standardising and reporting the rationale for buffer selection to minimise bias and enhance comparability and evidence synthesis across studies.

Background

Air pollution is the leading environmental risk factor for health. Assessing outdoor air pollution exposure with detailed spatial and temporal variability in urban areas is crucial for evaluating its health effects. Aim: We developed and compared Land Use Regression (LUR), dispersion (DM), and hybrid (HM) models to estimate outdoor concentrations for NO2, PM2.5, black carbon (BC), and PM2.5-constituents (Fe, Cu, Zn) in Barcelona.

Methods

Two monitoring campaigns were conducted. In the first, NO2 concentrations were measured twice at 984 home addresses and in the second, NO2, PM2.5, and BC were measured four times at 34 points across Barcelona. LUR and DM were constructed using conventional techniques, while HM was developed using Random Forest (RF). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and 10-fold cross-validation (10-CV) for LUR and HM, and by comparing DM and LUR estimates with routine monitoring stations. NO2 levels estimated by all models were externally validated using the home monitoring campaign. Agreement between models was assessed using Spearman correlation (rs) and Bland-Altman (BA) plots.

Results

Models showed moderate to good performance. LUR exhibited R2LOOCV of 0.62 (NO2), 0.45 (PM2.5), 0.83 (BC), and 0.85 to 0.89 (PM2.5-constituents). DM model comparison showed R2 values of 0.39 (NO2), 0.26 (PM2.5), and 0.65 (BC). HM models had higher R210-CV 0.64 (NO2), 0.66 (PM2.5), 0.86 (BC), and 0.44 to 0.70 (PM2.5-constituents). Validation for NO2 showed R2 values of 0.56 (LUR), 0.44 (DM), and 0.64 (HM). Correlations between models varied from −0.38 to 0.92 for long-term exposure, and − 0.23 to 0.94 for short-term exposure. BA plots showed good agreement between models, especially for NO2 and BC.

Conclusions

Our models varied substantially, with some models performing better in validation samples (NO2 and BC). Future health studies should use the most accurate methods to minimize bias from exposure measurement error.

Two key goals for sustainable spatial planning are to promote low-carbon travel in daily life and to enhance human wellbeing through diverse human-environment interactions. Yet, the integration of these goals has been underexplored. This study investigates the potential for experiential diversity via active travel in different residential contexts within the Gävle city-region, Sweden. Over 15 months, we collected spatiotemporal data from 165 participants, analyzing 4,362 reported experiences and 13,192 GPS-derived travel trajectories. Our analysis uncovered a significant spatial discrepancy: while the travelled distances to locations of positive experiences typically ranged from 1.5 km to 5 km, active travel predominated only within 1.5 km. This discrepancy persisted across urban, suburban, and peripheral contexts. Although residents in different contexts reported the same types of experiences, urban dwellers travelled about 50 % farther for nature experiences compared with other positive experiences, whereas peripheral dwellers travelled twice the distance for urbanicity experiences compared with other positive experiences. Consequently, urban residents mostly relied on active travel for urbanicity experiences and motorised travel for nature experiences, with the reverse trend observed among peripheral dwellers. These results illustrate the importance of spatial scale for promoting diverse positive experiences via active travel, regardless of residential context. Effective planning strategies may include enhancing environmental diversity near homes and developing infrastructure that favours active over motorised travel for short to moderate distances.

To inform spatial planning promoting low-carbon travel and well-being, we investigate the potential for experiential diversity by active travel across different residential contexts. We use spatiotemporal tracking and experience data from the Gävle city-region, Sweden, generated by 165 participants over the course of 15 months. Findings reveal a discrepancy between typical travel distances to locations of positive experiences (1.5–5 km) and the distances at which active travel dominates (up to 1.5 km). This discrepancy largely persists across urban, suburban, and peripheral contexts, with urban dwellers travelling further for nature experiences, whereas peripheral dwellers travel further for urbanicity experiences. These results illustrate the importance of spatial scale for promoting diverse positive experiences by active travel, regardless of residential context. Planning strategies include enhancing environmental diversity close to people’s homes and providing infrastructure that promotes switching from motorised to active travel for trips of a few kilometres.