Jules Kerckhoffs’s research while affiliated with Utrecht University and other places

Aims: We assessed the association between air pollution from pregnancy (in utero) to 18 years and cardiovascular health markers in early adulthood. Methods: Data from 3,767 individuals from a UK birth cohort were used. We explored the associations between modelled fine particulate matter (PM2.5), nitrogen dioxide (NO2) and black carbon (BC) across an 18-year period and eight cardiovascular health markers measured at 18 year of age. Long-term exposure to air pollution was assessed by averaging the air pollutants over time and by creating air pollutant trajectories. Linear regressions were used to assess the associations between air pollutants and cardiovascular health markers. Possible sensitive periods of exposure and sex differences in these associations were also explored. Results: Higher average levels of PM2.5 and NO2 were associated with higher peripheral (pDBP) and central diastolic blood pressure (cDBP); e.g., an interquartile range increase in PM2.5 was associated with 0.46 mmHg (95%CI 0.14, 0.78) higher pDBP and 0.50 mmHg (95%CI 0.17, 0.83) higher cDBP. Higher average PM2.5 levels were also associated with lower carotid intima-media thickness and higher BC levels were associated with higher heart rate (HR). Latent classes showed the same overall patterns of association, with the trajectory classes with the highest levels of air pollution exposure tending to have higher pDBP, cDBP and HR. There was little evidence of sensitive periods of exposure and sex differences in the associations. Conclusions: Higher lifetime exposure to air pollution up to 18 years was associated with markers of poorer cardiovascular health in early adulthood.

Urban street trees can affect air pollutant concentrations by reducing ventilation rates in polluted street canyons (increasing concentrations), or by providing surface area for deposition (decreasing concentrations). This paper examines these effects in Rotterdam, the Netherlands, using mobile measurements of nitrogen dioxide (NO2), particulate matter (PM), black carbon (BC), and ultrafine particulate matter (UFP). The effect of trees is accounted for in regulatory dispersion models (https://www.cimlk.nl) by the application of an empirically determined tree factor, dependent on the existence and density of the tree canopy, to concentrations due to traffic emissions. Here, we examine the effect of street trees on different pollutants using street-level mobile measurements in a detailed case study (repeated measurements of several neighboring streets) and a larger statistical analysis of measurements across the urban core of Rotterdam. We find that in the summertime, when trees are fully leafed-out, the major short-lived traffic-related pollutants of NO2 and BC have higher concentrations in streets with higher traffic and greater tree cover, while PM2.5 has slightly lower concentrations in streets with higher tree factor. UFP shows a less clear, but decreasing trend with tree factor. In low-traffic streets and in wintertime (fewer leaves on trees) measurements confirm the importance of leaves to pollutant trapping by trees, by finding no enhancement of NO2 and BC with increasing tree cover, rather a slightly decreasing trend in pollutant concentrations with tree factor. Our observations are consistent with the dominant effect of (leafed-out) trees being to trap traffic-emitted pollutants at the surface, but that PM2.5 in street canyons is more often added by transport from outside the street, which can be attenuated by tree cover. Overall, these measurements emphasize that both traffic-emitted and regional sources are important factors that determine air quality in Rotterdam streets, making the effect of street trees different for different pollutants and different seasons.

Scenario microsimulations like agent-based models can account for feedbacks and spatio-temporal and social heterogeneity when projecting future intervention impacts. Addressing air pollution exposure requires traffic scenario models (i.e. of car-free zones). Traditional air pollution models do not meet all requirements for traffic scenario microsimulation: isolating traffic emission, integrating relevant dispersion moderators, while computationally efficient, interoperable and valid. We propose a hybrid model of land use regression-based baseline concentrations and on-road emissions in conjunction with cellular automata-based off-road dispersion. The model efficiently assesses air pollution, while accounting for meteorological and morphological dispersion processes. We calibrate using genetic algorithms and externally validate the model based on mobile measurements and fixed-site routine monitoring data of NO2 concentrations across Amsterdam. Our model achieves an external validation R2 of 0.60 and 0.48 s computation time in a 50 m × 50 m raster. Further, we successfully projected the NO2 reduction of the first Covid-19 lockdown traffic scenario (R2 0.57).

Mobile air pollution measurements are typically aggregated by varying road segment lengths, grid cell sizes, and time intervals. How these spatiotemporal aggregation schemas affect the modeling performance of land use regression models has seldom been assessed. We used 5.7 million mobile nitrogen dioxide (NO2) measurements collected over 160 days in Amsterdam (The Netherlands) and subsampled them into five campaign durations (10–70 days). We aggregated the measurements from each campaign duration onto road segments and grid cells with five spatial scales (25–200 m). A stepwise linear regression (SLRs) and random forests (RFs) were trained for each aggregated dataset to predict NO2 concentrations. The model accuracies were validated using a 30% hold-out sample of mobile measurements and external Palmes long-term stationary measurements (n = 105). At increased spatial scales, the prediction accuracy decreased for RFs but increased for SLRs when validated against mobile measurements. Using long-term stationary measurements, prediction accuracy varied across scales without any clear pattern. Regardless of cells or road segments, the models performed similarly at small scales (i.e., 25 m and 50 m). Models based on road segments were less sensitive to spatial scales than those based on cells in mobile and long-term external validations. Longer campaign durations increased the prediction accuracies of long-term NO2 concentrations, though the gain in accuracy diminished after 50 days. In conclusion, our results suggest that road segments are preferred when the aggregation scale gets larger as this approach likely reduces scale-dependent influences. The campaign duration plays a more important role in long-term NO2 prediction than spatial scales.

Scenario microsimulations like agent-based models can account for feedbacks and spatiotemporal and social heterogeneity when projecting future intervention impacts. Addressing air pollution exposure requires traffic scenario models (e.g. of car-free zones). Traditional air pollution models do not meet all requirements for traffic scenario microsimulation: isolating traffic emission, integrating relevant dispersion moderators, while computationally efficient, interoperable and valid. We propose a hybrid model of land use regression-based baseline concentrations and on-road emissions in conjunction with cellular automata-based off-road dispersion. The model efficiently assesses air pollution, while accounting for meteorological and morphological dispersion processes. We calibrate using genetic algorithms and externally validate the model based on mobile measurements and fixed-site routine monitoring data of NO2 concentrations across Amsterdam. Our model achieves an external validation R2 of 0.60 and 0.48 seconds computation time in a 50mx50m raster. Further, we successfully projected the NO2 reduction of the first Covid-19 lockdown traffic scenario (R2 0.57).

Addressing the challenge of mapping hyperlocal air pollution in areas without local monitoring, we evaluated unsupervised transfer-learning-based land-use regression (LUR) models developed using mobile monitoring data from other cities: CORrelation ALignment (Coral) and its inverse distance-weighted modification (IDW_Coral). These models mitigated domain shifts and transferred patterns learned from mobile air quality monitoring campaigns in Copenhagen and Rotterdam to estimate annual average air pollution levels in Amsterdam (50m road segments) without involving any Amsterdam measurements in model development. For nitrogen dioxide (NO2), IDW_Coral outperformed Copenhagen and Rotterdam LUR models directly applied to Amsterdam, achieving MAE (4.47 µg/m3) and RMSE (5.36 µg/m3) comparable to a locally fitted LUR model (AMS_SLR) developed using Amsterdam mobile measurements collected for 160 days. IDW_Coral yielded an R2 of 0.35, similar to that of the AMS_SLR based on 20 collection days, suggesting a minimum requirement of 20-day mobile monitoring to capture city-specific insights. For Ultra Fine Particles (UFP), IDW_Coral’s citywide predictions strongly correlated with previously published mixed-effect models fitted with 160-day Amsterdam measurements (Pearson correlation of 0.71 for UFP and 0.72 for NO2). IDW_Coral demands no direct measurements in the target area, showcasing its potential for large-scale applications and offering significant economic efficiencies in executing mobile monitoring campaigns.