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Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO 2 and PM 2.5 , for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016-2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO 2 models performed overall well (5-fold cross validation R 2  = 0.50-0.78), while the PM 2.5 performed moderately (5-fold cross validation R 2  = 0.24-0.62). Both for NO 2 and PM 2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays'. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO 2 hourly R 2  = 0.35-0.70; PM 2.5 hourly R 2  = 0.01-0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: To investigate associations between annual average air pollution exposures and health, most epidemiological studies rely on estimated residential exposures because information on actual time-activity patterns can only be collected for small populations and short periods of time due to costs and logistic constraints. In the current study, we aim to compare exposure assessment methodologies that use data on time-activity patterns of children with residence-based exposure assessment. We compare estimated exposures and associations with lung function for residential exposures and exposures accounting for time activity patterns., Methods: We compared four annual average air pollution exposure assessment methodologies; two rely on residential exposures only, the other two incorporate estimated time activity patterns. The time-activity patterns were based on assumptions about the activity space and make use of available external data sources for the duration of each activity. Mapping of multiple air pollutants (NO 2 , NO X , PM 2.5 , PM 2.5 absorbance, PM 10 ) at a fine resolution as input to exposure assessment was based on land use regression modelling. First, we assessed the correlations between the exposures from the four exposure methods. Second, we compared estimates of the cross-sectional associations between air pollution exposures and lung function at age 8 within the PIAMA birth cohort study for the four exposure assessment methodologies., Results: The exposures derived from the four exposure assessment methodologies were highly correlated (R > 0.95) for all air pollutants. Similar statistically significant decreases in lung function were found for all four methods. For example, for NO 2 the decrease in FEV 1 was -1.40% (CI; -2.54, -0.24%) per IQR (9.14 μg/m 3 ) for front door exposure, and -1.50% (CI; -2.68, -0.30%) for the methodology which incorporates time activity pattern and actual school addresses., Conclusions: Exposure estimates from methods based on the residential location only and methods including time activity patterns were highly correlated and associated with similar decreases in lung function. Our study illustrates that the annual average exposure to air pollution for 8-year-old children in the Netherlands is sufficiently captured by residential exposures., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: In countries where air pollution stations are unavailable or scarce, station measurements from other countries and atmospheric remote sensing could jointly provide information to estimate ambient air quality at a sufficiently fine resolution to study the relationship between air pollution exposure and health. Predicting NO 2 concentration globally with sufficient spatial and temporal resolution and accuracy for health studies is, however, not a trivial task. Challenges are data deficiency, in terms of NO 2 measurements and NO 2 predictors, and the development of a statistical model that can typify the regional and continental differences, such as traffic regulations, energy sources, and local weather., Objective: We investigated the feasibility of mapping daytime and nighttime NO 2 globally at a high spatial resolution (25 m), by including TROPOMI (TROPOspheric Monitoring Instrument) data and comparing various statistical learning techniques., Method: We separated daytime (7:00 am - 9:59 pm) and nighttime (10:00 pm - 6:59 am) based on the local times. To study if one should build models for each country separately, national models in 4 selected countries (the US, China, Germany, Spain) were developed. We build the models for 2017 and used 3636 stations. Seven statistical learning techniques were applied and the impact of the predictors, model fitting, and predicting accuracy was compared between different techniques, national models, national and global models, and models with and without including the NO 2 vertical column density retrieved from TROPOMI., Result and Conclusion: The ensemble tree-based methods obtained higher accuracy compared to the linear regression-based methods in national and global models. The global tree-based methods obtained similar accuracy to national models. Different spatial prediction patterns are observed even when the prediction accuracy is very similar. Separating between day and night can be important for more accurate air pollution exposure assessment. The TROPOMI variable is ranked as one of the most important variables in the statistical learning techniques but adding it to global models that contain other precedent remote sensing products does not improve the prediction accuracy., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Long-term air pollution exposure may lead to an increase in incidences and mortality rates of chronic diseases and adversely affect human health. The effects of long-term air pollution exposure have not been comprehensively studied due to the lack of human mobility data collected over a long period. In this study, we develop and apply a personal mobility model to long-term hourly air pollution concentration predictions to quantify personal long-term air pollution exposure for all individuals. We implement our model assuming mobility patterns for commuters and homemakers, and separate between weekdays and weekend. Our results show that NO 2 exposure of commuters are on average slightly higher and vary less spatially as they are exposed to NO 2 at multiple locations., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Air pollution has been shown to promote cardiovascular disease in adults. Possible mechanisms include air pollution induced changes in arterial wall function and structure. Atherosclerotic vascular disease is a lifelong process and childhood exposure may play a critical role. We investigated whether air pollution is related to arterial wall changes in 5-year old children. To this aim, we developed an air pollution exposure methodology including time-weighted activity patterns improving upon epidemiological studies which assess exposure only at residential addresses., Methods: The study is part of an existing cohort study in which measurements of carotid artery intima-media thickness, carotid artery distensibility, elastic modulus, diastolic and systolic blood pressure have been obtained. Air pollution assessments were based on annual average concentration maps of Particulate Matter and Nitrogen Oxides at 5 m resolution derived from the European Study of Cohorts for Air Pollution Effects. We defined children's likely primary activities and for each activity we calculated the mean air pollution exposure within the assumed area visited by the child. The exposure was then weighted by the time spent performing each activity to retrieve personal air pollution exposure for each child. Time spent in these activities was based upon a Dutch mobility survey. To assess the relation between the vascular status and air pollution exposure we applied linear regressions in order to adjust for potential confounders., Results: Carotid artery distensibility was consistently associated with the exposures among the 733 5-years olds. Regression analysis showed that for air pollution exposures carotid artery distensibility decreased per standard deviation. Specifically, for NO 2, carotid artery distensibility decreased by - 1.53 mPa - 1 (95% CI: -2.84, - 0.21), for NO x by - 1.35 mPa - 1 (95% CI: -2.67, - 0.04), for PM 2.5 by - 1.38 mPa - 1 (95% CI: -2.73, - 0.02), for PM 10 by - 1.56 mPa - 1 (95% CI: -2.73, - 0.39), and for PM 2.5absorbance by - 1.63 (95% CI: -2.30, - 0.18). No associations were observed for the rest outcomes., Conclusions: The results of this study support the view that air pollution exposure may reduce arterial distensibility starting in young children. If the reduced distensibility persists, this may have clinical relevance later in life. The results of this study further stress the importance of reducing environmental pollutant exposures.

Background: Cohorts based on administrative data have size advantages over individual cohorts in investigating air pollution risks, but often lack in-depth information on individual risk factors related to lifestyle. If there is a correlation between lifestyle and air pollution, omitted lifestyle variables may result in biased air pollution risk estimates. Correlations between lifestyle and air pollution can be induced by socio-economic status affecting both lifestyle and air pollution exposure., Objectives: Our overall aim was to assess potential confounding by missing lifestyle factors on air pollution mortality risk estimates. The first aim was to assess associations between long-term exposure to several air pollutants and lifestyle factors. The second aim was to assess whether these associations were sensitive to adjustment for individual and area-level socioeconomic status (SES), and whether they differed between subgroups of the population. Using the obtained air pollution-lifestyle associations and indirect adjustment methods, our third aim was to investigate the potential bias due to missing lifestyle information on air pollution mortality risk estimates in administrative cohorts., Methods: We used a recent Dutch national health survey of 387,195 adults to investigate the associations of PM 10 , PM 2.5 , PM 2.5-10 , PM 2.5 absorbance, OP DTT, OP ESR and NO 2 annual average concentrations at the residential address from land use regression models with individual smoking habits, alcohol consumption, physical activity and body mass index. We assessed the associations with and without adjustment for neighborhood and individual SES characteristics typically available in administrative data cohorts. We illustrated the effect of including lifestyle information on the air pollution mortality risk estimates in administrative cohort studies using a published indirect adjustment method., Results: Current smoking and alcohol consumption were generally positively associated with air pollution. Physical activity and overweight were negatively associated with air pollution. The effect estimates were small (mostly <5% of the air pollutant standard deviations). Direction and magnitude of the associations depended on the pollutant, use of continuous vs. categorical scale of the lifestyle variable, and level of adjustment for individual and area-level SES. Associations further differed between subgroups (age, sex) in the population. Despite the small associations between air pollution and smoking intensity, indirect adjustment resulted in considerable changes of air pollution risk estimates for cardiovascular and especially lung cancer mortality., Conclusions: Individual lifestyle-related risk factors were weakly associated with long-term exposure to air pollution in the Netherlands. Indirect adjustment for missing lifestyle factors in administrative data cohort studies may substantially affect air pollution mortality risk estimates., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Air pollution has been shown to promote cardiovascular disease in adults. Possible mechanisms include air pollution induced changes in arterial wall function and structure. Atherosclerotic vascular disease is a lifelong process and childhood exposure may play a critical role. We investigated whether air pollution is related to arterial wall changes in 5-year old children. To this aim, we developed an air pollution exposure methodology including time-weighted activity patterns improving upon epidemiological studies which assess exposure only at residential addresses.Methods: The study is part of an existing cohort study in which measurements of carotid artery intima-media thickness, carotid artery distensibility, elastic modulus, diastolic and systolic blood pressure have been obtained. Air pollution assessments were based on annual average concentration maps of Particulate Matter and Nitrogen Oxides at 5 m resolution derived from the European Study of Cohorts for Air Pollution Effects. We defined children's likely primary activities and for each activity we calculated the mean air pollution exposure within the assumed area visited by the child. The exposure was then weighted by the time spent performing each activity to retrieve personal air pollution exposure for each child. Time spent in these activities was based upon a Dutch mobility survey. To assess the relation between the vascular status and air pollution exposure we applied linear regressions in order to adjust for potential confounders.Results: Carotid artery distensibility was consistently associated with the exposures among the 733 5-years olds. Regression analysis showed that for air pollution exposures carotid artery distensibility decreased per standard deviation. Specifically, for NO2, carotid artery distensibility decreased by - 1.53 mPa- 1 (95% CI: -2.84, - 0.21), for NOx by - 1.35 mPa- 1 (95% CI: -2.67, - 0.04), for PM2.5 by - 1.38 mPa- 1 (95% CI: -2.73, - 0.02), for PM10 by - 1.56 mPa- 1 (95% CI: -2.73, - 0.39), and for PM2.5absorbance by - 1.63 (95% CI: -2.30, - 0.18). No associations were observed for the rest outcomes.Conclusions: The results of this study support the view that air pollution exposure may reduce arterial distensibility starting in young children. If the reduced distensibility persists, this may have clinical relevance later in life. The results of this study further stress the importance of reducing environmental pollutant exposures. [ABSTRACT FROM AUTHOR]

Road traffic is an important source of noise and air pollution. Modelling of air pollution and noise therefore requires detailed information on annual average daily traffic (AADT) flows on all roads. Europe-wide estimates on traffic intensity are however not publicly available. This has hampered previous Europe-wide air pollution and noise modelling, used extensively in Europe-wide epidemiological studies of morbidity and mortality. We aim to estimate Europe-wide AADT and quantify potential improvements of previous Europe-wide air pollution models. We built separate random forests (RF) models for different road types in OpenStreetMap (highway, primary, secondary and tertiary, and residential roads). We collected observations on annual average daily traffic (AADT) from six European countries. We evaluated our AADT models using 5-fold cross-validation (CV) and by comparison of our Europe-wide traffic flow estimates with national traffic model estimates for Switzerland and the Netherlands. We evaluated whether adding our estimated AADT as predictors for Europe-wide air pollution models trained by more than 2000 routine monitoring sites improved the performance of the models based upon major road length in different buffer sizes. The 5-fold cross-validation result showed our estimates overall captured variations in AADT between road types (R2 = 0.82). Our result showed variability in AADT within and between road types, documenting the benefit of our model framework at a continental scale. Our AADT estimates modestly improved model performance of previous Europe-wide air pollution models for NO 2 , PM 10 , PM 2.5 , and O 3 , especially for NO 2 (3% improvement of geographically-weighted regression model). Improvement of model performance was larger in urban areas (5% and 8% increases in R2 for NO 2 and O 3). Importantly, more detailed intra-city near-road variations were captured for traffic-related air pollution. The resulting AADT estimates of all roads across Europe will be useful for further improving air pollution modelling and facilitating harmonized road traffic noise modelling in Europe. [Display omitted] • We used random forests models to estimate traffic counts on all roads across Europe. • 5-fold cross-validation accuracy was good for most road types. • Correlation between European and national traffic flow models was satisfactory. • Our AADTs improved previous Europe-wide air pollution models. • Incorporation of AADT resulted in finer-resolution urban air pollution maps. [ABSTRACT FROM AUTHOR]

• We included spatially-varying relationships in our Europe-wide models. • We built spatially- and temporally-varying models to estimate annual air pollution. • Spatially-varying linear regression was more robust than a machine learning method. Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO 2 , O 3 , PM 2.5 and PM 10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2 = NO 2 : 0.66; O 3 : 0.58; PM 10 : 0.62; PM 2.5 : 0.77), which are better than SLR (average R2 = NO 2 : 0.61; O 3 : 0.46; PM 10 : 0.51; PM 2.5 : 0.75) and RF (average R2 = NO 2 : 0.64; O 3 : 0.53; PM 10 : 0.56; PM 2.5 : 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2 > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models. [ABSTRACT FROM AUTHOR]

Land use regression (LUR) modeling has been applied to study the spatiotemporal patterns of air pollution, which when combined with human space-time activity, is important in understanding the health effects of air pollution. However, most of these studies focus either on the temporal or the spatial domain and do not consider the variability in both space and time. A temporally aggregated model does not reflect the temporal variability caused by traffic and atmospheric conditions and leads to inaccurate estimation of personal exposure. Besides, most studies focus on a single air pollutant (e.g., O 3 , NO 2 , or NO). These pollutants have a strong interaction due to photochemical processes. For studying relations between spatial and temporal patterns in these pollutants it is preferable to use a uniform data source and modelling approach which makes comparison of pollution surfaces between pollutants more reliable as they are produced with the same methodology. We developed temporal land use regression models of O 3 , NO 2 and NO to study the co-variability of these pollutants and the relations with typical weather conditions over the year. We use hourly concentrations from the measurement network of the Dutch National Institute for Public Health and the Environment and aggregate them by hour, for weekday/weekend and month, and fit a regression model for each hour of the day. 70 candidate predictors that are known to have a strong relationship with combustion-related emissions are evaluated in the LUR modelling process. For all pollutants, the optimal LUR was identified with 4 predictors and the temporal variability was determined by the explained variance of each temporal model. Our temporal models for O 3 , NO 2 , and NO strongly reflect the photochemical processes in space and time. O 3 shows a high background value throughout the day and only dips in the (close) vicinity of roads. The diminishing rate is affected by traffic intensity. The NO 2 LUR is validated against NO 2 measurements from the Traffic-Related Air pollution and Children's respiratory HEalth and Allergies (TRACHEA) study, resulting in an R2 of 0.61. • High resolution hourly LUR models at different temporal aggregations. • Spatiotemporal variations and interactions are modelled for NO, NO2, and O3. • Temporal variations of variable importance for air pollutant contributions. • Hourly and seasonally varying explained variations of each predictor. • Validation against an independent data set for NO 2. [ABSTRACT FROM AUTHOR]