High resolution annual average air pollution concentration maps for the Netherlands

Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

Short-term exposure sequences and anxiety symptoms: a time series clustering of smartphone-based mobility trajectories

BACKGROUND

Short-term environmental exposures, including green space, air pollution, and noise, have been suggested to affect health. However, the evidence is limited to aggregated exposure estimates which do not allow the capture of daily spatiotemporal exposure sequences. We aimed to (1) determine individuals' sequential exposure patterns along their daily mobility paths and (2) examine whether and to what extent these exposure patterns were associated with anxiety symptoms.

METHODS

We cross-sectionally tracked 141 participants aged 18-65 using their global positioning system (GPS) enabled smartphones for up to 7 days in the Netherlands. We estimated their location-dependent exposures for green space, fine particulate matter, and noise along their moving trajectories at 10-min intervals. The resulting time-resolved exposure sequences were then partitioned using multivariate time series clustering with dynamic time warping as the similarity measure. Respondents' anxiety symptoms were assessed with the Generalized Anxiety Disorders-7 questionnaire. We fitted linear regressions to assess the associations between sequential exposure patterns and anxiety symptoms.

RESULTS

We found four distinctive daily sequential exposure patterns across the participants. Exposure patterns differed in terms of exposure levels and daily variations. Regression results revealed that participants with a "moderately health-threatening" exposure pattern were significantly associated with fewer anxiety symptoms than participants with a "strongly health-threatening" exposure pattern.

CONCLUSIONS

Our findings support that environmental exposures' daily sequence and short-term magnitudes may be associated with mental health. We urge more time-resolved mobility-based assessments in future analyses of environmental health effects in daily life.

Healthy Cities, A comprehensive dataset for environmental determinants of health in England cities

This paper presents a fine-grained and multi-sourced dataset for environmental determinants of health collected from England cities. We provide health outcomes of citizens covering physical health (COVID-19 cases, asthma medication expenditure, etc.), mental health (psychological medication expenditure), and life expectancy estimations. We present the corresponding environmental determinants from four perspectives, including basic statistics (population, area, etc.), behavioural environment (availability of tobacco, health-care services, etc.), built environment (road density, street view features, etc.), and natural environment (air quality, temperature, etc.). To reveal regional differences, we extract and integrate massive environment and health indicators from heterogeneous sources into two unified spatial scales, i.e., at the middle layer super output area (MSOA) and the city level, via big data processing and deep learning. Our data holds great promise for diverse audiences, such as public health researchers and urban designers, to further unveil the environmental determinants of health and design methodology for a healthy, sustainable city.

Optimizing modeling windows to better capture the long-term variation of PM2.5 concentrations in China during 2005-2019

Including data of different time intervals during model development influences the predicting accuracy of PM2.5 but has not been widely discussed. Therefore, we included modeling data with multiple time windows to identify optimized modeling time windows for capturing the long-term variation of PM2.5 in China during 2005-2019. In general, we incorporated PM2.5 measurements, aerosol optical depth (AOD), meteorological parameters, land use data, and other predictors to train random forest models. The study period was separated into two phases (2013-2019 and 2005-2012) according to the availability of PM2.5 measurements. First, we trained models with two strategies of choosing time windows to compare model performance in predicting PM2.5 from 2013 to 2019, when measurements were available. Strategy 1a (ST1a) refers to training one model with all available data, and Strategy 1b (ST1b) refers to training multiple models each with one-year data. Second, we trained models with additional ten strategies (ST2a-ST2j) based on data from different time windows during 2013-2019 to compare the accuracy in predicting PM2.5 before 2013, when measurements were unavailable. The internal and external cross-validation (CV) indicated that the model performance of ST1b was better than ST1a. Predictions based on ST1a tended to underestimate PM2.5 levels in 2013 and 2014 when PM2.5 concentrations were high, and overestimate after 2017 when PM2.5 dropped dramatically. The external CV of predicting historical PM2.5 was the most robust in ST2i (averaged predictions from two models developed by 2013 and 2014 data, respectively). Models with data closer to historical years and PM2.5 levels performed better in predicting historical PM2.5 concentrations. Our results suggested that training models with data of current-years performed better during 2013-2019, and with data of 2013 and 2014 performed better in predicting historical PM2.5 before 2013 in China. The comparison provided evidence for choosing optimized time windows when predicting long-term PM2.5 concentrations in China.

Innovative experimental approach for spatial mapping of source-specific risk contributions of potentially toxic trace elements in PM10

Exposure to potentially toxic trace elements (PTTEs) in inhalable particulate matter (PM₁₀) is associated with an increased risk of developing cardiorespiratory diseases. Therefore, in multi-source polluted urban contexts, a spatially-resolved evaluation of health risks associated with exposure to PTTEs in PM is essential to identify critical risk areas. In this study, a very-low volume device for high spatial resolution sampling and analysis of PM₁₀ was employed in Terni (Central Italy) in a wide and dense network (23 sampling sites, about 1 km between each other) during a 15-month monitoring campaign. The soluble and insoluble fraction of 33 elements in PM₁₀ was analysed through a chemical fractionation procedure that increased the selectivity of the elements as source tracers. Total carcinogenic risk (CR) and non-carcinogenic risk (NCR) for adults and children due to concentrations of PTTEs in PM₁₀ were calculated and quantitative source-specific risk apportionment was carried out by applying Positive Matrix Factorization (PMF) to the spatially-resolved concentrations of the chemically fractionated elements. PMF analysis identified 5 factors: steel plant, biomass burning, brake dust, soil dust and road dust. Steel plant showed the greatest risk contribution. Total CR and NCR, and source-specific risk contributions at the 23 sites were interpolated using the ordinary kriging (OK) method and mapped to geo-reference the health risks of the identified sources in the whole study area. This also allowed risk estimation in areas not directly measured and the assessment of the risk contribution of individual sources at each point of the study area. This innovative experimental approach is an effective tool to localize the health risks of spatially disaggregated sources of PTTEs and it may allow for better planning of control strategies and mitigation measures to reduce airborne pollutant concentrations in urban settings polluted by multiple sources.

Daily space-time activities, multiple environmental exposures, and anxiety symptoms: A cross-sectional mobile phone-based sensing study

BACKGROUND

Few mobility-based studies have investigated the associations between multiple environmental exposures, including social exposures, and mental health.

OBJECTIVE

To assess how exposure to green space, blue space, noise, air pollution, and crowdedness along people's daily mobility paths are associated with anxiety symptoms.

METHODS

358 participants were cross-sectionally tracked with Global Positioning System (GPS)-enabled mobile phones. Anxiety symptoms were measured at baseline using the Generalized Anxiety Disorder-7 (GAD-7) questionnaire. Green space, blue space, noise, and air pollution were assessed based on concentric buffers of 50 m and 100 m around each GPS point. Crowdedness was measured by the number of nearby Bluetooth-enabled devices detected along the mobility paths. Multiple linear regressions with full covariate adjustment were fitted to examine anxiety-environmental exposures associations. Random forest models were applied to explore possible nonlinear associations and exposure interactions.

RESULTS

Regression results showed null linear associations between GAD-7 scores and environmental exposures. Random forest models indicated that GAD-7-environment associations varied nonlinearly with exposure levels. We found a negative association between green space and GAD-7 scores only for participants with moderate green space exposure. We observed a positive association between GAD-7 scores and noise levels above 60 dB and air pollution concentrations above 17.2 μg m^-3. Crowdedness was positively associated with GAD-7 scores, but exposure-response functions flattened out with pronounced crowdedness of >7.5. Blue space tended to be positively associated with GAD-7 scores. Random forest models ranked environmental exposures as more important to explain GAD-7 scores than linear models.

CONCLUSIONS

Our findings indicate possible nonlinear associations between mobility-based environmental exposures and anxiety symptoms. More studies are needed to obtain an in-depth understanding of underlying anxiety-environment mechanisms during daily life.

High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning

Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R²) value of 0.740 for PM_2.5, 0.754 for CO and 0.716 for SO₂. Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.

Implications of Mitigating Ozone and Fine Particulate Matter Pollution in the Guangdong-Hong Kong-Macau Greater Bay Area of China Using a Regional-To-Local Coupling Model

Ultrahigh-resolution air quality models that resolve sharp gradients of pollutant concentrations benefit the assessment of human health impacts. Mitigating fine particulate matter (PM_2.5) concentrations over the past decade has triggered ozone (O₃) deterioration in China. Effective control of both pollutants remains poorly understood from an ultrahigh-resolution perspective. We propose a regional-to-local model suitable for quantitatively mitigating pollution pathways at various resolutions. Sensitivity scenarios for controlling nitrogen oxide (NO_x) and volatile organic compound (VOC) emissions are explored, focusing on traffic and industrial sectors. The results show that concurrent controls on both sectors lead to reductions of 17%, 5%, and 47% in NO_x, PM_2.5, and VOC emissions, respectively. The reduced traffic scenario leads to reduced NO₂ and PM_2.5 but increased O₃ concentrations in urban areas. Guangzhou is located in a VOC-limited O₃ formation regime, and traffic is a key factor in controlling NO_x and O₃. The reduced industrial VOC scenario leads to reduced O₃ concentrations throughout the mitigation domain. The maximum decrease in median hourly NO₂ is >11 μg/m³, and the maximum increase in the median daily maximum 8-hr rolling O₃ is >10 μg/m³ for the reduced traffic scenario. When controls on both sectors are applied, the O₃ increase reduces to <7 μg/m³. The daily averaged PM_2.5 decreases by <2 μg/m³ for the reduced traffic scenario and varies little for the reduced industrial VOC scenario. An O₃ episode analysis of the dual-control scenario leads to O₃ decreases of up to 15 μg/m³ (8-hr metric) and 25 μg/m³ (1-hr metric) in rural areas.

Association between ozone exposure and prevalence of mumps: a time-series study in a Megacity of Southwest China

In the present study, we aim to evaluate the delayed and cumulative effect of ozone (O₃) exposure on mumps in a megacity with high population density and high humidity. We took Chongqing, a megacity in Southwest China, as the research area and 2013-2017 as the research period. A total of 49,258 confirmed mumps cases were collected from 122 hospitals of Chongqing. We employed the distributed lag nonlinear models with quasi-Poisson link to investigate the relationship between prevalence of mumps and O₃ exposure after adjusting for the effects of meteorological conditions. The results show that the effect of O₃ exposure on mumps was mainly manifested in the lag of 0-7 days. The single-day ;lag effect was the most obvious on the 4th day, with the relative risk (RR) of mumps occurs of 1.006 (95% CI: 1.003-1.007) per 10 μg/m³ in the O₃ exposure. The cumulative RR within 7 days was 1.025 (95% CI: 1.013-1.038). Our results suggest that O₃ exposure can increase the risk of mumps infection, which fills the gap of relevant research in mountainous areas with high population density and high humidity.

A comparison of associations with childhood lung function between air pollution exposure assessment methods with and without accounting for time-activity patterns

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 (NO₂, NO_X, PM_2.5, PM_2.5absorbance, PM₁₀) 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 NO₂ the decrease in FEV₁ was -1.40% (CI; -2.54, -0.24%) per IQR (9.14 μg/m³) 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.

Multiple environmental exposures along daily mobility paths and depressive symptoms: A smartphone-based tracking study

Few studies go beyond the residential environment in assessments of the environment-mental health association, despite multiple environments being encountered in daily life. This study investigated 1) the associations between multiple environmental exposures and depressive symptoms, both in the residential environment and along the daily mobility path, 2) examined differences in the strength of associations between residential- and mobility-based models, and 3) explored sex as a moderator. Depressive symptoms of 393 randomly sampled adults aged 18-65 were assessed using the Patient Health Questionnaire (PHQ-9). Respondents were tracked via global positioning systems- (GPS) enabled smartphones for up to 7 days. Exposure to green space (normalized difference vegetation index (NDVI)), blue space, noise (Lden) and air pollution (particulate matter (PM_2.5)) within 50 m and 100 m of each residential address and GPS point was computed. Multiple linear regression analyses were conducted separately for the residential- and mobility-based exposures. Wald tests were used to assess if the coefficients differed across models. Interaction terms were entered in fully adjusted models to determine if associations varied by sex. A significant negative relationship between green space and depressive symptoms was found in the fully adjusted residential- and mobility-based models using the 50 m buffer. No significant differences were observed in coefficients across models. None of the interaction terms were significant. Our results suggest that exposure to green space in the immediate environment, both at home and along the daily mobility path, is associated with a reduction in depressive symptoms. Further research is required to establish the utility of dynamic approaches to exposure assessment in studies on the environment and mental health.

Urban Air Pollution Mapping Using Fleet Vehicles as Mobile Monitors and Machine Learning

Spatially explicit urban air quality information is important for developing effective air quality control measures. Traditionally, urban air quality is measured by networks of stationary monitors that are not universally available and sparsely sited. Mobile air quality monitoring using equipped vehicles is a promising alternative but has focused on vehicle-level experiments and lacks fleet-level demonstration. Here, we equipped 260 electric vehicles in a ride-hailing fleet in Beijing, China with low-cost sensors to collect real-time, spatial-resolved data on fine particulate matter (PM_2.5) concentrations. Using this data, we developed a decision tree model to infer the distribution of PM_2.5 concentrations in Beijing at 1 km by 1 km and 1 h resolution. Our results are able to show both short- and long-term variations of urban PM_2.5 concentrations and identify local air pollution hotspots. Compared with a benchmark model that only uses data from stationary monitoring sits, our model has shown significant improvement with the coefficient of determination increased from 0.56 to 0.80 and root mean square error decreased from 12.6 to 8.1 μg/m³. To the best of our knowledge, this study collects the largest mobile sensor data for urban air quality monitoring, which are augmented by state-of-the-art machine learning techniques to derive high-quality urban air pollution mapping. Our results demonstrate the potential and necessity of using fleet vehicles as routine mobile sensors combined with advanced data science methods to provide high-resolution urban air quality monitoring.

A hybrid air pollution / land use regression model for predicting air pollution concentrations in Durban, South Africa

The objective of this paper was to incorporate source-meteorological interaction information from two commonly employed atmospheric dispersion models into the land use regression technique for predicting ambient nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and particulate matter (PM₁₀). The study was undertaken across two regions in Durban, South Africa, one with a high industrial profile and a nearby harbour, and the other with a primarily commercial and residential profile. Multiple hybrid models were developed by integrating air pollution dispersion modelling predictions for source specific NO₂, SO₂, and PM₁₀ concentrations into LUR models following the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to characterise exposure, in Durban. Industrial point sources, ship emissions, domestic fuel burning, and vehicle emissions were key emission sources. Standard linear regression was used to develop annual, summer and winter hybrid models to predict air pollutant concentrations. Higher levels of NO₂ and SO₂ were predicted in south Durban as compared to north Durban as these are industrial related pollutants. Slightly higher levels of PM₁₀ were predicted in north Durban as compared to south Durban and can be attributed to either traffic, bush burning or domestic fuel burning. The hybrid NO₂ models for annual, summer and winter explained 60%, 58% and 63%, respectively, of the variance with traffic, population and harbour being identified as important predictors. The SO₂ models were less robust with lower R² annual (44%), summer (53%) and winter (46%), in which industrial and traffic variables emerged as important predictors. The R² for PM₁₀ models ranged from 80% to 85% with population and urban land use type emerging as predictor variables.

Using Street View Imagery to Predict Street-Level Particulate Air Pollution

Land-use regression (LUR) models are frequently applied to estimate spatial patterns of air pollution. Traditional LUR often relies on fixed-site measurements and GIS-derived variables with limited spatial resolution. We present an approach that leverages Google Street View (GSV) imagery to predict street-level particulate air pollution (i.e., black carbon [BC] and particle number [PN] concentrations). We developed empirical models based on mobile monitoring data and features extracted from ∼52 500 GSV images using a deep learning model. We tested theory- and data-driven feature selection methods as well as models using images within varying buffer sizes (50-2000 m). Compared to LUR models with traditional variables, our models achieved similar model performance using the street-level predictors while also identifying additional potential hotspots. Adjusted R² (10-fold CV R²) with integrated feature selection was 0.57-0.64 (0.50-0.57) and 0.65-0.73 (0.61-0.66) for BC and PN models, respectively. Models using only features near the measurement locations (i.e., GSV images within 250 m) explained ∼50% of air pollution variability, indicating PN and BC are strongly affected by the street-level built environment. Our results suggest that GSV imagery, processed with computer vision techniques, is a promising data source to develop LUR models with high spatial resolution and consistent predictor variables across administrative boundaries.

Spatial and Spatiotemporal Variability of Regional Background Ultrafine Particle Concentrations in the Netherlands

Studies of the health effects of ultrafine particles (UFPs) in large nationwide cohorts are currently hampered by a lack of knowledge about spatial and spatiotemporal variations in regional background UFPs. We measured the UFP (10-300 nm) at 20 regional background locations (3 × 2 weeks) across the Netherlands and a reference site continuously over a total period of 14 months in 2016-2017. We compared the overall averages for each site and used kriging to create a regional background spatial map of the Netherlands. Spatiotemporal variability was analyzed by correlating time-series of 2 and 24 h average concentrations. The overall average measured UFP concentrations at the 20 locations ranged from 3814 to 7070 particles/cm³. We found the spatial correlation in the UFP concentrations up to 180 km and clear differences between the north and the more populated southern parts of the country. The average temporal correlation between 2 and 24 h average UFP concentrations was 0.50 (IQR: 0.36-0.61) and 0.58 (IQR: 0.44-0.75), respectively. Temporal correlation declined weakly with a distance between sites, from 0.58 for sites within 80 km of each other to 0.47 for sites farther away. The substantial spatial variation in the regional background UFP concentrations suggests that regional variation may contribute importantly to exposure contrast in nationwide health studies of UFP.

Spatial and Spatiotemporal Variability of Regional Background Ultrafine Particle Concentrations in the Netherlands

Studies of the health effects of ultrafine particles (UFPs) in large nationwide cohorts are currently hampered by a lack of knowledge about spatial and spatiotemporal variations in regional background UFPs. We measured the UFP (10-300 nm) at 20 regional background locations (3 × 2 weeks) across the Netherlands and a reference site continuously over a total period of 14 months in 2016-2017. We compared the overall averages for each site and used kriging to create a regional background spatial map of the Netherlands. Spatiotemporal variability was analyzed by correlating time-series of 2 and 24 h average concentrations. The overall average measured UFP concentrations at the 20 locations ranged from 3814 to 7070 particles/cm³. We found the spatial correlation in the UFP concentrations up to 180 km and clear differences between the north and the more populated southern parts of the country. The average temporal correlation between 2 and 24 h average UFP concentrations was 0.50 (IQR: 0.36-0.61) and 0.58 (IQR: 0.44-0.75), respectively. Temporal correlation declined weakly with a distance between sites, from 0.58 for sites within 80 km of each other to 0.47 for sites farther away. The substantial spatial variation in the regional background UFP concentrations suggests that regional variation may contribute importantly to exposure contrast in nationwide health studies of UFP.

Outdoor light at night, air pollution and depressive symptoms: A cross-sectional study in the Netherlands

BACKGROUND

Artificial light at night (ALAN) may be an anthropogenic stressor for mental health disturbing humans' natural day-night cycle. However, the few existing studies used satellite-based measures of radiances for outdoor ALAN exposure assessments, which were possibly confounded by traffic-related air pollutants.

OBJECTIVES

To assess 1) whether living in areas with increased exposure to outdoor ALAN is associated with depressive symptoms; and 2) to assess the potential confounding effects of air pollution.

METHODS

We used cross-sectional data from people (N = 10,482) aged 18-65 years in the Netherlands. Depressive symptoms were assessed with the Patient Health Questionnaire (PHQ-9). Satellite-measured annual ALAN were taken from the Visible Infrared Imaging Radiometer Suite. ALAN exposures were assessed at people's home address within 100 and 600 m buffers. We used generalized (geo)additive models to quantify associations between PHQ-9 scores and quintiles of ALAN adjusting for several potential confounders including PM_2.5 and NO₂.

RESULTS

Unadjusted estimates for the 100 m buffers showed that people in the 2^nd to 5^th ALAN quintile showed significantly higher PHQ-9 scores than those in the lowest ALAN quintile (β_Q2 = 0.503 [95% confidence intervals (CI): 0.207-0.798], β_Q3 = 0.587 [95% CI: 0.291-0.884], β_Q4 = 0.921 [95% CI: 0.623-1.218], β_Q5 = 1.322 [95% CI: 1.023-1.620]). ALAN risk estimates adjusted for individual and area-level confounders (i.e., PM_2.5, urbanicity, noise, land-use diversity, greenness, deprivation, and social fragmentation) were attenuated but remained significant for the 100 m buffer (β_Q2 = 0.420 [95% CI: 0.125-0.715], β_Q3 = 0.383 [95% CI: 0.071-0.696], β_Q4 = 0.513 [95% CI: 0.177-0.850], β_Q5 = 0.541 [95% CI: 0.141-0.941]). When adjusting for NO₂ per 100 m buffers, the air pollutant was associated with PHQ-9 scores, but ALAN did not display an exposure-response relationship. ALAN associations were insignificant for 600 m buffers.

CONCLUSION

Accounting for NO₂ exposure suggested that air pollution rather than outdoor ALAN correlated with depressive symptoms. Future evaluations of health effects from ALAN should consider potential confounding by traffic-related exposures (i.e., NO₂).

Greenery exposure and suicide mortality later in life: A longitudinal register-based case-control study

BACKGROUND

Exposure to residential greenery accumulates over people's lifetimes, and possibly has a protective association with suicide later in life.

OBJECTIVES

To examine the associations between suicide mortality and long-term residential greenery exposure in male and female adults.

METHODS

Our population-based nested case-control study used longitudinally georeferenced Dutch register data. Suicide cases aged 18-64 years between 2007 and 2016 were matched by gender, age, and date of suicide to 10 random controls. We measured long-term greenery exposure along people's 10-year residential address histories through longitudinal normalized difference vegetation indices (NDVI) from Landsat satellite imagery between 1997 and 2016. We assigned accumulated greenery exposures, weighted by people's exposure duration, within 300, 600, and 1,000 m concentric buffers around home addresses. To assess associations between suicide and greenery, we estimated gender-specific conditional logistic regressions without and with adjustment for individual-level and area-level confounders. Stratified models were fitted for areas with a high/low level of urbanicity and movers/non-movers.

RESULTS

Our study population consisted of 9,757 suicide cases and 95,641 controls. In our models adjusted for age, gender, and date of suicide, the odds ratios decreased significantly with higher quartiles of accumulated NDVI scores. NDVI associations were attenuated and did not remain significant after adjustment for socioeconomics, urbanicity, air pollution, social fragmentation, etc. for either males or females. For females, but not males, our model with 300 m buffers for areas with a low level of urbanicity showed a significant suicide risk reduction with increasing levels of NDVI. Individual risk factors (e.g., lack of labor market participation) outweighed the contribution of greenery.

CONCLUSION

We found limited evidence that long-term greenery exposure over people's lifetimes contributes to resilience against suicide mortality. Ensuring exposure to greenery may contribute to suicide prevention for specific population groups, but the effectiveness of such exposure should not be overstated.

Activity-based air pollution exposure assessment: Differences between homemakers and cycling commuters

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 NO₂ exposure of commuters are on average slightly higher and vary less spatially as they are exposed to NO₂ at multiple locations.

A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series

Exploring global anthropogenic heat and its effects on climate change is necessary and meaningful to gain a better understanding of human-environment interactions caused by growing energy consumption. However, the variation in regional energy consumption and limited data availability make estimating long-term global anthropogenic heat flux (AHF) challenging. Thus, using high-resolution population density data (30 arc-second) and a top-down inventory-based approach, this study developed a new global gridded AHF dataset covering 1970-2050 based historically on energy consumption data from the British Petroleum (BP); future projections were built on estimated future energy demands. The globally averaged terrestrial AHFs were estimated at 0.05, 0.13, and 0.16 W/m2 in 1970, 2015, and 2050, respectively, but varied greatly among countries and regions. Multiple validation results indicate that the past and future global gridded AHF (PF-AHF) dataset has reasonable accuracy in reflecting AHF at various scales. The PF-AHF dataset has longer time series and finer spatial resolution than previous data and provides powerful support for studying long-term climate change at various scales.

Relations between air pollution and vascular development in 5-year old children: a cross-sectional study in the Netherlands

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.