Long-term exposure to elemental components of fine particulate matter and all-natural and cause-specific mortality in a Danish nationwide administrative cohort study.

BACKGROUND: Fine particulate matter (PM2.5) is a well-recognized risk factor for premature death. However, evidence on which PM2.5 components are most relevant is unclear. METHODS: We evaluated the associations between mortality and long-term exposure to eight PM2.5 elemental components [copper (Cu), iron (Fe), zinc (Zn), sulfur (S), nickel (Ni), vanadium (V), silicon (Si), and potassium (K)]. Studied outcomes included death from diabetes, chronic kidney disease (CKD), dementia, and psychiatric disorders as well as all-natural causes, cardiovascular disease (CVD), respiratory diseases (RD), and lung cancer. We followed all residents in Denmark (aged ≥30 years) from January 1, 2000 to December 31, 2017. We used European-wide land-use regression models at a 100 × 100 m scale to estimate the residential annual mean levels of exposure to PM2.5 components. The models were developed with supervised linear regression (SLR) and random forest (RF). The associations were evaluated by Cox proportional hazard models adjusting for individual- and area-level socioeconomic factors and total PM2.5 mass. RESULTS: Of 3,081,244 individuals, we observed 803,373 death from natural causes during follow-up. We found significant positive associations between all-natural mortality with Si and K from both exposure modeling approaches (hazard ratios; 95% confidence intervals per interquartile range increase): SLR-Si (1.04; 1.03-1.05), RF-Si (1.01; 1.00-1.02), SLR-K (1.03; 1.02-1.04), and RF-K (1.06; 1.05-1.07). Strong associations of K and Si were detected with most causes of mortality except CKD and K, and diabetes and Si (the strongest associations for psychiatric disorders mortality). In addition, Fe was relevant for mortality from RD, lung cancer, CKD, and psychiatric disorders; Zn with mortality from CKD, RD, and lung cancer, and; Ni and V with lung cancer mortality. CONCLUSIONS: We present novel results of the relevance of different PM2.5 components for different causes of death, with K and Si seeming to be most consistently associated with mortality in Denmark.

Association between exposure to multiple air pollutants, transportation noise and cause-specific mortality in adults in Switzerland.

BACKGROUND: Long-term exposure to air pollution and noise is detrimental to health; but studies that evaluated both remain limited. This study explores associations with natural and cause-specific mortality for a range of air pollutants and transportation noise. METHODS: Over 4 million adults in Switzerland were followed from 2000 to 2014. Exposure to PM2.5, PM2.5 components (Cu, Fe, S and Zn), NO2, black carbon (BC) and ozone (O3) from European models, and transportation noise from source-specific Swiss models, were assigned at baseline home addresses. Cox proportional hazards models, adjusted for individual and area-level covariates, were used to evaluate associations with each exposure and death from natural, cardiovascular (CVD) or non-malignant respiratory disease. Analyses included single and two exposure models, and subset analysis to study lower exposure ranges. RESULTS: During follow-up, 661,534 individuals died of natural causes (36.6% CVD, 6.6% respiratory). All exposures including the PM2.5 components were associated with natural mortality, with hazard ratios (95% confidence intervals) of 1.026 (1.015, 1.038) per 5 µg/m3 PM2.5, 1.050 (1.041, 1.059) per 10 µg/m3 NO2, 1.057 (1.048, 1.067) per 0.5 × 10-5/m BC and 1.045 (1.040, 1.049) per 10 dB Lden total transportation noise. NO2, BC, Cu, Fe and noise were consistently associated with CVD and respiratory mortality, whereas PM2.5 was only associated with CVD mortality. Natural mortality associations persisted < 20 µg/m3 for PM2.5 and NO2, < 1.5 10-5/m BC and < 53 dB Lden total transportation noise. The O3 association was inverse for all outcomes. Including noise attenuated all outcome associations, though many remained significant. Across outcomes, noise was robust to adjustment to air pollutants (e.g. natural mortality 1.037 (1.033, 1.042) per 10 dB Lden total transportation noise, after including BC). CONCLUSION: Long-term exposure to air pollution and transportation noise in Switzerland contribute to premature mortality. Considering co-exposures revealed the importance of local traffic-related pollutants such as NO2, BC and transportation noise.

Predictors of healthy physiological aging across generations in a 30-year population-based cohort study: the Doetinchem Cohort Study.

BACKGROUND: Predicting healthy physiological aging is of major interest within public health research. However, longitudinal studies into predictors of healthy physiological aging that include numerous exposures from different domains (i.e. the exposome) are scarce. Our aim is to identify the most important exposome-related predictors of healthy physiological aging over the life course and across generations. METHODS: Data were used from 2815 participants from four generations (generation 1960s/1950s/1940s/1930s aged respectively 20-29/30-39/40-49/50-59 years old at baseline, wave 1) of the Doetinchem Cohort Study who were measured every 5 years for 30 years. The Healthy Aging Index, a physiological aging index consisting of blood pressure, glucose, creatinine, lung function, and cognitive functioning, was measured at age 46-85 years (wave 6). The average exposure and trend of exposure over time of demographic, lifestyle, environmental, and biological exposures were included, resulting in 86 exposures. Random forest was used to identify important predictors. RESULTS: The most important predictors of healthy physiological aging were overweight-related (BMI, waist circumference, waist/hip ratio) and cholesterol-related (using cholesterol lowering medication, HDL and total cholesterol) measures. Diet and educational level also ranked in the top of important exposures. No substantial differences were observed in the predictors of healthy physiological aging across generations. The final prediction model's performance was modest with an R2 of 17%. CONCLUSIONS: Taken together, our findings suggest that longitudinal cardiometabolic exposures (i.e. overweight- and cholesterol-related measures) are most important in predicting healthy physiological aging. This finding was similar across generations. More work is needed to confirm our findings in other study populations.

Predicting self-perceived general health status using machine learning: an external exposome study.

BACKGROUND: Self-perceived general health (SPGH) is a general health indicator commonly used in epidemiological research and is associated with a wide range of exposures from different domains. However, most studies on SPGH only investigated a limited set of exposures and did not take the entire external exposome into account. We aimed to develop predictive models for SPGH based on exposome datasets using machine learning techniques and identify the most important predictors of poor SPGH status. METHODS: Random forest (RF) was used on two datasets based on personal characteristics from the 2012 and 2016 editions of the Dutch national health survey, enriched with environmental and neighborhood characteristics. Model performance was determined using the area under the curve (AUC) score. The most important predictors were identified using a variable importance procedure and individual effects of exposures using partial dependence and accumulated local effect plots. The final 2012 dataset contained information on 199,840 individuals and 81 variables, whereas the final 2016 dataset had 244,557 individuals with 91 variables. RESULTS: Our RF models had overall good predictive performance (2012: AUC = 0.864 (CI: 0.852-0.876); 2016: AUC = 0.890 (CI: 0.883-0.896)) and the most important predictors were "Control of own life", "Physical activity", "Loneliness" and "Making ends meet". Subjects who felt insufficiently in control of their own life, scored high on the De Jong-Gierveld loneliness scale or had difficulty in making ends meet were more likely to have poor SPGH status, whereas increased physical activity per week reduced the probability of poor SPGH. We observed associations between some neighborhood and environmental characteristics, but these variables did not contribute to the overall predictive strength of the models. CONCLUSIONS: This study identified that within an external exposome dataset, the most important predictors for SPGH status are related to mental wellbeing, physical exercise, loneliness, and financial status.

Long-term exposure to low-level air pollution and incidence of asthma: the ELAPSE project.

BACKGROUND: Long-term exposure to ambient air pollution has been linked to childhood-onset asthma, although evidence is still insufficient. Within the multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), we examined the associations of long-term exposures to particulate matter with a diameter <2.5 µm (PM2.5), nitrogen dioxide (NO2) and black carbon (BC) with asthma incidence in adults. METHODS: We pooled data from three cohorts in Denmark and Sweden with information on asthma hospital diagnoses. The average concentrations of air pollutants in 2010 were modelled by hybrid land-use regression models at participants' baseline residential addresses. Associations of air pollution exposures with asthma incidence were explored with Cox proportional hazard models, adjusting for potential confounders. RESULTS: Of 98 326 participants, 1965 developed asthma during a mean follow-up of 16.6 years. We observed associations in fully adjusted models with hazard ratios of 1.22 (95% CI 1.04-1.43) per 5 µg·m-3 for PM2.5, 1.17 (95% CI 1.10-1.25) per 10 µg·m-3 for NO2 and 1.15 (95% CI 1.08-1.23) per 0.5×10-5 m-1 for BC. Hazard ratios were larger in cohort subsets with exposure levels below the European Union and US limit values and possibly World Health Organization guidelines for PM2.5 and NO2. NO2 and BC estimates remained unchanged in two-pollutant models with PM2.5, whereas PM2.5 estimates were attenuated to unity. The concentration-response curves showed no evidence of a threshold. CONCLUSIONS: Long-term exposure to air pollution, especially from fossil fuel combustion sources such as motorised traffic, was associated with adult-onset asthma, even at levels below the current limit values.

Effects of exposure to surrounding green, air pollution and traffic noise with non-accidental and cause-specific mortality in the Dutch national cohort.

BACKGROUND: Everyday people are exposed to multiple environmental factors, such as surrounding green, air pollution and traffic noise. These exposures are generally spatially correlated. Hence, when estimating associations of surrounding green, air pollution or traffic noise with health outcomes, the other exposures should be taken into account. The aim of this study was to evaluate associations of long-term residential exposure to surrounding green, air pollution and traffic noise with mortality. METHODS: We followed approximately 10.5 million adults (aged ≥ 30 years) living in the Netherlands from 1 January 2013 until 31 December 2018. We used Cox proportional hazard models to evaluate associations of residential surrounding green (including the average Normalized Difference Vegetation Index (NDVI) in buffers of 300 and 1000 m), annual average ambient air pollutant concentrations [including particulate matter (PM2.5), nitrogen dioxide (NO2)] and traffic noise with non-accidental and cause-specific mortality, adjusting for potential confounders. RESULTS: In single-exposure models, surrounding green was negatively associated with all mortality outcomes, while air pollution was positively associated with all outcomes. In two-exposure models, associations of surrounding green and air pollution attenuated but remained. For respiratory mortality, in a two-exposure model with NO2 and NDVI 300 m, the HR of NO2 was 1.040 (95%CI: 1.022, 1.059) per IQR increase (8.3 µg/m3) and the HR of NDVI 300 m was 0.964 (95%CI: 0.952, 0.976) per IQR increase (0.14). Road-traffic noise was positively associated with lung cancer mortality only, also after adjustment for air pollution or surrounding green. CONCLUSIONS: Lower surrounding green and higher air pollution were associated with a higher risk of non-accidental and cause-specific mortality. Studies including only one of these correlated exposures may overestimate the associations with mortality of that exposure.

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

The Impact of Short-Term Exposure to Air Pollution on the Exhaled Breath of Healthy Adults.

Environmental factors, such as air pollution, can affect the composition of exhaled breath, and should be well understood before biomarkers in exhaled breath can be used in clinical practice. Our objective was to investigate whether short-term exposures to air pollution can be detected in the exhaled breath profile of healthy adults. In this study, 20 healthy young adults were exposed 2-4 times to the ambient air near a major airport and two highways. Before and after each 5 h exposure, exhaled breath was analyzed using an electronic nose (eNose) consisting of seven different cross-reactive metal-oxide sensors. The discrimination between pre and post-exposure was investigated with multilevel partial least square discriminant analysis (PLSDA), followed by linear discriminant and receiver operating characteristic (ROC) analysis, for all data (71 visits), and for a training (51 visits) and validation set (20 visits). Using all eNose measurements and the training set, discrimination between pre and post-exposure resulted in an area under the ROC curve of 0.83 (95% CI = 0.76-0.89) and 0.84 (95% CI = 0.75-0.92), whereas it decreased to 0.66 (95% CI = 0.48-0.84) in the validation set. Short-term exposure to high levels of air pollution potentially influences the exhaled breath profiles of healthy adults, however, the effects may be minimal for regular daily exposures.

Development of Europe-Wide Models for Particle Elemental Composition Using Supervised Linear Regression and Random Forest.

We developed Europe-wide models of long-term exposure to eight elements (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) in particulate matter with diameter <2.5 µm (PM2.5) using standardized measurements for one-year periods between October 2008 and April 2011 in 19 study areas across Europe, with supervised linear regression (SLR) and random forest (RF) algorithms. Potential predictor variables were obtained from satellites, chemical transport models, land-use, traffic, and industrial point source databases to represent different sources. Overall model performance across Europe was moderate to good for all elements with hold-out-validation R-squared ranging from 0.41 to 0.90. RF consistently outperformed SLR. Models explained within-area variation much less than the overall variation, with similar performance for RF and SLR. Maps proved a useful additional model evaluation tool. Models differed substantially between elements regarding major predictor variables, broadly reflecting known sources. Agreement between the two algorithm predictions was generally high at the overall European level and varied substantially at the national level. Applying the two models in epidemiological studies could lead to different associations with health. If both between- and within-area exposure variability are exploited, RF may be preferred. If only within-area variability is used, both methods should be interpreted equally.

Residential surrounding green, air pollution, traffic noise and self-perceived general health.

Self-perceived general health (SGH) is one of the most inclusive and widely used measures of health status and a powerful predictor of mortality. However, only a limited number of studies evaluated associations of combined environmental exposures on SGH. Our aim was to evaluate associations of combined residential exposure to surrounding green, air pollution and traffic noise with poor SGH in the Netherlands. We linked data on long-term residential exposure to surrounding green based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and road- and rail-traffic noise with a Dutch national health survey, resulting in a study population of 354,827 adults. We analyzed associations of single and combined exposures with poor SGH. In single-exposure models, NDVI within 300 m was inversely associated with poor SGH [odds ratio (OR) = 0.91, 95% CI: 0.89, 0.94 per IQR increase], while NO2 was positively associated with poor SGH (OR = 1.07, 95% CI: 1.04, 1.11 per IQR increase). In multi-exposure models, associations with surrounding green and air pollution generally remained, but attenuated. Joint odds ratios (JOR) of combined exposure to air pollution, rail-traffic noise and decreased surrounding green were higher than the odds ratios of single-exposure models. Studies including only one of these correlated exposures may overestimate the risk of poor SGH attributed to the studied exposure, while underestimating the risk of combined exposures.

The associations of air pollution, traffic noise and green space with overweight throughout childhood: The PIAMA birth cohort study.

BACKGROUND: Air pollution, traffic noise and absence of green space may contribute to the development of overweight in children. OBJECTIVES: To investigate the combined associations of air pollution, traffic noise and green space with overweight throughout childhood. METHODS: We used data for 3680 participants of the Dutch PIAMA birth cohort. We estimated exposure to air pollution, traffic noise and green space (i.e. the average Normalized Difference Vegetation Index (NDVI) and percentages of green space in circular buffers of 300 m and 3000 m) at the children's home addresses at the time of parental reported weight and height measurements. Associations of these exposures with overweight from age 3 to 17 years were analyzed by generalized linear mixed models, adjusting for potential confounders. Odds ratios (OR's) are presented for an interquartile range increase in exposure. RESULTS: odds of being overweight increased with increasing exposure to NO2 (adjusted OR 1.40 [95% confidence interval (CI) 1.12-1.74] per 8.90 µg/m3) and tended to decrease with increasing exposure to green space in a 3000 m buffer (adjusted OR 0.86 [95% CI 0.71-1.04] per 0.13 increase in the NDVI; adjusted OR 0.86 [95% CI 0.71-1.03] per 29.5% increase in the total percentage of green space). After adjustment for NO2, the associations with green space in a 3000 m buffer weakened. No associations of traffic noise with overweight throughout childhood were found. In children living in an urban area, living further away from a park was associated with a lower odds of being overweight (adjusted OR 0.67 [95% CI 0.52-0.85] per 359.6 m). CONCLUSIONS: Exposure to traffic-related air pollution, but not traffic noise or green space, may contribute to childhood overweight. Future studies examining the associations of green space with childhood overweight should account for air pollution exposure.

Green space definition affects associations of green space with overweight and physical activity.

INTRODUCTION: In epidemiological studies, exposure to green space is inconsistently associated with being overweight and physical activity, possibly because studies differ widely in their definition of green space exposure, inclusion of important confounders, study population and data analysis. OBJECTIVES: We evaluated whether the association of green space with being overweight and physical activity depended upon definition of greenspace. METHODS: We conducted a cross-sectional study using data from a Dutch national health survey of 387,195 adults. Distance to the nearest park entrance and surrounding green space, based on the Normalized Difference Vegetation Index (NDVI) or a detailed Dutch land-use database (TOP10NL), was calculated for each residential address. We used logistic regression analyses to study the association of green space exposure with being overweight and being moderately or vigorously physically active outdoors at least 150min/week (self-reported). To study the shape of the association, we specified natural splines and quintiles. RESULTS: The distance to the nearest park entrance was not associated with being overweight or outdoor physical activity. Associations of surrounding green space with being overweight or outdoor physical activity were highly non-linear. For NDVI surrounding greenness, we observed significantly decreased odds of being overweight [300m buffer, odds ratio (OR) = 0.88; 95% CI: 0.86, 0.91] and increased odds for outdoor physical activity [300m buffer, OR = 1.14; 95% CI: 1.10, 1.17] in the highest quintile compared to the lowest quintile. For TOP10NL surrounding green space, associations were mostly non-significant. Associations were generally stronger for subjects living in less urban areas and for the smaller buffers. CONCLUSION: Associations of green space with being overweight and outdoor physical activity differed considerably between different green space definitions. Associations were strongest for NDVI surrounding greenness.

Children's respiratory health and oxidative potential of PM2.5: the PIAMA birth cohort study.

INTRODUCTION: The oxidative potential (OP) of particulate matter (PM) has been proposed as a health-relevant metric, but currently few epidemiological studies investigated associations of OP with health. Our main aim was to assess associations of long-term exposure to OP with respiratory health in children. Our second aim was to evaluate whether OP is more consistently associated with respiratory health than PM mass, PM composition or nitrogen dioxide (NO2). METHODS: For 3701 participants of a prospective birth cohort, annual average concentrations of OP (assessed by spin resonance (OP(ESR)) and dithiothreitol assay (OP(DTT))), PM with an aerodynamic diameter of less than 2.5 µm (PM2.5) mass, NO2, and PM2.5 constituents at the home addresses at birth and at all follow-up addresses were estimated by land-use regression. Repeated questionnaire reports of asthma and hay fever until age 14 years, and measurements of allergic sensitisation, lung function and fractional exhaled nitric oxide at age 12 years were linked with air pollution concentrations. RESULTS: Asthma incidence, prevalence of asthma symptoms and rhinitis were positively associated with OP(DTT) (adjusted OR (95% CI) per IQR increase in exposure 1.10 (1.01 to 1.20), 1.08 (1.02 to 1.16), 1.15 (1.05 to 1.26), respectively). These associations persisted after adjustment for most co-pollutants. Forced expiratory volume in 1s and forced vital capacity were negatively associated with OP(DTT). These associations were sensitive to adjustment for NO2. Respiratory health was not significantly associated with PM2.5 mass and OP(ESR). CONCLUSIONS: Respiratory health was more strongly associated with OP(DTT) than with PM2.5 mass; OP(DTT) associations with lung function, but not symptoms, were sensitive to adjustment for NO2.

Associations between three specific a-cellular measures of the oxidative potential of particulate matter and markers of acute airway and nasal inflammation in healthy volunteers.

INTRODUCTION: We evaluated associations between three a-cellular measures of the oxidative potential (OP) of particulate matter (PM) and acute health effects. METHODS: We exposed 31 volunteers for 5 h to ambient air pollution at five locations: an underground train station, two traffic sites, a farm and an urban background site. Each volunteer visited at least three sites. We conducted health measurements before exposure, 2 h after exposure and the next morning. We measured air pollution on site and characterised the OP of PM2.5 and PM10 using three a-cellular assays; dithiotreitol (OP(DTT)), electron spin resonance (OP(ESR)) and ascorbic acid depletion (OP(AA)). RESULTS: In single-pollutant models, all measures of OP were significantly associated with increases in fractional exhaled nitric oxide and increases in interleukin-6 in nasal lavage 2 h after exposure. These OP associations remained significant after adjustment for co-pollutants when only the four outdoor sites were included, but lost significance when measurements at the underground site were included. Other health end points including lung function and vascular inflammatory and coagulation parameters in blood were not consistently associated with OP. CONCLUSIONS: We found significant associations between three a-cellular measures of OP of PM and markers of airway and nasal inflammation. However, consistency of these effects in two-pollutant models depended on how measurements at the underground site were considered. Lung function and vascular inflammatory and coagulation parameters in blood were not consistently associated with OP. Our study, therefore, provides limited support for a role of OP in predicting acute health effects of PM in healthy young adults.

A national fine spatial scale land-use regression model for ozone.

Uncertainty about health effects of long-term ozone exposure remains. Land use regression (LUR) models have been used successfully for modeling fine scale spatial variation of primary pollutants but very limited for ozone. Our objective was to assess the feasibility of developing a national LUR model for ozone at a fine spatial scale. Ozone concentrations were measured with passive samplers at 90 locations across the Netherlands (19 regional background, 36 urban background, 35 traffic). All sites were measured simultaneously during four 2-weekly campaigns spread over the seasons. LUR models were developed for the summer average as the primary exposure and annual average using predictor variables obtained with Geographic Information Systems. Summer average ozone concentrations varied between 32 and 61 µg/m(3). Ozone concentrations at traffic sites were on average 9 µg/m(3) lower compared to regional background sites. Ozone correlated highly negatively with nitrogen dioxide and moderately with fine particles. A LUR model including small-scale traffic, large-scale address density, urban green and a region indicator explained 71% of the spatial variation in summer average ozone concentrations. Land use regression modeling is a promising method to assess ozone spatial variation, but the high correlation with NO2 limits application in epidemiology.

Agreement of central site measurements and land use regression modeled oxidative potential of PM2.5 with personal exposure.

Oxidative potential (OP) of ambient particulate matter (PM) has been suggested as a health-relevant exposure metric. In order to use OP for exposure assessment, information is needed about how well central site OP measurements and modeled average OP at the home address reflect temporal and spatial variation of personal OP. We collected 96-hour personal, home outdoor and indoor PM2.5 samples from 15 volunteers living either at traffic, urban or regional background locations in Utrecht, the Netherlands. OP was also measured at one central reference site to account for temporal variations. OP was assessed using electron spin resonance (OP(ESR)) and dithiothreitol (OP(DTT)). Spatial variation of average OP at the home address was modeled using land use regression (LUR) models. For both OP(ESR) and OP(DTT), temporal correlations of central site measurements with home outdoor measurements were high (R>0.75), and moderate to high (R=0.49-0.70) with personal measurements. The LUR model predictions for OP correlated significantly with the home outdoor concentrations for OP(DTT) and OP(ESR) (R=0.65 and 0.62, respectively). LUR model predictions were moderately correlated with personal OP(DTT) measurements (R=0.50). Adjustment for indoor sources, such as vacuum cleaning and absence of fume-hood, improved the temporal and spatial agreement with measured personal exposure for OP(ESR). OP(DTT) was not associated with any indoor sources. Our study results support the use of central site OP for exposure assessment of epidemiological studies focusing on short-term health effects.

Air pollution exposure affects circulating white blood cell counts in healthy subjects: the role of particle composition, oxidative potential and gaseous pollutants - the RAPTES project.

Studies have linked air pollution exposure to cardiovascular health effects, but it is not clear which components drive these effects. We examined the associations between air pollution exposure and circulating white blood cell (WBC) counts in humans. To investigate independent contributions of particulate matter (PM) characteristics, we exposed 31 healthy volunteers at five locations with high contrast and reduced correlations amongst pollutant components: two traffic sites, an underground train station, a farm and an urban background site. Each volunteer visited at least three sites and was exposed for 5 h with intermittent exercise. Exposure measurements on-site included PM mass and number concentration, oxidative potential (OP), elemental- and organic carbon, metals, O3 and NO2. Total and differential WBC counts were performed on blood collected before and 2 and 18 h post-exposure (PE). Changes in total WBC counts (2 and 18 h PE), number of neutrophils (2 h PE) and monocytes (18 h PE) were positively associated with PM characteristics that were high at the underground site. These time-dependent changes reflect an inflammatory response, but the characteristic driving this effect could not be isolated. Negative associations were observed for NO2 with lymphocytes and eosinophils. These associations were robust and did not change after adjustment for a large suite of PM characteristics, suggesting an independent effect of NO2. We conclude that short-term air pollution exposure at real-world locations can induce changes in WBC counts in healthy subjects. Future studies should indicate if air pollution exposure-induced changes in blood cell counts results in adverse cardiovascular effects in susceptible individuals.

Beyond the Runway: Respiratory health effects of ultrafine particles from aviation in children.

Aviation has been shown to cause high particle number concentrations (PNC) in areas surrounding major airports. Particle size distribution and composition differ from motorized traffic. The objective was to study short-term effects of aviation-related UFP on respiratory health in children. In 2017-2018 a study was conducted in a school panel of 7-11 year old children (n = 161) living North and South of Schiphol Airport. Weekly supervised spirometry and exhaled nitric oxide (eNO) measurements were executed. The school panel, and an additional group of asthmatic children (n = 19), performed daily spirometry tests at home and recorded respiratory symptoms. Hourly concentrations of various size fractions of PNC and black carbon (BC) were measured at three school yards. Concentrations of aviation-related particles were estimated at the residential addresses using a dispersion model. Linear and logistic mixed models were used to investigate associations between daily air pollutant concentrations and respiratory health. PNC20, a proxy for aviation-related UFP, was virtually uncorrelated with BC and PNC50-100 (reflecting primarily motorized traffic), supporting the feasibility of separating PNC from aviation and other combustion sources. No consistent associations were found between various pollutants and supervised spirometry and eNO. Major air pollutants were significantly associated with an increase in various respiratory symptoms. Odds Ratios for previous day PNC20 per 3,598pt/cm3 were 1.13 (95%CI 1.02; 1.24) for bronchodilator use and 1.14 (95%CI 1.03; 1.26) for wheeze. Modelled aviation-related UFP at the residential addresses was also positively associated with these symptoms, corroborating the PNC20 findings. PNC20 was not associated with daily lung function, but PNC50-100 and BC were negatively associated with FEV1. PNC of different sizes indicative of aviation and other combustion sources were independently associated with an increase of respiratory symptoms and bronchodilator use in children living near a major airport. No consistent associations between aviation-related UFP with lung function was observed.

Respiratory effects of a reduction in outdoor air pollution concentrations.

BACKGROUND: Air pollution has been associated with respiratory health effects. There is little direct evidence that reductions in air pollution related to abatement policies lead to actual improvement in respiratory health. We assessed whether a reduction in (traffic policy-related) air pollution concentrations was associated with changes in respiratory health. METHODS: Air pollution concentrations and respiratory health were measured in 2008 and 2010 at eight busy urban streets and at four suburban background control locations. Respiratory function was assessed twice in 661 residents by spirometry and measurements of airway resistance. Nitric oxide (NO) in exhaled air was measured as a marker for airway inflammation. RESULTS: Air pollution concentrations were lower in 2010 than in 2008. The declines in pollutants varied among locations, with the largest decline observed in a street with a large reduction in traffic intensity. In regression analyses adjusted for important covariates, reductions in concentrations of soot, NO2, NOx, Cu, and Fe were associated with increases in forced vital capacity (FVC) (∼1% increase per interquartile range [IQR] decline). Airway resistance decreased with a decline in particulate matter (PM10) and PM2.5 (9% per IQR), although these associations were somewhat less consistent. No associations were found with exhaled NO. Results were driven largely by one street where traffic-related air pollution showed the largest reduction. Forced expiratory volume and FVC improved by 3% to 6% in residents of this street compared with suburban background residents. This was accompanied by a suggestive reduction in airway resistance. CONCLUSIONS: Reductions in air pollution may lead to small improvements in respiratory function.

Acute nasal pro-inflammatory response to air pollution depends on characteristics other than particle mass concentration or oxidative potential: the RAPTES project.

OBJECTIVES: To investigate which air pollution characteristics are associated with biomarkers for acute nasal airway inflammation in healthy subjects. We hypothesised that associations would be strongest for oxidative potential (OP) of particles. METHODS: 31 volunteers were exposed to ambient air pollution at five sites in The Netherlands: two traffic sites, an underground train station, a farm and an urban background site. Each subject visited at least three sites between March and October 2009 and was exposed for 5 h per visit including exercise for 20 min every hour (h). Air pollution measurements during this 5-h-period included particulate matter (PM) mass concentration, elemental composition, elemental and organic carbon (OC), particle number concentration, OP, endotoxins, O3 and NO2. Pro-inflammatory biomarkers were measured before, 2 and 18 h postexposure, including cytokine IL-6 and IL-8, protein and lactoferrin in nasal lavage (NAL) as well as IL-6 in blood. One- and two-pollutant mixed models were used to analyse associations between exposure and changes in biomarkers. RESULTS: In two-pollutant models, cytokines in NAL were positively associated with OC, endotoxin and NO2; protein was associated with NO2; and lactoferrin was associated with all PM characteristics that were high at the underground site. In blood, associations with OC and endotoxin were negative. CONCLUSIONS: We observed no consistent effects in two-pollutant models for PM mass concentration and OP. Instead, we found consistent associations with nasal inflammatory markers for other PM characteristics, specifically OC, endotoxin and NO2.