111 results found
Chen J, de Hoogh K, Gulliver J, et al., 2020, Development of Europe-Wide Models for Particle Elemental Composition Using Supervised Linear Regression and Random Forest, ENVIRONMENTAL SCIENCE & TECHNOLOGY, Vol: 54, Pages: 15698-15709, ISSN: 0013-936X
Laine JE, Bodinier B, Robinson O, et al., 2020, Prenatal exposure to multiple air pollutants, mediating molecular mechanisms, and shifts in birthweight., Environmental Science and Technology (Washington), Vol: 54, Pages: 14502-14513, ISSN: 0013-936X
Mechanisms underlying adverse birth and later in life health effects from exposure to air pollution during the prenatal period have not been not fully elucidated, especially in the context of mixtures. We assessed the effects of prenatal exposure to mixtures of air pollutants of particulate matter (PM), PM2.5, PM10, nitrogen oxides, NO2, NO x , ultrafine particles (UFP), and oxidative potential (OP) of PM2.5 on infant birthweight in four European birth cohorts and the mechanistic underpinnings through cross-omics of metabolites and inflammatory proteins. The association between mixtures of air pollutants and birthweight z-scores (standardized for gestational age) was assessed for three different mixture models, using Bayesian machine kernel regression (BKMR). We determined the direct effect for PM2.5, PM10, NO2, and mediation by cross-omic signatures (identified using sparse partial least-squares regression) using causal mediation BKMR models. There was a negative association with birthweight z-scores and exposure to mixtures of air pollutants, where up to -0.21 or approximately a 96 g decrease in birthweight, comparing the 75th percentile to the median level of exposure to the air pollutant mixture could occur. Shifts in birthweight z-scores from prenatal exposure to PM2.5, PM10, and NO2 were mediated by molecular mechanisms, represented by cross-omics scores. Interleukin-17 and epidermal growth factor were identified as important inflammatory responses underlyingair pollution-associated shifts in birthweight. Our results signify that by identifying mechanisms through which mixtures of air pollutants operate, the causality of air pollution-associated shifts in birthweight is better supported, substantiating the need for reducing exposure in vulnerable populations.
Yang Z, Freni Sterrantino A, Fuller G, et al., 2020, Development and Transferability of Ultrafine Particle Land Use Regression Models in London, Science of the Total Environment, Vol: 740, ISSN: 0048-9697
Due to a lack of routine monitoring, bespoke measurements are required to develop ultrafine particle (UFP) land use regression (LUR) models, which is especially challenging in megacities due to their large area. As an alternative, for London, we developed separate models for three urban residential areas, models combining two areas, and models using all three areas. Models were developed against annual mean ultrafine particle count cm−3 estimated from repeated 30-min fixed-site measurements, in different seasons (2016–2018), at forty sites per area, that were subsequently temporally adjusted using continuous measurements from a single reference site within or close to each area. A single model and 10 models were developed for each individual area and combination of areas. Within each area, sites were split into 10 groups using stratified random sampling. Each of the 10 models were developed using 90% of sites. Hold-out validation was performed by pooling the 10% of sites held-out each time. The transferability of models was tested by applying individual and two-area models to external area(s). In model evaluation, within-area mean squared error (MSE) R2 ranged from 14% to 48%. Transferring individual- and combined-area models to external areas without calibration yielded MSE-R2 ranging from −18 to 0. MSE-R2 was in the range 21% to 41% when using particle number count (PNC) measurements in external areas to calibrate models. Our results suggest that the UFP models could be transferred to other areas without calibration in London to assess relative ranking in exposures but not for estimating absolute values of PNC.
Amadou A, Coudon T, Praud D, et al., 2020, Chronic Low-Dose Exposure to Xenoestrogen Ambient Air Pollutants and Breast Cancer Risk: XENAIR Protocol for a Case-Control Study Nested Within the French E3N Cohort., JMIR Res Protoc, Vol: 9, ISSN: 1929-0748
BACKGROUND: Breast cancer is the most frequent cancer in women in industrialized countries. Lifestyle and environmental factors, particularly endocrine-disrupting pollutants, have been suggested to play a role in breast cancer risk. Current epidemiological studies, although not fully consistent, suggest a positive association of breast cancer risk with exposure to several International Agency for Research on Cancer Group 1 air-pollutant carcinogens, such as particulate matter, polychlorinated biphenyls (PCB), dioxins, Benzo[a]pyrene (BaP), and cadmium. However, epidemiological studies remain scarce and inconsistent. It has been proposed that the menopausal status could modify the relationship between pollutants and breast cancer and that the association varies with hormone receptor status. OBJECTIVE: The XENAIR project will investigate the association of breast cancer risk (overall and by hormone receptor status) with chronic exposure to selected air pollutants, including particulate matter, nitrogen dioxide (NO2), ozone (O3), BaP, dioxins, PCB-153, and cadmium. METHODS: Our research is based on a case-control study nested within the French national E3N cohort of 5222 invasive breast cancer cases identified during follow-up from 1990 to 2011, and 5222 matched controls. A questionnaire was sent to all participants to collect their lifetime residential addresses and information on indoor pollution. We will assess these exposures using complementary models of land-use regression, atmospheric dispersion, and regional chemistry-transport (CHIMERE) models, via a Geographic Information System. Associations with breast cancer risk will be modeled using conditional logistic regression models. We will also study the impact of exposure on DNA methylation and interactions with genetic polymorphisms. Appropriate statistical methods, including Bayesian modeling, principal component analysis, and cluster analysis, will be used to assess the impact of multipollutant exposure. The f
Mancini FR, Laine JE, Tarallo S, et al., 2020, microRNA expression profiles and personal monitoring of exposure to particulate matter, Environmental Pollution, Vol: 263, ISSN: 0269-7491
An increasing number of findings from epidemiological studies support associations between exposure to air pollution and the onset of several diseases, including pulmonary, cardiovascular and neurodegenerative diseases, and malignancies. However, intermediate, and potentially mediating, biological mechanisms associated with exposure to air pollutants are largely unknown. Previous studies on the human exposome have shown that the expression of certain circulating microRNAs (miRNAs), regulators of gene expression, are altered upon exposure to traffic-related air pollutants. In the present study, we investigated the relationship between particulate matter (PM) smaller than 2.5 μm (PM2.5), PM2.5 absorbance (as a proxy of black carbon and soot), and ultrafine-particles (UFP, smaller than 0.1 μm), measured in healthy volunteers by 24 h personal monitoring (PEM) sessions and global expression levels of peripheral blood miRNAs. The PEM sessions were conducted in four European countries, namely Switzerland (Basel), United Kingdom (Norwich), Italy (Turin), and The Netherlands (Utrecht). miRNAs expression levels were analysed using microarray technology on blood samples from 143 participants. Seven miRNAs, hsa-miR-24-3p, hsa-miR-4454, hsa-miR-4763-3p, hsa-miR-425-5p, hsa-let-7d-5p, hsa-miR-502-5p, and hsa-miR-505-3p were significantly (FDR corrected) expressed in association with PM2.5 personal exposure, while no significant association was found between miRNA expression and the other pollutants. The results obtained from this investigation suggest that personal exposure to PM2.5 is associated with miRNA expression levels, showing the potential for these circulating miRNAs as novel biomarkers for air pollution health risk assessment.
Khan J, Kakosimos K, Jensen SS, et al., 2020, The spatial relationship between traffic-related air pollution and noise in two Danish cities: Implications for health-related studies, SCIENCE OF THE TOTAL ENVIRONMENT, Vol: 726, ISSN: 0048-9697
Cai Y, Hansell AL, Granell R, et al., 2020, Prenatal, early-life and childhood exposure to air pollution and lung function: the ALSPAC cohort, American Journal of Respiratory and Critical Care Medicine, Vol: 202, Pages: 112-123, ISSN: 1073-449X
RATIONALE: Exposure to air pollution during intrauterine development and through childhood may have lasting effects on respiratory health. OBJECTIVES: To investigate lung function at ages 8 and 15 years in relation to air pollution exposures during pregnancy, infancy and childhood in a UK population-based birth cohort. METHODS: Individual exposures to source-specific particulate matter with diameter ≤10µm (PM10) during each trimester, 0-6 months, 7-12 months (1990-1993) and up to age 15 years (1991-2008) were examined in relation to %predicted Forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) at ages 8(N=5,276) and 15(N=3,446) years, usinglinear regression models adjusted for potential confounders. A profile regression model was used to identify sensitive time periods. MEASUREMENTS AND MAIN RESULTS: We did not find clear evidence for a sensitive exposure period for PM10 from road-traffic: at age 8 years, 1µg/m3 higher exposure during the first trimester was associated with lower %predicted of FEV1(-0.826, 95%CI:-1.357 to -0.296) and FVC(-0.817, 95%CI:-1.357 to -0.276), but similar associations were seen for exposures for other trimesters, 0-6 months, 7-12 months, and 0-7 years. Associations were stronger among boys, children whose mother had a lower education level or smoked during pregnancy. For PM10 from all sources, the third trimester was associated with lower %predicted of FVC (-1.312, 95%CI: -2.100 to -0.525). At age 15 years, no adverse associations were seen with lung function. CONCLUSIONS: Exposure to road-traffic PM10 during pregnancy may result in small but significant reductions in lung function at age 8 years.
Hodgson S, Fecht D, Gulliver J, et al., 2020, Availability, access, analysis and dissemination of small area data, International Journal of Epidemiology, Vol: 49, Pages: i4-i14, ISSN: 1464-3685
In this era of ‘big data’, there is growing recognition of the value of environmental, health, social and demographic data for research. Open government data initiatives are growing in number and in terms of content. Remote sensing data are finding widespread use in environmental research, including in low- and middle-income settings. While our ability to study environment and health associations across countries and continents grows, data protection rules and greater patient control over the use of their data present new challenges to using health data in research. Innovative tools that circumvent the need for the physical sharing of data by supporting non-disclosive sharing of information, or that permit spatial analysis without researchers needing access to underlying patient data can be used to support analyses while protecting data confidentiality. User-friendly visualisations, allowing small area data to be seen and understood by non-expert audiences are revolutionising public and researcher interactions with data. The UK Small Area Health Statistics Unit’s Environment and Health Atlas for England and Wales, and the US National Environmental Public Health Tracking Network offer good examples. Open data facilitates user-generated outputs, and ‘mash-ups’, and user generated inputs from social media, mobile devices, and wearable tech are new data streams which will find utility in future studies, and bring novel dimensions with respect to ethical use of small area data.
Fecht D, Garwood K, Butters O, et al., 2020, Automation of cleaning and reconstructing residential address histories to assign environmental exposures in longitudinal studies, International Journal of Epidemiology, Vol: 49, Pages: i49-i56, ISSN: 1464-3685
Background: We have developed an open-source ALgorithm for Generating Address Exposures (ALGAE) that cleans residential address records to construct address histories and assign spatially-determined exposuresto cohort participants. The first application of this algorithm was to construct prenatal and early-life air pollution exposure for individuals of the Avon Longitudinal Study of Parents and Children (ALSPAC)in the South West of Englandusingpreviously estimated particulate matter ≤10 μm (PM10) concentrations. Methods: ALSPAC recruited 14,541 pregnant women between 1991and 1992. We assignedtrimester-specific estimated PM10exposures for 12,752 pregnancies,and first year of life exposures for 12,525births, based on maternal residence and residential mobility. Results: Average PM10exposure was32.6 μg/m3(StDev. 3.0 μg/m3) during pregnancy and 31.4 μg/m3(StDev. 2.6 μg/m3) during the first year of life. 6.7% ofwomen changedaddress during pregnancy, and 18.0% moved during first year of lifeof their infant. Exposure differences ranged from -5.3 μg/m3 to 12.4 μg/m3(up to 26% difference) during pregnancy and -7.22 μg/m3to 7.64 μg/m3(up to 27% difference) in the first year of life,when comparing estimated exposure using the address at birth and that assessedusing the complete cleaned address history. For the majority of individualsexposure changed by <5% but some relatively large changes were seen both in pregnancy and infancy.Conclusion: ALGAE provides a generic andadaptable, open-source solution to clean addresses stored in acohort contact database and assign life-stage specific exposureestimates with the potential to reduce exposure misclassification.
van Nunen E, Vermeulen R, Tsai M-Y, et al., 2020, Associations between modeled residential outdoor and measured personal exposure to ultrafine particles in four European study areas, Atmospheric Environment, Vol: 226, ISSN: 1352-2310
Land use regression (LUR) models for Ultrafine Particles (UFP) have been developed to assess health effects of long-term average UFP exposure in epidemiological studies. Associations between LUR modeled residential outdoor and measured long-term personal exposure to UFP have never been evaluated, adding uncertainty in interpretation of epidemiological studies of UFP. Our aim was to assess how predictions of recently developed LUR models for UFP compared to measured average personal UFP exposure in four European areas.Personal UFP exposure was measured in 154 adults from Basel (Switzerland), Amsterdam and Utrecht (the Netherlands), Norwich (United Kingdom), and Turin (Italy). Subjects performed three 24-h exposure measurements by carrying a real-time monitor measuring particles between 10 and 300 nm (MiniDisc). Subjects reported whereabouts and indoor sources of UFP in questionnaires. In Basel and the Netherlands contemporaneously residential outdoor UFP concentrations were monitored. Area-specific LUR models were applied to model residential outdoor UFP concentrations. Associations between modeled and measured UFP concentrations were assessed with linear regression.LUR model predictions were significantly associated with median but not mean personal UFP exposures, likely because of the high impact of indoor peaks on mean personal exposures. Regression slopes (±se) combined for the four areas were 0.12 ± 0.04 for median and −0.06 ± 0.17 for mean personal exposure. The LUR model explained variance of the median personal exposure less than variance of residential outdoor measurements. Associations did not change when personal exposure was calculated for the time spent at home or when presence of indoor sources was incorporated in the regression models. Regression slopes for measured residential outdoor versus personal exposure were smaller for UFP (0.16 ± 0.04) than for simultaneously measured PM2.5 and soot (0.32 ± 0.10 and 0.4
Toledano MB, Shaddick G, de Hoogh C, et al., 2020, Electric field and air ion exposures near high voltage overhead power lines and adult cancers: a case control study across England and Wales, International Journal of Epidemiology, Vol: 49, Pages: i57-i66, ISSN: 0300-5771
Background: Various mechanisms have been postulated to explain how electric fields emitted by high voltage overhead power lines, and the charged ions they produce, might be associated with possible adult cancer risk but this has not previously been systematically explored in large scale epidemiologic research. Methods: We investigated risks of adult cancers in relation to modelled air ion density (per cm3) within 600m (focusing analysis on mouth, lung, respiratory) and calculated electric field within 25m (focusing analysis on non-melanoma skin) of high voltage overhead power lines in England and Wales, 1974-2008. Results: With adjustment for age, sex, deprivation and rurality, odds ratios (OR) in the highest fifth of net air ion density (0.504-1) compared with the lowest (0-0.1879) ranged from 0.94 (95% CI 0.82 – 1.08) for mouth cancers to 1.03 (95% CI 0.97 -1.09) for respiratory system cancers, with no trends in risk. The pattern of cancer risk was similar using corona ion estimates from an alternative model proposed by others. For keratinocyte carcinoma, adjusted OR in the highest (1.06 - 4.11 kV/m) compared with the lowest (<0.70 kV/m) thirds of electric field strength was 1.23 (95% CI 0.65-2.34) with no trend in risk. Conclusions: Our results do not provide evidence to support hypotheses that air ion density or electric fields in the vicinity of power lines are associated with cancer risk in adults.
Roca Barcelo A, Douglas P, Fecht D, et al., 2020, Risk of respiratory hospital admission associated with modelled concentrations of Aspergillus fumigatus from composting facilities in England, Environmental Research, Vol: 183, Pages: 1-10, ISSN: 0013-9351
Bioaerosols have been associated with adverse respiratory-related health effects and are emitted in elevated concentrations from composting facilities. We usedmodelledAspergillus fumigatusconcentrations, a good indicator for bioaerosol emissions,to assess associations with respiratory-related hospital admissions. Mean dailyAspergillus fumigatusconcentrationswere estimated for each composting site for first full year of permit issuefrom2005 onwardsto 2014 for Census Output Areas (COAs) within 4km of 76 composting facilities in England, as previously described (Williams et al. 2019). We fitted ahierarchicalgeneralized mixed modelto examine therisk of hospital admission witha primary diagnosis of(i) any respiratory condition,(ii) respiratory infections,(iii) asthma,(iv) COPD,(v)diseases due to organic dust,and (vi)Cystic Fibrosis,inrelation to quartilesof Aspergillus fumigatusconcentrations. Models included a random intercept for each COAto account for over-dispersion,nested within composting facility, on whicha random intercept was fitted to account for clusteringof the data, with adjustmentsfor age, sex, ethnicity, deprivation, tobacco sales (smoking proxy) and traffic load (as a proxy for traffic-related air pollution). Weincluded 249,748 respiratory-related and 3,163 Cystic Fibrosis hospital admissions in 9,606 COAswith a population-weighted centroid within 4 km of the 76 included composting facilities. After adjustment for confounders, no statistically significant effect was observed for any respiratory-related (Relative Risk (RR)=0.99; 95% Confidence Interval (CI)0.96–1.01)or for Cystic Fibrosis (RR=1.01; 95% CI 0.56-1.83)hospital admissions for COAs in the highest quartile of exposure. Similar results were observed across all respiratory disease sub-groups.This study does not provide evidence for increased risks of respiratory-related hospitalisationsfor those livingnearcomposting facilities.However, given the limitations in the dispersion modelling, risks
Smith RB, Beevers SD, Gulliver J, et al., 2020, Impacts of air pollution and noise on risk of preterm birth and stillbirth in London, Environment International, Vol: 134, ISSN: 0160-4120
BackgroundEvidence for associations between ambient air pollution and preterm birth and stillbirth is inconsistent. Road traffic produces both air pollutants and noise, but few studies have examined these co-exposures together and none to date with all-cause or cause-specific stillbirths.ObjectivesTo analyse the relationship between long-term exposure to air pollution and noise at address level during pregnancy and risk of preterm birth and stillbirth.MethodsThe study population comprised 581,774 live and still births in the Greater London area, 2006–2010. Outcomes were preterm birth (<37 completed weeks gestation), all-cause stillbirth and cause-specific stillbirth. Exposures during pregnancy to particulate matter with diameter <2.5 μm (PM2.5) and <10 μm (PM10), ozone (O3), primary traffic air pollutants (nitrogen dioxide, nitrogen oxides, PM2.5 from traffic exhaust and traffic non-exhaust), and road traffic noise were estimated based on maternal address at birth.ResultsAn interquartile range increase in O3 exposure was associated with elevated risk of preterm birth (OR 1.15 95% CI: 1.11, 1.18, for both Trimester 1 and 2), all-cause stillbirth (Trimester 1 OR 1.17 95% CI: 1.07, 1.27; Trimester 2 OR 1.20 95% CI: 1.09, 1.32) and asphyxia-related stillbirth (Trimester 1 OR 1.22 95% CI: 1.01, 1.49). Odds ratios with the other air pollutant exposures examined were null or <1, except for primary traffic non-exhaust related PM2.5, which was associated with 3% increased odds of preterm birth (Trimester 1) and 7% increased odds stillbirth (Trimester 1 and 2) when adjusted for O3. Elevated risk of preterm birth was associated with increasing road traffic noise, but only after adjustment for certain air pollutant exposures.DiscussionOur findings suggest that exposure to higher levels of O3 and primary traffic non-exhaust related PM2.5 during pregnancy may increase risk of preterm birth and stillbirth; and a possible relationship between long-term traff
Cai Y, Blangiardo M, de Hoogh K, et al., 2020, Road traffic noise, air pollution and cardiorespiratory Health in European Cohorts: A harmonised approach in the BioShare project, Pages: 137-142
Background and aims: Few studies have investigated joint effects of road traffic noise and air pollution on cardiorespiratory outcomes. This project aims to quantify the joint and separate effects of both exposures on prevalent and incident cardiovascular disease and asthma as part of the EU-funded BioSHaRE project involving five European cohorts (EPIC-Oxford, EPIC-Turin, HUNT, Lifelines, UK Biobank). Methods: Health outcomes have been ascertained by self-report (prevalence) and medical record (incidence) and retrospectively harmonised across cohorts. Residential road traffic noise exposures for each participant are estimated using a European noise model based on Common Noise Assessment Methods in Europe (CNOSSOS-EU). Road traffic air pollution estimates at home address were derived from Land Use Regression models. Cross-sectional and incident epidemiological analyses are in progress, using individual level data, virtually pooled using DataSHIELD methodology. Results: In total, 742,950 men and women are included from all five cohorts, mostly >40 years. Prevalence of self-reported myocardial infarction from these five cohorts is 2.1% (N=15,031) while prevalence of self-reported stroke is 1.4% (N=10,077). Initial pooled analysis of EPIC-Oxford, HUNT and Lifelines showed median day-time (07:00-19:00) noise estimate of 51.8 dB(A) and night-time (23:00-07:00) noise estimate of 43.5 dB(A). Correlations between noise estimates and NO are generally low (r=0.1 to 0.4). Conclusions: Pooling of individual level harmonised data from established cohorts offers the large sample sizes and exposure variations needed to investigate effects of road traffic noise and ambient air pollution on cardio-respiratory diseases. 2
Halonen J, Hansell A, Gulliver J, et al., 2020, Associations of road traffic noise with mortality and hospital admissions in London, Pages: 119-123
Background and aims Previously published studies have found associations of road noise with hypertension, which is a risk factor for cardiovascular disease, especially for stroke. We aimed to examine the chronic effects of road traffic noise on mortality and hospital admissions for cardiovascular disease and stroke in a large general population. Methods The study population consisted of 8.61 million inhabitants in London. We assessed small-area level associations of day- (7:00-22:59) and night-time (23:00-06:59) road traffic noise with all-cause and cardiovascular mortality and cardiovascular hospital admissions in all adults (25+ years) with Poisson regression models applying the Integrated Nested Laplace Approximation (INLA) approach in the Bayesian framework. We adjusted the models for age and sex, area-level deprivation, ethnicity, smoking, air pollution and a random effect. Results Mean daytime exposure to road traffic noise was 55.6 dB. Daytime noise was associated with all-cause and cardiovascular mortality in adults; relative risks (RR) for all-cause mortality were 1.04 (95% CI 1.00-1.07) in areas with daytime road noise >60 dB vs. <55 dB. Exposure to daytime road traffic noise also increased the risk of hospital admission for stroke with RR 1.05 (95% CI 1.02-1.09) in areas >60 dB vs. <55 dB. Night-time noise was not associated with road traffic noise in adults of all ages. Conclusions This is the largest study to date to investigate environmental noise and cardiovascular disease. It suggests that road traffic noise is associated with small increased risks of all-cause mortality and cardiovascular disease.
Gulliver J, Morley D, Fecht D, et al., 2020, Feasibility study for using the CNOSSOS-EU road traffic noise prediction model with low resolution inputs for exposure estimation on a Europe-wide scale, Pages: 481-486
A noise model based on the CNOSSOS-EU method was developed to estimate exposures to road traffic noise at individual address locations for studies of noise and health in European cohorts in the EU FP7 BioSHaRE project. We assessed the loss in model performance from necessarily (i.e. at national scale) using low resolution data on traffic flows, road geography and land cover. To assess the feasibility of this approach in terms of the loss of model performance, we applied CNOSSOS-EU with different combinations of high- and low-resolution inputs (e.g. high resolution road geography with low resolution land cover) and compared noise level estimates with measurements of L from 38 locations in Leicester, a medium sized city in the UK. The lowest resolution model performed reasonably well in terms of correlation [r = 0.75; p = 0.000)] but with relatively large model errors [RMSE = 4.46 dB(A)]. For a sample of postcode (zip code) locations (n=721) in Leicester, in comparing output from Model A (highest resolution) and Model F (lowest resolution), 81.8% and 72.8% of exposure estimates remained in the lowest and highest of three equal exposure categories, respectively. Aeq1hr s
Fecht D, Sheridan CE, Roscoe CJ, et al., 2019, Inequalities in exposure to nitrogen dioxide in parks and playgrounds in Greater London, International Journal of Environmental Research and Public Health, Vol: 16, Pages: 1-11, ISSN: 1660-4601
Elevated levels of nitrogen dioxide (NO2) have been associated with adverse health outcomes in children including reduced lung function and increased rates of asthma. Many parts ofLondon continue to exceed the annual average NO2 concentration of 40µg/m3 set by the EU directive. Using high-resolution maps of annual average NO2 for 2016 from the London Atmospheric Emissions Inventory and detailed maps of open spaces from Britain’s national mapping agency, Ordnance Survey, we estimated average NO2 concentrations for every open space in Greater London and analysed geospatial patterns comparing Inner verses Outer London and the 32 London Boroughs. Across Greater London, 24% of play spaces, 67% of private parks and 27% of public parks had average levels of NO2 that exceeded the EU limit for NO2. Rates of exceedance were higher in Inner London; open spaces in the City of London had the highest average NO2 values among all the London Boroughs. The closest play space for more than 250,000 children (14%) under 16 years old in Greater London had NO2 concentrations above recommended levels. Of these children, 66% (~165,000 children) live in the most deprived areas of London as measured by the Index of Multiple Deprivations where average NO2 concentrations in play spaces where on average 6 µg/m3 higher than for play spaces in the least deprived quintile. More action is needed to reduce NO2 in open spaces to safe levels through pollution reduction and mitigation efforts as currently open spaces in Greater London including play spaces, parks and gardens still have dangerously high levels of NO2 according to the most recent NO2 map.
Chen J, de Hoogh K, Gulliver J, et al., 2019, A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide, ENVIRONMENT INTERNATIONAL, Vol: 130, ISSN: 0160-4120
Boyd A, Thomas R, Hansell AL, et al., 2019, Data resource profile: the ALSPAC birth cohort as a platform to study the relationship of environment and health and social factors, International Journal of Epidemiology, Vol: 48, Pages: 1038-1039k, ISSN: 1464-3685
This resource profile describes the information about the physical and social environment collected within the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort. This includes spatial and temporal information gathered on three generations about: area-level built and social characteristics (e.g. density and location of fast-food outlets, crime rates within a neighbourhood); exposure measurements (e.g. air pollution concentrations, temperature records); participant-reported data directly related to the spaces and places they inhabit (e.g. neighbourhood safety, presence of damp within a home); information directly measured from participants (e.g. blood lead and total mercury concentrations, physical activity); the location information needed to link these diverse data.We describe the platform’s previous uses, strengths and weaknesses and access arrangements, emphasizing confidentiality safeguard controls. This profile highlights a particular class of ALSPAC data (with distinct access arrangements) to promote the potential for incorporating physical environment and other spatially-dependent data into research investigations.
Donaire-Gonzalez D, Valentin A, van Nunen E, et al., 2019, ExpoApp: An integrated system to assess multiple personal environmental exposures, Environment International, Vol: 126, Pages: 494-503, ISSN: 0160-4120
To assess environmental exposures at the individual level, new assessment methods and tools are required. We developed an exposure assessment system (ExpoApp) for smartphones. ExpoApp integrates: (i) geo-location and accelerometry measurements from a waist attached smartphone, (ii) data from portable monitors, (iii) geographic information systems, and (iv) individual's information. ExpoApp calculates time spent in microenvironments, physical activity level, inhalation rate, and environmental exposures and doses (e.g., green spaces, inhaled ultrafine particles- UFP). We deployed ExpoApp in a panel study of 158 adults from five cities (Amsterdam and Utrecht- the Netherlands, Basel- Switzerland, Norwich- UK, and Torino- Italy) with an UFP monitor. To evaluate ExpoApp, participants also carried a reference accelerometer (ActiGraph) and completed a travel-activity diary (TAD). System reliability and validity of measurements were evaluated by comparing the monitoring failure rate and the agreement on time spent in microenvironments and physical activity with the reference tools. There were only significant failure rate differences between ExpoApp and ActiGraph in Norwich. Agreement on time in microenvironments and physical activity level between ExpoApp and reference tools was 86.6% (86.5–86.7) and 75.7% (71.5–79.4), respectively. ExpoApp estimated that participants inhaled 16.5 × 1010 particles/day of UFP and had almost no contact with green spaces (24% of participants spent ≥30 min/day in green spaces). Participants with more contact with green spaces had higher inhaled dose of UFP, except for the Netherlands, where the relationship was the inverse. ExpoApp is a reliable system and provides accurate individual's measurements, which may help to understand the role of environmental exposures on the origin and course of diseases.
Mitsakou C, Dimitroulopoulou S, Heaviside C, et al., 2019, Environmental public health risks in European metropolitan areas within the EURO-HEALTHY project, Science of the Total Environment, Vol: 658, Pages: 1630-1639, ISSN: 0048-9697
Urban areas in Europe are facing a range of environmental public health challenges, such as air pollution, traffic noise and road injuries. The identification and quantification of the public health risks associated with exposure to environmental conditions is important for prioritising policies and interventions that aim to diminish the risks and improve the health of the population. With this purpose in mind, the EURO-HEALTHY project used a consistent approach to assess the impact of key environmental risk factors and urban environmental determinants on public health in European metropolitan areas. A number of environmental public health indicators, which are closely tied to the physical and built environment, were identified through stakeholder consultation; data were collected from six European metropolitan areas (Athens, Barcelona, Lisbon, London, Stockholm and Turin) covering the period 2000–2014, and a health impact assessment framework enabled the quantification of health effects (attributable deaths) associated with these indicators. The key environmental public health indicators were related to air pollution and certain urban environmental conditions (urban green spaces, road safety). The air pollution was generally the highest environmental public health risk; the associated number of deaths in Athens, Barcelona and London ranged between 800 and 2300 attributable deaths per year. The number of victims of road traffic accidents and the associated deaths were lowest in the most recent year compared with previous years. We also examined the positive impacts on health associated with urban green spaces by calculating reduced mortality impacts for populations residing in areas with greater green space coverage; results in Athens showed reductions of all-cause mortality of 26 per 100,000 inhabitants for populations with benefits of local greenspace. Based on our analysis, we discuss recommendations of potential interventions that could be implemented to r
Ghosh RE, Freni-Sterrantino A, Douglas P, et al., 2019, Fetal growth, stillbirth, infant mortality and other birth outcomes near UK municipal waste incinerators; retrospective population based cohort and case-control study, Environment International, Vol: 122, Pages: 151-158, ISSN: 0160-4120
Background: Some studies have reported associations between municipal waste incinerator (MWI) exposures and adverse birth outcomes but there are few studies of modern MWIs operating to current European Union (EU) Industrial Emissions Directive standards. Methods: Associations between modelled ground-level particulate matter ≤10 μm in diameter (PM10) from MWI emissions (as a proxy for MWI emissions) within 10 km of each MWI, and selected birth and infant mortality outcomes were examined for all 22 MWIs operating in Great Britain 2003–10. We also investigated associations with proximity of residence to a MWI. Outcomes used were term birth weight, small for gestational age (SGA) at term, stillbirth, neonatal, post-neonatal and infant mortality, multiple births, sex ratio and preterm delivery sourced from national registration data from the Office for National Statistics. Analyses were adjusted for relevant confounders including year of birth, sex, season of birth, maternal age, deprivation, ethnicity and area characteristics and random effect terms were included in the models to allow for differences in baseline rates between areas and in incinerator feedstock. Results: Analyses included 1,025,064 births and 18,694 infant deaths. There was no excess risk in relation to any of the outcomes investigated during pregnancy or early life of either mean modelled MWI PM10 or proximity to an MWI. Conclusions: We found no evidence that exposure to PM10 from, or living near to, an MWI operating to current EU standards was associated with harm for any of the outcomes investigated. Results should be generalisable to other MWIs operating to similar standards.
Downward GS, van Nunen EJHM, Kerckhoffs J, et al., 2018, Long-Term Exposure to Ultrafine Particles and Incidence of Cardiovascular and Cerebrovascular Disease in a Prospective Study of a Dutch Cohort, ENVIRONMENTAL HEALTH PERSPECTIVES, Vol: 126, ISSN: 0091-6765
de Hoogh K, Chen J, Gulliver J, et al., 2018, Spatial PM2.5, NO2, O-3 and BC models for Western Europe - Evaluation of spatiotemporal stability, ENVIRONMENT INTERNATIONAL, Vol: 120, Pages: 81-92, ISSN: 0160-4120
Mostafavi N, Vermeulen R, Ghantous A, et al., 2018, Acute changes in DNA methylation in relation to 24 h personal air pollution exposure measurements: a panel study in four European countries, Environment International, Vol: 120, Pages: 11-21, ISSN: 0160-4120
BackgroundOne of the potential mechanisms linking air pollution to health effects is through changes in DNA-methylation, which so far has mainly been analyzed globally or at candidate sites.ObjectiveWe investigated the association of personal and ambient air pollution exposure measures with genome-wide DNA-methylation changes.MethodsWe collected repeated 24-hour personal and ambient exposure measurements of particulate matter (PM2.5), PM2.5 absorbance, and ultrafine particles (UFP) and peripheral blood samples from a panel of 157 healthy non-smoking adults living in four European countries. We applied univariate mixed-effects models to investigate the association between air pollution and genome-wide DNA-methylation perturbations at single CpG (cytosine-guanine dinucleotide) sites and in Differentially Methylated Regions (DMRs). Subsequently, we explored the association of air pollution-induced methylation alterations with gene expression and serum immune marker levels measured in the same subjects.ResultsPersonal exposure to PM2.5 was associated with methylation changes at 13 CpG sites and 69 DMRs. Two of the 13 identified CpG sites (mapped to genes KNDC1 and FAM50B) were located within these DMRs. In addition, 42 DMRs were associated with personal PM2.5 absorbance exposure, 16 DMRs with personal exposure to UFP, 4 DMRs with ambient exposure to PM2.5, 16 DMRs with ambient PM2.5 absorbance exposure, and 15 DMRs with ambient UFP exposure. Correlation between methylation levels at identified CpG sites and gene expression and immune markers was generally moderate.ConclusionThis study provides evidence for an association between 24-hour exposure to air pollution and DNA-methylation at single sites and regional clusters of CpGs. Analysis of differentially methylated regions provides a promising avenue to further explore the subtle impact of environmental exposures on DNA-methylation.
Carey IM, Anderson HR, Atkinson RW, et al., 2018, Are noise and air pollution related to the incidence of dementia? A cohort study in London, England, BMJ Open, Vol: 8, ISSN: 2044-6055
OBJECTIVE: To investigate whether the incidence of dementia is related to residential levels of air and noise pollution in London. DESIGN: Retrospective cohort study using primary care data. SETTING: 75 Greater London practices. PARTICIPANTS: 130 978 adults aged 50-79 years registered with their general practices on 1 January 2005, with no recorded history of dementia or care home residence. PRIMARY AND SECONDARY OUTCOME MEASURES: A first recorded diagnosis of dementia and, where specified, subgroups of Alzheimer's disease and vascular dementia during 2005-2013. The average annual concentrations during 2004 of nitrogen dioxide (NO2), particulate matter with a median aerodynamic diameter </=2.5 microm (PM2.5) and ozone (O3) were estimated at 20x20 m resolution from dispersion models. Traffic intensity, distance from major road and night-time noise levels (Lnight) were estimated at the postcode level. All exposure measures were linked anonymously to clinical data via residential postcode. HRs from Cox models were adjusted for age, sex, ethnicity, smoking and body mass index, with further adjustments explored for area deprivation and comorbidity. RESULTS: 2181 subjects (1.7%) received an incident diagnosis of dementia (39% mentioning Alzheimer's disease, 29% vascular dementia). There was a positive exposure response relationship between dementia and all measures of air pollution except O3, which was not readily explained by further adjustment. Adults living in areas with the highest fifth of NO2 concentration (>41.5 microg/m(3)) versus the lowest fifth (<31.9 microg/m(3)) were at a higher risk of dementia (HR=1.40, 95% CI 1.12 to 1.74). Increases in dementia risk were also observed with PM2.5, PM2.5 specifically from primary traffic sources only and Lnight, but only NO2 and PM2.5 remained statistically significant in multipollutant models. Associations were more consistent for Alzheimer's disease than vascular dementia. CONCLUSIONS: We have found evidence of a
Aldred R, Goodman A, Gulliver J, et al., 2018, Cycling injury risk in London: A case-control study exploring the impact of cycle volumes, motor vehicle volumes, and road characteristics including speed limits, ACCIDENT ANALYSIS AND PREVENTION, Vol: 117, Pages: 75-84, ISSN: 0001-4575
Pimpin L, Retat L, Fecht D, et al., 2018, Estimating the costs of air pollution to the National Health Service and social care: An assessment and forecast up to 2035, PLoS Medicine, Vol: 15, ISSN: 1549-1277
BACKGROUND: Air pollution damages health by promoting the onset of some non-communicable diseases (NCDs), putting additional strain on the National Health Service (NHS) and social care. This study quantifies the total health and related NHS and social care cost burden due to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in England. METHOD AND FINDINGS: Air pollutant concentration surfaces from land use regression models and cost data from hospital admissions data and a literature review were fed into a microsimulation model, that was run from 2015 to 2035. Different scenarios were modelled: (1) baseline 'no change' scenario; (2) individuals' pollutant exposure is reduced to natural (non-anthropogenic) levels to compute the disease cases attributable to PM2.5 and NO2; (3) PM2.5 and NO2 concentrations reduced by 1 μg/m3; and (4) NO2 annual European Union limit values reached (40 μg/m3). For the 18 years after baseline, the total cumulative cost to the NHS and social care is estimated at £5.37 billion for PM2.5 and NO2 combined, rising to £18.57 billion when costs for diseases for which there is less robust evidence are included. These costs are due to the cumulative incidence of air-pollution-related NCDs, such as 348,878 coronary heart disease cases estimated to be attributable to PM2.5 and 573,363 diabetes cases estimated to be attributable to NO2 by 2035. Findings from modelling studies are limited by the conceptual model, assumptions, and the availability and quality of input data. CONCLUSIONS: Approximately 2.5 million cases of NCDs attributable to air pollution are predicted by 2035 if PM2.5 and NO2 stay at current levels, making air pollution an important public health priority. In future work, the modelling framework should be updated to include multi-pollutant exposure-response functions, as well as to disaggregate results by socioeconomic status.
Morley DW, Gulliver J, 2018, A land use regression variable generation, modelling and prediction tool for air pollution exposure assessment, Environmental Modelling and Software, Vol: 105, Pages: 17-23, ISSN: 1364-8152
Land use regression (LUR) is commonly used to estimate air pollution exposures for epidemiological studies. By statistically relating a set of geolocated measured pollutant values with explanatory variables defining sources and modifiers of air pollution patterns, such as land cover characteristics, traffic flow and intensity, it is possible to predict pollution levels at unsampled locations. LUR utilises simple linear regression, but the generation of predictor variables, application of the model and the supervised iterative approach to model development means an analyst must be a competent user of both GIS and statistical packages. Here we present an application to simplify the LUR modelling process for exposure scientists and environmental epidemiologists. RLUR is a user-friendly application built using the statistical and GIS capabilities of the R programming language. The main aim of this software is to provide an introduction to the LUR process without the need for specific GIS or statistical expertise.
Tonne C, Milà C, Fecht D, et al., 2018, Socioeconomic and ethnic inequalities in exposure to air and noise pollution in London, Environment International, Vol: 115, Pages: 170-179, ISSN: 0160-4120
BACKGROUND: Transport-related air and noise pollution, exposures linked to adverse health outcomes, varies within cities potentially resulting in exposure inequalities. Relatively little is known regarding inequalities in personal exposure to air pollution or transport-related noise. OBJECTIVES: Our objectives were to quantify socioeconomic and ethnic inequalities in London in 1) air pollution exposure at residence compared to personal exposure; and 2) transport-related noise at residence from different sources. METHODS: We used individual-level data from the London Travel Demand Survey (n = 45,079) between 2006 and 2010. We modeled residential (CMAQ-urban) and personal (London Hybrid Exposure Model) particulate matter <2.5 μm and nitrogen dioxide (NO2), road-traffic noise at residence (TRANEX) and identified those within 50 dB noise contours of railways and Heathrow airport. We analyzed relationships between household income, area-level income deprivation and ethnicity with air and noise pollution using quantile and logistic regression. RESULTS: We observed inverse patterns in inequalities in air pollution when estimated at residence versus personal exposure with respect to household income (categorical, 8 groups). Compared to the lowest income group (<£10,000), the highest group (>£75,000) had lower residential NO2 (-1.3 (95% CI -2.1, -0.6) μg/m3 in the 95th exposure quantile) but higher personal NO2 exposure (1.9 (95% CI 1.6, 2.3) μg/m3 in the 95th quantile), which was driven largely by transport mode and duration. Inequalities in residential exposure to NO2 with respect to area-level deprivation were larger at lower exposure quantiles (e.g. estimate for NO2 5.1 (95% CI 4.6, 5.5) at quantile 0.15 versus 1.9 (95% CI 1.1, 2.6) at quantile 0.95), reflecting low-deprivation, high residential NO2 areas in the city centre. Air pollution exposure at residence consistently overestimated personal exposure; this overestimation varied with age
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