Long-term associations of outdoor air pollution with mortality in Great Britain

Early life PM2.5 exposure, childhood cognitive ability and mortality between age 11 and 86: A record-linkage life-course study from Scotland

BACKGROUND

Living in areas with high air pollution concentrations is associated with all-cause and cause-specific mortality. Exposure in sensitive developmental periods might be long-lasting but studies with very long follow-up are rare, and mediating pathways between early life exposure and life-course mortality are not fully understood.

METHODS

Data were drawn from the Scottish Longitudinal Study Birth Cohort of 1936, a representative record-linkage study comprising 5% of the Scottish population born in 1936. Participants had valid age 11 cognitive ability test scores along with linked mortality data until age 86. Fine particle (PM2.5) concentrations estimated with the EMEP4UK atmospheric chemistry transport model were linked to participants' residential address derived from the National Identity Register in 1939 (age 3). Confounder-adjusted Cox regression estimated associations between PM2.5 and mortality; regression-based causal mediation analysis explored mediation through childhood cognitive ability.

RESULTS

The final sample consisted of 2734 individuals with 1608 deaths registered during the 1,833,517 person-months at risk follow-up time. Higher early life PM2.5 exposure increased the risk of all-cause mortality (HR = 1.03, 95% CI: 1.01-1.04 per 10 μg m-3 increment), associations were stronger for mortality between age 65 and 86. PM2.5 increased the risk of cancer-related mortality (HR = 1.05, 95% CI: 1.02-1.08), especially for lung cancer among females (HR = 1.11, 95% CI: 1.02-1.21), but not for cardiovascular and respiratory diseases. Higher PM2.5 in early life (≥50 μg m-3) was associated with lower childhood cognitive ability, which, in turn, increased the risk of all-cause mortality and mediated 25% of the total associations.

CONCLUSIONS

In our life-course study with 75-year of continuous mortality records, we found that exposure to air pollution in early life was associated with higher mortality in late adulthood, and that childhood cognitive ability partly mediated this relationship. Findings suggest that past air pollution concentrations will likely impact health and longevity for decades to come.

Short and long-term association of exposure to ambient black carbon with all-cause and cause-specific mortality: A systematic review and meta-analysis

Black carbon (BC) is a product of incomplete or inefficient combustion and may be associated with a variety of adverse effects on human health. The objective of this study was to analyze the association between various mortalities and long-/short-term exposure to BC as an independent pollutant. In this systematic review, we searched 4 databases for original research in English up to 6^th October 2022, that investigated population-wide mortality due to BC exposure. We pooled mortality estimates and expressed them as relative risk (RR) per 10 μg/m³ increase in BC. We used a random-effect model to derive the pooled RRs. Of the 3186 studies identified, 29 articles met the eligibility criteria, including 18 long-term exposure studies and 11 short-term exposure studies. In the major meta-analysis and sensitivity analysis, positive associations were found between BC and total mortality and cause-specific disease mortalities. Among them, the short-term effects of BC on total mortality, cardiovascular disease mortality, respiratory disease mortality, and the long-term effects of BC on total mortality, ischemic heart disease mortality, respiratory disease mortality and lung cancer mortality were found to be statistically significant. The heterogeneity of the meta-analysis results was much lower for short-term studies than for long-term. Few studies were at a high risk of bias in any domain. The certainty of the evidence for most of the exposure-outcome pairs was moderate. Our study showed a significantly positive association between short-/long-term BC exposure and various mortalities. We speculate that BC has a higher adverse health effect on the respiratory system than on the cardiovascular system. This is different from the effect of PM_2.5. Therefore, more studies are needed to consider BC as a separate pollutant, and not just as a component of PM_2.5.

The Short- and Long-Run Impacts of Air Pollution on Human Health: New Evidence from China

Under the background of the far-reaching impact of the COVID-19 epidemic on global economic development, the interactive effect of economic recovery and pollution rebound makes the research topic of air pollution and human health receive attention again. Matching a series of new datasets and employing thermal inversion as an instrumental variable, this study investigates the physical and mental health effect of air pollution jointly in China. We find that in the short run, the above inference holds for both physical and mental health. These short-run influences are credible after a series of robustness checks and vary with different individual characteristics and geographical locations. We also find that in the long run, air pollution only damages mental health. Finally, this study calculates the health cost of air pollution. The above findings indicate that in China, the effect of air pollution on physical and mental health cannot be ignored. The government needs to consider the heterogeneity and long-run and short-run differences in the health effects of air pollution when formulating corresponding environmental and medical policies.

Carcinogenic risk assessment and changes in Spirometric indices in casting and welding workers exposed to Metal fumes

INTRODUCTION

The aim of this study was to investigate exposure to dust, and metal fumes, changes in pulmonary function indices among industrial workers to estimate the carcinogenic and non-carcinogenic risk of exposure to occupational metal fume.

Methods: This cross-sectional study was performed on 98 workers exposed to metal fumes. Air sampling was performed according to the NIOSH 0500 method and was analyzed by gravimetry and metal levels were analyzed by atomic absorption spectrometry. Spirometric results for 2010-2016 were collected. Carcinogenic and non-carcinogenic risk assessments were performed according to the US Environmental Protection Agency guidelines. Data were analyzed by SPSS 20 software.

RESULTS

The mean occupational exposure of the subjects to workplace dust and iron fumes was 15.95 ± 6.65 mg/m3 and 13.18 ± 3.06 mg/m3 respectively. During these 6 years, the FVC (P=0.04), PEFR (P=0.04), and FEV1 (P=0.03) indices decreased significantly among welders, but there was no significant difference between FEV1/ FVC indexes. Also, the mean of FEV1 and PEFR decreased significantly amongst casting workers, but FVC and FEV1/ FVC had no significant difference. Multivariate regression showed that in both jobs, BMI and work history were related to pulmonary function indices. The mean total excess ifetime carcinogenic risk (ELCR) of hexavalent chromium in the study population was 0.708 per 1000 people and the mean non-carcinogenic risk of hexavalent chromium was HQ = 19.62.

CONCLUSIONS

The results showed that exposure to metal fumes in casting and welding jobs reduces pulmonary function indices. Although the average occupational exposure to hexavalent chromium is lower than the recommended limit and the risk of carcinogenesis is within an acceptable range, the risk of non-carcinogenic effects among workers is significant Therefore, it is important to prevent this problem, by adequate ventilation and using respiratory masks.

Association between ambient air pollution and laryngeal neoplasms incidence in twelve major Chinese cities, 2006-2013

Epidemiological evidence has suggested that ambient air pollution is an increasingly important risk factor for respiratory diseases without assessing its influence on laryngeal neoplasms incidence in China. We constructed two-way fixed effect models and Poisson regression models to explore the effects of ambient air pollutants including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and particulate matter less than or equal to 10 μm in aerodynamic diameter (PM₁₀) on incidence of laryngeal neoplasms in twelve major cities in China over the period 2006-2013. The annual average concentration for PM₁₀, SO₂, and NO₂ was 107.22 μg/m³, 44.07 μg/m³, and 46.71 μg/m³ with standard deviations of 24.84 μg/m³, 13.68 μg/m³, and 9.19 μg/m³, respectively. We observed that ambient air pollutants were significantly positively correlated with the incidence of laryngeal neoplasms, especially for NO₂. The relative risks of overall incidence of laryngeal neoplasms in the current period were 1.20, 1.04, and 1.00 for NO₂, SO₂, and PM₁₀, with 95% confidence intervals (CIs) of 1.01-1.43, 0.93-1.16, and 0.96-1.05, respectively. Moreover, this deleterious impact was stronger in the male than in the female, likely due to genetic predisposition caused by longer exposure to more serious air pollution for men. Our findings complement the epidemiological evidence of laryngeal neoplasms due to ambient air pollution and reinforce the necessity of policy efforts to control the noxious air pollution emissions.

Mortality risks due to long-term ambient sulphur dioxide exposure: large variability of relative risk in the literature

Several studies have been published about the potential health effects due to long-term exposure to sulphur dioxide (SO₂) and the relative risks (RRs) for different causes of mortality. Broad differences in the RR values are found, however. In this study, we performed an analysis of these studies aiming finding potential explanations for the high variability of the RR reported. The RRs for stratified subgroups were also analysed to identify more susceptible subgroups. A total of 14 studies were identified. Some of them related strong associations between mortality and long-term ambient SO₂ exposure, while others found insignificant or no associations to the same mortality indexes. The mean RR values ranged from 0.95 to 1.14 for mortality due to all causes, 0.99 to 3.05 for lung cancer, 0.87 to 1.3 for respiratory diseases, 0.96 to 1.14 cardiovascular diseases and 0.97 to 1.05 for cardiopulmonary diseases mortality. Among the factors that may affect the RR estimations, only the size of studied population and the spatial scales used in exposure assessment showed notable influences. The female population was found to be more susceptible to long-term SO₂ exposure. For other stratified subgroups including age, smoking status and income levels, no obvious relationship with RR was observed.

Exposure to urban particulate matter and its association with human health risks

Human health and environmental risks are increasing following air pollution associated with vehicular and industrial emissions in which particulate matter is a constituent. The purpose of this review was to assess studies on the health effects and mortality induced by particles published for the last 15 years. The literature survey indicated the existence of strong positive associations between fine and ultrafine particles' exposure and cardiovascular, hypertension, obesity and type 2 diabetes mellitus, cancer health risks, and mortality. Its exposure is also associated with increased odds of hypertensive and diabetes disorders of pregnancy and premature deaths. The ever increasing hospital admission and mortality due to heart failure, diabetes, hypertension, and cancer could be due to long-term exposure to particles in different countries. Therefore, its effect should be communicated for legal and scientific actions to minimize emissions mainly from traffic sources.

Advances in mapping population and demographic characteristics at small-area levels

Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health. In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys. We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.

Small-area methods for investigation of environment and health

Small-area studies offer a powerful epidemiological approach to study disease patterns at the population level and assess health risks posed by environmental pollutants. They involve a public health investigation on a geographical scale (e.g. neighbourhood) with overlay of health, environmental, demographic and potential confounder data. Recent methodological advances, including Bayesian approaches, combined with fast-growing computational capabilities, permit more informative analyses than previously possible, including the incorporation of data at different scales, from satellites to individual-level survey information. Better data availability has widened the scope and utility of small-area studies, but has also led to greater complexity, including choice of optimal study area size and extent, duration of study periods, range of covariates and confounders to be considered and dealing with uncertainty. The availability of data from large, well-phenotyped cohorts such as UK Biobank enables the use of mixed-level study designs and the triangulation of evidence on environmental risks from small-area and individual-level studies, therefore improving causal inference, including use of linked biomarker and -omics data. As a result, there are now improved opportunities to investigate the impacts of environmental risk factors on human health, particularly for the surveillance and prevention of non-communicable diseases.

Photocatalytic degradation of atmospheric fine particulate matter (PM2.5) collected on TiO2 supporting quartz fibre filter

Carbonaceous constituents in fine particulate matter (PM_2.5) are often associated with adverse health effects in humans. Although air filtration technology is widely used for preventing exposure to PM_2.5, the trapped PM_2.5 still has hazardous property if not treated subsequently. Thus, this study aimed to realise detoxification of PM_2.5 with a photocatalytic decomposition of carbonaceous compounds in PM_2.5 samples collected on a quartz fibre filter coated with titanium dioxide (TiO₂). The mass of PM_2.5 gradually decreased with time during the UV irradiation with a significant release of carbon dioxide (CO₂) as a product. The analysis of organic carbon (OC) and elemental carbon (EC) using a thermal/optical carbon analyser following the IMPROVE protocol showed that carbonaceous constituents such as OC1, OC2, OC3, OC4, and EC1 fractions were successfully decomposed by UV-irradiated TiO₂, whereas EC2 and EC4 fractions were inert to the photocatalysis. However, a majority of the carbon content, approximately 92% of the total carbon, was reduced by the proposed method. This shows that the photon-induced TiO₂ potentially reduces the hazardous effects of PM_2.5.

Association of Ambient Air Pollution with Nasopharyngeal Carcinoma Incidence in Ten Large Chinese Cities, 2006-2013

Large cities in China are experiencing severe ambient air pollution. Although China accounts for more than 45% of new cases of nasopharyngeal carcinoma worldwide in 2018, few studies have examined the association between ambient air pollution and the high nasopharyngeal carcinoma (NPC) incidence in China. Thus, we aim to investigate whether exposure to ambient air pollution (including nitrogen dioxide, sulfur dioxide, and PM10) would significantly affect NPC incidence in large Chinese cities. We collected panel data of ten Chinese provincial cities about local NPC incidence, air pollution level, meteorology, and city profiles during 2006 to 2013 to construct a two-way fixed-effects model to explore the association between ambient air pollution and NPC incidence, as well as possible regional and gender differences behind the association. We found that NO2 had the strongest association with NPC incidence, and the relative risks were 2.2995 (95% CI, 1.2567-4.2075) for males and 1.3010 (95% CI, 0.8212-2.0620) for females, respectively. Under cumulative exposure, it was still NO2 that had the strongest association with NPC incidence, with a relative risk of 1.8836 (95% CI, 1.2416-2.8577), compared to 1.0857 (95% CI, 0.9474-1.2450) and 1.0547 (95% CI, 0.8790-1.2663) for SO2 and PM10, respectively. In addition, males were found more sensitive to ambient air pollution than females. We also found that southern Chinese cities were more sensitive to NO2 than northern cities, which might be related to a higher humidity there. Our study reveals that exposure to ambient air pollutants like SO2, PM10, and particularly NO2, is significantly positively associated with NPC incidence in China.

Physico-chemical characterization and in vitro inflammatory and oxidative potency of atmospheric particles collected in Dakar city's (Senegal)

Exposure to atmospheric pollutants has been recognized as a major risk factor of respiratory and cardiovascular diseases. Fine particles (PM_2.5) and a coarser fraction (PM_>2.5) sampled at an urban site in Dakar (HLM), characterized by high road traffic emissions, were compared with particles sampled at a rural area, Toubab Dialaw located about 40 km from Dakar. The physicochemical characteristics of samples revealed that PMs differ for their physical (surface area) and chemical properties (in terms of CHN, metals, ions, paraffins, VOCs and PAHs) that were 65-75% higher in urban samples. Moreover the fine PMs contain higher amounts of anthropogenic related pollutants than the PM_>2.5 one. These differences are sustained by the ratios reported for the analysed PAHs which suggest as predominant primary emission sources vehicle exhausts at urban site and biomass combustion at the rural site. The inflammatory response and the oxidative damages were evaluated in BEAS-2B cells by the quantification of 4 selected inflammatory cytokines (TNF-α, IL-1β, IL-6, IL-8) and of total carbonylated proteins and the oxidative DNA adduct 8-OHdG after 8 or 24 h exposure. In accordance with the different sources and different physical and chemical properties, the inflammatory response and the oxidative damages were found higher in bronchial cells exposed to urban PMs. These data confirm the importance, also for West African countries, to evaluate the correlation between PM physico-chemical properties and potential biological impacts.

Remote Sensing in Environmental Justice Research—A Review

Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people’s physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses.

Mortality Characteristics of Two Populations in the Northern Mediterranean (Croatia) in the Period 1960⁻2012: An Ecological Study

In the second half of the 20th century, the town of Bakar (Primorje-Gorski Kotar County, Croatia), where a coking plant was operational 1978⁻1994, experienced intensive industrialisation. The town of Mali Lošinj (Primorje-Gorski Kotar County, Croatia) in this period based its economy on non-industrial sectors. The study goal was comparing mortality characteristics of these populations in the northern Mediterranean for 1960⁻2012. An ecological study design was used. Data were analysed for 1960⁻2012 for the deceased with recorded place of residence in the study area. Data on the deceased for 1960⁻1993 were taken from death reports, for 1994⁻2012 from digital archives of the Teaching Institute of Public Health, Primorje-Gorski Kotar County. Data on causes of death for 1960⁻1994 were recoded to the three-digit code of underlying cause of death according to the International Classification of Diseases (ICD⁻10). Among studied populations significant difference was found among the causes of deaths coded within ICD⁻10 chapters: neoplasms (particularly stomach carcinoma), mental and behavioural disorders and diseases of the respiratory system (particularly chronic obstructive pulmonary disease, (COPD)). Increase in mortality from neoplasms, increase in respiratory diseases for the area exposed to industrial pollution, also stomach carcinoma and COPD particularly in the town Bakar require further research.

Evaluating the long-term consequences of air pollution in early life: geographical correlations between coal consumption in 1951/1952 and current mortality in England and Wales

OBJECTIVE

To evaluate associations between early life air pollution and subsequent mortality.

DESIGN

Geographical study.

SETTING

Local government districts within England and Wales.

EXPOSURE

Routinely collected geographical data on the use of coal and related solid fuels in 1951-1952 were used as an index of air pollution.

MAIN OUTCOME MEASURES

We evaluated the relationship between these data and both all-cause and disease-specific mortality among men and women aged 35-74 years in local government districts between 1993 and 2012.

RESULTS

Domestic (household) coal consumption had the most powerful associations with mortality. There were strong correlations between domestic coal use and all-cause mortality (relative risk per SD increase in fuel use 1.124, 95% CI 1.123 to 1.126), and respiratory (1.238, 95% CI 1.234 to 1.242), cardiovascular (1.138, 95% CI 1.136 to 1.140) and cancer mortality (1.073, 95% CI 1.071 to 1.075). These effects persisted after adjustment for socioeconomic indicators in 1951, current socioeconomic indicators and current pollution levels.

CONCLUSION

Coal was the major cause of pollution in the UK until the Clean Air Act of 1956 led to a rapid decline in consumption. These data suggest that coal-based pollution, experienced over 60 years ago in early life, affects human health now by increasing mortality from a wide variety of diseases.

Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty

The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 challenges, with inference based on Markov chain Monte Carlo simulation. The first stage is a multivariate spatio-temporal fusion model for predicting areal level average concentrations of multiple pollutants from both monitored and modelled pollution data. The second stage is a spatio-temporal model for estimating the health impact of multiple correlated pollutants simultaneously, which accounts for the uncertainty in the estimated pollution concentrations. The novel methodology is motivated by a new study of the impact of both particulate matter and nitrogen dioxide concentrations on respiratory hospital admissions in Scotland between 2007 and 2011, and the results suggest that both pollutants exhibit substantial and independent health effects.

Long-term effects of total and source-specific particulate air pollution on incident cardiovascular disease in Gothenburg, Sweden

BACKGROUND AND AIMS

Long-term exposure to air pollution increases cardiopulmonary morbidity and mortality, but it is not clear which components of air pollution are the most harmful, nor which time window of exposure is most relevant. Further studies at low exposure levels have also been called for. We analyzed two Swedish cohorts to investigate the effects of total and source-specific particulate matter (PM) on incident cardiovascular disease for different time windows of exposure.

METHODS

Two cohorts initially recruited to study predictors of cardiovascular disease (the PPS cohort and the GOT-MONICA cohort) were followed from 1990 to 2011. We collected data on residential addresses and assigned each individual yearly total and source-specific PM and Nitrogen Oxides (NO_x) exposures based on dispersion models. Using multivariable Cox regression models with time-dependent exposure, we studied the association between three different time windows (lag 0, lag 1-5, and exposure at study start) of residential PM and NO_x exposure, and incidence of ischemic heart disease, stroke, heart failure and atrial fibrillation.

RESULTS AND DISCUSSION

During the study period, there were 2266 new-onset cases of ischemic heart disease, 1391 of stroke, 925 of heart failure and 1712 of atrial fibrillation. The majority of cases were in the PPS cohort, where participants were older. Exposure levels during the study period were moderate (median: 13µg/m³ for PM₁₀ and 9µg/m³ for PM_2.5), and similar in both cohorts. Road traffic and residential heating were the largest local sources of PM air pollution, and long distance transportation the largest PM source in total. In the PPS cohort, there were positive associations between PM in the last five years and both ischemic heart disease (HR: 1.24 [95% CI: 0.98-1.59] per 10µg/m³ of PM₁₀, and HR: 1.38 [95% CI: 1.08-1.77] per 5µg/m³ of PM_2.5) and heart failure. In the GOT-MONICA cohort, there were positive but generally non-significant associations between PM and stroke (HR: 1.48 [95% CI: 0.88-2.49] per 10µg/m³ of PM₁₀, and HR: 1.50 [95% CI: 0.90-2.51] per 5µg/m³ of PM_2.5, in the last five years). Effect estimates were stronger for women, non-smokers, and higher socioeconomic classes. Exposure in the last five years seemed to be more strongly associated with outcomes than other exposure time windows. Associations between source-specific PM air pollution and outcomes were mixed and generally weak. High correlations between the main pollutants limited the use of multi-pollutant models.

CONCLUSIONS

The main PM air pollutants were associated with ischemic heart disease and stroke (in women) at the relatively low exposure levels in Gothenburg, Sweden. The associations tended to be stronger for women than for men, for non-smokers than for smokers, and for higher socioeconomic classes than for lower. The associations could not be attributed to a specific PM source or type, and differed somewhat between the two cohorts. The results of this study confirm that further efforts to reduce air pollution exposure should be undertaken in Sweden to reduce the negative health effects in the general population.

A chronology of ratios between black smoke and PM10 and PM2.5 in the context of comparison of air pollution epidemiology concentration-response functions

BACKGROUND

For many air pollution epidemiological studies in Europe, 'black smoke' (BS) was the only measurement available to quantify ambient particulate matter (PM), particularly for exposures prior to the mid-1990s when quantification via the PM₁₀ and/or PM_2.5 metrics was introduced. The aim of this work was to review historic BS and PM measurements to allow comparison of health concentration-response functions (CRF) derived using BS as the measure of exposure with CRFs derived using PM₁₀ or PM_2.5.

METHODS

The literature was searched for quantitative information on measured ratios of BS:PM₁₀, BS:PM_2.5, and chemical composition of PM; with specific focus on the United Kingdom (UK) between 1970 and the early 2000s when BS measurements were discontinued.

RESULTS

The average BS:PM₁₀ ratio in urban background air was just below unity at the start of the 1970s, decreased rapidly to ≈ 0.7 in the mid-1970s and to ≈ 0.5 at the end of the 1970s, with continued smaller declines in the 1980s, and was within the range 0.2-0.4 by the end of the 1990s. The limited data for the BS:PM_2.5 ratio suggest it equalled or exceeded unity at the start of the 1970s, declined to ≈ 0.7 by the end of the 1970s, with slower decline thereafter to a range 0.4-0.65 by the end of the 1990s. For an epidemiological study that presents a CRF _BS value, the corresponding CRF _PM10 value can be estimated as R _BS:PM10 × CRF _BS where R _BS:PM10 is the BS:PM₁₀ concentration ratio, if the toxicity of PM₁₀ is assumed due only to the component quantified by a BS measurement. In the general case of some (but unknown) contribution of toxicity from non-BS components of PM₁₀ then CRF _PM10 > R _BS:PM10 × CRF _BS, with CRF _PM10 exceeding CRF_BS if the toxicity of the other components in PM₁₀ is greater than the toxicity of the component to which the BS metric is sensitive. Similar analyses were applied to relationships between CRF _PM2.5 and CRF _BS.

CONCLUSIONS

Application of this analysis to example published CRF _BS values for short and long-term health effects of PM suggest health effects from other components in the PM mixture in addition to the fine black particles characterised by BS.

A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health

In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale.

Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England

BACKGROUND

Estimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future.

METHODS

We conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO2); ozone (O3); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM2.5), and less than 10 micrometers (PM10); and sulphur dioxide (SO2). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP).

RESULTS

NO2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO2 emissions and concentrations.

CONCLUSIONS

NO2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.

Air pollution and cardiovascular mortality with over 25years follow-up: A combined analysis of two British cohorts

BACKGROUND

Adverse effects of air pollution on cardiovascular disease (CVD) mortality are well established. There are comparatively fewer studies in Europe, and in the UK particularly, than in North America. We examined associations in two British cohorts with >25years of follow-up.

METHODS

Annual average NO₂, SO₂ and black smoke (BS) air pollution exposure estimates for 1991 were obtained from land use regression models using contemporaneous monitoring data. From the European Study of Cohorts and Air Pollution (ESCAPE), air pollution estimates in 2010-11 were obtained for NO₂, NO_x, PM₁₀, PM_coarse and PM_2.5. The exposure estimates were assigned to place of residence 1989 for participants in a national birth cohort born in 1946, the MRC National Study of Health and Development (NSHD), and an adult multi-ethnic London cohort, Southall and Brent Revisited (SABRE) recruited 1988-91. The combined median follow-up was 26years. Single-pollutant competing risk models were employed, adjusting for individual risk factors.

RESULTS

Elevated non-significant hazard ratios for CVD mortality were seen with 1991 BS and SO₂ and with ESCAPE PM₁₀ and PM_2.5 in fully adjusted linear models. Per 10μg/m³ increase HRs were 1.11 [95% CI: 0.76-1.61] for BS, 1.05 [95% CI: 0.91-1.22] for SO₂, 1.16 [95% CI: 0.70-1.92] for PM₁₀ and 1.30 [95% CI: 0.39-4.34] for PM_2.5, with largest effects seen in the fourth quartile of BS and PM_2.5 compared to the first with HR 1.24 [95% CI: 0.91-1.61] and 1.21 [95% CI: 0.88-1.66] respectively. There were no consistent associations with other ESCAPE pollutants, or with 1991 NO₂. Modelling using Cox regression led to similar results.

CONCLUSION

Our results support a detrimental long-term effect for air pollutants on cardiovascular mortality.

Mitigating the effects of preferentially selected monitoring sites for environmental policy and health risk analysis

The potential effects of air pollution are a major concern both in terms of the environment and in relation to human health. In order to support both environmental and health policy there is a need for accurate estimates of the exposures that populations might experience. The information for this typically comes from environmental monitoring networks but often the locations of monitoring sites are preferentially located in order to detect high levels of pollution. Using the information from such networks has the potential to seriously affect the estimates of pollution that are obtained and that might be used in health risk analyses. In this context, we explore the topic of preferential sampling within a long-standing network in the UK that monitored black smoke due to concerns about its effect on public health, the extent of which came to prominence during the famous London fog of 1952. Abatement measures led to a decline in the levels of black smoke and a subsequent reduction in the number of monitoring locations that were thought necessary to provide the information required for policy support. There is evidence of selection bias during this process with sites being kept in the most polluted areas. We assess the potential for this to affect the estimates of risk associated air pollution and show how using Bayesian spatio-temporal exposure models may be used to attempt to mitigate the effects of preferential sampling in this case.

Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health

Performing studies on the risks of environmental hazards on human health requires accurate estimates of exposures that might be experienced by the populations at risk. Often there will be missing data and in many epidemiological studies, the locations and times of exposure measurements and health data do not match. To a large extent this will be due to the health and exposure data having arisen from completely different data sources and not as the result of a carefully designed study, leading to problems of both ‘change of support’ and ‘misaligned data’. In such cases, a direct comparison of the exposure and health outcome is often not possible without an underlying model to align the two in the spatial and temporal domains. The Bayesian approach provides the natural framework for such models; however, the large amounts of data that can arise from environmental networks means that inference using Markov Chain Monte Carlo might not be computationally feasible in this setting. Here we adapt the integrated nested Laplace approximation to implement spatio–temporal exposure models. We also propose methods for the integration of large-scale exposure models and health analyses. It is important that any model structure allows the correct propagation of uncertainty from the predictions of the exposure model through to the estimates of risk and associated confidence intervals. The methods are demonstrated using a case study of the levels of black smoke in the UK, measured over several decades, and respiratory mortality.

How robust are the estimated effects of air pollution on health? Accounting for model uncertainty using Bayesian model averaging

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We explored the sensitivity of the pollution-health effect to three factors.

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Estimation of NO2, choice of deprivation and choice of spatial autocorrelation model.

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Choice of these factors leads to a wide variation in pollution-health effects.

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BMA is utilised to estimate an overall effect while accounting for model uncertainty.

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Overall, a positive but borderline pollution-health effect was obtained.

The long-term impact of air pollution on human health can be estimated from small-area ecological studies in which the health outcome is regressed against air pollution concentrations and other covariates, such as socio-economic deprivation. Socio-economic deprivation is multi-factorial and difficult to measure, and includes aspects of income, education, and housing as well as others. However, these variables are potentially highly correlated, meaning one can either create an overall deprivation index, or use the individual characteristics, which can result in a variety of pollution-health effects. Other aspects of model choice may affect the pollution-health estimate, such as the estimation of pollution, and spatial autocorrelation model. Therefore, we propose a Bayesian model averaging approach to combine the results from multiple statistical models to produce a more robust representation of the overall pollution-health effect. We investigate the relationship between nitrogen dioxide concentrations and cardio-respiratory mortality in West Central Scotland between 2006 and 2012.

Historic air pollution exposure and long-term mortality risks in England and Wales: prospective longitudinal cohort study

INTRODUCTION

Long-term air pollution exposure contributes to mortality but there are few studies examining effects of very long-term (>25 years) exposures.

METHODS

This study investigated modelled air pollution concentrations at residence for 1971, 1981, 1991 (black smoke (BS) and SO2) and 2001 (PM10) in relation to mortality up to 2009 in 367,658 members of the longitudinal survey, a 1% sample of the English Census. Outcomes were all-cause (excluding accidents), cardiovascular (CV) and respiratory mortality.

RESULTS

BS and SO2 exposures remained associated with mortality decades after exposure-BS exposure in 1971 was significantly associated with all-cause (OR 1.02 (95% CI 1.01 to 1.04)) and respiratory (OR 1.05 (95% CI 1.01 to 1.09)) mortality in 2002-2009 (ORs expressed per 10 μg/m(3)). Largest effect sizes were seen for more recent exposures and for respiratory disease. PM10 exposure in 2001 was associated with all outcomes in 2002-2009 with stronger associations for respiratory (OR 1.22 (95% CI 1.04 to 1.44)) than CV mortality (OR 1.12 (95% CI 1.01 to 1.25)). Adjusting PM10 for past BS and SO2 exposures in 1971, 1981 and 1991 reduced the all-cause OR to 1.16 (95% CI 1.07 to 1.26) while CV and respiratory associations lost significance, suggesting confounding by past air pollution exposure, but there was no evidence for effect modification. Limitations include limited information on confounding by smoking and exposure misclassification of historic exposures.

CONCLUSIONS

This large national study suggests that air pollution exposure has long-term effects on mortality that persist decades after exposure, and that historic air pollution exposures influence current estimates of associations between air pollution and mortality.

Association between long-term exposure to air pollution and mortality in France: A 25-year follow-up study

INTRODUCTION

Long-term exposure to air pollution (AP) has been shown to have an impact on mortality in numerous countries, but since 2005 no data exists for France.

OBJECTIVES

We analyzed the association between long-term exposure to air pollution and mortality at the individual level in a large French cohort followed from 1989 to 2013.

METHODS

The study sample consisted of 20,327 adults working at the French national electricity and gas company EDF-GDF. Annual exposure to PM10, PM10–2.5, PM2.5, NO2, O3, SO2, and benzene was assessed for the place of residence of participants using a chemistry-transport model and taking residential history into account. Hazard ratios were estimated using a Cox proportional-hazards regression model, adjusted for selected individual and contextual risk factors. Hazard ratios were computed for an interquartile range (IQR) increase in air pollutant concentrations.

RESULTS

The cohort recorded 1967 non-accidental deaths. Long-term exposures to b aseline PM2.5, PM10-25, NO2 and benzene were associated with an increase in non-accidental mortality (Hazard Ratio, HR = 1.09; 95% CI: 0.99, 1.20 per 5.9 μg/m3, PM10-25; HR=1.09; 95% CI: 1.04, 1.15 per 2.2 μg/m3, NO2: HR=1.14; 95% CI: 0.99, 1.31 per 19.3 μg/m3 and benzene: HR=1.10; 95% CI: 1.00, 1.22 per 1.7 μg/m3).The strongest association was found for PM10: HR = 1.14; 95% CI: 1.05, 1.25 per 7.8 μg/m3. PM10, PM10-25 and SO2 were associated with non-accidental mortality when using time varying exposure. No significant associations were observed between air pollution and cardiovascular and respiratory mortality.

CONCLUSION

Long-term exposure to fine particles, nitrogen dioxide, sulfur dioxide and benzene is associated with an increased risk of non-accidental mortality in France. Our results strengthen existing evidence that outdoor air pollution is a significant environmental risk factor for mortality. Due to the limited sample size and the nature of our study (occupational), further investigations are needed in France with a larger representative population sample.

Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies

The health impact of long-term exposure to air pollution is now routinely estimated using spatial ecological studies, owing to the recent widespread availability of spatial referenced pollution and disease data. However, this areal unit study design presents a number of statistical challenges, which if ignored have the potential to bias the estimated pollution-health relationship. One such challenge is how to control for the spatial autocorrelation present in the data after accounting for the known covariates, which is caused by unmeasured confounding. A second challenge is how to adjust the functional form of the model to account for the spatial misalignment between the pollution and disease data, which causes within-area variation in the pollution data. These challenges have largely been ignored in existing long-term spatial air pollution and health studies, so here we propose a novel Bayesian hierarchical model that addresses both challenges and provide software to allow others to apply our model to their own data. The effectiveness of the proposed model is compared by simulation against a number of state-of-the-art alternatives proposed in the literature and is then used to estimate the impact of nitrogen dioxide and particulate matter concentrations on respiratory hospital admissions in a new epidemiological study in England in 2010 at the local authority level. © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd.

Long term effects of residential NO(x) exposure on total and cause-specific mortality and incidence of myocardial infarction in a Swedish cohort

BACKGROUND AND AIMS

Exposure to air pollution has been linked to total and cardiopulmonary mortality. However, few studies have examined the effects of exposure over decades, or which time windows of long term exposure are most relevant. We investigated the long term effects of residential air pollution on total and cause-specific mortality and incidence of myocardial infarction in a well-characterized cohort of men in Sweden.

METHODS

A cohort of 7494 men in Gothenburg was examined in 1970-1973 and followed subsequently to determine predictors of cardiovascular disease. We collected data on residential address and cause-specific mortality for the years 1973-2007. Each individual was assigned yearly nitrogen oxides (NOx) exposure based on dispersion models. Using multivariable Cox regression and generalized additive models with time-dependent exposure, we studied the association between three different time windows of residential NOx exposure, and selected outcomes.

RESULTS

In the years 1973-2007, a total of 5669 deaths, almost half of which were due to cardiovascular diseases, occurred in the cohort. Levels of NOx exposure decreased during the study period, from a median of 38 µg/m(3) in 1973 to 17 µg/m(3) in 2007. Total non-accidental mortality was associated with participants' NOx exposure in the last year (the year of outcome) (HR 1.03, 95% CI 1.01-1.05, per 10 µg/m(3)), with the mean NOx exposure during the last 5 years, and with the mean NOx exposure since enrolment (HR 1.02, 95% CI 1.01-1.04 for both). The associations were similar (HR 1.01-1.03), but generally not statistically significant, for cardiovascular, ischemic heart disease, and acute myocardial infarction mortality, and weaker for cerebrovascular and respiratory mortality. There was no association between NOx exposure and incident myocardial infarction.

DISCUSSION AND CONCLUSIONS

Long term residential exposure to NOx at these relatively low exposure levels in Gothenburg was associated with total non-accidental mortality. The association was as strong for NOx exposure in the last year as for longer exposure windows. The effect was near linear, and only marginally affected by confounders and effect modifiers. The improved air quality in Gothenburg has by these estimates led to a 6% decrease in excess non-accidental mortality during the study period.

A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London

It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices.

Long-term exposure to air pollution and cardiovascular mortality: an analysis of 22 European cohorts

BACKGROUND

Air pollution has been associated with cardiovascular mortality, but it remains unclear as to whether specific pollutants are related to specific cardiovascular causes of death. Within the multicenter European Study of Cohorts for Air Pollution Effects (ESCAPE), we investigated the associations of long-term exposure to several air pollutants with all cardiovascular disease (CVD) mortality, as well as with specific cardiovascular causes of death.

METHODS

Data from 22 European cohort studies were used. Using a standardized protocol, study area-specific air pollution exposure at the residential address was characterized as annual average concentrations of the following: nitrogen oxides (NO2 and NOx); particles with diameters of less than 2.5 μm (PM2.5), less than 10 μm (PM10), and 10 μm to 2.5 μm (PMcoarse); PM2.5 absorbance estimated by land-use regression models; and traffic indicators. We applied cohort-specific Cox proportional hazards models using a standardized protocol. Random-effects meta-analysis was used to obtain pooled effect estimates.

RESULTS

The total study population consisted of 367,383 participants, with 9994 deaths from CVD (including 4,992 from ischemic heart disease, 2264 from myocardial infarction, and 2484 from cerebrovascular disease). All hazard ratios were approximately 1.0, except for particle mass and cerebrovascular disease mortality; for PM2.5, the hazard ratio was 1.21 (95% confidence interval = 0.87-1.69) per 5 μg/m and for PM10, 1.22 (0.91-1.63) per 10 μg/m.

CONCLUSION

In a joint analysis of data from 22 European cohorts, most hazard ratios for the association of air pollutants with mortality from overall CVD and with specific CVDs were approximately 1.0, with the exception of particulate mass and cerebrovascular disease mortality for which there was suggestive evidence for an association.

Controlling for localised spatio-temporal autocorrelation in long-term air pollution and health studies

Estimating the long-term health impact of air pollution using an ecological spatio-temporal study design is a challenging task, due to the presence of residual spatio-temporal autocorrelation in the health counts after adjusting for the covariate effects. This autocorrelation is commonly modelled by a set of random effects represented by a Gaussian Markov random field (GMRF) prior distribution, as part of a hierarchical Bayesian model. However, GMRF models typically assume the random effects are globally smooth in space and time, and thus are likely to be collinear to any spatially and temporally smooth covariates such as air pollution. Such collinearity leads to poor estimation performance of the estimated fixed effects, and motivated by this epidemiological problem, this paper proposes new GMRF methodology to allow for localised spatio-temporal smoothing. This means random effects that are either geographically or temporally adjacent are allowed to be autocorrelated or conditionally independent, which allows more flexible autocorrelation structures to be represented. This increased flexibility results in improved fixed effects estimation compared with global smoothing models, which is evidenced by our simulation study. The methodology is then applied to the motivating study investigating the long-term effects of air pollution on respiratory ill health in Greater Glasgow, Scotland between 2007 and 2011.

Modeling exposure-lag-response associations with distributed lag non-linear models

In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure-lag-response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others.

A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution

Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models.

Development and back-extrapolation of NO2 land use regression models for historic exposure assessment in Great Britain

Modeling historic air pollution exposures is often restricted by availability of monitored concentration data. We evaluated back-extrapolation of land use regression (LUR) models for annual mean NO2 concentrations in Great Britain for up to 18 years earlier. LUR variables were created in a geographic information system (GIS) using land cover and road network data summarized within buffers, site coordinates, and altitude. Four models were developed for 2009 and 2001 using 75% of monitoring sites (in different groupings) and evaluated on the remaining 25%. Variables selected were generally stable between models. Within year, hold-out validation yielded mean-squared-error-based R(2) (MSE-R(2)) (i.e., fit around the 1:1 line) values of 0.25-0.63 and 0.51-0.65 for 2001 and 2009, respectively. Back-extrapolation was conducted for 2009 and 2001 models to 1991 and for 2009 models to 2001, adjusting to the year using two background NO2 monitoring sites. Evaluation of back-extrapolated predictions used 100% of sites from an historic national NO2 diffusion tube network (n = 451) for 1991 and 70 independent sites from automatic monitoring in 2001. Values of MSE-R(2) for back-extrapolation to 1991 were 0.42-0.45 and 0.52-0.55 for 2001 and 2009 models, respectively, but model performance varied by region. Back-extrapolation of LUR models appears valid for exposure assessment for NO2 back to 1991 for Great Britain.

Effects of Eyjafjallajökull volcanic ash on innate immune system responses and bacterial growth in vitro

BACKGROUND

On 20 March 2010, the Icelandic volcano Eyjafjallajökull erupted for the first time in 190 years. Despite many epidemiological reports showing effects of volcanic ash on the respiratory system, there are limited data evaluating cellular mechanisms involved in the response to ash. Epidemiological studies have observed an increase in respiratory infections in subjects and populations exposed to volcanic eruptions.

METHODS

We physicochemically characterized volcanic ash, finding various sizes of particles, as well as the presence of several transition metals, including iron. We examined the effect of Eyjafjallajökull ash on primary rat alveolar epithelial cells and human airway epithelial cells (20-100 µg/cm(2)), primary rat and human alveolar macrophages (5-20 µg/cm(2)), and Pseudomonas aeruginosa (PAO1) growth (3 µg/104 bacteria).

RESULTS

Volcanic ash had minimal effect on alveolar and airway epithelial cell integrity. In alveolar macrophages, volcanic ash disrupted pathogen-killing and inflammatory responses. In in vitro bacterial growth models, volcanic ash increased bacterial replication and decreased bacterial killing by antimicrobial peptides.

CONCLUSIONS

These results provide potential biological plausibility for epidemiological data that show an association between air pollution exposure and the development of respiratory infections. These data suggest that volcanic ash exposure, while not seriously compromising lung cell function, may be able to impair innate immunity responses in exposed individuals.

Mortality associations with long-term exposure to outdoor air pollution in a national English cohort

RATIONALE

Cohort evidence linking long-term exposure to outdoor particulate air pollution and mortality has come largely from the United States. There is relatively little evidence from nationally representative cohorts in other countries.

OBJECTIVES

To investigate the relationship between long-term exposure to a range of pollutants and causes of death in a national English cohort.

METHODS

A total of 835,607 patients aged 40-89 years registered with 205 general practices were followed from 2003-2007. Annual average concentrations in 2002 for particulate matter with a median aerodynamic diameter less than 10 (PM(10)) and less than 2.5 μm (PM(2.5)), nitrogen dioxide (NO(2)), ozone, and sulfur dioxide (SO(2)) at 1 km(2) resolution, estimated from emission-based models, were linked to residential postcode. Deaths (n = 83,103) were ascertained from linkage to death certificates, and hazard ratios (HRs) for all- and cause-specific mortality for pollutants were estimated for interquartile pollutant changes from Cox models adjusting for age, sex, smoking, body mass index, and area-level socioeconomic status markers.

MEASUREMENTS AND MAIN RESULTS

Residential concentrations of all pollutants except ozone were positively associated with all-cause mortality (HR, 1.02, 1.03, and 1.04 for PM(2.5), NO(2), and SO(2), respectively). Associations for PM(2.5), NO(2), and SO(2) were larger for respiratory deaths (HR, 1.09 each) and lung cancer (HR, 1.02, 1.06, and 1.05) but nearer unity for cardiovascular deaths (1.00, 1.00, and 1.04).

CONCLUSIONS

These results strengthen the evidence linking long-term ambient air pollution exposure to increased all-cause mortality. However, the stronger associations with respiratory mortality are not consistent with most US studies in which associations with cardiovascular causes of death tend to predominate.

Long-term exposure to outdoor air pollution and incidence of cardiovascular diseases

BACKGROUND

Evidence based largely on US cohorts suggests that long-term exposure to fine particulate matter is associated with cardiovascular mortality. There is less evidence for other pollutants and for cardiovascular morbidity. By using a cohort of 836,557 patients age 40 to 89 years registered with 205 English general practices in 2003, we investigated relationships between ambient outdoor air pollution and incident myocardial infarction, stroke, arrhythmia, and heart failure over a 5-year period.

METHODS

Events were identified from primary care records, hospital admissions, and death certificates. Annual average concentrations in 2002 for particulate matter with a median aerodynamic diameter <10 (PM10) and <2.5 microns, nitrogen dioxide (NO2), ozone, and sulfur dioxide at a 1 × 1 km resolution were derived from emission-based models and linked to residential postcode. Analyses were performed using Cox proportional hazards models adjusting for relevant confounders, including social and economic deprivation and smoking.

RESULTS

While evidence was weak for relationships with myocardial infarction, stroke, or arrhythmia, we found consistent associations between pollutant concentrations and incident cases of heart failure. An interquartile range change in PM10 and in NO2 (3.0 and 10.7 µg/m, respectively) both produced a hazard ratio of 1.06 (95% confidence interval = 1.01-1.11) after adjustment for confounders. There was some evidence that these effects were greater in more affluent areas.

CONCLUSIONS

This study of an English national cohort found evidence linking long-term exposure to particulate matter and NO2 with the development of heart failure. We did not, however, replicate associations for other cardiovascular outcomes that have been reported elsewhere.

Ambient air pollution exposures and risk of rheumatoid arthritis: results from the Swedish EIRA case-control study

OBJECTIVE

Environmental factors may play a role in the development of rheumatoid arthritis (RA). We examined whether long-term exposures to air pollution were associated with the risk of RA in the Swedish Epidemiological Investigation of Rheumatoid Arthritis Study.

METHODS

We studied 1497 incident RA cases and 2536 controls. Local levels of particulate matter (PM10) and gaseous pollutants (sulphur dioxide (SO2) and nitrogen dioxide (NO2)) from traffic and home heating were predicted for all residential addresses. We examined the association of an IQR increase (2 µg/m3 for PM10, 8 µg/m3 for SO2 and 9 µg/m3 for NO2) in each pollutant at different time points before symptom onset and average exposure with the risk of all RA and the risk of the rheumatoid factor and anti-citrullinated protein antibody (ACPA) RA phenotypes.

RESULTS

There was no evidence of an increased risk of RA with PM10. Total RA risks were modestly elevated for the gaseous pollutants, but were not statistically significant after adjustment for smoking and education (OR 1.18, 95% CI 0.97 to 1.43 and OR 1.09, 95% CI 0.99 to 1.19 for SO2 and NO2 in the 10th year before onset). Stronger elevated risks were observed for individuals with less than a university education and with the ACPA-negative RA phenotype.

CONCLUSIONS

No consistent overall associations between air pollution in the Stockholm area and the risk of RA were observed. However, there was a suggestion of increased risks of RA incidence with increases in NO2 from local traffic and SO2 from home heating sources with stronger associations for the ACPA-negative phenotype.

Air pollution and mortality in New Zealand: cohort study

BACKGROUND

Few cohort studies of the health effects of urban air pollution have been published. There is evidence, most consistently in studies with individual measurement of social factors, that more deprived populations are particularly sensitive to air pollution effects.

METHODS

Records from the 1996 New Zealand census were anonymously and probabilistically linked to mortality data, creating a cohort study of the New Zealand population followed up for 3 years. There were 1.06 million adults living in urban areas for which data were available on all covariates. Estimates of exposure to air pollution (measured as particulate matter with an aerodynamic diameter less than 10 μm, PM(10)) were available for census area units from a previous land use regression study. Logistic regression analyses were conducted to investigate associations between cause-specific mortality rates and average exposure to PM(10) in urban areas, with control for confounding by age, sex, ethnicity, social deprivation, income, education, smoking history and ambient temperature.

RESULTS

The odds of all-cause mortality in adults (aged 30-74 years at census) increased by 7% per 10 μg/m(3) increase in average PM(10) exposure (95% CI 3% to 10%) and 20% per 10 μg/m(3) among Maori, but with wide CI (7% to 33%). Associations were stronger for respiratory and lung cancer deaths.

CONCLUSIONS

An association of PM(10) with mortality is reported in a country with relatively low levels of air pollution. The major limitation of the study is the probable misclassification of PM(10) exposure. On balance, this means the strength of association was probably underestimated. The apparently greater association among Maori might be due to different levels of co-morbidity.

Relationship between physicochemical characterization and toxicity of fine particulate matter (PM2.5) collected in Dakar city (Senegal)

The massive increase in emissions of air pollutants due to economic and industrial growth in developing countries has made air quality a crucial health problem in this continent. Hence, it is somewhat critical to have a better knowledge on the air pollution in Sub-Saharan Africa countries. Three air pollution PM2.5 samples were also collected in two urban sites (i.e., Fann and Faidherbe) in Dakar (Senegal) and in a rural site near Dakar (i.e., Ngaparu). The two urban sites mainly differ in the type of used vehicles: in Fann, most of the traffic is made of buses, which are absent, in Faidherbe. The physicochemical characteristics of the three PM2.5 samples revealed their high heterogeneities and complexities, related to the multiple natural and anthropogenic emission sources. Results from 5-bromodeoxyuridine incorporation into DNA, mitochondrial dehydrogenase activity, and extracellular lactate dehydrogenase activity in PM2.5-exposed BEAS-2B cells suggested the exposure conditions (i.e., 3 and 12 μg PM/cm² during 24, 48, and 72 h) to further consider. The organic fractions (i.e., mainly PAHs) of the PM(2.5) samples were able to induce a time and/or concentration-dependent gene expression of CYP1A1 and CYP1B1, and, to a lesser extent, NQO1. There was a time and/or dose-dependent increase of both the gene expression and/or protein secretion of inflammatory mediators (i.e., TNF-α, IL-1β, IL-6, and/or IL-8) in PM(2.5)-exposed BEAS-2B cells. In agreement with the physicochemical characterization, urban PM(2.5) samples caused greater biological responses in BEAS-2B cells than the rural one. Variable concentrations of transition metals (i.e., Fe, Al, Pb, Mn, Zn) and organic compounds (i.e., PAHs) founded in the three PM2.5 samples might be firmly involved in a time- and/or dose-dependent toxicity, relying on inflammatory processes.

Land use regression modeling to estimate historic (1962-1991) concentrations of black smoke and sulfur dioxide for Great Britain

Land-use regression modeling was used to develop maps of annual average black smoke (BS) and sulfur dioxide (SO(2)) concentrations in 1962, 1971, 1981, and 1991 for Great Britain on a 1 km grid for use in epidemiological studies. Models were developed in a GIS using data on land cover, the road network, and population, summarized within circular buffers around air pollution monitoring sites, together with altitude and coordinates of monitoring sites to consider global trend surfaces. Models were developed against the log-normal (LN) concentration, yielding R(2) values of 0.68 (n = 534), 0.68 (n = 767), 0.41 (n = 771), and 0.39 (n = 155) for BS and 0.61 (n = 482), 0.65 (n = 733), 0.38 (n = 756), and 0.24 (n = 153) for SO(2) in 1962, 1971, 1981, and 1991, respectively. Model evaluation was undertaken using concentrations at an independent set of monitoring sites. For BS, values of R(2) were 0.56 (n = 133), 0.41 (n = 191), 0.38 (n = 193), and 0.34 (n = 37), and for SO(2) values of R(2) were 0.71 (n = 121), 0.57 (n = 183), 0.26 (n = 189), and 0.31 (n = 38) for 1962, 1971, 1981, and 1991, respectively. Models slightly underpredicted (fractional bias: 0∼-0.1) monitored concentrations of both pollutants for all years. This is the first study to produce historic concentration maps at a national level going back to the 1960s.