Epidemiological Studies Of Health Effects Research Articles

A multi-resolution ensemble model of three decision-tree-based algorithms to predict daily NO2 concentration in France 2005–2022

Understanding and managing the health effects of Nitrogen Dioxide (NO2) requires high resolution spatiotemporal exposure maps. Here, we developed a multi-stage multi-resolution ensemble model that predicts daily NO2 concentration across continental France from 2005 to 2022. Innovations of this work include the computation of daily predictions at a 200 m resolution in large urban areas and the use of a spatio-temporal blocking procedure to avoid data leakage and ensure fair performance estimation. Predictions were obtained after three cascading stages of modeling: (1) predicting NO2 total column density from Ozone Monitoring Instrument satellite; (2) predicting daily NO2 concentrations at a 1 km spatial resolution using a large set of potential predictors such as predictions obtained from stage 1, land-cover and road traffic data; and (3) predicting residuals from stage 2 models at a 200 m resolution in large urban areas. The latter two stages used a generalized additive model to ensemble predictions of three decision-tree algorithms (random forest, extreme gradient boosting and categorical boosting). Cross-validated performances of our ensemble models were overall very good, with a ten-fold cross-validated R2 for the 1 km model of 0.83, and of 0.69 for the 200 m model. All three basis learners participated in the ensemble predictions to various degrees depending on time and space. In sum, our multi-stage approach was able to predict daily NO2 concentrations with a relatively low error. Ensembling the predictions maximizes the chance of obtaining accurate values if one basis learner fails in a specific area or at a particular time, by relying on the other learners. To the best of our knowledge, this is the first study aiming to predict NO2 concentrations in France with such a high spatiotemporal resolution, large spatial extent, and long temporal coverage. Exposure estimates are available to investigate NO2 health effects in epidemiological studies.

Environmental variable importance for under-five mortality in Malaysia: A random forest approach

BackgroundEnvironmental factors have been associated with adverse health effects in epidemiological studies. The main exposure variable is usually determined via prior knowledge or statistical methods. It may be challenging when evidence is scarce to support prior knowledge, or to address collinearity issues using statistical methods. This study aimed to investigate the importance level of environmental variables for the under-five mortality in Malaysia via random forest approach. MethodWe applied a conditional permutation importance via a random forest (CPI-RF) approach to evaluate the relative importance of the weather- and air pollution-related environmental factors on daily under-five mortality in Malaysia. This study spanned from January 1, 2014 to December 31, 2016. In data preparation, deviation mortality counts were derived through a generalized additive model, adjusting for long-term trend and seasonality. Analyses were conducted considering mortality causes (all-cause, natural-cause, or external-cause) and data structures (continuous, categorical, or all types [i.e., include all variables of continuous type and all variables of categorical type]). The main analysis comprised of two stages. In Stage 1, Boruta selection was applied for preliminary screening to remove highly unimportant variables. In Stage 2, the retained variables from Boruta were used in the CPI-RF analysis. The final importance value was obtained as an average value from a 10-fold cross-validation. ResultSome heat-related variables (maximum temperature, heat wave), temperature variability, and haze-related variables (PM10, PM10-derived haze index, PM10- and fire-derived haze index, fire hotspot) were among the prominent variables associated with under-five mortality in Malaysia. The important variables were consistent for all- and natural-cause mortality and sensitivity analyses. However, different most important variables were observed between natural- and external-cause under-five mortality. ConclusionHeat-related variables, temperature variability, and haze-related variables were consistently prominent for all- and natural-cause under-five mortalities, but not for external-cause.

Bayesian hierarchical dose-response meta-analysis of epidemiological studies: Modeling and target population prediction methods

When assessing the human risks due to exposure to environmental chemicals, traditional dose-response analyses are not straightforward when there are numerous high-quality epidemiological studies of priority cancer and non-cancer health outcomes. Given this wealth of information, selecting a single “best” study on which to base dose-response analyses is difficult and would potentially ignore much of the available data. Therefore, systematic approaches are necessary for the analysis of these rich databases. Examples are meta-analysis (and further, meta-regression), which are well established methods that consider and incorporate information from multiple studies into the estimation of risks due to exposure to environmental contaminants. In this paper, we propose a hierarchical, Bayesian meta-analysis approach for the dose-response analysis of multiple epidemiological studies. This paper is the second of two papers detailing this approach; the first covered “pre-analysis” steps necessary to prepare the data for dose-response modeling. This paper focuses on the hierarchical Bayesian approach to dose-response modeling and extrapolation of risk to populations of interest using the association between bladder cancer and oral inorganic arsenic (iAs) exposure as an illustrative case study. In particular, this paper addresses the modeling of both case-control and cohort studies with a flexible, logistic model in a hierarchical Bayesian framework that estimates study-specific slopes, as well as a pooled slope across all studies. This approach is akin to a random effects model in which no assumption is made a priori that there is a single, common slope for all included studies. Further, this paper also details extrapolation of the estimates of logistic slope to extra risk in a target population using a lifetable analysis and basic assumptions about background iAs exposure levels. In this case, the target population was the general United States population and information on all-cause mortality and incidence and mortality from bladder cancer was used to perform the lifetable analysis. The methods herein were developed for general use in investigating the association between any pollutant and observed health-effects in epidemiological studies. In order to demonstrate these methods, inorganic arsenic was chosen as a case study given the large epidemiological database that exists for this contaminant.

Can cyanotoxins penetrate human skin during water recreation to cause negative health effects?

Cyanobacteria blooms and associated cyanotoxins pose significant public health risks during water recreation. Oral ingestion is the only recognized route of toxin exposure in water recreation guidelines. This review examines human skin as a barrier for the prevention of cyanotoxin absorption and investigates the likelihood of negative health effects through dermal exposure. Epidemiological studies of health effects from recreational exposure to algal blooms and toxins are summarized to highlight the importance of better understanding the toxicological effect of dermal exposure. The ability of a specific cyanotoxin to penetrate human skin is inferred by its physiochemical properties according to transdermal drug studies. The review identifies a disparity between the human health effects described in algal bloom exposure case studies and the toxicological skin exposure data. Skin penetration by algal toxins is likely and deserves further investigation.

Exposure Measurement Error in Air Pollution Studies: The Impact of Shared, Multiplicative Measurement Error on Epidemiological Health Risk Estimates.

Spatiotemporal air pollution models are increasingly being used to estimate health effects in epidemiological studies. Although such exposure prediction models typically result in improved spatial and temporal resolution of air pollution predictions, they remain subject to shared measurement error, a type of measurement error common in spatiotemporal exposure models which occurs when measurement error is not independent of exposures. A fundamental challenge of exposure measurement error in air pollution assessment is the strong correlation and sometimes identical (shared) error of exposure estimates across geographic space and time. When exposure estimates with shared measurement error are used to estimate health risk in epidemiological analyses, complex errors are potentially introduced, resulting in biased epidemiological conclusions. We demonstrate the influence of using a three-stage spatiotemporal exposure prediction model and introduce formal methods of shared, multiplicative measurement error (SMME) correction of epidemiological health risk estimates. Using our three-stage, ensemble learning based nitrogen oxides (NOx) exposure prediction model, we quantified SMME. We conducted an epidemiological analysis of wheeze risk in relation to NOx exposure among school-aged children. To demonstrate the incremental influence of exposure modeling stage, we iteratively estimated the health risk using assigned exposure predictions from each stage of the NOx model. We then determined the impact of SMME on the variance of the health risk estimates under various scenarios. Depending on the stage of the spatiotemporal exposure model used, we found that wheeze odds ratio ranged from 1.16 to 1.28 for an interquartile range increase in NOx. With each additional stage of exposure modeling, the health effect estimate moved further away from the null (OR=1). When corrected for observed SMME, the health effects confidence intervals slightly lengthened, but our epidemiological conclusions were not altered. When the variance estimate was corrected for the potential "worst case scenario" of SMME, the standard error further increased, having a meaningful influence on epidemiological conclusions. Our framework can be expanded and used to understand the implications of using exposure predictions subject to shared measurement error in future health investigations.

A Quantitative General Population Job Exposure Matrix for Occupational Noise Exposure.

Occupational noise exposure is a known risk factor for hearing loss and also adverse cardiovascular effects have been suggested. A job exposure matrix (JEM) would enable studies of noise and health on a large scale. The objective of this study was to create a quantitative JEM for occupational noise exposure assessment of the general working population. Between 2001-2003 and 2009-2010, we recruited workers from companies within the 10 industries with the highest reporting of noise-induced hearing loss according to the Danish Working Environment Authority and in addition workers of financial services and children day care to optimize the range in exposure levels. We obtained 1343 personal occupational noise dosimeter measurements among 1140 workers representing 100 different jobs according to the Danish version of the International Standard Classification of Occupations 1988 (DISCO 88). Four experts used 35 of these jobs as benchmarks and rated noise levels for the remaining 337 jobs within DISCO 88. To estimate noise levels for all 372 jobs, we included expert ratings together with sex, age, occupational class, and calendar year as fixed effects, while job and worker were included as random effects in a linear mixed regression model. The fixed effects explained 40% of the total variance: 72% of the between-jobs variance, -6% of the between-workers variance and 4% of the within-worker variance. Modelled noise levels showed a monotonic increase with increasing expert score and a 20 dB difference between the highest and lowest exposed jobs. Based on the JEM estimates, metal wheel-grinders were among the highest and finance and sales professionals among the lowest exposed. This JEM of occupational noise exposure can be used to prioritize preventive efforts of occupational noise exposure and to provide quantitative estimates of contemporary exposure levels in epidemiological studies of health effects potentially associated with noise exposure.

Exposure Beyond Mass

PDS 76: Source specific outdoor air pollution studies, Exhibition Hall (PDS), Ground floor, August 26, 2019, 4:30 PM - 5:30 PM Background: Most epidemiological studies of health effects of atmospheric particulate matter use conventional filter measurements. These measurements quantify total mass concentration but may oversimplify air pollution exposure estimates as they are blind to many critical physiochemical properties of atmospheric particles. These properties likely have larger exposure variability than particulate matter mass. Objectives: This study is designed to measure and predict the spatial variations of particulate matter exposure using state-of-the-art single-particle mass spectrometry to resolve detailed physicochemical properties, namely number, size, source and chemical mixing state of individual particles in a populous urban area to study their population exposure variability and the contrasts with mass exposure. Methods: We performed mobile sampling using an advanced single-particle mass spectrometer to measure the within-city variability of number concentration of source-resolved 50 – 1000 nm particles and particle mixing state in Pittsburgh, PA, USA. We built land use regression (LUR) models to estimate their spatial patterns. We coupled demographic data to estimate population exposure. Results: Particle number concentration has a much larger spatial variability (factor of ~5) than mass concentration (factor of 2) within the city. Fresh primary particles emitted from traffic and cooking drive the exposure variability on the number concentration basis, but the mass exposure is dominated by aged background particles composed of secondary material. In addition, we show that people exposed to elevated number concentrations of atmospheric particles are also exposed to more externally mixed, primary particles. Conclusions: There is a large intra-city spatial heterogeneity of exposure to traffic and cooking particles on the number basis. Such heterogeneity is not captured by conventional mass exposure measurements. In addition, the resulting strong spatial patterns of particle chemical mixing state may potentially be significant when evaluating health effects of particulate matter exposure.

Influence of exposure measurement errors on results from epidemiologic studies of different designs.

In epidemiologic studies of health effects of air pollution, measurements or models are used to estimate exposure. Exposure estimates have errors that propagate to effect estimates in exposure-response models. We critically evaluate how types of exposure measurement error influenced bias and precision of effect estimates to understand conditions affecting interpretation of exposure-response models for epidemiologic studies of exposure to PM2.5, NO2, and SO2. We reviewed available literature on exposure measurement error for time-series and long-term exposure epidemiologystudies. For time-series studies, time-activity error (daily exposure concentration did not account for variation in exposure due to time-activity during a day) and nonambient (indoor) sources negatively biased the effect estimates and increasedstandard error, so uncertainty grew with increasing bias while underestimating the true health effect in these studies. Spatial error (deviation between true exposure concentration at an individual's location and concentration at a receptor) was ascribed to negatively biased effect estimates in most cases. Positive bias occurred for spatially variable pollutants when the variance of error correlated with the exposure estimate. For long-term exposure studies, most spatial errors did not bias the effect estimate. For both time-series and long-term exposure studies reviewed, large uncertainties were observed when exposure concentration was modeled with low spatial and temporal resolution for a spatially variable pollutant.

Review on Ultrafine Particle Health Effects

Background: Due to their small size, inhaled ultrafine particles (UFPs) may enter into alveoli and are even capable of penetrating cell membranes. Toxicological studies suggest that UFPs contribute to the development and progression of various diseases. However, epidemiological evidence for health effects of UFPs is scarce. The aim of this project is to give an overview of the epidemiological literature on the effects of UFPs on health.Methods: We conducted a systematic literature review searching MEDLINE and LUDOK data bases (time period January 2011 - May 2017) as well as a by hand search for review articles and conference proceedings. We focused on epidemiologic studies that investigated health effects of UFPs, included quantitative effect measures and had a common UFPs-measure. Data extraction and quality assessment using standard instruments was conducted by two reviewers.Results: Overall, 85 unique references were included in the review. Most of these studies were conducted in North America (n=37) or Western Europe (n=27). The majority of the studies investigated short-term effects (n=75). Ten studies investigated long-term associations. Short-term studies were dominated by panel studies with repeated measures, scripted exposure studies and time-series studies. The investigation of health effects in epidemiological studies has substantially developed during the last 7 years. Specifically, the more recent studies have undertaken efforts to control for co-pollutants in order to identify independent effects of UFPs. Despite the advances in the field, the evidence on health effects remains inconclusive or insufficient for most of the studied outcomes.Conclusions: A future challenge is the development of enhanced spatiotemporal models that are able to contribute to a more precise exposure assessment across larger areas and incorporate multipollutant models to estimate independent UFP effects.

Biomarkers of oxidative stress in blood of workers exposed to non-cholinesterase inhibiting pesticides

In occupational settings workers are often exposed to pesticides at relatively high doses compared to environmental exposures. Long-term exposure to pesticides has been associated with numerous adverse health effects in epidemiological studies, and oxidative stress is often claimed as one of the underlying mechanisms. In fact, different pesticides have been reported to induce oxidative stress due to the generation of free radicals and/or alteration in antioxidant defense enzymes. The present study examined greenhouse workers regularly exposed to diverse pesticides under integrated production system, and a group of controls of the same geographic area without any chemical exposure. Two different periods of the same crop season were assessed, one of high exposure (with greater use of pesticides) and other of low exposure (in which a less use of these compounds was made). Non-specific biomarkers of oxidative stress, e.g. thiobarbituric acid reactive substances (TBARS), ferric reducing ability of serum (FRAS), total thiol groups (SHT), gamma-glutamyl transpeptidase (GGT) and paraoxonase-1 (PON1) were measured in serum samples from all study subjects, alongside erythrocyte acetylcholinesterase (AChE). Results are suggestive of a mild increase in oxidative stress associated with pesticide exposure, which was compensated by an adaptive response to raise the antioxidant defenses and thus counter the detrimental effects of sustained oxidative stress. This response led to significantly increased levels of FRAS, SHT and PON1 in greenhouse workers relative to controls. Furthermore, AChE was decreased likely as a result of oxidative stress as workers did not use organophosphate insecticides.

Assessing Exposures to Magnetic Resonance Imaging's Complex Mixture of Magnetic Fields for In Vivo, In Vitro, and Epidemiologic Studies of Health Effects for Staff and Patients.

A complex mixture of electromagnetic fields is used in magnetic resonance imaging (MRI): static, low-frequency, and radio frequency magnetic fields. Commonly, the static magnetic field ranges from one to three Tesla. The low-frequency field can reach several millitesla and with a time derivative of the order of some Tesla per second. The radiofrequency (RF) field has a magnitude in the microtesla range giving rise to specific absorption rate values of a few Watts per kilogram. Very little attention has been paid to the case where there is a combined exposure to several different fields at the same time. Some studies have shown genotoxic effects in cells after exposure to an MRI scan while others have not demonstrated any effects. A typical MRI exam includes muliple imaging sequences of varying length and intensity, to produce different types of images. Each sequence is designed with a particular purpose in mind, so one sequence can, for example, be optimized for clearly showing fat water contrast, while another is optimized for high-resolution detail. It is of the utmost importance that future experimental studies give a thorough description of the exposure they are using, and not just a statement such as “An ordinary MRI sequence was used.” Even if the sequence is specified, it can differ substantially between manufacturers on, e.g., RF pulse height, width, and duty cycle. In the latest SCENIHR opinion, it is stated that there is very little information regarding the health effects of occupational exposure to MRI fields, and long-term prospective or retrospective cohort studies on workers are recommended as a high priority. They also state that MRI is increasingly used in pediatric diagnostic imaging, and a cohort study into the effects of MRI exposure on children is recommended as a high priority. For the exposure assessment in epidemiological studies, there is a clear difference between patients and staff and further work is needed on this. Studies that explore the possible differences between MRI scan sequences and compare them in terms of exposure level are warranted.

Particulate matter components and health: a literature review on exposure assessment

A number of studies have provided evidence on the association between particulate matter (PM) and adverse health effects. However, PM is a mix of heterogeneous composition that varies greatly by season and region. Epidemiological studies rely primarily on spatial variation of the air pollutants to estimate the associations between exposure to air pollutants and health effects. Since PM components exhibit a great deal of spatial variability, additional monitoring campaign and more refined exposure models are still needed. To our knowledge, the exposure assessment methods for PM components in epidemiological studies haven’t been reviewed. Hence, in this review, we summarize several exposure assessment methods which have been applied in estimating the associations between PM components and adverse health effects in epidemiological studies. Many concerns have been accumulated on the potential health effects of PM components with very little evidence focusing on the exposure assessment of PM components in living and work place. Thus, it is quite important to develop exposure models that cover both outdoor and indoor environments for PM components.

Estimation of residential fine particulate matter infiltration in Shanghai, China

Ambient concentrations of fine particulate matter (PM2.5) concentration is often used as an exposure surrogate to estimate PM2.5 health effects in epidemiological studies. Ignoring the potential variations in the amount of outdoor PM2.5 infiltrating into indoor environments will cause exposure misclassification, especially when people spend most of their time indoors. As it is not feasible to measure the PM2.5 infiltration factor (Finf) for each individual residence, we aimed to build models for residential PM2.5Finf prediction and to evaluate seasonal Finf variations among residences. We repeated collected paired indoor and outdoor PM2.5 filter samples for 7 continuous days in each of the three seasons (hot, cold and transitional seasons) from 48 typical homes of Shanghai, China. PM2.5-bound sulfur on the filters was measured by X-ray fluorescence for PM2.5Finf calculation. We then used stepwise-multiple linear regression to construct season-specific models with climatic variables and questionnaire-based predictors. All models were evaluated by the coefficient of determination (R2) and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV). The 7-day mean (±SD) of PM2.5Finf across all observations was 0.83 (±0.18). Finf was found higher and more varied in transitional season (12-25°C) than hot (>25°C) and cold (<12°C) seasons. Air conditioning use and meteorological factors were the most important predictors during hot and cold seasons; Floor of residence and building age were the best transitional season predictors. The models predicted 60.0%-68.4% of the variance in 7-day averages of Finf, The LOOCV analysis showed an R2 of 0.52 and an RMSE of 0.11. Our finding of large variation in residential PM2.5Finf between seasons and across residences within season indicated the important source of outdoor-generated PM2.5 exposure heterogeneity in epidemiologic studies. Our models based on readily available data may potentially improve the accuracy of estimates of the health effects of PM2.5 exposure.

The sensitivities of emissions reductions for the mitigation of UK PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt;

Abstract. The reduction of ambient concentrations of fine particulate matter (PM2.5) is a key objective for air pollution control policies in the UK and elsewhere. Long-term exposure to PM2.5 has been identified as a major contributor to adverse human health effects in epidemiological studies and underpins ambient PM2.5 legislation. As a range of emission sources and atmospheric chemistry transport processes contribute to PM2.5 concentrations, atmospheric chemistry transport models are an essential tool to assess emissions control effectiveness. The EMEP4UK atmospheric chemistry transport model was used to investigate the impact of reductions in UK anthropogenic emissions of primary PM2.5, NH3, NOx, SOx or non-methane VOC on surface concentrations of PM2.5 in the UK for a recent year (2010) and for a future current legislation emission (CLE) scenario (2030). In general, the sensitivity to UK mitigation is rather small. A 30 % reduction in UK emissions of any one of the above components yields (for the 2010 simulation) a maximum reduction in PM2.5 in any given location of ∼ 0.6 µg m−3 (equivalent to ∼ 6 % of the modelled PM2.5). On average across the UK, the sensitivity of PM2.5 concentrations to a 30 % reduction in UK emissions of individual contributing components, for both the 2010 and 2030 CLE baselines, increases in the order NMVOC, NOx, SOx, NH3 and primary PM2.5; however there are strong spatial differences in the PM2.5 sensitivities across the UK. Consequently, the sensitivity of PM2.5 to individual component emissions reductions varies between area and population weighting. Reductions in NH3 have the greatest effect on area-weighted PM2.5. A full UK population weighting places greater emphasis on reductions of primary PM2.5 emissions, which is simulated to be the most effective single-component control on PM2.5 for the 2030 scenario. An important conclusion is that weighting corresponding to the average exposure indicator metric (using data from the 45 model grids containing a monitor whose measurements are used to calculate the UK AEI) further increases the emphasis on the effectiveness of primary PM2.5 emissions reductions (and of NOx emissions reductions) relative to the effectiveness of NH3 emissions reductions. Reductions in primary PM2.5 have the largest impact on the AEI in both 2010 and the 2030 CLE scenario. The summation of the modelled reductions to the UK PM2.5 AEI from 30 % reductions in UK emissions of primary PM2.5, NH3, SOx, NOx and VOC totals 1.17 and 0.82 µg m−3 for the 2010 and 2030 CLE simulations, respectively (not accounting for non-linearity).

Induction of micronuclei and superoxide production in neuroblastoma and glioma cell lines exposed to weak 50 Hz magnetic fields.

Extremely low-frequency (ELF) magnetic fields (MF) have been associated with adverse health effects in epidemiological studies. However, there is no known mechanism for biological effects of weak environmental MFs. Previous studies indicate MF effects on DNA integrity and reactive oxygen species, but such evidence is limited to MFs higher (greater than or equal to 100 µT) than those generally found in the environment. Effects of 10 and 30 µT fields were studied in SH-SY5Y and C6 cells exposed to 50-Hz MFs for 24 h. Based on earlier findings, menadione (MQ) was used as a cofactor. Responses to MF were observed in both cell lines, but the effects differed between the cell lines. Micronuclei were significantly increased in SH-SY5Y cells at 30 µT. This effect was largest at the highest MQ dose used. Increased cytosolic and mitochondrial superoxide levels were observed in C6 cells. The effects on superoxide levels were independent of MQ, enabling further mechanistic studies without co-exposure to MQ. The micronucleus and mitochondrial superoxide data were consistent with a conventional rising exposure-response relationship. For cytosolic superoxide, the effect size was unexpectedly large at 10 µT. The results indicate that the threshold for biological effects of ELF MFs is 10 µT or less.

Effects of Long-term Exposure to Fine Particulate Matter on Mortality: Systematic Review and Meta-analysis on Assessment of Exposure and Health Effects in Epidemiological Studies

Effects of Long-term Exposure to Fine Particulate Matter on Mortality: Systematic Review and Meta-analysis on Assessment of Exposure and Health Effects in Epidemiological Studies

Residential NOx exposure in a 35-year cohort study. Changes of exposure, and comparison with back extrapolation for historical exposure assessment

In this study we aimed to investigate the effects on historical NOx estimates on time trends, spatial distributions, exposure contrasts, the effect of relocation patterns and the effects of back extrapolation. Historical levels of nitrogen oxides (NOx) from 1975 to 2009 were modeled with high resolution in Gothenburg, Sweden, using historical emission databases and Gaussian models. Yearly historical addresses were collected and geocoded from a population-based cohort of Swedish men from 1973 to 2007, with a total of 160,568 address years. Of these addresses, 146,675 (91%) were within our modeled area and assigned a NOx level.NOx levels decreased substantially from a maximum median level of 43.9 μg m−3 in 1983 to 16.6 μg m−3 in 2007, mainly due to lower emissions per vehicle km. There was a considerable variability in concentrations within the cohort, with a ratio of 3.5 between the means in the highest and lowest quartile. About 50% of the participants changed residential address during the study, but the mean NOx exposure was not affected. About half of these moves resulted in a positive or negative change in NOx exposure of >10 μg m−3, and thus changed the exposure substantially. Back extrapolation of NOx levels using the time trend of a background monitoring station worked well for 5–7 years back in time, but extrapolation more than ten years back in time resulted in substantial scattering compared to the “true” dispersion models for the corresponding years. These findings are important to take into account since accurate exposure estimates are essential in long term epidemiological studies of health effects of air pollution.

Land use regression models for crustal and traffic-related PM2.5 constituents in four areas of the SAPALDIA study

Many studies have documented adverse health effects of long-term exposure to fine particulate matter (PM2.5), but there is still limited knowledge regarding the causal relationship between specific sources of PM2.5 and such health effects. The spatial variability of PM2.5 constituents and sources, as a exposure assessment strategy for investigating source contributions to health effects, has been little explored so far.Between 2011 and 2012, three measurement campaigns of PM and nitrogen dioxide (NO2) were performed in 80 sites across four areas of the Swiss Study on Air Pollution and Lung and heart Diseases in Adults (SAPALDIA). Reflectance analysis and energy dispersive X-ray fluorescence (XRF) were performed on PM2.5 filter samples to estimate light absorbance and trace element concentrations, respectively. Three air pollution source factors were identified using principal-component factor analysis: vehicular, crustal, and long-range transport. Land use regression (LUR) models were developed for temporally-adjusted scores of each factor, combining the four study areas. Model performance was assessed using two cross-validation methods.Model explained variance was high for the vehicular factor (R2=0.76), moderate for the crustal factor (R2=0.46), and low for the long-range transport factor (R2=0.19). The cross-validation methods suggested that models for the vehicular and crustal factors moderately accounted for both the between and within-area variability, and therefore can be applied to the four study areas to estimate long-term exposures within the SAPALDIA study population.The combination of source apportionment techniques and LUR modelling may help in identifying air pollution sources and disentangling their contribution to observed health effects in epidemiologic studies.

Biomarker as a research tool in linking exposure to air particles and respiratory health.

Some of the environmental toxicants from air pollution include particulate matter (PM10), fine particulate matter (PM2.5), and ultrafine particles (UFP). Both short- and long-term exposure could result in various degrees of respiratory health outcomes among exposed persons, which rely on the individuals' health status. Methods. In this paper, we highlight a review of the studies that have used biomarkers to understand the association between air particles exposure and the development of respiratory problems resulting from the damage in the respiratory system. Data from previous epidemiological studies relevant to the application of biomarkers in respiratory system damage reported from exposure to air particles are also summarized. Results. Based on these analyses, the findings agree with the hypothesis that biomarkers are relevant in linking harmful air particles concentrations to increased respiratory health effects. Biomarkers are used in epidemiological studies to provide an understanding of the mechanisms that follow airborne particles exposure in the airway. However, application of biomarkers in epidemiological studies of health effects caused by air particles in both environmental and occupational health is inchoate. Conclusion. Biomarkers unravel the complexity of the connection between exposure to air particles and respiratory health.

Daily Ambient NO2Concentration Predictions Using Satellite Ozone Monitoring Instrument NO2Data and Land Use Regression

Although ground measurements have contributed to revealing the association between ambient air pollution and health effects in epidemiological studies, exposure measurement errors are likely to be caused because of the sparse spatial distribution of ground monitors. In this study, we estimate daily ground NO2 concentrations in the New England region, U.S., for the period 2005-2010 using satellite remote sensing data in combination with land use regression. To estimate ground-level NO2 concentrations, we constructed a mixed effects model by taking advantage of spatial and temporal variability in satellite Ozone Monitoring Instrument (OMI) tropospheric column NO2 densities. Using fine-scale land use parameters, we derived NO2 concentrations at point locations, which can be further used for subject-specific exposure estimates in epidemiological studies. A mixed effects model showed a reasonably high predictive power for daily NO2 concentrations (cross-validation R(2) = 0.79). We observed that the model performed similarly in each season, year, and state. The spatial patterns of model estimates reflected emission source areas (such as high populated/traffic areas) in the study region and revealed the seasonal characteristics of NO2. This study suggests that a combination of satellite remote sensing and land use regression can be useful for both spatially and temporally resolved exposure assessments of NO2.