Individual Air Pollution Exposure Research Articles

EFFECT OF EXPOSURE TO AMBIENT PM2.5 ON BLOOD PRESSURE AND HYPERTENSION AMONG PARTICIPANTS OF THE GLOBALLY CONDUCTED MAY MEASUREMENT MONTH 2018-2019

Objective: Low-middle-income countries (LMICs) face most of the burden of air pollution and increasing incidence and mortality from increased blood pressure (BP) and hypertension. Yet, there is a paucity of studies linking air pollution and BP conducted in LMICs. We aim to extend the spatial coverage of the evidence using data from countries with different income and air pollution levels. We investigated the effect of exposure to short and long-term ambient PM2.5 on BP and prevalent hypertension in 125,198 participants from 29 countries during May Measurement Month (MMM) BP screening campaigns in 2018 and 2019. Design and method: We conducted a cross-sectional study using satellite-derived data on monthly average PM2.5 and assigned exposure (averaged for 1month, 3months, 6 months, and 1 year) to participants for the month in which BP measurement was recorded based on the coordinates of the MMM screening site. We used generalized additive models with penalized splines for BP and generalized linear models for prevalent hypertension, accounting for potential confounders and country-level clustering effects. Results: Short and long-term exposure to ambient PM2.5 was associated with increased SBP and DBP at an individual level but not at the country level. We found the relationship non-linear, with significantly higher effects beyond 70ug/m3. An interquartile range increase (17.6ug/m3) in annual PM2.5 was associated with a higher prevalence of hypertension with an odds ratio of 1.12 [95% CI: 1.09, 1.15]. Stratified analysis showed that individuals with high BMI and those with hypertension, particularly among those untreated and participants with comorbidities, were more susceptible to the effect of air pollution on BP. Conclusions: More high-quality data are required to evaluate individual air pollution exposure (using robust and granular data) with cardiovascular risk factors such as BP and major adverse cardiovascular events. Meanwhile, health policies promoting air pollution reduction are needed, particularly for those at the highest risk.

Short-term effects of air pollution on respiratory diseases among young children in Wuhan city, China

BackgroundThe high risks for childhood respiratory diseases are associated with exposure to ambient air pollution. However, there are few studies that have explored the association between air pollution exposure and respiratory diseases among young children (particularly aged 0–2 years) based on the entire population in a megalopolis.MethodsDaily hospital admission records were obtained from 54 municipal hospitals in Wuhan city, China. We included all children (aged 0–2 years) hospitalized with respiratory diseases between January 2017 and December 2018. Individual air pollution exposure assessment was used in Land Use Regression model and inverse distance weighted. Case-crossover design and conditional logistic regression models were adopted to estimate the hospitalization risk associated with air pollutants.ResultsWe identified 62,425 hospitalizations due to respiratory diseases, of which 36,295 were pneumonia. Particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) and nitrogen dioxide (NO2) were significantly associated with respiratory diseases and pneumonia. ORs of pneumonia were 1.0179 (95% CI 1.0097–1.0260) for PM2.5 and 1.0131 (95% CI 1.0042–1.0220) for NO2 at lag 0–7 days. Subgroup analysis suggested that NO2, Ozone (O3) and sulfur dioxide (SO2) only showed effects on pneumonia hospitalizations on male patients, but PM2.5 had effects on patients of both genders. Except O3, all pollutants were strongly associated with pneumonia in cold season. In addition, children who aged elder months and who were in central urban areas had a higher hospitalization risk.ConclusionsAir pollution is associated with higher hospitalization risk for respiratory diseases, especially pneumonia, among young children, and the risk is related to gender, month age, season and residential location.

Multiple environmental exposures and obesity in eastern China: An individual exposure evaluation model

Obesity has caused a huge burden of disease. Few studies have explored individuals’ environmental exposure level and the impact of multiple environmental exposures on obesity. The aim of this study was to explore individual air pollution exposure evaluation, and the association between and multiple environmental factors and obesity among adult residents in rural areas of China. In this study, 8400 residents of 14 districts and counties in eastern of China were selected by multistage stratified cluster sampling, and a total of 8377 residents were included in the final analysis. We adopted BMI (Body Mass Index) > 28 kg/m2 as the definition of obesity. First, an individual air pollution evaluation model was established based on the monitoring data of air pollution stations closest to residential address, different demographic characteristics of residents and daily living habits using generalized linear model and random forest model. Then, we used Bayesian Kernel Machine Regression (BKMR) and Quantile g-Computation (QgC) models to explore multiple environmental exposures on obesity. The results showed that six air pollutants were significantly positively associated with obesity, and green space had a significant protective effect on obesity. The BKMR model showed that the effects of different air pollutants on obesity were significantly enhanced by each other, while green space significantly reduced the positive effect of air pollution on obesity. The QgC model showed a significant positive association with obesity when all environmental factors were exposed as a whole, especially in males, higher household incomes and young people. It suggested that relevant authorities should improve regional air quality and green space to reduce the burden of disease caused by obesity.

Air pollution exposure monitoring using portable low-cost air quality sensors

Urban environments with a high degree of industrialization are infested with hazardous chemicals and airborne pollutants. These pollutants can have devastating effects on human health, causing both acute and chronic diseases such as respiratory infections, lung cancer, and heart disease. Air pollution monitoring is vital not only to citizens, warning them on the health risks of air pollutants, but also to policy-makers, assisting them on drafting regulations and laws that aim at minimizing those health risks. Currently, air pollution monitoring predominantly relies on expensive high-end static sensor stations. These stations produce only aggregated information about air pollutants, and are unable to capture variations in individual's air pollution exposure. As an alternative, this article develops a citizen-based air pollution monitoring system that captures individual exposure levels to air pollutants during daily indoor and outdoor activities. We present a low-cost portable sensor and carry out a measurement campaign using the sensors to demonstrate the validity and benefits of citizen-based pollution measurements. Specifically, we (i) successfully classify the data into indoor and outdoor, and (ii) validate the consistency and accuracy of our outdoor-classified data to the measurements of a high-end reference monitoring station. Our experimental results further prove the effectiveness of our campaign by (i) providing fine-grained air pollution insights over a wide geographical area, (ii) identifying probable causes of air pollution dependent on the area, and (iii) providing citizens with personalized insights about air pollutants in their daily commute.

Validity of self-reported air pollution annoyance to assess long-term exposure to air pollutants in Belgium

In epidemiological studies, assessment of long term exposure to air pollution is often estimated using air pollution measurements at fixed monitoring stations, and interpolated to the residence of survey participants through Geographical Information Systems (GIS). However, obtaining georeferenced address data from national registries requires a long and cumbersome administrative procedure, since this kind of personal data is protected by privacy regulations. This paper aims to assess whether information collected in health interview surveys, including air pollution annoyance, could be used to build prediction models for assessing individual long term exposure to air pollution, removing the need for data on personal residence address.Analyses were carried out based on data from the Belgian Health Interview Survey (BHIS) 2013 linked to GIS-modelled air pollution exposure at the residence place of participants older than 15 years (n = 9347). First, univariate linear regressions were performed to assess the relationship between air pollution annoyance and modelled exposure to each air pollutant. Secondly, a multivariable linear regression was performed for each air pollutant based on a set of variables selected with elastic net cross-validation, including variables related to environmental annoyance, socio-economic and health status of participants. Finally, the performance of the models to classify individuals in three levels of exposure was assessed by means of a confusion matrix.Our results suggest a limited validity of self-reported air pollution annoyance as a direct proxy for air pollution exposure and a weak contribution of environmental annoyance variables in prediction models. Models using variables related to the socio-economic status, region, urban level and environmental annoyance allow to predict individual air pollution exposure with a percentage of error ranging from 8% to 18%. Although these models do not provide very accurate predictions in terms of absolute exposure to air pollution, they do allow to classify individuals in groups of relative exposure levels, ranking participants from low over medium to high air pollution exposure. This model represents a rapid assessment tool to identify groups within the BHIS participants undergoing the highest levels of environmental stress.

Air Pollution Exposure Affects Severity and Cellular Endotype of Chronic Rhinosinusitis With Nasal Polyps.

Air pollution has emerged as an important environmental risk factor for chronic rhinosinusitis (CRS) progression. This study assessed exposure to five types of air pollution (PM2.5/10 , SO2 , NO2 , CO, O3 ) and explored their effects on CRS with nasal polyps (CRSwNP) severity and endotype. Retrospective cohort study. Air pollution data from monitoring sites in Beijing were obtained to assess individual air pollution exposure. Outcomes of CRSwNP (n=282) including Lund-Mackay (L-M) score, Lund-Kennedy (L-K) score, visual analogue scale (VAS) score and nasal patency/airflow resistance and so on were measured to analyze correlations with air pollution and compare groups with different exposure types. Multivariable-adjusted binary logistic regression was used to determine potential air pollution risk factors of the endotype of eosinophilic CRSwNP (ECRSwNP). Short-term exposures to PM2.5/10 , SO2 , CO, NO2 , and O3 were weak but significantly associated with increased L-M scores. Short-term exposures to PM10 , CO, and NO2 were correlated with increased VAS headache/facial pain scores. The L-M scores of the group of the highest PM2.5 (≥150 μg/m3 ) exposure were significantly higher than those of control group. For each increased unit of the average concentration of PM2.5 , there was a 1.047-fold (95% confidence interval, 1.005-1.091) increased risk of the endotype of ECRSwNP. Air pollution exposure exacerbated CRSwNP severity and PM2.5 could be a risk factor for endotype of ECRSwNP, suggesting the role of air pollution in CRSwNP pathogenesis. 4 Laryngoscope, 132:2103-2110, 2022.

INSIDE Project: Individual Air Pollution Exposure, Extracellular Vesicles Signaling and Hypertensive Disorder Development in Pregnancy.

Hypertensive disorders are common complications during pregnancy (HDP) with substantial public health impact. Acute and chronic particulate matter (PM) exposure during pregnancy increases the risk of HDP, although the underlying molecular mechanisms remain unclear. Extracellular vesicles (EVs) may be the ideal candidates for mediating the effects of PM exposure in pregnancy as they are released in response to environmental stimuli. The INSIDE project aims to investigate this mechanism in pregnancy outcomes. The study population is enrolled at the Fetal Medicine Unit of Fondazione IRCCS Ca’Granda—Ospedale Maggiore Policlinico at 10–14 weeks of gestation. Exposure to PM10 and PM2.5 is assessed using the flexible air quality regional model (FARM) and Bayesian geostatistical models. Each woman provides a blood sample for EV analysis and circulating biomarker assessment. Moreover, a subgroup of recruited women (n = 85) is asked to participate in a cardiovascular screening program including a standard clinical evaluation, a non-invasive assessment of right ventricular function, and pulmonary circulation at rest and during exercise. These subjects are also asked to wear a personal particulate sampler, to measure PM10, PM2.5, and PM1. The INSIDE study is expected to identify the health impacts of PM exposure on pregnancy outcomes.

Validity of air pollution annoyance to assess long-term exposure to air pollution in Belgium

Abstract Background In environmental epidemiology, air pollution exposure is often estimated at the population level. To avoid the risk of exposure misclassification, one possibility is to interpolate air pollution measures at the residence through Geographical Information Systems. However, this might imply cumbersome administrative procedures. Data on air pollution annoyance from surveys can be an alternative to assess individual exposure to air pollution. This study investigates the association between air pollution annoyance and individual air pollution exposure. Methods Analyses were carried out based on a linkage of data from the Belgian Health Interview Survey (HIS 2008 and 2013; >15 years; n = 9347) and annual means of air pollution concentration at the residence. Self-reported air pollution annoyance was assessed through a five-point Likert scale. Statistical analyses included Spearman correlation coefficient, analysis of variance and multivariable ordinal logistic regressions (OLR). Results A significant exposure-response relationship was observed between long-term air pollution exposure and self-reported air pollution annoyance. However, Spearman coefficients were low (0.18-0.24), meaning a high heterogeneity of annoyance levels for a given exposure. In multivariable OLR, the odds of being annoyed by air pollution was 2.10 (95% CI: 1.86-2.67) times higher for each Interquartile range (IQR) increase in NO2 concentration (PM2.5: 1.77 [95% CI: 1.53-2.07], PM10: 1.61 [95% CI: 1.33-1.67], black carbon: 1.45 [95% CI: 1.27-1.67]). Air pollution annoyance depended largely on individual factors especially health status. Conclusions Air pollution exposure has a significant influence on self-reported air pollution annoyance. However, many other factors were found to influence annoyance, independently of the exposure. We found a limited validity of self-reported air pollution annoyance to assess individual long-term exposure to air pollution. Key messages Significant exposure-response relationship between individual long-term air pollution exposure and self-reported air pollution annoyance. Limited validity of self-reported air pollution annoyance for assessing individual long term exposure to air pollution.

Long-term exposure to air pollution and atherosclerosis in the carotid arteries in the Malmö diet and cancer cohort

BackgroundLong-term exposure to air pollution increases the risk of cardiovascular morbidity and mortality, but the mechanisms are not fully known. Current evidence suggests that air pollution exposure contributes to the development of atherosclerosis. There are few studies investigating associations between air pollution and carotid plaques, a well-known precursor of cardiovascular disease. MethodsA Swedish population-based cohort (aged 45–64 years at recruitment) was randomly selected from the Malmö Diet and Cancer study between 1991 and 1994, of which 6103 participants underwent ultrasound examination of the right carotid artery to determine carotid plaque presence and carotid intima media thickness (CIMT). Participants were assigned individual residential air pollution exposure (source-specific PM2.5, PM10, NOx, BC) at recruitment from Gaussian dispersion models. Logistic and linear regression models, adjusted for potential confounders and cardiovascular risk factors, were used to investigate associations between air pollutants and prevalence of carotid plaques, and CIMT, respectively. ResultsThe prevalence of carotid plaques was 35%. The mean levels of PM2.5 and PM10 at recruitment were 11 and 14 μg/m3, most of which was due to long range transport. The exposure contrast within the cohort was relatively low. PM2.5 exposure was associated with carotid plaques in a model including age and sex only (OR 1.10 (95% CI 1.01–1.20) per 1 μg/m3), but after adjustment for cardiovascular risk factors and socioeconomic status (SES) the association was weak and not significant (OR 1.05 (95% CI 0.96–1.16) per 1 μg/m3). The pattern was similar for PM10 and NOx exposure. Associations between air pollutants and plaques were slightly stronger for long-term residents and in younger participants with hypertension. There was no clear linear trend between air pollution exposure and plaque prevalence. Non-significant slightly positive associations were seen between air pollution exposures and CIMT. ConclusionsIn this large, well-controlled cross-sectional study at low exposure levels we found no significant associations between air pollution exposures and subclinical atherosclerosis in the carotid arteries, after adjusting for cardiovascular risk factors and SES. Further epidemiological studies of air pollution and intermediate outcomes are needed to explain the link between air pollution and cardiovascular events.

Predicting ambient PM2.5 concentrations in Ulaanbaatar, Mongolia with machine learning approaches.

Accurately assessing individual ambient air pollution exposure is a crucial part of epidemiological studies looking at the adverse health effect of poor air quality. This is particularly challenging in developing countries with high levels of air pollution, mostly due to sparse monitoring networks with a lack of consistent data. We evaluated the performance of six different machine learning algorithms in predicting fine particulate matter (PM2.5) concentrations in Ulaanbaatar, Mongolia using data between 2010 and 2018. We found that the algorithms produce robust results based on performance metrics. Random forest (RF) and gradient boosting models performed the best with leave-one-location-out cross-validated R2 of 0.82 for when using data from the entire study period. After applying tuned models on the hold-out test set, R2 increased to 0.96 for the RF and 0.90 for the gradient boosting model. We also predicted PM2.5 concentrations for each administrative area (khoroo) of the city using RF and maps of predictions show spatiotemporal variations that are in line with the location of the high-emission area (ger district), city center, and population density. Our results provide evidence of the advantage and feasibility of machine learning approaches in predicting ambient PM2.5 levels in a setting with limited resources and extreme air pollution levels.

Associations of maternal ozone exposures during pregnancy with maternal blood pressure and risk of hypertensive disorders of pregnancy: A birth cohort study in Guangzhou, China

Although studies have assessed the associations of maternal exposure to ozone (O3) during pregnancy with blood pressure and the risk of hypertensive disorders of pregnancy (HDP), the results were inconsistent. Furthermore, no studies have been conducted in China where the ambient O3 concentration continuedly increased. The present study aimed to estimate the effects of maternal exposure to O3 during pregnancy on the HDP risk, systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP). All participants of pregnant women were selected from the prospective birth cohort study on Prenatal Environments and Offspring Health conducted in Guangzhou, China. A spatiotemporal land-use-regression model was used to estimate individual monthly air pollution exposure from three months before pregnancy to childbirth date. Information on HDP, SBP, DBP and PP was obtained from maternal medical records. A Logistic regression model and a mixed linear model were used to estimate the associations of maternal exposure to O3 with the risk of HDP and blood pressure (SBP, DBP and PP), respectively. We found significant associations of maternal exposure to O3 during the third (OR = 1.31, 95%CI: 1.07, 1.60) and the second month (OR = 1.25, 95%CI: 1.02, 1.51) before pregnancy with the risk of HDP. Observed significantly positive associations of O3 exposures with SBP, DBP and PP during the two months before pregnancy and during the early pregnancy. The peak effects of O3 exposure on SBP, DBP and PP were respectively observed during the second month of pregnancy (β = 1.07 mmHg, 95%CI: 0.84, 1.31 mmHg), the first month before pregnancy (β = 0.40 mmHg, 95%CI: 0.21, 0.50 mmHg) and the second month of pregnancy (β = 0.78 mmHg, 95%CI: 0.59, 0.97 mmHg). Our results suggest that maternal exposure to O3 were positively associated with blood pressure and the risk of HDP, and the period from three months before pregnancy to the first trimester might be the critical exposure window.

Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data.

The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. With the advantages of massive users, wide spatial coverage and passive acquisition capability, mobile phone data have become an emerging data source for compiling exposure estimates. However, compared with air pollution monitoring data, the temporal granularity of mobile phone data is not high enough, which limits the performance of individual exposure estimation. To mitigate this problem, we present a novel method of estimating dynamic individual air pollution exposure levels using trajectories reconstructed from mobile phone data. Using the city of Shanghai as a case study, we compared three different types of exposure estimates using (1) reconstructed mobile phone trajectories, (2) recorded mobile phone trajectories, and (3) residential locations. The results demonstrate the necessity of trajectory reconstruction in exposure and health risk assessment. Additionally, we measure the potential health effects of air pollution from both individual and geographical perspectives. This helped reveal the temporal variations in individual exposures and the spatial distribution of residential areas with high exposure levels. The proposed method allows us to perform large-area and long-term exposure estimations for a large number of residents at a high spatiotemporal resolution, which helps support policy-driven environmental actions and reduce potential health risks.

Long term exposure to air pollution and atherosclerotic plaques in the Malmö Diet and Cancer Cohort

TPS 671: Cardiometabolic effects of long-term air pollution exposure, Exhibition Hall, Ground floor, August 26, 2019, 3:00 PM - 4:30 PM Background: Long-term exposure to air pollution increases the risk of cardiovascular morbidity and mortality, but the mechanism is not fully known. It has been suggested that air pollution exposure contributes to the development of atherosclerosis, such as carotid artery plaques, a well known precursor of cardiovascular disease. Methods: A Swedish population-based cohort (age 46–67 years) was recruited within the Malmo Diet and Cancer study between 1991 and 1994, of which 6103 underwent ultrasound examination of the right carotid artery. The definition of carotid artery plaque was “a focal thickening of the intima-media complex of >1.2 mm and with an area of ≥10 mm2”. Also intima-media thickness (IMT) was measured (1 cm proximal to the bifurcation). Participants were assigned individual residential air pollution exposure (source-specific PM2.5, PM10, NOx, BC) at recruitment from Gaussian dispersion models. Logistic and linear regression was used to examine associations between air pollution and the prevalence of carotid artery plaques, and IMT, adjusted for potential confounders and usual cardiovascular risk factors. Results: The prevalence of carotid plaques was 34%. The mean levels of PM2.5 and PM10 at recruitment were about 10 and 14 µg/m3, most of which was due to long range transport. The mean NOx level was 39 µg/m3, most of which was due to local emissions. There was only a limited and non-significant association between PM2.5 and plaque prevalence (OR: 1.07, 95% CI 0.91 – 1.26, per µg/m3), and no association with PM10, NOx or BC. There was also no significant linear trend between plaque and air pollution levels in quartiles. There was a slight positive association between IMT and PM2.5, but not statistically significant. Conclusions: Overall we found only limited support for the hypothesis that air pollution exposure for PM10, PM2.5, BC or NOx increases the risk of atherosclerosis in the carotid arteries.

Evaluation of the inhaled dose across different microenvironments

The principal aim of the INSIDE project (INdividual air pollution exposure, extracellular vescicles SIgnaling and hypertensive disorder DEvelopment in pregnancy) is to assess the molecular effects of environmental exposure to airborne particulate matter (PM) of susceptible subject. Different approaches are considered to evaluate these effects, including an exposure-effect study performed on a selected population. The short-term exposure to different pollutants (PM and NO2) was evaluated considering 51 subjects recruited from October 2017 to April 2018. Each subject was asked to carry personal instruments for few hours before a clinical evaluation (blood and cardiological examination) from home to hospital. Instruments used in the study were: (I) CairClip – CairPol (NO2) and (II) Aerocet 831 - Aerosol Mass Monitor, Met One Instruments (size-fractionated PM). Moreover, a (III) smartphone with a GPS application and a (IV) Time Activity Diary (TAD) were used in this study to acquire information about the microenvironments (MEs) visited by subjects during the monitoring sessions.The experimental design of the project allowed to further investigate issues related to the mode of exposure: through the analysis of TADs and GPS data, it was possible to document the time spent by each subject in the different MEs and characterize the average exposure and inhaled dose associated to different MEs.The microenvironmental inhaled dose of pollutants was estimated considering the average exposure to PM and NO2, the time spent across these MEs and the specific ventilation rate of each subject. Moreover, to understand which of these parameters has the major impact of the dose model, a sensitivity analysis was performed, on the total and on the MEs dataset.

Bio3Air, an integrative system for monitoring individual-level air pollutant exposure with high time and spatial resolution

BackgroundAir pollution is a leading cause of global disease burden. Lack of suitable methods for long term measuring exposure level at individual level is crippling environmental epidemiology research of air pollution. MethodsWe report an integrative system, Bio3Air, for long term measurement of individual level air pollution exposure, currently focusing on ambient particulate matter (PM). The novel system in real-time quantifies individual's outdoor/indoor status, geological location, lung ventilation rate and PM concentration of individual's surrounding environment, and these metrics are subsequently incorporated in calculating PM exposure. ResultsThe system is fully developed and tested in China, USA and Canada, and has been successfully applied in epidemiology study. Bio3Air offers high reliability, sensitivity, reproducibility (>99%) and accuracy. It has high time- and spatial- resolution (≤ 2 min and ≤ 20 m, respectively). Bio3Air achieved 91.89% consistency with “gold-standard” method (membrane collection and off-line analysis). ConclusionsBio3Air represents a substantial methodological advance in environmental health research of air pollution. It captures information relevant in measuring individual's PM exposure (e.g. real-time outdoor/indoor status, location and lung ventilation rate). Such information is typically missed by conventional approaches. Additional features of Bio3Air include easy-to-use, cost-effectiveness and automated data collection, making it a powerful tool facilitating studies of air pollution exposure and health consequences.

Evaluation of the Danish AirGIS air pollution modeling system against measured concentrations of PM2.5, PM10, and black carbon

Background: Adverse health effects of air pollution have been reported in previous studies with varying methodological approaches to the exposure assessment. Measuring individual air pollution exposure for large-scale epidemiological studies is infeasible, calling for refined modeling tools. We evaluated the performance of the Geographical Information System–based air pollution and human exposure modeling system (AirGIS). Methods: Modeled concentrations were evaluated against measured concentrations of particulate matter (PM) less than 10 and 2.5 μm in aerodynamic diameter (PM10/PM2.5) from two fixed-site monitoring stations (background and street) and from two measurement campaigns in Copenhagen, Denmark. Modeled concentrations of black carbon (BC) were evaluated against measured PM2.5 absorbance and PM10 absorbance. Results: Mean concentrations measured in the four series were in the range of 10.4–15.3 μg/m3 for PM2.5 and 17.8–25.1 μg/m3 for PM10. The model underestimated by 7%–13% in comparison to the fixed-site monitoring stations. Correlation coefficients of 0.82 and 0.73 were observed for monthly and daily averages of measured and modeled PM2.5 at the background site and, correspondingly, 0.85 and 0.74 at the street site. The spatial variation, as evaluated from the two measurement campaigns, was also well reproduced. Correlation coefficients of 0.77 and 0.79 were observed for BC and PM2.5 absorbance and 0.76 for PM10 absorbance. Conclusion: The AirGIS framework showed an overall high degree of accuracy and will be applicable to future epidemiological studies on health effects of PM and BC.

Genome-Wide Interaction Analysis of Air Pollution Exposure and Childhood Asthma with Functional Follow-up

The evidence supporting an association between traffic-related air pollution exposure and incident childhood asthma is inconsistent and may depend on genetic factors. To identify gene-environment interaction effects on childhood asthma using genome-wide single-nucleotide polymorphism (SNP) data and air pollution exposure. Identified loci were further analyzed at epigenetic and transcriptomic levels. We used land use regression models to estimate individual air pollution exposure (represented by outdoor NO2 levels) at the birth address and performed a genome-wide interaction study for doctors' diagnoses of asthma up to 8 years in three European birth cohorts (n = 1,534) with look-up for interaction in two separate North American cohorts, CHS (Children's Health Study) and CAPPS/SAGE (Canadian Asthma Primary Prevention Study/Study of Asthma, Genetics and Environment) (n = 1,602 and 186 subjects, respectively). We assessed expression quantitative trait locus effects in human lung specimens and blood, as well as associations among air pollution exposure, methylation, and transcriptomic patterns. In the European cohorts, 186 SNPs had an interaction P < 1 × 10-4 and a look-up evaluation of these disclosed 8 SNPs in 4 loci, with an interaction P < 0.05 in the large CHS study, but not in CAPPS/SAGE. Three SNPs within adenylate cyclase 2 (ADCY2) showed the same direction of the interaction effect and were found to influence ADCY2 gene expression in peripheral blood (P = 4.50 × 10-4). One other SNP with P < 0.05 for interaction in CHS, rs686237, strongly influenced UDP-Gal:betaGlcNAc β-1,4-galactosyltransferase, polypeptide 5 (B4GALT5) expression in lung tissue (P = 1.18 × 10-17). Air pollution exposure was associated with differential discs, large homolog 2 (DLG2) methylation and expression. Our results indicated that gene-environment interactions are important for asthma development and provided supportive evidence for interaction with air pollution for ADCY2, B4GALT5, and DLG2.

Human Exposure Assessment for Air Pollution.

Assessment of human exposure to air pollution is a fundamental part of the more general process of health risk assessment. The measurement methods for exposure assessment now include personal exposure monitoring, indoor-outdoor sampling, mobile monitoring, and exposure assessment modeling (such as proximity models, interpolation model, air dispersion models, and land-use regression (LUR) models). Among these methods, personal exposure measurement is considered to be the most accurate method of pollutant exposure assessment until now, since it can better quantify observed differences and better reflect exposure among smaller groups of people at ground level. And since the great differences of geographical environment, source distribution, pollution characteristics, economic conditions, and living habits, there is a wide range of differences between indoor, outdoor, and individual air pollution exposure in different regions of China. In general, the indoor particles in most Chinese families comprise infiltrated outdoor particles, particles generated indoors, and a few secondary organic aerosol particles, and in most cases, outdoor particle pollution concentrations are a major contributor to indoor concentrations in China. Furthermore, since the time, energy, and expense are limited, it is difficult to measure the concentration of pollutants for each individual. In recent years, obtaining the concentration of air pollutants by using a variety of exposure assessment models is becoming a main method which could solve the problem of the increasing number of individuals in epidemiology studies.

Effects of air pollution exposure on glucose metabolism in Los Angeles minority children.

Growing evidence indicates that ambient (AAP: NO2 , PM2.5 and O3 ) and traffic-related air pollutants (TRAP) contribute to metabolic disease risk in adults; however, few studies have examined these relationships in children. Metabolic profiling was performed in 429 overweight and obese African-American and Latino youth living in urban Los Angeles, California. This cross-sectional study estimated individual residential air pollution exposure and used linear regression to examine relationships between air pollution and metabolic outcomes. AAP and TRAP exposure were associated with adverse effects on glucose metabolism independent of body fat percent. PM2.5 was associated with 25.0% higher fasting insulin (p < 0.001), 8.3% lower insulin sensitivity (p < 0.001), 14.7% higher acute insulin response to glucose (p = 0.001) and 1.7% higher fasting glucose (p < 0.001). Similar associations were observed for increased NO2 exposure. TRAP from non-freeway roads was associated with 12.1% higher insulin (p < 0.001), 6.9% lower insulin sensitivity (p = 0.02), 10.8% higher acute insulin response to glucose (p = 0.003) and 0.7% higher fasting glucose (p = 0.047). Elevated air pollution exposure was associated with a metabolic profile that is characteristic of increased risk for type 2 diabetes. These results indicate that increased prior year exposure to air pollution may adversely affect type 2 diabetes-related pathophysiology in overweight and obese minority children.

Association of Atmospheric Particulate Matter and Ozone with Gestational Diabetes Mellitus.

BackgroundAmbient air pollution has been linked to the development of gestational diabetes mellitus (GDM). However, evidence of the association is very limited, and no study has estimated the effects of ozone.ObjectiveOur aim was to determine the association of prenatal exposures to particulate matter ≤ 2.5 μm (PM2.5) and ozone (O3) with GDM.MethodsWe used Florida birth vital statistics records to investigate the association between the risk of GDM and two air pollutants (PM2.5 and O3) among 410,267 women who gave birth in Florida between 2004 and 2005. Individual air pollution exposure was assessed at the woman’s home address at time of delivery using the hierarchical Bayesian space–time statistical model. We further estimated associations between air pollution exposures during different trimesters and GDM.ResultsAfter controlling for nine covariates, we observed increased odds of GDM with per 5-μg/m3 increase in PM2.5 (ORTrimester1 = 1.16; 95% CI: 1.11, 1.21; ORTrimester2 = 1.15; 95% CI: 1.10, 1.20; ORPregnancy = 1.20; 95% CI: 1.13, 1.26) and per 5-ppb increase in O3 (ORTrimester1 = 1.09; 95% CI: 1.07, 1.11; ORTrimester2 = 1.12; 95% CI: 1.10, 1.14; ORPregnancy = 1.18; 95% CI: 1.15, 1.21) during both the first trimester and second trimester as well as the full pregnancy in single-pollutant models. Compared with the single-pollutant model, the ORs for O3 were almost identical in the co-pollutant model. However, the ORs for PM2.5 during the first trimester and the full pregnancy were attenuated, and no association was observed for PM2.5 during the second trimester in the co-pollutant model (OR = 1.02; 95% CI: 0.98, 1.07).ConclusionThis population-based study suggests that exposure to air pollution during pregnancy is associated with increased risk of GDM in Florida, USA.CitationHu H, Ha S, Henderson BH, Warner TD, Roth J, Kan H, Xu X. 2015. Association of atmospheric particulate matter and ozone with gestational diabetes mellitus. Environ Health Perspect 123:853–859; http://dx.doi.org/10.1289/ehp.1408456