Evaluating the application of multipollutant exposure metrics in air pollution health studies

Non-linear association between air pollutants and secondary sensitive skin in acne patients

BACKGROUND

There is a growing number of patients suffering from sensitive skin secondary to acne, but its prevalence and influencing factors are not yet well-understood.

OBJECTIVE

The aim of this study is to investigate the nonlinear relationship between air pollutants and secondary sensitive skin in acne patients.

METHODS

A cross-sectional study comprising 4325 acne outpatients in China was carried out between September 2021 and December 2022, employing a simple random sampling approach. Air pollutants data was derived from the nearest air quality monitoring station corresponding to the subjects' residential locations. Furthermore, socio-economic characteristics, biological attributes, and lifestyle data of patients were acquired via questionnaire surveys. The data were subsequently analyzed utilizing the XGBoost machine learning model.

RESULTS

A nonlinear relationship has been observed between secondary sensitive skin in acne patients and various factors, including particulate matter (PM2.5), inhalable particulate matter (PM10), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), the severity of depression, different levels of exercise intensity, acne grading, frequency of sunscreen application, gender, and age.

CONCLUSION

The occurrence of secondary sensitive skin in acne patients be mitigated through the implementation of measures such as the control of air pollutant emissions, regulation of negative emotions, and improvement of personal lifestyle.

Air pollution exposure, accelerated biological aging, and increased thyroid dysfunction risk: Evidence from a nationwide prospective study

BACKGROUND

Long-term air pollution exposure is a major health concern, yet its associations with thyroid dysfunction (hyperthyroidism and hypothyroidism) and biological aging remain unclear. We aimed to determine the association of long-term air pollution exposure with thyroid dysfunction and to investigate the potential roles of biological aging.

METHODS

A prospective cohort study was conducted on 432,340 participants with available data on air pollutants including particulate matter (PM2.5, PM10, and PM2.5-10), nitrogen dioxide (NO2), and nitric oxide (NO) from the UK Biobank. An air pollution score was calculated using principal component analysis to reflect joint exposure to these pollutants. Biological aging was assessed using the Klemera-Doubal method biological age and the phenotypic age algorithms. The associations of individual and joint air pollutants with thyroid dysfunction were estimated using the Cox proportional hazards regression model. The roles of biological aging were explored using interaction and mediation analyses.

RESULTS

During a median follow-up of 12.41 years, 1,721 (0.40 %) and 9,296 (2.15 %) participants developed hyperthyroidism and hypothyroidism, respectively. All air pollutants were observed to be significantly associated with an increased risk of incident hypothyroidism, while PM2.5, PM10, and NO2 were observed to be significantly associated with an increased risk of incident hyperthyroidism. The hazard ratios (HRs) for hyperthyroidism and hypothyroidism were 1.15 (95 % confidence interval: 1.00-1.32) and 1.15 (1.08-1.22) for individuals in the highest quartile compared with those in the lowest quartile of air pollution score, respectively. Additionally, we noticed that individuals with higher pollutant levels and biologically older generally had a higher risk of incident thyroid dysfunction. Moreover, accelerated biological aging partially mediated 1.9 %-9.4 % of air pollution-associated thyroid dysfunction.

CONCLUSIONS

Despite the possible underestimation of incident thyroid dysfunction, long-term air pollution exposure may increase the risk of incident thyroid dysfunction, particularly in biologically older participants, with biological aging potentially involved in the mechanisms.

Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA

Air toxics are atmospheric pollutants with hazardous effects on health and the environment. Although methodological constraints have limited the number of air toxics assessed for associations with health and disease, advances in machine learning (ML) enable the assessment of a much larger set of environmental exposures. We used ML methods to conduct a retrospective study to identify combinations of 109 air toxics associated with asthma symptoms among 269 elementary school students in Spokane, Washington. Data on the frequency of asthma symptoms for these children were obtained from Spokane Public Schools. Their exposure to air toxics was estimated by using the Environmental Protection Agency's Air Toxics Screening Assessment and National Air Toxics Assessment. We defined three exposure periods: the most recent year (2019), the last three years (2017-2019), and the last five years (2014-2019). We analyzed the data using the ML-based Data-driven ExposurE Profile (DEEP) extraction method. DEEP identified 25 air toxic combinations associated with asthma symptoms in at least one exposure period. Three combinations (1,1,1-trichloroethane, 2-nitropropane, and 2,4,6-trichlorophenol) were significantly associated with asthma symptoms in all three exposure periods. Four air toxics (1,1,1-trichloroethane, 1,1,2,2-tetrachloroethane, BIS (2-ethylhexyl) phthalate (DEHP), and 2,4-dinitrophenol) were associated only in combination with other toxics, and would not have been identified by traditional statistical methods. The application of DEEP also identified a vulnerable subpopulation of children who were exposed to 13 of the 25 significant combinations in at least one exposure period. On average, these children experienced the largest number of asthma symptoms in our sample. By providing evidence on air toxic combinations associated with childhood asthma, our findings may contribute to the regulation of these toxics to improve children's respiratory health.

Indoor pollution control based on surrogate model for residential buildings

Individuals typically spend most of their lives indoors, predominantly in spaces like offices and residences. Consequently, prolonged indoor exposure underscores the critical significance of maintaining optimal indoor air quality (IAQ) to safeguard one's health. The primary impediment to attaining efficient regulation of Indoor Air Quality (IAQ) is the challenge of monitoring the IAQ parameters, particularly within the immediate vicinity of an individual's breathing space. Current heating, ventilation, and air conditioning systems lack the ability to rapidly predict and optimize the quality of indoor air. The objective of this study is to acquire the distribution features of indoor pollutants and precise indoor environment data in order to efficiently forecast and enhance the IAQ. To achieve this objective, a proposed surrogate model was developed using computational fluid dynamics (CFD). Notably, the Kriging surrogate model can rapidly predict IAQ while using a limited number of CFD runs. CFD are widely used as numerical simulation methods to obtain the accurate information. Surrogate models can rapidly forecast indoor environmental conditions using CFD simulation data simultaneously. Optimization algorithms can efficiently achieve desirable indoor ambient conditions, offering highly effective and intelligent control techniques for indoor atmospheric ventilation.

Drinking Water Contaminant Concentrations and Birth Outcomes

Previous research in the US has found negative health effects of contamination when it triggers regulatory violations. An important question is whether levels of contamination that do not trigger a health-based violation impact health. We study the impact of drinking water contamination in community water systems on birth outcomes using drinking water sampling results data in Pennsylvania. We focus on the effects of water contamination for births not exposed to regulatory violations. Our most rigorous specification employs mother fixed effects and finds changing from the 10th to the 90th percentile of water contamination (among births not exposed to regulatory violations) increases low birth weight by 12% and preterm birth by 17%.

Mammographic density in the environs of multiple industrial sources

BACKGROUND

Mammographic density (MD), defined as the percentage of dense fibroglandular tissue in the breast, is a modifiable marker of the risk of developing breast cancer. Our objective was to evaluate the effect of residential proximity to an increasing number of industrial sources in MD.

METHODS

A cross-sectional study was conducted on 1225 premenopausal women participating in the DDM-Madrid study. We calculated distances between women's houses and industries. The association between MD and proximity to an increasing number of industrial facilities and industrial clusters was explored using multiple linear regression models.

RESULTS

We found a positive linear trend between MD and proximity to an increasing number of industrial sources for all industries, at distances of 1.5 km (p-trend = 0.055) and 2 km (p-trend = 0.083). Moreover, 62 specific industrial clusters were analyzed, highlighting the significant associations found between MD and proximity to the following 6 industrial clusters: cluster 10 and women living at ≤1.5 km (β = 10.78, 95 % confidence interval (95%CI) = 1.59; 19.97) and at ≤2 km (β = 7.96, 95%CI = 0.21; 15.70); cluster 18 and women residing at ≤3 km (β = 8.48, 95%CI = 0.01; 16.96); cluster 19 and women living at ≤3 km (β = 15.72, 95%CI = 1.96; 29.49); cluster 20 and women living at ≤3 km (β = 16.95, 95%CI = 2.90; 31.00); cluster 48 and women residing at ≤3 km (β = 15.86, 95%CI = 3.95; 27.77); and cluster 52 and women living at ≤2.5 km (β = 11.09, 95%CI = 0.12; 22.05). These clusters include the following industrial activities: surface treatment of metals/plastic, surface treatment using organic solvents, production/processing of metals, recycling of animal waste, hazardous waste, urban waste-water treatment plants, inorganic chemical industry, cement and lime, galvanization, and food/beverage sector.

CONCLUSIONS

Our results suggest that women living in the proximity to an increasing number of industrial sources and those near certain types of industrial clusters have higher MD.

Long-term Exposure to Multiple Ambient Air Pollutants and Association With Incident Depression and Anxiety

Importance

Air pollution is increasingly recognized as an important environmental risk factor for mental health. However, epidemiologic evidence on long-term exposure to low levels of air pollutants with incident depression and anxiety is still very limited.

Objectives

To investigate the association of long-term joint exposure to multiple air pollutants with incident depression and anxiety.

Design, Setting, and Participants

This prospective, population-based cohort study used data from the UK Biobank. The participants were recruited between March 13, 2006, and October 1, 2010, and included individuals who had never been diagnosed with depression or anxiety at baseline and had full information on exposure and covariates. Data were analyzed from May 1 to October 10, 2022.

Exposures

Annual mean air pollution concentrations of particulate matter (PM) with aerodynamic diameter of 2.5 μm or less (PM2.5) and PM with aerodynamic diameter between 2.5 μm and 10 μm (PM2.5-10). Nitrogen dioxide (NO2) and nitric oxide (NO) were estimated for each participant's residential address using the land use regression model, and joint exposure to air pollution reflected by air pollution score was calculated by principal components analysis.

Main Outcomes and Measures

Incidence of diagnosed depression (F32-F33) and anxiety (F40-F48) were ascertained with International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes.

Results

During a median (IQR) follow-up of 10.9 (10.1-11.6) years, among 389 185 participants (mean [SD] age, 56.7 [8.1] years, 205 855 female individuals [52.9%]), a total of 13 131 and 15 835 patients were diagnosed with depression and anxiety, respectively. The median (IQR) concentration of pollutants was as follows: PM2.5, 9.9 (9.3-10.6) μg/m3; PM2.5-10, 6.1 (5.8-6.6) μg/m3; NO2, 26.0 (21.3-31.1) μg/m3; and NO, 15.9 (11.6-20.6) μg/m3. Long-term estimated exposure to multiple air pollutants was associated with increased risk of depression and anxiety, and the exposure-response curves were nonlinear, with steeper slopes at lower concentrations and plateauing trends at higher exposure. The hazard ratios (HRs) and 95% CIs for depression and anxiety were 1.16 (95% CI, 1.09-1.23; P < .001) and 1.11 (95% CI, 1.05-1.17; P < .001) in the highest quartile compared with the lowest quartile of air pollution score, respectively. Similar trends were shown for PM2.5, NO2, and NO. Subgroup analysis showed the association between PM2.5 and anxiety tended to be higher in male individuals than in female individuals (quartile 4: male individuals, 1.18; 95% CI, 1.08-1.29; female individuals, 1.07; 95% CI, 1.00-1.14; P = .009).

Conclusions and Relevance

Study results suggest that estimates of long-term exposure to multiple air pollutants was associated with increased risk of depression and anxiety. The nonlinear associations may have important implications for policy making in air pollution control. Reductions in joint exposure to multiple air pollutants may alleviate the disease burden of depression and anxiety.

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

BACKGROUND

Long-term exposure to air pollution and noise is detrimental to health; but studies that evaluated both remain limited. This study explores associations with natural and cause-specific mortality for a range of air pollutants and transportation noise.

METHODS

Over 4 million adults in Switzerland were followed from 2000 to 2014. Exposure to PM_2.5, PM_2.5 components (Cu, Fe, S and Zn), NO₂, black carbon (BC) and ozone (O₃) from European models, and transportation noise from source-specific Swiss models, were assigned at baseline home addresses. Cox proportional hazards models, adjusted for individual and area-level covariates, were used to evaluate associations with each exposure and death from natural, cardiovascular (CVD) or non-malignant respiratory disease. Analyses included single and two exposure models, and subset analysis to study lower exposure ranges.

RESULTS

During follow-up, 661,534 individuals died of natural causes (36.6% CVD, 6.6% respiratory). All exposures including the PM_2.5 components were associated with natural mortality, with hazard ratios (95% confidence intervals) of 1.026 (1.015, 1.038) per 5 µg/m³ PM_2.5, 1.050 (1.041, 1.059) per 10 µg/m³ NO₂, 1.057 (1.048, 1.067) per 0.5 × 10^-5/m BC and 1.045 (1.040, 1.049) per 10 dB Lden total transportation noise. NO₂, BC, Cu, Fe and noise were consistently associated with CVD and respiratory mortality, whereas PM_2.5 was only associated with CVD mortality. Natural mortality associations persisted < 20 µg/m³ for PM_2.5 and NO₂, < 1.5 10^-5/m BC and < 53 dB Lden total transportation noise. The O₃ association was inverse for all outcomes. Including noise attenuated all outcome associations, though many remained significant. Across outcomes, noise was robust to adjustment to air pollutants (e.g. natural mortality 1.037 (1.033, 1.042) per 10 dB Lden total transportation noise, after including BC).

CONCLUSION

Long-term exposure to air pollution and transportation noise in Switzerland contribute to premature mortality. Considering co-exposures revealed the importance of local traffic-related pollutants such as NO₂, BC and transportation noise.

Investigating the association between ambient particulate matter (PM10) exposure and blood pressure values: Results from the link between the Portuguese Health Examination Survey and air quality data

INTRODUCTION AND OBJECTIVES

High blood pressure (BP) remains a major modifiable cardiovascular (CV) risk factor. Several epidemiologic studies have been performed to assess the association between air pollution exposure and this CV risk factor but results remain inconsistent. This study aims to estimate the effect of short-term PM₁₀ exposure (average previous three-day concentration) on diastolic (DBP) and systolic (SBP) blood pressure values of the resident mainland Portuguese population.

METHODS

Our study was based on available DBP and SBP data from 2272 participants from the first Portuguese Health Examination Survey (INSEF, 2015) living within a 30 km radius of at least one air quality monitoring station, with available measurements of particulate matter with an aerodynamic equivalent diameter ≤10 μm (PM₁₀). We used data from the air quality monitoring network of the Portuguese Environment Agency to obtain the individual allocated PM₁₀ concentrations. Generalized linear models were used to assess the effect of PM₁₀ exposure on DBP and SBP values.

RESULTS

No statistically significant association was found between PM₁₀ exposure and both DBP and SBP values (0.42% DBP change per 10 μg/m³ of PM₁₀ increment (95% confidence interval (CI): -0.85; 1.70) and 0.47% SBP change per 10 μg/m³ of PM₁₀ increment (95% CI: -0.86; 1.79)). Results remain unchanged after restricting the analysis to hypertensive or obese participants or changing the PM₁₀ assessment methodology.

CONCLUSIONS

In view of the PM₁₀ levels observed in 2015, our results suggests that exposure to PM₁₀ concentrations have a small or no effect on the blood pressure values. Other air pollutants and mixtures of pollutants that were not included in our study should considered in future studies.

Association between residential greenness and all-cause mortality and the joint mediation effect of air pollutants among old people with disability: A prospective cohort study

BACKGROUND

Residential greenness offers health benefits to old people, but evidence of its association with the health of old people with disability is scarce. Moreover, due to the limited mobility of this vulnerable population, air pollutants may play an indispensable mediating role in that association, which however remains understudied.

OBJECTIVES

This study aimed to investigate the association between residential greenness and all-cause mortality risk and the joint mediation effect of air pollutants among old people with disability.

METHODS

A total of 34,075 old people with disability were included in the Chengdu Long-term Care Insurance cohort. Participants' residential greenness exposure was measured by an enhanced vegetation index within the 500 m buffer zone (EVI_500m). Causal mediation analysis was conducted to assess the total effect (TE) of residential greenness and the natural indirect effect (NIE) through PM_2.5, CO, NO₂, SO₂, and O₃ on all-cause mortality.

RESULTS

The TE of EVI_500m on the all-cause mortality risk in overall participants showed negative, which, decreased from the 2^nd quartile (HR = 0.93, 95 % CI: 0. 91, 0.95) to the 4^th quartile (HR = 0.81, 95 % CI: 0.76, 0.85); the NIE through the five air pollutants also decreased from the 2^nd quartile (HR = 0.96, 95 % CI: 0.95, 0.98) to the 4^th quartile (HR = 0.90, 95 % CI: 0.88, 0.93), with the proportion mediated decreased from 48 % to 44 %. The stronger TE or NIE were observed in participants aged <80 years old, men, with mild-moderate disability, and having outdoor experience every week.

CONCLUSION

Exposure to residential greenness was associated with a decreased risk of mortality, partially through the pathways of air pollutants, which varied by age, sex, degree of disability, and frequency of weekly outdoors. Our findings would provide evidence to develop aging-friendly cities.

Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign

Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We designed an innovative and extensive mobile monitoring campaign to characterize TRAP exposure levels for the Adult Changes in Thought (ACT) study, a Seattle-based cohort. The campaign measured particle number concentration (PNC) to capture ultrafine particles (UFP), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2) at 309 roadside sites within a large, 1200 land km2 (463 mi2) area representative of the cohort. We collected about 29 two-minute measurements at each site during all seasons, days of the week, and most times of the day over a 1-year period. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and monitoring agency sites (R2 = 0.68-0.73). Universal kriging-partial least squares models of annual average pollutant concentrations had cross-validated mean square error-based R2 (and root mean square error) values of 0.77 (1177 pt/cm3) for PNC, 0.60 (102 ng/m3) for BC, 0.77 (1.3 ppb) for NO2, 0.70 (0.3 μg/m3) for PM2.5, and 0.51 (4.2 ppm) for CO2. Overall, we found that the design of this extensive campaign captured the spatial pollutant variations well and these were explained by sensible land use features, including those related to traffic.

Machine learning-driven identification of early-life air toxic combinations associated with childhood asthma outcomes

Air pollution is a well-known contributor to asthma. Air toxics are hazardous air pollutants that cause or may cause serious health effects. Although individual air toxics have been associated with asthma, only a limited number of studies have specifically examined combinations of air toxics associated with the disease. We geocoded air toxic levels from the US National Air Toxics Assessment (NATA) to residential locations for participants of our AiRway in Asthma (ARIA) study. We then applied Data-driven ExposurE Profile extraction (DEEP), a machine learning-based method, to discover combinations of early-life air toxics associated with current use of daily asthma controller medication, lifetime emergency department visit for asthma, and lifetime overnight hospitalization for asthma. We discovered 20 multi-air toxic combinations and 18 single air toxics associated with at least 1 outcome. The multi-air toxic combinations included those containing acrylic acid, ethylidene dichloride, and hydroquinone, and they were significantly associated with asthma outcomes. Several air toxic members of the combinations would not have been identified by single air toxic analyses, supporting the use of machine learning-based methods designed to detect combinatorial effects. Our findings provide knowledge about air toxic combinations associated with childhood asthma.

PM10 exposure interacts with abdominal obesity to increase blood triglycerides: a cross-sectional linkage study

Background

Blood lipids and glucose levels dysregulation represent potential mechanisms intermediating the adverse cardiovascular effects of ambient particulate matter (PM) exposure. This study aims to estimate the effect of long-term PM₁₀ exposure on blood lipids and glucose levels and to assess the potential mediation and/or modification action of abdominal obesity (AO) (waist-to-height ratio).

Methods

Our study was based on 2,390 participants of the first Portuguese Health Examination Survey (INSEF, 2015) with available data on blood lipids and glucose parameters and living within a 30-km radius of an air quality monitoring station with available PM₁₀ measurements. PM₁₀ concentrations were acquired from the air quality monitoring network of the Portuguese Environment Agency. Generalized linear models were used to assess the effect of 1-year PM₁₀ exposure on blood lipids and glucose levels. An interaction term was introduced in the models to test the modification action of AO.

Results

We found an association between PM₁₀ and non-fasting blood triglycerides (TG) after adjustment for age, sex, education, occupation, lifestyles-related variables and temperature but only in participants with AO. Per each 1 µg/m³ PM₁₀ increment, there was a 1.84% (95% confidence interval: 0.02–3.69) increase in TG. For the remaining blood lipid and glucose parameters, no associations were found.

Conclusions

Our study demonstrates that even at low levels of exposure, long-term PM₁₀ exposure interacts with AO to increase blood TG. Our findings suggest that reducing both AO prevalence and PM₁₀ below current standards would result in additional health benefits for the population.

Evaluating the energy, health efficiency, and productivity in OECD

In the past, energy and environmental research focused on the performance of individual efficiencies. In order to make up for the research deficiencies, this research uses SBM (slack-based measures) dynamic network DEA (DN-SBM) to assess energy, health efficiency, and DN-TFP productivity changes from 2011 to 2015. This research uses forest area as the carryover that can objectively measure the performance of OECD energy, health, and total efficiency, and calls for the importance of forest protection and planting. The empirical results show that Estonia, Finland, Hungary, Iceland, Mexico, New Zealand, Portugal, Slovenia, Sweden, and Turkey have the best overall efficiency performance, while Ireland (0.4469), Israel (0.4179), and the Netherlands (0.3697) are the three worst. In total, 29 economies show progress in terms of productivity. Moreover, Chile (0.9706), Mexico (0.9995), Slovak Republic (0.9942), Turkey (0.9815), and the UK (0.9886) exhibit a slight decline. The overall efficiency value of 20 countries is greater than the average, and their productivity is showing an upward trend. Only the UK (0.5081, 0.9886) has an overall efficiency value that is less than the overall average with productivity that is showing a drop. About research method, this study utilizes dynamic intertemporal data to evaluate the changes in the overall efficiency and productivity of OECD members with DN-SBM and DN-TFP indices in order to offer more objective research results for various economies that are useful for formulating policies related to energy, national health, and forest conservation.

Dynamic Linkages Among Energy Consumption, Environment and Health Sustainability: Evidence from the Different Income Level Countries

Environment pollution was closely related to human health. The energy consumption is one of the important sources of environmental pollution in the development of economy. This paper used undesirable two-stage meta-frontier DDF (distance difference function) data envelopment analysis model to explore the impact of environment pollutants from energy consumption on the mortality of children and the aged, survival rate of 65 years old and health expenditure efficiency in 27 high income countries, 21 upper middle income countries, and 16 lower middle income countries from 2010 to 2014. High income countries had higher efficiency of energy and health than middle income countries in general. But whether in high income or middle income countries, the efficiency of non-renewable energy is higher than renewable energy. There was much room for both high income countries and middle income countries to improve renewable energy efficiency. Besides, middle income countries need to improve the efficiency of non-renewable energy and reduce pollutant emissions per unit of GDP. In terms of health efficiency, upper middle income countries performed worse than lower income countries. This phenomenon might indicate there was a U-shaped relationship between health efficiency and income level. Upper income countries should pay more attention to the environmental and health problems and cross the U-shaped turning point. The contribution of this article was to consider the heterogeneous performance of energy efficiency, environmental efficiency, and health efficiency under the influence of income level differences, and found that there might be a U-shaped relationship between health efficiency and income level.

Effects of using different exposure data to estimate changes in premature mortality attributable to PM2.5 and O3 in China

The assessment of premature mortality associated with the dramatic changes in fine particulate matter (PM_2.5) and ozone (O₃) has important scientific significance and provides valuable information for future emission control strategies. Exposure data are particularly vital but may cause great uncertainty in health burden assessments. This study, for the first time, used six methods to generate the concentration data of PM_2.5 and O₃ in China between 2014 and 2018, and then quantified the changes in premature mortality due to PM_2.5 and O₃ using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results show that PM_2.5-related premature mortality in China decreases by 263 (95% confidence interval (CI95): 142-159) to 308 (CI95: 213-241) thousands from 2014 to 2018 by using different concentration data, while O₃-related premature mortality increases by 67 (CI95: 26-104) to 103 (CI95: 40-163) thousands. The estimated mean changes are up to 40% different for the PM_2.5-related mortality, and up to 30% for the O₃-related mortality if different exposure data are chosen. The most significant difference due to the exposure data is found in the areas with a population density of around 10³ people/km², mostly located in Central China, for both PM_2.5 and O₃. Our results demonstrate that the exposure data source significantly affects mortality estimations and should thus be carefully considered in health burden assessments.

Commuter types identified using clustering and their associations with source-specific PM2.5

Traffic-related fine particulate matter air pollution (tr-PM_2.5) has been associated with adverse health outcomes such as cardiopulmonary morbidity and mortality, with in-vehicle tr-PM_2.5 exposure contributing to total personal pollution exposure. Trip characteristics, including time of day, day of the week, and traffic congestion, are associated with in-vehicle PM_2.5 exposures. We hypothesized that some commuter characteristics, such as whether commuters travel primarily during rush hour, would also be associated with increased tr-PM_2.5 exposures. The commute data consisted of unscripted personal vehicle trips of 46 commuters in the Washington, D.C. metro area over 48-h, with a total of 320 trips. We identified commuter types using sparse K-means clustering, which identifies the hours throughout the day important for clustering commuters. Source-specific PM_2.5 over 48 h was estimated using Positive Matrix Factorization. Linear regression was used to estimate differences in source-specific PM_2.5 by commuter cluster. Two commuter clusters were identified using the clustering approach: rush hour commuters, who primarily travelled during rush hour, and sporadic commuters, who travelled throughout the day. The hours given the largest weights by sparse K-means were 7-8 a.m. and 6-7 p.m., corresponding to peak travel times. Integrated black carbon (BC) was higher for rush hour commuters (median = 3.1 μg/m³ (IQR = 1.5)) compared to sporadic commuters (2.0 μg/m³ (IQR = 1.9)). Mobile PM_2.5, consisting primarily of tailpipe emissions and brake/tire wear, was also higher for rush hour commuters (2.9 μg/m³ (IQR = 1.6)) compared to sporadic commuters (2.1 μg/m³ (IQR = 2.4)), though this difference was not statistically significant in regression models. Estimated differences between commuter types for secondary/mixed PM_2.5 and road salt PM_2.5 were smaller. Further research may elucidate whether commuter characteristics are an efficient way to identify individuals with highest tr-PM_2.5 exposures associated with commuting and to develop effective mitigation strategies.

Effect of air pollution on health care expenditure: Evidence from respiratory diseases

Recent reports show that at least 95% of the world's population is breathing polluted air. However, the impact of air quality on air pollution-related medical expenditure and utilization is sparse. This study estimates the short-term health care cost impacts of air pollution using a meteorological phenomenon-thermal inversion-as an instrumental variable for air quality. Using information on outpatient care for respiratory diseases from universal health insurance claim data in Taiwan during 2006-2012, our estimates suggest that a one-unit reduction in the air quality index (AQI) leads to NT$2.3 billion (nearly US$74 million) of savings in respiratory-related outpatient expenditure per year. Given that the average AQI is equal to 32 during our study period, completely removing air pollution would reduce the national health expenditure by approximately 8% annually. Our results provide the important implication that the cost of controlling air pollutant emissions can be offset by curtailing health care expenditure.

Liaison between exposure to sub-micrometric particulate matter and allergic response in children from a petrochemical industry city

The study examines the association between exposure to sub-micrometric Particulate Matter (PM₁) and allergic response in a group of sensitive young children (age: 2-10 years) from Ploiesti city, Romania. The city is the only urban agglomeration in Europe surrounded by four oil refineries. A panel study was conducted by collecting medical information from children with respiratory illnesses and atopy (n = 135). Hot Spot Analysis revealed the areas of the city that are susceptible to high levels of PM₁. We found a close interaction between exposure to PM₁ outdoor concentrations and various physiological changes and clinical symptoms in children including triggering of allergic reactions, rhinitis, alteration of lung function, upper and lower respiratory tract symptoms, and bronchial asthma. During the 2-year study period, the incidence of hospitalizations was 40.7%. Strong correlations (p < 0.001) were observed between the PM₁ exposure and hospitalizations, and exposure and Immunoglobulin E (IgE). PM₁ exposure was also correlated with eosinophils (p < 0.05). Another positive correlation was observed between hospitalizations and IgE levels (p < 0.05). The mean results of tested indicators were as follows: wheezing (5.3, 95% CI (1.4-1.8); Coeff. of var. (CV) = 30%), IgE (382, 95% CI (349-445); CV = 102%), and EO% (5.3, 95% CI (3.3-4.2); CV = 69.5%). We can conclude that exposure to PM₁ influenced the frequency of wheezing episodes, increased hospitalizations, and the levels of allergic blood indicators in children, especially in infants and pre-schoolers. CAPSULE: Exposure to sub-micrometric particles (PM₁) influences the frequency of wheezing episodes, hospitalizations, and the levels of allergic blood indicators in children, especially in infants and pre-schoolers.

Integrated Evaluation of Indoor Particulate Exposure: The VIEPI Project

Despite the progress made in recent years, reliable modeling of indoor air quality is still far from being obtained. This requires better chemical characterization of the pollutants and airflow physics included in forecasting tools, for which field observations conducted simultaneously indoors and outdoors are essential. The project “Integrated Evaluation of Indoor Particulate Exposure” (VIEPI) aimed at evaluating indoor air quality and exposure to particulate matter (PM) of humans in workplaces. VIEPI ran from February 2016 to December 2019 and included both numerical simulations and field campaigns carried out in universities and research environments located in urban and non-urban sites in the metropolitan area of Rome (Italy). VIEPI focused on the role played by micrometeorology and indoor airflow characteristics in determining indoor PM concentration. Short- and long-term study periods captured diurnal, weekly, and seasonal variability of airflow and PM concentration. Chemical characterization of PM10, including the determination of elements, ions, elemental carbon, organic carbon, and bioaerosol, was also carried out. Large differences in the composition of PM10 were detected between inside and outside as well as between different periods of the day and year. Indoor PM composition was related to the presence of people, to the season, and to the ventilation regime.

Chronic Low-Dose Exposure to Xenoestrogen Ambient Air Pollutants and Breast Cancer Risk: XENAIR Protocol for a Case-Control Study Nested Within the French E3N Cohort

Background

Breast cancer is the most frequent cancer in women in industrialized countries. Lifestyle and environmental factors, particularly endocrine-disrupting pollutants, have been suggested to play a role in breast cancer risk. Current epidemiological studies, although not fully consistent, suggest a positive association of breast cancer risk with exposure to several International Agency for Research on Cancer Group 1 air-pollutant carcinogens, such as particulate matter, polychlorinated biphenyls (PCB), dioxins, Benzo[a]pyrene (BaP), and cadmium. However, epidemiological studies remain scarce and inconsistent. It has been proposed that the menopausal status could modify the relationship between pollutants and breast cancer and that the association varies with hormone receptor status.

Objective

The XENAIR project will investigate the association of breast cancer risk (overall and by hormone receptor status) with chronic exposure to selected air pollutants, including particulate matter, nitrogen dioxide (NO2), ozone (O3), BaP, dioxins, PCB-153, and cadmium.

Methods

Our research is based on a case-control study nested within the French national E3N cohort of 5222 invasive breast cancer cases identified during follow-up from 1990 to 2011, and 5222 matched controls. A questionnaire was sent to all participants to collect their lifetime residential addresses and information on indoor pollution. We will assess these exposures using complementary models of land-use regression, atmospheric dispersion, and regional chemistry-transport (CHIMERE) models, via a Geographic Information System. Associations with breast cancer risk will be modeled using conditional logistic regression models. We will also study the impact of exposure on DNA methylation and interactions with genetic polymorphisms. Appropriate statistical methods, including Bayesian modeling, principal component analysis, and cluster analysis, will be used to assess the impact of multipollutant exposure. The fraction of breast cancer cases attributable to air pollution will be estimated.

Results

The XENAIR project will contribute to current knowledge on the health effects of air pollution and identify and understand environmental modifiable risk factors related to breast cancer risk.

Conclusions

The results will provide relevant evidence to governments and policy-makers to improve effective public health prevention strategies on air pollution. The XENAIR dataset can be used in future efforts to study the effects of exposure to air pollution associated with other chronic conditions.

International Registered Report Identifier (IRRID)

DERR1-10.2196/15167

Using Latent Class Modeling to Jointly Characterize Economic Stress and Multipollutant Exposure

BACKGROUND

Work is needed to better understand how joint exposure to environmental and economic factors influence cancer. We hypothesize that environmental exposures vary with socioeconomic status (SES) and urban/rural locations, and areas with minority populations coincide with high economic disadvantage and pollution.

METHODS

To model joint exposure to pollution and SES, we develop a latent class mixture model (LCMM) with three latent variables (SES Advantage, SES Disadvantage, and Air Pollution) and compare the LCMM fit with K-means clustering. We ran an ANOVA to test for high exposure levels in non-Hispanic black populations. The analysis is at the census tract level for the state of North Carolina.

RESULTS

The LCMM was a better and more nuanced fit to the data than K-means clustering. Our LCMM had two sublevels (low, high) within each latent class. The worst levels of exposure (high SES disadvantage, low SES advantage, high pollution) are found in 22% of census tracts, while the best levels (low SES disadvantage, high SES advantage, low pollution) are found in 5.7%. Overall, 34.1% of the census tracts exhibit high disadvantage, 66.3% have low advantage, and 59.2% have high mixtures of toxic pollutants. Areas with higher SES disadvantage had significantly higher non-Hispanic black population density (NHBPD; P < 0.001), and NHBPD was higher in areas with higher pollution (P < 0.001).

CONCLUSIONS

Joint exposure to air toxins and SES varies with rural/urban location and coincides with minority populations.

IMPACT

Our model can be extended to provide a holistic modeling framework for estimating disparities in cancer survival.See all articles in this CEBP Focus section, "Environmental Carcinogenesis: Pathways to Prevention."

Improved risk communications with a Bayesian multipollutant Air Quality Health Index

Establishing an optimal indicator to communicate health risks of multiple air pollutants to public is much important. The Air Quality Health Index (AQHI) has been developed in many countries as a communication tool of multiple air pollutants related health risks. However, the current AQHI is based on the sum of the excess health risks which are typically derived from the single-pollutant statistical models. Such a strategy may overestimate the joint effect of multiple pollutants. We proposed an improved strategy to construct the AQHI based on a Bayesian multipollutant weighted model. Using this strategy, two improved indices - Bayesian multipollutant AQHI (BMP-AQHI) and Bayesian multipollutant AQHI with seasonal specificity (SBMP-AQHI) were calculated to present the multiple pollutants related health risks to the cardiovascular system based on data collected in Chengdu, China during 2013 to 2018. The two improved indices were compared to current Air Quality Index (AQI) and AQHI to evaluate the effectiveness of the improved indices in characterizing multipollutant health risks. The AQI risk classification suggested much smaller health risks than AQHIs. Among three AQHI types, the BMP-AQHI and SBMP-AQHI suggested slightly lower health risks to the cardiovascular system than the current AQHI. In the evaluation analysis, the SBMP-AQHI had the strongest association with the mortality of cardiovascular disease (CVD) (2.66%; 95%CI, 1.57%, 3.76%). In the subgroup analysis, an interquartile increase (IQR) of the SBMP-AQHI was associated with 3.21% (95%CI, 2.06%, 4.38%), 1.34% (95%CI, -0.13%, 2.82%), and 4.20% (95%CI, 2.59%, 5.84%) increases for CVD mortality in the elderly, male, and female subgroups, respectively. The study shows that the improved AQHIs can communicate the health information of multiple air pollutants more efficiently. The study also indicates the necessity to consider seasonal specificity in the construction of the AQHI.

The simultaneous effects of thermal stress and air pollution on body temperature of Tehran traffic officers

PURPOSE

Global warming and air pollution are among the most important problems all over the world. Considering the key role of traffic officers who saliently deal with traffic management and are in full, constant and direct exposure to thermal stress and air pollution index, this study aims to investigate the simultaneous effects of these factors on the body temperature of traffic officers in the main squares of Tehran.

METHODS

This study was conducted among 119 traffic officers who were working in 29 squares of Tehran, located near the active pollutant's stations during 2017. Samples were selected by the census method. Environmental parameters such as air temperature (dry and wet), radiation temperature, the level of air pollution in the main squares and characteristics of officers such as body temperature and the Wet-Bulb-Globe-Temperature (WBGT) index were evaluated. Data were analyzed through independent samples t-test and factorial ANOVA with a p value of p ≤ 0.05 in SPSS software.

RESULTS

There was no significant relationship between air pollution and ear temperature, but there was a statistically significant difference between the wet-bulb temperature and the ear temperature (t = 26.4, P < 0.001). The interaction effect of air pollution and wet-bulb temperature on the ear temperature was also significant (F = 3.98, P = 0.048).

CONCLUSION

Exposure to heat and air pollution affects body temperature, with its greatest impact on the temperature of the ear. More studies are recommended to be conducted in these field and other factors such as demographic and environmental factors at different times of the year should be investigated. Accordingly, some interventions should be implemented to reduce the vulnerability of officers based on the findings of the research.

Evaluating a multipollutant metric for use in characterizing traffic-related air pollution exposures within near-road environments

Accurately characterizing human exposures to traffic-related air pollutants (TRAPs) is critical to public health protection. However, quantifying exposure to this single source is challenging, given its extremely heterogeneous chemical composition. Efforts using single-species tracers of TRAP are, thus, lacking in their ability to accurately reflect exposures to this complex mixture. There have been recent discussions centered on adopting a multipollutant perspective for sources with many emitted pollutants to maximize the benefits of control expenditures as well as to minimize population and ecosystem exposure. As part of a larger study aimed to assess a complete emission-to-exposure pathway of primary traffic pollution and understand exposure of individuals in the near-road environment, an intensive field campaign measured TRAPs and related data (e.g., meteorology, traffic counts, and regional air pollutant levels) in Atlanta along one of the busiest highway corridors in the US. Given the dynamic nature of the near-road environment, a multipollutant exposure metric, the Integrated Mobile Source Indicator (IMSI), which was generated based on emissions-based ratios, was calculated and compared to traditional single-species methods for assessing exposure to mobile source emissions. The current analysis examined how both traditional and non-traditional metrics vary spatially and temporally in the near-road environment, how they compare with each other, and whether they have the potential to offer more accurate means of assigning exposures to primary traffic emissions. The results indicate that compared to the traditional single pollutant specie, the multipollutant IMSI metric provided a more spatially stable method for assessing exposure, though variations occurred based on location with varying results among the six sites within a kilometer of the highway.

Obesity may enhance the adverse effects of NO2 exposure in urban schools on asthma symptoms in children

BACKGROUND

Sparse data address the effects of nitrogen dioxide (NO₂) exposure in inner-city schools on obese students with asthma.

OBJECTIVE

We sought to evaluate relationships between classroom NO₂ exposure and asthma symptoms and morbidity by body mass index (BMI) category.

METHODS

The School Inner-City Asthma Study enrolled students aged 4 to 13 years with asthma from 37 inner-city schools. Students had baseline determination of BMI percentile. Asthma symptoms, morbidity, pulmonary inflammation, and lung function were monitored throughout the subsequent academic year. Classroom NO₂ data, linked to enrolled students, were collected twice per year. We determined the relationship between classroom NO₂ levels and asthma outcomes by BMI stratification.

RESULTS

A total of 271 predominantly black (35%) or Hispanic students (35%) were included in analyses. Fifty percent were normal weight (5-84th BMI percentile), 15% overweight (≥85-94th BMI percentile), and 35% obese (≥95th BMI percentile). For each 10-parts per billion increase in NO₂, obese students had a significant increase in the odds of having an asthma symptom day (odds ratio [OR], 1.86; 95% CI, 1.15-3.02) and in days caregiver changed plans (OR, 4.24; 95% CI, 2.33-7.70), which was significantly different than normal weight students who exhibited no relationship between NO₂ exposure and symptom days (OR, 0.90; 95% CI, 0.57-1.42; pairwise interaction P = .03) and change in caregiver plans (OR, 1.37; 95% CI, 0.67-2.82; pairwise interaction P = .02). Relationships between NO₂ levels and lung function and fractional exhaled nitric oxide did not differ by BMI category. If we applied a conservative Holm-Bonferroni correction for 16 comparisons (obese vs normal weight and overweight vs normal weight for 8 outcomes), these findings would not meet statistical significance (all P > .003).

CONCLUSIONS

Obese BMI status appears to increase susceptibility to classroom NO₂ exposure effects on asthma symptoms in inner-city children. Environmental interventions targeting indoor school NO₂ levels may improve asthma health for obese children. Although our findings would not remain statistically significant after adjustment for multiple comparisons, the large effect sizes warrant future study of the interaction of obesity and pollution in pediatric asthma.

Applications of nutritional functional units in commodity-level life cycle assessment (LCA) of agri-food systems

PURPOSE

The nutritional quality of final products is attracting an increased level of attention within life cycle assessment (LCA) literature of agri-food systems. The majority of these studies, however, are based on comparisons at the dietary level and, therefore, are unable to offer immediate implications for farmers as to how best to produce food. This article evaluates recent literature examining the nutrition-environment nexus at the commodity level, with the aim to identify potential pathways towards sustainability analysis that can inform both consumers and producers.

METHODS

A systematic search of literature was carried out to produce a shortlist of studies, and strict exclusion criteria were applied to them afterwards to eliminate irrelevant material. The studies thus selected were classified into one of three tiers based on the level of complexity with regard to their functional units: (1) based on single nutrients, (2) based on composite indicators derived from multiple nutrients and (3) based on commodity-level analysis in a dietary context.

RESULTS AND DISCUSSION

Sixteen papers were identified for inclusion in the review. All of them accounted for climate change either directly or indirectly, whilst only five addressed different impact categories at the same time. Nine studies estimated environmental impacts under functional units associated with nutrient density scores, and the others utilised alternative approaches to account for nutritional value such as linear programming and end-point modelling combined with epidemiological data. A recently developed method to calculate the marginal contribution of a commodity to the overall nutritional value of a specific diet was considered to be a successful first step in bridging the aforementioned knowledge gap.

CONCLUSIONS

The LCA community should continue the ongoing effort to link farm management decisions to diet-level environmental impacts through an enhanced focus on human nutrition across the entire value chain. Future research comparing environmental performances of multiple food groups or multiple production systems should acknowledge differences in nutritional composition and bioavailability between the final products and, ideally, the effects of these nutrients on overall dietary quality.

Impact of Media Reports and Environmental Pollution on Health and Health Expenditure Efficiency

Over the past few decades, China’s rapid economic, energy, and industrial developments have caused serious environmental damage. However, as there are large resource, energy use, economic, and environmental damage differences across Chinese regions, the Chinese government is seeking to reduce city pollution across the country. Most previous analyses have only looked at these issues on a single level; for example, the impact of environmental pollution on health, or energy and environmental efficiency analyses, but there have been few studies that have conducted overall analyses. Further, many of the methods that have been used in previous research have employed one-stage radial or non-radial analyses without considering regional differences. Therefore, this paper developed a meta undesirable two-stage EBM DEA model to analyze the energy, environment, health, and media communication efficiencies in 31 Chinese cities, from which it was found that the productivity efficiency in most cities was better than the health treatment efficiencies, the GDP and fixed asset efficiency improvements were small, the air quality index (AQI) and CO₂ efficiencies varied widely between the cities, media report and governance inputs were generally inefficient, the birth rate efficiencies were better than the respiratory disease efficiencies, and the technical gap was best in Guangzhou, Shanghai, and Lhasa. Also, it found that high-income cities have a higher technology gap than upper middle–income cities, and media reports efficiency have a high correlation with respiratory diseases and CO₂.

Ambient air pollution and lipid profile: Systematic review and meta-analysis

Ambient air pollution (AAP) is recognized a cardiovascular risk factor and lipid profile dysregulation seems to be one of the potential mediators involved. However, results from epidemiologic research on the association between exposure to AAP and altered lipid profile have been inconsistent. This study aims to systematically review and meta-analyse epidemiologic evidence on the association between exposure to ambient air pollutants (particulate matter, nitrogen oxides, sulphur dioxide, ozone, carbon monoxide, back carbon) and lipid profile parameters (Total cholesterol; High-Density Lipoprotein Cholesterol; Low-Density Lipoprotein Cholesterol; TG-Triglycerides) or dyslipidaemia. Systematic electronic literature search was performed in PubMed, Web of Science and Scopus databases (last search on 24th May 2019) using keywords related to the exposure (ambient air pollutants) and to the outcomes (lipid profile parameters/dyslipidaemia). Qualitative and quantitative information of the studies were extracted and fixed or random-effects models were used to obtain a pooled effect estimate per each pollutant/outcome combination. 22 studies were qualitatively analysed and, from those, 3 studies were quantitatively analysed. Particulate matters were the most studied pollutants and a considerable heterogeneity in air pollution assessment methods and outcomes definitions was detected. Age, obesity related measures, tobacco consumption, sex and socioeconomic factors were the most frequent considered variables for confounding adjustment in the models. In a long-term exposure scenario, we found a 3.14% (1.36%-4.95%) increase in TG levels per 10 μg/m³ PM₁₀ increment and a 4.24% (1.37%-7.19%) increase in TG levels per 10 μg/m³ NO₂ increment. No significant associations were detected for the remaining pollutant/outcome combinations. Despite the few studies included in the meta-analysis, our study suggests some epidemiologic evidence supporting the association between PM₁₀ and NO₂ exposures and increased TG levels. Due to the very low level of evidence, more studies are needed to clarify the role of lipid profile dysregulation as a mediator on the AAP adverse cardiovascular effects.

A Two-stage Dynamic Undesirable Data Envelopment Analysis Model Focused on Media Reports and the Impact on Energy and Health Efficiency

Past research on energy and environmental issues in China has generally focused on energy and environmental efficiencies with no models having included the public health associations or the role of the media. Therefore, to fill this research gap, this paper used a modified Undesirable Dynamic Network model to analyze the efficiency of China’s energy, environment, health and media communications, from which it was found that the urban production efficiency stage was better than the health treatment stage, and that the energy efficiencies across the Chinese regions varied significantly, with only Beijing, Guangzhou, Lhasa and Nanning being found to have high efficiencies. Large urban gaps and low efficiencies were found for health expenditure, with the best performances being found in Fuzhou, Guangzhou, Haikou, Hefei, Nanning, and Urumqi. The regions with the best media communication efficiencies were Fuzhou, Guangzhou, Haikou, Hefei, Lhasa, Nanning and Urumqi, and the cities with the best respiratory disease efficiencies were Fuzhou, Guangzhou, Haikou, Lhasa, Nanning, Wuhan, Urumqi, Xian, and Yinchuan. Overall, significant efficiency improvements were needed in health expenditure and in particular in respiratory diseases as there were major differences across the country.

Spatial PM2.5 mobile source impacts using a calibrated indicator method

Motor vehicles are major sources of fine particulate matter (PM_2.5), and the PM_2.5 from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM_2.5, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NO_x) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NO_x estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM_2.5, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects. Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM_2.5 mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM_2.5 source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.

Associations between multipollutant day types and select cardiorespiratory outcomes in Columbia, South Carolina, 2002 to 2013

Background

Health studies of air pollution are increasingly aiming to study associations between air pollutant mixtures and health.

Objective

Estimate associations between observed combinations of ambient air pollutants and select cardiorespiratory outcomes in Columbia, SC during 2002 to 2013.

Methods

We estimate associations using a two-stage approach. First, we identified a collection of observed pollutant combinations, which we define as multipollutant day types (MDTs), by applying a self-organizing map (SOM) to daily measures of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter ≤ 2.5 microns (PM_2.5). Then, overdispersed Poisson time-series models were used to estimate associations between MDTs and each outcome using a 'clean' MDT referent and controlling for long-term, seasonal, and day-of-the-week trends and meteorology. Outcomes included daily emergency department visits for asthma and upper respiratory infection (URI), and hospital admissions for congestive heart failure (CHF) and ischemic heart disease (IHD).

Results

We found that a number of MDTs were significantly and positively associated (point estimates ranged from~2-5%) with cardiorespiratory outcomes in Columbia when compared to days with low pollution. Estimated associations revealed that outcomes for asthma, URIs, and IHD increased 2-4% on warm, dry days experiencing elevated levels of O₃ and PM_2.5. We also found that cooler days with higher NO₂ pollution associated with increased asthma, CHF, and IHD outcomes (2-5%).

Conclusion

Our analysis continues support for using self-organizing maps to develop multipollutant exposure metrics and further illustrates how such metrics can be applied to explore associations between pertinent pollutant combinations and health.

Traffic-Related Particulate Matter and Cardiometabolic Syndrome: A Review

Traffic-related particulate matter (PM) is a major source of outdoor air pollution worldwide. It has been recently hypothesized to cause cardiometabolic syndrome, including cardiovascular dysfunction, obesity, and diabetes. The environmental and toxicological factors involved in the processes, and the detailed mechanisms remain to be explored. The objective of this study is to assess the current scientific evidence of traffic-related PM-induced cardiometabolic syndrome. We conducted a literature review by searching the keywords of “traffic related air pollution”, “particulate matter”, “human health”, and “metabolic syndrome” from 1980 to 2018. This resulted in 25 independent research studies for the final review. Both epidemiological and toxicological findings reveal consistent correlations between traffic-related PM exposure and the measured cardiometabolic health endpoints. Smaller sizes of PM, particularly ultrafine particles, are shown to be more harmful due to their greater concentrations, reactive compositions, longer lung retention, and bioavailability. The active components in traffic-related PM could be attributed to metals, black carbon, elemental carbon, polyaromatic hydrocarbons, and diesel exhaust particles. Existing evidence points out that the development of cardiometabolic symptoms can occur through chronic systemic inflammation and increased oxidative stress. The elderly (especially for women), children, genetically susceptible individuals, and people with pre-existing conditions are identified as vulnerable groups. To advance the characterization of the potential health risks of traffic-related PM, additional research is needed to investigate the detailed chemical compositions of PM constituents, atmospheric transformations, and the mode of action to induce adverse health effects. Furthermore, we recommend that future studies could explore the roles of genetic and epigenetic factors in influencing cardiometabolic health outcomes by integrating multi-omics approaches (e.g., genomics, epigenomics, and transcriptomics) to provide a comprehensive assessment of biological perturbations caused by traffic-related PM.

Mutagenic and DNA repair activity in traffic policemen: a case-crossover study

Background

Emissions from vehicles are composed of heterogeneous mixtures of hazardous substances; several pollutants such as Polycyclic Aromatic Hydrocarbons (PAHs) are amongst the most dangerous substances detected in urban monitoring. A cohort of traffic policemen usually occupationally exposed to PAHs present in the urban environment were examined in order to assess the mutagenicity and DNA capacity repair.

Methods

Seventy-two urban traffic policemen working in Catania's metropolitan area were enrolled in the study. Two spot urine samples were collected from each subject during the whole working cycle as follows: sample 1 (S1), pre-shift on day 1; sample 2 (S2) post-shift on day 6. 1-hydroxypyrene (1-OHP) was measured to serve as an indirect exposure indicator. Urinary mutagenic activity was assessed through the plate incorporation pre-incubation technique with S9, using YG1024 Salmonella typhimurium strain over-sensitive to PAH metabolite. Concentrations of urinary 8-oxodG were measured using liquid chromatography tandem mass spectrometry.

Results

As regards the exposure to PAHs, results highlighted a statistically significant difference (p < 0.001) between pre-shift on day 1 and post-shift on day 6 levels. Mutagenic activity was detected in 38 (66%) workers on S1 and in 47 (81%) on S2. Also 8-oxodG analysis showed a statistically significant difference between S1 and S2 sampling.

Conclusions

This study demonstrated that occupational exposure to pollutants from traffic emission, assessed via 1-OHP measurements in urine, may lead to DNA repair and mutagenic activity, in line with other studies.

Assessing the cumulative health effect following short term exposure to multiple pollutants: An evaluation of methodological approaches using simulations and real data

BACKGROUND

Assessment of the cumulative effect of correlated exposures is an open methodological issue in environmental epidemiology. Most previous studies have applied regression models with interaction terms or dimension reduction methods. The combined effect of pollutants has been also evaluated through the use of exposure scores that incorporate weights based on the strength of the component-specific associations with health outcomes.

METHODS

We compared three approaches addressing multi-pollutant exposures in epidemiological models: main effects models, the adaptive least absolute shrinkage and selection operator (LASSO) and a weighted exposure score. We assessed the performance of the methods by simulations under various scenarios for the pollutants' correlations. We further applied these methods to time series data from Athens, Greece in 2007-12 to investigate the combined effect of short-term exposure to six regulated pollutants on all-cause and respiratory mortality.

RESULTS

The exposure score provided the least biased estimate under all correlation scenarios for both mortality outcomes. The adaptive LASSO performed well in the case of low and medium correlation between exposures while the main effect model resulted in severe bias. In the real data application, the cumulative effect estimate was similar between approaches for all-cause mortality ranging from 0.7% increase per interquartile range (IQR) (score) to 1.1% (main effects), while for respiratory mortality conclusions were contradictive and ranged from - 0.6% (adaptive LASSO) to 2.8% (score).

CONCLUSIONS

Τhe use of a weighted exposure score to address cumulative effects of correlated metrics may perform well under different exposure correlation and variability in the health outcomes.

Pollutant composition modification of the effect of air pollution on progression of coronary artery calcium: the Multi-Ethnic Study of Atherosclerosis

Background:

Differences in traffic-related air pollution (TRAP) composition may cause heterogeneity in associations between air pollution exposure and cardiovascular health outcomes. Clustering multipollutant measurements allows investigation of effect modification by TRAP profiles.

Methods:

We measured TRAP components with fixed-site and on-road instruments for two 2-week periods in Baltimore, Maryland. We created representative TRAP profiles for cold and warm seasons using predictive k-means clustering. We predicted cluster membership for 1005 participants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution with follow-up between 2000 and 2012. We estimated cluster-specific relationships between coronary artery calcification (CAC) progression and long-term exposure to fine particulate matter (PM_2.5) and oxides of nitrogen (NO_X).

Results:

We identified two clusters in the cold season, notable for higher ratios of gases and ultrafine particles, respectively. A 5-μg/m³ difference in PM_2.5 was associated with 17.0 (95% confidence interval [CI] = 7.2, 26.7) and 42.6 (95% CI = 25.7, 59.4) Agatston units/year CAC progression among participants in clusters 1 and 2, respectively (effect modification P = 0.006). A 40 ppb difference in NO_X was associated with 22.2 (95% CI = 7.7, 36.7) and 41.9 (95% CI = 23.7, 60.2) Agatston units/year CAC progression in clusters 1 and 2, respectively (P = 0.08). Similar trends occurred using clusters identified from warm season measurements. Clusters correlated highly with baseline pollution level.

Conclusions:

Clustering TRAP measurements identified spatial differences in composition. We found evidence of greater CAC progression rates per unit PM_2.5 exposures among people living in areas characterized by high ratios of ultrafine particle counts relative to NO_X concentrations.

Recent Approaches to Estimate Associations Between Source-Specific Air Pollution and Health

PURPOSE OF REVIEW

Estimating health effects associated with source-specific exposure is important for better understanding how pollution impacts health and for developing policies to better protect public health. Although epidemiologic studies of sources can be informative, these studies are challenging to conduct because source-specific exposures (e.g., particulate matter from vehicles) often are not directly observed and must be estimated. We reviewed recent studies that estimated associations between pollution sources and health to identify methodological developments designed to address important challenges.

RECENT FINDINGS

Notable advances in epidemiologic studies of sources include approaches for (1) propagating uncertainty in source estimation into health effect estimates, (2) assessing regional and seasonal variability in emissions sources and source-specific health effects, and (3) addressing potential confounding in estimated health effects. Novel methodological approaches to address challenges in studies of pollution sources, particularly evaluation of source-specific health effects, are important for determining how source-specific exposure impacts health.

Acute respiratory symptoms associated with short term fluctuations in ambient pollutants among schoolchildren in Durban, South Africa

Ambient air pollution has been associated with adverse respiratory outcomes, especially among children with asthma. This study reports on associations between daily ambient air pollutant concentrations and the respiratory symptoms of schoolchildren living in Durban, South Africa. This city is Africa's busiest port and a key hub for imported crude oil and exported refined petroleum and petrochemical products, and it experiences a mixture of air pollutants that reflects emissions from industry, traffic and biomass burning. Children in four communities in the highly industrialized southern portion of the city were compared to children of similar socio-economic profiles living in the north of the city. One school was selected in each community. A total of 423 children were recruited. Symptom logs were completed every 1.5-2 h over 3-week period in each of four seasons. Ambient concentrations of NO₂, NO, SO₂, CO, O_3, PM_2.5 and PM₁₀ were measured throughout the study. Generalized estimating equation (GEE) models were used to estimate odds ratios (ORs) and assess lag effects (1-5 days) using single pollutant (single lags or distributed lags) models. Concentrations of SO₂ and NO_x were markedly higher in the south, while PM₁₀ did not vary. Significant increase in the odds ratios of cough were identified for the various lags analyzed. The OR of symptoms was further increased among those living in the south compared to the north. In conclusion, in this analysis of over 70,000 observations, we provide further evidence that exposure to PM₁₀, SO₂, NO₂ and NO is associated with significantly increased occurrence of respiratory symptoms among children. This was evident for cough, shortness of breath, and chest tightness, across the four pollutants and for different lags of exposure. This is the first study describing these changes in sub-Saharan Africa.

Spatio-temporal analysis of particulate matter intake fractions for vehicular emissions: Hourly variation by micro-environments in the Greater Toronto and Hamilton Area, Canada

Previous investigations have reported intake fraction (iF) for different environments, which include ambient concentrations (outdoor exposure) and microenvironments (indoor exposure). However, little is known about iF variations due to space-time factors, especially in microenvironments. In this paper, we performed a spatio-temporal analysis of particulate matter (PM_2.5) intake fractions for vehicular emissions. Specifically, we investigated hourly variation (12:00am-11:00pm) by micro-environments (residences and workplaces) in the Greater Toronto and Hamilton Area (GTHA), Canada. We used GIS modeling to estimate air pollution data (ambient concentration, and traffic emission) and population data in each microenvironment. Our estimates showed that the total iF at residences and workplaces accounts for 85% and 15%, respectively. Workplaces presented the highest 24h average iF (1.06ppm), which accounted for 25% higher than residences. Observing the iF by hour at residences, our estimates showed the highest average iF at 2:00am (iF=3.72ppm). These estimates indicate that approximately 4g of PM_2.5 emitted from motor vehicles are inhaled for every million grams of PM_2.5 emitted. For the workplaces, the highest exposure was observed at 10:00am, with average iF equal to 2.04ppm. The period of the day with the lower average iF for residences was at 8:00am (average iF=0.11ppm), while for the workplaces was at 4:00am (average iF=0.47ppm). Our approach provides a new perspective on human exposure to air pollution. Our results showed significant hourly variation in iF across the GTHA. Our findings can be incorporated in future investigations to advance environmental health effects research and human health risk assessment.

Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children

Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was -1.19 points (95% CI -1.94, -0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be applied to other populations, exposures and outcomes to generate hypotheses within high-dimensional exposure data.

Association between Pesticide Profiles Used on Agricultural Fields near Maternal Residences during Pregnancy and IQ at Age 7 Years

We previously showed that potential prenatal exposure to agricultural pesticides was associated with adverse neurodevelopmental outcomes in children, yet the effects of joint exposure to multiple pesticides is poorly understood. In this paper, we investigate associations between the joint distribution of agricultural use patterns of multiple pesticides (denoted as "pesticide profiles") applied near maternal residences during pregnancy and Full-Scale Intelligence Quotient (FSIQ) at 7 years of age. Among a cohort of children residing in California's Salinas Valley, we used Pesticide Use Report (PUR) data to characterize potential exposure from use within 1 km of maternal residences during pregnancy for 15 potentially neurotoxic pesticides from five different chemical classes. We used Bayesian profile regression (BPR) to examine associations between clustered pesticide profiles and deficits in childhood FSIQ. BPR identified eight distinct clusters of prenatal pesticide profiles. Two of the pesticide profile clusters exhibited some of the highest cumulative pesticide use levels and were associated with deficits in adjusted FSIQ of -6.9 (95% credible interval: -11.3, -2.2) and -6.4 (95% credible interval: -13.1, 0.49), respectively, when compared with the pesticide profile cluster that showed the lowest level of pesticides use. Although maternal residence during pregnancy near high agricultural use of multiple neurotoxic pesticides was associated with FSIQ deficit, the magnitude of the associations showed potential for sub-additive effects. Epidemiologic analysis of pesticides and their potential health effects can benefit from a multi-pollutant approach to analysis.

COVARIATE-ADAPTIVE CLUSTERING OF EXPOSURES FOR AIR POLLUTION EPIDEMIOLOGY COHORTS

Cohort studies in air pollution epidemiology aim to establish associations between health outcomes and air pollution exposures. Statistical analysis of such associations is complicated by the multivariate nature of the pollutant exposure data as well as the spatial misalignment that arises from the fact that exposure data are collected at regulatory monitoring network locations distinct from cohort locations. We present a novel clustering approach for addressing this challenge. Specifically, we present a method that uses geographic covariate information to cluster multi-pollutant observations and predict cluster membership at cohort locations. Our predictive k-means procedure identifies centers using a mixture model and is followed by multi-class spatial prediction. In simulations, we demonstrate that predictive k-means can reduce misclassification error by over 50% compared to ordinary k-means, with minimal loss in cluster representativeness. The improved prediction accuracy results in large gains of 30% or more in power for detecting effect modification by cluster in a simulated health analysis. In an analysis of the NIEHS Sister Study cohort using predictive k-means, we find that the association between systolic blood pressure (SBP) and long-term fine particulate matter (PM2.5) exposure varies significantly between different clusters of PM2.5 component profiles. Our cluster-based analysis shows that for subjects assigned to a cluster located in the Midwestern U.S., a 10 μg/m3 difference in exposure is associated with 4.37 mmHg (95% CI, 2.38, 6.35) higher SBP.

Fine Particulate Matter in Urban Environments: A Trigger of Respiratory Symptoms in Sensitive Children

The overall objective of this research was to study children’s respiratory illness levels in Targoviste (Romania) in relationship to the outdoor concentrations of airborne particulate matter with an aerodynamic diameter below 2.5 µm (PM_2.5). We monitored and analysed the PM_2.5 concentrations according to a complex experimental protocol. The health trial was conducted over three months (October–December 2015) and required the active cooperation of the children’s parents to monitor carefully the respiratory symptoms of the child, i.e., coughing, rhinorrhoea, wheezing, and fever, as well as their outdoor program. We selected the most sensitive children (n = 25; age: 2–10 years) with perturbed respiratory health, i.e., wheezing, asthma, and associated symptoms. The estimated average PM_2.5 doses were 0.8–14.5 µg·day⁻¹ for weekdays, and 0.4–6.6 µg·day⁻¹ for the weekend. The frequency and duration of the symptoms decreased with increasing age. The 4- to 5-year old children recorded the longest duration of symptoms, except for rhinorrhoea, which suggested that this age interval is the most vulnerable to exogenous trigger agents (p < 0.01) compared to the other age groups. PM_2.5 air pollution was found to have a direct positive correlation with the number of wheezing episodes (r = 0.87; p < 0.01) in November 2015. Monitoring of wheezing occurrences in the absence of fever can provide a reliable assessment of the air pollution effect on the exacerbation of asthma and respiratory disorders in sensitive children.

Pediatric emergency department visits and ambient Air pollution in the U.S. State of Georgia: a case-crossover study

BACKGROUND

Estimating the health effects of ambient air pollutant mixtures is necessary to understand the risk of real-life air pollution exposures.

METHODS

Pediatric Emergency Department (ED) visit records for asthma or wheeze (n = 148,256), bronchitis (n = 84,597), pneumonia (n = 90,063), otitis media (n = 422,268) and upper respiratory tract infection (URI) (n = 744,942) were obtained from Georgia hospitals during 2002-2008. Spatially-contiguous daily concentrations of 11 ambient air pollutants were estimated from CMAQ model simulations that were fused with ground-based measurements. Using a case-crossover study design, odds ratios for 3-day moving average air pollutant concentrations were estimated using conditional logistic regression, matching on ZIP code, day-of-week, month, and year.

RESULTS

In multipollutant models, the association of highest magnitude observed for the asthma/wheeze outcome was with "oxidant gases" (O₃, NO₂, and SO₂); the joint effect estimate for an IQR increase of this mixture was OR: 1.068 (95% CI: 1.040, 1.097). The group of "secondary pollutants" (O₃ and the PM_2.5 components SO₄^2-, NO^3-, and NH⁴⁺) was strongly associated with bronchitis (OR: 1.090, 95% CI: 1.050, 1.132), pneumonia (OR: 1.085, 95% CI: 1.047, 1.125), and otitis media (OR: 1.059, 95% CI: 1.042, 1.077). ED visits for URI were strongly associated with "oxidant gases," "secondary pollutants," and the "criteria pollutants" (O₃, NO₂, CO, SO₂, and PM_2.5).

CONCLUSIONS

Short-term exposures to air pollution mixtures were associated with ED visits for several different pediatric respiratory diseases.

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models

BACKGROUND

Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health.

METHODS

ZIP-code level estimates of exposure for six pollutants (CO, NO_x, EC, PM_2.5, SO₄, O₃) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs.

RESULTS

Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3-85% for population error, and 31-85% for total error. When CO, NO_x or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate.

CONCLUSIONS

The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present.

Quantifying the impact of PM2.5 and associated heavy metals on respiratory health of children near metallurgical facilities

The aim of this study was to link the concentrations of particulate matter with an aerodynamic diameter below 2.5 μm (PM2.5) and associated heavy metals with occurrence of wheezing and hospitalizations due to wheezing in 111 children who live near metallurgical plants in Targoviste City, Romania. A group of 72 children with high levels of immunoglobulin E (IgE) and eosinophils, as well as frequent wheezing episodes, was geolocated on digital thematic maps. Monitoring campaigns and medical assessments were performed over two consecutive years (2013-2014). The multiannual average concentrations of PM2.5 ranged from 4.6 to 22.5 μg m(-3), up to a maximum value of 102 μg m(-3). Significant correlations (p < 0.01) were observed between the locations of the children with respiratory issues and the PM2.5 multiannual average (r = 0.985) and PM2.5 maximum (r = 0.813). Fe, Ni, Cd, and Cr were the main marker elements of the emissions from steel production and metal-working facilities in the Targoviste area. The results support the hypothesis that increased PM2.5 levels directly influence wheezing symptom and asthma attacks in the analyzed group. IgE, eosinophils, and wheezing episodes may be considered key indicators with which to evaluate the adverse effects of PM2.5 air pollution on children's health.

Association between prenatal exposure to ambient diesel particulate matter and perchloroethylene with children's 3rd grade standardized test scores

The objective of this research was to determine if prenatal exposure to two common urban air pollutants, diesel and perchloroethylene, affects children's 3rd grade standardized test scores in mathematics and English language arts (ELA). Exposure estimates consisted of annual average ambient concentrations of diesel particulate matter and perchloroethylene obtained from the Environmental Protection Agency's 1996 National Air Toxics Assessment for the residential census tract at birth. Outcome data consisted of linked birth and educational records for 201,559 singleton, non-anomalous children born between 1994 and 1998 who attended New York City public schools. Quantile regression models were used to estimate the effects of these exposures on multiple points within the continuous distribution of standardized test scores. Modified Poisson regression models were used to calculate risk ratios (RR) and 95% confidence intervals (CI) of failing to meet curricula standards, an indicator derived from test scores. Models were adjusted for a number of maternal, neighborhood and childhood factors. Results showed that math scores were approximately 6% of a standard deviation lower for children exposed to the highest levels of both pollutants as compared to children with low levels of both pollutants. Children exposed to high levels of both pollutants also had the largest risk of failing to meet math test standards when compared to children with low levels of exposure to the pollutants (RR 1.10 95%CI 1.07,1.12 RR high perchloroethylene only 1.03 95%CI 1.00,1.06; RR high diesel PM only 1.02 95%CI 0.99,1.06). There was no association observed between exposure to the pollutants and failing to meet ELA standards. This study provides preliminary evidence of associations between prenatal exposure to urban air pollutants and lower academic outcomes. Additionally, these findings suggest that individual pollutants may additively impact health and point to the need to study the collective effects of air pollutant mixtures.

KEY WORDS

air toxics, academic outcomes, urban health, tetrachloroethylene, air pollutant mixtures.

Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County

Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neighborhood cluster, both contribute to TLBW while controlling for the other.

Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia

BACKGROUND

Exposure metrics that identify spatial contrasts in multipollutant air quality are needed to better understand multipollutant geographies and health effects from air pollution. Our aim is to improve understanding of: (1) long-term spatial distributions of multiple pollutants; and (2) demographic characteristics of populations residing within areas of differing air quality.

METHODS

We obtained average concentrations for ten air pollutants (p=10) across a 12 km grid (n=253) covering Atlanta, Georgia for 2002-2008. We apply a self-organizing map (SOM) to our data to derive multipollutant patterns observed across our grid and classify locations under their most similar pattern (i.e, multipollutant spatial type (MST)). Finally, we geographically map classifications to delineate regions of similar multipollutant characteristics and characterize associated demographics.

RESULTS

We found six MSTs well describe our data, with profiles highlighting a range of combinations, from locations experiencing generally clean air to locations experiencing conditions that were relatively dirty. Mapping MSTs highlighted that downtown areas were dominated by primary pollution and that suburban areas experienced relatively higher levels of secondary pollution. Demographics show the largest proportion of the overall population resided in downtown locations experiencing higher levels of primary pollution. Moreover, higher proportions of nonwhites and children in poverty reside in these areas when compared to suburban populations that resided in areas exhibiting relatively lower pollution.

CONCLUSION

Our approach reveals the nature and spatial distribution of differential pollutant combinations across urban environments and provides helpful insights for identifying spatial exposure and demographic contrasts for future health studies.

Short-term exposure to traffic-related air pollution and daily mortality in London, UK

Epidemiological studies have linked daily concentrations of urban air pollution to mortality, but few have investigated specific traffic sources that can inform abatement policies. We assembled a database of >100 daily, measured and modelled pollutant concentrations characterizing air pollution in London between 2011 and 2012. Based on the analyses of temporal patterns and correlations between the metrics, knowledge of local emission sources and reference to the existing literature, we selected, a priori, markers of traffic pollution: oxides of nitrogen (general traffic); elemental and black carbon (EC/BC) (diesel exhaust); carbon monoxide (petrol exhaust); copper (tyre), zinc (brake) and aluminium (mineral dust). Poisson regression accounting for seasonality and meteorology was used to estimate the percentage change in risk of death associated with an interquartile increment of each pollutant. Associations were generally small with confidence intervals that spanned 0% and tended to be negative for cardiovascular mortality and positive for respiratory mortality. The strongest positive associations were for EC and BC adjusted for particle mass and respiratory mortality, 2.66% (95% confidence interval: 0.11, 5.28) and 2.72% (0.09, 5.42) per 0.8 and 1.0 μg/m(3), respectively. These associations were robust to adjustment for other traffic metrics and regional pollutants, suggesting a degree of specificity with respiratory mortality and diesel exhaust containing EC/BC.