Is the air pollution health research community prepared to support a multipollutant air quality management framework?

The association between PM2.5 components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models

BACKGROUND

PM2.5 is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develop an analytical methodology combining traditional and novel machine learning models to evaluate PM2.5's combined effects on blood pressure (BP) and identify the most toxic components.

METHODS

We measured late-pregnancy BP of 1138 women from the Heshan cohort while simultaneously analyzing 31 PM2.5 components. We utilized multiple linear regression modeling to establish the relationship between PM2.5 components and late-pregnancy BP and applied Random Forest (RF) and generalized Weighted Quantile Sum (gWQS) regression to identify the most toxic components contributing to elevated BP and to quantitatively evaluate the cumulative effect of the PM2.5 component mixtures.

RESULTS

The results revealed that 16 PM2.5 components, such as EC, OC, Ti, Fe, Mn, Cu, Cd, Mg, K, Pb, Se, Na+, K+, Cl-, NO3-, and F-, contributed to elevated systolic blood pressure (SBP), while 26 components, including two carbon components (EC, OC), fourteen metallics (Ti, Fe, Mn, Cr, Mo, Co, Cu, Zn, Cd, Na, Mg, Al, K, Pb), one metalloid (Se), and nine water-soluble ions (Na+, K+, Mg2+, Ca2+, NH4+, Cl-, NO3-, SO42-, F-), contributed to elevated diastolic blood pressure (DBP). Mn and Cr were the most toxic components for elevated SBP and DBP, respectively, as analyzed by RF and gWQS models and verified against each other. Exposure to PM2.5 component mixtures increased SBP by 1.04 mmHg (95% CI: 0.33-1.76) and DBP by 1.13 mmHg (95% CI: 0.47-1.78).

CONCLUSIONS

Our study highlights the effectiveness of combining traditional and novel models as an analytical strategy to quantify the health effects of PM2.5 constituent mixtures.

Mapping soil trace metal distribution using remote sensing and multivariate analysis

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia

BACKGROUND

Air pollution risk assessments do not generally quantify health impacts using multipollutant risk estimates, but instead use results from single-pollutant or copollutant models. Multipollutant epidemiological models account for pollutant interactions and joint effects but can be computationally complex and data intensive. Risk estimates from multipollutant studies are therefore challenging to implement in the quantification of health impacts.

OBJECTIVES

Our objective was to conduct a case study using a developmental multipollutant version of the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) to estimate the health impact associated with changes in multiple air pollutants using both a single and multipollutant approach.

METHODS

BenMAP-CE was used to estimate the change in the number of pediatric asthma emergency department (ED) visits attributable to simulated changes in air pollution between 2011 and 2025 in Atlanta, Georgia, applying risk estimates from an epidemiological study that examined short-term single-pollutant and multipollutant (with and without first-order interactions) exposures. Analyses examined individual pollutants (i.e., ozone, fine particulate matter, carbon monoxide, nitrogen dioxide (NO 2 ), sulfur dioxide, and particulate matter components) and combinations of these pollutants meant to represent shared properties or predefined sources (i.e., oxidant gases, secondary pollutants, traffic, power plant, and criteria pollutants). Comparisons were made between multipollutant health impact functions (HIF) and the sum of single-pollutant HIFs for the individual pollutants that constitute the respective pollutant groups.

RESULTS

Photochemical modeling predicted large decreases in most of the examined pollutant concentrations between 2011 and 2025 based on sector specific (i.e., source-based) estimates of growth and anticipated controls. Estimated number of avoided asthma ED visits attributable to any given multipollutant group were generally higher when using results from models that included interaction terms in comparison with those that did not. We estimated the greatest number of avoided pediatric asthma ED visits for pollutant groups that include NO 2 (i. e., criteria pollutants, oxidants, and traffic pollutants). In models that accounted for interaction, year-round estimates for pollutant groups that included NO 2 ranged from 27.1 [95% confidence interval (CI): 1.6, 52.7; traffic pollutants] to 55.4 (95% CI: 41.8, 69.0; oxidants) avoided pediatric asthma ED visits. Year-round results using multipollutant risk estimates with interaction were comparable to the sum of the single-pollutant results corresponding to most multipollutant groups [e.g., 52.9 (95% CI: 43.6, 62.2) for oxidants] but were notably lower than the sum of the single-pollutant results for some pollutant groups [e.g., 77.5 (95% CI: 66.0, 89.0) for traffic pollutants].

DISCUSSION

Performing a multipollutant health impact assessment is technically feasible but computationally complex. It requires time, resources, and detailed input parameters not commonly reported in air pollution epidemiological studies. Results estimated using the sum of single-pollutant models are comparable to those quantified using a multipollutant model. Although limited to a single study and location, assessing the trade-offs between a multipollutant and single-pollutant approach is warranted. https://doi.org/10.1289/EHP12969.

Occurrences, sources and health hazard estimation of potentially toxic elements in the groundwater of Garhwal Himalaya, India

High concentrations of potentially toxic elements (PTEs) in potable water can cause severe human health disorders. Present study examined the fitness of groundwater for drinking purpose based on the occurrence of nine PTEs in a heavy pilgrim and tourist influx region of the Garhwal Himalaya, India. The concentrations of analyzed PTEs in groundwater were observed in the order of Zn > Mn > As > Al > Cu > Cr > Se > Pb > Cd. Apart from Mn and As, other PTEs were within the corresponding guideline values. Spatial maps were produced to visualize the distribution of the PTEs in the area. Estimated water pollution indices and non-carcinogenic risk indicated that the investigated groundwater is safe for drinking purpose, as the hazard index was < 1 for all the water samples. Assessment of the cancer risk of Cr, As, Cd, and Pb also indicated low health risks associated with groundwater use, as the values were within the acceptable range of ≤ 1 × 10-6 to 1 × 10-4. Multivariate statistical analyses were used to describe the various possible geogenic and anthropogenic sources of the PTEs in the groundwater resources although the contamination levels of the PTEs were found to pose no serious health risk. However, the present study recommends to stop the discharge of untreated wastewater and also to establish cost-effective as well as efficient water treatment facility nearby the study area. Present work's findings are vital as they may protect the health of the massive population from contaminated water consumption. Moreover, it can help the researchers, governing authorities and water supplying agencies to take prompt and appropriate decisions for water security.

Low level exposure to hydrogen sulfide: a review of emissions, community exposure, health effects, and exposure guidelines

Hydrogen sulfide (H2S) is a toxic gas that is well-known for its acute health risks in occupational settings, but less is known about effects of chronic and low-level exposures. This critical review investigates toxicological and experimental studies, exposure sources, standards, and epidemiological studies pertaining to chronic exposure to H2S from both natural and anthropogenic sources. H2S releases, while poorly documented, appear to have increased in recent years from oil and gas and possibly other facilities. Chronic exposures below 10 ppm have long been associated with odor aversion, ocular, nasal, respiratory and neurological effects. However, exposure to much lower levels, below 0.03 ppm (30 ppb), has been associated with increased prevalence of neurological effects, and increments below 0.001 ppm (1 ppb) in H2S concentrations have been associated with ocular, nasal, and respiratory effects. Many of the studies in the epidemiological literature are limited by exposure measurement error, co-pollutant exposures and potential confounding, small sample size, and concerns of representativeness, and studies have yet to consider vulnerable populations. Long-term community-based studies are needed to confirm the low concentration findings and to refine exposure guidelines. Revised guidelines that incorporate both short- and long-term limits are needed to protect communities, especially sensitive populations living near H2S sources.

Prenatal Exposure to Ambient PM2.5 and Early Childhood Growth Impairment Risk in East Africa

Height for age is an important and widely used population-level indicator of children's health. Morbidity trends show that stunting in young children is a significant public health concern. Recent studies point to environmental factors as an understudied area of child growth failure in Africa. Data on child measurements of height-for-age and confounders were obtained from fifteen waves of the Demographic and Health Surveys (DHS) for six countries in East Africa. Monthly ambient PM_2.5 concentration data was retrieved from the Atmospheric Composition Analysis Group (ACAG) global surface PM_2.5 estimates and spatially integrated with DHS data. Generalized additive models with linear and logistic regression were used to estimate the exposure-response relationship between prenatal PM_2.5 and height-for-age and stunting among children under five in East Africa (EA). Fully adjusted models showed that for each 10 µg/m³ increase in PM_2.5 concentration there is a 0.069 (CI: 0.097, 0.041) standard deviation decrease in height-for-age and 9% higher odds of being stunted. Our study identified ambient PM_2.5 as an environmental risk factor for lower height-for-age among young children in EA. This underscores the need to address emissions of harmful air pollutants in EA as adverse health effects are attributable to ambient PM_2.5 air pollution.

Synergistic or Antagonistic Health Effects of Long- and Short-Term Exposure to Ambient NO2 and PM2.5: A Review

Studies on adverse health effects associated with air pollution mostly focus on individual pollutants. However, the air is a complex medium, and thus epidemiological studies face many challenges and limitations in the multipollutant approach. NO2 and PM2.5 have been selected as both originating from combustion processes and are considered to be the main pollutants associated with traffic; moreover, both elicit oxidative stress responses. An answer to the question of whether synergistic or antagonistic health effects of combined pollutants are demonstrated by pollutants monitored in ambient air is not explicit. Among the analyzed studies, only a few revealed statistical significance. Exposure to a single pollutant (PM2.5 or NO2) was mostly associated with a small increase in non-accidental mortality (HR:1.01-1.03). PM2.5 increase of <10 µg/m3 adjusted for NO2 as well as NO2 adjusted for PM2.5 resulted in a slightly lower health risk than a single pollutant. In the case of cardiovascular heart disease, mortality evoked by exposure to PM2.5 or NO2 adjusted for NO2 and PM2.5, respectively, revealed an antagonistic effect on health risk compared to the single pollutant. Both short- and long-term exposure to PM2.5 or NO2 adjusted for NO2 and PM2.5, respectively, revealed a synergistic effect appearing as higher mortality from respiratory diseases.

Joint effect of indoor size-fractioned particulate matters and black carbon on cardiopulmonary function and relevant metabolic mechanism: A panel study among school children

Indoor particulate matter (PM) and black carbon (BC) are associated with adverse cardiopulmonary effect. However, the cumulative and interactive effects of the mixture of size-fractioned PMs and BC on cardiopulmonary function are not well understood, and the underlying biological mechanisms remain unclear. This repeated-measure study was conducted to assess the joint cardiopulmonary effect and metabolic mechanisms of multiple-size particles and BC among 46 children. PM_0.5, PM₁, PM_2.5, PM₅, PM₁₀ and BC were monitored for 5 weekdays. Cardiorespiratory function measurements and urine samples collection were conducted three times. Untargeted-metabolomics and meet-in-metabolite approach were applied to mechanism investigation. Bayesian machine kernel regression was adopted to analyze associations among PMs, cardiopulmonary function and metabolites. Lung function and heart rate variability significantly decreased with the increased PMs and BC co-exposure (p < 0.05). The effective particles were BC, PM_1-2.5 and PM_0.5 in turn. No interaction effects of different particles on cardiopulmonary function were observed at different lag days. BC-related glucose and fatty acid increase, and PM_1-2.5-related branched-chain amino acid degradation were primarily observed. Other metabolisms were successively disturbed. The greatest joint effects of PMs and BC on metabolism were mainly at lag0 and lag01 day. They occurred earlier than the strongest effects on cardiopulmonary function, which were at lag01 and lag02 day. BC, PM_1-2.5 and PM_0.5 were mainly associated with cardiorespiratory indices by disturbing amino acids, glucose, lipid, isoflavone and purine metabolism. Mitochondrial productivity and antioxidation reduction are pivotal to the relevant metabolic alterations. More attention should be paid to BC and smaller-size PMs to control indoor PM pollution and its adverse effect on children.

Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network

As part of our low-cost sensor network, we colocated multipollutant monitors containing sensors for particulate matter, carbon monoxide, ozone, nitrogen dioxide, and nitrogen monoxide at a reference field site in Baltimore, MD, for 1 year. The first 6 months were used for training multiple regression models, and the second 6 months were used to evaluate the models. The models produced accurate hourly concentrations for all sensors except ozone, which likely requires nonlinear methods to capture peak summer concentrations. The models for all five pollutants produced high Pearson correlation coefficients (r > 0.85), and the hourly averaged calibrated sensor and reference concentrations from the evaluation period were within 3-12%. Each sensor required a distinct set of predictors to achieve the lowest possible root-mean-square error (RMSE). All five sensors responded to environmental factors, and three sensors exhibited cross-sensitives to another air pollutant. We compared the RMSE from models (NO₂, O₃, and NO) that used colocated regulatory instruments and colocated sensors as predictors to address the cross-sensitivities to another gas, and the corresponding model RMSEs for the three gas models were all within 0.5 ppb. This indicates that low-cost sensor networks can yield useable data if the monitoring package is designed to comeasure key predictors. This is key for the utilization of low-cost sensors by diverse audiences since this does not require continual access to regulatory grade instruments.

From Theory to Praxis: 'Go Sustainable Living' Survey for Exploring Individuals Consciousness Level of Decision-Making and Action-Taking in Daily Life Towards a Green Citizenship

This study aims at embedding sustainability practices by exploring sustainable actions of individuals consisting the educated workforce of Greece. A tailored questionnaire was created and sent via e-mails to 500 respondents, to identify a snapshot of participants daily buying and consuming actions. 483 responses received and analyzed using statistical tools. They respond to recommendations for enhancing sustainability consciousness at individual level, inspiring people to buy sustainable, creating new consumption attitudes that are key factors for moving towards a sustainable citizenship. The findings will further provide information for a second paper on developing the ‘Go Sustainable Living’ digital application to be uploaded in individuals’ mobile phones, for rewarding users with points that correspond to each sustainable action and can later be used for discounts in all participating stores. The analysis showed that <30% of consumers are considered sustainability-conscious, 57.6% are in a transition phase, while 13% fell into the category of non-conscious. To make sustainable decisions and actions in every daily life, individuals need to have knowledge of sustainability, awareness, consciousness of their actions, and be active citizens. An educated workforce armed with sustainability perceptions and competencies is an asset for societies and businesses poised to respond to the sustainability call. Sustainability should not be only an ‘utopia’ in our societies but an ‘eutopia’ entailing a life with ecological and social health and prosperity at a local, regional, and global level.

Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results

BACKGROUND

Most epidemiological studies estimate associations without considering exposure measurement error. While some studies have estimated the impact of error in single-exposure models we aimed to quantify the effect of measurement error in multi-exposure models, specifically in time-series analysis of PM_2.5, NO₂, and mortality using simulations, under various plausible scenarios for exposure errors. Measurement error in multi-exposure models can lead to effect transfer where the effect estimate is overestimated for the pollutant estimated with more error to the one estimated with less error. This complicates interpretation of the independent effects of different pollutants and thus the relative importance of reducing their concentrations in air pollution policy.

METHODS

Measurement error was defined as the difference between ambient concentrations and personal exposure from outdoor sources. Simulation inputs for error magnitude and variability were informed by the literature. Error-free exposures with their consequent health outcome and error-prone exposures of various error types (classical/Berkson) were generated. Bias was quantified as the relative difference in effect estimates of the error-free and error-prone exposures.

RESULTS

Mortality effect estimates were generally underestimated with greater bias observed when low ratios of the true exposure variance over the error variance were assumed (27.4% underestimation for NO₂). Higher ratios resulted in smaller, but still substantial bias (up to 19% for both pollutants). Effect transfer was observed indicating that less precise measurements for one pollutant (NO₂) yield more bias, while the co-pollutant (PM_2.5) associations were found closer to the true. Interestingly, the sum of single-pollutant model effect estimates was found closer to the summed true associations than those from multi-pollutant models, due to cancelling out of confounding and measurement error bias.

CONCLUSIONS

Our simulation study indicated an underestimation of true independent health effects of multiple exposures due to measurement error. Using error parameter information in future epidemiological studies should provide more accurate concentration-response functions.

Sex-specific associations of plasma metals and metal mixtures with glucose metabolism: An occupational population-based study in China

Studies with multi-pollutant approach on the relationships between multiple metals and fasting plasma glucose (FPG) are limited. Few studies are available on the potential sex-specific associations between metal exposures and glucose metabolism. We explored the associations between 22 plasma metals and FPG level among the 769 participants from the manganese-exposed workers healthy cohort in China. We applied a sparse partial least squares (sPLS) regression followed by ordinary least-squares regression to evaluate multi-pollutant association. Bayesian kernel machine regression (BKMR) model was used to deal with metal mixtures and evaluate their joint effects on FPG level. In the sPLS model, negative associations on FPG levels were observed for plasma iron (belta = -0.066), cobalt (belta = -0.075), barium (belta = -0.109), and positive associations for strontium (belta = 0.082), and selenium (belta = 0.057) in men, which overlapped with the results among the overall participants. Among women, plasma copper (belta = 0.112) and antimony (belta = 0.137) were positively associated with elevated FPG level. Plasma magnesium was negatively associated with FPG level in both sexes (belta = -0.071 in men and belta = -0.144 in women). The results of overlapped for plasma magnesium was selected as the significant contributor to decreasing FPG level in the multi-pollutant, single-metal, and multi-metal models. BKMR model showed a significantly negative over-all effect of six metal mixtures (magnesium, iron, cobalt, selenium, strontium and barium) on FPG level among the overall participants from all the metals fixed at 50th percentile. In summary, our findings underline the probable role of metals in glucose homeostasis with potential sex-dependent heterogeneities, and suggest more researches are needed to explore the sex-specific associations of metal exposures with risk of diabetes.

IDENTIFYING MAIN EFFECTS AND INTERACTIONS AMONG EXPOSURES USING GAUSSIAN PROCESSES

This article is motivated by the problem of studying the joint effect of different chemical exposures on human health outcomes. This is essentially a nonparametric regression problem, with interest being focused not on a black box for prediction but instead on selection of main effects and interactions. For interpretability we decompose the expected health outcome into a linear main effect, pairwise interactions and a nonlinear deviation. Our interest is in model selection for these different components, accounting for uncertainty and addressing nonidentifiability between the linear and nonparametric components of the semiparametric model. We propose a Bayesian approach to inference, placing variable selection priors on the different components, and developing a Markov chain Monte Carlo (MCMC) algorithm. A key component of our approach is the incorporation of a heredity constraint to only include interactions in the presence of main effects, effectively reducing dimensionality of the model search. We adapt a projection approach developed in the spatial statistics literature to enforce identifiability in modeling the nonparametric component using a Gaussian process. We also employ a dimension reduction strategy to sample the nonlinear random effects that aids the mixing of the MCMC algorithm. The proposed MixSelect framework is evaluated using a simulation study, and is illustrated using data from the National Health and Nutrition Examination Survey (NHANES). Code is available on GitHub.

Prenatal Exposure to Multiple Air Pollutants, Mediating Molecular Mechanisms, and Shifts in Birthweight

Mechanisms underlying adverse birth and later in life health effects from exposure to air pollution during the prenatal period have not been not fully elucidated, especially in the context of mixtures. We assessed the effects of prenatal exposure to mixtures of air pollutants of particulate matter (PM), PM2.5, PM10, nitrogen oxides, NO2, NOx, ultrafine particles (UFP), and oxidative potential (OP) of PM2.5 on infant birthweight in four European birth cohorts and the mechanistic underpinnings through cross-omics of metabolites and inflammatory proteins. The association between mixtures of air pollutants and birthweight z-scores (standardized for gestational age) was assessed for three different mixture models, using Bayesian machine kernel regression (BKMR). We determined the direct effect for PM2.5, PM10, NO2, and mediation by cross-omic signatures (identified using sparse partial least-squares regression) using causal mediation BKMR models. There was a negative association with birthweight z-scores and exposure to mixtures of air pollutants, where up to -0.21 or approximately a 96 g decrease in birthweight, comparing the 75th percentile to the median level of exposure to the air pollutant mixture could occur. Shifts in birthweight z-scores from prenatal exposure to PM2.5, PM10, and NO2 were mediated by molecular mechanisms, represented by cross-omics scores. Interleukin-17 and epidermal growth factor were identified as important inflammatory responses underlyingair pollution-associated shifts in birthweight. Our results signify that by identifying mechanisms through which mixtures of air pollutants operate, the causality of air pollution-associated shifts in birthweight is better supported, substantiating the need for reducing exposure in vulnerable populations.

The joint effect of ambient air pollution and agricultural pesticide exposures on lung function among children with asthma

BACKGROUND

Ambient environmental pollutants have been shown to adversely affect respiratory health in susceptible populations. However, the role of simultaneous exposure to multiple diverse environmental pollutants is poorly understood.

OBJECTIVE

We applied a multidomain, multipollutant approach to assess the association between pediatric lung function measures and selected ambient air pollutants and pesticides.

METHODS

Using data from the US EPA and California Pesticide Use Registry, we reconstructed three months prior exposure to ambient air pollutants ((ozone (O₃), nitrogen dioxide (NO₂), particulate matter with a median aerodynamic diameter < 2.5 μm (PM_2.5) and <10 μm (PM₁₀)) and pesticides (organophosphates (OP), carbamates (C) and methyl bromide (MeBr)) for 153 children with mild intermittent or mild persistent asthma from the San Joaquin Valley of California, USA. We implemented Bayesian kernel machine regression (BKMR) to estimate the association between simultaneous exposures to air pollutants and pesticides and lung function measures (forced expiratory volume in 1 s (FEV₁), forced vital capacity (FVC), and forced expiratory flow between 25% and 75% of vital capacity (FEF_25-75)).

RESULTS

In BKMR analysis, the overall effect of mixtures (pollutants and pesticides) was associated with reduced FEV₁ and FVC, particularly when all the environmental exposures were above their 60th percentile. For example, the effect of the overall mixture at the 70th percentile (compared to the median) was a -0.12SD (-50 mL, 95% CI: -180 mL, 90 mL) change in the FEV₁ and a -0.18SD (-90 mL, 95% CI: -240 mL, 60 mL) change in the FVC. However, 95% credible intervals around all of the joint effect estimates contained the null value.

CONCLUSION

At this agricultural-urban interface, we observed results from multipollutant analyses, suggestive of adverse effects on some pediatric lung function measures following a cumulative increase in ambient air pollutants and agricultural pesticides. Given the uncertainty in effect estimates, this approach should be explored in larger studies.

Low level prenatal exposure to a mixture of Sr, Se and Mn and neurocognitive development of 2-year-old children

The excess or deficiency of trace metals can cause adverse neurocognitive development. Evidence of health effect of trace metal mixtures on neurocognitive development is limited in children. We evaluated associations of prenatal exposure to trace metals and metal mixtures with neurocognitive development of 2-year-old children. A total of 544 mother-child pairs were included in the study. The concentrations of 10 trace metals in maternal urine were monitored before delivery. Neurocognitive development indexes, including mental development index (MDI) and psychomotor development index (PDI), were assessed using the Bayley Scales of Infant Development. Linear regression analysis was performed to explore the effects of single-metal and multi-metal exposures. Bayesian Kernel Machine regression (BKMR) was used to investigate overall effect of exposure to metal mixtures and potential interactions among mixture components. We found positive associations of urinary strontium (Sr) and Selenium (Se) levels with MDI scores among all children in the single-metal model. Sr was positively related to MDI, while Manganese (Mn) was negatively associated with PDI in the multi-metal model. The results from BKMR model in girls revealed that MDI scores were improved with the increasing concentrations of Sr, Se and Mn mixture until the concentrations reached their 30th percentiles (Sr: 149.49 μg/g creatinine, Se:18.38 μg/g creatinine, Mn:1.96 μg/g creatinine), with no effect after that threshold level. Sr played a positive role in mental development among mixture components, which was consistent with the results of Sr in the multi-metal models. No signification association of mixture with MDI/PDI was found in boys. The study suggested potential sex-specific association of Sr, Se and Mn mixture levels (at or below their 30th percentiles) with improved mental development, and beneficial role of Sr.

The association of urine metals and metal mixtures with cardiovascular incidence in an adult population from Spain: the Hortega Follow-Up Study

BACKGROUND

The association of low-level exposure to metals and metal mixtures with cardiovascular incidence in the general population has rarely been studied. We flexibly evaluated the association of urinary metals and metal mixtures concentrations with cardiovascular diseases in a representative sample of a general population from Spain.

METHODS

Urine antimony (Sb), barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), molybdenum (Mo), vanadium (V) and zinc (Zn) were measured in 1171 adults without clinical cardiovascular diseases, who participated in the Hortega Study. Cox proportional hazard models were used for evaluating the association between single metals and cardiovascular incidence. We used a Probit extension of Bayesian Kernel Machine Regression (BKMR-P) to handle metal mixtures in a survival setting.

RESULTS

In single-metal models, the hazard ratios [confidence intervals (CIs)] of cardiovascular incidence, comparing the 80th to the 20th percentiles of metal distributions, were 1.35 (1.06, 1.72) for Cu, 1.43 (1.07, 1.90) for Zn, 1.51 (1.13, 2.03) for Sb, 1.46 (1.13, 1.88) for Cd, 1.64 (1.05, 2.58) for Cr and 1.31 (1.01, 1.71) for V. BKMR-P analysis was confirmatory of these findings, supporting that Cu, Zn, Sb, Cd, Cr and V are related to cardiovascular incidence in the presence of the other metals. Cd and Sb showed the highest posterior inclusion probabilities.

CONCLUSIONS

Urine Cu, Zn, Sb, Cd, Cr and V were independently associated with increased cardiovascular risk at levels relevant for the general population of Spain. Urine metals in the mixture were also jointly associated with cardiovascular incidence, with Cd and Sb being the most important components of the mixture.

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.

Early-life exposure to ambient fine particulate air pollution and infant mortality: pooled evidence from 43 low- and middle-income countries

BACKGROUND

Many low- and middle-income countries are experiencing high and increasing exposure to ambient fine particulate air pollution (PM2.5). The effect of PM2.5 on infant and child mortality is usually modelled using concentration response curves extrapolated from studies conducted in settings with low ambient air pollution, which may not capture its full effect.

METHODS

We pool data on more than half a million births from 69 nationally representative Demographic and Health Surveys that were conducted in 43 low- and middle-income countries between 1998 and 2014, and we calculate early-life exposure (exposure in utero and post partum) to ambient PM2.5 using high-resolution calibrated satellite data matched to the child's place of residence. We estimate the association between the log of early-life PM2.5 exposure, both overall and separated by type, and the odds of neonatal and infant mortality, adjusting for child-level, parent-level and household-level characteristics.

RESULTS

We find little evidence that early-life exposure to overall PM2.5 is associated with higher odds of mortality relative to low exposure to PM2.5. However, about half of PM2.5 is naturally occurring dust and sea-salt whereas half is from other sources, comprising mainly carbon-based compounds, which are mostly due to human activity. We find a very strong association between exposure to carbonaceous PM2.5 and infant mortality, particularly neonatal mortality, i.e. mortality in the first 28 days after birth. We estimate that, at the mean level of exposure in the sample to carbonaceous PM2.5-10.9 µg/m3-the odds of neonatal mortality are over 50% higher than in the absence of pollution.

CONCLUSION

Our results suggest that the current World Health Organization guideline of limiting the overall ambient PM2.5 level to less than 10 µg/m³ should be augmented with a lower limit for harmful carbonaceous PM2.5.

Interdisciplinary-driven hypotheses on spatial associations of mixtures of industrial air pollutants with adverse birth outcomes

BACKGROUND

Adverse birth outcomes (ABO) such as prematurity and small for gestational age confer a high risk of mortality and morbidity. ABO have been linked to air pollution; however, relationships with mixtures of industrial emissions are poorly understood. The exploration of relationships between ABO and mixtures is complex when hundreds of chemicals are analyzed simultaneously, requiring the use of novel approaches.

OBJECTIVE

We aimed to generate robust hypotheses spatially linking mixtures and the occurrence of ABO using a spatial data mining algorithm and subsequent geographical and statistical analysis. The spatial data mining approach aimed to reduce data dimensionality and efficiently identify spatial associations between multiple chemicals and ABO.

METHODS

We discovered co-location patterns of mixtures and ABO in Alberta, Canada (2006-2012). An ad-hoc spatial data mining algorithm allowed the extraction of primary co-location patterns of 136 chemicals released into the air by 6279 industrial facilities (National Pollutant Release Inventory), wind-patterns from 182 stations, and 333,247 singleton live births at the maternal postal code at delivery (Alberta Perinatal Health Program), from which we identified cases of preterm birth, small for gestational age, and low birth weight at term. We selected secondary patterns using a lift ratio metric from ABO and non-ABO impacted by the same mixture. The relevance of the secondary patterns was estimated using logistic models (adjusted by socioeconomic status and ABO-related maternal factors) and a geographic-based assignment of maternal exposure to the mixtures as calculated by kernel density.

RESULTS

From 136 chemicals and three ABO, spatial data mining identified 1700 primary patterns from which five secondary patterns of three-chemical mixtures, including particulate matter, methyl-ethyl-ketone, xylene, carbon monoxide, 2-butoxyethanol, and n-butyl alcohol, were subsequently analyzed. The significance of the associations (odds ratio > 1) between the five mixtures and ABO provided statistical support for a new set of hypotheses.

CONCLUSION

This study demonstrated that, in complex research settings, spatial data mining followed by pattern selection and geographic and statistical analyses can catalyze future research on associations between air pollutant mixtures and adverse birth outcomes.

Characterizing the joint effects of pesticide exposure and criteria ambient air pollutants on pediatric asthma morbidity in an agricultural community

Supplemental Digital Content is available in the text.

Background:

Environmental contributions to pediatric asthma morbidity have been studied extensively in urban settings; exposures characteristic of agricultural and rural communities have received less attention despite a comparable burden of morbidity.

Methods:

We obtained repeated urine samples (n = 139) from 16 school-age children with asthma in the Yakima Valley of Washington State between July and October 2012. Biomarkers of organophosphate (OP) pesticide exposure (dialkyl phosphates [DAPs]) and asthma exacerbation (leukotriene E4 [LTE4]) were analyzed in samples. Corresponding 24-hour average particulate matter <2.5 μg (PM_2.5) and maximum 8-hour ozone concentration data for the study period were available from local monitoring stations. We evaluated the independent and multi-pollutant associations between LTE4 and exposure to ambient air pollutants and DAPs using generalized estimating equations. For multi-domain and multi-pollutant models, we created categorized pollution combination levels and estimated the relative health impact of exposure to pollutant mixtures.

Results:

In single-pollutant models, an interquartile range increase in exposures to DAPs was associated with increase in LTE4 levels (β: 4.1 [0.6–7.6] pg/mg). PM_2.5 and ozone were also associated with increase in LTE4, though confidence intervals contained the null value. Increase in LTE4 levels was consistently associated with increase in median-dichotomized multi-pollutant combination exposures; the highest effect estimates were observed with joint highest (vs. the lowest) category of the three-pollutant exposure (PM_2.5, ozone, and OP; β: 53.5, 95% confidence interval = 24.2, 82.8 pg/mg).

Conclusion:

Concurrent short-term exposure to criteria air pollutants and OPs in an agricultural community was associated with an increase in a marker of asthma morbidity.

Cancer Risk from Exposure to Particulate Matter and Ozone According to Obesity and Health-Related Behaviors: A Nationwide Population-Based Cross-Sectional Study

BACKGROUND

There is little evidence of an association between cancer risk and long-term exposure to ambient particulate matter <10 μm (PM₁₀) and ozone (O₃), according to obesity and health-related behaviors.

METHODS

In the 2012 Korean Community Health Survey, survey data on socioeconomic characteristics, health-related behaviors, and previous cancer history were collected from 100,867 participants. Daily average concentrations of PM₁₀ and O₃ (2003-2012) were obtained from the Korean Air Pollutants Emission Service. The cancer risks for interquartile increases in PM₁₀ and O₃ were evaluated using multiple logistic regression and were stratified by age, sex, obesity, and health-related behaviors.

RESULTS

Increased cancer risk was found among obese subjects aged ≥50 years after adjusting for confounding factors [PM₁₀: ≥60 years: OR 1.34, 95% confidence interval (CI) 1.03-1.74; 50-60 years: OR 1.40, CI 1.01-1.96; O₃: ≥60 years: OR 1.12, CI 1.04-1.20; 50-60 years: OR 1.20, CI 1.08-1.33]. However, we did not observe similar trends in the nonobese subjects. Among obese subjects aged ≥50 who had been exposed to PM₁₀, men, ever smokers, and inactive subjects were at increased cancer risk. Regarding O₃, the cancer risk was significantly higher among obese adults >50 years old, regardless of sex or health-related behaviors.

CONCLUSIONS

Long-term exposure to PM₁₀ and O₃ was found to increase cancer risk. In particular, the risk differed according to obesity status, age, sex, and health-related behaviors.

IMPACT

The effect of air pollution on cancer risk was compounded by obesity, smoking, and physical inactivity among subjects over 50 years old.

Statistical Approaches to Address Multi-Pollutant Mixtures and Multiple Exposures: the State of the Science

PURPOSE OF REVIEW

The purpose of this review is to describe the most recent statistical approaches to estimate the effect of multi-pollutant mixtures or multiple correlated exposures on human health.

RECENT FINDINGS

The health effects of environmental chemicals or air pollutants have been widely described. Often, there exists a complex mixture of different substances, potentially highly correlated with each other and with other (environmental) stressors. Single-exposure approaches do not allow disentangling effects of individual factors and fail to detect potential interactions between exposures. In the last years, sophisticated methods have been developed to investigate the joint or independent health effects of multi-pollutant mixtures or multiple environmental exposures. A classification of the most recent methods is proposed. A non-technical description of each method is provided, together with epidemiological applications and operational details for implementation with standard software.

A cross-disciplinary evaluation of evidence for multipollutant effects on cardiovascular disease

BACKGROUND

The current single-pollutant approach to regulating ambient air pollutants is effective at protecting public health, but efficiencies may be gained by addressing issues in a multipollutant context since multiple pollutants often have common sources and individuals are exposed to more than one pollutant at a time.

OBJECTIVE

We performed a cross-disciplinary review of the effects of multipollutant exposures on cardiovascular effects.

METHODS

A broad literature search for references including at least two criteria air pollutants (particulate matter [PM], ozone [O₃], oxides of nitrogen, sulfur oxides, carbon monoxide) was conducted. References were culled based on scientific discipline then searched for terms related to cardiovascular disease. Most multipollutant epidemiologic and experimental (i.e., controlled human exposure, animal toxicology) studies examined PM and O₃ together.

DISCUSSION

Epidemiologic and experimental studies provide some evidence for O₃ concentration modifying the effect of PM, although PM did not modify O₃ risk estimates. Experimental studies of combined exposure to PM and O₃ provided evidence for additivity, synergism, and/or antagonism depending on the specific health endpoint. Evidence for other pollutant pairs was more limited.

CONCLUSIONS

Overall, the evidence for multipollutant effects was often heterogeneous, and the limited number of studies inhibited making a conclusion about the nature of the relationship between pollutant combinations and cardiovascular disease.

Exposure to Ambient Fine Particulate Air Pollution in Utero as a Risk Factor for Child Stunting in Bangladesh

Pregnant mothers in Bangladesh are exposed to very high and worsening levels of ambient air pollution. Maternal exposure to fine particulate matter has been associated with low birth weight at much lower levels of exposure, leading us to suspect the potentially large effects of air pollution on stunting in children in Bangladesh. We estimate the relationship between exposure to air pollution in utero and child stunting by pooling outcome data from four waves of the nationally representative Bangladesh Demographic and Health Survey conducted between 2004 and 2014, and calculating children’s exposure to ambient fine particulate matter in utero using high resolution satellite data. We find significant increases in the relative risk of child stunting, wasting, and underweight with higher levels of in utero exposure to air pollution, after controlling for other factors that have been found to contribute to child anthropometric failure. We estimate the relative risk of stunting in the second, third, and fourth quartiles of exposure as 1.074 (95% confidence interval: 1.014–1.138), 1.150 (95% confidence interval: 1.069–1.237, and 1.132 (95% confidence interval: 1.031–1.243), respectively. Over half of all children in Bangladesh in our sample were exposed to an annual ambient fine particulate matter level in excess of 46 µg/m³; these children had a relative risk of stunting over 1.13 times that of children in the lowest quartile of exposure. Reducing air pollution in Bangladesh could significantly contribute to the Sustainable Development Goal of reducing child stunting.

Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining

OBJECTIVES

Traditional studies on effects of outdoor pollution on asthma have been criticized for questionable statistical validity and inefficacy in exploring the effects of multiple air pollutants, alone and in combination. Association rule mining (ARM), a method easily interpretable and suitable for the analysis of the effects of multiple exposures, could be of use, but the traditional interest metrics of support and confidence need to be substituted with metrics that focus on risk variations caused by different exposures.

METHODS

We present an ARM-based methodology that produces rules associated with relevant odds ratios and limits the number of final rules even at very low support levels (0.5%), thanks to post-pruning criteria that limit rule redundancy and control for statistical significance. The methodology has been applied to a case-crossover study to explore the effects of multiple air pollutants on risk of asthma in pediatric subjects.

RESULTS

We identified 27 rules with interesting odds ratio among more than 10,000 having the required support. The only rule including only one chemical is exposure to ozone on the previous day of the reported asthma attack (OR=1.14). 26 combinatory rules highlight the limitations of air quality policies based on single pollutant thresholds and suggest that exposure to mixtures of chemicals is more harmful, with odds ratio as high as 1.54 (associated with the combination day0 SO₂, day0 NO, day0 NO₂, day1 PM).

CONCLUSIONS

The proposed method can be used to analyze risk variations caused by single and multiple exposures. The method is reliable and requires fewer assumptions on the data than parametric approaches. Rules including more than one pollutant highlight interactions that deserve further investigation, while helping to limit the search field.

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.

Atypical microglial response to biodiesel exhaust in healthy and hypertensive rats

Accumulating evidence suggests a deleterious role for urban air pollution in central nervous system (CNS) diseases and neurodevelopmental disorders. Microglia, the resident innate immune cells and sentinels in the brain, are a common source of neuroinflammation and are implicated in air pollution-induced CNS effects. While renewable energy, such as soy-based biofuel, is of increasing public interest, there is little information on how soy biofuel may affect the brain, especially in people with preexisting disease conditions. To address this, male spontaneously hypertensive rats (SHR) and normotensive Wistar Kyoto (WKY) rats were exposed to 100% Soy-based Biodiesel Exhaust (100SBDE; 0, 50, 150 and 500μg/m³) by inhalation, 4h/day for 4 weeks (5 days/week). Ionized calcium-binding adapter molecule-1 (IBA-1) staining of microglia in the substantia nigra revealed significant changes in morphology with 100SBDE exposure in rats from both genotypes, where SHR were less sensitive. Aconitase activity was inhibited in the frontal cortex and cerebellum of WKY rats exposed to 100SBDE. No consistent changes occurred in pro-inflammatory cytokine expression, nitrated protein, or arginase1 expression in brain regions from either rat strain exposed to 100SBDE. However, while IBA-1 mRNA expression was not modified, CX3CR1 mRNA expression was lower in the striatum of 100SBDE exposed rats regardless of genotype, suggesting a downregulation of the fractalkine receptor on microglia in this brain region. Together, these data indicate that while microglia are detecting and responding to 100SBDE exposure with changes in morphology, there is reduced expression of CX3CR1 regardless of genetic background and the activation response is atypical without traditional inflammatory markers of M1 or M2 activation in the brain.

A national study of the association between traffic-related air pollution and adverse pregnancy outcomes in Canada, 1999-2008

Numerous studies have examined the association of air pollution with preterm birth and birth weight outcomes. Traffic-related air pollution has also increasingly been identified as an important contributor to adverse health effects of air pollution. We employed a national nitrogen dioxide (NO2) exposure model to examine the association between NO2 and pregnancy outcomes in Canada between 1999 and 2008. National models for NO2 (and particulate matter of median aerodynamic diameter <2.5µm (PM2.5) as a covariate) were developed using ground-based monitoring data, estimates from remote-sensing, land use variables and, for NO2, deterministic gradients relative to road traffic sources. Generalized estimating equations were used to examine associations with preterm birth, term low birth weight (LBW), small for gestational age (SGA) and term birth weight, adjusting for covariates including infant sex, gestational age, maternal age and marital status, parity, urban/rural place of residence, maternal place of birth, season, year of birth and neighbourhood socioeconomic status and per cent visible minority. Associations were reduced considerably after adjustment for individual covariates and neighbourhood per cent visible minority, but remained significant for SGA (odds ratio 1.04, 95%CI 1.02-1.06 per 20ppb NO2) and term birth weight (16.2g reduction, 95% CI 13.6-18.8g per 20ppb NO2). Associations with NO2 were of greater magnitude in a sensitivity analysis using monthly monitoring data, and among births to mothers born in Canada, and in neighbourhoods with higher incomes and a lower proportion of visible minorities. In two pollutant models, associations with NO2 were less sensitive to adjustment for PM2.5 than vice versa, and there was consistent evidence of a dose-response relationship for NO2 but not PM2.5. In this study of approximately 2.5 million Canadian births between 1999 and 2008, we found significant associations of NO2 with SGA and term birth weight which remained significant after adjustment for PM2.5, suggesting that traffic may be a particularly important source with respect to the role of air pollution as a risk factor for adverse pregnancy outcomes.

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.

Microglial priming through the lung-brain axis: the role of air pollution-induced circulating factors

Air pollution is implicated in neurodegenerative disease risk and progression and in microglial activation, but the mechanisms are unknown. In this study, microglia remained activated 24 h after ozone (O3) exposure in rats, suggesting a persistent signal from lung to brain. Ex vivo analysis of serum from O3-treated rats revealed an augmented microglial proinflammatory response and β-amyloid 42 (Aβ42) neurotoxicity independent of traditional circulating cytokines, where macrophage-1 antigen-mediated microglia proinflammatory priming. Aged mice exhibited reduced pulmonary immune profiles and the most pronounced neuroinflammation and microglial activation in response to mixed vehicle emissions. Consistent with this premise, cluster of differentiation 36 (CD36)(-/-) mice exhibited impaired pulmonary immune responses concurrent with augmented neuroinflammation and microglial activation in response to O3 Further, aging glia were more sensitive to the proinflammatory effects of O3 serum. Together, these findings outline the lung-brain axis, where air pollutant exposures result in circulating, cytokine-independent signals present in serum that elevate the brain proinflammatory milieu, which is linked to the pulmonary response and is further augmented with age.-Mumaw, C. L., Levesque, S., McGraw, C., Robertson, S., Lucas, S., Stafflinger, J. E., Campen, M. J., Hall, P., Norenberg, J. P., Anderson, T., Lund, A. K., McDonald, J. D., Ottens, A. K., Block, M. L. Microglial priming through the lung-brain axis: the role of air pollution-induced circulating factors.

Characterizing multi-pollutant air pollution in China: Comparison of three air quality indices

Multi-pollutant air pollution (i.e., several pollutants reaching very high concentrations simultaneously) frequently occurs in many regions across China. Air quality index (AQI) is used worldwide to inform the public about levels of air pollution and associated health risks. The current AQI approach used in China is based on the maximum value of individual pollutants, and does not consider the combined health effects of exposure to multiple pollutants. In this study, two novel alternative indices--aggregate air quality index (AAQI) and health-risk based air quality index (HAQI)--were calculated based on data collected in six megacities of China (Beijing, Shanghai, Guangzhou, Shjiazhuang, Xi'an, and Wuhan) during 2013 to 2014. Both AAQI and HAQI take into account the combined health effects of various pollutants, and the HAQI considers the exposure (or concentration)-response relationships of pollutants. AAQI and HAQI were compared to AQI to examine the effectiveness of the current AQI in characterizing multi-pollutant air pollution in China. The AAQI and HAQI values are higher than the AQI on days when two or more pollutants simultaneously exceed the Chinese Ambient Air Quality Standards (CAAQS) 24-hour Grade II standards. The results of the comparison of the classification of risk categories based on the three indices indicate that the current AQI approach underestimates the severity of health risk associated with exposure to multi-pollutant air pollution. For the AQI-based risk category of 'unhealthy', 96% and 80% of the days would be 'very unhealthy' or 'hazardous' if based on AAQI and HAQI, respectively; and for the AQI-based risk category of 'very unhealthy', 67% and 75% of the days would be 'hazardous' if based on AAQI and HAQI, respectively. The results suggest that the general public, especially sensitive population groups such as children and the elderly, should take more stringent actions than those currently suggested based on the AQI approach during high air pollution events. Sensitivity studies were conducted to examine the assumptions used in the AAQI and HAQI approaches. Results show that AAQI is sensitive to the choice of pollutant irrelevant constant. HAQI is sensitive to the choice of both threshold values and pollutants included in total risk calculation.

Framing air pollution epidemiology in terms of population interventions, with applications to multipollutant modeling

Air pollution epidemiology continues moving toward the study of mixtures and multipollutant modeling. Simultaneously, there is a movement in epidemiology to estimate policy-relevant health effects that can be understood in reference to specific interventions. Scaling regression coefficients from a regression model by an interquartile range (IQR) is one common approach to presenting multipollutant health effect estimates. We are unaware of guidance on how to interpret these effect estimates as an intervention. To illustrate the issues of interpretability of IQR-scaled air pollution health effects, we analyzed how daily concentration changes in 2 air pollutants (nitrogen dioxide and particulate matter with aerodynamic diameter ≤ 2.5 μm) related to one another within 2 seasons (summer and winter), within 3 cities with distinct air pollution profiles (Burbank, California; Houston, Texas; and Pittsburgh, Pennsylvania). In each city season, we examined how realistically IQR scaling in multipollutant lag-1 time-series studies reflects a hypothetical intervention that is possible given the observed data. We proposed 2 causal conditions to explicitly link IQR-scaled effects to a clearly defined hypothetical intervention. Condition 1 specified that the index pollutant had to experience a daily concentration change of greater than 1 IQR, reflecting the notion that the IQR is an appropriate measure of variability between consecutive days. Condition 2 specified that the copollutant had to remain relatively constant. We found that in some city seasons, there were very few instances in which these conditions were satisfied (eg, 1 day in Pittsburgh during summer). We discuss the practical implications of IQR scaling and suggest alternative approaches to presenting multipollutant effects that are supported by empirical data.

Exposure to Endocrine-Disrupting Chemicals during Pregnancy and Weight at 7 Years of Age: A Multi-pollutant Approach

BACKGROUND

Prenatal exposure to endocrine-disrupting chemicals (EDCs) may induce weight gain and obesity in children, but the obesogenic effects of mixtures have not been studied.

OBJECTIVE

We evaluated the associations between pre- and perinatal biomarker concentrations of 27 EDCs and child weight status at 7 years of age.

METHODS

In pregnant women enrolled in a Spanish birth cohort study between 2004 and 2006, we measured the concentrations of 10 phthalate metabolites, bisphenol A, cadmium, arsenic, and lead in two maternal pregnancy urine samples; 6 organochlorine compounds in maternal pregnancy serum; mercury in cord blood; and 6 polybrominated diphenyl ether congeners in colostrum. Among 470 children at 7 years, body mass index (BMI) z-scores were calculated, and overweight was defined as BMI > 85th percentile. We estimated associations with EDCs in single-pollutant models and applied principal-component analysis (PCA) on the 27 pollutant concentrations.

RESULTS

In single-pollutant models, HCB (hexachlorobenzene), βHCH (β-hexachlorocyclohexane), and polychlorinated biphenyl (PCB) congeners 138 and 180 were associated with increased child BMI z-scores; and HCB, βHCH, PCB-138, and DDE (dichlorodiphenyldichloroethylene) with overweight risk. PCA generated four factors that accounted for 43.4% of the total variance. The organochlorine factor was positively associated with BMI z-scores and with overweight (adjusted RR, tertile 3 vs. 1: 2.59; 95% CI: 1.19, 5.63), and these associations were robust to adjustment for other EDCs. Exposure in the second tertile of the phthalate factor was inversely associated with overweight.

CONCLUSIONS

Prenatal exposure to organochlorines was positively associated with overweight at age 7 years in our study population. Other EDCs exposures did not confound this association.

Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications

BACKGROUND

Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.

OBJECTIVE

Present a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health.

METHODS

First, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather.

RESULTS

Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the null.

CONCLUSIONS

We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.

The relationship between daily cardiovascular mortality and daily ambient concentrations of particulate pollutants (sulfur, arsenic, selenium, and mercury) and daily source contributions from coal power plants and smelters (individually, combined, and with interaction) in Phoenix, AZ, 1995-1998: A multipollutant approach to acute, time-series air pollution epidemiology: I

The objective of this paper is to estimate the increase in risk of daily cardiovascular mortality due to an increase in the daily ambient concentration of the individual particulate pollutants sulfur (S), arsenic (As), selenium (Se), and mercury (Hg) using single-pollutant models (SPMs) and to compare this risk to the combined increase in risk due to an increase in all four pollutants by including all four pollutants in the same model (multipollutant model, MPM) and to the risks from source contributions from power plants and smelters. Individual betas in a multipollutant model (MPM) were summed to give a combined beta. Interaction was investigated with a pollutant product term. SPMs (controlling for time trends, temperature, and relative humidity), for an interquartile range (IQR) increase in the pollutant concentration on lag day 0, gave these percent excess risks (±95% confidence levels): S, 6.9% (1.3-12%); As, 2.9% (0.4-5.5%); Se, 1.4% (-1.7 to 4.6); Hg, 9.6% (4.8-14.6%). The SPM beta for S (as sulfate) was higher than found in other studies. The SPM beta for Hg gave the largest t-statistic and beta per unit mass of any pollutant studied. An (IQR) increase in all four pollutants gave an excess risk of 15.4% (7.5-23.8%), slightly smaller than the combination of S and Hg, 16.7% (9.1-24.9%). The combined beta was 71% of the sum of the four individual SPM betas, indicating a reduction in confounding among pollutants in the combined model. As and Se were shown to be noncausal; their SPM betas could be explained as confounding by S.

IMPLICATIONS

The combined effect of several pollutants can be estimated by including the appropriate pollutants in the same statistical model, summing their individual betas to give a combined beta, and using a variance-covariance matrix to obtain the standard error. This approach identifies and reduces confounding among the species in the multipollutant model and can be used to identify confounded species that have no independent relationship with mortality. The effect of several pollutants acting together may be higher than that of one pollutant. Further work is needed to understand the strong relationship of mortality with particulate mercury and sulfate.

Joint effects of ambient air pollutants on pediatric asthma emergency department visits in Atlanta, 1998-2004

BACKGROUND

Because ambient air pollution exposure occurs as mixtures, consideration of joint effects of multiple pollutants may advance our understanding of the health effects of air pollution.

METHODS

We assessed the joint effect of air pollutants on pediatric asthma emergency department visits in Atlanta during 1998-2004. We selected combinations of pollutants that were representative of oxidant gases and secondary, traffic, power plant, and criteria pollutants, constructed using combinations of criteria pollutants and fine particulate matter (PM2.5) components. Joint effects were assessed using multipollutant Poisson generalized linear models controlling for time trends, meteorology, and daily nonasthma upper respiratory emergency department visit counts. Rate ratios (RRs) were calculated for the combined effect of an interquartile range increment in each pollutant's concentration.

RESULTS

Increases in all of the selected pollutant combinations were associated with increases in warm-season pediatric asthma emergency department visits (eg, joint-effect RR = 1.13 [95% confidence interval = 1.06-1.21] for criteria pollutants, including ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, and PM2.5). Cold-season joint effects from models without nonlinear effects were generally weaker than warm-season effects. Joint-effect estimates from multipollutant models were often smaller than estimates based on single-pollutant models, due to control for confounding. Compared with models without interactions, joint-effect estimates from models including first-order pollutant interactions were largely similar. There was evidence of nonlinear cold-season effects.

CONCLUSIONS

Our analyses illustrate how consideration of joint effects can add to our understanding of health effects of multipollutant exposures and also illustrate some of the complexities involved in calculating and interpreting joint effects of multiple pollutants.

Effect of fuel zinc content on toxicological responses of particulate matter from pellet combustion in vitro

Significant amounts of transition metals such as zinc, cadmium and copper can become enriched in the fine particle fraction during biomass combustion with Zn being one of the most abundant transition metals in wood combustion. These metals may have an important role in the toxicological properties of particulate matter (PM). Indeed, many epidemiological studies have found associations between mortality and PM Zn content. The role of Zn toxicity on combustion PM was investigated. Pellets enriched with 170, 480 and 2300 mg Zn/kg of fuel were manufactured. Emission samples were generated using a pellet boiler and the four types of PM samples; native, Zn-low, Zn-medium and Zn-high were collected with an impactor from diluted flue gas. The RAW 264.7 macrophage cell line was exposed for 24h to different doses (15, 50,150 and 300 μg ml(-1)) of the emission samples to investigate their ability to cause cytotoxicity, to generate reactive oxygen species (ROS), to altering the cell cycle and to trigger genotoxicity as well as to promote inflammation. Zn enriched pellets combusted in a pellet boiler produced emission PM containing ZnO. Even the Zn-low sample caused extensive cell cycle arrest and there was massive cell death of RAW 264.7 macrophages at the two highest PM doses. Moreover, only the Zn-enriched emission samples induced a dose dependent ROS response in the exposed cells. Inflammatory responses were at a low level but macrophage inflammatory protein 2 reached a statistically significant level after exposure of RAW 264.7 macrophages to ZnO containing emission particles. ZnO content of the samples was associated with significant toxicity in almost all measured endpoints. Thus, ZnO may be a key component producing toxicological responses in the PM emissions from efficient wood combustion. Zn as well as the other transition metals, may contribute a significant amount to the ROS responses evoked by ambient PM.

Comparing multipollutant emissions-based mobile source indicators to other single pollutant and multipollutant indicators in different urban areas

A variety of single pollutant and multipollutant metrics can be used to represent exposure to traffic pollutant mixtures and evaluate their health effects. Integrated mobile source indicators (IMSIs) that combine air quality concentration and emissions data have recently been developed and evaluated using data from Atlanta, Georgia. IMSIs were found to track trends in traffic-related pollutants and have similar or stronger associations with health outcomes. In the current work, we apply IMSIs for gasoline, diesel and total (gasoline + diesel) vehicles to two other cities (Denver, Colorado and Houston, Texas) with different emissions profiles as well as to a different dataset from Atlanta. We compare spatial and temporal variability of IMSIs to single-pollutant indicators (carbon monoxide (CO), nitrogen oxides (NO_x) and elemental carbon (EC)) and multipollutant source apportionment factors produced by Positive Matrix Factorization (PMF). Across cities, PMF-derived and IMSI gasoline metrics were most strongly correlated with CO (r = 0.31–0.98), while multipollutant diesel metrics were most strongly correlated with EC (r = 0.80–0.98). NO_x correlations with PMF factors varied across cities (r = 0.29–0.67), while correlations with IMSIs were relatively consistent (r = 0.61–0.94). In general, single-pollutant metrics were more correlated with IMSIs (r = 0.58–0.98) than with PMF-derived factors (r = 0.07–0.99). A spatial analysis indicated that IMSIs were more strongly correlated (r > 0.7) between two sites in each city than single pollutant and PMF factors. These findings provide confidence that IMSIs provide a transferable, simple approach to estimate mobile source air pollution in cities with differing topography and source profiles using readily available data.

Health effects of multi-pollutant profiles

BACKGROUND

The association between exposure to particle mass and mortality is well established; however, there are still uncertainties as to whether certain chemical components are more harmful than others. Moreover, understanding the health effects associated with exposure to pollutant mixtures may lead to new regulatory strategies.

OBJECTIVES

Recently we have introduced a new approach that uses cluster analysis to identify distinct air pollutant mixtures by classifying days into groups based on their pollutant concentration profiles. In Boston during the years 1999-2009, we examined whether the effect of PM2.5 on total mortality differed by distinct pollution mixtures.

METHODS

We applied a time series analysis to examine the association of PM2.5 with daily deaths. Subsequently, we included an interaction term between PM2.5 and the pollution mixture clusters.

RESULTS

We found a 1.1% increase (95% CI: 0.0, 2.2) and 2.3% increase (95% CI: 0.9-3.7) in total mortality for a 10 μg/m(3) increase in the same day and the two-day average of PM2.5 respectively. The association is larger in a cluster characterized by high concentrations of the elements related to primary traffic pollution and oil combustion emissions with a 3.7% increase (95% CI: 0.4, 7.1) in total mortality, per 10 μg/m(3) increase in the same day average of PM2.5.

CONCLUSIONS

Our study shows a higher association of PM2.5 on total mortality during days with a strong contribution of traffic emissions, and fuel oil combustion. Our proposed method to create multi-pollutant profiles is robust, and provides a promising tool to identify multi-pollutant mixtures which can be linked to the health effects.

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

BACKGROUND

Health effects associated with air pollution are typically evaluated using a single pollutant approach, yet people are exposed to mixtures consisting of multiple pollutants that may have independent or combined effects on human health. Development of exposure metrics that represent the multipollutant environment is important to understand the impact of ambient air pollution on human health.

OBJECTIVES

We reviewed existing multipollutant exposure metrics to evaluate how they can be applied to understand associations between air pollution and health effects.

METHODS

We conducted a literature search using both targeted search terms and a relational search in Web of Science and PubMed in April and December 2013. We focused on exposure metrics that are constructed from ambient pollutant concentrations and can be broadly applied to evaluate air pollution health effects.

RESULTS

Multipollutant exposure metrics were identified in 57 eligible studies. Metrics reviewed can be categorized into broad pollutant grouping paradigms based on: 1) source emissions and atmospheric processes or 2) common health outcomes.

DISCUSSION

When comparing metrics, it is apparent that no universal exposure metric exists; each type of metric addresses different research questions and provides unique information on human health effects. Key limitations of these metrics include the balance between complexity and simplicity as well as the lack of an existing "gold standard" for multipollutant health effects and exposure.

CONCLUSIONS

Future work on characterizing multipollutant exposure error and joint effects will inform development of improved multipollutant metrics to advance air pollution health effects research and human health risk assessment.

Using self-organizing maps to develop ambient air quality classifications: a time series example

BACKGROUND

Development of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies.

OBJECTIVE

Present a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles.

METHODS

Eight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques.

RESULTS

Our analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (<2 day persistence), and long-term and seasonal trends. Meteorological summaries revealed strong day type weather dependencies and pollutant concentration summaries provided interesting scenarios for further investigation. Comparison with traditional methods found SOM produced similar classifications with added insight regarding between-class relationships.

CONCLUSION

We find SOM to be an attractive framework for developing ambient air quality classification because the approach eases interpretation of results by allowing users to visualize classifications on an organized map. The presented approach provides an appealing tool for developing multipollutant metrics of air quality that can be used to support multipollutant health studies.

Systematic review and metaanalysis of air pollution exposure and risk of diabetes

The present systematic review and metaanalysis of published observational studies was conducted to assess the health effects of exposure to air pollution on diabetes risk. Online databases were searched through January 2013, and the reference lists of pertinent articles reporting observational studies in humans were examined. Pooled relative risks and 95 % confidence intervals were calculated with a random-effects model. Exposure to air pollution was associated with slight increase in risk of diabetes and susceptibility of people with diabetes to air pollution. These results were consistent between time-series, case-crossover and cohort studies and between studies conducted in North America and Europe. The association between exposure to air pollution and diabetes was stronger for gaseous pollutants than for particulate matter. Our metaanalysis suggests that exposure to air pollution may be a risk factor for diabetes and increase susceptibility of people with diabetes to air pollution.

Chemical mixtures and children's health

PURPOSE OF REVIEW

Humans are routinely exposed to multiple chemicals simultaneously or sequentially. There is evidence that the toxicity of individual chemicals may depend on the presence of other chemicals. Studies on chemical mixtures are limited, however, because of the lack of sufficient exposure data, limited statistical power, and difficulty in the interpretation of multidimensional interactions. This review summarizes the recent literature examining chemical mixtures and pediatric health outcomes, with an emphasis on metal mixtures.

RECENT FINDINGS

Several studies report significant interactions between metals in relation to pediatric health outcomes. Two prospective studies found interactive effects of early-life lead and manganese exposures on cognition. In two different cohorts, interactions between lead and cadmium exposures were reported on reproductive hormone levels and on neurodevelopment. Effects of lead exposure on impulsive behavior and cognition were modified by mercury exposure in studies from Canada and Denmark. However, there is little consistency related to exposure indicators and statistical approaches for evaluating interaction.

SUMMARY

Several studies suggest that metals interact to cause health effects that are different from exposure to each metal alone. Despite the nearly infinite number of possible chemical combinations, mixtures research represents real-life exposure scenarios and warrants more attention, particularly in the context of uniquely vulnerable children.

Classification and regression trees for epidemiologic research: an air pollution example

BACKGROUND

Identifying and characterizing how mixtures of exposures are associated with health endpoints is challenging. We demonstrate how classification and regression trees can be used to generate hypotheses regarding joint effects from exposure mixtures.

METHODS

We illustrate the approach by investigating the joint effects of CO, NO2, O3, and PM2.5 on emergency department visits for pediatric asthma in Atlanta, Georgia. Pollutant concentrations were categorized as quartiles. Days when all pollutants were in the lowest quartile were held out as the referent group (n = 131) and the remaining 3,879 days were used to estimate the regression tree. Pollutants were parameterized as dichotomous variables representing each ordinal split of the quartiles (e.g. comparing CO quartile 1 vs. CO quartiles 2-4) and considered one at a time in a Poisson case-crossover model with control for confounding. The pollutant-split resulting in the smallest P-value was selected as the first split and the dataset was partitioned accordingly. This process repeated for each subset of the data until the P-values for the remaining splits were not below a given alpha, resulting in the formation of a "terminal node". We used the case-crossover model to estimate the adjusted risk ratio for each terminal node compared to the referent group, as well as the likelihood ratio test for the inclusion of the terminal nodes in the final model.

RESULTS

The largest risk ratio corresponded to days when PM2.5 was in the highest quartile and NO2 was in the lowest two quartiles (RR: 1.10, 95% CI: 1.05, 1.16). A simultaneous Wald test for the inclusion of all terminal nodes in the model was significant, with a chi-square statistic of 34.3 (p = 0.001, with 13 degrees of freedom).

CONCLUSIONS

Regression trees can be used to hypothesize about joint effects of exposure mixtures and may be particularly useful in the field of air pollution epidemiology for gaining a better understanding of complex multipollutant exposures.

Cense: a tool to assess combined exposure to environmental health stressors in urban areas

This paper describes the structure of the Combined Environmental Stressors' Exposure (CENSE) tool. Individuals are exposed to several environmental stressors simultaneously. Combined exposure represents a more serious hazard to public health. Consequently, there is a need to address co-exposure in a holistic way. Rather than viewing chemical and physical health stressors separately for decision making and environmental sustainability considerations, the possibility of an easy-to-comprehend co-exposure assessment is herein considered. Towards this aim, the CENSE tool is developed in the programming environment of Delphi. The graphical user's interface facilitates its tractable application. Studying different scenarios is easy since the execution time required is negligible. The tool incorporates co-exposure indicators and takes into account the potential dose of each chemical stressor by considering the physical activities of each citizen in an urban (micro)environment. The capabilities of the CENSE tool are demonstrated through its application for the case of Thessaloniki, Greece. The test case highlights usability and validation insights and incorporates health stressors and local characteristics of the area considered into a well identified user/decision maker interface. The main conclusion of the work reported is that a decision maker can trust CENSE for urban planning and environmental sustainability considerations, since it supports a holistic assessment of the combined potential damage attributed to multiple health stressors. CENSE abandons the traditional approach of viewing chemical and physical stressors separately, which represents the most commonly adopted strategy in real life decision support cases.