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

Decomposing PM2.5 concentrations in urban environments into meaningful factors: 1. Separating the contribution of local anthropogenic activities from background and long-range transport

Fine particulate matter (PM2.5) is a complex mixture of aerosol particles with varying properties and sources, both local and distant. In areas lacking detailed monitoring of PM2.5 speciation, the common source-apportionment analyses are not applicable. This study demonstrates an alternative framework for estimating sources and processes that affect observed PM2.5 concentrations when information on the particle composition is unavailable. Eight years (2012-2019) of half-hourly PM2.5 observations from 10 air quality monitoring (AQM) stations, clustered according to their airmass transport sector were analyzed, using Non-negative Matrix Factorization (NMF). Factors were determined based on their variation in time, space, and between airmass sectors. Employing a supervised machine-learning model provided insights into the relationships between the extracted factors, meteorological parameters and co-measured airborne pollutants. Factor interpretations were evaluated through comparisons with measurements of PM2.5 species from a nearby Surface PARTiculate mAtter Network (SPARTAN) station. The NMF successfully separated background factors from an urban anthropogenic-activity factor, with the latter accounting for approximately 60 % of the observed PM2.5 levels in Tel Aviv (∼10±6μg/m3). Positive monotonic relationships were observed between the PM2.5 urban anthropogenic-activity factor and measurements of nitrogen oxides (NOx) and absolute humidity (AH), representing the impact of traffic emissions and hygroscopic growth, respectively. The summer background factor was found to represent long-range transport (LRT) from Europe, showing a good agreement (R2 = 0.81) with ammonium sulphate concentrations. Our results demonstrate that a spatial NMF analysis can reliably estimate contributions of different sources with distinct compositions and properties to the total observed PM2.5. Using such an analysis, future environmental health studies could assess health risks associated with exposure to distinct PM2.5 fractions. This information may assist decision makers to set environmental targets for abating PM2.5 with specific compositions and properties.

Decomposing PM2.5 concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions

Vehicle-emitted fine particulate matter (PM2.5) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic-related exhaust emissions (TREE) to observed PM2.5 using a novel factorization framework. Specifically, co-measured nitrogen oxides (NOx) concentrations served as a marker of vehicle-tailpipe emissions and were integrated into the optimization of a Non-negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012-2019) PM2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co-measured black carbon (BC) and PM2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM2.5 concentrations at an AQM station from the first location showed close agreement (R2=0.79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7-11 % of the observed PM2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60-78 % of the traffic-related PM2.5 concentrations could be attributed to particulate traffic-exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic-related air pollution abatement.

Sources, trigger points, and effect size of associations between PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) and fractional exhaled nitric oxide (FeNO): A panel study with 16 follow-up visits over 4 years

Exposure to fine particulate matter (PM2.5) is a significant concern for respiratory health. However, the sources, trigger points, and effect size of specific associations between PM2.5 components, particularly polycyclic aromatic hydrocarbons (PAHs) and the airway inflammatory marker fractional exhaled nitric oxide (FeNO) have not been fully explored. In this study, 69 healthy college students were enrolled and followed up 16 times from 2014 to 2018. Individual FeNO was measured and ambient air PM2.5 samples were collected for 7 consecutive days before each follow-up. PAHs were quantified using Gas Chromatography-Mass Spectrometry. Linear mixed-effect regression models were employed to evaluate the associations between PM2.5-bound PAHs and FeNO. Additionally, PMF (Positive Matrix Factorization) was utilized to identify sources of PM2.5-bound PAHs and assess their impact on FeNO. Throughout the study, the average (SD) of ΣPAHs concentrations was 78.50 (128.9) ng/m3. PM2.5 and PM2.5-bound PAHs were significantly associated with FeNO at various lag days. Single-day lag analyses revealed maximum effects of PM2.5 on FeNO, with an increase of 7.71% (95% CI: 4.67%, 10.83%) per interquartile range (IQR) (48.10 μg/m3) increase of PM2.5 at lag2, and ΣPAHs showed a maximum elevation in FeNO of 6.40% (95% CI: 2.33%, 10.63%) at lag4 per IQR (57.39 ng/m3) increase. Individual PAHs exhibited diversity peak effects on FeNO at lag3 (6 of 17), lag4 (9 of 17) in the single-day model, and lag0-5 (8 of 17) (from lag0-1 to lag0-6) in the cumulative model. Source apportionment indicated coal combustion as the primary contributor (accounting for 30.7%). However, a maximum effect on FeNO (an increase of 21.57% (95% CI: 13.58%, 30.13%) per IQR increase) was observed with traffic emissions at lag4. The findings imply that strategic regulation of particular sources of PAHs, like traffic emissions, during specific periods could significantly contribute to safeguarding public health.

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Ambient fine particulate matter exposures and oxidative protein damage in early pregnant women

The association between oxidative protein damage in early pregnant women and ambient fine particulate matter (PM_2.5) is unknown. We estimated the effect of PM_2.5 exposures within seven days before blood collection on serum 3-nitrotyrosine (3-NT) and advanced oxidation protein products (AOPP) in 100 women with normal early pregnancy (NEP) and 100 women with clinically recognized early pregnancy loss (CREPL). Temporally-adjusted land use regression model was applied for estimation of maternal daily PM_2.5 exposure. Daily nitrogen dioxide (NO₂) exposure of each participant was estimated using city-level concentrations of NO₂. Single-day lag effect of PM_2.5 was analyzed using multivariable linear regression model. Net cumulative effect and distributed lag effect of PM_2.5 and NO₂ within seven days were analyzed using distributed lag non-linear model. In all 200 subjects, the serum 3-NT were significantly increased with the single-day lag effects (4.72%-8.04% increased at lag 0-2), distributed lag effects (2.32%-3.49% increased at lag 0-2), and cumulative effect within seven days (16.91% increased). The single-day lag effects (7.41%-10.48% increased at lag 0-1), distributed lag effects (3.42%-5.52% increased at lag 0-2), and cumulative effect within seven days (24.51% increased) of PM_2.5 significantly increased serum 3-NT in CREPL group but not in NEP group. The distributed lag effects (2.62%-4.54% increased at lag 0-2) and cumulative effect within seven days (20.25% increased) of PM_2.5 significantly increased serum AOPP in early pregnant women before the coronavirus disease (COVID-19) pandemic but not after that, similarly to the effects of NO₂ exposures. In conclusion, PM_2.5 exposures were associated with oxidative stress to protein in pregnant women in the first trimester, especially in CREPL women. Analysis of NO₂ exposures suggested that combustion PM_2.5 was the crucial PM_2.5 component. Wearing masks may be potentially preventive in PM_2.5 exposure and its related oxidative protein damage.

Personal exposure to fine particulate matter and blood pressure: Variations by particulate sources

Fine particulate matter (PM_2.5) is a complex mixture of components which has been associated with various cardiovascular effects, such as elevated blood pressure (BP). However, evidences on specific sources behind these effects remain uncertain. Based on 140 72-h personal measurements among a panel of 36 health college students in Shanghai, China, we assessed associations between source-apportioned PM_2.5 exposure and BP changes. Based on personal filter samples, PM_2.5 source apportionment was conducted using Positive Matrix Factorization (PMF) model. Linear mixed-effects models were applied to evaluate associations of source-specific PM_2.5 exposure with BP changes. Seven sources were identified in PMF analysis. Among them, secondary sulfate (41%) and nitrate (24%) sources contributed most to personal PM_2.5, followed by industrial emissions (15%), traffic-related source (10%), coal combustion (6.2%), dust (2.4%) and aged sea salt (1.1%). We found nitrate, traffic-related source and coal combustion were significantly associated with increased BP. For example, an interquartile range increase in PM_2.5 from traffic-related source was significantly associated with increase in systolic BP [1.5 (95% CI: 0.26, 2.7) mmHg], diastolic BP [1.2 (95% CI: 0.10, 2.2) mmHg] and mean arterial pressure [1.2 (95% CI: 0.15, 2.2) mmHg]. This is the first investigation linking personal PM_2.5 source profile and BP changes. This study provides evidence that several anthropogenic emissions (especially traffic-related emission) may be particularly responsible for BP increases, and highlights that the importance of development of health-oriented PM_2.5 source control strategies.

Source-specific contributions of particulate matter to asthma-related pediatric emergency department utilization

Ambient particulate matter smaller than 2.5 μm (PM_2.5) is associated with different chronic diseases. It is crucial to identify the sources of ambient particulate matter to reduce the impact on health. Still, only a few studies have been linked with specific ambient particulate matter sources. In this study, we estimated the contributions of sources of PM_2.5 and examined their association with daily asthma hospital utilization in Cincinnati, Ohio, USA. We used a model-based clustering method to group days with similar source-specific contributions into six distinct clusters. Specifically, elevated PM_2.5 concentrations occurring on days characterized by low coal combustion contributions showed a significantly reduced risk of hospital utilization for asthma (rate ratio: 0.86, 95% CI: [0.77, 0.95]) compared to other clusters. Reducing coal combustion contribution to PM_2.5 levels could be an effective intervention for lowering asthma-related hospital utilization.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-021-00141-z.

Exposure to traffic-related particle matter and effects on lung function and potential interactions in a cross-sectional analysis of a cohort study in west Sweden

OBJECTIVES

To investigate the long-term effects of source-specific particle matter (PM) on lung function, effects of Surfactant Protein A (SP-A) and glutathione S-transferase (GST) genes GSTP1 and GSTT1 gene variants and effect modification by single-nucleotide polymorphism (SNP) genotype.

DESIGN

Cohort study with address-based annual PM exposure assigned from annual estimates of size (PM₁₀, PM_2.5 and PM_BC) and source-specific (traffic, industry, marine traffic and wood burning) dispersion modelling.

SETTING

Gothenburg, Sweden.

PARTICIPANTS

The ADult-Onset asthma and NItric oXide Study had 6685 participants recruited from the general population, of which 5216 (78%) were included in the current study with information on all variables of interest. Mean age at the time of enrolment was 51.4 years (range 24-76) and 2427 (46.5%) were men.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV₁). Secondary outcome measures were effects and gene-environment interactions of SP-A and GSTT1 and GSTP1 genotypes.

RESULTS

Exposure to traffic-related PM₁₀ and PM_2.5 was associated with decreases in percent-predicted (% predicted) FEV₁ by -0.48% (95% CI -0.89% to -0.07%) and -0.47% (95% CI -0.88% to -0.07%) per IQR 3.05 and 2.47 µg/m³, respectively, and with decreases in % predicted FVC by -0.46% (95% CI -0.83% to -0.08%) and -0.47% (95% CI -0.83% to -0.10%). Total and traffic-related PM_BC was strongly associated with both FEV₁ and FVC by -0.53 (95% CI -0.94 to -0.13%) and -0.43% (95% CI -0.77 to -0.09%) per IQR, respectively, for FVC, and similarly for FEV₁. Minor allele carrier status for two GSTP1 SNPs and the GSTT1 null genotype were associated with decreases in % predicted lung function. Three SP-A SNPs showed effect modification with exposure to PM_2.5 from industry and marine traffic.

CONCLUSIONS

PM exposure, specifically traffic related, was associated with FVC and FEV₁ reductions and not modified by genotype. Genetic effect modification was suggested for industry and marine traffic PM_2.5.

Source-Apportioned PM2.5 and Cardiorespiratory Emergency Department Visits: Accounting for Source Contribution Uncertainty

BACKGROUND

Despite evidence suggesting that air pollution-related health effects differ by emissions source, epidemiologic studies on fine particulate matter (PM2.5) infrequently differentiate between particles from different sources. Those that do rarely account for the uncertainty of source apportionment methods.

METHODS

For each day in a 12-year period (1998-2010) in Atlanta, GA, we estimated daily PM2.5 source contributions from a Bayesian ensemble model that combined four source apportionment methods including chemical transport and receptor-based models. We fit Poisson generalized linear models to estimate associations between source-specific PM2.5 concentrations and cardiorespiratory emergency department visits (n = 1,598,117). We propagated uncertainty in the source contribution estimates through analyses using multiple imputation.

RESULTS

Respiratory emergency department visits were positively associated with biomass burning and secondary organic carbon. For a 1 µg/m increase in PM2.5 from biomass burning during the past 3 days, the rate of visits for all respiratory outcomes increased by 0.4% (95% CI 0.0%, 0.7%). There was less evidence for associations between PM2.5 sources and cardiovascular outcomes, with the exception of ischemic stroke, which was positively associated with most PM2.5 sources. Accounting for the uncertainty of source apportionment estimates resulted, on average, in an 18% increase in the standard error for rate ratio estimates for all respiratory and cardiovascular emergency department visits, but inflation varied across specific sources and outcomes, ranging from 2% to 39%.

CONCLUSIONS

This study provides evidence of associations between PM2.5 sources and some cardiorespiratory outcomes and quantifies the impact of accounting for variability in source apportionment approaches.

[Combined effects of different environmental factors on health: air pollution, temperature, green spaces, pollen, and noise]

Environmental factors affect the health and wellbeing of urban residents. However, they do not act individually on humans, but instead show potential synergistic or antagonistic effects. Questions that arise from this are: How does a combination of air pollutants with other environmental factors impact health? How well are these associations evidenced? What methods can we use to look at them? In this article, methodical approaches regarding the effects of a combination of various environmental factors are first described. Environmental factors are then examined, which together with different air pollutants, have an impact on human health such as ambient temperature, noise, and pollen as well as the effect of green spaces. Physical activity and nutrition are addressed regarding the attenuation of health effects from air pollution.While there is often clear evidence of health effects of single environmental stressors, there are still open questions in terms of their interaction. The research methods required for this still need to be further developed. The interrelationship between the different environmental factors make it clear that (intervention) measures for reducing single indicators are also interlinked. Regarding traffic, switching from passive to active transport (e.g., due to safe cycle paths and other measures) leads to less air pollutants, smaller increases in temperature in the long term, and at the same time improved health of the individual. As a result, sensible planning of the built environment has great potential to reduce environmental stressors and improve people's health and wellbeing.

Fine particulate air pollution and adult hospital admissions in 200 Chinese cities: a time-series analysis

BACKGROUND

The association between short-term exposure to ambient fine particulate matter (PM2.5) and morbidity risk in developing countries is not fully understood. We conducted a nationwide time-series study to estimate the short-term effect of PM2.5 on hospital admissions in Chinese adults.

METHODS

Daily counts of hospital admissions for 2014-16 were obtained from the National Urban Employee Basic Medical Insurance database. We identified more than 58 million hospitalizations from 0.28 billion insured persons in 200 Chinese cities for subjects aged 18 years or older. Generalized additive models with quasi-Poisson regression were applied to examine city-specific associations of PM2.5 concentrations with hospital admissions. National-average estimates of the association were obtained from a random-effects meta-analysis. We also investigated potential effect modifiers, such as age, sex, temperature and relative humidity.

RESULTS

An increase of 10 μg/m3 in same-day PM2.5 concentrations was positively associated with a 0.19% (95% confidence interval: 0.07-0.30) increase in the daily number of hospital admissions at the national level. PM2.5 exposure remained positively associated with hospital admissions on days when the daily concentrations met the current Chinese Ambient Air Quality Standards (75 μg/m3). Estimates of admission varied across cities and increased in cities with lower PM2.5 concentrations (p = 0.044) or higher temperatures (p = 0.002) and relative humidity (p = 0.003). The elderly were more sensitive to PM2.5 exposure (p < 0.001).

CONCLUSIONS

Short-term exposure to PM2.5 was positively associated with adult hospital admissions in China, even at levels below current Chinese Ambient Air Quality Standards.

High level of source-specific particulate matter air pollution associated with cardiac arrhythmias

Epidemiological evidence linking source-specific ambient particulate matter with aerodynamic diameter <2.5 μm (PM_2.5) and cardiac arrhythmias is limited. In this study, we investigated the impact of source-specific PM_2.5 on cardiac arrhythmias in a panel of forty-five healthy adults living in Beijing, China, between 2015 and 2016. Repeated measures of 24-hour electrocardiograms were conducted during clinical visits, and daily counts of four arrhythmia events including supraventricular premature beat (SVPB), atrial tachycardia (AT), premature ventricular contraction (PVC) and ventricular tachycardia (VT) were recorded. One hundred forty-seven constituents in PM_2.5 were analyzed for collected particulate samples, in which fifty-six of them above laboratory detection limits were selected for source apportionment analysis using positive matrix factorization. The average contributions of identified five major sources to PM_2.5 were 45.9% from secondary nitrate/sulfate, 18.0% from coal combustion, 16.9% from crustal soil, 13.8% from biomass burning, and 5.4% from cooking. Generalized estimating equation models were used to estimate relative risks (RR) of arrhythmias in association with interquartile-range (IQR) increases in PM_2.5 constituents and specific sources. Total PM_2.5 mass as well as several combustion related constituents were found of significant impacts on increased risks of arrhythmia events. Among the identified sources of PM_2.5, coal burning has been found the major source that associated with increased risks of SVPB, PVC and VT with RR of 1.19 [95% confidence intervals (CI): 1.04, 1.36] to 1.64 (95% CI: 1.35, 2.00). PM_2.5 from combustion related secondary nitrate/sulfate was also found of significant impact on SVPB and AT, followed by PM_2.5 from biomass burning and crustal soil. Our results indicated that PM_2.5 from anthropogenic activity related sources were most responsible for increased risks of arrhythmia events. Our findings enhance the understanding of increased risks of arrhythmias from exposure to PM_2.5, and provide evidence on source-specific PM control priorities.

The pro-inflammatory effects of particulate matter on epithelial cells are associated with elemental composition

BACKGROUND

Adverse health effects of particulate matter (PM) vary with chemical composition; however, evidence regarding which elements are the most detrimental is limited. The roof space area provides an open and stable environment for outdoor PM to settle and deposit. Therefore, this study used roof space PM samples as a proxy of residential cumulative exposure to outdoor air pollution to investigate their pro-inflammatory effects on human lung cells and the contribution of the endotoxin and chemical content.

METHODS

Roof space PM samples of 36 different homes were collected and analysed using standardised techniques. We evaluated cytotoxicity and cytokine production of BEAS-2B cells after PM exposure using MTS and ELISA, respectively. Principle component analysis (PCA) and linear regression analyses were employed to assess the associations between cytokine production and the PM components.

RESULTS

PM caused significant time- and dose-dependent increases in cellular cytokine production (p < 0.05). PCA identified four factors that explained 68.33% of the variance in the chemical composition. An increase in Factor 1 (+Fe, +Al, +Mn) score and a decrease in Factor 2 (-Ca, +Pb, +PAH) score were associated with increased interleukin (IL)-6 (Factor 1; p = 0.010; Factor 2; p = 0.006) and IL-8 (Factor 1; p = 0.003; Factor 2; p = 0.020) production, however, only the association with Factor 1 was evident after correcting for endotoxin and particle size.

CONCLUSIONS

Our study provides novel insight into the positive associations between pro-inflammatory effects of roof space PM samples with Fe, Al and Mn levels.

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.