Impact of ambient fine particulate matter carbon measurement methods on observed associations with acute cardiorespiratory morbidity

Independent risk evaluation associated with short-term black carbon exposure on mortality in two megacities of Yangtze River Delta, China

The adverse health effects of PM_2.5 have been well demonstrated by many studies. However, as a component of PM_2.5, evidence on the mortality risk of black carbon (BC) is still limited. In this study, based on the data of daily mean PM_2.5 concentration, BC concentration, meteorological factors, total non-accidental (all-cause) and cardiovascular mortality in Shanghai and Nanjing during 2015-2016, a semi-parameter generalized additive model (GAM) in the time series and the constituent residual approach were employed to explore the exposure-response relationship between BC and human mortality in these two megacities of Yangtze River Delta, China. The main objective was to separate the health effects of BC from total PM_2.5, and compare the difference of mortality ER related to BC original concentration and adjusted concentration after controlling PM_2.5. Results showed that there were all significantly associated with daily mortality for PM_2.5 and BC. The percentage excess risk (ER) increases in all-cause and cardiovascular categories were 1.68 % (95 % s 1.28, 2.08) and 2.16 % (95 % CI: 1.54, 2.79) with 1 μg/m³ increment in original BC concentration in Shanghai. And the ER in Nanjing was smaller than that in Shanghai. After eliminating PM_2.5 confounding effects by a constituent residual approach, the BC residual concentration still had a strong significant ER. The ER for BC residual in Shanghai got an obvious increase, and ER of the cardiovascular mortality for all, females and males increased by 0.55 %, 1.46 % and 0.62 %, respectively, while the ER in Nanjing decreased slightly. It also revealed that females were more sensitive to the health risk associated with short-term BC exposure than males. Our findings provide additional important evidence and ER for mortality related to independent BC exposure. Therefore, BC emission reduction should be paid more attention in air pollution control strategies to reduce BC-related health burdens.

Air pollution exposure and heart failure: A systematic review and meta-analysis

While the literature strongly supports a positive association between particulate matter with diameter ≤ 2.5 μm (PM_2.5) exposure and heart failure (HF), there is uncertainty regarding the other pollutants and the dose and duration of exposure that triggers an adverse response. To comprehensively assess and quantify the association of air pollution exposure with HF incidence and mortality, we performed separate meta-analyses according to pollutant types [PM_2.5, PM₁₀, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃)], and exposure duration (short- and long-term). We systematically searched PubMed, EMBASE, and Web of Science for relevant articles with publication dates up to July 12, 2022, identifying 35 eligible studies. Random-effects models were used to summarize the pooled odds ratios (ORs) and 95 % confidence intervals (95 % CIs). For long-term exposure, the growing risk of HF was significantly associated with each 10 μg/m³ increase in PM_2.5 (OR = 1.196, 95 % CI: 1.079-1.326; I² = 76.8 %), PM₁₀ (1.190, 1.045-1.356; I² = 76.2 %), and NO₂ (1.072, 1.028-1.118; I² = 78.3 %). For short-term exposure, PM_2.5, PM₁₀, NO₂, and O₃ (per 10 μg/m³ increment) increased the risk of HF, with estimated ORs of 1.019 (1.008-1.030; I² = 39.9 %), 1.012 (1.007-1.017; I² = 28.3 %), 1.016 (1.005-1.026; I² = 53.7 %), and 1.006 (1.002-1.010; I² = 0.0 %), respectively. No significant effects of SO₂ and CO exposure on the risk of HF were observed. In summary, our study powerfully highlights the deleterious impact of PM_2.5, PM₁₀, and NO₂ exposure (either short- or long-term) on HF risk. Serious efforts should be made to improve air quality through legislation and interdisciplinary cooperation.

Factors Modulating COVID-19: A Mechanistic Understanding Based on the Adverse Outcome Pathway Framework

Addressing factors modulating COVID-19 is crucial since abundant clinical evidence shows that outcomes are markedly heterogeneous between patients. This requires identifying the factors and understanding how they mechanistically influence COVID-19. Here, we describe how eleven selected factors (age, sex, genetic factors, lipid disorders, heart failure, gut dysbiosis, diet, vitamin D deficiency, air pollution and exposure to chemicals) influence COVID-19 by applying the Adverse Outcome Pathway (AOP), which is well-established in regulatory toxicology. This framework aims to model the sequence of events leading to an adverse health outcome. Several linear AOPs depicting pathways from the binding of the virus to ACE2 up to clinical outcomes observed in COVID-19 have been developed and integrated into a network offering a unique overview of the mechanisms underlying the disease. As SARS-CoV-2 infectibility and ACE2 activity are the major starting points and inflammatory response is central in the development of COVID-19, we evaluated how those eleven intrinsic and extrinsic factors modulate those processes impacting clinical outcomes. Applying this AOP-aligned approach enables the identification of current knowledge gaps orientating for further research and allows to propose biomarkers to identify of high-risk patients. This approach also facilitates expertise synergy from different disciplines to address public health issues.

Is short-term and long-term exposure to black carbon associated with cardiovascular and respiratory diseases? A systematic review and meta-analysis based on evidence reliability

OBJECTIVE

Adverse health effects of fine particles (particulate matter_2.5) have been well documented by a series of studies. However, evidences on the impacts of black carbon (BC) or elemental carbon (EC) on health are limited. The objectives were (1) to explored the effects of BC and EC on cardiovascular and respiratory morbidity and mortality, and (2) to verified the reliability of the meta-analysis by drawing p value plots.

DESIGN

The systematic review and meta-analysis using adapted Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach and p value plots approach.

DATA SOURCES

PubMed, Embase and Web of Science were searched from inception to 19 July 2021.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Time series, case cross-over and cohort studies that evaluated the associations between BC/EC on cardiovascular or respiratory morbidity or mortality were included.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently selected studies, extracted data and assessed risk of bias. Outcomes were analysed via a random effects model and reported as relative risk (RR) with 95% CI. The certainty of evidences was assessed by adapted GRADE. The reliabilities of meta-analyses were analysed by p value plots.

RESULTS

Seventy studies met our inclusion criteria. (1) Short-term exposure to BC/EC was associated with 1.6% (95% CI 0.4% to 2.8%) increase in cardiovascular diseases per 1 µg/m³ in the elderly; (2) Long-term exposure to BC/EC was associated with 6.8% (95% CI 0.4% to 13.5%) increase in cardiovascular diseases and (3) The p value plot indicated that the association between BC/EC and respiratory diseases was consistent with randomness.

CONCLUSIONS

Both short-term and long-term exposures to BC/EC were related with cardiovascular diseases. However, the impact of BC/EC on respiratory diseases did not present consistent evidence and further investigations are required.

PROSPERO REGISTRATION NUMBER

CRD42020186244.

Guidance to Reduce the Cardiovascular Burden of Ambient Air Pollutants: A Policy Statement From the American Heart Association

In 2010, the American Heart Association published a statement concluding that the existing scientific evidence was consistent with a causal relationship between exposure to fine particulate matter and cardiovascular morbidity and mortality, and that fine particulate matter exposure is a modifiable cardiovascular risk factor. Since the publication of that statement, evidence linking air pollution exposure to cardiovascular health has continued to accumulate and the biological processes underlying these effects have become better understood. This increasingly persuasive evidence necessitates policies to reduce harmful exposures and the need to act even as the scientific evidence base continues to evolve. Policy options to mitigate the adverse health impacts of air pollutants must include the reduction of emissions through action on air quality, vehicle emissions, and renewable portfolio standards, taking into account racial, ethnic, and economic inequality in air pollutant exposure. Policy interventions to improve air quality can also be in alignment with policies that benefit community and transportation infrastructure, sustainable food systems, reduction in climate forcing agents, and reduction in wildfires. The health care sector has a leadership role in adopting policies to contribute to improved environmental air quality as well. There is also potentially significant private sector leadership and industry innovation occurring in the absence of and in addition to public policy action, demonstrating the important role of public-private partnerships. In addition to supporting education and research in this area, the American Heart Association has an important leadership role to encourage and support public policies, private sector innovation, and public-private partnerships to reduce the adverse impact of air pollution on current and future cardiovascular health in the United States.

A hybrid modeling framework to estimate pollutant concentrations and exposures in near road environments

Traffic related air pollution is one of the major local sources of pollution challenging most urban populations. Current air quality modeling approaches can estimate the concentrations of air pollutants on either regional or local scales but cannot effectively estimate concentrations from the combination of regional and local sources at both local and regional scales simultaneously. This study describes a hybrid modeling framework, HYCAMR, combining a regional model, CAMx, and a local-scale dispersion model, R-LINE, to estimate concentrations of both primary and secondary species at high temporal (hourly) and spatial (40 m) resolution. HYCAMR utilizes all the chemical and physical processes available in CAMx and the Particulate Matter Source Apportionment Technology (PSAT) tool to estimate concentrations from both onroad and nonroad emission sources. HYCAMR employs R-LINE, to estimate the normalized dispersion of pollutant mass from onroad emission sources, from primary and secondary roads, at high resolution. Applying R-LINE for one day per month using average daily meteorology yields seasonally-resolved spatial dispersion profiles at low computational cost. Combining the R-LINE spatial dispersion profile with CAMx concentration estimates yields an estimate of the combined concentrations for a range of pollutants at high spatial and temporal resolution. In three major cities in Connecticut, HYCAMR shows strong temporal and seasonal variability in NOx, PM_2.5, and elemental carbon (EC) concentrations. This study evaluates HYCAMR year 2011 estimates of NO₂ and PM_2.5 against two sources: satellite-based estimates at coarse resolution and regression model estimates at census block group resolution. In this evaluation, HYCAMR demonstrates improved agreement with the land-use regression modeling and mixed agreement with satellite-based estimates when compared to the regional CAMx estimates.

A multicity study of air pollution and cardiorespiratory emergency department visits: Comparing approaches for combining estimates across cities

Determining how associations between ambient air pollution and health vary by specific outcome is important for developing public health interventions. We estimated associations between twelve ambient air pollutants of both primary (e.g. nitrogen oxides) and secondary (e.g. ozone and sulfate) origin and cardiorespiratory emergency department (ED) visits for 8 specific outcomes in five U.S. cities including Atlanta, GA; Birmingham, AL; Dallas, TX; Pittsburgh, PA; St. Louis, MO. For each city, we fitted overdispersed Poisson time-series models to estimate associations between each pollutant and specific outcome. To estimate multicity and posterior city-specific associations, we developed a Bayesian multicity multi-outcome (MCM) model that pools information across cities using data from all specific outcomes. We fitted single pollutant models as well as models with multipollutant components using a two-stage chemical mixtures approach. Posterior city-specific associations from the MCM models were somewhat attenuated, with smaller standard errors, compared to associations from time-series regression models. We found positive associations of both primary and secondary pollutants with respiratory disease ED visits. There was some indication that primary pollutants, particularly nitrogen oxides, were also associated with cardiovascular disease ED visits. Bayesian models can help to synthesize findings across multiple outcomes and cities by providing posterior city-specific associations building on variation and similarities across the multiple sources of available information.

Association between air pollution and cardiovascular mortality in China: a systematic review and meta-analysis

Air pollutant levels in many Chinese cities remained significantly higher than the upper limits stated in World Health Organization guidelines. In light of limited evidence in China, we conducted a meta-analysis summarizing the association between acute exposure of air pollution and cardiovascular mortality. We searched PubMed, and CNKI databases etc. for literature published in English or Chinese up to January 2017. Outcomes were pooled and compared using random-effects model. Excess risks (ERs) per 10 μg/m³ increase in PM_2.5, PM₁₀, NO₂, SO₂ and O₃ were evaluated. Subgroup analysis was conducted according to lag patterns (lags 0, 1, 2, 0–1, 0–2 days), gender (male vs. female), temperature (cool vs. warm) and age (< 65 vs. ≥ 65). Study bias was detected using Begg’s and Egger’s test. Of 299 articles identified, 30 met inclusion criteria. Each 10 μg/m³ increase in the concentration was associated with a higher incidence of cardiovascular mortality for PM_2.5 (0.68%, 95% CI: 0.39–0.97%), PM₁₀ (0.39%, 95% CI: 0.26–0.53%), NO₂ (1.12%, 95% CI: 0.76–1.48%), SO₂ (0.75%, 95% CI: 0.42–1.09%), and O₃ (0.62%, 95% CI: 0.33–0.92%), respectively. Air pollution conferred greater adverse impacts on cardiovascular mortality for longer duration of exposures. Strongest associations were seen for lag 0–1 day of exposure among all pollutants. Female, lower temperature, and age > 65 years were associated with greater risks of cardiovascular mortality for all pollutants. Higher concentrations of air pollutants correlated with a greater short-term increase in cardiovascular mortality. Further high-quality studies in China are urgently warranted to determine the susceptible population, which would offer reference for policy-making to minimize adverse health effects.

Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities

BACKGROUND

Short-term exposure to ambient fine particulate matter (PM2.5) concentrations has been associated with increased mortality and morbidity. Determining which sources of PM2.5 are most toxic can help guide targeted reduction of PM2.5. However, conducting multicity epidemiologic studies of sources is difficult because source-specific PM2.5 is not directly measured, and source chemical compositions can vary between cities.

OBJECTIVES

We determined how the chemical composition of primary ambient PM2.5 sources varies across cities. We estimated associations between source-specific PM2.5 and respiratory disease emergency department (ED) visits and examined between-city heterogeneity in estimated associations.

METHODS

We used source apportionment to estimate daily concentrations of primary source-specific PM2.5 for four U.S. cities. For sources with similar chemical compositions between cities, we applied Poisson time-series regression models to estimate associations between source-specific PM2.5 and respiratory disease ED visits.

RESULTS

We found that PM2.5 from biomass burning, diesel vehicle, gasoline vehicle, and dust sources was similar in chemical composition between cities, but PM2.5 from coal combustion and metal sources varied across cities. We found some evidence of positive associations of respiratory disease ED visits with biomass burning PM2.5; associations with diesel and gasoline PM2.5 were frequently imprecise or consistent with the null. We found little evidence of associations with dust PM2.5.

CONCLUSIONS

We introduced an approach for comparing the chemical compositions of PM2.5 sources across cities and conducted one of the first multicity studies of source-specific PM2.5 and ED visits. Across four U.S. cities, among the primary PM2.5 sources assessed, biomass burning PM2.5 was most strongly associated with respiratory health. Citation: Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE. 2017. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ Health Perspect 125:97-103; http://dx.doi.org/10.1289/EHP271.

Current Methods and Challenges for Epidemiological Studies of the Associations Between Chemical Constituents of Particulate Matter and Health

Epidemiological studies have been critical for estimating associations between exposure to ambient particulate matter (PM) air pollution and adverse health outcomes. Because total PM mass is a temporally and spatially varying mixture of constituents with different physical and chemical properties, recent epidemiological studies have focused on PM constituents. Most studies have estimated associations between PM constituents and health using the same statistical methods as in studies of PM mass. However, these approaches may not be sufficient to address challenges specific to studies of PM constituents, namely assigning exposure, disentangling health effects, and handling measurement error. We reviewed large, population-based epidemiological studies of PM constituents and health and describe the statistical methods typically applied to address these challenges. Development of statistical methods that simultaneously address multiple challenges, for example, both disentangling health effects and handling measurement error, could improve estimation of associations between PM constituents and adverse health outcomes.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age

Previous studies have found strong associations between asthma morbidity and major ambient air pollutants. Relatively little research has been conducted to assess whether age is a factor conferring susceptibility to air pollution-related asthma morbidity. We investigated the short-term relationships between asthma emergency department (ED) visits and ambient ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5) in Atlanta (1993-2009), Dallas (2006-2009), and St. Louis (2001-2007). City-specific daily time-series analyses were conducted to estimate associations by age group (0-4, 5-18, 19-39, 40-64, and 65+ years). Sub-analyses were performed stratified by race and sex. City-specific rate ratios (RRs) were combined by inverse-variance weighting to provide an overall association for each strata. The overall RRs differed across age groups, with associations for all pollutants consistently strongest for children aged 5-18 years. The patterns of association across age groups remained generally consistent when models were stratified by sex and race, although the strong observed associations among 5-18 year olds appeared to be partially driven by non-white and male patients. Our findings suggest that age is a susceptibility factor for asthma exacerbations in response to air pollution, with school-age children having the highest susceptibility.

Reactive Oxygen Species Generation Linked to Sources of Atmospheric Particulate Matter and Cardiorespiratory Effects

Exposure to atmospheric fine particulate matter (PM2.5) is associated with cardiorespiratory morbidity and mortality, but the mechanisms are not well understood. We assess the hypothesis that PM2.5 induces oxidative stress in the body via catalytic generation of reactive oxygen species (ROS). A dithiothreitol (DTT) assay was used to measure the ROS-generation potential of water-soluble PM2.5. Source apportionment on ambient (Atlanta, GA) PM2.5 was performed using the chemical mass balance method with ensemble-averaged source impact profiles. Linear regression analysis was used to relate PM2.5 emission sources to ROS-generation potential and to estimate historical levels of DTT activity for use in an epidemiologic analysis for the period of 1998-2009. Light-duty gasoline vehicles (LDGV) exhibited the highest intrinsic DTT activity, followed by biomass burning (BURN) and heavy-duty diesel vehicles (HDDV) (0.11 ± 0.02, 0.069 ± 0.02, and 0.052 ± 0.01 nmol min(-1) μg(-1)source, respectively). BURN contributed the largest fraction to total DTT activity over the study period, followed by LDGV and HDDV (45, 20, and 14%, respectively). DTT activity was more strongly associated with emergency department visits for asthma/wheezing and congestive heart failure than PM2.5. This work provides further epidemiologic evidence of a biologically plausible mechanism, that of oxidative stress, for associations of adverse health outcomes with PM2.5 mass and supports continued assessment of the utility of the DTT activity assay as a measure of ROS-generating potential of particles.