Nitrogen dioxide exposure, attentional function, and working memory in children from 4 to 8 years: Periods of susceptibility from pregnancy to childhood.

BACKGROUND: Air pollution exposure during pregnancy and childhood has been linked to executive function impairment in children, however, very few studies have assessed these two exposure periods jointly to identify susceptible periods of exposure. We sought to identify potential periods of susceptibility of nitrogen dioxide (NO2) exposure from conception to childhood on attentional function and working memory in school-aged children. METHODS: Within the Spanish INMA Project, we estimated residential daily NO2 exposures during pregnancy and up to 6 years of childhood using land use regression models (n = 1,703). We assessed attentional function at 4-6 years and 6-8 years, using the Conners Kiddie Continuous Performance Test and the Attention Network Test, respectively, and working memory at 6-8 years, using the N-back task. We used distributed lag non-linear models to assess the periods of susceptibility of each outcome, adjusting for potential confounders and correcting for multiple testing. We also stratified all models by sex. RESULTS: Higher exposure to NO2 between 1.3 and 1.6 years of age was associated with higher hit reaction time standard error (HRT-SE) (0.14 ms (95 % CI 0.05; 0.22) per 10 µg/m3 increase in NO2) and between 1.5 and 2.2 years of age with more omission errors (1.02 (95 % CI 1.01; 1.03) of the attentional function test at 4-6 years. Higher exposure to NO2 between 0.3 and 2.2 years was associated with higher HRT-SE (10.61 ms (95 % CI 3.46; 17.75) at 6-8 years only in boys. We found no associations between exposure to NO2 and working memory at 6-8 years. CONCLUSION: Our findings suggest that NO2 exposure during the first two years of life is associated with poorer attentional function in children from 4 to 8 years of age, especially in boys. These findings highlight the importance of exploring long-term effects of traffic-related air pollution exposure in older age groups.

Dynamic and stationary monitoring of air pollutant exposures and dose during marathons.

Marathon running significantly increases breathing volumes and, consequently, air pollution inhalation doses. This is of special concern for elite athletes who ventilate at very high rates. However, race organizers and sport governing bodies have little guidance to support events scheduling to protect runners. A key limitation is the lack of hyper-local, high temporal resolution air quality data representative of exposure along the racecourse. This work aimed to understand the air pollution exposures and dose inhaled by athletes, by means of a dynamic monitoring methodology designed for road races. Air quality monitors were deployed during three marathons, monitoring nitrogen dioxide (NO2), ozone (O3), particulate matter (PMx), air temperature, and relative humidity. One fixed monitor was installed at the Start/Finish line and one mobile monitor followed the women elite runner pack. The data from the fixed monitors, deployed prior the race, described daily air pollution trends. Mobile monitors in combination with heatmap analysis facilitated the hyper-local characterization of athletes' exposures and helped identify local hotspots (e.g., areas prone to PM resuspension) which should be preferably bypassed. The estimation of inhaled doses disaggregated by gender and ventilation showed that doses inhaled by last finishers may be equal or higher than those inhaled by first finishers for O3 and PMx, due to longer exposures as well as the increase of these pollutants over time (e.g., 58.2 ± 9.6 and 72.1 ± 23.7 µg of PM2.5 for first and last man during Rome marathon). Similarly, men received significantly higher doses than women due to their higher ventilation rate, with differences of 31-114 µg for NO2, 79-232 µg for O3, and 6-41 µg for PMx. Finally, the aggregated data obtained during the 4 week- period prior the marathon can support better race scheduling by the organizers and provide actionable information to mitigate air pollution impacts on athletes' health and performance.

Long-term exposure to major constituents of fine particulate matter and neurodegenerative diseases: A population-based survey in the Pearl River Delta Region, China.

BACKGROUND: Exposure to PM2.5 has been linked to neurodegenerative diseases, with limited understanding of constituent-specific contributions. OBJECTIVES: To explore the associations between long-term exposure to PM2.5 constituents and neurodegenerative diseases. METHODS: We recruited 148,274 individuals aged ≥ 60 from four cities in the Pearl River Delta region, China (2020 to 2021). We calculated twenty-year average air pollutant concentrations (PM2.5 mass, black carbon (BC), organic matter (OM), ammonium (NH4+), nitrate (NO3-) and sulfate (SO42-)) at the individuals' home addresses. Neurodegenerative diseases were determined by self-reported doctor-diagnosed Alzheimer's disease (AD) and Parkinson's disease (PD). Generalized linear mixed models were employed to explore associations between pollutants and neurodegenerative disease prevalence. RESULTS: PM2.5 and all five constituents were significantly associated with a higher prevalence of AD and PD. The observed associations generally exhibited a non-linear pattern. For example, compared with the lowest quartile, higher quartiles of BC were associated with greater odds for AD prevalence (i.e., the adjusted odds ratios were 1.81; 95% CI, 1.45-2.27; 1.78; 95% CI, 1.37-2.32; and 1.99; 95% CI, 1.54-2.57 for the second, third, and fourth quartiles, respectively). CONCLUSIONS: Long-term exposure to PM2.5 and its constituents, particularly combustion-related BC, OM, and SO42-, was significantly associated with higher prevalence of AD and PD in Chinese individuals. ENVIRONMENTAL IMPLICATION: PM2.5 is a routinely regulated mixture of multiple hazardous constituents that can lead to diverse adverse health outcomes. However, current evidence on the specific contributions of PM2.5 constituents to health effects is scarce. This study firstly investigated the association between PM2.5 constituents and neurodegenerative diseases in the moderately to highly polluted Pearl River Delta region in China, and identified hazardous constituents within PM2.5 that have significant impacts. This study provides important implications for the development of targeted PM2.5 prevention and control policies to reduce specific hazardous PM2.5 constituents.

A review of air pollution as a driver of cardiovascular disease risk across the diabetes spectrum.

The prevalence of diabetes is estimated to reach almost 630 million cases worldwide by the year 2045; of current and projected cases, over 90% are type 2 diabetes. Air pollution exposure has been implicated in the onset and progression of diabetes. Increased exposure to fine particulate matter air pollution (PM2.5) is associated with increases in blood glucose and glycated hemoglobin (HbA1c) across the glycemic spectrum, including normoglycemia, prediabetes, and all forms of diabetes. Air pollution exposure is a driver of cardiovascular disease onset and exacerbation and can increase cardiovascular risk among those with diabetes. In this review, we summarize the literature describing the relationships between air pollution exposure, diabetes and cardiovascular disease, highlighting how airborne pollutants can disrupt glucose homeostasis. We discuss how air pollution and diabetes, via shared mechanisms leading to endothelial dysfunction, drive increased cardiovascular disease risk. We identify portable air cleaners as potentially useful tools to prevent adverse cardiovascular outcomes due to air pollution exposure across the diabetes spectrum, while emphasizing the need for further study in this particular population. Given the enormity of the health and financial impacts of air pollution exposure on patients with diabetes, a greater understanding of the interventions to reduce cardiovascular risk in this population is needed.

Seasonal extreme temperatures and short-term fine particulate matter increases pediatric respiratory healthcare encounters in a sparsely populated region of the intermountain western United States.

BACKGROUND: Western Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Evaluating while accounting for these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health is becoming more important. METHODS: We explored short-term exposure to air pollution on children's respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated healthcare events. The main outcome measure included individual-based address located respiratory-related healthcare visits for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for ages 0-17 from 2017-2020. We used a time-stratified, case-crossover analysis with distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 14 prior-days modified by temperature or season. RESULTS: For asthma, increases of 1 µg/m3 in PM2.5 exposure 7-13 days prior a healthcare visit date was associated with increased odds that were magnified during median to colder temperatures and winter periods. For LRTIs, 1 µg/m3 increases during 12 days of cumulative PM2.5 with peak exposure periods between 6-12 days before healthcare visit date was associated with elevated LRTI events, also heightened in median to colder temperatures but no seasonal effect was observed. For URTIs, 1 unit increases during 13 days of cumulative PM2.5 with peak exposure periods between 4-10 days prior event date was associated with greater risk for URTIs visits that were intensified during median to hotter temperatures and spring to summer periods. CONCLUSIONS: Delayed, short-term exposure increases of PM2.5 were associated with elevated odds of all three pediatric respiratory healthcare visit categories in a sparsely population area of the inter-Rocky Mountains, USA. PM2.5 in colder temperatures tended to increase instances of asthma and LRTIs, while PM2.5 during hotter periods increased URTIs.

Confounder-dependent Bayesian mixture model: Characterizing heterogeneity of causal effects in air pollution epidemiology.

Several epidemiological studies have provided evidence that long-term exposure to fine particulate matter (pm2.5) increases mortality rate. Furthermore, some population characteristics (e.g., age, race, and socioeconomic status) might play a crucial role in understanding vulnerability to air pollution. To inform policy, it is necessary to identify groups of the population that are more or less vulnerable to air pollution. In causal inference literature, the group average treatment effect (GATE) is a distinctive facet of the conditional average treatment effect. This widely employed metric serves to characterize the heterogeneity of a treatment effect based on some population characteristics. In this paper, we introduce a novel Confounder-Dependent Bayesian Mixture Model (CDBMM) to characterize causal effect heterogeneity. More specifically, our method leverages the flexibility of the dependent Dirichlet process to model the distribution of the potential outcomes conditionally to the covariates and the treatment levels, thus enabling us to: (i) identify heterogeneous and mutually exclusive population groups defined by similar GATEs in a data-driven way, and (ii) estimate and characterize the causal effects within each of the identified groups. Through simulations, we demonstrate the effectiveness of our method in uncovering key insights about treatment effects heterogeneity. We apply our method to claims data from Medicare enrollees in Texas. We found six mutually exclusive groups where the causal effects of pm2.5 on mortality rate are heterogeneous.

Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling.

BACKGROUND: Distributed lag non-linear models (DLNMs) are the reference framework for modelling lagged non-linear associations. They are usually used in large-scale multi-location studies. Attempts to study these associations in small areas either did not include the lagged non-linear effects, did not allow for geographically-varying risks or downscaled risks from larger spatial units through socioeconomic and physical meta-predictors when the estimation of the risks was not feasible due to low statistical power. METHODS: Here we proposed spatial Bayesian DLNMs (SB-DLNMs) as a new framework for the estimation of reliable small-area lagged non-linear associations, and demonstrated the methodology for the case study of the temperature-mortality relationship in the 73 neighbourhoods of the city of Barcelona. We generalized location-independent DLNMs to the Bayesian framework (B-DLNMs), and extended them to SB-DLNMs by incorporating spatial models in a single-stage approach that accounts for the spatial dependence between risks. RESULTS: The results of the case study highlighted the benefits of incorporating the spatial component for small-area analysis. Estimates obtained from independent B-DLNMs were unstable and unreliable, particularly in neighbourhoods with very low numbers of deaths. SB-DLNMs addressed these instabilities by incorporating spatial dependencies, resulting in more plausible and coherent estimates and revealing hidden spatial patterns. In addition, the Bayesian framework enriches the range of estimates and tests that can be used in both large- and small-area studies. CONCLUSIONS: SB-DLNMs account for spatial structures in the risk associations across small areas. By modelling spatial differences, SB-DLNMs facilitate the direct estimation of non-linear exposure-response lagged associations at the small-area level, even in areas with as few as 19 deaths. The manuscript includes an illustrative code to reproduce the results, and to facilitate the implementation of other case studies by other researchers.

Explore the effect of apparent temperature and air pollutants on the admission rate of acute myocardial infarction in Chongqing, China: a time-series study.

OBJECTIVE: Limited research has been conducted on the correlation between apparent temperature and acute myocardial infarction (AMI), as well as the potential impact of air pollutants in modifying this relationship. The objective of this study is to investigate the lagged effect of apparent temperature on AMI and assess the effect modification of environmental pollutants on this association. DESIGN: A time-series study. SETTING AND PARTICIPANTS: The data for this study were obtained from the Academy of Medical Data Science at Chongqing Medical University, covering daily hospitalisations for AMI between 1 January 2015 and 31 December 2016. Meteorological and air pollutant data were provided by China's National Meteorological Information Centre. OUTCOME MEASURES: We used a combined approach of quasi-Poisson generalised linear model and distributed lag non-linear model to thoroughly analyse the relationships. Additionally, we employed a generalised additive model to investigate the interaction between air pollutants and apparent temperature on the effect of AMI. RESULT: A total of 872 patients admitted to hospital with AMI were studied based on the median apparent temperature (20.43°C) in Chongqing. Low apparent temperature (10th, 7.19℃) has obvious lagged effect on acute myocardial infarction, first appearing on the 8th day (risk ratio (RR) 1.081, 95% CI 1.010 to 1.158) and the greatest risk on the 11th day (RR 1.094, 95% CI 1.037 to 1.153). No lagged effect was observed at high apparent temperature. In subgroup analysis, women and individuals aged 75 and above were at high risk. The interaction analysis indicates that there exist significant interactions between PM2.5 and high apparent temperature, as well as nitrogen dioxide (NO2) and low apparent temperature. CONCLUSION: The occurrence of decreased apparent temperature levels was discovered to be linked with a heightened relative risk of hospitalisations for AMI. PM2.5 and NO2 have an effect modification on the association between apparent temperature and admission rate of AMI.

Exposure to Concurrent Heatwaves and Ozone Pollution and Associations with Mortality Risk: A Nationwide Study in China.

BACKGROUND: Concurrent extreme events are projected to occur more frequently under a changing climate. Understanding the mortality risk and burden of the concurrent heatwaves and ozone (O3) pollution may support the formulation of adaptation strategies and early warning systems for concurrent events in the context of climate change. OBJECTIVES: We aimed to estimate the mortality risk and excess deaths of concurrent heatwaves and O3 pollution across 250 counties in China. METHODS: We collected daily mortality, meteorological, and air pollution data for the summer (1 June to 30 September) during 2013-2018. We defined heatwaves and high O3 pollution days, then we divided the identified days into three categories: a) days with only heatwaves (heatwave-only event), b) days with only high O3 pollution (high O3 pollution-only event), and c) days with concurrent heatwaves and high O3 pollution (concurrent event). A generalized linear model with a quasi-Poisson regression was used to estimate the risk of mortality associated with extreme events for each county. Then we conducted a random-effects meta-analysis to pool the county-specific estimates to derive the overall effect estimates. We used mixed-effects meta-regression to identify the drivers of the heterogeneity. Finally, we estimated the excess death attributable to extreme events (heatwave-only, high O3 pollution-only, and concurrent events) from 2013 to 2020. RESULTS: A higher all-cause mortality risk was associated with exposure to the concurrent heatwaves and high O3 pollution than exposure to a heatwave-only or a high O3 pollution-only event. The effects of a concurrent event on circulatory and respiratory mortality were higher than all-cause and nonaccidental mortality. Sex and age significantly impacted the association of concurrent events and heatwave-only events with all-cause mortality. We estimated that annual average excess deaths attributed to the concurrent events were 6,249 in China from 2017 to 2020, 5.7 times higher than the annual average excess deaths attributed to the concurrent events from 2013 to 2016. The annual average proportion of excess deaths attributed to the concurrent events in the total excess deaths caused by three types of events (heatwave-only events, high O3 pollution-only events, and concurrent events) increased significantly in 2017-2020 (31.50%; 95% CI: 26.73%, 35.53%) compared with 2013-2016 (9.65%; 95% CI: 5.67%, 10.81%). Relative excess risk due to interaction revealed positive additive interaction considering the concurrent effect of heatwaves and high O3 pollution. DISCUSSION: Our findings may provide scientific basis for establishing a concurrent event early warning system to reduce the adverse health impact of the concurrent heatwaves and high O3 pollution. https://doi.org/10.1289/EHP13790.

A toxicological perspective on climate change and the exposome.

Climate change is accompanied by changes in the exposome, including increased heat, ground-level ozone, and other air pollutants, infectious agents, pollens, and psychosocial stress. These exposures alter the internal component of the exposome and account for some of the health effects of climate change. The adverse outcome pathways describe biological events leading to an unfavorable health outcome. In this perspective study, I propose to use this toxicological framework to better describe the biological steps linking a stressor associated with climate change to an adverse outcome. Such a framework also allows for better identification of possible interactions between stressors related to climate change and others, such as chemical pollution. More generally, I call for the incorporation of climate change as part of the exposome and for improved identification of the biological pathways involved in its health effects.

A comprehensive review of urban vegetation as a Nature-based Solution for sustainable management of particulate matter in ambient air.

Air pollution is one of the most pressing environmental threats worldwide, resulting in several health issues such as cardiovascular and respiratory disorders, as well as premature mortality. The harmful effects of air pollution are particularly concerning in urban areas, where mismanaged anthropogenic activities, such as growth in the global population, increase in the number of vehicles, and industrial activities, have led to an increase in the concentration of pollutants in the ambient air. Among air pollutants, particulate matter is responsible for most adverse impacts. Several techniques have been implemented to reduce particulate matter concentrations in the ambient air. However, despite all the threats and awareness, efforts to improve air quality remain inadequate. In recent years, urban vegetation has emerged as an efficient Nature-based Solution for managing environmental air pollution due to its ability to filter air, thereby reducing the atmospheric concentrations of particulate matter. This review characterizes the various mitigation mechanisms for particulate matter by urban vegetation (deposition, dispersion, and modification) and identifies key areas for further improvements within each mechanism. Through a systematic assessment of existing literature, this review also highlights the existing gaps in the present literature that need to be addressed to maximize the utility of urban vegetation in reducing particulate matter levels. In conclusion, the review emphasizes the urgent need for proper air pollution management through urban vegetation by integrating different fields, multiple stakeholders, and policymakers to support better implementation.

Air Pollutants and Mortality Risk in Patients with Aortic Dissection: Evidence from a Clinical Cohort, Single-Cell Sequencing, and Proteomics.

We aimed to evaluate the association between air pollutants and mortality risk in patients with acute aortic dissection (AAD) in a longitudinal cohort and to explore the potential mechanisms of adverse prognosis induced by fine particulate matter (PM2.5). Air pollutants data, including PM2.5, PM10.0, nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3), were collected from official monitoring stations, and multivariable Cox regression models were applied. Single-cell sequencing and proteomics of aortic tissue were conducted to explore the potential mechanisms. In total, 1,267 patients with AAD were included. Exposure to higher concentrations of air pollutants was independently associated with an increased mortality risk. The high-PM2.5 group carried approximately 2 times increased mortality risk. There were linear associations of PM10, NO2, CO, and SO2 exposures with long-term mortality risk. Single-cell sequencing revealed an increase in mast cells in aortic tissue in the high-PM2.5 exposure group. Enrichment analysis of the differentially expressed genes identified the inflammatory response as one of the main pathways, with IL-17 and TNF signaling pathways being among the top pathways. Analysis of proteomics also identified these pathways. This study suggests that exposure to higher PM2.5, PM10, NO2, CO, and SO2 are associated with increased mortality risk in patients with AAD. PM2.5-related activation and degranulation of mast cells may be involved in this process.

The impact of co-exposure to air and noise pollution on the incidence of metabolic syndrome from a health checkup cohort.

Previous studies have found associations between the incidence of metabolic syndrome (MetS) and exposure to air pollution or road traffic noise. However, investigations on environmental co-exposures are limited. This study aimed to investigate the association between co-exposure to air pollution and road traffic noise and MetS and its subcomponents. Participants living in Taipei City who underwent at least two health checkups between 2010 and 2016 were included in the study. Data were sourced from the MJ Health database, a longitudinal, large-scale cohort in Taiwan. The monthly traffic noise exposure (Lden and Lnight) was computed using a dynamic noise map. Monthly fine particulate data at one kilometer resolution were computed from satellite imagery data. Cox proportional hazards regression models with month as the underlying time scale were used to estimate hazard ratios (HRs) for the impact of PM2.5 and road traffic noise exposure on the risk of developing MetS or its subcomponents. Data from 10,773 participants were included. We found significant positive associations between incident MetS and PM2.5 (HR: 1.88; 95% CI 1.67, 2.12), Lden (HR: 1.10; 95% CI 1.06, 1.15), and Lnight (HR: 1.07; 95% CI 1.02, 1.13) in single exposure models. Results further showed significant associations with an elevated risk of incident MetS in co-exposure models, with HRs of 1.91 (95% CI 1.69, 2.16) and 1.11 (95% CI 1.06, 1.16) for co-exposure to PM2.5 and Lden, and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM2.5 and Lnight. The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM2.5 and noise exposure may elevate the risk of incident MetS and its components in both single and co-exposure models. Therefore, preventive approaches to mitigate the risk of MetS and its subcomponents should consider reducing exposure to PM2.5 and noise pollution.

Indoor sulfur dioxide prediction through air quality modeling and assessment of sulfur dioxide and nitrogen dioxide levels in industrial and non-industrial areas.

In this study, the levels of sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO2 and NO2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO2 was the highest in summer, while the mean indoor NO2 concentration was the highest in winter in both areas. The ratio of NO2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO2 for the training set (RMSE = 2.108, MAE = 1.780, and R2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant.

Household Air Pollution Interventions to Improve Health in Low- and Middle-Income Countries: An Official American Thoracic Society Research Statement.

Background: An estimated 3 billion people, largely in low- and middle-income countries, rely on unclean fuels for cooking, heating, and lighting to meet household energy needs. The resulting exposure to household air pollution (HAP) is a leading cause of pneumonia, chronic lung disease, and other adverse health effects. In the last decade, randomized controlled trials of clean cooking interventions to reduce HAP have been conducted. We aim to provide guidance on how to interpret the findings of these trials and how they should inform policy makers and practitioners.Methods: We assembled a multidisciplinary working group of international researchers, public health practitioners, and policymakers with expertise in household air pollution from within academia, the American Thoracic Society, funders, nongovernmental organizations, and global organizations, including the World Bank and the World Health Organization. We performed a literature search, convened four sessions via web conference, and developed consensus conclusions and recommendations via the Delphi method.Results: The committee reached consensus on 14 conclusions and recommendations. Although some trials using cleaner-burning biomass stoves or cleaner-cooking fuels have reduced HAP exposure, the committee was divided (with 55% saying no and 45% saying yes) on whether the studied interventions improved measured health outcomes.Conclusions: HAP is associated with adverse health effects in observational studies. However, it remains unclear which household energy interventions reduce exposure, improve health, can be scaled, and are sustainable. Researchers should engage with policy makers and practitioners working to scale cleaner energy solutions to understand and address their information needs.

An umbrella review of health co-benefits from actions to reduce greenhouse gas emissions.

BACKGROUND: There have been many modelled studies of potential health co-benefits from actions to reduce greenhouse gas emissions, but so far there have been no large-scale attempts to compare the magnitude of health and climate effects across sectors, countries, and study designs. METHODS: As part of the Pathfinder Initiative project an umbrella review of studies was done, and 26 previous reviews were identified with 57 primary studies included. Studies included in the review were required to have quantified changes in greenhouse gas emissions and health effects (or risk factors) from defined actions to reduce climate effects. Study data were extracted and harmonised by standardising impact measures per 100 000 of the national population (or urban population for city-level actions), averaging effects over a 1-year period and aggregating actions into their respective sectors by use of a predefined framework. FINDINGS: From 200 mitigation actions, the majority were in the agriculture, forestry, and land use sector (103 actions [52%]), followed by the transport sector (43 actions [22%]). The largest effects on greenhouse gas emissions were seen from actions in the energy sector, and these actions also had substantial health co-benefits in lower middle-income countries, although benefits were smaller in high-income settings. The greatest health benefits were seen from actions to change diets and introduce clean cookstoves. The major pathways to health were through reduced air pollution, healthier diets, and increased physical activity from switching to active travel modes. Effect sizes tended to be larger from national modelling studies and smaller from localised or implemented actions. INTERPRETATION: The potential co-benefits to health from actions to reduce climate change are large, but most evidence still comes from modelling studies and from high-income and middle-income countries. There are also major context-dependent differences in the magnitude of effects found, so actions need to be tailored to the local context and careful attention needs to be paid to potential trade-offs and spillover effects. FUNDING: The Wellcome Trust and the Oak Foundation.

The Urban Environment and Cardiometabolic Health.

Urban environments contribute substantially to the rising burden of cardiometabolic diseases worldwide. Cities are complex adaptive systems that continually exchange resources, shaping exposures relevant to human health such as air pollution, noise, and chemical exposures. In addition, urban infrastructure and provisioning systems influence multiple domains of health risk, including behaviors, psychological stress, pollution, and nutrition through various pathways (eg, physical inactivity, air pollution, noise, heat stress, food systems, the availability of green space, and contaminant exposures). Beyond cardiometabolic health, city design may also affect climate change through energy and material consumption that share many of the same drivers with cardiometabolic diseases. Integrated spatial planning focusing on developing sustainable compact cities could simultaneously create heart-healthy and environmentally healthy city designs. This article reviews current evidence on the associations between the urban exposome (totality of exposures a person experiences, including environmental, occupational, lifestyle, social, and psychological factors) and cardiometabolic diseases within a systems science framework, and examines urban planning principles (eg, connectivity, density, diversity of land use, destination accessibility, and distance to transit). We highlight critical knowledge gaps regarding built-environment feature thresholds for optimizing cardiometabolic health outcomes. Last, we discuss emerging models and metrics to align urban development with the dual goals of mitigating cardiometabolic diseases while reducing climate change through cross-sector collaboration, governance, and community engagement. This review demonstrates that cities represent crucial settings for implementing policies and interventions to simultaneously tackle the global epidemics of cardiovascular disease and climate change.

Air Pollution Drives Macrophage Senescence through a Phagolysosome-15-Lipoxygenase Pathway.

Urban particulate matter (PM; uPM) poses significant health risks, particularly to the respiratory system. Fine particles, such as PM2.5, can penetrate deep into the lungs and exacerbate a range of health problems, including emphysema, asthma, and lung cancer. PM exposure is also linked to extrapulmonary disorders such as heart and neurodegenerative diseases. Moreover, prolonged exposure to elevated PM levels can reduce overall life expectancy. Senescence is a dysfunctional cell state typically associated with age but can also be precipitated by environmental stressors. This study aimed to determine whether uPM could drive senescence in macrophages, an essential cell type involved in particulate phagocytosis-mediated clearance. Although it is known that uPM exposure impairs immune function, this deficit is multifaceted and incompletely understood, partly because of the use of particulates such as diesel exhaust particles as a surrogate for true uPM. uPM was collected from several locations in the United States, including Baltimore, Houston, and Phoenix. Bone marrow-derived macrophages were stimulated with uPM or reference particulates (e.g., diesel exhaust particles) to assess senescence-related parameters. We report that uPM-exposed bone marrow-derived macrophages adopt a senescent phenotype characterized by increased IL-1α secretion, senescence-associated ß-galactosidase activity, and diminished proliferation. Exposure to allergens failed to elicit such a response, supporting a distinction between different types of environmental exposure. uPM-induced senescence was independent of key macrophage activation pathways, specifically inflammasome and scavenger receptors. However, inhibition of the phagolysosome pathway abrogated senescence markers, supporting this phenotype's attribution to uPM phagocytosis. These data suggest that uPM exposure leads to macrophage senescence, which may contribute to immunopathology.

Evaluation the impact of electricity consumption on China's air pollution at the provincial level.

As the world's largest electricity-consuming country, China faces the challenge of energy conservation and environmental pollution. Therefore, it is imperative that China takes decisive action to address these issues. Based on the panel data of 30 provinces (cities, districts) in China from 2011 to 2020, we use the entropy method to measure the air pollution index in different provinces, construct two fixed effects models, panel quantile model, and spatial Durbin model to empirically analyze the impact of electricity consumption on air pollution in China's provincial regions. The experimental results show that: (1) Electricity consumption has a significant positive impact on the provincial air pollution index in China and the higher the index is, the more serious the air pollution is. When the electricity consumption increases 1%, the air pollution index will increase of by 0.0649% as accompanied. (2) Through comparison of different times, we found that the degree of increase in air pollution index caused by electricity consumption would be reduced due to the improvement of environmental protection efforts. From the perspective of different geographical locations, the electricity consumption in the southeast side of the "Hu Line" has exacerbated the impact on air pollution index. (3) According to the panel quantile regression results, the marginal effect of electricity consumption on air pollution is positive. With the increase of quantiles, the impact of electricity consumption on air pollution is increasing. (4) Spatial effect analysis shows that electricity consumption has a significant positive spatial spillover effect on air pollution index. The increase in electricity consumption not only increases the air pollution index in the local region, but also leads to an increase in the air pollution index in surrounding areas. These findings contribute to the governance of air pollution and the promotion of sustainable economic, environmental and energy development.

The Associations of Prenatal Exposure to Fine Particulate Matter and Its Chemical Components with Allergic Rhinitis in Children and the Modification Effect of Polyunsaturated Fatty Acids: A Birth Cohort Study.

BACKGROUND: Polyunsaturated fatty acids (PUFAs) have been shown to protect against fine particulate matter <2.5µm in aerodynamic diameter (PM2.5)-induced hazards. However, limited evidence is available for respiratory health, particularly in pregnant women and their offspring. OBJECTIVES: We aimed to investigate the association of prenatal exposure to PM2.5 and its chemical components with allergic rhinitis (AR) in children and explore effect modification by maternal erythrocyte PUFAs. METHODS: This prospective birth cohort study involved 657 mother-child pairs from Guangzhou, China. Prenatal exposure to residential PM2.5 mass and its components [black carbon (BC), organic matter (OM), sulfate (SO42-), nitrate (NO3-), and ammonium (NH4+)] were estimated by an established spatiotemporal model. Maternal erythrocyte PUFAs during pregnancy were measured using gas chromatography. The diagnosis of AR and report of AR symptoms in children were assessed up to 2 years of age. We used Cox regression with the quantile-based g-computation approach to assess the individual and joint effects of PM2.5 components and examine the modification effects of maternal PUFA levels. RESULTS: Approximately 5.33% and 8.07% of children had AR and related symptoms, respectively. The average concentration of prenatal PM2.5 was 35.50±5.31 µg/m3. PM2.5 was positively associated with the risk of developing AR [hazard ratio (HR)=1.85; 95% confidence interval (CI): 1.16, 2.96 per 5 µg/m3] and its symptoms (HR=1.79; 95% CI: 1.22, 2.62 per 5 µg/m3) after adjustment for confounders. Similar associations were observed between individual PM2.5 components and AR outcomes. Each quintile change in a mixture of components was associated with an adjusted HR of 3.73 (95% CI: 1.80, 7.73) and 2.69 (95% CI: 1.55, 4.67) for AR and AR symptoms, with BC accounting for the largest contribution. Higher levels of n-3 docosapentaenoic acid and lower levels of n-6 linoleic acid showed alleviating effects on AR symptoms risk associated with exposure to PM2.5 and its components. CONCLUSION: Prenatal exposure to PM2.5 and its chemical components, particularly BC, was associated with AR/symptoms in early childhood. We highlight that PUFA biomarkers could modify the adverse effects of PM2.5 on respiratory allergy. https://doi.org/10.1289/EHP13524.