Global Association of Air Pollution and Cardiorespiratory Diseases: A Systematic Review, Meta-Analysis, and Investigation of Modifier Variables

Long-term exposure to PM2.5 and cardiorespiratory mortality: an ecological small-area study in five cities in Colombia

Long-term exposure to the fine particulate matter (PM2.5) is a risk factor for cardiorespiratory mortality. However, little is known about its distribution and health impact in large cities in low-middle-income countries where population exposure has increased during the last decades. This ecological study evaluated the association between PM2.5 concentration and adult cardiorespiratory mortality at the intraurban census sector (CS) level of Colombia's five most populated cities (2015-2019). We estimated incidence rate ratios (IRR; per 5µg/m3) by fitting negative binomial regressions to smoothed Bayesian mortality rates (BMR) on PM2.5 predicted from land use regression (LUR) models, adjusting for CS demographic structure, multidimensional poverty index, and spatial autocorrelation. CS median PM2.5 ranged from 8.1µg/m3 in Bucaramanga to 18.7µg/m3 in Medellín, whereas Bogotá had the highest variability (IQR = 29.5µg/m3) and cardiorespiratory mortality (BMR = 2,560 per 100,000). Long-term exposure to PM2.5 increased cardiorespiratory mortality in Bucaramanga (IRR = 1.15; 95%CI: 1.02; 1.31), without evidence of spatial clustering, and cardiovascular (IRR = 1.06; 95%CI: 1.01; 1.12) and respiratory (IRR = 1.07; 95%CI: 1.02; 1.13) mortality in Medellín. Cardiorespiratory mortality spatially clustered in some Colombian cities and was associated with long-term exposure to PM2.5 in urban areas where the LUR models had the highest predictive accuracy. These findings highlight the need to incorporate high-quality, high-resolution exposure assessments to better understand the health impact of air pollution and inform public health interventions in urban environments.

Influence of air pollution and climate variability on dengue in Singapore: a time-series analysis

The associations between respiratory and cardiovascular health outcomes with air quality have been well examined. Less conclusive are the studies assessing the relationship between air quality and dengue, a mosquito-borne illness which continues to pose risk to more than half the world's population. We examined this relationship in Singapore, a tropical city-state located in the South-East Asian region where the burden of dengue is among the highest across the globe. We analyzed the short-term associations between all laboratory-confirmed dengue reports and variations in PM2.5, PM10, CO, O3, NO2 and SO2 in Singapore from 2009 to 2019 using the Distributed Lag Non-linear Model (DLNM) framework. PM2.5 (RR90th percentile: 1.28, 95% CI 1.11,1.49), PM10 (RR90th percentile: 1.30, 95% CI 1.12, 1.51), and CO (RR90th percentile: 1.30, 95% CI 1.06, 1.61), were positively associated with dengue up to a period of 48 days with observed maximum threshold effects, while O3 and NO2 were negatively associated with dengue. There was a positive, non-linear association between ambient temperature and dengue. We observed strong evidence of ambient temperature modifying the association between particulate matter and dengue risk. Variations in the concentrations of these air pollutant types may inform short-term dengue control resource augmentation plans.

Frequency of Exacerbations of Chronic Obstructive Pulmonary Disease Associated with the Long-Term Exposure to Air Pollution in the AIREPOC Cohort

Background

Exacerbations of chronic obstructive pulmonary disease (COPD-E) have been associated with levels of air pollution. The occurrence of COPD-E is associated with increased mortality in this population.

Purpose

To determine the association between long-term exposure to PM2.5 and NO2, and the frequency of COPD-E in patients belonging to AIREPOC, an institutional integrated care program for COPD in Bogota, Colombia.

Patients and Methods

Retrospective cohort study included patients with COPD living in Bogotá, between 2018 and 2021, who received health care in the AIREPOC program. Each patient´s home address was geolocated. Information from local air quality network stations was used to estimate daily and annual mean PM2.5 and NO2 exposure level for each patient using the inverse distance squared weighted regression (IDWR) method. The effect of PM2.5 and NO2 concentrations categorized at 15 µg/m3 and 25 µg/m3 respectively on the frequency of COPD-E was estimated using a zero-truncated negative binomial model adjusted for potential confounders. Goodness-of-fit was assessed by residuals.

Results

During the observation period, 580 COPD-E occurred in 722 patients. Significant associations were found between COPD-E and NO2 concentrations ≥25 µg/m3 (incidence density ratio, RDI: 1.29, 95% CI: 1.02-1.67) after adjustment for sun exposure, COPD severity, depression, and ambient humidity. No association was found between the frequency of COPD-E and PM2.5 concentrations ≥15µg/m3.

Conclusion

Prolonged exposure to high levels of NO2 increases the frequency of COPD exacerbations in patients residing in Bogotá. These results highlight the importance of strengthening air quality control measures and educating people with COPD to know and interpret the local air quality indices and to follow the recommendations derived from its alterations.

Mediating effects of carbon monoxide on the development of hypertension in China

OBJECTIVES

Understanding the relationship between ambient carbon monoxide (CO) exposure and hypertension is crucial for advancing public health research. We investigated the association between air pollution CO exposure and hypertension and determined whether various health indicators played a mediating role.

STUDY DESIGN

Cross-sectional population-based study.

METHODS

Our study examined the data of 13,240 participants in the China Health and Aging Longitudinal Study (CHARLS) conducted in 2015. A logistic regression approach was used to investigate the association between CO concentration and hypertension. The mediating effects (MEs) of health indicators on the association between carbon monoxide and hypertension prevalence were explored using mediation models.

RESULTS

Hypertension was diagnosed in 2953 of the participants. A significantly increased prevalence of hypertension was associated with higher concentrations of air pollutant CO after adjusting for all covariates (OR: 1.19, 95% CI: 1.04-1.37) in the matched group (OR: 1.21, 95% CI: 1.03-1.42). The study also showed that proportional mediation was 3.7% by TG, -5.3% by TC, -2.9% by LDL-C and 19.0% by HDL-C for blood lipid; 4.6% by FPG and 12.2% by HbA1c for blood glucose; -2.0% by grip strength and 16.2% by chair rising time for muscle strength; 59.6% by WC and 1.8% by BMI for obesity indicator and -2.3% by nighttime sleep and 7.8% by daytime nap for sleep duration.

CONCLUSIONS

A significant positive association has been found between environmental CO exposure and hypertension, and obesity, muscle strength, sleep duration, and metabolic factors may play a significant role in mediating this association.

Long-term effects of air quality on hospital readmission for heart failure in patients with acute myocardial infarction

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of death worldwide, with air pollution posing significant risks to cardiovascular health. The effect of air quality on heart failure (HF) readmission in acute myocardial infarction (AMI) patients is unclear.The aim of this study was to evaluate the role of a single measure of air pollution exposure collected on the day of first hospitalization.

METHODS

We retrospectively analyzed data from 12,857 acute coronary syndrome (ACS) patients (January 2015-March 2023). After multiple screenings, 4023 AMI patients were included. The air pollution data is updated by the automatic monitoring data of the national urban air quality monitoring stations in real time and synchronized to the China Environmental Monitoring Station. Cox proportional hazards regression assessed the impact of air quality indicators on admission and outcomes in 4013 AMI patients. A decision tree model identified the most susceptible groups.

RESULTS

After adjusting for confounders, NO2 (HR 1.009, 95% CI 1.004-1.015, P = 0.00066) and PM10 (HR 1.006, 95% CI 1.002-1.011, P = 0.00751) increased the risk of HF readmission in ST-segment elevation myocardial infarction (STEMI) patients. No significant effect was observed in non-STEMI (NSTEMI) patients (P > 0.05). STEMI patients had a 2.8-fold higher risk of HF readmission with NO2 > 13 μg/m3 (HR 2.857, 95% CI 1.439-5.670, P = 0.00269) and a 1.65-fold higher risk with PM10 > 55 μg/m3 (HR 1.654, 95% CI 1.124-2.434, P = 0.01064).

CONCLUSION

NO2 and PM10 are linked to increased HF readmission risk in STEMI patients, particularly when NO2 exceeds 13 μg/m3 and PM10 exceeds 55 μg/m3. Younger, less symptomatic male STEMI patients with fewer underlying conditions are more vulnerable to these pollutants.

Prenatal and early life exposure to air pollution and the risk of severe lower respiratory tract infections during early childhood: the Espoo Cohort Study

BACKGROUND

There is inconsistent evidence of the effects of exposure to ambient air pollution on the occurrence of lower respiratory tract infections (LRTIs) in early childhood. We assessed the effects of individual-level prenatal and early life exposure to air pollutants on the risk of LRTIs in early life.

METHODS

We studied 2568 members of the population-based Espoo Cohort Study born between 1984 and 1990 and living in 1991 in the City of Espoo, Finland. Exposure assessment was based on dispersion modelling and land-use regression for lifetime residential addresses. The outcome was a LRTI based on data from hospital registers. We applied Poisson regression to estimate the incidence rate ratio (IRR) of LTRIs, contrasting incidence rates in the exposure quartiles to the incidence rates in the first quartile. We used weighted quantile sum (WQS) regression to estimate the joint effect of the studied air pollutants.

RESULTS

The risk of LRTIs during the first 2 years of life was significantly related to exposure to individual and multiple air pollutants, measured with the Multipollutant Index (MPI), including primarily sulphur dioxide (SO2), particulate matter with a dry diameter of up to 2.5 µm (PM2.5) and nitrogen dioxide (NO2) exposures in the first year of life, with an adjusted IRR of 1.72 per unit increase in MPI (95% CI 1.20 to 2.47). LRTIs were not related to prenatal exposure.

CONCLUSIONS

We provide evidence that ambient air pollution exposure during the first year of life increases the risk of LRTIs during the first 2 years of life. SO2, PM2.5 and NO2 were found to contribute the highest weights on health effects.

Association Between Long‑Term Exposure to Air Pollution and the Rate of Mortality After Hip Fracture Surgery in Patients Older Than 60 Years: Nationwide Cohort Study in Taiwan

BACKGROUND

To enhance postoperative patient survival, particularly in older adults, understanding the predictors of mortality following hip fracture becomes paramount. Air pollution, a prominent global environmental issue, has been linked to heightened morbidity and mortality across a spectrum of diseases. Nevertheless, the precise impact of air pollution on hip fracture outcomes remains elusive.

OBJECTIVE

This retrospective study aims to comprehensively investigate the profound influence of a decade-long exposure to 12 diverse air pollutants on the risk of post-hip fracture mortality among older Taiwanese patients (older than 60 years). We hypothesized that enduring long-term exposure to air pollution would significantly elevate the 1-year mortality rate following hip fracture surgery.

METHODS

From Taiwan's National Health Insurance Research Database, we obtained the data of patients who underwent hip fracture surgery between July 1, 2003, and December 31, 2013. Using patients' insurance registration data, we estimated their cumulative exposure levels to sulfur dioxide (SO2), carbon dioxide (CO2), carbon monoxide (CO), ozone (O3), particulate matter having a size of <10 μm (PM10), particulate matter having a size of <2.5 μm (PM2.5), nitrogen oxides (NOX), nitrogen monoxide (NO), nitrogen dioxide (NO2), total hydrocarbons (THC), nonmethane hydrocarbons (NMHC), and methane (CH4). We quantified the dose-response relationship between these air pollutants and the risk of mortality by calculating hazard ratios associated with a 1 SD increase in exposure levels over a decade.

RESULTS

Long-term exposure to SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 demonstrated significant associations with heightened all-cause mortality risk within 1 year post hip fracture surgery among older adults. For older adults, each 1 SD increment in the average exposure levels of SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 corresponded to a substantial escalation in mortality risk, with increments of 14%, 49%, 18%, 12%, 41%, 33%, 38%, 20%, 9%, and 26%, respectively. We further noted a 35% reduction in the hazard ratio for O3 exposure suggesting a potential protective effect, along with a trend of potentially protective effects of CO2.

CONCLUSIONS

This comprehensive nationwide retrospective study, grounded in a population-based approach, demonstrated that long-term exposure to specific air pollutants significantly increased the risk of all-cause mortality within 1 year after hip fracture surgery in older Taiwanese adults. A reduction in the levels of SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 may reduce the risk of mortality after hip fracture surgery. This study provides robust evidence and highlights the substantial impact of air pollution on the outcomes of hip fractures.

Exploring the optimal threshold of FDI inflows for carbon-neutral growth in Africa

This study investigates the relationship between foreign direct investment (FDI) and CO2 emissions in Africa, primarily emphasizing carbon-neutral growth. Employing advanced econometric methods like the Generalized Method of Moments (GMM), fixed effect, and Two-Stage Least Squares (2SLS), we identify critical threshold values for key variables, including economic growth, trade openness, human capital, financial development, inflation, and population growth. Our findings indicate that GDP significantly influences the FDI-CO2 emissions relationship as economies expand, shifting from negative to positive, potentially leading to increased carbon emissions. Higher trade-to-GDP ratios are associated with reduced CO2 emissions due to cleaner technologies and greener production practices. Additionally, financial development plays a pivotal role, enabling investment in sustainable technologies. Nations with a more skilled workforce are more likely to adopt sustainable practices. The influence of population growth on CO2 emissions is complex, balancing increased demand with investments in clean technologies. The study recommends that African policymakers prioritize FDI aligned with carbon-neutral growth by promoting sustainability, investing in human capital, and carefully balancing population growth with sustainability.

Impact of air pollution on postoperative outcomes following organ transplantation: Evidence from clinical investigations

INTRODUCTION

Air pollution is a worldwide problem affecting human health via various body systems, resulting in numerous significant adverse events. Air pollutants, including particulate matter < or = 2.5 microns (PM2.5), particulate matter < or = 10 microns (PM10), ozone (O3 ), nitrogen dioxide (NO2 ), and traffic-related air pollution (TRAP), have demonstrated the negative effects on human health (e.g., increased cerebrovascular, cardiovascular, and respiratory diseases, malignancy, and mortality). Organ transplant patients, who are taking immunosuppressive agents, are especially vulnerable to the adverse effects of air pollutants. The evidence from clinical investigation has shown that exposure to air pollution after organ transplantation is associated with organ rejection, cardiovascular disease, coronary heart disease, cerebrovascular disease, infection-related mortality, and vitamin D deficiency.

OBJECTIVES AND METHOD

This review aims to summarize and discuss the association of exposure to air pollutants and serum 25-hydroxyvitamin D level and outcomes after transplantation. Controversial findings are also included and discussed.

CONCLUSION

All of the findings suggest that air pollution results in a hazardous environment, which not only impacts human health worldwide but also affects post-transplant outcomes.

Detecting the spatial clustering of exposure-response relationships with estimation error: a novel spatial scan statistic

Detecting the spatial clustering of the exposure-response relationship (ERR) between environmental risk factors and health-related outcomes plays important roles in disease control and prevention, such as identifying highly sensitive regions, exploring the causes of heterogeneous ERRs, and designing region-specific health intervention measures. However, few studies have focused on this issue. A possible reason is that the commonly used cluster-detecting tool, spatial scan statistics, cannot be used for multivariate spatial datasets with estimation error, such as the ERR, which is often defined by a vector with its covariance estimated by a regression model. Such spatial datasets have been produced in abundance in the last decade, which suggests the importance of developing a novel cluster-detecting tool applicable for multivariate datasets with estimation error. In this work, by extending the classic scan statistic, we developed a novel spatial scan statistic called the estimation-error-based scan statistic (EESS), which is applicable for both univariate and multivariate datasets with estimation error. Then, a two-stage analytic process was proposed to detect the spatial clustering of ERRs in practical studies. A published motivating example and a simulation study were used to validate the performance of EESS. The results show that the clusters detected by EESS can efficiently reflect the clustering heterogeneity and yield more accurate ERR estimates by adjusting for such heterogeneity.

A Multi-Pollutant and Meteorological Analysis of Cardiorespiratory Mortality among the Elderly in São Paulo, Brazil-An Artificial Neural Networks Approach

Traditionally, studies that associate air pollution with health effects relate individual pollutants to outcomes such as mortality or hospital admissions. However, models capable of analyzing the effects resulting from the atmosphere mixture are demanded. In this study, multilayer perceptron neural networks were evaluated to associate PM₁₀, NO₂, and SO₂ concentrations, temperature, wind speed, and relative air humidity with cardiorespiratory mortality among the elderly in São Paulo, Brazil. Daily data from 2007 to 2019 were considered and different numbers of neurons on the hidden layer, algorithms, and a combination of activation functions were tested. The best-fitted artificial neural network (ANN) resulted in a MAPE equal to 13.46%. When individual season data were analyzed, the MAPE decreased to 11%. The most influential variables in cardiorespiratory mortality among the elderly were PM₁₀ and NO₂ concentrations. The relative humidity variable is more important during the dry season, and temperature is more important during the rainy season. The models were not subjected to the multicollinearity issue as with classical regression models. The use of ANNs to relate air quality to health outcomes is still very incipient, and this work highlights that it is a powerful tool that should be further explored.

High-dimensional spatiotemporal visual analysis of the air quality in China

Air quality is a significant environmental issue among the Chinese people and even the global population, and it affects both human health and the Earth's long-term sustainability. In this study, we proposed a multiperspective, high-dimensional spatiotemporal data visualization and interactive analysis method, and we studied and analyzed the relationship between the air quality and several influencing factors, including meteorology, population, and economics. Six visualization methods were integrated in this study, each specifically designed and improved for visualization analysis purposes. To reveal the spatiotemporal distribution and potential impact of the air quality, we designed a comprehensive coupled visual interactive analysis approach visually express both high-dimensional and spatiotemporal attributes, reveal the overall situation and explain the relationship between attributes. We clarified the current spatiotemporal distribution, development trends, and influencing factors of the air quality in China through interactive visual analysis of a 25-dimensional dataset involving 31 Chinese provinces. We also verified the correctness and effectiveness of relevant policies and demonstrated the advantages of our method.

Selecting relevant moderators with Bayesian regularized meta-regression

When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To overcome these challenges, we introduce Bayesian Regularized Meta-Analysis (BRMA), which selects relevant moderators from a larger set of candidates by shrinking small regression coefficients towards zero with regularizing (LASSO or horseshoe) priors. This method is suitable when there are many potential moderators, but it is not known beforehand which of them are relevant. A simulation study compared BRMA against state-of-the-art random effects meta-regression using restricted maximum likelihood (RMA). Results indicated that BRMA outperformed RMA on three metrics: BRMA had superior predictive performance, which means that the results generalized better; BRMA was better at rejecting irrelevant moderators, and worse at detecting true effects of relevant moderators, while the overall proportion of Type I and Type II errors was equivalent to RMA. BRMA regression coefficients were slightly biased towards zero (by design), but its residual heterogeneity estimates were less biased than those of RMA. BRMA performed well with as few as 20 studies, suggesting its suitability as a small sample solution. We present free open source software implementations in the R-package pema (for penalized meta-analysis) and in the stand-alone statistical program JASP. An applied example demonstrates the use of the R-package.

Health and economic benefits of meeting WHO air quality guidelines, Western Pacific Region

Objective

To quantify the number of avoidable annual deaths and associated economic benefits from meeting the World Health Organization (WHO) air quality guidelines for ambient concentrations for fine particulate matter (PM_2.5) for Member States of the WHO Western Pacific Region.

Methods

Using the AirQ+ software, we performed a quantitative health impact assessment comparing country-level PM_2.5 concentrations with the 2005 and 2021 air quality guidelines recommended maximum concentrations of 10 and 5 μg/m³, respectively. We obtained PM_2.5 data from the WHO Global Health Observatory (latest available year 2016), and population and mortality estimates from the United Nations World Population Prospects database for the latest 5-year period available (2015-2019), which we averaged to 1-year estimates. A risk estimate for all-cause mortality, based on a meta-analysis, was embedded within AirQ+ software. Our economic assessment used World Bank value of a statistical life adjusted to country-specific gross domestic product (latest available year 2014).

Findings

Data were complete for 21 of 27 Member States. If these countries achieved the 2021 guidelines for PM_2.5, an estimated 3.1 million deaths would be avoided annually, which are 0.4 million more deaths avoided than meeting the 2005 guidelines. China would avoid the most deaths per 100 000 population (303 deaths) and Brunei Darussalam the least (5 deaths). The annual economic benefit per capita ranged from 5781 United States dollars (US$) in Singapore to US$ 143 in Solomon Islands.

Conclusion

Implementing effective measures to reduce PM_2.5 emissions would save a substantial number of lives and money across the Region.

Investigation of a river-tunnel effect on PM2.5 concentrations in New York City subway stations

It is well-documented that subway stations exhibit high fine particulate matter (PM2.5) concentrations. Little is known about the potential of river-tunnels to increase PM2.5 concentrations in subways. We hypothesized a "river-tunnel" effect exists: Stations adjacent to poorly ventilated tunnels that travel beneath rivers exhibit higher PM2.5 concentrations than more distant stations. Accordingly, the PM2.5 concentrations were monitored at stations adjacent to and two- and three-stations distant from the river-tunnel. Multivariate linear regression analysis was conducted to disentangle how proximity to a river-tunnel and other factors (e.g., depth) influence concentrations. Stations adjacent to a river-tunnel had 80-130% higher PM2.5 concentrations than more distant stations. Moreover, distance from a river-tunnel was the strongest PM2.5-influencing factor This distance effect was not observed at underground stations adjacent to a river-bridge. The "river-tunnel" effect explains some of the inter-station variability in subway PM2.5 concentrations. These results support the need for improving ventilation systems in subways.

A comparative study of three models to analyze the impact of air pollutants on the number of pulmonary tuberculosis cases in Urumqi, Xinjiang

In this paper, we separately constructed ARIMA, ARIMAX, and RNN models to determine whether there exists an impact of the air pollutants (such as PM2.5, PM10, CO, O3, NO2, and SO2) on the number of pulmonary tuberculosis cases from January 2014 to December 2018 in Urumqi, Xinjiang. In addition, by using a new comprehensive evaluation index DISO to compare the performance of three models, it was demonstrated that ARIMAX (1,1,2) × (0,1,1)12 + PM2.5 (lag = 12) model was the optimal one, which was applied to predict the number of pulmonary tuberculosis cases in Urumqi from January 2019 to December 2019. The predicting results were in good agreement with the actual pulmonary tuberculosis cases and shown that pulmonary tuberculosis cases obviously declined, which indicated that the policies of environmental protection and universal health checkups in Urumqi have been very effective in recent years.

Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions

BACKGROUND

Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have been identified as risk factors for chronic diseases such as ischemic heart disease. However, the likelihood evaluation of the disease occurrence probability due to environmental factors is missing.

METHOD

We defined people aged 51-90 years who were free from ischemic heart disease (ICD9: 410-414) in 1996-2002 as the susceptible group. A Bayesian conditional logistic regression model based on a case-crossover design was utilized to construct a risk information system and applied to data from three databases in Taiwan: air quality variables from the Environmental Protection Administration (EPA), meteorological parameters from the Central Weather Bureau (CWB), and subject information from the National Health Insurance Research Database (NHIRD).

RESULTS

People living in different geographic regions in Taiwan were found to have different risk factors; thus, disease risk alert intervals varied in the three regions.

CONCLUSIONS

Disease risk alert intervals can be a reference for weather bureaus to issue health warnings. With early warnings, susceptible groups can take measures to avoid exacerbation of disease when meteorological conditions and air pollution become hazardous to their health.

Meta analysis of health effects of ambient air pollution exposure in low- and middle-income countries

It is well established that exposure to ambient air pollution affects human health. A majority of literature concentrated on health effects of air pollution in high income countries. Only fewer studies analyzing health effects of air pollution in Low- and Middle-Income Countries (LMICs) are available. To bridge this gap in literature, this study investigated short term and long-term health impacts of ambient air pollutants focussed in LMICs. We evaluated Total Non-accidental mortality, Respiratory Mortality, Stroke Mortality, Cardio-vascular Mortality, Chronic Obstructive Pulmonary Disease (COPD), Ischemic Heart Disease (IHD) and Lung Cancer Mortality in LMICs particularly. Random Effects Model was utilised to derive overall risk estimate. Relative Risk (RR) estimates per 10 μg/m³ was used as input for model. Subgroup and Sensitivity Analysis by Design and Country was conducted. A total of 152 studies were included for quantitative analysis. We found positive associations between pollutants and Total Non-accidental mortality for PM₁₀ ((RR:1.0043-1.0036), p < 0.0001), NO₂ (RR:1.0222 (1.0111-1.0336), p < 0.0001), SO₂ (RR:1.0107 - (1.0073-1.0140), p < 0.0001), O₃ (RR: 1.0038 (1.0023-1.0053), p < 0.0001) and PM_2.5 (RR: 1.0048 (1.0037-1.0059), p < 0.0001) for every 10 μg/m³ increase. We found positive association between Long-term exposure to PM₁₀ and Total Non-accidental mortality (RR: 1.0430 (1.0278-1.0583), p < 0.0001) We also found statistically significant positive associations between pollutants and Cardiorespiratory and Cardiovascular morbidity. The positive associations persisted when analysed amongst sub-groups. However, the high heterogeneity amongst studies persisted even after performing sub-group analysis. The study has found statistically significant positive associations between short-term and long-term exposure to Ambient air pollution with various health-outcome combinations.

Health impacts from TRAPs and carbon emissions in the projected electric vehicle growth and energy generation mix scenarios in Malaysia

Road transport contributes over 70% of air pollution in urban areas and is the second largest contributor to the total carbon dioxide emissions in Malaysia at 21% in 2016. Transport-related air pollutants (TRAPs) such as NO_x, SO₂, CO and particulate matter (PM) pose significant threats to the urban population's health. Malaysia has targeted to deploy 885,000 EV cars on the road by 2030 in the Low Carbon Mobility Blueprint (LCMB). This study aims to quantify the health co-benefits of electric vehicle adoption from their impacts on air quality in Malaysia. Two EV uptake projections, i.e. LCMB and Revised EV Adoption (REVA) projections, and five electricity generation mix scenarios were modelled up to 2040. We used comparative health risk assessment to estimate the potential changes in mortality and burden of diseases (BoD) from the emissions in each scenario. Intake fractions and exposure-risk functions were used to calculate the burden from respiratory diseases (PM_2.5, NO_x, SO₂, CO), cardiovascular diseases and lung cancer (PM_2.5). Results showed that along with a net reduction of carbon emissions across all scenarios, there could be reduced respiratory mortality from NO_x by 10,200 mortality (176,200 DALYs) and SO₂ by 2600 mortality (45,400 DALYs) per year in 2040. However, there could also be additional 719 mortality (9900 DALYs) per year from PM_2.5 and 329 mortality (5600 DALYs) from CO per year. The scale of reduction in mortality and BoD from NO_x and SO₂ are significantly larger than the scale of increase from PM_2.5 and CO, indicating potential net positive health impacts from the EV adoption in the scenarios. The health cost savings from the reduced BoD of respiratory mortality could reach up to RM 7.5 billion per year in 2040. In conclusion, EV is a way forward in promoting a healthy and sustainable future transport in Malaysia.

Study Protocol. Evaluating the life-course health impact of a city-wide system approach to improve air quality in Bradford, UK: A quasi-experimental study with implementation and process evaluation

BACKGROUND

Air quality is a major public health threat linked to poor birth outcomes, respiratory and cardiovascular disease, and premature mortality. Deprived groups and children are disproportionately affected. Bradford will implement a Clean Air Zone (CAZ) as part of the Bradford Clean Air Plan (B-CAP) in 2022 to reduce pollution, providing a natural experiment. The aim of the current study is to evaluate the impact of the B-CAP on health outcomes and air quality, inequalities and explore value for money. An embedded process and implementation evaluation will also explore barriers and facilitators to implementation, impact on attitudes and behaviours, and any adverse consequences.

METHODS

The study is split into 4 work packages (WP). WP1A: 20 interviews with decision makers, 20 interviews with key stakeholders; 10 public focus groups and documentary analysis of key reports will assess implementation barriers, acceptability and adverse or unanticipated consequences at 1 year post-implementation (defined as point at which charging CAZ goes 'live'). WP1B: A population survey (n = 2000) will assess travel behaviour and attitudes at baseline and change at 1 year post-implementation). WP2: Routine air quality measurements will be supplemented with data from mobile pollution sensors in 12 schools collected by N = 240 pupil citizen scientists (4 within, 4 bordering and 4 distal to CAZ boundary). Pupils will carry sensors over four monitoring periods over a 12 month period (two pre, and two post-implementation). We will explore whether reductions in pollution vary by CAZ proximity. WP3A: We will conduct a quasi-experimental interrupted time series analysis using a longitudinal routine health dataset of > 530,000 Bradford residents comparing trends (3 years prior vs 3 years post) in respiratory health (assessed via emergency/GP attendances. WP3B: We will use the richly-characterised Born in Bradford cohort (13,500 children) to explore health inequalities in respiratory health using detailed socio-economic data. WP4: will entail a multi-sectoral health economic evaluation to determine value for money of the B-CAP.

DISCUSSION

This will be first comprehensive quasi-experimental evaluation of a city-wide policy intervention to improve air quality. The findings will be of value for other areas implementing this type of approach.

TRIAL REGISTRATION

ISRCTN67530835 https://doi.org/10.1186/ISRCTN67530835.

Long-term air pollution exposure and incident stroke in American older adults: A national cohort study

Aims

Stroke is a leading cause of death and disability for Americans, and growing evidence suggests that air pollution may play an important role. To facilitate pollution control efforts, the National Academy of Sciences and the World Health Organization have prioritized determining which air pollutants are most toxic. However, evidence is limited for the simultaneous effects of multiple air pollutants on stroke.

Methods and results

We constructed a nationwide population-based cohort study, using the Medicare Chronic Conditions Warehouse (2000-2017) and high-resolution air pollution data, to investigate the impact of long-term exposure to ambient PM2.5, NO2, and ground-level O3 on incident stroke. Hazard ratios (HR) for stroke incidence were estimated using single-, bi-, and tri-pollutant Cox proportional hazards models. We identified ~2.2 million incident stroke cases among 17,443,900 fee-for-service Medicare beneficiaries. Per interquartile range (IQR) increase in the annual average PM2.5 (3.7 μg/m3), NO2 (12.4 ppb), and warm-season O3 (6.5 ppb) one-year prior to diagnosis, the HRs were 1.022 (95% CI: 1.017-1.028), 1.060 (95% CI: 1.054-1.065), and 1.021 (95% CI: 1.017-1.024), respectively, from the tri-pollutant model. There was strong evidence of linearity in concentration-response relationships for all three air pollutants in single-pollutant models. This linear relationship remained robust for NO2 and O3 in tri-pollutant models while the effect of PM2.5 attenuated at the lower end of concentrations.

Conclusion

Using a large nationwide cohort, our study suggests that long-term exposure to PM2.5, NO2, and O3 may independently increase the risk of stroke among the US elderly, among which traffic-related air pollution plays a particularly crucial role.

Adaptive resilience of roadside trees to vehicular emissions via leaf enzymatic, physiological, and anatomical trait modulations

Unplanned urbanization and heavy automobile use by the rapidly growing population contribute to a variety of environmental issues. Roadside plants can mitigate air pollution by modifying their enzymatic activity, physiological and anatomical traits. Plant enzymes, physiological and anatomical traits play an important role in adaptation and mitigation mechanisms against vehicular emissions. There is a significant gap in understanding of how plant enzymes and anatomical traits respond or how they participate in modulating the effect of vehicular emissions/air pollution. Modulation of leaf anatomical traits is also useful in regulating plant physiological behavior. Hence, the present study was conducted to evaluate the effects of vehicular pollution on the enzymatic activity, physiological, and anatomical traits of plant species that grow in forests (S1) and alongside roads (S2-1 km away from the S1 site) during different seasons. The present study examines four commonly found roadside tree species i.e. Grevillea robusta, Cassia fistula, Quercus leucotrichophora and Cornus oblonga. The study found that the activities of catalase and phenylalanine ammonium enzymes were higher in G. robusta species of roadside than control site (S1). Non-enzymatic antioxidants such as flavonoid and phenol were also found in higher concentrations in roadside tree species during the summer season. However, the measured values of physiological traits were higher in Q. leucotrichophora tree species of S1 during the summer season. When compared to the other species along the roadside, Q. leucotrichophora had the highest number of stomata and epidermal cells during the summer season. Hence, we found that tree species grown along the roadside adapted towards vehicular emissions by modulating their enzymatic, physiological, and anatomical traits to mitigate the effect of air pollution.

Acute cardiorespiratory response to air quality index in healthy young adults

BACKGROUND

Little is known about the acute health impacts of air quality index (AQI) on cardiorespiratory risk factors.

OBJECTIVES

To assess the short-term links of AQI with cardiorespiratory risk factors in young healthy adults.

METHODS

We performed a longitudinal panel study with 4 repeated visits in 40 healthy young adults in Hefei, Anhui Province, China from August to October 2021. Cardiorespiratory factors included systolic blood pressure (BP), diastolic BP (DBP), mean arterial pressure (MAP) and fractional exhaled nitric oxide (FeNO). We collected hourly AQI data from a nearby air quality monitoring site. Linear mixed-effects model was applied to assess the effects of AQI on BP and FeNO.

RESULTS

The study participants (75.0% females) provided 160 pairs of valid health measurements with average age of 24 years. The mean AQI level was 44.43 during the study period. There were significant positive associations of AQI with three BP parameters and FeNO at different lag periods. For example, an interquartile range increase in AQI (26.54 unit) over lag 0-24 h was associated with increments of 6.69 mmHg (95%CI: 2.95-10.44), 5.71 mmHg (95%CI: 3.30-8.13), 6.04 mmHg (95%CI: 3.46-8.62) and 5.67% (95%CI: 1.05%-16.05%) in SBP, DBP, MAP and FeNO, respectively. The results were robust after controlling for PM₁. We did not find effect modifications by gender, BMI, physical activity, or AQI category level on the associations.

CONCLUSIONS

The current findings on associations of AQI with cardiorespiratory factors might add evidence of the acute adverse cardiorespiratory consequences following air pollution.

Short-Term Exposure to Ambient Air Pollution and Hospital Admissions for Sequelae of Stroke in Chinese Older Adults

Extensive evidence suggests that ambient air pollution contributes to a higher risk of hospital admissions for cerebrovascular diseases; however, its association with admissions for sequelae of stroke remains unclear. A time-stratified case-crossover study was conducted among 31,810 older adults who were admitted to hospital for sequelae of stroke in Guangzhou, China during 2016-2019. For each subject, daily residential exposure to fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) was extracted from a validated grid data set. Conditional logistic regression models were used for exposure-response analyses. In single-pollutant models, each interquartile range (IQR) increase of lag 04-day exposure to CO (IQR: 0.25 mg/m3) and lag 3-day exposure to O3 (69.6 μg/m3) was significantly associated with a 4.53% (95% confidence interval: 1.67%, 7.47%) and 5.63% (1.92%, 9.48%) increase in odds of hospital admissions for sequelae of stroke, respectively. These associations did not significantly vary across age or sex. With further adjustment for each of the other pollutants in 2-pollutant models, the association for CO did not change significantly, while the association for O3 disappeared. We estimated that 7.72% of the hospital admissions were attributable to CO exposures. No significant or consistent association was observed for exposure to PM2.5, PM10, SO2, or NO2. In conclusion, short-term exposure to ambient CO, even at levels below the WHO air quality guideline, was significantly associated with an increased odds of hospital admissions for sequelae of stroke, which may lead to considerable excess hospital admissions.

Long-term exposure to low air pollutant concentrations and hospitalisation for respiratory diseases in older men: A prospective cohort study in Perth, Australia

Background

Acute exposure to ambient air pollution even at low concentrations has been associated with increased hospitalisation for respiratory diseases but the effects of long-term exposure are less certain. In this study, we investigated the associations between long-term exposures to PM_2.5, PM_2.5 absorbance and NO₂ and hospitalisation for asthma, chronic obstructive pulmonary disease and pneumonia in a cohort of older men living in Perth, Western Australia, a city where the levels of air pollutants are well below the world standards.

Materials and methods

The study population of 11,156 men with no prior hospitalisation for respiratory disease was drawn from the Health in Men Study (HIMS) cohort of men aged >65 years living in Perth, Western Australia between 1996-1999. PM_2.5, PM_2.5 absorbance (PM_2.5a) and NO₂ were measured across the Perth metropolitan area over three seasons in 2012. Land use regression (LUR) models were used to estimate annual concentrations of PM_2.5, PM_2.5 absorbance and NO₂ at the residential address of each participant from inception (1996) to 2015. Hospitalisation for respiratory disease between inception and 2015 was ascertained using the Western Australian Data Linkage System. The association between exposure to air pollution with hospitalisation for respiratory disease was examined using Cox regression analysis.

Results

No statistically significant associations were observed in the fully adjusted models. However, positive associations were observed with first hospitalisation for pneumonia (HR 1.08, 95% CI: 1.01–1.16) when adjusted for age, year of enrolment, smoking status, education, BMI and physical activity.

Conclusions

In this longitudinal study of older men we found no evidence of associations between increased long-term exposure to low-level air pollution with increased risk of hospitalisation for respiratory diseases in Perth, Australia. More studies on respiratory morbidity associated with exposure to low levels of air pollution are needed for more comprehensive understanding of the overall risk.

Impact of ambient air pollution on outdoor employees’ performance: Mediating role of anxiety

This paper aims to examine the direct and indirect impact of ambient air pollution (AAP) on employees’ performance. This study has used cross sectional survey design to collect the data from the outdoor employees of the pharmaceutical industry of Pakistan. The data were collected in time lags from 299. Partial least squares- structural equation modeling (PLS-SEM) approach was applied to analyze the data. The results show that AAP has a significant negative impact on the employees’ performance, and anxiety partially mediates the association between AAP and employees’ performance. This study reveals that AAP brings anxiety among outdoor employees, which in turn decreases their working performance. The implications, limitations, and future research directions are presented in the last section of this study.

Neuroendocrine stress hormones associated with short-term exposure to nitrogen dioxide and fine particulate matter in individuals with and without chronic obstructive pulmonary disease: A panel study in Beijing, China

Air pollution is a major trigger of chronic obstructive pulmonary disease (COPD). Dysregulation of the neuroendocrine hypothalamic-pituitary-adrenal (HPA) and sympathetic-adrenal medullary (SAM) axes is essential in progression of COPD. However, it is not clear whether air pollution exposure is associated with neuroendocrine responses in individuals with and without COPD. Based on a panel study of 51 stable COPD patients and 78 non-COPD participants with 384 clinical visits, we measured the morning serum levels of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), cortisol, norepinephrine, and epinephrine as indicators of stress hormones released from the HPA and SAM axes. Ambient nitrogen dioxide (NO₂), fine particulate matter (PM_2.5), and meteorological conditions were continuously monitored at the station from 2 weeks before the start of clinical visits. Linear mixed-effects models were used to estimate associations between differences in stress hormones following an average of 1-14-day exposures to NO₂ and PM_2.5. The average 1 day air pollutant levels prior to the clinical visits were 24.4 ± 14.0 ppb for NO₂ and 55.6 ± 41.5 μg/m³ for PM_2.5. We observed significant increases in CRH, ACTH, and norepinephrine, and decreases in cortisol and epinephrine with interquartile range increase in the average NO₂ and PM_2.5 concentrations in all participants. In the stratified analyses, we identified significant between-group difference in epinephrine following NO₂ exposure in individuals with and without COPD. These results may suggest the susceptibility of COPD patients to the neuroendocrine responses associated with short-term air pollution exposure.

A systematic review and meta-analysis on the association between ambient air pollution and pulmonary tuberculosis

There is inconclusive evidence on the association between ambient air pollution and pulmonary tuberculosis (PTB) incidence, tuberculosis-related hospital admission and mortality. This review aimed to assess the extent to which selected air pollutants are associated to PTB incidence, hospital admissions and mortality. This was a systematic review of studies published in English from January 1st, 1946, through May 31st, 2022, that quantitatively assessed the association between PM_2.5, PM₁₀, NO₂, SO₂, CO, O₃ and the incidence of, hospital admission or death from PTB. Medline, Embase, Scopus and The Cochrane Library were searched. Extracted data from eligible studies were analysed using STATA software. Random-effect meta-analysis was used to derive pooled adjusted risk and odds ratios. A total of 24 studies (10 time-series, 5 ecologic, 5 cohort, 2 case–control, 1 case cross-over, 1 cross-sectional) mainly from Asian countries were eligible and involved a total of 437,255 tuberculosis cases. For every 10 μg/m³ increment in air pollutant concentration, there was a significant association between exposure to PM_2.5 (pooled aRR = 1.12, 95% CI: 1.06–1.19, p < 0.001, N = 6); PM₁₀ (pooled aRR = 1.06, 95% CI: 1.01–1.12, p = 0.022, N = 8); SO₂ (pooled aRR = 1.08, 95% CI: 1.04–1.12, p < 0.001, N = 9); and the incidence of PTB. There was no association between exposure to CO (pooled aRR = 1.04, 95% CI: 0.98–1.11, p = 0.211, N = 4); NO₂ (pooled aRR = 1.08, 95% CI: 0.99–1.17, p = 0.057, N = 7); O₃ (pooled aRR = 1.00, 95% CI: 0.99–1.02, p = 0.910, N = 6) and the incidence of PTB. There was no association between the investigated air pollutants and mortality or hospital admissions due to PTB. Overall quality of evidence was graded as low (GRADE approach). Exposure to PM_2.5, PM₁₀ and SO₂ air pollutants was found to be associated with an increased incidence of PTB, while exposure to CO, NO₂ and O₃ was not. There was no observed association between exposure to these air pollutants and hospital admission or mortality due to PTB. The quality of the evidence generated, however, remains low. Addressing the tuberculosis epidemic by 2030 as per the 4th Sustainable Development Goal may require a more rigorous exploration of this association.

The art of Patient and Public Involvement: exploring ways to research and reduce air pollution through art-based community workshops - a reflective paper

In this reflective paper we outline and discuss our art-based Patient and Public Involvement (PPI) approach.  This exercise held two broad objectives. Firstly, to assist policy makers in understanding the types of interventions communities will find acceptable to address the problem of poor air quality, and secondly, to ascertain community views about our research plans to explore the impact of the planned interventions on neighbourhoods.  We reflect on both our approach and the emergent conversations from the PPI activity.  Attendees contributed to the process and stressed the importance of not burdening poor neighbourhoods with costly charges as that would ameliorate one health problem but generate others as a consequence of additional financial burden.  Equally, they stressed the need to conduct research on matters which they could connect with such as the impact of clean air plans on young children and how information about air pollution is disseminated in their neighbourhoods as and when research findings emerge.  This paper offers a conceptual analysis of the art-based PPI method and uniquely draws a connection to the philosophical traditions of Ludwig Wittgenstein.  Specifically, we demonstrate how art is conducive to creating a dialogue which is specifically helpful for PPI purposes for both researchers and implementers, and conversely, why traditional conversational approaches may have fallen short of the adequacy mark in this regard.

Systematic review and meta-analysis of studies between short-term exposure to ambient carbon monoxide and non-accidental, cardiovascular, and respiratory mortality in China

Although a growing number of original epidemiological studies imply a link between ambient pollution exposure and mortality risk, the findings associated with carbon monoxide (CO) exposure are inconsistent. Thus, we conducted a systematic review and meta-analysis of epidemiological studies to evaluate the correlations between ambient CO and non-accidental, cardiovascular, and respiratory mortality in China. Eight databases were searched from inception to 15 May 2021. A random-effect model was used to calculate the pooled relative risks (RRs) and 95% confidence intervals (CIs). Subgroup analyses as well as sensitivity analyses were performed. The I square value (I²) was used to assess heterogeneity among different studies. The assessment of publication bias on included studies was examined by funnel plot and Egger's test. The influence of a potential publication bias on findings was explored by using the trim-and-fill procedure. Ultimately, a total of 19 studies were included in our analysis. The pooled relative risk for each 1 mg/m³ increase of ambient carbon monoxide was 1.0220 (95%CI: 1.0102-1.0339) for non-accidental mortality, 1.0304 (95%CI:1.0154-1.0457) for cardiovascular mortality, and 1.0318 (95%CI:1.0132-1.0506) for respiratory mortality. None of subgroup analyses could explain the source of heterogeneity. Exclusion of any single study did not materially alter the pooled effect estimates. Although it was suggestive of publication bias, findings were generally similar with principal findings when we explored the influence of a potential publication bias using the trim-and-fill method. Our meta-analysis demonstrated that exposure to ambient CO was positive with risk of deaths from all non-accidental causes, total cardiovascular, and respiratory diseases. Based on these findings, tougher intervention policies and initiatives to reduce the health effects of CO exposure should be established.

An Epidemiological Study to Investigate Links between Atmospheric Pollution from Farming and SARS-CoV-2 Mortality

Exposure to atmospheric particulate matter and nitrogen dioxide has been linked to SARS-CoV-2 infection and death. We hypothesized that long-term exposure to farming-related air pollutants might predispose to an increased risk of COVID-19-related death. To test this hypothesis, we performed an ecological study of five Italian Regions (Piedmont, Lombardy, Veneto, Emilia-Romagna and Sicily), linking all-cause mortality by province (administrative entities within regions) to data on atmospheric concentrations of particulate matter (PM_2.5 and PM₁₀) and ammonia (NH₃), which are mainly produced by agricultural activities. The study outcome was change in all-cause mortality during March-April 2020 compared with March-April 2015-2019 (period). We estimated all-cause mortality rate ratios (MRRs) by multivariate negative binomial regression models adjusting for air temperature, humidity, international import-export, gross domestic product and population density. We documented a 6.9% excess in MRR (proxy for COVID-19 mortality) for each tonne/km² increase in NH₃ emissions, explained by the interaction of the period variable with NH₃ exposure, considering all pollutants together. Despite the limitations of the ecological design of the study, following the precautionary principle, we recommend the implementation of public health measures to limit environmental NH₃ exposure, particularly while the COVID-19 pandemic continues. Future studies are needed to investigate any causal link between COVID-19 and farming-related pollution.

Estimation of the effects PM2.5, NO2, O3 pollutants on the health of Shahrekord residents based on AirQ+ software during (2012-2018)

Research objectives

Intertwined with modern life, air pollution is not a new phenomenon. Air pollution imposes a significant number of deaths and disease complications on society, and therefore it is very important to determine the extent of its effects on health in any society. This study sought to evaluate the concentration and short-term and long-term excess mortality attributed to PM2.5, NO₂ and O3 observed in Shahrekord.

Procedure

Hourly concentrations of PM2.5, O₃, and NO₂ measured at different stations of the Shahrekord Monitoring Network were obtained from the Shahrekord Department of Environment (DOE). Then, for different air quality monitoring stations, the average 24-hour PM2.5 concentration, the one-hour average NO₂ concentration and the maximum 8-hour daily O₃ concentration were calculated using Excel 2010. When the maximum 8-hour daily ozone level exceeds 35, it drops below 35 to calculate the SOMO35 index for modeling.

Results

The death rates of IHD, COPD, lung cancer and ALRI and stroke related to PM2.5 were 176, 7, 0, 10, 105, respectively. The effect of ozone on respiratory mortality was zero. During the study period in Shahrekord, no respiratory mortality was determined due to ozone and acute lower respiratory tract infection (ALRI). this study is first ever study on health effects of air pollution in shahrekord city.

Conclusion

A significant number of deaths due to air pollutants in Shahrekord have been reported. It can be concluded that by designing and implementing strategies and measures to control air pollution, both health effects and economic losses are prevented.

Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework

Given the serious adverse health effects associated with many pollutants, and the inequitable distribution of these effects between socioeconomic groups, air pollution is often a focus of environmental justice (EJ) research. However, EJ analyses that aim to illuminate whether and how air pollution hazards are inequitably distributed may present a unique set of requirements for estimating pollutant concentrations compared to other air quality applications. Here, we perform a scoping review of the range of data analytic and modeling methods applied in past studies of air pollution and environmental injustice and develop a guidance framework for selecting between them given the purpose of analysis, users, and resources available. We include proxy, monitor-based, statistical, and process-based methods. Upon critically synthesizing the literature, we identify four main dimensions to inform method selection: accuracy, interpretability, spatiotemporal features of the method, and usability of the method. We illustrate the guidance framework with case studies from the literature. Future research in this area includes an exploration of increasing data availability, advanced statistical methods, and the importance of science-based policy.

Toxicological effects of air settled particles from the Vitoria Metropolitan Area mediated by oxidative stress, pro-inflammatory mediators and NFΚB pathway

Atmospheric pollution is a major environmental and public health risk due to its effect on global air quality and climate. Increase in pollutants concentrations, especially particulate matter (PM), are associated with increased respiratory diseases. The pathophysiology of respiratory diseases involves molecular and cellular mechanisms as inflammatory biomarkers and reactive oxygen species production. Thus, the present study aimed to investigate the in vitro cytotoxic and pro-inflammatory effects of particulate matter (PM) of six monitoring stations (1-6) from the Vitoria Metropolitan Area (VMA), Espirito Santo, Brazil in 2018. The PM was chemically characterized by inductively coupled plasma mass spectrometry. In vitro cytotoxic effects of PM (3.12-200.0 μg/mL) were analyzed in human lung epithelial cells (A549) and macrophage cells (RAW 264.7) by MTT assay (3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide). To investigate the pro-inflammatory effects of PM in RAW 264.7 cells, the levels of proinflammatory mediators such as nitric oxide (NO), superoxide anion (O₂^•-), tumor necrosis factor (TNF-α), interleukin 6 (IL-6), and the activation of nuclear factor kappa B (NF- κB) were measured. The comet assay evaluated genotoxicity. Cell cycle, oxidative stress (DCF and DHE), and apoptosis were analyzed by flow cytometry. Chemical analysis of PM revealed aluminum (Al) and Iron (Fe) as the major chemical elements in all studied monitoring stations. In addition, worrying concentrations of mercury (Hg) were detected in the PM. The in vitro results showed that PM presents a dose-dependent cytotoxic effect in macrophage and pulmonary epithelial cell lines. The PM increased the production of NO, O₂^•-, and pro-inflammatory cytokines TNF-α and IL-6. PM also promoted alterations in the cell cycle, increased apoptosis frequency, and DNA damage. Moreover, PM increased the expression NF-κB. In addition, a positive correlation between Al and Fe and ROS production was observed. Based on the results obtained during the study period, it was concluded that the sedimented particles from the VMA might have deleterious effects on human health, which was evidenced by the increase in oxidative stress, an increase in pro-inflammatory mediators, and genotoxic effects partially mediated by the NF-κB pathway. These results add aspects to elucidate the molecular mechanisms involved in the effects of sedimented particles in vivo and in vitro.

Smart Multi-Sensor System for Remote Air Quality Monitoring Using Unmanned Aerial Vehicle and LoRaWAN

Deaths caused by respiratory and cardiovascular diseases have increased by 10%. Every year, exposure to high levels of air pollution is the cause of 7 million premature deaths and the loss of healthy years of life. Air pollution is generally caused by the presence of CO, NO₂, NH₃, SO₂, particulate matter PM₁₀ and PM_2.5, mainly emitted by economic activities in large metropolitan areas. The problem increases considerably in the absence of national regulations and the design, installation, and maintenance of an expensive air quality monitoring network. A smart multi-sensor system to monitor air quality is proposed in this work. The system uses an unmanned aerial vehicle and LoRa communication as an alternative for remote and in-situ atmospheric measurements. The instrumentation was integrated modularly as a node sensor to measure the concentration of carbon monoxide (CO), nitrogen dioxide (NO₂), ammonia (NH₃), sulfur dioxide (SO₂), and suspended particulate mass PM₁₀ and PM_2.5. The optimal design of the multi-sensor system has been developed under the following constraints: A low weight, compact design, and low power consumption. The integration of the multi-sensor device, UAV, and LoRa communications as a single system adds aeeded flexibility to currently fixed monitoring stations.

An Integration Method for Regional PM2.5 Pollution Control Optimization Based on Meta-Analysis and Systematic Review

PM_2.5 pollution in China is becoming increasingly severe, threatening public health. The major goal of this study is to evaluate the mortality rate attributed to PM_2.5 pollution and design pollution mitigation schemes in a southern district of China through a two-objective optimization model. The mortality rate is estimated by health effect evaluation model. Subjected to limited data information, it is assumed that the meta-analysis method, through summarizing and combining the research results on the same subject, was suitable to estimate the percentage of deaths caused by PM_2.5 pollution. The critical parameters, such as the total number of deaths and the background concentration of PM_2.5, were obtained through on-site survey, data collection, literature search, policy analysis, and expert consultation. The equations for estimating the number of deaths caused by PM_2.5 pollution were established by incorporating the relationship coefficient of exposure to reaction, calculated residual PM_2.5 concentration of affected region, and statistical total base number of deaths into a general framework. To balance the cost from air quality improvement and human health risks, a two-objective optimization model was developed. The first objective is to minimize the mortality rate attributable to PM_2.5 pollution, and the second objective is to minimize the total system cost over three periods. The optimization results demonstrated that the combination of weights assigned to the two objectives significantly influenced the model output. For example, a high weight value assigned to minimizing the number of deaths results in the increased use of treatment techniques with higher efficiencies and a dramatic decrease in pollutant concentrations. In contrast, a model weighted more toward minimizing economic loss may lead to an increase in the death toll due to exposure to higher air pollution levels. The effective application of this model in the Nanshan District of Shenzhen City, China, is expected to serve as a basis for similar work in other parts of the world in the future.

Particulate pollution over an urban Himalayan site: Temporal variability, impact of meteorology and potential source regions

Five-year (2013-2017) particulate matter (PM) data observed at an urban site, Srinagar, Kashmir Himalaya, India was used to examine the temporal variability, meteorological impacts and potential source regions of PM. The daily mean PM₁₀ and PM_2.5 concentration was 135 ± 112 μg/m³ and 87 ± 93 μg/m³ respectively with significant intra- and inter-daily variation. The annual PM₁₀ and PM_2.5 concentration was 2.0-3.2 and 1.7-2.8 times higher than the annual Indian National Ambient Air Quality Standards (PM₁₀ = 60 μg/m³ and PM_2.5 = 40 μg/m³). PM concentration shows a bimodal diurnal pattern with morning and evening peaks, which coincide with the increased anthropogenic activity and shallow planetary boundary layer (PBL). The combined effect of the low temperature, low wind speed, shallow and stable PBL and geomorphic setup of Kashmir valley leads to the accumulation of particulate pollution during autumn and winter and the converse meteorological conditions leads to dispersion, dilution and deposition during spring and summer. High precipitation rate (>15 mm/day) removes the coarse particles (PM₁₀) more efficiently than fine particles (PM_2.5), while as the moderate to high humid conditions (55-95%) leads to the accumulation and growth of more PM. It was observed that ~80% of the air masses arriving at the site during spring, autumn and winter are westerlies. Source contribution analysis revealed that highly potential source regions of PM at the site are neighboring Pakistan, Afghanistan, parts of Iran and Trans-Gangetic Plains, which could contribute high concentration of the PM₁₀ (>250 μg/m³) and PM_2.5 (>150 μg/m³) during autumn and winter. The high PM load observed at the site during autumn and winter, with major contribution from the anthropogenic source emissions like biomass and coal burning, fossil fuel combustion and suspension of road dust, is aggravated by the geomorphic and meteorological setup of the Kashmir valley.

Extracellular Vesicles: Footprints of environmental exposures in the aging process?

PURPOSE OF THE REVIEW

Extracellular vesicles (EVs) are nano-sized lipid particles that participate in intercellular signaling through the trafficking of bioactive molecules from parental cells to recipient ones. This well-orchestrated communication system is crucial for the organism to respond to external cues in a coordinated manner; indeed, environmental and lifestyle exposures can modify both EV number and content, with consequences on cellular metabolism and homeostasis. In particular, a growing body of evidence suggests that exposome-induced changes in EV profile could regulate the aging process, both at the cellular and organismal levels. Here, we provide an overview of the role played by ambient-induced EVs on aging and age-related diseases. Among the several environmental factors that can affect the communication network operated by EVs, we focused on air pollution, ultraviolet light, diet, and physical exercise. Moreover, we performed a miRNA target analysis, to support the role of EV-miRNA emerging from the literature in the context of aging.

RECENT FINDINGS

The overall emerging picture strongly supports a key regulatory role for EVs at the interface between external stimuli and cellular/organismal aging, thus providing novel insights into the molecular mechanisms linking a "healthy exposome" to well-being in old age. In addition, this knowledge will pave the way for research aimed at developing innovative antiaging strategies based on EVs.

Acute responses of airway oxidative stress, inflammation, and hemodynamic markers to ambient PM2.5 and their trace metal contents among healthy adolescences: A panel study in highly polluted versus low polluted regions

Particulate air pollutants are known contributors to global cardiorespiratory mortality through several pathways. We examined the effects of varied exposure to PM_2.5 and trace metals on biological markers of airway inflammation, oxidative stress, and hemodynamic function of young individuals living in two different exposure settings. We enrolled and followed a panel of 97 healthy nonsmoking participants aged 15-18 years living in a highly polluted metropolitan city of Tabriz (TBZ) and a much less polluted semi-urban town of Hadishahr (HDS). For five consecutive months, the subjects were examined by a physician, and fractional exhaled nitric oxide levels (FE_NO) were measured. Samples of exhaled breath condensation (EBC) were obtained for measuring interleukin 6 (IL-6), tumor necrosis factor α (TNF-α), and total nitric oxide (NO_x). We measured daily outdoor PM_2.5 mass concentration in a fixed station in each location for all this period. The PM-metal content was analyzed by ICP-MS. The linear mixed-effects regression models were applied for data analysis. The averages of PM_2.5 mass and total metals in TBZ were nearly two and four times higher than in HDS, respectively. In TBZ, an increased IQR of PM_2.5 mass during 0-5 days was -correlated with a significant rise in diastolic blood pressure, heart rate, TNF-α, FE_NO, and NO_x and reduction of IL-6. Moreover, exposure to low PM_2.5 concentration is significantly -correlated with an elevation in diastolic blood pressure in HDS. We also observed that exposure to metal constituents in the highly polluted region is correlated with increased TNF-α and IL-6 with 131.80% (95% CI: 56.01, 244.39) and 47.51% (95% CI: 33.01, 62.05) per IQR of Hg, respectively. This study suggests that exposure to ambient PM_2.5 and their metal contents in highly polluted areas may incite significant changes in airway inflammation, oxidative stress, and hemodynamic parameters in healthy subjects.

Long- and Short-Term Exposures to PM10 Can Shorten Telomere Length in Individuals Affected by Overweight and Obesity

Reduced telomere length (TL) has been associated with increased risk of age-related diseases, most likely through oxidative stress and inflammation, which have also been claimed as mechanisms underlying health effects of air pollution exposure. We aimed to verify whether exposure to particulate matter with diameter ≤10 µm (PM₁₀) affects TL. We recruited 1792 participants with overweight/obesity in Milan (Italy) in 2010-2015 who completed a structured questionnaire on sociodemographic data, gave a blood sample for TL measurement by real-time PCR, and were assigned air pollution and meteorological data of their residential address. In multivariate mixed-effects linear models (with a random intercept on PCR plate), we observed a -0.51% change in TL (95% confidence interval (CI): -0.98; -0.05)) per 10 µg/m³ increase in PM₁₀ at the day of recruitment. A similar decreasing trend in TL was observed up to two weeks before withdrawal, with percentage changes as low as -1.53% (average exposure of the 12 days before recruitment). Mean annual exposure to PM₁₀ was associated with -2.57% TL reduction (95%CI: -5.06; -0.08). By showing consistent associations between short- and long-term PM₁₀ exposures and reduced TL, our findings shed light on the potential mechanisms responsible for the excess of age-related diseases associated with air pollution exposure.

Heat-related mortality under climate change and the impact of adaptation through air conditioning: A case study from Thessaloniki, Greece

Climate change is expected to increase heat-related mortality across the world. Health Impact Assessment (HIA) studies are used to quantify the impact of higher temperatures, taking into account the effect of population adaptation. Although air-conditioning (AC) is one of the main drivers of technological adaptation to heat, the health impacts associated with AC-induced air pollution have not been examined in detail. This study uses the city of Thessaloniki, Greece as a case study and aims to estimate the future heat-related mortality, the residential cooling demand, and the adaptation trade-off between averted heat-related and increased air pollution cardiorespiratory mortality. Using temperature and population projections under different Coupled Model Intercomparison Project Phase 6 (CIMP6) Shared Socioeconomic Pathways scenarios (SSPs), a HIA model was developed for the future heat and air pollution cardiorespiratory mortality. Counterfactual scenarios of either black carbon (BC) or natural gas (NG) being the fuel source for electricity generation were included in the HIA. The results indicate that the heat-related cardiorespiratory mortality in Thessaloniki will increase and the excess of annual heat-related deaths in 2080-2099 will range from 2.4 (95% CI: 0.0-20.9) under SSP1-2.6 to 433.7 (95% CI: 66.9-1070) under SSP5-8.5. Population adaptation will attenuate the heat-related mortality, although the latter may be counterbalanced by the higher air pollution-related mortality due to increased AC, especially under moderate SSP scenarios and coal-fired power plants. Future studies examining the health effects of warmer temperatures need to account for the impact of both adaptation and increased penetration and use of AC.

Taking a deep breath: a qualitative study exploring acceptability and perceived unintended consequences of charging clean air zones and air quality improvement initiatives amongst low-income, multi-ethnic communities in Bradford, UK

BACKGROUND

Poor air quality is the one of the biggest causes of early death and illness across the lifespan. In the UK, 28 local authorities with illegal pollution levels have been mandated by the Government to develop plans to rapidly reduce pollution to legal limits. These plans include consideration of implementing one of four of charging 'Clean Air Zone' (CAZ) classes in areas of high pollution which would charge older polluting vehicles a daily charge to enter. While this offers a potential to improve air quality, the extent to which CAZ might impact (for example, economically) on socio-economically deprived groups and local businesses is unclear.

AIMS

To explore the acceptability and perceived unintended consequences of a CAZ and other initiatives to improve air quality with seldom-heard communities living in deprived, multi-ethnic areas within the city of Bradford, UK.

METHODS

Ten semi-structured focus groups were conducted with people who live in areas of high pollution and deprivation. A total of 87 people participated from a diverse range of ethnic backgrounds with the majority of Pakistani origin. Recorded data were transcribed, coded and analysed using thematic analysis.

FINDINGS

As poor air quality was not always visible it was seen as a hidden issue by many, and not prioritised over other more visible environmental issues (e.g. fly-tipping, littering). There was resistance to proposals which included charging private vehicles. Many felt that low-income families did not have the resources to purchase compliant vehicles or pay daily charges, placing a disproportionate burden on them. It was also felt that low-income taxi drivers would be disproportionately affected financially by proposals. Public transport infrastructure was felt to be inadequate. Other traffic management or emission reduction activities were also explored. Views towards these initiatives were more positive if they did not directly affect individuals financially.

CONCLUSION

Air quality initiatives such as CAZs were felt to be likely to financially disadvantage communities already living in socio-economic and environmental poverty. Policy makers need to carefully consider appropriate mitigation strategies to ensure that health and economic inequalities are not increased by implementation of CAZ. Given air quality is low priority for some groups, careful engagement and communication will be required to increase acceptance interventions such as CAZs.

The association between air pollution and COVID-19 related mortality in Santiago, Chile: A daily time series analysis

BACKGROUND

Exposure to ambient air pollution is a risk factor for morbidity and mortality from lung and heart disease.

RESEARCH QUESTION

Does short term exposure to ambient air pollution influence COVID-19 related mortality?

STUDY DESIGN AND METHODOLOGY

Using time series analyses we tested the association between daily changes in air pollution measured by stationary monitors in and around Santiago, Chile and deaths from laboratory confirmed or suspected COVID-19 between March 16 and August 31, 2020. Results were adjusted for temporal trends, temperature and humidity, and stratified by age and sex.

RESULTS

There were 10,069 COVID-19 related deaths of which 7659 were laboratory confirmed. Using distributed lags, the cumulative relative risk (RR) (95% CI) of mortality for an interquartile range (IQR) increase in CO, NO₂ and PM_2.5 were 1.061 (1.033-1.089), 1.067 (1.023-1.103) and 1.058 (1.034-1.082), respectively There were no significant differences in RR by sex.. In those at least 85 years old, an IQR increase in NO₂ was associated with a 12.7% (95% CI 4.2-22.2) increase in daily mortality.

CONCLUSION

This study provides evidence that daily increases in air pollution increase the risk of dying from COVID-19, especially in the elderly.

Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases

The prediction of hospital emergency room visits (ERV) for respiratory diseases after the outbreak of PM_2.5 is of great importance in terms of public health, medical resource allocation, and policy decision support. Recently, the machine learning methods bring promising solutions for ERV prediction in view of their powerful ability of short-term forecasting, while their performances still exist unknown. Therefore, we aim to check the feasibility of machine learning methods for ERV prediction of respiratory diseases. Three different machine learning models, including autoregressive integrated moving average (ARIMA), multilayer perceptron (MLP), and long short-term memory (LSTM), are introduced to predict daily ERV in urban areas of Beijing, and their performances are evaluated in terms of the mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²). The results show that the performance of ARIMA is the worst, with a maximum R² of 0.70 and minimum MAE, RMSE, and MAPE of 99, 124, and 26.56, respectively, while MLP and LSTM perform better, with a maximum R² of 0.80 (0.78) and corresponding MAE, RMSE, and MAPE of 49 (33), 62 (42), and 14.14 (9.86). In addition, it demonstrates that MLP cannot detect the time lag effect properly, while LSTM does well in the description and prediction of exposure-response relationship between PM_2.5 pollution and infecting respiratory disease.

Bayesian Methods for Meta-Analyses of Binary Outcomes: Implementations, Examples, and Impact of Priors

Bayesian methods are an important set of tools for performing meta-analyses. They avoid some potentially unrealistic assumptions that are required by conventional frequentist methods. More importantly, meta-analysts can incorporate prior information from many sources, including experts’ opinions and prior meta-analyses. Nevertheless, Bayesian methods are used less frequently than conventional frequentist methods, primarily because of the need for nontrivial statistical coding, while frequentist approaches can be implemented via many user-friendly software packages. This article aims at providing a practical review of implementations for Bayesian meta-analyses with various prior distributions. We present Bayesian methods for meta-analyses with the focus on odds ratio for binary outcomes. We summarize various commonly used prior distribution choices for the between-studies heterogeneity variance, a critical parameter in meta-analyses. They include the inverse-gamma, uniform, and half-normal distributions, as well as evidence-based informative log-normal priors. Five real-world examples are presented to illustrate their performance. We provide all of the statistical code for future use by practitioners. Under certain circumstances, Bayesian methods can produce markedly different results from those by frequentist methods, including a change in decision on statistical significance. When data information is limited, the choice of priors may have a large impact on meta-analytic results, in which case sensitivity analyses are recommended. Moreover, the algorithm for implementing Bayesian analyses may not converge for extremely sparse data; caution is needed in interpreting respective results. As such, convergence should be routinely examined. When select statistical assumptions that are made by conventional frequentist methods are violated, Bayesian methods provide a reliable alternative to perform a meta-analysis.

A Population-Based Cohort Study of Respiratory Disease and Long-Term Exposure to Iron and Copper in Fine Particulate Air Pollution and Their Combined Impact on Reactive Oxygen Species Generation in Human Lungs

Metal components in fine particulate matter (PM_2.5) from nontailpipe emissions may play an important role in underlying the adverse respiratory effects of PM_2.5. We investigated the associations between long-term exposure to iron (Fe) and copper (Cu) in PM_2.5 and their combined impact on reactive oxygen species (ROS) generation in human lungs, and the incidence of asthma, chronic obstructive pulmonary disease (COPD), COPD mortality, pneumonia mortality, and respiratory mortality. We conducted a population-based cohort study of ∼0.8 million adults in Toronto, Canada. Land-use regression models were used to estimate the concentrations of Fe, Cu, and ROS. Outcomes were ascertained using validated health administrative databases. We found positive associations between long-term exposure to Fe, Cu, and ROS and the risks of all five respiratory outcomes. The associations were more robust for COPD, pneumonia mortality, and respiratory mortality than for asthma incidence and COPD mortality. Stronger associations were observed for ROS than for either Fe or Cu. In two-pollutant models, adjustment for nitrogen dioxide somewhat attenuated the associations while adjustment for PM_2.5 had little influence. Long-term exposure to Fe and Cu in PM_2.5 and estimated ROS concentration in lung fluid was associated with increased incidence of respiratory diseases, suggesting the adverse respiratory effects of nontailpipe emissions.