Adverse Effects Of Air Pollution Research Articles

Investigating the patterns of air pollution exposure on major cardiovascular events by cluster analysis. Insights from the EP-PARTICLES study on a Polish smog cohort

Abstract Introduction Exposure to air pollution (AP) is a modifiable risk factor for cardiovascular diseases. Aim To analyse the short-term effects of AP on the occurrence of acute coronary syndromes (ACS). Methods The EP-PARTICLES study consists of a retrospective nationwide cohort of Eastern Poland residents (N= 8,077,671). The area is characterized by a specific type of pollution - Polish smog, featuring high concentrations of particulate matter (PM), benzo(a)pyrenes (BaP) and favourable atmospheric conditions of formation. Medical data were collected from the National Health Fund and the Central Statistical Office. The records included sex and age and the causes of hospitalization. The analysis utilized air quality modeling data from the GEM-AQ system, which allowed to provide daily pollution concentration at the level of each LAU (ie. local administrative units, at municipality level), throughout the observation period. We estimated LAU specific associations of AP concentration using quasi-Poisson generalized additive models. We controlled our models for long-term trends and weather conditions. In the second stage, we pooled LAU-specific estimates with a random-effects meta-analysis. The results are presented as % risk ratio (RR), and 95% confidence intervals (95% CIs) for the two-day moving average of AP concentration on the present day and the day after exposure (ie. LAG0-1). Cluster analysis was performed on LAU levels from 19 variables using the k-mens clustering using the Ward minimum variance method. The set of variables included demographic, ecological, socioeconomic, environmental conditions. Results Over the years 2011-20, we noted 2,141,213 hospitalizations due to CVD, including 139,697 ACS cases (45.2% STEMI). The main triggers for ACS were PM2.5 (RRLAG0-1 1.04 95%CI 1.028-1.053), BaP (RRLAG0-1 1.021 95%CI 1.01-1.032). We identified three distinct clusters: Cluster 1 was characterized by high population density and GPD per capita and highest prevalence of atrial fibrillation. Cluster 2 (40% of the entire population) exhibited significantly high variability, characterized by low population density and GPD per capita, the highest prevalence of heart failure, people drinking alcohol and smoking tobacco. Cluster 3 (7% of the entire population) was in the middle but showed the highest prevalence of hypertension, diabetes and renal failure. Cluster 2 inhabited areas with low concentrations of PMs and BaP and demonstrated increased vulnerability to the effects of PM2.5 and BaP from 3% to 5.2% (P<0.001). For details see Figure 1. Conclusions PM and BaP are important factors associated with the occurrence of MACE. AP exposure should be considered in CVD prevention models. Cluster classification helps in risk stratification and prognosis. Adverse effects of AP can be mitigated by attention to improving socio-economic status and hazardous habits.Associations between AP exposure and ACS

Impact of physical activity levels on the association between air pollution exposures and glycemic indicators in older individuals

BackgroundAir pollution may exacerbate diabetes-related indicators; however, the longitudinal associations between air pollutant concentrations and glycemic markers remain unclear. In this prospective cohort study, we examined the longitudinal associations between air pollution and glycemic indicators among older individuals with normoglycemia at baseline and determined whether these associations differed according to changes in physical activity levels.MethodsOverall, 1,856 participants (mean age, 70.9 years) underwent baseline and 4-year follow-up surveys. We used linear mixed-effect models to examine the associations between previous 1-year exposures to air pollutants and glycemic indicators. We further investigated associations between previous 5-year exposures to air pollutants and glycemic indicators after the inverse probability of treatment weighting (IPTW). We explored effect modifications by the level of physical activity maintenance and changes in metabolic equivalent of task (METs) for physical activity.ResultsLevels of particulate matter with aerodynamic diameters ≤ 10 μm (PM10) and ≤ 2.5 μm, and nitrogen dioxide (NO2) were significantly associated with increased fasting blood glucose, Hemoglobin A1c, insulin, and homeostatic model assessment for insulin resistance (HOMA-IR) values. After IPTW, the associations remained significant for PM10 and NO2. The positive associations of NO2 with insulin and HOMA-IR remained significant in the maintained inactive group, but not in the maintained moderate-to-vigorous active group. The positive associations of PM10 or NO2 with insulin and HOMA-IR remained significant in the group with increased METs, but not in those with decreased METs. In the post-hoc analysis of non-linear relationships between an increase in METs and glycemic indicators, insulin and HOMA-IR remarkably increased in the higher PM10 and NO2 exposure group from the point of 12,000 and 13,500 METs-min/week increase, respectively.ConclusionsWe demonstrated longitudinal associations between air pollution exposures and increased insulin resistance in older individuals. Maintaining moderate-to-vigorous physical activity may mitigate the adverse effects of air pollution on insulin resistance. In older individuals dwelling in highly polluted areas, an increase of less than 12,000 METs-min/week may be beneficial for insulin resistance.

Air pollution exposure and inflammatory bowel disease: a systematic literature review of epidemiological and mechanistic studies

This systematic literature review investigates the relationship between air pollution exposure and inflammatory bowel disease (IBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC). Despite the growing concern over air pollution's impact on various health outcomes, studies specifically addressing effects on the digestive system, particularly IBD, are relatively rare. This review aims to synthesize the current knowledge on this topic, focusing on the mechanisms underlying these associations and the role of different air pollutants. Following PRISMA guidelines, a systematic literature search of PubMed and Web of Science databases was conducted, yielding 13 epidemiological studies and six mechanistic (toxicological) studies meeting the inclusion criteria. The epidemiological studies examined associations between IBD and various air pollutants, including PM2.5 (particles with an aerodynamic diameter smaller than, or equal to, 2.5 µm), PM10 (particles with an aerodynamic diameter smaller than, or equal to, 10 µm), PM2.5–10 (coarse particles with an aerodynamic diameter in the range of 2.5–10 µm), BC (black carbon), NO2 (nitrogen dioxide), NO (nitrogen monoxide), NOx (nitrogen oxides), N2O (nitrous oxide), CO (carbon monoxide), SO2 (sulfur dioxide), VOC (volatile organic compounds), O3 (ozone), Ox (oxidant capacity), and traffic load. Study methodologies varied among these 13 epidemiological studies, including four cohort studies, two ecological studies, three case-control studies, two studies using two-sample Mendelian randomization, and two longitudinal time-series studies. Eight studies investigated associations with Crohn's disease and ulcerative colitis separately, while five studies analyzed IBD as a whole without distinguishing between CD and UC. Eleven studies found statistically significant associations between air pollution exposure and IBD, although inconsistent results were found in several of these studies. A total number of six mechanistic (toxicological) studies were retrieved. Among these six studies, five were using particulate matter as exposure metric, and one was based on NO2 and O3 as exposure metrics. With a combination of animal, human, and in vitro studies, the results in terms of biological mechanisms indicate that air pollution exposure influences the composition of the gut microbiome, altering metabolic functions within the gut, and creates immunological reactions with inflammation contributing to the development of IBD. Consequently, the results suggest a link between air pollution exposure and both the onset and exacerbation of IBD. However, differences in study design, exposure assessment, and pollutant types make it challenging to draw any firm conclusions. Moreover, the lack of multi-pollutant models in most epidemiological studies makes it difficult to estimate the individual effect of specific air pollutants. This review highlights the need for further research utilizing robust study designs and standardized exposure assessment methods to better understand the impact of air pollution on IBD. By elucidating these associations, policymakers and healthcare professionals can develop effective strategies to mitigate the adverse effects of air pollution on digestive health.

AYURVEDIC LIFESTYLE RECOMMENDATIONS FOR LIVING IN HIGH POLLU-TION AREAS - A REVIEW

Air pollution refers to the presence of harmful or excessive quantities of substances in the air that can be detrimental to human health, the environment, and the climate. These substances, often called pollutants, can originate from natural and human-made sources. Air pollution is a significant risk to public health, particularly in municipal and industrialised regions. This article explores Ayurvedic lifestyle recommendations to diminish the adverse effects of air pollution on health. Ayurved is an ancient system of natural healing. It can offer a holistic approach to strengthening the body's defence system and promoting overall well-being in polluted environments. Key recommendations include dietary modifications, herbal Rasayanas, breathing exercises, and daily routines that enhance the body's natural detoxification processes. By integrating Ayurvedic practices with modern preventive measures, individuals can better protect themselves from the harmful impacts of air pollution and improve their quality of life.

Emerging Scientific Approaches for Identifying Ecologically Adverse Effects of Air Pollution.

Now more than ever, complex socio-ecological challenges require timely and integrated responses from scientists and policymakers. Air quality is one such challenge. Under the Clean Air Act, the U.S. Environmental Protection Agency establishes ambient air quality standards to protect public welfare from known or anticipated adverse effects of air pollutants. As our understanding of the environment and awareness of social values grow, there is a need to improve characterization of "adversity to the public welfare." Scientific assessment can link ecological effects to public welfare using modern scientific approaches that incorporate ecological complexity and multiple value systems held by the public. We propose ideas for the future of scientific assessments meant to inform air quality and other environmental decision-making, including concrete ways we can focus on vulnerable species and ecosystems, incorporate a multiplicity of values, climate and multiple stressors, and partner to diversify the knowledge upon which protective policies are based.

Air pollution and issuance credit spread of municipal investment bond

This paper explores the impact of city-level air pollution on the financing costs of local and regional governments (LRGs) in China. The empirical results demonstrate that cities with poor air quality face higher credit spreads on bonds issued by local financing platforms (LFPs), underscoring the critical role of air quality in determining LRGs financing costs. Air pollution negatively affects the credit rating of municipal investment bonds and increases fiscal pressure on LRGs. However, when LRGs invest more in environmental protection, reduce reliance on tax revenue, enforce stricter environmental regulations, and implement auditing policy for natural resources management, the adverse effect of air pollution on bond credit spreads are mitigated. This study enriches the research on whether and how air pollution affects bond issuance pricing and provides insights into strategies for reducing LFPs financing costs and addressing LRGs debt crises.

Accurate PM2.5 urban air pollution forecasting using multivariate ensemble learning Accounting for evolving target distributions

Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fine particulate matter (PM2.5), a key component reflecting air quality, is the fourth leading cause of death worldwide after cardiovascular disease, smoking, and diet. Various research efforts have aimed to develop PM2.5 forecasting models that can be integrated into a solution to mitigate the adverse effects of air pollution. However, PM2.5 forecasting is challenging because air pollution data are non-stationary and influenced by multiple random effects. This paper proposes an effective multivariate multi-step ensemble machine learning model for predicting continuous 24-h PM2.5 concentrations, considering meteorological conditions, the rolling mean of PM2.5 time series, and temporal features. PM2.5 is strongly correlated with space and time. Therefore, forecasting results from one location are insufficient to represent the level of air pollution for an entire city. In this study, we established six real-time air quality monitoring sites in different regions, including traffic, residential, and industrial areas in Ho Chi Minh City (HCMC), and generated forecasting results for each station. Various statistical methods are incorporated to evaluate the performance of the model. The experimental results confirm that the model performs well, substantially improving its forecasting accuracy compared to existing PM2.5 forecasting models developed for HCMC. In addition, we analyze to determine the contribution of different feature groups to model performance. The model can serve as a reference for citizens scheduling local travel and for healthcare providers to provide early warnings.

Forecasting ground-level ozone and fine particulate matter concentrations at Craiova city using a meta-hybrid deep learning model

Air quality forecasting is vital for managing and mitigating the adverse effects of air pollution on human health, crops, and the environment. This study aims to forecast daily time series of ozone and fine particulate matter (PM) concentrations using a meta-hybrid deep NARMAX (Nonlinear Auto-Regressive Moving Average with eXogenous inputs) model. Two datasets were utilised: (a) data on meteorological parameters (temperature, air pressure, relative humidity) and air pollutant concentrations (particulate matter, ozone, dioxide of carbon, volatile organic compounds, formaldehyde) provided by a sensor model A3 situated in the centre of Craiova city, and (b) data on wind direction, wind speed, and sunshine duration provided by the National Meteorological Administration. The data sets covered a time interval from December 10, 2020, to January 05, 2024. Initially, a statistical analysis was conducted to assess the correlation between variables. Results revealed that ozone concentration is primarily influenced by meteorological variables such as temperature (r = 0.79), sunshine duration (r = 0.55), and relative humidity (r = −0.48), and secondarily by air pollution indicators including VOC (r = −0.34), PM concentrations (r = −0.34), and CO2 (r = −0.3). In the subsequent stage, thirteen Machine Learning (ML) models were employed in conjunction with an integral feature selection (IFS) method to identify the best combinations of predictor variables for predicting ozone and PMs. Finally, a deep NARMAX model was developed to forecast the next periods of ozone and PMs based on the optimal combinations identified earlier. Results indicated the selection of sixty best models for ozone forecasting and four best models for PMs. The R2 values surpassed 0.97 for ozone and exceeded 0.8 for PMs, demonstrating the efficacy of the forecasting approach.

Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season

Increasing air pollution has necessitated the prediction of pollutants over time. Deterministic, statistical, and machine-learning methods have been adopted to predict and forecast pollutant levels. It aids in planning and adopting measures to overcome the adverse effects of air pollution. This study employs long short-term memory (LSTM). This study used the hourly data from a meteorological station in a low-town area, Mohammedia City, Morocco. The model prediction accuracy was evaluated based on hourly, weekly, and seasonal (summer and winter) readings for the summer and winter of 2019, 2020 and 2021. Root mean square error (RMSE), mean absolute error (MAE) and mean arctangent absolute percentage error (MAAPE) were calculated to evaluate the accuracy of the developed LSTM model. The MAE value of 0.026 was observed to be less in winter than 0.029 during summer in 2019. Also, it was observed that MAE values decreased from Year 2019-2021, indicating increased prediction accuracy. MAAPE also observed a similar trend. However, RMSE values indicated the opposite for 2019 and 2020; in 2021, the RMSE value was 0.21 for summer and 0.14 for winter for hourly readings. Based on the error calculation, the study found weekly hourly readings were the most accurate for predicting SO2 concentration. Also, the LSTM model was more accurate in predicting winter SO2 concentration than in the summer season. Further studies must incorporate local incidences affecting the SO2 concentration into the LSTM model to increase its accuracy.

Air pollution and firm performance: the role of innovation and happiness

Purpose The detrimental effects of air pollution on the continuity of corporations attract more and more attention in the economic and financial studies. Prior literature investigates the impact of air pollution on corporate financial performance. This study aims to extend this research area by exploring the role of corporate innovation and happiness as factors that mitigate the adverse effects of air pollution and moderate the relationship between air pollution and financial performance. Design/methodology/approach This study uses two-step system generalized method of moments models to analyze the data of 200 firms listed on Istanbul Stock Exchange over the period 2009–2022. Findings The results show that firms located in regions with higher air pollution are more likely to invest in innovation. In addition, firms that are more exposed to air pollution and have investments in research and development (R&D) have less ability to improve their financial performance compared to firms that have no investments in R&D. In a similar vein, although R&D has positive effect on financial performance, this effect diminishes in the presence of higher air pollution. The results also show that happiness has no significant moderating effect on the relationship between air pollution and financial performance. Practical implications The findings of this study related to the role of corporate innovation in determining the effect of air pollution on financial performance indicate that the costs of investment in R&D weaken the firm’s ability to mitigate the adverse impact of air pollution on financial performance, which provides important signals to policymakers to concentrate more on supporting investment in corporate innovation by providing the necessary facilities for firms to improve their innovative performance and decrease the costs of investment in innovation. Originality/value To the author’s knowledge, this research is the first to explore the influence of happiness on the air pollution–financial performance relationship. In addition, this study differs from most prior ones by examining how responding to air pollution through investment in innovation can moderate the association between air pollution and financial performance.

Microcontroller-Based Air Pollution Monitoring System

The increase in air pollution is a serious environmental issue in many major cities in Indonesia. This study aims to develop an effective and efficient air pollution monitoring system using a microcontroller. The designed system consists of air pollution sensors capable of detecting various pollutants such as PM10, CO2, and NO2. The data collected from the sensors are processed by the microcontroller and displayed in real-time to users through a web-based interface. This method allows users to accurately and promptly monitor the air quality in their surroundings. The results show that the microcontroller-based monitoring system operates stably and provides accurate data on the concentration of pollutants in the air. This system is expected to be a valuable tool for the general public and authorities in monitoring and managing air quality, as well as taking preventive measures against the adverse effects of air pollution.

The association between airborne particulate matter (PM2.5) exposure level and primary open-angle glaucoma

The eye is vulnerable to the adverse effects of air pollution. Previous experimental study found that fine particulate matter (PM2.5) had a direct toxic effect on intraocular tissues. However, clinical evidence for the impact of air pollutants exposure on functional and structural changes in glaucoma remains scarce. A total of 120 patients with primary open-angle glaucoma (POAG) who met the inclusion criteria were included in this retrospective study. The standardized ophthalmic examination, such as intraocular pressure (IOP), visual field, optical coherence tomography, and comprehensive physical examination, were performed. The air pollution data, including PM2.5 concentration and air quality index (AQI), were collected. PM2.5 and AQI for the day of the medical examination, as well as one month, and three months before the medical examination date, were investigated. In our results, higher average exposure levels for one-month and three-month, were associated with increased IOP (r=0.229, P=0.013; r=0.204, P=0.028, respectively) and decreased visual field mean sensitivity (MS) (r=-0.212, P=0.037; r=-0.305, P=0.002, respectively). PM2.5 concentrations for the day of the medical examination was not significantly associated with ocular parameters. In multiple linear regression analysis adjusted for demographic and clinical factors, higher PM2.5 exposure for one month was associated with elevated IOP (P=0.040, β=0.173, 95 %CI=0.008–0.337). We also found an association between PM2.5 and MS (one-month exposure: β=-0.160, P=0.029; three-month exposure: β=-0.238, P=0.002). The logistic regression analysis found that three-month average PM2.5 exposure level was significantly associated with the disease severity (β=0.043, P=0.025, 95 %CI=1.005–1.084). In conclusion, this study is the first to investigate the relationship between air pollution and detailed ocular parameters of POAG patients in Shanghai over a three-year period, and to explore the effects of different exposure times of PM2.5 on glaucoma. This study found that PM2.5 exposure was correlated with elevated IOP and decreased MS. The one-month PM2.5 exposure level had the most significant effects on IOP. The three-month PM2.5 exposure level was an independent risk factor for POAG severity. Current evidence suggests there may be an association between PM2.5 exposure and POAG.

Effects of air pollution on global health: evidence from the global burden of disease study in the BRICS countries.

Considering the dynamic influence of environmental, social, economic, and political factors in the emergence and growth of the BRICS countries (Brazil, Russia, India, China, and South Africa) over the years and pre-existing differences, the adverse effects of air pollution on the health and well-being of the people have remained major areas of academic inquiry and policy interventions. The present study examines the global trend of deaths and Disability Adjusted Life Years (DALYs) attributable to air pollution with particular reference to the BRICS countries for the period 1990 to 2019. This study has used the global burden of disease estimates by using different rounds of the Global Burden of Disease (GBD) study report published by the Institute of Health Metrics Evaluation. This study has calculated the cause of death and DALYs due to environmental risk factors (i.e. Air pollution). Data analysis has been done by using the standard formula for the calculation of death (mortality) rate and DALYs rate. Similarly, we calculated the age and gender-wise death and DALYs rate by using the appropriate numerator and denominator. The study discovered a significant shift in disease patterns over this period, as communicable diseases like respiratory infections and tuberculosis were replaced by non-communicable diseases such as ischemic heart disease (17.2 million), chronic obstructive pulmonary disease (14.59 million), and stroke (17.02 million) as the primary causes of air pollution-related deaths in 2019 at the global level. Additionally, the study identified a worrying increase in deaths linked to neonatal disorders and respiratory infections caused by ambient particulate matter pollution in South Africa, India, and Brazil. The impact of air pollution on public health is evident across different age groups and genders, with people aged 50-69 years, those aged 70 and above, and children under 5 years being more vulnerable. Furthermore, the male population is disproportionately affected by communicable and noncommunicable diseases caused by air pollution. The study highlights the need for policymakers to implement evidence-based interventions to tackle this global health problem. The interventions should aim to reduce the emerging crisis of non-communicable diseases related to air pollution, particularly among vulnerable age groups and the male population, ultimately improving public health outcomes.

Several Factors Responsible For Sick Building Syndrome in Urban Settings: Literature Review

Introduction: Urban dwellers are more susceptible to the adverse effects of air pollution and climate change. Sick building syndrome (SBS) relates to health issues experienced by workers as a result of indoor activities, air pollution, and climate change. The incidence of SBS is strongly correlated with environmental factors both within and outside the workplace. Objective: To identify current factors associated with the incidence of sick building syndrome in urban areas Method: Review of the literature compiled in both English and Indonesian from the databases of PubMed, Scopus, and Google Scholar. The literature, which is available in full text publications, was collected during the period of the last five years, from 2019 to 2024. Results: Temperature, relative humidity, microbes, air pollution, psychological factors, light, and ventilation in a room or workplace were some of the variables that affect the incidence of sick building syndrome in urban settings. Conclusion: SBS was related to a number of factors, including temperature, relative humidity, ventilation, lighting, pathogens, and psychosocial factors. These elements may have an individual or combined effect on worker productivity and the development of SBS.

Adverse Effects Of Air Pollution Vary By ASCVD Risk: Insights From The PROMISE Trial

Adverse Effects Of Air Pollution Vary By ASCVD Risk: Insights From The PROMISE Trial

Health effect of multiple air pollutant mixture on sarcopenia among middle-aged and older adults in China

BackgroundAs the global aging process accelerates, the health challenges posed by sarcopenia among middle-aged and older adults are becoming increasingly prominent. However, the available evidence on the adverse effects of air pollution on sarcopenia is limited, particularly in the Western Pacific region. This study aimed to explore relationships of multiple air pollutants with sarcopenia and related biomarkers using the nationally representative database. MethodsTotally, 6585 participants aged over 45 years were enrolled from the China Health and Retirement Longitudinal Study (CHARLS) in 2011 and 3443 of them were followed up until 2015. Air pollutants were estimated from high-resolution satellite-based spatial-temporal models. In the cross-sectional analysis, we used generalized linear regression, unconditional logistic regression analytical and restricted cubic spline (RCS) methods to assess the single-exposure and non-linear effects of multiple air pollutants on sarcopenia and related surrogate biomarkers (serum creatinine and cystatin C). Several popular mixture analysis techniques such as Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regression, and quantile-based g-computation (Qgcomp) were further used to examinate the combined effects of multiple air pollutants. Logistic regression was used to further analyze the longitudinal association between air pollution and sarcopenia. ResultsEach interquartile range increase in PM2.5, PM10 and NO2 was significantly associated with an increased risk of sarcopenia, with adjusted odds ratios (aORs) of 1.09 [95 % confidence interval (CI): 1.01, 1.20], 1.24 (95 % CI: 1.14, 1.35) and 1.18 (95 % CI: 1.08, 1.28), respectively. Our findings also showed that five air pollutants were significantly associated with the sarcopenia index. In addition, employing a mixture analysis approach, we confirmed significant combined effects of air pollution mixtures on sarcopenia risk and associated biomarkers, with PM10 and PM2.5 identified as major contributors to the combined effect. The results of the exposure-response (E-R) relationships, subgroup analysis, longitudinal analysis and sensitivity analysis all showed the unfavorable impact of air pollution on sarcopenia risk and related vulnerable populations. ConclusionsSingle-exposure and co-exposure to multiple air pollutants were positively associated with sarcopenia among middle-aged and older adults in China. Our study provided new evidence that air pollution mixture was significantly associated with sarcopenia related biomarkers.

Exploring the Neurobiological Mechanisms Mediating the Impact of Air Pollution on Cognitive and Emotional Health in Urban Residents of New Delhi

As urbanization accelerates globally, the adverse effects of air pollution on public health are becoming increasingly evident, particularly concerning mental health outcomes. This growing concern necessitates a multifaceted approach integrating policy development, innovative technology, and community engagement to mitigate the impacts of air pollution. Effective strategies include enhancing air quality standards, improving pollution monitoring, encouraging public transport, and promoting green urban spaces. Additionally, the adoption of innovative technologies such as electric vehicles and advanced air filtration systems represents a forward-thinking approach to reducing environmental pollutants. Community-based initiatives and global cooperation are also crucial in addressing the pervasive challenges of air pollution. This comprehensive approach aims not only to reduce air pollution but also to mitigate its broader health impacts, thereby enhancing community resilience and sustainability.

The impact of air pollution on household vulnerability to poverty: An empirical study from household data in China

Understanding the relationship between air pollution and poverty is essential for improving livelihoods and fostering common prosperity. While existing studies have concentrated on the impact of extreme climate and natural disasters on poverty, the adverse effects of air pollution are neglected. Using the China Family Panel Studies (CFPS) from 2016 to 2018, this study investigates air pollution's effect on household vulnerability to poverty, including the mechanisms and heterogeneity. Results shows that air pollution significantly heightens household vulnerability to poverty. This effect stems from air pollution lowering incomes and increasing healthcare burden. Heterogeneity analysis shows that air pollution negatively impacts households with low wealth, poorly educated, weak social networks, and those in developing, non-coastal, and high-terrain cities. Furthermore, air pollution more severely affects households with high consumption volatility and low wealth compared to those with low vulnerability. We also discover that the adverse effects of air pollution on household vulnerability to poverty diminish with increased physical capital. Local governments are encouraged to intensify efforts in reducing emissions through improved environmental regulations, protecting residents from pollution-induced harm.

Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM2.5) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Long-term exposure to air pollution and road traffic noise and incidence of dementia in the Danish Nurse Cohort.

We examined the association of long-term exposure to air pollution and road traffic noise with dementia incidence in the Danish Nurse Cohort. Female nurses were followed for dementia incidence (hospital contact or medication prescription) from 1993/1999 to 2020. Air pollution and road traffic noise levels were estimated at nurses' residences, and their associations with dementia were examined using Cox regression models. Of 25,233 nurses 1409 developed dementia. Particulate matter with a diameter of ≤2.5µm (PM2.5) was associated with dementia incidence, after adjusting for lifestyle, socioeconomic status, and road traffic noise (hazard ratio [95% confidence interval] 1.35 [1.15-1.59] per interquartile range of 2.6µg/m3). There was no association of PM2.5 with dementia in physically active nurses. Association with road traffic noise diminished after adjusting for PM₂.₅ (1.02 [0.93-1.11] per 7.6dB). Long-term exposure to air pollution increases risk of dementia, and physical activity may moderate this risk. Long-term exposure to air pollution was associated with increased risk of dementia among female nurses from the Danish Nurse Cohort. Association of air pollution with dementia was independent of road traffic noise. Association of road traffic noise with dementia diminished after adjusting for air pollution. Physical activity moderated adverse effects of air pollution on dementia.