Ambient air pollution and its influence on human health and welfare: an overview

Spatio-temporal variation of particulate matter with health impact assessment and long-range transport - case study: Ankara, Türkiye

A clean atmosphere should be provided as a right for human beings to live. The reality is that a significant proportion of the population is exposed to air pollution. This study presents an in-depth investigation into the spatio-temporal dynamics of PM2.5 concentrations in Ankara, Türkiye, spanning over three years. With particular emphasis on the impact of COVID-19 lockdown measures and local air quality management strategies, data from eight air pollution monitoring stations were analyzed. The findings indicate a significant reduction in PM2.5 levels during lockdown periods, with an average decrease of 18 % observed across the city. Implementing the Ankara Provincial Clean Air Action Plan further contributed to a 9.1 % decrease in PM2.5 concentrations in 2021, followed by an additional 6.6 % decrease in 2022 compared to 2020. The spatial distribution of PM2.5 concentrations reveals the influence of industrial and urban areas on pollution levels. Potential Source Contribution Function (PSCF) and Concentration-Weighted Trajectory (CWT) methods were employed to investigate the spatial and temporal variation of long-range transport source regions contributing to the PM2.5 levels in Ankara. PSCF and CWT analyses revealed a decreasing trend in anthropogenic contribution to PM2.5 from 2020 to 2022. The AirQ+ model was employed to predict the long-term mortality rates attributable to PM2.5 across different monitoring stations. Based on the estimations, all stations' average estimated attributable proportion is 9.8 % (3.3 %-27.8 %). The results depict varying trends in estimated mortality rates, emphasizing the importance of targeted interventions to mitigate the public health risks arising from exposure to polluted air. Overall, the results of this study show significant measures for the development of effective clean air quality strategies can effectively change the direction of the adverse impact of air pollution on public health.

Objective and subjective measures of air pollution and self-rated health: the evidence from Chile

PURPOSE

The literature exploring individual differences in self-rated health has grown fast in recent years. Self-rated health (SRH) is a good indicator of general health status. This empirical study explores the association between outdoor air pollution and SRH in Chile. This type of analysis is infrequent in Latin America.

METHODS

We used objective and subjective air pollution measures. The first corresponds to PM2.5, and the latter to the perception of a high level of air pollution. Drawing on data from two independent and repeated nationwide surveys over the period 2006-2017 at the individual level in Chile, we performed repeated cross-sectional analyses for each year of survey application. Ordered Logit (OL) and Logit (L) multivariate models were used to investigate the association between SRH and air pollution measures, considering other socioeconomic and demographic covariates.

RESULTS

We found that the higher is the level of air pollution, the lower the SRH in Chile, regardless of whether air pollution is physically measured or perceived by respondents. The results were consistent over the years in the sign and significance of regression coefficients using two surveys and two forms of the outcome variable.

CONCLUSIONS

Our findings add evidence that air pollution is a relevant determinant of SRH. In addition, they show that subjective measures of air pollution can be as reliable as physical measures in the analysis of the association between air pollution and human health.

Unveiling the health consequences of air pollution in the world's most polluted nations

Air pollution poses a persuasive threat to global health, demonstrating widespread detrimental effects on populations worldwide. Exposure to pollutants, notably particulate matter with a diameter of 2.5 µm (PM2.5), has been unequivocally linked to a spectrum of adverse health outcomes. A nuanced understanding of the relationship between them is crucial for implementing effective policies. This study employs a comprehensive investigation, utilizing the extended health production function framework alongside the system generalized method of moments (SGMM) technique, to scrutinize the interplay between air pollution and health outcomes. Focusing on a panel of the top twenty polluted nations from 2000 to 2021, the findings yield substantial insights. Notably, PM2.5 concentration emerges as a significant factor, correlating with a reduction in life expectancy by 3.69 years and an increase in infant mortality rates by 0.294%. Urbanization is found to increase life expectancy by 0.083 years while concurrently decreasing infant mortality rates by 0.00022%. An increase in real per capita gross domestic product corresponds with an improvement in life expectancy by 0.21 years and a decrease in infant mortality rates by 0.00065%. Similarly, an elevated school enrollment rate is associated with a rise in life expectancy by 0.17 years and a decline in infant mortality rates by 0.00032%. However, a higher population growth rate is found to modestly decrease life expectancy by 0.019 years and slightly elevate infant mortality rates by 0.000016%. The analysis reveals that per capita greenhouse gas emissions exert a negative impact, diminishing life expectancy by 0.486 years and elevating infant mortality rates by 0.00061%, while per capita energy consumption marginally reduces life expectancy by 0.026 years and increases infant mortality rates by 0.00004%. Additionally, economic volatility shock presents a notable decrement in life expectancy by 0.041 years and an increase in infant mortality rates by 0.000045%, with inflationary shock further exacerbating adverse health outcomes by lowering life expectancy by 0.70 years and elevating infant mortality rates by 0.00025%. Moreover, the study scrutinizes the role of institutional quality, revealing a constructive impact on health outcomes. Specifically, the institutional quality index is associated with an increase in life expectancy by 0.66% and a decrease in infant mortality rates by 0.0006%. Extending the analysis to examine the nuanced dimensions of institutional quality, the findings discern that economic institutions wield a notably stronger positive influence on health outcomes compared to political and institutional governance indices. Finally, the results underscore the pivotal moderating role of institutional quality in mitigating the deleterious impact of PM2.5 concentration on health outcomes, counterbalancing the influence of external shocks, and improving the relationships between explanatory variables and health outcome indicators. These findings offer critical insights for guiding evidence-based policy implications, with a focus on fostering resilient, sustainable, and health-conscious societies.

Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment

In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.

Early-life exposure to PM2.5 leads to ASD-like phenotype in male offspring rats through activation of PI3K-AKT signaling pathway

Previous studies have shown that early-life exposure to fine particulate matter (PM2.5) is associated with an increasing risk of autism spectrum disorder (ASD), however, the specific sensitive period of ASD is unknown. Here, a model of dynamic whole-body concentrated PM2.5 exposure in pre- and early-postnatal male offspring rats (MORs) was established. And we found that early postnatal PM2.5 exposed rats showed more typical ASD behavioral characteristics than maternal pregnancy exposure rats, including poor social interaction, novelty avoidance and anxiety disorder. And more severe oxidative stress and inflammatory responses were observed in early postnatal PM2.5 exposed rats. Moreover, the expression level of phosphatase and tensin homolog deleted on chromosome ten (PTEN) was down-regulated and the ratios of p-PI3K/PI3K and p-AKT/AKT were up-regulated in early postnatal PM2.5 exposed rats. This study suggests that early postnatal exposure to PM2.5 is more susceptible to ASD-like phenotype in offspring than maternal pregnancy exposure and the activation of PI3K-AKT signaling pathway may represent underlying mechanisms.

The economics of nature's healing touch: A systematic review and conceptual framework of green space, pharmaceutical prescriptions, and healthcare expenditure associations

Green spaces play a crucial role in promoting sustainable and healthy lives. Recent evidence shows that green space also may reduce the need for healthcare, prescription medications, and associated costs. This systematic review provides the first comprehensive assessment of the available literature examining green space exposure and its associations with healthcare prescriptions and expenditures. We applied Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to search MEDLINE, Scopus, and Web of Science for observational studies published in English through May 6, 2023. A quality assessment of the included studies was conducted using the Office of Health Assessment and Translation (OHAT) tool, and the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) assessment was used to evaluate the overall quality of evidence. Our search retrieved 26 studies that met the inclusion criteria and were included in our review. Among these, 20 studies (77 % of the total) showed beneficial associations of green space exposure with healthcare prescriptions or expenditures. However, most studies had risks of bias, and the overall strength of evidence for both outcomes was limited. Based on our findings and related bodies of literature, we present a conceptual framework to explain the possible associations and complex mechanisms underlying green space and healthcare outcomes. The framework differs from existing green space and health models by including upstream factors related to healthcare access (i.e., rurality and socioeconomic status), which may flip the direction of associations. Additional research with lower risks of bias is necessary to validate this framework and better understand the potential for green space to reduce healthcare prescriptions and expenditures.

Effects of PM10 Airborne Particles from Different Regions of a Megacity on In Vitro Secretion of Cytokines by a Monocyte Line during Different Seasons

Several epidemiological studies have demonstrated that particulate matter (PM) in air pollution can be involved in the genesis or aggravation of different cardiovascular, respiratory, perinatal, and cancer diseases. This study assessed the in vitro effects of PM10 on the secretion of cytokines by a human monocytic cell line (THP-1). We compared the chemotactic, pro-inflammatory, and anti-inflammatory cytokines induced by PM10 collected for two years during three different seasons in five different Mexico City locations. MIP-1α, IP-10, MCP-1, TNF-α, and VEGF were the main secretion products after stimulation with 80 μg/mL of PM10 for 24 h. The THP-1 cells showed a differential response to PM10 obtained in the different sites of Mexico City. The PM10 from the north and the central city areas induced a higher pro-inflammatory cytokine response than those from the south. Seasonal pro-inflammatory cytokine secretion always exceeded anti-inflammatory secretion. The rainy-season-derived particles caused the lowest pro-inflammatory effects. We concluded that toxicological assessment of airborne particles provides evidence supporting their potential role in the chronic exacerbation of local or systemic inflammatory responses that may worsen the evolution of some chronic diseases.

Association Between Nitrogen Dioxide Pollution and Cause-Specific Mortality in China: Cross-Sectional Time Series Study

BACKGROUND

Nitrogen dioxide (NO2) has been frequently linked to a range of diseases and associated with high rates of mortality and morbidity worldwide. However, there is limited evidence regarding the risk of NO2 on a spectrum of causes of mortality. Moreover, adjustment for potential confounders in NO2 analysis has been insufficient, and the spatial resolution of exposure assessment has been limited.

OBJECTIVE

This study aimed to quantitatively assess the relationship between short-term NO2 exposure and death from a range of causes by adjusting for potential confounders in Guangzhou, China, and determine the modifying effect of gender and age.

METHODS

A time series study was conducted on 413,703 deaths that occurred in Guangzhou during the period of 2010 to 2018. The causes of death were classified into 10 categories and 26 subcategories. We utilized a generalized additive model with quasi-Poisson regression analysis using a natural cubic splines function with lag structure of 0 to 4 days to estimate the potential lag effect of NO2 on cause-specific mortality. We estimated the percentage change in cause-specific mortality rates per 10 μg/m3 increase in NO2 levels. We stratified meteorological factors such as temperature, humidity, wind speed, and air pressure into high and low levels with the median as the critical value and analyzed the effects of NO2 on various death-causing diseases at those high and low levels. To further identify potentially vulnerable subpopulations, we analyzed groups stratified by gender and age.

RESULTS

A significant association existed between NO2 exposure and deaths from multiple causes. Each 10 μg/m3 increment in NO2 density at a lag of 0 to 4 days increased the risks of all-cause mortality by 1.73% (95% CI 1.36%-2.09%) and mortality due to nonaccidental causes, cardiovascular disease, respiratory disease, endocrine disease, and neoplasms by 1.75% (95% CI 1.38%-2.12%), 2.06% (95% CI 1.54%-2.59%), 2.32% (95% CI 1.51%-3.13%), 2.40% (95% CI 0.84%-3.98%), and 1.18% (95% CI 0.59%-1.78%), respectively. Among the 26 subcategories, mortality risk was associated with 16, including intentional self-harm, hypertensive disease, and ischemic stroke disease. Relatively higher effect estimates of NO2 on mortality existed for low levels of temperature, relative humidity, wind speed, and air pressure than with high levels, except a relatively higher effect estimate was present for endocrine disease at a high air pressure level. Most of the differences between subgroups were not statistically significant. The effect estimates for NO2 were similar by gender. There were significant differences between the age groups for mortality due to all causes, nonaccidental causes, and cardiovascular disease.

CONCLUSIONS

Short-term NO2 exposure may increase the risk of mortality due to a spectrum of causes, especially in potentially vulnerable populations. These findings may be important for predicting and modifying guidelines for NO2 exposure in China.

Efficient air pollution mapping in extensive regions with fully autonomous unmanned aerial vehicles: A numerical perspective

It is noteworthy that comprehensive exploration of atmospheric measurements in the horizontal plane using aerial platforms, necessitating high autonomy, has not been extensively covered in the existing literature. This research presents a systematic numerical approach to effective air pollution mapping achieved through the integration of horizontal and vertical air pollution measurements conducted using a fully autonomous unmanned aerial vehicle (UAV) platform. The developed robust navigation model enables the UAV to efficiently scan the extensive measurement area, which is subdivided into smaller sub-areas using the polygonal decomposition technique, resulting in a comprehensive map of the entire region. Furthermore, technical analysis determines the optimal flight speed, leading to air pollution measurements in up to 30 % more areas and ensuring more consistent results. The simulation results illustrate the effective mapping of the entire area by aggregating air pollution measurements from sub-areas, with seamless transitions emphasizing the accuracy and consistency of the employed air pollution mapping technique. This systematic method offers numerous advantages, including rapid air pollution source identification and swift response capabilities. Moreover, this approach holds potential for various applications, such as forest fire monitoring and natural resources assessment, by equipping UAVs with additional equipment like cameras alongside atmospheric sensors.

Air Pollutants and Risk of Parkinson's Disease among Women in the Sister Study

BACKGROUND

Air pollutants may contribute to the development of Parkinson's disease (PD), but empirical evidence is limited and inconsistent.

OBJECTIVES

This study aimed to prospectively investigate the associations of PD with ambient exposures to fine particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) and nitrogen dioxide (NO 2 ).

METHODS

We analyzed data from 47,108 US women from the Sister Study, enrolled from 2003-2009 (35-80 years of age) and followed through 2018. Exposures of interest included address-level ambient PM 2.5 and NO 2 in 2009 and their cumulative averages from 2009 to PD diagnosis with varying lag-years. The primary outcome was PD diagnosis between 2009 and 2018 (n = 163 ). We used multivariable Cox proportional hazards and time-varying Cox models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs).

RESULTS

NO 2 exposure in 2009 was associated with PD risk in a dose-response manner. The HR and 95% CI were 1.22 (95% CI: 1.03, 1.46) for one interquartile [4.8 parts per billion (ppb)] increment in NO 2 , adjusting for age, race and ethnicity, education, smoking status, alcohol drinking, caffeine intake, body mass index, physical activity, census region, residential area type, area deprivation index (ADI), and self-reported health status. The association was confirmed in secondary analyses with time-varying averaged cumulative exposures. For example, the multivariable adjusted HR for PD per 4.8   ppb increment in NO 2 was 1.25 (95% CI: 1.05, 1.50) in the 2-year lag analysis using cumulative average exposure. Post hoc subgroup analyses overall confirmed the association. However, statistical interaction analyses found that the positive association of NO 2 with PD risk was limited to women in urban, rural, and small town areas and women with ≥ 50 th percentile ADI but not among women from suburban areas or areas with < 50 th percentile ADI. In contrast, PM 2.5 exposure was not associated with PD risk with the possible exception for women from the Midwest region of the US (HR interquartile -range = 2.49 , 95% CI: 1.20, 5.14) but not in other census regions.

DISCUSSION

In this nationwide cohort of US women, higher level exposure to ambient NO 2 is associated with a greater risk of PD. This finding needs to be independently confirmed and the underlying mechanisms warrant further investigation. https://doi.org/10.1289/EHP13009.

Association between short-term exposure to ambient air pollutants and biomarkers indicative of inflammation and oxidative stress: a cross-sectional study using KoGES-HEXA data

BACKGROUND

Air pollution-induced systemic inflammation and oxidative stress are hypothesized to be the major biological mechanisms underlying pathological outcomes. We examined the association between short-term exposure to ambient air pollutants and biomarkers of inflammation and oxidative stress in 2199 general middle-aged Korean population residing in metropolitan areas.

METHODS

Serum levels of inflammatory cytokines (interleukin [IL]-1β, IL-6, IL-8, IL-10, and tumor necrosis factor [TNF]-α) and urinary levels of 8-hydroxy-2'-deoxyguanosine (8-OHdG) were measured. Daily concentrations of a series of air pollutants (particulate matter [PM]10, PM2.5, SO2, NO2, CO, and O3) were predicted using the Community Multiscale Air Quality modeling system, and participant-level pollutant exposure was determined using geocoded residential addresses. Short-term exposure was defined as the 1- to 7-day moving averages.

RESULTS

The multivariable-adjusted linear models controlling for the sociodemographic, lifestyle, temporal, and meteorological factors identified positive associations of PM with IL-1β, IL-8, IL-10, TNF-α, and 8-OHdG levels; SO2 with IL-10 levels, CO with IL-1β, IL-10, and TNF-α levels; and O3 with IL-1β, IL-8, and 8-OHdG levels. O3 levels were inversely associated with IL-10 levels. For each pollutant, the strongest associations were observed for the 7-day average PM and CO with IL-1β (per 10-µg/m3 increase in PM10: 2.7%, 95% confidence interval [CI] = 0.6-4.8; per 10-µg/m3 increase in PM2.5: 6.4%, 95% CI = 2.4-10.5; per 0.1-ppm increase in CO: 3.3%, 95% CI = 0.3-6.5); the 2-day average SO2 with IL-10 levels (per 1-ppb increase in SO2: 1.1%, 95% CI = 0.1-2.1); and the 7-day average O3 with IL-8 levels (per 1-ppb increase in O3: 1.3%, 95% CI = 0.7-1.9).

CONCLUSIONS

Short-term exposure to ambient air pollutants may induce oxidative damage and pro-inflammatory roles, together with counter-regulatory anti-inflammatory response.

Disruption of Atrial Rhythmicity by the Air Pollutant 1,2-Naphthoquinone: Role of Beta-Adrenergic and Sensory Receptors

The combustion of fossil fuels contributes to air pollution (AP), which was linked to about 8.79 million global deaths in 2018, mainly due to respiratory and cardiovascular-related effects. Among these, particulate air pollution (PM2.5) stands out as a major risk factor for heart health, especially during vulnerable phases. Our prior study showed that premature exposure to 1,2-naphthoquinone (1,2-NQ), a chemical found in diesel exhaust particles (DEP), exacerbated asthma in adulthood. Moreover, increased concentration of 1,2-NQ contributed to airway inflammation triggered by PM2.5, employing neurogenic pathways related to the up-regulation of transient receptor potential vanilloid 1 (TRPV1). However, the potential impact of early-life exposure to 1,2-naphthoquinone (1,2-NQ) on atrial fibrillation (AF) has not yet been investigated. This study aims to investigate how inhaling 1,2-NQ in early life affects the autonomic adrenergic system and the role played by TRPV1 in these heart disturbances. C57Bl/6 neonate male mice were exposed to 1,2-NQ (100 nM) or its vehicle at 6, 8, and 10 days of life. Early exposure to 1,2-NQ impairs adrenergic responses in the right atria without markedly affecting cholinergic responses. ECG analysis revealed altered rhythmicity in young mice, suggesting increased sympathetic nervous system activity. Furthermore, 1,2-NQ affected β1-adrenergic receptor agonist-mediated positive chronotropism, which was prevented by metoprolol, a β1 receptor blocker. Capsazepine, a TRPV1 blocker but not a TRPC5 blocker, reversed 1,2-NQ-induced cardiac changes. In conclusion, neonate mice exposure to AP 1,2-NQ results in an elevated risk of developing cardiac adrenergic dysfunction, potentially leading to atrial arrhythmia at a young age.

Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic

The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.

The impact of ambient air pollution on hospital admissions, length of stay and hospital costs for patients with diabetes mellitus and comorbid respiratory diseases in Panzhihua, Southwest China

Background

There is limited evidence on association between air pollutants and hospital admissions, hospital cost and length of stay (LOS) among patients with diabetes mellitus (DM) and comorbid respiratory diseases (RD), especially in low- and middle-income countries (LMICs) with low levels of air pollution.

Methods

Daily data on RD-DM patients were collected in Panzhihua from 2016 to 2020. A generalised additive model (GAM) was used to explore the effect of air pollutants on daily hospital admissions, LOS and hospital cost. Attributable risk was employed to estimate RD-DM's burden due to exceeding air pollution exposure, using both 0 microgrammes per cubic metre (μg/m3) and WHO's 2021 air quality guidelines as reference.

Results

For each 10 ug/m3 increase of particles with an aerodynamic diameter <2.5 micron (μm) (PM2.5), particles with an aerodynamic diameter <10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3), the admissions of RD-DM patients increased by 7.25% (95% CI = 4.26 to 10.33), 5.59% (95% CI = 3.79 to 7.42), 10.10% (95% CI = 7.29 to 12.98), 12.33% (95% CI = 8.82 to 15.95) and -2.99% (95% CI = -4.08 to -1.90); per 1 milligramme per cubic metre (mg/m3) increase of carbon monoxide (CO) corresponded to a 25.77% (95% CI = 17.88 to 34.19) increment for admissions of RD-DM patients. For LOS and hospital cost, the six air pollutants showed similar effect. Given 0 μg/m3 as the reference, NO2 showed the maximum attributable fraction of 32.68% (95% CI = 25.12 to 39.42%), corresponding to an avoidable burden of 5661 (95% CI = 3611 to 5860) patients with RD-DM.

Conclusions

There is an association between PM2.5, PM10, SO2, NO2, and CO with increased hospital admissions, LOS and hospital cost in patients with RD-DM. Disease burden of RD-DM may be improved by formulating policies related to air pollutants exposure reduction, especially in LMICs with low levels of air pollution.

Assessment of air quality using AERMOD modeling: a case study in the Middle East

Emissions of greenhouse gases from industrial facilities, such as refineries, are one of the most significant environmental problems in many countries. This study aimed to assess the present status of emission sources near a gas refinery region, and the contribution of sources to air pollution was estimated by monitoring CO for a year at a fixed station. This descriptive-analytical study was conducted between January and December 2020. A simulation of CO gas distribution and pollutant concentration prediction was carried out. The results show that the maximum concentration of CO in the 1-h period was 2260 µg/m3, which corresponds to the peak concentration in spring, and in the 8-h period, it was 573 µg/m3, which corresponds to the peak concentration in winter. The studied area's maximum pollutant concentration was also compared to national and international standards for clean air. In all four seasons, the maximum simulated CO concentrations were lower than the Iranian and EPA standards for clean air. Maximum concentrations have occurred in the southern slopes of the study area's heights, and, due to the appropriate wind speed, maximum concentrations in the northeastern mountain peaks occurred at a more considerable distance due to the high altitude of the mountains and the lack of suitable conditions for pollutant escape. Furthermore, because of the height of smokestacks and flares from the ground and the effect of wind on the release height, the concentration of pollutants at the foot of the stacks is low and decreases gradually over a certain distance. Finally, the distribution and deposition of pollutants in the pathway of the smoke were influenced by the type of topography.

Association of Air Pollution with the Number of Common Respiratory Visits in Children in a Heavily Polluted Central City, China

Children's respiratory health is vulnerable to air pollution. Based on data collected from June 2019 to June 2022 at a children's hospital in Zhengzhou, China, this study utilized Spearman correlation analysis and a generalized additive model (GAM) to examine the relationship between daily visits for common respiratory issues in children and air pollutant concentrations. Results show that the number of upper respiratory tract infection (URTI), pneumonia (PNMN), bronchitis (BCT), and bronchiolitis (BCLT) visits in children showed a positive correlation with PM2.5, PM10, NO2, SO2, and CO while exhibiting a negative correlation with temperature and relative humidity. The highest increases in PNMN visits in children were observed at lag 07 for NO2, SO2, and CO. A rise of 10 μg/m3 in NO2, 1 μg/m3 in SO2, and 0.1 mg/m3 in CO corresponded to an increase of 9.7%, 2.91%, and 5.16% in PNMN visits, respectively. The effects of air pollutants on the number of BCT and BCLT visits were more pronounced in boys compared to girls, whereas no significant differences were observed in the number of URTI and PNMN visits based on sex. Overall, air pollutants significantly affect the prevalence of respiratory diseases in children, and it is crucial to improve air quality to protect the children's respiratory health.

Climate change and mortality rates of COPD and asthma: A global analysis from 2000 to 2018

BACKGROUND

Climate change plays a significant role in global health threats, particularly with respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma, but the long-term global-scale impact of climate change on these diseases' mortality remains unclear.

OBJECTIVE

This study aims to investigate the impact of climate change on the age-standardized mortality rates (ASMR) of COPD and asthma at national levels.

METHODS

We used Global Burden of Disease (GBD) data of ASMR of COPD and asthma from 2000 to 2018. The climate change index was represented as the deviance percentage of temperature (DPT) and relative humidity (DPRH), calculated based on 19-year temperature and humidity averages. Annual temperature, RH, and fine particulate matter (PM2.5) levels in 185 countries/regions were obtained from ERA5 and the OECD's environmental statistics database. General linear mixed-effect regression models were used to examine the associations between climate change with the log of ASMR (LASMR) of COPD and asthma.

RESULTS

After adjusting for annual PM2.5, SDI level, smoking prevalence, and geographical regions, a 0.26% increase in DPT was associated with decreases of 0.016, 0.017, and 0.014 per 100,000 people in LASMR of COPD and 0.042, 0.046, and 0.040 per 100,000 people in LASMR of asthma for both genders, males, and females. A 2.68% increase in DPRH was associated with increases of 0.009 and 0.011 per 100,000 people in LASMR of COPD. We observed a negative association of DPT with LASMR for COPD in countries/regions with temperatures ranging from 3.8 to 29.9 °C and with LASMR for asthma ranging from -5.3-29.9 °C. However, we observed a positive association of DPRH with LASMR for both COPD and asthma in the RH range of 41.2-67.2%.

CONCLUSION

Climate change adaptation and mitigation could be crucial in reducing the associated COPD and asthma mortality rates, particularly in regions most vulnerable to temperature and humidity fluctuations.

PM2.5 contributed to pulmonary epithelial senescence and ferroptosis by regulating USP3-SIRT3-P53 axis

Pulmonary epithelial cells act as the first line of defense against various air pollutant particles. Previous studies have reported that particulate matter 2.5 (PM2.5) could trigger pulmonary inflammation and fibrosis by inducing pulmonary epithelial senescence and ferroptosis. Sirtuin 3 (SIRT3) is one of critical the mitochondrial NAD+-dependent deacetylases, exerting antioxidant and anti-aging effects in multiple diseases. The present study aimed to explore the role of SIRT3 in PM2.5-induced lung injury as well as possible mechanisms. The role of SIRT3 in PM2.5-induced lung injury was investigated by SIRT3 genetic depletion, adenovirus-mediated overexpression in type II alveolar epithelial (AT2) cells, and pharmacological activation by melatonin. The protein level and activity of SIRT3 in lung tissues and AT2 cells were significantly downregulated after PM2.5 stimulation. SIRT3 deficiency in AT2 cells aggravated inflammatory response and collagen deposition in PM2.5-treated lung tissues. RNA-sequence and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the differentially expressed genes (DEGs) between SIRT3 flox and SIRT3 CKO mice were mainly enriched in ferroptosis and cellular longevity. Western blot further showed that SIRT3 deficiency in AT2 cells significantly upregulated the proteins associated with ferroptosis and cell senescence in PM2.5-treated lung tissues. In vitro experiments also showed that SIRT3 overexpression could decrease the levels of ferroptosis and cell senescence in PM2.5-treated AT2 cells. In addition, we found that PM2.5 could increase the acetylation of P53 via triggering DNA damage in AT2 cells. And SIRT3 could deacetylate P53 at lysines 320 (K320), thus reducing its transcriptional activity. PM2.5 decreased the protein level of SIRT3 by inducing proteasome pathway through downregulating USP3. Finally, we found that SIRT3 agonist, melatonin treatment could alleviate PM2.5-induced senescence and ferroptosis in mice. In conclusion, targeting USP3-SIRT3-P53 axis may be a novel therapeutic strategy against PM2.5-induced pulmonary inflammation and fibrosis by decreasing pulmonary epithelial senescence and ferroptosis.

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China

Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluates the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprises meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China, from January 1, 2016 to August 20, 2022. We use support vector regression (SVR) to build models that enable analysis of the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. Spearman correlation analysis and SVR model results indicate that NO2, PM2.5, and PM10 are correlated with the occurrence of respiratory diseases, with the strongest correlation relating to pneumonia. An increase in the daily average temperature and daily relative humidity decreases the number of patients with pneumonia and chronic lower respiratory diseases but increases the number of patients with acute upper respiratory infections. The SVR modeling has the potential to predict the number of respiratory-related hospital visits. This work demonstrates that machine learning can be combined with meteorological and air pollution data for disease prediction, providing a useful tool whereby policymakers can take preventive measures.

Type 2 diabetes and thyroid cancer: Synergized risk with rising air pollution

Diabetes is a complex condition, and the causes are still not fully understood. However, a growing body of evidence suggests that exposure to air pollution could be linked to an increased risk of diabetes. Specifically, exposure to certain pollutants, such as particulate Matter and Ozone, has been associated with higher rates of diabetes. At the same time, air pollution has also been linked to an increased risk of thyroid cancer. While there is less evidence linking air pollution to thyroid cancer than to diabetes, it is clear that air pollution could have severe implications for thyroid health. Air pollution could increase the risk of diabetes and thyroid cancer through several mechanisms. For example, air pollution could increase inflammation in the body, which is linked to an increased risk of diabetes and thyroid cancer. Air pollution could also increase oxidative stress, which is linked to an increased risk of diabetes and thyroid cancer. Additionally, air pollution could increase the risk of diabetes and thyroid cancer by affecting the endocrine system. This review explores the link between diabetes and air pollution on thyroid cancer. We will discuss the evidence for an association between air pollution exposure and diabetes and thyroid cancer, as well as the potential implications of air pollution for thyroid health. Given the connections between diabetes, air pollution, and thyroid cancer, it is essential to take preventive measures to reduce the risk of developing the condition.

Nanostructured Bioaerogels as a Potential Solution for Particulate Matter Pollution

Particulate matter (PM) pollution is a significant environmental and public health issue globally. Exposure to high levels of PM, especially fine particles, can have severe health consequences. These particles can come from a variety of sources, including natural events like dust storms and wildfires, as well as human activities such as industrial processes and transportation. Although an extensive development in air filtration techniques has been made in the past few years, fine particulate matter still poses a serios and dangerous threat to human health and to our environment. Conventional air filters are fabricated from non-biodegradable and non-ecofriendly materials which can cause further environmental pollution as a result of their excessive use. Nanostructured biopolymer aerogels have shown great promise in the field of particulate matter removal. Their unique properties, renewable nature, and potential for customization make them attractive materials for air pollution control. In the present review, we discuss the meaning, properties, and advantages of nanostructured aerogels and their potential in particulate matter removal. Particulate matter pollution, types and sources of particulate matter, health effect, environmental effect, and the challenges facing scientists in particulate matter removal are also discussed in the present review. Finally, we present the most recent advances in using nanostructured bioaerogels in the removal of different types of particulate matter and discuss the challenges that we face in these applications.

Environmental impacts of air pollution and its abatement by plant species: A comprehensive review

Air pollution is one of the major global environmental issues urgently needed attention for its control through sustainable approaches. The release of air pollutants from various anthropogenic and natural processes imposes serious threats to the environment and human health. The green belt development using air pollution-tolerant plant species has become popular approach for air pollution remediation. Plants' biochemical and physiological attributes, especially relative water content, pH, ascorbic acid, and total chlorophyll content, are taken into account for assessing air pollution tolerance index (APTI). In contrast, anticipated performance index (API) is assessed based on socio-economic characteristics including "canopy structure, type, habit, laminar structure, economic value and APTI score" of plant species. Based on previous work, plants with high dust-capturing capacity are identified in Ficus benghalensis L. (0.95 to 7.58 mg/cm2), and highest overall PM accumulation capacity was observed in Ulmus pumila L. (PM10 = 72 µg/cm2 and PM2.5 = 70 µg/cm2) in the study from different regions. According to APTI, the plant species such as M. indica (11 to 29), Alstonia scholaris (L.) R. Br. (6 to 24), and F. benghalensis (17 to 26) have been widely reported as high air pollution-tolerant species and good to best performer in terms of API at different study sites. Statistically, previous studies show that ascorbic acid (R2 = 0.90) has good correlation with APTI among all the parameters. The plant species with high pollution tolerance capacity can be recommended for future plantation and green belt development.

Assessment of ambient air quality in heavy industrial localities: a case study of Yanbu industrial city

The objective of this study was to provide an appropriate evaluation of ambient air quality in industrial localities and the surrounding residential areas in its vicinity. Therefore, an assessment of gaseous emissions from industrial sectors was performed. For this purpose, concentrations were measured for SO₂, H₂S, NO₂, O₃, CO, PM_2.5, and PM₁₀ in five spatially diverse monitoring stations (AQMS) over different temporal intervals (daily, monthly, and annual) for the years 2015-2020. The impact on the environment and public health was evaluated through comparison with the corresponding regional and international standards. In the case study region, a substantial spatiotemporal variation was observed in the gaseous contaminants, due to the predominance of characterized meteorological parameters interfering with contributions from existing chemical facilities and anthropogenic activities. The exceedances for the investigated emissions were routinely violated the standard concentrations. According to AQI classifications, these violations were assigned to be within the acceptable limits for the gaseous emissions, moderately polluted for PM_2.5, and unhealthy for sensitive groups for PM10. The proper distribution of the AQMSs within the industrial locality provides enough spatial and temporal observatory data, such that the exceedances were reduced with the subsequent years, hence appropriate evaluation of the relevant measurements revealed effective qualitative policies taken into action by authorities to maintain less accumulation of the gaseous emissions into ambient air beyond the harmful limits for public health and environment.

Air pollution as a substantial threat to the improvement of agricultural total factor productivity: Global evidence

OBJECTIVE

This study aims to provide empirical evidence about whether and to what extent air pollution affects the global agricultural total factor productivity (TFP).

METHODS

The research sample covers 146 countries all over the world during 2010-2019. Two-way fixed effects panel regression models are used to estimate air pollution's impacts. A random forest analysis is conducted to assess the relative importance of independent variables.

RESULTS

The results show that, on average, a 1% increase in fine particulate matter (PM_2.5) and tropospheric ozone (O₃) concentration would cause the agricultural TFP to decline by 0.104% and 0.207%, respectively. Air pollution's adverse impact widely exists in various countries with different development levels, pollution degrees, and industrial structures. This study also finds that temperature has a moderating effect on the relationship between PM_2.5 and agricultural TFP. PM_2.5 pollution's detrimental impact is weaker (stronger) in a warmer (cooler) climate. In addition, the random forest analysis confirms that air pollution is among the most crucial predictors of agricultural productivity.

CONCLUSIONS

Air pollution is a substantial threat to the improvement of global agricultural TFP. Worldwide actions should be taken to ameliorate air quality, for the sake of agricultural sustainability and global food security.

Bacterial Biological Factories Intended for the Desulfurization of Petroleum Products in Refineries

The removal of sulfur by deep hydrodesulfurization is expensive and environmentally unfriendly. Additionally, sulfur is not separated completely from heterocyclic poly-aromatic compounds. In nature, several microorganisms (Rhodococcus erythropolis IGTS8, Gordonia sp., Bacillus sp., Mycobacterium sp., Paenibacillus sp. A11-2 etc.) have been reported to remove sulfur from petroleum fractions. All these microbes remove sulfur from recalcitrant organosulfur compounds via the 4S pathway, showing potential for some organosulfur compounds only. Activity up to 100 µM/g dry cell weights is needed to meet the current demand for desulfurization. The present review describes the desulfurization capability of various microorganisms acting on several kinds of sulfur sources. Genetic engineering approaches on Gordonia sp. and other species have revealed a variety of good substrate ranges of desulfurization, both for aliphatic and aromatic organosulfur compounds. Whole genome sequence analysis and 4S pathway inhibition by a pTeR group inhibitor have also been discussed. Now, emphasis is being placed on how to commercialize the microbes for industrial-level applications by incorporating biodesulfurization into hydrodesulfurization systems. Thus, this review summarizes the potentialities of microbes for desulfurization of petroleum. The information included in this review could be useful for researchers as well as the economical commercialization of bacteria in petroleum industries.

High-resolution modeling for criteria air pollutants and the associated air quality index in a metropolitan city

The Air Quality Index (AQI), which jointly accounts for levels of criteria air pollutants relative to their guidelines, is largely reported at the city level. Little is known about the spatial patterns of the AQI in terms of the magnitude, temporal variability, and predominant air pollutant contributions at the hyperlocal scale within a city. To fill this research gap, we developed spatiotemporal models for each criteria air pollutant based on an advanced geostatistical framework and estimated daily AQI levels at 100-meter resolution in a metropolitan city in 2019. The model prediction ability (cross-validation, CV, Coefficient of determination, R², and root mean square error, RMSE) ranged from 0.43 and 1.86 µg/m³ for sulfur dioxide (SO₂) to 0.92 and 6.25 µg/m³ for fine particulate matter (PM_2.5) across the six air pollutants, leading to good performance in the subsequent AQI estimations (CV R² = 0.86, RMSE = 10.05). The AQI varies substantially over space at a fine scale and differs from the distributions of individual air pollutants. The unhealthy air quality (AQI > 100 over 75 days) spatial pattern was dominated by excessive ground-level ozone exposure in a large area. Our research provides a useful tool for accurately estimating AQI spatiotemporal variations for population health studies.

Advances of microfluidic lung chips for assessing atmospheric pollutants exposure

Atmospheric pollutants, including particulate matters, nanoparticles, bioaerosols, and some chemicals, have posed serious threats to the environment and the human's health. The lungs are the responsible organs for providing the interface betweenthecirculatory system and the external environment, where pollutant particles can deposit or penetrate into bloodstream circulation. Conventional studies to decipher the mechanismunderlying air pollution and human health are quite limited, due to the lack of reliable models that can reproduce in vivo features of lung tissues after pollutants exposure. In the past decade, advanced near-to-native lung chips, combining cell biology with bioengineered technology, present a new strategy for atmospheric pollutants assessment and narrow the gap between 2D cell culture and in vivo animal models. In this review, the key features of artificial lung chips and the cutting-edge technologies of the lung chip manufacture are introduced. The recent progresses of lung chip technologies for atmospheric pollutants exposure assessment are summarized and highlighted. We further discuss the current challenges and the future opportunities of the development of advanced lung chips and their potential utilities in atmospheric pollutants associated toxicity testing and drug screening.

Prenatal exposure to ambient air pollution is associated with neurodevelopmental outcomes at 2 years of age

BACKGROUND

Higher prenatal ambient air pollution exposure has been associated with impaired neurodevelopment in preschoolers and school-aged children. The purpose of this study was to explore the relationships between prenatal ambient air pollution exposure and neurodevelopment during infancy.

METHODS

This study examined 161 Latino mother-infant pairs from the Southern California Mother's Milk Study. Exposure assessments included prenatal nitrogen dioxide (NO₂) and particulate matter smaller than 2.5 and 10 microns in diameter (PM_2.5 and PM₁₀, respectively). The pregnancy period was also examined as three windows, early, mid, and late, which describe the first, middle, and last three months of pregnancy. Infant neurodevelopmental outcomes at 2 years of age were measured using the Bayley-III Scales of Infant and Toddler Development. Multivariable linear models and distributed lag linear models (DLM) were used to examine relationships between prenatal exposures and neurodevelopmental scores, adjusting for socioeconomic status, breastfeeding frequency, time of delivery, pre-pregnancy body mass index, and infant birthweight and sex.

RESULTS

Higher prenatal exposure to PM₁₀ and PM_2.5 was negatively associated with composite cognitive score (β = -2.01 [-3.89, -0.13] and β = -1.97 [-3.83, -0.10], respectively). In addition, higher average prenatal exposure to PM₁₀ was negatively associated with composite motor (β = -2.35 [-3.95, -0.74]), scaled motor (β = -0.77 [-1.30, -0.24]), gross motor (β = -0.37 [-0.70, -0.04]), fine motor (β = -0.40 [-0.71, -0.09]), composite language (β = -1.87 [-3.52, -0.22]), scaled language (β = -0.61 [-1.18, -0.05]) and expressive communication scaled scores (β = -0.36 [-0.66, -0.05]). DLMs showed that higher prenatal air pollution exposure during mid and late pregnancy was inversely associated with motor, cognitive, and communication language scores.

CONCLUSIONS

Higher exposure to air pollutants during pregnancy, particularly in the mid and late prenatal periods, was inversely associated with scaled and composite motor, cognitive, and language scores at 2 years. These results indicate that prenatal ambient air pollution may negatively impact neurodevelopment in early life.

Spatio-Temporal Evolution and Influencing Factors of Ecological Well-Being Performance from the Perspective of Strong Sustainability: A Case Study of the Three Gorges Reservoir Area, China

The concept of strong sustainability suggests that natural capital is irreplaceable and emphasizes that human natural consumption cannot exceed the carrying capacity of the ecological environment. In the realistic context of tightening resource constraints and ecological degradation, how to explore the optimal economic and well-being output under certain ecological constraints has become an important topic in sustainability research. Ecological well-being performance (EWP) can comprehensively reflect the efficiency of natural resources and ecological inputs into the well-being level and has become an important tool for sustainable development research. Based on strong sustainability, this paper constructs an EWP evaluation index system. It decomposes EWP into two stages: ecological economy and economic well-being, which opens the "black box" of the ecological well-being conversion process. PCA-DEA, the network super-efficiency Epsilon-based measure (Network SEBM) model, and Geodetector are used to dynamically measure the EWP in the Three Gorges Reservoir Area (TGRA) from 2010 to 2020 and analyze its spatial and temporal characteristics and influencing factors. The results show that (1) temporally, the EWP in the TGRA shows an increasing trend from 2010 to 2020, but the overall level is low. Spatially, it shows a high pattern in the east and low in the west, and spatial differences are gradually decreasing; (2) ecological economic efficiency is significantly lower than economic well-being efficiency, and ecological economic efficiency is the main reason limiting the improvement in EWP in the TGRA. The ecological well-being situation of the TGRA is not optimistic; (3) there is an overall problem of excessive ecological input and insufficient per-capita GDP and well-being output in the TGRA, and decisions should be made according to local conditions; (4) the level of economic development has an EWP level that plays a dominant role and also has a greater relationship with the policy system, socioeconomic conditions, and natural environment.

A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic

China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as - 25.88 in Wuhan and - 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.

Intensive carbon combustion in sintering packed bed via steam spraying: An experimental study on carbon monoxide emission reduction

Improving the combustion efficiency of solid fuels is important for reducing carbon monoxide emissions in the iron ore sintering process. In this paper, the surface steam spraying technology is introduced in the sintering process based on the auxiliary combustion effect of steam on coke, and its potential to reduce carbon monoxide emissions is demonstrated. Thermogravimetric analysis experiments of coke breeze in air and air-steam mixed atmosphere are carried out, and the results show that the introduction of steam can reduce the concentration of carbon monoxide in the exhaust gas from 183×10^-6 to 78×10^-6. At the same time, the mechanisms of carbon monoxide emission reduction by surface steam spraying technology are analyzed from the thermodynamic and kinetic perspectives. Then, a series of laboratory-scale sintering pot tests are carried out under no spraying operation, interval spraying operation, and continuous spraying operation. The results indicate that both interval and continuous spraying operations can reduce carbon monoxide emissions. The optimal mode of steam spraying under the present experimental conditions is continuously spraying for 13 min at a volume rate of 0.053 m³/min. Compared with no spraying, the average carbon monoxide concentration in the exhaust gas is reduced from 7565×10^-6 to 6231×10^-6, and total carbon monoxide emissions for per ton sinter are reduced from 13.46 m³/t to 9.51 m³/t.

Engineers in Medicine: Foster Innovation by Traversing Boundaries

Engineers play a critical role in the advancement of biomedical science and the development of diagnostic and therapeutic technologies for human well-being. The complexity of medical problems requires the synthesis of diverse knowledge systems and clinical experiences to develop solutions. Therefore, engineers in the healthcare and biomedical industries are interdisciplinary by nature to innovate technical tools in sophisticated clinical settings. In academia, engineering is usually divided into disciplines with dominant characteristics. Since biomedical engineering has been established as an independent curriculum, the term "biomedical engineers" often refers to the population from a specific discipline. In fact, engineers who contribute to medical and healthcare innovations cover a broad range of engineering majors, including electrical engineering, mechanical engineering, chemical engineering, industrial engineering, and computer sciences. This paper provides a comprehensive review of the contributions of different engineering professions to the development of innovative biomedical solutions. We use the term "engineers in medicine" to refer to all talents who integrate the body of engineering knowledge and biological sciences to advance healthcare systems.

Urbanicity-Perspectives from Neuroscience and Public Health: A Scoping Review

Urban residency is associated with exposure to environmental factors, which can influence health in many ways. Neuroscientific research, as well as Public Health research, aim towards broadening evidence in the field of Urban Health. However, it is unclear whether the association between urban living and mental illnesses is causal rather than explainable by other selective effects. This review seeks to gather information on the current evidence regarding urban living and neurological outcomes to demonstrate how Public Health and Neuroscience could complement each other in the field of Urban Health. A scoping review was conducted in four electronic databases according to the PRISMA-statement guidelines. 25 empirical studies were included. Outcomes such as schizophrenia and psychotic disorders, social and cognitive functioning were scrutinised. Evidence was found for alteration of brain functioning and brain structure. Most studies researching cognitive functioning or cognitive decline displayed possible protective effects of urban living compared to rural living. The different study designs in Public Health and Neuroscience could profit from each other. Although the comparability of studies is limited by the inconsistent assessments of urbanity. Synergies and potentials to combine aspects of Public Health and Neuroscience in the field of Urban Health to improve population health became apparent.

Effects of Ambient Air Pollution on Precocious Puberty: A Case-Crossover Analysis in Nanjing, China

BACKGROUND

Ambient air pollution is closely related to a variety of health outcomes. Few studies have focused on the correlations between air pollution exposure and children's sexual development. In this study, we investigated the potential effects of exposure to air pollution on precocious puberty among children using real-world evidence.

METHODS

We conducted a case-crossover study (n = 2201) to investigate the effect of ambient air pollution exposure on precocious puberty from January 2016 to December 2021. Average exposure levels of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ before diagnosis were calculated by using the inverse distance weighting (IDW) method. Distributed lag nonlinear model (DLNM) was used to assess the effect of air pollutants exposure on precocious puberty.

RESULTS

The mean age of the children who were diagnosed with precocious puberty was 7.47 ± 1.24 years. The average concentration of PM_2.5 and PM₁₀ were 38.81 ± 26.36 μg/m³ and 69.77 ± 41.07 μg/m³, respectively. We found that exposure to high concentrations of PM_2.5 and PM₁₀ might increase the risk of precocious puberty using the DLNM model adjusted for the age, SO₂, NO₂, CO, and O₃ levels. The strongest effects of the PM_2.5 and PM₁₀ on precocious puberty were observed in lag 27 (OR = 1.72, 95% CI: 1.01-2.92) and lag 16 (OR = 1.95, 95% CI: 1.33-2.85), respectively.

CONCLUSION

Our findings supported that short-term exposure to air pollution was the risk factor for precocious puberty. Every effort should be made to protect children from air pollution.

A scientometrics study of the nanomedicines assisted in respiratory diseases

Nanomedicine has been extensively studied for its versatility and broad-spectrum applications of theranostics in the research of respiratory disease. However, to the best of our knowledge, a scientometrics study based on the scientific knowledge assay of the overall situation on nanomedicine applied in the research of respiratory disease has not been reported so far, which would be of major importance to relevant researchers. To explore and exhibit the research status and developing trend of nanomedicines deployed in basic or clinical research in respiratory disease, the research ecosystem and exciting subareas were profiled based on the massive data mining and visualization from the relevant works reported from 2006 to 2021. Data were collected from the Web of Science database. Data statistics software and bibliometric analysis software were employed to visualize the research trend and the relationship between respiratory diseases and nanomedicines in each representative direction. The cluster analysis and burst detections indicated that the improvement of drug delivery and vaccine developments are the up-to-date key directions in nanomedicines for respiratory disease research and treatments. Furthermore, we emphatically studied four branch areas in this field including COVID-19, nanotube, respiratory syncytial virus, and mRNA vaccine those are selected for in-depth mining and bibliometric coupling analysis. Research trends signify the future focuses will center on preventing respiratory diseases with mRNA vaccines using nanoparticle-based approaches. We anticipate our study will enable researchers to have the panorama and deep insights in this area, thus inspiriting further exploitations especially the nanobiomaterial-based systems for theranostic applications in respiratory disease treatment.

Assessment of air quality before and during the COVID-19 and its potential health impacts in an arid oasis city: Urumqi, China

As a key node city of the "Silk Road Economic Belt" Urumqi has been listed as one of the ten most polluted cities in the world, posing a serious threat to the urban environment and residents' health. This study analyzed the air quality before and during the COVID-19 (Coronavirus disease 2019) pandemic and its potential health effects based on the data of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃_8h levels from 10 air quality monitoring stations in Urumqi from January 1, 2017, to December 31, 2021. As per the results, the concentrations of the air pollutants PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃_8h in Urumqi from 2017 to 2021 showed a cyclical trend, and the implementation of COVID-19 prevention and control measures could effectively reduce the concentration(ρ) of air pollutants. The mean value of ρ(PM_2.5) decreased from 2017 to 2021, whereas ρ(O₃_8h) showed a waveform change trend (increased in 2017-2018, decreased in 2018-2020, and increased after 2020). Meanwhile, the maximum annual average values of ρ(PM_2.5) and ρ(O₃_8h) for the six monitoring stations during 2017-2021 occurred at sites S2 (74.37 µg m^-3) and S6 (91.80 µg m^-3), respectively; rapid industrialization had a greater impact on PM_2.5 and O₃_8h concentrations compared to commercial and residential areas. In addition, the air quality index data series can characterize the fluctuation trend of PM_2.5. The high pollution levels (Class IV and V) of the air pollutants PM_2.5 and O₃_8h in Urumqi have been decreasing annually, and good days can account for 80-95% of the total number of days in the year, indicating that the number of days with a potential threat to residents' health is gradually decreasing. Therefore, more attention should be paid in controlling and managing air pollution in Urumqi.

Different levels of circadian (de)synchrony -- where does it hurt?

A network of cellular timers ensures the maintenance of homeostasis by temporal modulation of physiological processes across the day. These so-called circadian clocks are synchronized to geophysical time by external time cues (or zeitgebers). In modern societies, natural environmental cycles are disrupted by artificial lighting, around-the-clock availability of food or shift work. Such contradictory zeitgeber input promotes chronodisruption, i.e., the perturbation of internal circadian rhythms, resulting in adverse health outcomes. While this phenomenon is well described, it is still poorly understood at which level of organization perturbed rhythms impact on health and wellbeing. In this review, we discuss different levels of chronodisruption and what is known about their health effects. We summarize the results of disrupted phase coherence between external and internal time vs. misalignment of tissue clocks amongst each other, i.e., internal desynchrony. Last, phase incoherence can also occur at the tissue level itself. Here, alterations in phase coordination can emerge between cellular clocks of the same tissue or between different clock genes within the single cell. A better understanding of the mechanisms of circadian misalignment and its effects on physiology will help to find effective tools to prevent or treat disorders arising from modern-day chronodisruptive environments.

Ambient fine particulate pollution hysteresis triggers wake-up stroke and rapidly triggers non-wake-up stroke: a case-crossover study

Atmospheric pollutants increase the risk of acute ischemic stroke (AIS) which has been widely reported. However, little is known about the relationships between air pollution and specific subsets of AIS, such as wake-up stroke (WUS) and non-wake-up stroke (non-WUS). This study aimed to explore the relationship between WUS and non-WUS and atmospheric pollutants. A total of 1432 patients (331 WUS patients and 1101 non-WUS patients) were admitted to a tertiary hospital from 2016 to 2019. A time-stratified case-crossover design and a conditional logistic regression model to study the associations of change in pollutant concentration with WUS and non-WUS events were constructed. Data analysis revealed that WUS-related risks increased 48 to 72 h after the increase in the PM_2.5 concentration (each 10 μg/m³ increase, lag 0-72 h) [threshold OR (95% CI):18 μg/m³ 1.03 (0.94-1.11), 35 μg/m³ 1.01 (0.92-1.12), 50 μg/m³ 1.04 (0.91-1.19)]; the non-WUS-related risk increased 1 to 6 h after the increase in the PM_2.5 concentration (each 10 μg/m³ increase, lag 0-1 h) [threshold OR (95% CI):18 μg/m³ 1.01 (0.98-1.03), 35 μg/m³ 1.00 (0.97-1.04), 50 μg/m³ 1.01 (0.96-1.05)] (lag 0-6 h) [threshold OR (95% CI): 18 μg/m³ 1.00 (0.97-1.03), 35 μg/m³ 1.00 (0.97-1.04), 50 μg/m³ 1.01 (0.97-1.06)]; O₃ exposure was related to WUS events, and its impact on WUS events was stronger and longer-lasting (1-96 h) than its impact on non-WUS events (1-6 h). Greater than or equal to 65 years of age, overweight (BMI ≥ 25), and diabetes had a significantly greater risk of WUS associated with increased PM_2.5 concentration in the previous 12-96 h than patients without these conditions. Patients with hypertension and smoking had a significant risk of non-WUS associated with increased PM_2.5 concentration in the previous 1-6 h. The increase in PM_2.5 concentration in the cold season increased the risk of both WUS and non-WUS events. Ambient air pollution hysteresis triggers WUS and rapidly triggers non-WUS, even if the degree of pollutant is relatively low. Patients with elderly, overweight, and diabetes appeared particularly susceptible to WUS, and patients with hypertension and smoking history were susceptible to non-WUS. We need to expand the sample for further investigation into mechanisms by which environmental pollutants trigger WUS or non-WUS.

Neurotoxic effects of environmental contaminants-measurements, mechanistic insight, and environmental relevance

Pollution is a significant and growing concern for any population regardless of age because these environmental contaminants exhibit different neurodegenerative effects on persons of different ages. These environmental contaminants are the products of human welfare projects like industry, automobile exhaust, clinical and research laboratory extrudes, and agricultural chemicals. These contaminants are found in various forms in environmental matrices like nanoparticles, particulate matter, lipophilic vaporized toxicants, and ultrafine particulate matter. Because of their small size, they can easily cross blood-brain barriers or use different cellular mechanisms for assistance. Other than this, these contaminants cause an innate immune response in different cells of the central nervous system and cause neurotoxicity. Considering the above critiques and current needs, this review summarizes different protective strategies based on bioactive compounds present in plants. Various bioactive compounds from medicinal plants with neuroprotective capacities are discussed with relevant examples. Many in vitro studies on clinical trials have shown promising outcomes using plant-based bioactive compounds against neurological disorders.

Traffic costs of air pollution: the effect of PM2.5 on traffic violation

Although emerging studies have investigated the effect of air pollution on traffic crashes, it is unclear to scholars whether air pollution affects another road safety problem-traffic violations. To address this gap, the current paper constructs a data set from 1,390,221 traffic violation records of 640,971 drivers from the Wuhan Traffic Management Bureau between January 2018 and December 2018. An ordered logistic regression was conducted to verify our hypotheses. The result shows that PM_2.5 has no overall impact on the severity of traffic violations, but each 1% increase in the daily concentration of PM_2.5 leads to a 1.02-fold increase in the odds of serious inexperience-related violations and a 0.99-fold decrease in the odds of serious overconfidence-related violations. This effect is the strongest in PM_2.5, followed by NO₂, and has not been observed in CO and O₃. In addition, robustness tests indicate that the relationship between air pollution and traffic violations is consistent among the different subsets (e.g., clear weather, no rain and snow, and good visibility). We also provide valuable practical advice for drivers and traffic authorities.

Locally weighted total least-squares variance component estimation for modeling urban air pollution

Land use regression (LUR) models are one of the standard methods for estimating air pollution concentration in urban areas. These models are usually low accurate due to inappropriate stochastic models (weight matrix). Furthermore, the measurement or modeling of dependent and independent variables used in LUR models is affected by various errors, which indicates the need to use an efficient stochastic and functional model to achieve the best estimation. This study proposes a locally weighted total least-squares variance component estimation (LW-TLS-VCE) for modeling urban air pollution. In the proposed method, in the first step, a locally weighted total least-squares (LW-TLS) regression is developed to simultaneously considers the non-stationary effects and errors of dependent and independent variables. In the second step, the variance components of the stochastic model are estimated to achieve the best linear unbiased estimation of unknowns. The efficiency of the proposed method is evaluated by modeling PM_2.5 concentrations via meteorological, land use, and traffic variables in Isfahan, Iran. The benefits provided by the proposed method, including considering non-stationary effects and random errors of all variables, besides estimating the actual variance of observations, are evaluated by comparing four consecutive methods. The obtained results demonstrate that using a suitable stochastic and functional model will significantly increase the proposed method's efficiency in PM_2.5 modeling.

Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

Background

Artificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution.

Methods

All literature on the application of AI to air pollution was searched from the Web of Science database. CiteSpace 5.8.R1 was used to analyze countries/regions, institutions, authors, keywords and references cited, and to reveal hot spots and frontiers of AI in atmospheric pollution.

Results

Beginning in 1994, publications on AI in air pollution have increased in number, with a surge in research since 2017. The leading country and institution were China (N = 524) and the Chinese Academy of Sciences (N = 58), followed by the United States (N = 455) and Tsinghua University (N = 33), respectively. In addition, the United States (0.24) and the England (0.27) showed a high degree of centrality. Most of the identified articles were published in journals related to environmental science; the most cited journal was Atmospheric Environment, which reached nearly 1,000 citations. There were few collaborations among authors, institutions and countries. The hot topics were machine learning, air pollution and deep learning. The majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods, low-cost air quality sensors, indoor air quality, and thermal comfort.

Conclusion

Researches in the field of AI and air pollution are expanding rapidly in recent years. The majority of scholars are from China and the United States, and the Chinese Academy of Sciences is the dominant research institution. The United States and the England contribute greatly to the development of the cooperation network. Cooperation among research institutions appears to be suboptimal, and strengthening cooperation could greatly benefit this field of research. The prediction of air pollutant concentrations, particularly PM_2.5, low-cost air quality sensors, and thermal comfort are the current research hotspot.

How Does Digitalization Affect Haze Pollution? The Mediating Role of Energy Consumption

In the context of digital technology innovation, an in-depth investigation into the impact of digitalization on haze pollution is of great significance for scientifically understanding environmental effects of digitalization and building a livable civic environment. From the perspective of energy consumption intensity and structure, this paper theoretically analyzes the direct and indirect effects of digitalization on haze pollution. On this basis, the impact of digitalization on haze pollution for 81 countries over the period 2010-2019 is empirically investigated by using the system GMM and mediating effects model. Empirical results show that digitalization can effectively suppress haze pollution, and there is significant heterogeneity in this inhibiting effect. In addition, digitalization can indirectly restrain haze pollution by reducing energy consumption intensity and optimizing energy consumption structure. The findings of this paper can provide enlightenment for countries to promote digitalization, combat haze pollution, and thus enhance the health of community residents.

Effect of Short- to Long-Term Exposure to Ambient Particulate Matter on Cognitive Function in a Cohort of Middle-Aged and Older Adults: KoGES

Exposure to ambient air pollution and its threat to human health is a global concern, especially in the elderly population. Therefore, more in-depth studies are required to understand the extent of the harmful effects of particulate matter (PM) based on duration and levels of exposure. An investigation was conducted to determine the association between short- (1-14 days), medium- (1, 3, and 6 months), and long-term (1, 2, and 3 years) exposure to air pollutants (PM_2.5 and PM₁₀) and cognitive function among Koreans (4175 participants, mean age 67.8 years, 55.2% women) aged over 50 years. Higher levels of PM_2.5 exposure for short to long term and PM₁₀ exposure for medium to long term were found to be associated with decreased cognitive function, as indicated by lower scores of the Mini-Mental State Examination adopted in Korean (K-MMSE). There were significant effect modifications by sex, age group, alcohol consumption, physical activity, and smoking status in the association between long-term PM_2.5 and PM₁₀ exposure and cognitive function. These findings, which underscore the importance of the efforts to reduce the exposure levels and durations of air pollutants, especially in the vulnerable elderly population, provide evidence for establishing more stringent policies for air pollution regulations.

Label-free detection and quantification of ultrafine particulate matter in lung and heart of mouse and evaluation of tissue injury

While it is known that air borne ultrafine particulate matter (PM) may pass through the pulmonary circulation of blood at the alveolar level between lung and heart and cross the air-blood barrier, the mechanism and effects are not completely clear. In this study the imaging method fluorescence lifetime imaging microscopy is adopted for visualization with high spatial resolution and quantification of ultrafine PM particles in mouse lung and heart tissues. The results showed that the median numbers of particles in lung of mice exposed to ultrafine particulate matter of diameter less than 2.5 µm was about 2.0 times more than that in the filtered air (FA)-treated mice, and about 1.3 times more in heart of ultrafine PM-treated mice than in FA-treated mice. Interestingly, ultrafine PM particles were more abundant in heart than lung, likely due to how ultrafine PM particles are cleared by phagocytosis and transport via circulation from lungs. Moreover, heart tissues showed inflammation and amyloid deposition. The component analysis of concentrated airborne ultrafine PM particles suggested traffic exhausts and industrial emissions as predominant sources. Our results suggest association of ultrafine PM exposure to chronic lung and heart tissue injuries. The current study supports the contention that industrial air pollution is one of the causative factors for rising levels of chronic pulmonary and cardiac diseases.

Environmental Pollutants and Oxidative Stress in Terrestrial and Aquatic Organisms: Examination of the Total Picture and Implications for Human Health

There is current great international concern about the contribution of environmental pollution to the global burden of disease particularly in the developing, low- and medium-income countries. Industrial activities, urbanization, developmental projects as well as various increased anthropogenic activities involving the improper generation, management and disposal of pollutants have rendered today’s environment highly polluted with various pollutants. These pollutants include toxic metals (lead, cadmium, mercury, arsenic), polycyclic aromatic hydrocarbons, polychlorinated biphenyls, pesticides and diesel exhaust particles most of which appear to be ubiquitous as well as have long-term environmental persistence with a wide range of toxicities such as oxidative stress among others. Oxidative stress, which may arise from increased production of damaging free radicals emanating from increased pollutant burden and depressed bioavailability of antioxidant defenses causes altered biochemical and physiological mechanisms and has been implicated in all known human pathologies most of which are chronic. Oxidative stress also affects both flora and fauna and plants are very important components of the terrestrial environment and significant contributors of nutrients for both man and animals. It is also remarkable that the aquatic environment in which sea animals and creatures are resident is also highly polluted, leading to aquatic stress that may affect the survival of the aquatic animals, sharing in the oxidative stress. These altered terrestrial and aquatic environments have an overarching effect on human health. Antioxidants neutralize the damaging free radicals thus, they play important protective roles in the onset, progression and severity of the unmitigated generation of pollutants that ultimately manifest as oxidative stress. Consequently, human health as well as that of aquatic and terrestrial organisms may be protected from environmental pollution by mitigating oxidative stress and employing the principles of nutritional medicine, essentially based on antioxidants derived mainly from plants, which serve as the panacea of the vicious state of environmental pollutants consequently, the health of the population. Understanding the total picture of oxidative stress and integrating the terrestrial and aquatic effects of environmental pollutants are central to sustainable health of the population and appear to require multi-sectoral collaborations from diverse disciplinary perspectives; basically the environmental, agricultural and health sectors.

A replicable strategy for mapping air pollution's community-level health impacts and catalyzing prevention

BACKGROUND

Air pollution was responsible for an estimated 6.7 million deaths globally in 2019 and 197,000 deaths in the United States. Fossil fuel combustion is the major source.

HYPOTHESIS

Mapping air pollution's health impacts at the community level using publicly available data and open-source software will provide a replicable strategy for catalyzing pollution prevention.

METHODS

Using EPA's Environmental Benefits Mapping and Analysis (BenMAP-CE) software and state data, we quantified the effects of airborne fine particulate matter (PM_2.5) pollution on disease, death and children's cognitive function (IQ Loss) in each city and town in Massachusetts. To develop a first-order estimate of PM_2.5 pollution's impact on child IQ, we derived a concentration-response coefficient through literature review.

FINDINGS

The annual mean PM_2.5 concentration in Massachusetts in 2019 was 6.3 μg/M³, a level below EPA's standard of 12 μg/M³ and above WHO's guideline of 5 μg/M³. In adults, PM_2.5 pollution was responsible for an estimated 2780 (Confidence Interval [CI] 2726 - 2853) deaths: 1677 (CI, 1346 - 1926) from cardiovascular disease, 2185 (CI, 941-3409) from lung cancer, 200 (CI, 66-316) from stroke, and 343 (CI, 222-458) from chronic respiratory disease. In children, PM_2.5 pollution was responsible for 308 (CI, 105-471) low-weight births, 15,386 (CJ, 5433-23,483) asthma cases, and a provisionally estimated loss of nearly 2 million Performance IQ points; IQ loss impairs children's school performance, reduces graduation rates and decreases lifetime earnings. Air-pollution-related disease, death and IQ loss were most severe in low-income, minority communities, but occurred in every city and town in Massachusetts regardless of location, demographics or median family income.

CONCLUSION

Disease, death and IQ loss occur at air pollution exposure levels below current EPA standards. Prevention of disease and premature death and preservation of children's cognitive function will require that EPA air quality standards be tightened. Enduring prevention will require government-incentivized transition to renewable energy coupled with phase-outs of subsidies and tax breaks for fossil fuels. Highly localized information on air pollution's impacts on health and on children's cognitive function has potential to catalyze pollution prevention.

White matter pathology in alzheimer's transgenic mice with chronic exposure to low-level ambient fine particulate matter

Background

Air pollution, especially fine particulate matter (PM), can cause brain damage, cognitive decline, and an increased risk of neurodegenerative disease, especially alzheimer’s disease (AD). Typical pathological findings of amyloid and tau protein accumulation have been detected in the brain after exposure in animal studies. However, these observations were based on high levels of PM exposure, which were far from the WHO guidelines and those present in our environment. In addition, white matter involvement by air pollution has been less reported. Thus, this experiment was designed to simulate the true human world and to discuss the possible white matter pathology caused by air pollution.

Results

6 month-old female 3xTg-AD mice were divided into exposure and control groups and housed in the Taipei Air Pollutant Exposure System (TAPES) for 5 months. The mice were subjected to the Morris water maze test after exposure and were then sacrificed with brain dissection for further analyses. The mean mass concentration of PM_2.5 during the exposure period was 13.85 μg/m³. After exposure, there was no difference in spatial learning function between the two groups, but there was significant decay of memory in the exposure group. Significantly decreased total brain volume and more neuronal death in the cerebral and entorhinal cortex and demyelination of the corpus callosum were noted by histopathological staining after exposure. However, there was no difference in the accumulation of amyloid or tau on immunohistochemistry staining. For the protein analysis, amyloid was detected at significantly higher levels in the cerebral cortex, with lower expression of myelin basic protein in the white matter. A diffuse tensor image study also revealed insults in multiple white matter tracts, including the optic tract.

Conclusions

In conclusion, this pilot study showed that even chronic exposure to low PM_2.5 concentrations still caused brain damage, such as gross brain atrophy, cortical neuron damage, and multiple white matter tract damage. Typical amyloid cascade pathology did not appear prominently in the vulnerable brain region after exposure. These findings imply that multiple pathogenic pathways induce brain injury by air pollution, and the optic nerve may be another direct invasion route in addition to olfactory nerve.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12989-022-00485-8.

Recent Insights into Particulate Matter (PM2.5)-Mediated Toxicity in Humans: An Overview

Several epidemiologic and toxicological studies have commonly viewed ambient fine particulate matter (PM_2.5), defined as particles having an aerodynamic diameter of less than 2.5 µm, as a significant potential danger to human health. PM_2.5 is mostly absorbed through the respiratory system, where it can infiltrate the lung alveoli and reach the bloodstream. In the respiratory system, reactive oxygen or nitrogen species (ROS, RNS) and oxidative stress stimulate the generation of mediators of pulmonary inflammation and begin or promote numerous illnesses. According to the most recent data, fine particulate matter, or PM_2.5, is responsible for nearly 4 million deaths globally from cardiopulmonary illnesses such as heart disease, respiratory infections, chronic lung disease, cancers, preterm births, and other illnesses. There has been increased worry in recent years about the negative impacts of this worldwide danger. The causal associations between PM_2.5 and human health, the toxic effects and potential mechanisms of PM_2.5, and molecular pathways have been described in this review.