Association Between Maternal Prenatal Exposure to Household Air Pollution and Child Respiratory Health: A Systematic Review and Meta-analysis.

Maternal prenatal exposure to household air pollution (HAP) is a critical public health concern with potential long-term implications for child respiratory health. The objective of this study is to assess the level of association between prenatal household air pollution and child respiratory health, and to identify which HAP pollutants are associated with specific respiratory illnesses or symptoms and to what degree. Relevant studies were retrieved from PubMed databases up to April 27, 2010, and their reference lists were reviewed. Random effects models were applied to estimate summarized relative risks (RRs) and 95% confidence intervals (CIs). The analysis involved 11 studies comprising 387 767 mother-child pairs in total, assessing various respiratory health outcomes in children exposed to maternal prenatal HAP. Children with prenatal exposure to HAP pollutants exhibited a summary RR of 1.26 (95% CI=1.08-1.33) with moderate between-study heterogeneity (I²=49.22%) for developing respiratory illnesses. Specific associations were found between prenatal exposure to carbon monoxide (CO) (RR=1.11, 95% CI: 1.09-1.13), Nitrogen Oxides (NOx) (RR=1.46, 95% CI: 1.09-1.60), and particulate matter (PM) (RR=1.26, 95% CI: 1.2186-1.3152) and child respiratory illnesses (all had I² close to 0%, indicating no heterogeneity). Positive associations with child respiratory illnesses were also found with ultrafine particles (UFP), polycyclic aromatic hydrocarbons (PAH), and ozone (O3). However, no significant association was observed for prenatal exposure to sulfur dioxide (SO2). In summary, maternal prenatal exposure to HAP may contribute to a higher risk of child respiratory health issues, emphasizing the need for interventions to reduce this exposure during pregnancy. Targeted public health strategies such as improved ventilation, cleaner cooking technologies, and awareness campaigns should be implemented to minimize adverse respiratory effects on children.

Exposure to air pollution is associated with DNA methylation changes in sperm.

Exposure to air pollutants has been associated with adverse health outcomes in adults and children who were prenatally exposed. In addition to reducing exposure to air pollutants, it is important to identify their biologic targets in order to mitigate the health consequences of exposure. One molecular change associated with prenatal exposure to air pollutants is DNA methylation (DNAm), which has been associated with changes in placenta and cord blood tissues at birth. However, little is known about how air pollution exposure impacts the sperm epigenome, which could provide important insights into the mechanism of transmission to offspring. In the present study, we explored whether exposure to particulate matter less than 2.5 microns in diameter, particulate matter less than 10 microns in diameter, nitrogen dioxide (NO2), or ozone (O3) was associated with DNAm in sperm contributed by participants in the Early Autism Risk Longitudinal Investigation prospective pregnancy cohort. Air pollution exposure measurements were calculated as the average exposure for each pollutant measured within 4 weeks prior to the date of sample collection. Using array-based genome-scale methylation analyses, we identified 80, 96, 35, and 67 differentially methylated regions (DMRs) significantly associated with particulate matter less than 2.5 microns in diameter, particulate matter less than 10 microns in diameter, NO2, and O3, respectively. While no DMRs were associated with exposure to all four pollutants, we found that genes overlapping exposure-related DMRs had a shared enrichment for gene ontology biological processes related to neurodevelopment. Together, these data provide compelling support for the hypothesis that paternal exposure to air pollution impacts DNAm in sperm, particularly in regions implicated in neurodevelopment.

Evaluating the mortality and health rate caused by the PM2.5 pollutant in the air of several important Iranian cities and evaluating the effect of variables with a linear time series model.

All over the world, the level of special air pollutants that have the potential to cause diseases is increasing. Although the relationship between exposure to air pollutants and mortality has been proven, the health risk assessment and prediction of these pollutants have a therapeutic role in protecting public health, and need more research. The purpose of this research is to evaluate the ill-health caused by PM2.5 pollution using AirQ + software and to evaluate the different effects on PM2.5 with time series linear modeling by R software version 4.1.3 in the cities of Arak, Esfahan, Ahvaz, Tabriz, Shiraz, Karaj and Mashhad during 2019-2020. The pollutant hours, meteorology, population and mortality information were calculated by the Environmental Protection Organization, Meteorological Organization, Statistics Organization and Statistics and Information Technology Center of the Ministry of Health, Treatment and Medical Education for 24 h of PM2.5 pollution with Excel software. In addition, having 24 h of PM2.5 pollutants and meteorology is used to the effect of variables on PM2.5 concentration. The results showed that the highest and lowest number of deaths due to natural deaths, ischemic heart disease (IHD), lung cancer (LC), chronic obstructive pulmonary disease (COPD), acute lower respiratory infection (ALRI) and stroke in The effect of disease with PM2.5 pollutant in Ahvaz and Arak cities was 7.39-12.32%, 14.6-17.29%, 16.48-8.39%, 10.43-18.91%, 12.21-22.79% and 14.6-18.54 % respectively. Another result of this research was the high mortality of the disease compared to the mortality of the nose. The analysis of the results showed that by reducing the pollutants in the cities of Karaj and Shiraz, there is a significant reduction in mortality and linear modeling provides a suitable method for air management planning.

An ecological analysis of associations between ambient air pollution and cancer incidence rates in Taiwan.

Air pollution is deemed a human carcinogen and can be linked to certain types of cancer other than lung cancer. The present study aimed to investigate the pollutant-cancer associations in a population-level cohort. We obtained the annual age-standardized incidence rates of 28 different cancer types between 2015 to 2019 from the Taiwan Cancer Registry. Outdoor concentrations of particulate matter with a diameter of 10 µm or less (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ground-level ozone (O3), and carbon monoxide (CO) between 2001 to 2010 were retrieved from the Taiwan Air Quality Monitoring Network. Weighted quantile sum (WQS) regression models were used to determine the combined effects of five air pollutants on the relationship to cancer incidence rates after controlling for sex ratio, age, average disposable income per household, overweight/obesity prevalence, current smoking rate, and drinking rate. Trend analyses showed that NO2 and CO concentrations tended to decrease, while SO2 concentrations increased in some counties. WQS regression analyses revealed significantly positive correlations between air pollutants and liver cancer, lung and tracheal cancer, colorectal cancer, thyroid cancer, kidney cancer, and small intestine cancer. Altogether, the results from this ecological study unravel that exposure to ambient air pollution is associated with the incidence of several non-lung cancer types.

Causal relationship between air pollution and chronic obstructive pulmonary disease in European and East Asian populations: a Mendelian randomization study.

Epidemiologic studies have suggested a possible association between air pollution and chronic obstructive pulmonary disease (COPD), but it is controversial and difficult to draw causal inferences. Five methods were adopted to evaluate the causal relationship between air pollution and COPD in European and East Asian populations by using MR Analysis. A statistically significant causal relationship between PM2.5 and COPD was observed in the European population (OR: 2.34; 95% CI: 1.06-5.05; p = 0.033). Statistical significance remained after adjustment for confounding factors (adjusted OR: 2.28; 95% CI: 1.01-5.20; p = 0.048). In East Asian populations, PM2.5 absorbance, a proxy for black carbon, was statistically associated with COPD (OR: 1.41; 95% CI: 1.09-1.81; p = 0.007). We did not adjust for confounders in East Asian populations, as the association was independent of known confounders (e.g. smoking, respiratory tract infections, etc.). In conclusion, increased concentrations of PM2.5 and PM2.5 absorbance were associated with an increased risk of COPD.

Joint effects of prenatal exposure to indoor air pollution and psychosocial factors on early life inflammation.

It is hypothesized that air pollution and stress impact the central nervous system through neuroinflammatory pathways Despite this, the association between prenatal exposure to indoor air pollution and psychosocial factors on inflammatory markers in infancy has been underexplored in epidemiology studies. This study investigates the individual and joint effects of prenatal exposure to indoor air pollution and psychosocial factors on early life inflammation (interleukin-1ß (IL-1ß), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α)). We analyzed data from the South African Drakenstein Child Health Study (N = 225). Indoor air pollution and psychosocial factor measurements were taken in the 2nd trimester of pregnancy. Circulating inflammatory markers (IL-1ß, Il-6, and TNF-α) were measured in serum in the infants at 6 weeks postnatal. Linear regression models were used to investigate associations between individual exposures and inflammatory markers. To investigate joint effects of environmental and psychosocial factors, Self-Organizing Maps (SOM) were used to create exposure profile clusters. These clusters were added to linear regression models to investigate the associations between exposure profiles and inflammatory markers. All models were adjusted for maternal age, maternal HIV status, and ancestry to control for confounding. Most indoor air pollutants were positively associated with inflammatory markers, particularly benzene and TNF-α in single pollutant models. No consistent patterns were found for psychosocial factors in single-exposure linear regression models. In joint effects analyses, the SOM profile with high indoor air pollution, low SES, and high maternal depressive symptoms were associated with higher inflammation. Indoor air pollutants were consistently associated with increased inflammation in both individual and joint effects models, particularly in combination with low SES and maternal depressive symptoms. The trend for individual psychosocial factors was not as clear, with mainly null associations. As we have observed pro- and anti-inflammatory effects, future research should investigate joint effects of these exposures on inflammation and their health effects.

Investigating the impact of air pollution and temperature changes on emergency admissions (cardiovascular + respiratory) disease in the city of Bojnord, northeastern Iran.

Cardiovascular (CVD) + Respiratory diseases are recognized as the main cause of death worldwide. Fluctuations in temperature and air pollution have been reported as one of the most important causes of cardiovascular & respiratory diseases. Therefore, in the current study, we assessed the relationship between ambient air temperature and pollution on the number of total emergency hospital admission due to cardiovascular and respiratory conditions in the City of Bojnord, northeastern Iran. The meteorological data, including daily temperature, relative humidity and concentrations of five air pollutants CO, NO2, NOX SO2, and PM10 were obtained from online electronic sensors at the Bojnurd meteorological station from 21th March 2018 to 20th March 2020. Statistical analysis, penalized distributed lag non-linear method was applied using R Software. Also, sensitivity analysis test was calculated by using appropriate application. The results of the study revealed that the effect of higher and lower temperatures was observed immediately from the first day and the second week, respectively. Also result showed with increase and decrease temperature, significantly increased the risk of hospitalization by 36% (RR, 1.36; 95% CI (1), 0.95 to 1.95) and 17% (RR, 1.17; 95% CI (1), 0.88 to 1.55) until the lag 25th day, respectively. Based on the results, increasing temperature significantly increased the hospitalization rate of cardiopulmonary patients, but the effect of cold was not significant on the population as well as age and gender subgroups. Study have also proved that there is no significance correlation between air pollutant and Cardiovascular & respiratory diseases.

Influence of building spatial patterns on wind environment and air pollution dispersion inside an industrial park based on CFD simulation.

The "spatial pattern-wind environment-air pollution" within building clusters is closely interconnected, where different spatial pattern parameters may have varying degrees of impact on the wind environment and pollutant dispersion. Due to the complex spatial structure within industrial parks, this complexity may lead to the accumulation and retention of air pollutants within the parks. Therefore, to alleviate the air pollution situation in industrial parks in China and achieve the circular transformation and construction of parks, this study takes Hefei Circular Economy Demonstration Park as the research object. The microscale Fluent model in computational fluid dynamics (CFD) is used to finely simulate the wind flow field and the diffusion process of pollutants within the park. The study analyzes the triad relationship and influence mechanism of "spatial pattern-wind environment-air pollution" within the park and studies the influence of different spatial pattern parameters on the migration and diffusion of pollutants. The results show a significant negative correlation between the content of pollutants and wind speed inside the industrial park. The better the wind conditions, the higher the air quality. The spatial morphology parameters of the building complex are the main influences on the condition of its internal wind environment. Building coverage ratio and degree of enclosure have a significant negative correlation with wind conditions. Maintaining them near 0.23 and 0.37, respectively, is favorable to the quality of the surrounding environment. Moreover, the average height of the building is positively correlated with the wind environment condition. The rate of transport and dissipation of pollutants gradually increases as the average building height reaches 16 m. Therefore, a reasonable building planning strategy and arrangement layout can effectively improve the wind environment condition inside the park, thus alleviating the pollutant retention situation. The obtained results serve as a theoretical foundation for optimizing morphological structure design within urban industrial parks.

Effects of ambient air pollution on the risk of small- and large-for-gestational-age births: an analysis using national birth data in Japan.

OBJECTIVES: Small-for-gestational-age (SGA) and large-for-gestational-age (LGA) births are major adverse birth outcomes related to newborn health. In contrast, the association between ambient air pollution levels and SGA or LGA births has not been investigated in Japan; hence, the purpose of our study is to investigate this association. METHODS: We used birth data from Vital Statistics in Japan from 2017 to 2021 and municipality-level data on air pollutants, including nitrogen dioxide (NO2), sulfur dioxide (SO2), photochemical oxidants, and particulate matter 2.5 (PM2.5). Ambient air pollution levels throughout the first, second, and third trimesters, as well as the whole pregnancy, were calculated for each birth. The association between SGA/LGA and ambient levels of the air pollutants was investigated using crude and adjusted log-binomial regression models. In addition, a regression model with spline functions was also used to detect the non-linear association. RESULTS: We analyzed data from 2,434,217 births. Adjusted regression analyses revealed statistically significant and positive associations between SGA birth and SO2 level, regardless of the exposure period. Specifically, the risk ratio for average SO2 values throughout the whole pregnancy was 1.014 (95% confidence interval [CI] 1.009, 1.019) per 1 ppb increase. In addition, regression analysis with spline functions indicated that an increase in risk ratio for SGA birth depending on SO2 level was linear. Furthermore, statistically significant and negative associations were observed between LGA birth and SO2 except for the third trimester. CONCLUSIONS: It was suggested that ambient level of SO2 during the pregnancy term is a risk factor for SGA birth in Japan.

Air pollution and diabetes mellitus: Association and validation in a desert area in China.

BACKGROUND: Despite the growing evidence pointing to the detrimental effects of air pollution on diabetes mellitus (DM), the relationship remains poorly explored, especially in desert-adjacent areas characterized by high aridity and pollution. METHODS: We conducted a cross-sectional study with health examination data from over 2.9 million adults in two regions situated in the southern part of the Taklamakan Desert, China. We assessed three-year average concentrations (2018-2020) of particulate matter (PM1, PM2.5, and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) through a space-time extra-trees model. After adjusting for various covariates, we employed generalized linear mixed models to evaluate the association between exposure to air pollutants and DM. RESULTS: The odds ratios for DM associated with a 10 µg/m3 increase in PM1, PM2.5, PM10, CO, and NO2 were 1.898 (95% CI: 1.741, 2.070), 1.07 (95% CI: 1.053, 1.086), 1.013 (95% CI: 1.008, 1.018), 1.009 (95% CI: 1.007, 1.011), and 1.337 (95% CI: 1.234, 1.449), respectively. Notably, men, individuals aged ≥50 years, those with lower educational attainment, nonsmokers, and those not engaging in physical exercise displayed more susceptible to the adverse effects of air pollution. Multiple sensitivity analyses confirmed the stability of these findings. CONCLUSIONS: Our study provides robust evidence of a correlation between prolonged exposure to air pollution and the prevalence of DM among individuals living in the desert-adjacent areas. This research contributes to the expanding knowledge on the relationship between air pollution exposure and DM prevalence in desert-adjacent areas.

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

BACKGROUND: Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. OBJECTIVE: Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. METHODS: We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. RESULTS: The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV- R 2 = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and R 2 = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV- R 2 = 0.51 (with LCS). IMPACT: We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Gelato: a new hybrid deep learning-based Informer model for multivariate air pollution prediction.

The increase in air pollutants and its adverse effects on human health and the environment has raised significant concerns. This implies the necessity of predicting air pollutant levels. Numerous studies have aimed to provide new models for more accurate prediction of air pollutants such as CO2, O3, and PM2.5. Most of the models used in the literature are deep learning models with Transformers being the best for time series prediction. However, there is still a need to enhance accuracy in air pollution prediction using Transformers. Alongside the need for increased accuracy, there is a significant demand for predicting a broader spectrum of air pollutants. To encounter this challenge, this paper proposes a new hybrid deep learning-based Informer model called "Gelato" for multivariate air pollution prediction. Gelato takes a leap forward by taking several air pollutants into consideration simultaneously. Besides introducing new changes to the Informer structure as the base model, Gelato utilizes Particle Swarm Optimization for hyperparameter optimization. Moreover, XGBoost is used at the final stage to achieve minimal errors. Applying the proposed model on a dataset containing eight important air pollutants, including CO2, O3, NO, NO2, SO2, PM10, NH3, and PM2.5, the Gelato performance is assessed. Comparing the results of Gelato with other models shows Gelato's superiority over them, proving it is a high-confidence model for multivariate air pollution prediction.

Exploring the impact of lifestyle and environmental exposures on appetite hormone levels in children and adolescents: An observational study.

BACKGROUND: Appetite hormones are considered a promising target in fighting obesity as impaired appetite hormone levels have already been associated with obesity. However, further insights in the drivers of appetite hormone levels are needed. OBJECTIVES: In this study, we investigated the associations of fasting appetite hormone levels with lifestyle and environmental exposures in children and adolescents. METHODS: A total of 534 fasting blood samples were collected from children and adolescents (4-16y,50% boys) and appetite hormone levels (glucagon-like peptide-1 (GLP-1), peptide YY (PYY), pancreatic polypeptide (PP), leptin and ghrelin) were measured. Exposures included dietary quality (fiber-rich food intake, sugar propensity, fat propensity), psychosocial stress (happiness, negative emotions, negative life events and emotional problems), sleep duration, physical activity and environmental quality (long term black carbon (BC), particulate matter <2.5 µM (PM2.5), nitrogen dioxide (NO2) exposure, and green space in a 100 m and 2000 m radius around the residence). A multi-exposure score was calculated to combine all the exposures at study in one measure. Associations of individual exposures and multi-exposure score with appetite hormone levels were evaluated using linear mixed regression models adjusting for sex, age, socioeconomic status, waist-to-height ratio and multiple testing. RESULTS: GLP-1 was associated with air pollution exposure (NO2 ß* = -0.13, BC ß* = -0.15, PM2.5 ß* = -0.16, all p < 0.001). Leptin was associated with green space in a 100 m radius around the residence (ß* = -0.11; p = 0.002). Ghrelin was associated with negative emotions (active ghrelin ß* = -0.16; p = 0.04, total ghrelin ß* = -0.23; p = 0.0051) and happiness (active ghrelin ß* = 0.25; p < 0.001, total ghrelin ß* = 0.26; p < 0.001). Furthermore, total ghrelin levels were associated with the multi-exposure score, reflecting unhealthy exposures and lifestyle (ß* = -0.22; p = 0.036). DISCUSSION: Our findings provide new insights into the associations of exposures with appetite hormone levels, which are of high interest for preventive obesity research. Further research is crucial to reveal the underlying mechanisms of the observed associations.

Short-term air pollution levels and sickle cell disease hospital encounters in South Carolina: A case-crossover analysis.

BACKGROUND: Sickle cell disease (SCD) is a genetic disorder and symptoms may be sensitive to environmental stressors. Although it has been hypothesized that exposure to outdoor air pollution could trigger acute SCD events, evidence is limited. METHODS: We obtained SCD administrative data on hospital encounters in South Carolina from 2002 to 2019. We estimated outdoor air pollutant (particulate matter<2.5 µm (PM2.5), ozone (O3), and PM2.5 elemental carbon (EC) concentrations at residential zip codes using spatio-temporal models. Using a random bi-directional, fixed-interval case-crossover study design, we investigated the relationship between air pollution exposure over 1-, 3-, 5-, 9-, and14-day periods with SCD hospital encounters. RESULTS: We studied 8410 patients with 144,129 hospital encounters. We did not observe associations among all patients with SCD and adults for PM2.5, O3, and EC. We observed positive associations among children for 9- and 14-day EC (OR: 1.05 (95% confidence interval (CI): 1.02, 1.08) and OR: 1.05 (95% CI: 1.02, 1.09), respectively) and 9- and 14-day O3 (OR: 1.04 (95%CI: 1.00, 1.08)) for both. CONCLUSIONS: Our findings suggest that short-term (within two-weeks) levels of EC and O3 and may be associated with SCD hospital encounters among children. Two-pollutant model results suggest that EC is more likely responsible for effects on SCD than O3. More research is needed to confirm our findings.

Research on the cascading mechanism of "urban built environment-air pollution-respiratory diseases": a case of Wuhan city.

Background: Most existing studies have only investigated the direct effects of the built environment on respiratory diseases. However, there is mounting evidence that the built environment of cities has an indirect influence on public health via influencing air pollution. Exploring the "urban built environment-air pollution-respiratory diseases" cascade mechanism is important for creating a healthy respiratory environment, which is the aim of this study. Methods: The study gathered clinical data from 2015 to 2017 on patients with respiratory diseases from Tongji Hospital in Wuhan. Additionally, daily air pollution levels (sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM2.5, PM10), and ozone (O3)), meteorological data (average temperature and relative humidity), and data on urban built environment were gathered. We used Spearman correlation to investigate the connection between air pollution and meteorological variables; distributed lag non-linear model (DLNM) was used to investigate the short-term relationships between respiratory diseases, air pollutants, and meteorological factors; the impacts of spatial heterogeneity in the built environment on air pollution were examined using the multiscale geographically weighted regression model (MGWR). Results: During the study period, the mean level of respiratory diseases (average age 54) was 15.97 persons per day, of which 9.519 for males (average age 57) and 6.451 for females (average age 48); the 24 h mean levels of PM10, PM2.5, NO2, SO2 and O3 were 78.056 µg/m3, 71.962 µg/m3, 54.468 µg/m3, 12.898 µg/m3, and 46.904 µg/m3, respectively; highest association was investigated between PM10 and SO2 (r = 0.762, p < 0.01), followed by NO2 and PM2.5 (r = 0.73, p < 0.01), and PM10 and PM2.5 (r = 0.704, p < 0.01). We observed a significant lag effect of NO2 on respiratory diseases, for lag 0 day and lag 1 day, a 10 µg/m3 increase in NO2 concentration corresponded to 1.009% (95% CI: 1.001, 1.017%) and 1.005% (95% CI: 1.001, 1.011%) increase of respiratory diseases. The spatial distribution of NO2 was significantly influenced by high-density urban development (population density, building density, number of shopping service facilities, and construction land, the bandwidth of these four factors are 43), while green space and parks can effectively reduce air pollution (R2 = 0.649). Conclusion: Previous studies have focused on the effects of air pollution on respiratory diseases and the effects of built environment on air pollution, while this study combines these three aspects and explores the relationship between them. Furthermore, the theory of the "built environment-air pollution-respiratory diseases" cascading mechanism is practically investigated and broken down into specific experimental steps, which has not been found in previous studies. Additionally, we observed a lag effect of NO2 on respiratory diseases and spatial heterogeneity of built environment in the distribution of NO2.

Peroxyacetyl nitrate can be used as a comprehensive indicator of air pollution complex.

With the acceleration of air cleaning activities in China, air pollution has entered a new stage characterized by seasonal interplay and predominance of fine particulate matter (PM2.5) and ozone (O3) pollutants. However, the differing peak seasons of these two pollution preclude the use of a unified indicator for air pollution complex. Given that peroxyacetyl nitrate (PAN) originates from secondary formation and persists under low-temperature conditions for extended periods, it is vital to determine whether its concentration can be used as an indicator to represent air pollution, not only in summer but also in winter. Here, PAN observational data from 2018 to 2022 for Beijing were analyzed. The results showed that during photochemical pollution events in summer, secondary formation of PAN was intense and highly correlated with O3 (R = 0.8), while during PM2.5 pollution events in winter, when the lifetime of PAN is extended due to the low temperature, the PAN concentration was highly consistent with the PM2.5 concentration (R = 0.9). As a result, the PAN concentration essentially exhibited consistency with both the seasonal trends in the exceedance of air pollution (R = 0.6) and the air quality index (R = 0.8). When the daily average concentration exceeds 0.5 and 0.9 ppb, the PAN concentration can be used as a complementary indicator of the occurrence of primary and secondary standard pollution, respectively. This study demonstrated the unique role of PAN as an indicator of air pollution complex, highlighting the comprehensive ability for air quality characterization and reducing the burden of atmospheric environment management.

Multiple dietary patterns and the association between long-term air pollution exposure with type 2 diabetes risk: Findings from UK Biobank cohort study.

BACKGROUND: Evidence of modifying effect of various dietary patterns (DPs) on risk of type 2 diabetes (T2D) induced by long-term exposure to air pollution (AP) is still rather lacking, which therefore we aimed to explore in this study. METHODS: We included 78,230 UK Biobank participants aged 40-70 years with at least 2 typical 24-hour dietary assessments and without baseline diabetes. The annual average concentration of particulate matter with diameter micrometers ≤2.5 (PM2.5) and ≤10 (PM10), nitrogen dioxide (NO2), and nitrogen oxides (NOX) estimated by land use regression model was the alternative proxy of long-term AP exposure. Three well-known prior DPs such as Mediterranean diet (MED), dietary approaches to stop hypertension diet (DASH), and empirical dietary inflammatory pattern (EDIP), as well as three posterior DPs derived by the rank reduced regression model were used to capture participants' dietary habits. Cox regression models were used to estimate AP-T2D and DP-T2D associations. Modifying effect of DPs on AP-T2D association was assessed using stratified analysis and heterogeneity test. RESULTS: During a median follow-up 12.19 years, 1,693 participants developed T2D. PM2.5, PM10, NO2, and NOX significantly increased the T2D risk (P <0.05), with hazard ratio (HR) and 95% confidence interval (95% CI) for per interquartile range increase being 1.09 (1.02,1.15), 1.04 (1.00, 1.09), 1.11 (1.04, 1.18), and 1.08 (1.03, 1.14), respectively. Comparing high with low adherence, healthy DPs were associated with a 14-41% lower T2D risk. Participants with high adherence to MED, DASH, and anti-EDIP, alongside the posterior anti-oxidative dietary pattern (AODP) had attenuated and statistically non-significant NO2-T2D and NOX-T2D associations (Pmodify <0.05). CONCLUSIONS: Multiple forms of healthy DPs help reduce the T2D risk associated with long-term exposure to NO2 and NOX. Our findings indicate that adherence to healthy DPs is a feasible T2D prevention strategy for people long-term suffering from NO2 and NOX pollution.

Ambient air pollution exposure and the risk of probable sarcopenia: A prospective cohort study.

BACKGROUND: Sarcopenia is characterized by decreased muscle mass and strength, posing threat to quality of life. Air pollutants are increasingly recognized as risk factors for diseases, while the relationship between the two remains to be elucidated. This study investigated whether exposure to ambient air pollution contributes to the development of sarcopenia. METHODS: We employed the data from the UK Biobank with 303,031 eligible participants. Concentrations of PM2·5, NO2, and NOx were estimated. Cox proportional hazard regression models were applied to investigate the associations between pollutants and sarcopenia. RESULTS: 30,766 probable sarcopenia cases was identified during the follow-up. We observed that exposure to PM2.5 (HR, 1.232; 95% CI, 1.053-1.440), NO2 (HR, 1.055; 95% CI, 1.032-1.078) and NOx (HR, 1.016; 95% CI, 1.007-1.026) were all significantly associated with increased risk for probable sarcopenia for each 10 µg/m3 increase in pollutant concentration. In comparison with individuals in the lowest quartiles of exposure, those in the upper quartiles had significantly increased risk of probable sarcopenia. Sarcopenia-related factors, e.g., reduced lean muscle mass, diminished walking pace, and elevated muscle fat infiltration ratio, also exhibited positive associations with exposure to ambient air pollution. On the contrary, high level physical activity significantly mitigated the influence of air pollutants on the development of probable sarcopenia. CONCLUSIONS: Air pollution exposure elevated the risk of developing sarcopenia and related manifestations in a dose-dependent manner, while physical activity maintained protective under this circumstance. Efforts should be made to control air pollution and emphasize the importance of physical activity for skeletal muscle health under this circumstance.

Association of ambient air pollution with hemoglobin levels and anemia in the general population of Korean adults.

BACKGROUND: Emerging evidence has suggested significant associations between ambient air pollution and changes in hemoglobin levels or anemia in specific vulnerable groups, but few studies have assessed this relationship in the general population. This study aimed to evaluate the association between long-term exposure to air pollution and hemoglobin concentrations or anemia in general adults in South Korea. METHODS: A total of 69,830 Korean adults from a large-scale nationwide survey were selected for our final analysis. Air pollutants included particulate matter with an aerodynamic diameter less than or equal to 10 micrometers (PM10), particulate matter with an aerodynamic diameter less than or equal to 2.5 micrometers, nitrogen dioxide, sulfur dioxide (SO2), and carbon monoxide (CO). We measured the serum hemoglobin concentration to assess anemia for each participant. RESULTS: In the fully adjusted model, exposure levels to PM10, SO2, and CO for one and two years were significantly associated with decreased hemoglobin concentrations (all p < 0.05), with effects ranging from 0.15 to 0.62% per increase in interquartile range (IQR) for each air pollutant. We also showed a significant association of annual exposure to PM10 with anemia (p = 0.0426); the odds ratio (OR) [95% confidence interval (CI)] for anemia per each increase in IQR in PM10 was estimated to be 1.039 (1.001-1.079). This association was also found in the 2-year duration of exposure (OR = 1.046; 95% CI = 1.009-1.083; adjusted Model 2). In addition, CO exposure during two years was closely related to anemia (OR = 1.046; 95% CI = 1.004-1.091; adjusted Model 2). CONCLUSIONS: This study provides the first evidence that long-term exposure to air pollution, especially PM10, is significantly associated with reduced hemoglobin levels and anemia in the general adult population.