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.

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.

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

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

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

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

Long-term exposure to ambient air pollution and risk of microvascular complications among patients with type 2 diabetes: a prospective study.

BACKGROUND: Patients with type 2 diabetes (T2D) may disproportionately suffer the adverse cardiovascular effects of air pollution, but relevant evidence on microvascular outcome is lacking. We aimed to examine the association between air pollution exposure and the risk of microvascular complications among patients with T2D. METHODS: This prospective study included 17 995 participants with T2D who were free of macro- and micro-vascular complications at baseline from the UK Biobank. Annual average concentrations of particulate matter (PM) with diameters <2.5 µm (PM2.5), <10 µm (PM10), nitrogen dioxide (NO2) and nitrogen oxides (NOx) were assessed using land use regression models. Cox proportional hazards regression was used to estimate the associations of air pollution exposure with incident diabetic microvascular complications. The joint effects of the air pollutant mixture were examined using quantile-based g-computation in a survival setting. RESULTS: In single-pollutant models, the adjusted hazard ratios (95% confidence intervals) for composite diabetic microvascular complications per interquartile range increase in PM2.5, PM10, NO2 and NOx were 1.09 (1.04-1.14), 1.06 (1.01-1.11), 1.07 (1.02-1.12) and 1.04 (1.00-1.08), respectively. Similar significant results were found for diabetic nephropathy and diabetic neuropathy, but not for diabetic retinopathy. The associations of certain air pollutants with composite microvascular complications and diabetic nephropathy were present even at concentrations below the World Health Organization limit values. Multi-pollutant analyses demonstrated that PM2.5 contributed most to the elevated risk associated with the air pollutant mixture. In addition, we found no interactions between air pollution and metabolic risk factor control on the risk of diabetic microvascular complications. CONCLUSIONS: Long-term individual and joint exposure to PM2.5, PM10, NO2 and NOx, even at low levels, was associated with an increased risk of diabetic microvascular complications, with PM2.5 potentially being the main contributor.

Association of early-life exposure to air and noise pollution with youth mental health: findings from the ALSPAC cohort.

BACKGROUND: Increasing evidence suggests that air pollution exposure contributes to the development of mental health problems, including psychosis and depression. However, little is known about the importance of early-life exposure, nor the potential role of noise pollution, a correlate of air pollution. We examined the association of exposure to air and noise pollution from pregnancy to age 12 years with three mental health problems assessed at ages 12, 18, and 24 years. METHODS: Data were from the Avon Longitudinal Study of Parents and Children (ALSPAC), which tracks the development of about 14 000 babies who had expected delivery dates between April 1, 1991, and Dec 31, 1992, in Avon, UK. This was linked with novel data on nitrogen dioxide, PM2·5, and noise pollution in pregnancy, childhood (ages 1-9 years), and adolescence (ages 10-12 years). Psychotic experiences, depression, and anxiety were measured at ages 12, 18, and 24 years. Logistic regression models were controlled for individual-level, family-level, and area-level confounders, and e-values were calculated to estimate residual confounding. FINDINGS: Participants exposed to higher PM2.5, particularly during pregnancy, had greater odds for psychotic experiences (adjusted odds ratio 1·17 [95% CI 1·05-1·30]) and depression (1·11 [1·01-1·22]). There was little evidence associating nitrogen dioxide or noise pollution with psychotic experiences or depression. Conversely, higher nitrogen dioxide (but not PM2·5) exposure in pregnancy (1·16 [1·01-1·33]), and higher noise pollution in childhood (1·20 [1·06-1·37]) and adolescence (1·17 [1·02-1·35]), were associated with greater odds for anxiety. INTERPRETATION: Our study builds on evidence linking air pollution to psychosis and depression and provides rare longitudinal evidence linking noise pollution to anxiety. Our findings indicate that air pollution exposure earlier in development (eg, during pregnancy) might be particularly important, and suggest a degree of specificity in terms of pollutant-outcome associations. If causal, our findings suggest that interventions to reduce air pollution would improve global mental health. FUNDING: Wellcome Trust, UK Medical Research Council-Wellcome, and University of Bristol.

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.

Impacts of urban emissions and air quality in São Paulo State, Brazil.

Daily violations of air quality have an impact on urban populations and cause damage to the environment. Thus, the study evaluated the violations of the daily concentrations of SO2, NO2, and PM10, in regions of the State of São Paulo (SSP), based on the National Environment Council (CONAMA) resolution no 491/2018 and the World Health Organization (WHO - World Health Organization. (2016). Ambient air pollution: a global assessment of exposure and burden of disease.) criteria. Daily SO2, NO2, and PM10data from 6 air quality stations operated by Environmental Company of the State of São Paulo CETESB (1996-2011) were organized and submitted to quality control, with data faults (gaps) being identified. The imputation of data via spline proved satisfactory in filling in the gaps (r > 0.7 and low values of Standard Error of the Estimate (SEE) and Root Mean Square Error (RMSE). The cluster analysis (CA) applied to SO2 formed only one homogeneous group (G1). Contrariwise, NO2 and PM10 formed two homogeneous groups (G1 and G2) each. The stations that showed the greatest similarity according to the CA were Cerqueira Cesar and Osasco. The cophenetic matrix generated for SO2 (0.83), NO2 (0.79), and PM10 (0.77) indicate a satisfactory adjustment of the dendrograms. The exploratory statistics applied to groups G1 and G2 point to the high variability of outliers. The WHO criteria are more restrictive than CONAMA regarding daily violations, with a reduction in SO2 and an increase in specific years for NO2 and PM10. Such variability is due to the adoption of public policies by the SSP and the influence of meteorological systems, being confirmed by the Run test that indicated oscillations in the time series, mainly in PM10, and also recognized well-defined biannual cycles.

Exposure to ambient air pollution and cognitive function: an analysis of the English Longitudinal Study of Ageing cohort.

BACKGROUND: An increasing number of studies suggest adverse effects of exposure to ambient air pollution on cognitive function, but the evidence is still limited. We investigated the associations between long-term exposure to air pollutants and cognitive function in the English Longitudinal Study of Ageing (ELSA) cohort of older adults. METHODS: Our sample included 8,883 individuals from ELSA, based on a nationally representative study of people aged ≥ 50 years, followed-up from 2002 until 2017. Exposure to air pollutants was modelled by the CMAQ-urban dispersion model and assigned to the participants' residential postcodes. Cognitive test scores of memory and executive function were collected biennially. The associations between these cognitive measures and exposure to ambient concentrations of NO2, PM10, PM2.5 and ozone were investigated using mixed-effects models adjusted for time-varying age, physical activity and smoking status, as well as baseline gender and level of education. RESULTS: Increasing long-term exposure per interquartile range (IQR) of NO2 (IQR: 13.05 µg/m3), PM10 (IQR: 3.35 µg/m3) and PM2.5 (IQR: 2.7 µg/m3) were associated with decreases in test scores of composite memory by -0.10 (95% confidence interval [CI]: -0.14, -0.07), -0.02 [-0.04, -0.01] and -0.08 [-0.11, -0.05], respectively. The same increases in NO2, PM10 and PM2.5 were associated with decreases in executive function score of -0.31 [-0.38, -0.23], -0.05 [-0.08, -0.02] and -0.16 [-0.22, -0.10], respectively. The association with ozone was inverse across both tests. Similar results were reported for the London-dwelling sub-sample of participants. CONCLUSIONS: The present study was based on a long follow-up with several repeated measurements per cohort participant and long-term air pollution exposure assessment at a fine spatial scale. Increasing long-term exposure to NO2, PM10 and PM2.5 was associated with a decrease in cognitive function in older adults in England. This evidence can inform policies related to modifiable environmental exposures linked to cognitive decline.

Investigation of formaldehyde sources and its relative emission intensity in shipping channel environment.

Formaldehyde (HCHO) is considered one of the most abundant gas-phase carbonyl compounds in the atmosphere, which can be directly emitted through transportation sources. Long-Path Differential Optical Absorption Spectroscopy (LP-DOAS) was used to observe HCHO in the river channel of Wusong Wharf in Shanghai, China for the whole year of 2019. Due to the impact of ship activity, the annual average HCHO level in the channel is about 2.5 times higher than that in the nearby campus environment. To explain the sources of HCHO under different meteorological conditions, the tracer-pair of CO and Ox (NO2+O3) was used on the clustered air masses. The results of the source appointment show that primary, secondary and background account for 24.14% (3.34 ± 1.19 ppbv), 44.78% (6.20 ± 2.04 ppbv) and 31.09% (4.31 ± 2.33 ppbv) of the HCHO in the channel when the air masses were from the mixed direction of the city and channel, respectively. By performing background station subtraction at times of high primary HCHO values and resolving the plume peaks, directly emitted HCHO/NO2 in the channel environment and plume were determined to be mainly distributed between 0.2 and 0.3. General cargo ships with higher sailing speeds or main engine powers tend to have higher HCHO/NO2 levels. With the knowledge of NO2 (or NOx) emission levels from ships, this study may provide data support for the establishment of HCHO emission factors.

Association between ambient air pollutants and short-term mortality risks during 2015-2019 in Guangzhou, China.

With the development of technology and industry, the problem of global air pollution has become difficult to ignore. We investigated the association between air pollutant concentrations and daily all-cause mortality and stratified the analysis by sex, age, and season. Data for six air pollutants [fine particulate matter (PM2.5), inhalable particles (PM10), nitric dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO)] and daily mortality rates were collected from 2015 to 2019 in Guangzhou, China. A time-series study using a quasi-Poisson generalized additive model was used to examine the relationships between environmental pollutant concentrations and mortality. Mortality data for 296,939 individuals were included in the analysis. The results showed that an increase of 10 µg/m3 in the concentrations of PM2.5, PM10, SO2, O3, NO2, and CO corresponded to 0.84% [95% confidence interval (CI): 0.47, 1.21%], 0.70% (0.44, 0.96%), 3.59% (1.77, 5.43%), 0.21% (0.05, 0.36%), 1.06% (0.70, 1.41%), and 0.05% (0.02, 0.09%), respectively. The effects of the six air pollutants were more significant for male individuals than female individuals, the cool season than the warm season, and people 75 years or older than those younger than 75 years. PM2.5, PM10, SO2, and NO2 were all associated with neoplasms and circulatory and respiratory diseases. The two-pollutant models found that PM2.5, PM10, and NO2 may independently affect the risk of mortality. The results showed that exposure to PM2.5, PM10 and NO2 may increase the risk of daily all-cause excessive mortality in Guangzhou.

Physical Activity-Induced Modification of the Association of Long-Term Air Pollution Exposure with the Risk of Depression in Older Adults.

PURPOSE: Evidence suggests that long-term air pollution exposures may induce depression; however, the influence of physical activity on this effect is unclear. We investigated modification of the associations between air pollution exposures and depression by the intensity of physical activity. MATERIALS AND METHODS: This cross-sectional study included 1454 Korean adults. Depression was defined as a Geriatric Depression Scale score ≥8. Concentrations of particulate matter (PM10 and PM2.5: diameter ≤10 µm and ≤2.5 µm, respectively) and nitrogen dioxide (NO2) level at each participant's residential address were estimated. Based on metabolic equivalents, physical activity intensity was categorized as inactive, minimally active, or health-enhancing physical activity (HEPA). RESULTS: Each 1-part per billion (ppb) NO2 concentration increase was significantly associated with a 6% [95% confidence interval (CI), 4%-8%] increase in depression risk. In older adults (≥65 years), a 1-ppb NO2 increase was associated (95% CI) with a 4% (1%-7%), 9% (5%-13%), and 21% (9%-33%) increase in depression risk in the inactive, minimally active, and HEPA groups, respectively. Compared with the inactive group, the minimally active (p=0.039) and HEPA groups (p=0.004) had higher NO2 exposure-associated depression risk. Associations of PM10 and PM2.5 with depression did not significantly differ by the intensity of physical activity. CONCLUSION: We suggest that older adults who vigorously exercise outdoors may be susceptible to air pollution-related depression.

Investigating the spatiotemporal associations between meteorological conditions and air pollution in the federal state Baden-Württemberg (Germany).

When analyzing health data in relation to environmental stressors, it is crucial to identify which variables to include in the statistical model to exclude dependencies among the variables. Four meteorological parameters: temperature, ultraviolet radiation, precipitation, and vapor pressure and four outdoor air pollution parameters: ozone ( O 3 ), nitrogen dioxide ( NO 2 ), particulate matter ( P M 2.5 , P M 10 ) were studied on a daily basis for Baden-Württemberg (Germany). This federal state covers urban and rural compartments including mountainous and river areas. A temporal and spatial analysis of the internal relationships was performed among the variables using (a) cross-correlations, both on the grand ensemble of data as well as within subsets, and (b) the Local Indications of Spatial Association (LISA) method. Meteorological and air pollution variables were strongly correlated within and among themselves in time and space. We found a strong interaction between nitrogen dioxide and ozone, with correlation coefficients varying over time. The coefficients ranged from negative correlations in January (-0.84), April (-0.47), and October (-0.54) to a positive correlation in July (0.45). The cross-correlation plot showed a noticeable change in the correlation direction for O 3 and NO 2 . Spatially, NO 2 , P M 2.5 , and P M 10 concentrations were significantly higher in urban than rural regions. For O 3 , this effect was reversed. A LISA analysis confirmed distinct hot and cold spots of environmental stressors. This work examined and quantified the spatio-temporal relationship between air pollution and meteorological conditions and recommended which variables to prioritize for future health impact analyses. The results found are in line with the underlying physico-chemical atmospheric processes. It also identified postal code areas with dominant environmental stressors for further studies.

Population exposure to multiple air pollutants and its compound episodes in Europe.

Air pollution remains as a substantial health problem, particularly regarding the combined health risks arising from simultaneous exposure to multiple air pollutants. However, understanding these combined exposure events over long periods has been hindered by sparse and temporally inconsistent monitoring data. Here we analyze daily ambient PM2.5, PM10, NO2 and O3 concentrations at a 0.1-degree resolution during 2003-2019 across 1426 contiguous regions in 35 European countries, representing 543 million people. We find that PM10 levels decline by 2.72% annually, followed by NO2 (2.45%) and PM2.5 (1.72%). In contrast, O3 increase by 0.58% in southern Europe, leading to a surge in unclean air days. Despite air quality advances, 86.3% of Europeans experience at least one compound event day per year, especially for PM2.5-NO2 and PM2.5-O3. We highlight the improvements in air quality control but emphasize the need for targeted measures addressing specific pollutants and their compound events, particularly amidst rising temperatures.

Air pollution exposure may impact the composition of human milk oligosaccharides.

Human milk oligosaccharides (HMOs) impact neonate immunity and health outcomes. However, the environmental factors influencing HMO composition remain understudied. This study examined the associations between ambient air pollutant (AAP) exposure and HMOs at 1-month postpartum. Human milk samples were collected at 1-month postpartum (n = 185). AAP (PM2.5, PM10, NO2) exposure included the 9-month pregnancy period through 1-month postpartum. Associations between AAP with (1) HMO diversity, (2) the sum of sialylated and fucosylated HMOs, (3) 6 a priori HMOs linked with infant health, and (4) all HMOs were examined using multivariable linear regression and principal component analysis (PCA). Exposure to AAP was associated with lower HMO diversity. PM2.5 and PM10 exposure was positively associated with the HMO 3-fucosyllactose (3FL); PM2.5 exposure was positively associated with the sum of total HMOs, sum of fucosylated HMOs, and the HMO 2'-fucosyllactose (2'FL). PCA indicated the PM2.5, PM10, and NO2 exposures were associated with HMO profiles. Individual models indicated that AAP exposure was associated with five additional HMOs (LNFP I, LNFP II, DFLNT, LNH). This is the first study to demonstrate associations between AAP and breast milk HMOs. Future longitudinal studies will help determine the long-term impact of AAP on human milk composition.

Short-term exposure to air pollution and hospital admission after COVID-19 in Catalonia: the COVAIR-CAT study.

BACKGROUND: A growing body of evidence has reported positive associations between long-term exposure to air pollution and poor COVID-19 outcomes. Inconsistent findings have been reported for short-term air pollution, mostly from ecological study designs. Using individual-level data, we studied the association between short-term variation in air pollutants [nitrogen dioxide (NO2), particulate matter with a diameter of <2.5 µm (PM2.5) and a diameter of <10 µm (PM10) and ozone (O3)] and hospital admission among individuals diagnosed with COVID-19. METHODS: The COVAIR-CAT (Air pollution in relation to COVID-19 morbidity and mortality: a large population-based cohort study in Catalonia, Spain) cohort is a large population-based cohort in Catalonia, Spain including 240 902 individuals diagnosed with COVID-19 in the primary care system from 1 March until 31 December 2020. Our outcome was hospitalization within 30 days of COVID-19 diagnosis. We used individual residential address to assign daily air-pollution exposure, estimated using machine-learning methods for spatiotemporal prediction. For each pandemic wave, we fitted Cox proportional-hazards models accounting for non-linear-distributed lagged exposure over the previous 7 days. RESULTS: Results differed considerably by pandemic wave. During the second wave, an interquartile-range increase in cumulative weekly exposure to air pollution (lag0_7) was associated with a 12% increase (95% CI: 4% to 20%) in COVID-19 hospitalizations for NO2, 8% (95% CI: 1% to 16%) for PM2.5 and 9% (95% CI: 3% to 15%) for PM10. We observed consistent positive associations for same-day (lag0) exposure, whereas lag-specific associations beyond lag0 were generally not statistically significant. CONCLUSIONS: Our study suggests positive associations between NO2, PM2.5 and PM10 and hospitalization risk among individuals diagnosed with COVID-19 during the second wave. Cumulative hazard ratios were largely driven by exposure on the same day as hospitalization.

Assessing the Global Impact of Ambient Air Pollution on Cancer Incidence and Mortality: A Comprehensive Meta-Analysis.

PURPOSE: This study aims to examine the association between exposure to major ambient air pollutants and the incidence and mortality of lung cancer and some nonlung cancers. METHODS: This meta-analysis used PubMed and EMBASE databases to access published studies that met the eligibility criteria. Primary analysis investigated the association between exposure to air pollutants and cancer incidence and mortality. Study quality was assessed using the Newcastle Ottawa Scale. Meta-analysis was conducted using R software. RESULTS: The meta-analysis included 61 studies, of which 53 were cohort studies and eight were case-control studies. Particulate matter 2.5 mm or less in diameter (PM2.5) was the exposure pollutant in half (55.5%), and lung cancer was the most frequently studied cancer in 59% of the studies. A pooled analysis of exposure reported in cohort and case-control studies and cancer incidence demonstrated a significant relationship (relative risk [RR], 1.04 [95% CI, 1.02 to 1.05]; I2, 88.93%; P < .05). A significant association was observed between exposure to pollutants such as PM2.5 (RR, 1.08 [95% CI, 1.04 to 1.12]; I2, 68.52%) and nitrogen dioxide (NO2) (RR, 1.03 [95% CI, 1.01 to 1.05]; I2, 73.52%) and lung cancer incidence. The relationship between exposure to the air pollutants and cancer mortality demonstrated a significant relationship (RR, 1.08 [95% CI, 1.07 to 1.10]; I2, 94.77%; P < .001). Among the four pollutants, PM2.5 (RR, 1.15 [95% CI, 1.08 to 1.22]; I2, 95.33%) and NO2 (RR, 1.05 [95% CI, 1.02 to 1.08]; I2, 89.98%) were associated with lung cancer mortality. CONCLUSION: The study confirms the association between air pollution exposure and lung cancer incidence and mortality. The meta-analysis results could contribute to community cancer prevention and diagnosis and help inform stakeholders and policymakers in decision making.

Long-term variations in surface ozone at the Longfengshan Regional Atmosphere Background Station in Northeast China and related influencing factors.

Surface ozone (O3) is a crucial air pollutant that affects air quality, human health, agricultural production, and climate change. Studies on long-term O3 variations and their influencing factors are essential for understanding O3 pollution and its impact. Here, we conducted an analysis of long-term variations in O3 during 2006-2022 at the Longfengshan Regional Atmosphere Background Station (LFS; 44.44°N, 127.36°E, 330.5 m a.s.l.) situated on the northeastern edge of the Northeast China Plains. The maximum daily 8-h average (MDA8) O3 fluctuated substantially, with the annual MDA8 decreasing significantly during 2006-2015 (-0.62 ppb yr-1, p < 0.05), jumping during 2015-2016 and increasing clearly during 2020-2022. Step multiple linear regression models for MDA8 were obtained using meteorological variables, to decompose anthropogenic and meteorological contributions to O3 variations. Anthropogenic activities acted as the primary drivers of the long-term trends of MDA8 O3, contributing 73% of annual MDA8 O3 variability, whereas meteorology played less important roles (27%). Elevated O3 at LFS were primarily associated with airflows originating from the North China Plain, Northeast China Plain, and coastal areas of North China, primarily occurring during the warm months (May-October). Based on satellite products of NO2 and HCHO columns, the O3 photochemical regimes over LFS revealed NOx-limited throughout the period. NO2 increased first, reaching peak in 2011, followed by substantial decrease; while HCHO exhibited significant increase, contributing to decreasing trend in MDA8 O3 during 2006-2015. The plateauing NO2 and decreasing HCHO may contribute to the increase in MDA8 O3 in 2016. Subsequently, both NO2 and HCHO exhibited notable fluctuations, leading to significant changes in O3. The study results fill the gap in the understanding of long-term O3 trends in high-latitude areas in the Northeast China Plain and offer valuable insights for assessing the impact of O3 on crop yields, forest productivity, and climate change.

Exploring the association between early-life air pollution exposure and autism spectrum disorders in children: A systematic review and meta-analysis.

The objective of this meta-analysis is to investigate the association between air pollution and the vulnerability of children to autism spectrum disorders (ASD). A thorough examination and analysis of data obtained from a compilation of 14 studies was undertaken, with a particular emphasis on investigating the effects of nitrogen dioxide (NO2), oxide of nitrogen (NOx), ozone (O3), and particulate matter (PM10 and PM2.5) on individuals diagnosed with ASD. The findings demonstrate a moderate association between exposure to nitrogen dioxide (NO2) and ASD, as indicated by a combined odds ratio (OR) of 1.13 and a 95% confidence interval (CI) spanning from 0.77 to 1.549. O3 shows a combined odds ratio (OR) of 0.82, along with a 95% confidence interval (CI) ranging from 0.49 to 1.14. NOx shows a moderate level of heterogeneity (I² = 75.9%, p = 0.002), suggesting that the impact of NOx on the risk of ASD. There is a statistically significant relationship between exposure to O3 and ASD, although the strength of this relationship is diminished. The findings demonstrated a noteworthy correlation between exposure to PM10 and PM2.5 and the occurrence of ASD. The study found a significant correlation, in relation to PM2.5, with a combined odds ratio (OR) of 1.22 and a 95% confidence interval (CI) ranging from 1.11 to 1.34. The findings have significant implications for the formulation of programs aimed at reducing exposure to harmful chemicals, especially among vulnerable groups such as children.

Real-time application and modelling of the NOx-sorption reaction on a particulate calcium carbonate surface-flow filter exposed to combustion exhaust.

Of major interest, especially in city environments, and increasingly inside vehicles or industrial plants, is the drive to reduce human exposure to nitrogen oxides (NOx). This trend has drawn increasing attention to filtration, which has developed remarkably owing to the capabilities of recently developed mathematical models and novel filter concepts. This paper reports on the study of the kinetic modelling of adsorption of nitrogen dioxide (NO2), collected from the tailpipe of a diesel engine, reacting to calcium nitrate salt (Ca(NO3)2) on a surface flow filter consisting of a coating of fine ground limestone or marble (CaCO3) in combination with micro-nanofibrillated cellulose (MNFC) acting as binder and humectant applied onto a multiply recycled newsprint substrate. The coating and substrate are both porous, but on different pore size scales, with the coating having significantly lower permeability. To maximise gas-coating contact, therefore, the coating deposition is pixelated, achieved by pin coating. An axially dispersed gaseous plug flow model (dispersion model) was used to simulate the transport within the coating pore network structure, following earlier flow modelling studies, and a kinetic reaction model was used to examine NO2 to NO3- conversion in correlation with experimental results. Modelling results indicate a 60.38% conversion of exposed NO2 gas to Ca(NO3)2 under the specific conditions applied, with an absolute relative error between the predicted and experimentally estimated value being 0.81%. The model additionally enabled a prediction of effects of changing parameters over a limited perturbation range, thus assisting in predicting filter element consumption, with attention given to the active component CaCO3 surface as a function of particle size in relation to the gas contact exchange, promoting the reaction over time. It is intended that the Ca(NO3)2 formed from the reaction can go on to be used as a value-added fertiliser, thus contributing to circular economy.