Land Use Regression Models Research Articles

Association between Long-Term Exposure to Traffic-Related Air Pollution and Cardio-Metabolic Phenotypes: An MRI Data-Based Analysis.

Long-term exposure to traffic-related air pollution (TRAP) is associated with cardiometabolic disease; however, its role in subclinical stages of disease development is unclear. Thus, we aimed to explore this association in a cross-sectional analysis, with cardiometabolic phenotypes derived from magnetic resonance imaging (MRI). Phenotypes of the left (LV) and right cardiac ventricle, whole-body adipose tissue (AT), and organ-specific AT were obtained by MRI in 400 participants of the KORA cohort. Land-use regression models were used to estimate residential long-term exposures to TRAP, e.g., nitrogen dioxides (NO2) or particle number concentration (PNC). Associations between TRAP and MRI phenotypes were modeled using linear regression. Participants' mean age was 56 ± 9 years, and 42% were female. Long-term exposure to TRAP was associated with decreased LV wall thickness; a 6.0 μg/m3 increase in NO2 was associated with a -1.9% [95% confidence interval: -3.7%; -0.1%] decrease in mean global LV wall thickness. Furthermore, we found associations between TRAP and increased cardiac AT. A 2,242 n/cm3 increase in PNC was associated with a 4.3% [-1.7%; 10.4%] increase in mean total cardiac AT. Associations were more pronounced in women and in participants with diabetes. Our exploratory study indicates that long-term exposure to TRAP is associated with subclinical cardiometabolic disease states, particularly in metabolically vulnerable subgroups.

Land use regression model to predict nitrogen dioxide in the greater Philadelphia area

Because of greater concern of nitrogen dioxide (NO2) pollution for urban residents, monitoring efforts are typically focused in urban areas. However, large populations in suburban areas near urban centers may also experience elevated NO2 levels. This study focuses on the application of a land use regression (LUR) model to predict NO2 pollution in the suburban and urban areas around the city of Philadelphia, Pennsylvania, USA. Air pollutant measurements used for the LUR was obtained from Ogawa™ passive samplers, which were deployed at several sites (n = 15) throughout the Greater Philadelphia area. Passive samplers were deployed for two weeks each during the fall, winter, and spring months between 2021 and 2022. Predictor data included various spatial buffers of land use, landcover, and traffic counts, as well as distances to various roadway types. The best LUR model based on regression statistics (F-statistic with 4 and 10 degrees of freedom = 5.111, p < 0.05) showed that mean NO2 concentrations increase within a 1000m buffer of annual average daily traffic. The model shows a patchwork of NO2 concentrations throughout the study area that could indicate regional and local emissions. The results highlight spatial variations of elevated concentrations influenced by a mix of land use, including roads, residential, and industrial areas.

Association of fine particulate matter and residential green space with rheumatoid arthritis

BackgroundFine particulate matter (PM2.5) is a recognized risk factor for respiratory and cardiovascular diseases, but the association between PM2.5 and rheumatoid arthritis (RA) is still controversial. Additionally, evidence on the relationship of green space with RA is scarce. This study aimed to investigate the separate and combined associations of PM2.5 and green space with risk of RA. MethodsOur study involved 30,684 participants from the Yinzhou cohort in Ningbo, China. PM2.5 concentrations were determined using a land-use regression model. Residential green space was assessed using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from satellite images. We employed the Cox proportional hazard model to evaluate the relationships of PM2.5 and green space with RA. ResultsDuring the 176,894 person-years of follow-up period, 354 cases of RA were identified. Hazard ratio (HR) and the corresponding 95% confidence interval (95% CI) for every interquartile range (IQR) increase in PM2.5 were 1.23 (95% CI: 1.02, 1.49). Compared with lower exposure to residential green space, individuals living in areas with more green space had a decreased risk of RA (HR was 0.80 (95% CI: 0.70, 0.92), 0.80 (95% CI: 0.70, 0.92), and 0.79 (95% CI: 0.70, 0.89) for 250m, 500m, and 1000m NDVI buffers, respectively). Similar results were observed for the association between EVI and RA. Furthermore, a significant multiplicative interaction was observed between PM2.5 and green space (NDVI 250m and EVI 250m). No mediating effect of PM2.5 on the relationship between green space and RA was observed. ConclusionOur findings indicated that living in areas with higher green space was linked to a reduced risk of RA, whereas living in areas with higher PM2.5 was associated with an increased risk of RA. The beneficial effect of high green space may be offset by exposure to PM2.5.

Environmental exposures associated with early childhood recurrent wheezing in the mother and child in the environment birth cohort: a time-to-event study

BackgroundAntenatal factors and environmental exposures contribute to recurrent wheezing in early childhood.AimTo identify antenatal and environmental factors associated with recurrent wheezing in children from birth to 48 months in the...

Ambient air pollution, low-grade inflammation, and lung function: Evidences from the UK Biobank

The associations of ambient air pollution exposure and low-grade inflammation with lung function remain uncertain. In this study, 276,289 subjects were enrolled in the UK Biobank. Individual exposure to ambient air pollution (including nitrogen dioxide [NO2], nitrogen oxides [NOx]), and particulate matter [PM2.5, PM10, PMcoarse]) were estimated by using the land-use regression model. Forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) were tested, and low-grade inflammation score (INFLA score) was calculated for each subject. In this cross-sectional study, the median concentrations of air pollution were 9.89 µg/m3 for PM2.5, 15.98 µg/m3 for PM10, 6.09 µg/m3 for PMcoarse, 25.60 µg/m3 for NO2, and 41.46 µg/m3 for NOx, respectively. We observed that PM2.5, PM10, PMcoarse, NO2, NOx was negatively associated with lung function. Besides, significant positive associations between PM exposure and low-grade inflammation were noted. Per interquartile range (IQR) increase in PM2.5, PM10, and PMcoarse was related to higher INFLA score, and the β (95 % CI) was 0.06 (0.03, 0.08), 0.03 (0.02, 0.05), and 0.03 (0.01, 0.04), respectively. Additionally, we found significant negative associations between INFLA scores and lung function. One-unit increase in INFLA score was linked with 12.41- and 11.31-ml decreases in FVC and FEV1, respectively. Compared with individuals with low air pollution exposure and low INFLA scores, participants with high air pollution and high INFLA scores had the lowest FVC and FEV1. Additionally, we observed that INFLA scores could modify the relationships of PM2.5, NO2, and NOx with FVC and FEV1 (Pinteraction <0.05). The negative impact of air pollutants on lung function was more pronounced in subjects with high INFLA scores in comparison to those with low INFLA scores. In conclusion, we demonstrated negative associations between ambient air pollution and lung function, and the observed associations were strengthened and modified by low-grade inflammation.

Residential ambient air pollution exposure and the development of white matter microstructure throughout adolescence

BackgroundRecent evidence suggests an association of air pollution exposure with brain development, but evidence on white matter microstructure in children is scarce. We investigated how air pollution exposure during pregnancy and childhood impacts longitudinal development of white matter microstructure throughout adolescence. MethodsOur study population consisted of 4108 participants of Generation R, a large population-based birth cohort from Rotterdam, the Netherlands. Residential air pollution exposure to 14 air pollutants during pregnancy and childhood was estimated with land-use regression models. Diffusion tensor images were obtained around age 10 and 14, resulting in a total of 5422 useable scans (n = 3082 for wave 1 and n = 2340 for wave 2; n = 1314 for participants with data on both waves). We calculated whole-brain fractional anisotropy (FA) and mean diffusivity (MD) and performed single- and multi-pollutant analyses using mixed effects models adjusted for life-style and socioeconomic status variables. ResultsHigher exposure to PM2.5 during pregnancy, and PM10, PM2.5, PM2.5-10, and NOX during childhood was associated with a consistently lower whole-brain FA throughout adolescence (e.g. – 0.07 × 10−2 FA [95%CI -0.12; −0.02] per 1 standard deviation higher PM2.5 exposure during pregnancy). Higher exposure to silicon (Si) in PM2.5 and oxidative potential of PM2.5 during pregnancy, and PM2.5 during childhood was associated with an initial higher MD followed by a faster decrease in MD throughout adolescence (e.g. – 0.02 × 10−5 mm2/s MD [95%CI -0.03; −0.00] per year of age per 1 standard deviation higher Si exposure during pregnancy). Results were comparable when performing the analysis in children with complete data on the outcome for both neuroimaging assessments. ConclusionsExposure to several pollutants was associated with a consistently lower whole-brain FA throughout adolescence. The association of few pollutants with whole-brain MD at baseline attenuated throughout adolescence. These findings suggest both persistent and age-limited associations of air pollution exposure with white matter microstructure.

Integrating Doppler LiDAR and machine learning into land-use regression model for assessing contribution of vertical atmospheric processes to urban PM2.5 pollution

Air pollution has been recognized as a global issue, through adverse effects on environment and health. While vertical atmospheric processes substantially affect urban air pollution, traditional epidemiological research using Land-use regression (LUR) modeling usually focused on ground-level attributes without considering upper-level atmospheric conditions. This study aimed to integrate Doppler LiDAR and machine learning techniques into LUR models (LURF-LiDAR) to comprehensively evaluate urban air pollution in Hong Kong, and to assess complex interactions between vertical atmospheric processes and urban air pollution from long-term (i.e., annual) and short-term (i.e., two air pollution episodes) views in 2021. The results demonstrated significant improvements in model performance, achieving CV R2 values of 0.81 (95 % CI: 0.75–0.86) for the long-term PM2.5 prediction model and 0.90 (95 % CI: 0.87–0.91) for the short-term models. Approximately 69 % of ground-level air pollution arose from the mixing of ground- and lower-level (105 m–225 m) particles, while 21 % was associated with upper-level (825 m–945 m) atmospheric processes. The identified transboundary air pollution (TAP) layer was located at ~900 m above the ground. The identified Episode one (E1: 7 Jan–22 Jan) was induced by the accumulation of local emissions under stable atmospheric conditions, whereas Episode two (E2: 13 Dec–24 Dec) was regulated by TAP under instable and turbulent conditions. Our improved air quality prediction model is accurate and comprehensive with high interpretability for supporting urban planning and air quality policies.

Determining sources of air pollution exposure inequity in New York City through land-use regression modeling of PM2.5 constituents

Determining sources of air pollution exposure inequity in New York City through land-use regression modeling of PM2.5 constituents

Effects of indoor environments and outdoor air pollutants in residential areas on acute exacerbation in patients with severe asthma

ABSTRACT This study aimed to determine the effects of indoor environment (IE) and outdoor air pollutants (OAPs) in residential areas on acute exacerbation (AE) in patients with severe asthma. A total of 115 participants were recruited. To characterize IE, we used structured questionnaires and estimated OAP concentrations using a land-use regression model. Participants who were exposed to passive smoking and lived in houses where the kitchen and living room were not separated showed a significantly higher rate of AE (p = 0.014 and 0.0016, respectively). The mean concentration of PM2.5 in residential areas during the last 3 years was significantly higher in participants with AE than that in those without AE (19.8 ± 3.1 vs. 21.0 ± 2.5 µg/m3, p = 0.033). Moreover, the serum level of 8-hydroxy-2′-deoxyguanosine significantly increased in participants with AE compared to those without AE (56.9 ± 30.0 vs. 94.7 ± 44.5 ng/mL, p = 0.0047) suggesting enhanced oxidative stress in those with AE.

Circulating metabolomics revealed novel associations between multiple ambient air pollutants exposure and chronic obstructive pulmonary disease incidence: Evidence from a prospective cohort study

The mechanisms underlying relationships between ambient air pollution and chronic obstructive pulmonary disease (COPD) risk remained largely uncertain. In this study, we aim to evaluate whether metabolic signature comprising multiple circulating metabolites can characterize metabolic response to the multiple air pollution; and to assess whether the identified metabolic signature contribute to COPD risk. A total of 227,962 participants with complete data were included from the UK biobank study. Concentrations of nitrogen dioxide (NO2), nitrogen oxides (NOx), and particulate matter (PM2.5 and PM10) were evaluated by land-use regression models. We newly computed an air pollution score to reflect joint exposure to multiple air pollutants. Circulating metabolome was quantified by nuclear magnetic resonance (NMR) spectroscopy. During a median of 12.78 years of follow-up, a total of 8685 incident COPD cases were documented. After multiple correction, the Cox regression models showed that 102 of 143 metabolites were significantly associated with COPD risk. Utilizing elastic net regularized regressions, we identified a metabolic signature comprising 106 metabolites (including lipid, fatty acids, glycolysis and amino acids et al.) were robustly related to air pollution score. In the multivariate-adjusted Cox regression models, the derived metabolic signature showed a positive correlation with incident COPD [HR per SD = 1.20 (95% CI: 1.17–1.22)]. Casual mediation analysis further noted that the constructed metabolic signature mediated 10.5 % (8.3%–13.1%) of the air pollution-COPD associations. Taken together, our findings identified a metabolic signature that captured metabolic response to various air pollutants exposure jointly, and predicted future COPD risk independent of known risk factors.

National, satellite-based land-use regression models for estimating long-term annual NO2 exposure across India

In India, scarcity of ground-based measurements of nitrogen dioxide (NO2) is a major challenge for estimating long-term exposure and associated health impacts. This study aimed to develop and validate a national-scale annual NO2 exposure model for India for 2019 and determine if model cross-validation predictive ability was improved by including non-continuous (manual) measurements along with reference-grade, continuous measurements.We used a supervised forward-addition linear regression method to fit land use regression (LUR) models developed with up to 804 Central Pollution Control Board ground monitoring stations (n = 157 continuous, n = 647 manual) and 209 spatial predictor variables, including satellite-based tropospheric NO2 columns. Two models were developed: one using continuous sites only and one using continuous and manual sites, with standard diagnostics and cross-validation (CV) methods. We also assessed if the kriging of final model residuals reduced spatial autocorrelation and improved model CV results. LUR coefficients for the best-performing model were applied to predictors for 2015–2021 and gridded at 100 m to estimate population-weighted exposure.The continuous sites-only model and combined continuous and manual sites models had CV-R2 values of 0.59 (root-mean-square error [RMSE]: 9.4 μg/m3) and 0.54 (RMSE: 8.3 μg/m3), respectively, and both included the satellite NO2 predictor. Kriging residuals increased the CV-R2 of the combined model to 0.70 (RMSE: 7.2 μg/m3) but offered no improvement for the continuous site model. National population-weighted average NO2 was 22.1 μg/m3 in 2019. We estimated over 92% of the Indian population was exposed to annual NO2 exceeding the WHO air quality guideline (10 μg/m3). In Delhi, Mumbai, and Kolkata, an estimated 45%, 100%, and 100% of the population, respectively, experienced annual NO2 levels that surpassed Indian standards (40 μg/m3). To our knowledge, this is the first such long-term NO2 LUR model specific to India, and predictions are available to interested researchers.

Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring.

Addressing the challenge of mapping hyperlocal air pollution in areas without local monitoring, we evaluated unsupervised transfer learning-based land-use regression (LUR) models developed using mobile monitoring data from other cities: CORrelation ALignment (Coral) and its inverse distance-weighted modification (IDW_Coral). These models mitigated domain shifts and transferred patterns learned from mobile air quality monitoring campaigns in Copenhagen and Rotterdam to estimate annual average air pollution levels in Amsterdam (50m road segments) without involving any Amsterdam measurements in model development. For nitrogen dioxide (NO2), IDW_Coral outperformed Copenhagen and Rotterdam LUR models directly applied to Amsterdam, achieving MAE (4.47 μg/m3) and RMSE (5.36 μg/m3) comparable to a locally fitted LUR model (AMS_SLR) developed using Amsterdam mobile measurements collected for 160 days. IDW_Coral yielded an R2 of 0.35, similar to that of the AMS_SLR based on 20 collection days, suggesting a minimum requirement of 20-day mobile monitoring to capture city-specific insights. For ultrafine particles (UFP), IDW_Coral's citywide predictions strongly correlated with previously published mixed-effect models fitted with 160-day Amsterdam measurements (Pearson correlation of 0.71 for UFP and 0.72 for NO2). IDW_Coral demands no direct measurements in the target area, showcasing its potential for large-scale applications and offering significant economic efficiencies in executing mobile monitoring campaigns.

Investigating the impact of urban-environmental factors on air pollutants: a land use regression model approach and health risk assessment.

The presence of pollutants in the earth's atmosphere has a direct impact on human health and the environment. So that pollutants such as carbon monoxide (CO) and particulate matter (PM) cause respiratory diseases, cough headache, etc. Since the amount of pollutants in the air is related to environmental and urban factors, the aim of the current research is to investigate the relationship between the concentration of CO, PM2.5 and PM10 with urban-environmental factors including land use, wind speed and wind direction, topography, traffic, road network, and population through a Land use regression (LUR) model. The concentrations of CO, PM2.5 and PM10 were measured during four seasons from 26th of March 2022 to 16th of March 2023 at 25 monitoring stations and then the information about pollutant measurement points and Land use data were entered into the ArcGIS software. The annual average concentrations of CO, PM2.5 and PM10 were 0.7ppm, 18.94 and 60.76µg/m3, respectively, in which the values of annual average concentration of CO and PMs were outside the air quality guideline standard. The results of the health risk assessment showed that the hazard quotient values for all three investigated pollutants were lower than 1 and therefore, they were not in adverse conditions in terms of health effects. Among the urban-environmental factors affecting air pollution, the traffic variable is the most important factor affecting the annual LUR model of CO, PM2.5 and PM10, and then the topography variable is the second most effective factor on the annual LUR model of the aforementioned pollutants.

Air Pollution, Genetic Susceptibility, and Risk of Incident Systemic Lupus Erythematosus: A Prospective Cohort Study.

There are few existing studies that investigate the risk of systemic lupus erythematosus (SLE) associated with long-term exposure to air pollutants. This study aimed to explore associations between long-term exposure to air pollutants and incident SLE and further evaluate interactions and joint effects of genetic risk and air pollutants. A total of 459,815 participants were included from UK Biobank. The concentrations of air pollutants (fine particulate matter with diameter ≤2.5 μm [PM2.5], particulate matter diameter ≤10 μm [PM10], nitrogen dioxide [NO2], and nitrogen oxides [NOx]) were estimated by land-use regression model. We applied Cox proportional hazards model to explore linkages of air pollutants and incident SLE. The polygenic risk score (PRS) was used for further assessing the interactions and joint effects of genetic risk and air pollutants. A total of 399 patients with SLE were identified during a median follow-up of 11.77 years. There were positive associations between air pollutant exposure and incident SLE, as the adjusted hazard ratios were 1.18 (95% confidence interval [95% CI] 1.06-1.32), 1.23 (1.10-1.39), 1.27 (1.14-1.41), and 1.13 (1.03-1.23) for each interquartile range increase in PM2.5, PM10, NO2, and NOx, respectively. Moreover, participants with high genetic risk and high air pollution exposure had the highest risk of incident SLE compared with those with low genetic risk and low air pollution exposure (adjusted hazard ratio: PM2.5, 4.16 [95% CI 2.67-6.49]; PM10, 5.31 [95% CI 3.30,-8.55]; NO2, 5.61 [95% CI 3.45-9.13]; and NOx, 4.80 [95% CI 3.00-7.66]). There was a significant multiplicative interaction between NO2 and PRS. Long-term exposure to air pollutants (PM2.5, PM10, NO2, and NOx) may increase the risk of developing SLE.

Air pollution exposure and mortality from neurodegenerative diseases in the Netherlands: A population-based cohort study

BackgroundLong-term exposure to ambient air pollution has been linked with all-cause mortality and cardiovascular and respiratory diseases. Suggestive associations between ambient air pollutants and neurodegeneration have also been reported, but due to the small effect and relatively rare outcomes evidence is yet inconclusive. Our aim was to investigate the associations between long-term air pollution exposure and mortality from neurodegenerative diseases. MethodsA Dutch national cohort of 10.8 million adults aged ≥30 years was followed from 2013 until 2019. Annual average concentrations of air pollutants (ultra-fine particles (UFP), nitrogen dioxide (NO2), fine particles (PM2.5 and PM10) and elemental carbon (EC)) were estimated at the home address at baseline, using land-use regression models. The outcome variables were mortality due to amyotrophic lateral sclerosis (ALS), Parkinson's disease, non-vascular dementia, Alzheimer's disease, and multiple sclerosis (MS). Hazard ratios (HR) were estimated using Cox models, adjusting for individual and area-level socio-economic status covariates. ResultsWe had a follow-up of 71 million person-years. The adjusted HRs for non-vascular dementia were significantly increased for NO2 (1.03; 95% confidence interval (CI) 1.02–1.05) and PM2.5 (1.02; 95%CI 1.01–1.03) per interquartile range (IQR; 6.52 and 1.47 μg/m3, respectively). The association with PM2.5 was also positive for ALS (1.02; 95%CI 0.97–1.07). These associations remained positive in sensitivity analyses and two-pollutant models. UFP was not associated with any outcome. No association with air pollution was found for Parkinson's disease and MS. Inverse associations were found for Alzheimer's disease. ConclusionOur findings, using a cohort of more than 10 million people, provide further support for associations between long-term exposure to air pollutants (PM2.5 and particularly NO2) and mortality of non-vascular dementia. No associations were found for Parkinson and MS and an inverse association was observed for Alzheimer's disease.

Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany.

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods' black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m3 to 3.6 µg/m3 and the root mean squared error (RMSE) from 9.86 µg/m3 to 4.23 µg/m3 when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM2.5 concentrations with an MAE of less than 5 µg/m3 for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R2 from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas.

Winter and Summer PM2.5 Land Use Regression Models for the City of Novi Sad, Serbia

In this study, we describe the development of seasonal winter and summer (heating and non-heating season) land use regression (LUR) models for PM2.5 mass concentration for the city of Novi Sad, Serbia. The PM2.5 data were obtained through an extensive seasonal measurement campaign conducted at 21 locations in urban, urban/industrial, industrial and background areas in the period from February 2020–July 2021. At each location, PM2.5 samples were collected on quartz fibre filters for 10 days per season using a reference gravimetric pump. The developed heating season model had two predictors, the first can be associated with domestic heating over a larger area and the second with local traffic. These predictors contributed to the adjusted R2 of 0.33 and 0.55, respectively. The developed non-heating season model had one predictor which can be associated with local traffic, which contributed to the adjusted R2 of 0.40. Leave-one-out cross-validation determined RMSE/mean absolute error for the heating and non-heating season model were 4.04/4.80 μg/m3 and 2.80/3.17 μg/m3, respectively. For purposes of completeness, developed LUR models were also compared to a simple linear model which utilizes satellite aerosol optical depth data for PM2.5 estimation, and showed superior performance. The developed LUR models can help with quantification of differences between seasonal levels of air pollution, and, consequently, air pollution exposure and association between seasonal long-term exposure and possible health risk implications.

Associations of gestational exposure to air pollution with maternal vitamin D levels: a meta-analysis.

Maternal vitamin D level is an important determinant of pregnancy and child health outcomes. Exposure to air pollution is suspected to increase the risk of vitamin D deficiency, but the evidence is scarce. We investigated the association between air pollution during pregnancy and maternal vitamin D levels. A total of 15,935 pregnant women from 5 birth cohorts in Europe and U.S were included. Averaged concentrations of nitrogen oxides, fine and coarse particles, and composition of fine particles from conception until vitamin D measurement were estimated at participants' residential addresses using land-use regression or other spatiotemporal models. Cohorts measured vitamin D as 25(OH)D or 25(OH)D3 levels in serum or plasma at early or mid-pregnancy. We defined suboptimal vitamin D levels as levels below 20 ng/mL. We performed logistic regression models for each cohort to estimate the association between air pollution exposure and suboptimal vitamin D levels and pooled cohort-specific estimates in a random-effect meta-analysis. Models were adjusted for sociodemographic and lifestyle characteristics and month of conception. We found an association between PM2.5 and higher odds of suboptimal vitamin D levels (i.e., below 20 ng/mL) (odds ratio per 5 μg/m3 increase in PM2.5, 1.43 95%CI: 1.02, 1.99). There was no association between other air pollutant exposure and vitamin D levels. PM2.5 exposure might contribute to suboptimal levels of vitamin D in pregnancy. Reducing air pollution exposure should be a priority because vitamin D deficiency may adversely influence offspring development.

Geospatial analysis for environmental noise mapping: A land use regression approach in a metropolitan city

Environmental noise can lead to adverse health outcomes. Understanding the spatial variability of environmental noise is crucial for mitigating potential health risks and developing influential urban strategies for reducing noise levels. This study aimed to measure noise levels and develop a land use regression (LUR) model to determine the spatial variability of environmental noise in Shiraz, Iran. A grid-based technique was used to establish 191 noise measurement sites (summer) across the city to generate the LUR model based on two noise metrics: Lden and Lnight. Leave-one-out cross-validation (LOOCV) and 38 additional measurement sites (winter) were used for the LUR model assessment. The mean values of Lden and Lnight during summer were 68.20 (±8.05) and 58.95 (±9.55), respectively, while during winter, the corresponding values were 69.46 (±5.46) and 58.81 (±6.79). The LUR models explained 67% and 65% of the spatial variability in Lden and Lnight, respectively. LOOCV analysis demonstrated R2 values of 0.64 and 0.61. Moreover, findings indicated mean absolute error (MAE) values of 3.96 dB(A) for Lden and 4.74 dB(A) for Lnight. Validation based on an additional set of 38 measurement sites revealed R2 values of 0.62 for both Lden and Lnight, with MAE of 2.78 and 3.31, respectively. In addition, the adjusted R2 values were 0.54 and 0.53. The results indicated no significant temporal variations between summer and winter. The results revealed that road-related variables significantly influenced noise levels. Moreover, the results indicated that Lden and Lnight levels were higher than the World Health Organization recommendations for exposure to road traffic noise. The results of our study showed that the LUR modeling approach based on geographical predictors is an effective tool for assessing changes in ambient noise levels in other cities in Iran and around the globe.

Air pollution, genetic factors, and chronic rhinosinusitis: A prospective study in the UK Biobank

BackgroundChronic rhinosinusitis (CRS) is a prevalent upper respiratory condition that manifests in two primary subtypes: CRS with nasal polyps (CRSwNP) and CRS without nasal polyps (CRSsNP). While previous studies indicate a correlation between air pollution and CRS, the role of genetic predisposition in this relationship remains largely unexplored. We hypothesized that higher air pollution exposure would lead to the development of CRS, and that genetic susceptibility might modify this association. MethodsThis cohort study involving 367,298 adult participants from the UK Biobank, followed from March 2006 to October 2021. Air pollution metrics were estimated at residential locations using land-use regression models. Cox proportional hazard models were employed to explore the associations between air pollution exposure and CRS, CRSwNP, and CRSsNP. A polygenic risk score (PRS) was constructed to evaluate the joint effect of air pollution and genetic predisposition on the development of CRS. ResultsWe found that the risk of CRS increased under long-term exposure to PM2.5 [the hazard ratios (HRs) with 95 % CIs: 1.59 (1.26–2.01)], PM10 [1.64 (1.26–2.12)], NO2 [1.11 (1.04–1.17)], and NOx [1.18 (1.12–1.25)], respectively. These effects were more pronounced among participants with CRSwNP, although the differences were not statistically significant. Additionally, we found that the risks for CRS and CRSwNP increased in a graded manner among participants with higher PRS or higher exposure to PM2.5, PM10, or NOx concentrations. However, no multiplicative or additive interactions were observed. ConclusionsLong-term exposure to air pollution increases the risk of CRS, particularly CRSwNP underscoring the need to prioritize clean air initiatives and environmental regulations.