Land Use Regression Research Articles (Page 1)

This study assessed combined effects of air pollutants on arrhythmia, cardiovascular disease (CVD), and mortality, and explored genetic susceptibility’s role. In 435,893 participants from the UK Biobank, air pollutant concentrations (PM2.5, PM2.5–10, PM10, NO2, and NO) were obtained from established land-use regression models linked to the database.An air pollution scores integrated pollutant levels weighted by CVD/arrhythmia-associated coefficients from single-pollutant Cox models. Genetic risk scores (GRS) and pollutant-attributable fractions (PAFs) were analyzed, with GRS primarily examined in the context of gene–pollution interactions rather than as an independent risk factor. Over 12.7 median follow-up years, 56,354 incident arrhythmia cases occurred. Adjusted models showed higher quartiles of NO2, NO, and PM2.5 increased risks of arrhythmia, CVD, and mortality (all P-trend <0.001; P-trend indicated trend tests across increasing exposure categories). Men exhibited weaker associations between pollution scores and arrhythmia/CVD than women. NO2 was the primary risk factor for arrhythmia [RR:1.04 (1.01–1.08); PAF=3.45%] and CVD [RR:1.11 (1.08–1.14); PAF=8.11%], while NO and PM2.5 dominated CVD mortality [RR:1.20 (1.08–1.33); PAF=13.62%] and all-cause mortality [RR:1.10 (1.06–1.13); PAF=7.10%], respectively. Furthermore, significant interactions were observed between air pollution and GRS, indicating higher susceptibility among individuals with elevated genetic risk. Combined exposure to five pollutants heightened arrhythmia, CVD, and mortality risks, with NO2 as the key driver for arrhythmia/CVD. Genetic susceptibility modified pollution-related risks. Mitigating air pollution, particularly NO2, may reduce cardiovascular burden.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-22491-4.

Background: Air pollution is a significant risk factor for type 2 diabetes (T2D). While dietary factors may help mitigate T2D risk, traditional dietary assessments may not accurately capture individual metabolic responses to diet and its potential interactions with environmental exposures. This study aims to compare the associations of metabolomics-based versus questionnaire-based EAT-Lancet diet with T2D risk and evaluate their modification effect of air pollution-related T2D risk. Research Questions: Can metabolomics-based dietary assessment better capture diet-disease relationships and modify air pollution-related T2D risk compared to traditional questionnaire-based assessment? Methods: This study included 49,625 UK Biobank participants without baseline T2D. Air pollutant concentrations (PM2.5, PM10, PM2.5-10, NO2, and NOx) were estimated using land-use regression models. An integrated air pollution score was constructed to assess their joint exposure. Diet quality was evaluated using questionnaire-based and metabolomics-based approaches to the EAT-Lancet diet. Hazard ratio (HR) and absolute risk difference (ARD) were used to estimate the associations of diet and air pollution with incident T2D. Both additive and multiplicative interactions were examined. Results: Higher integrated air pollution score was associated with higher T2D risk (HR: 1.10; 95% CI: 1.03-1.18). The metabolomics-based EAT-Lancet diet showed a stronger association with incident T2D (per SD increment HR: 0.88; 95% CI: 0.83-0.93) than the questionnaire-based assessment (per SD increment HR: 0.96; 95% CI: 0.91-1.02) (PZ-test=0.047). Participants with the lowest diet scores and highest air pollution exposure had a higher T2D risk than those with the highest diet and lowest pollution (HR: 1.63; 95% CI: 1.24-2.15 and ARD: 27%; 95% CI: 20%-33%). The relative excess risk due to interaction between high air pollution and low diet quality was 0.31 (95% CI: 0.03-0.58), accounting for 31% of T2D risk. No significant interactions were observed with questionnaire-based assessment. Conclusions: Air pollution was positively associated with T2D risk. The metabolomics-based, but not questionnaire-based, EAT-Lancet diet was inversely associated with T2D risk and significantly modified air pollution-related T2D risk.

Mapping urban ultrafine particle exposure in Guangzhou, China: Development of a high-resolution land-use regression framework for megacity.

ObjectiveTo investigate the interplay between the genetic predisposition to successful ageing and air pollution on lung disease in healthy aged German women under the hypothesis that ageing and lung diseases share mechanisms of oxidative stress and inflammation that can be regulated by genetic predisposition and environmental factors.DesignGerman Study on the influence of Air pollution on Lung function, Inflammation and Aging prospective cohort between baseline (1985–1994) and follow-up (2007–2010).SettingUrban Ruhr area and the adjacent rural Münsterland in Germany.ParticipantsAt baseline, 4874 women aged 55 years living between 1985 and 1994 in the setting and at follow-up examination, 834 of them participated.Main outcome measuresChronic lung disease was defined as any of asthma, chronic bronchitis, cough (with sputum) or chronic obstructive pulmonary disease. Chronic individual exposures to nitrogen dioxide (NO2), nitrogen oxides, particulate matter with median aerodynamic diameters <2.5 (PM2.5), PM10, PMcoarse and PM2.5 absorbance based on European Study of Cohorts for Air Pollution Effects land-use regression models were used. Main and interaction effects between the genetic risk score (77 single-nucleotide polymorphisms (SNPs) related to successful ageing) and air pollutant exposures were investigated using adjusted logistic regression models.ResultsIn 560 women (67–80 years), chronic lung disease was present in 156. Higher exposure to air pollution was associated with increased odds by up to 43% per IQR-increase in NO2 (IQR=11.6 µg/m³, 95% CI 1.15 to 1.77). The genetic make-up reduced the negative impact of air pollution (gene–environment interaction with NO2: OR=0.66, 95% CI 0.45 to 0.96), while a healthy lifestyle further strengthens this association.ConclusionsIn elderly women, genetic predisposition based on successful ageing SNPs likely reduces the negative impact of air pollution on chronic lung disease, while a healthy lifestyle further strengthens this association.

Long-term exposure to traffic-related air pollution is associated with epigenetic age acceleration.

The application of a land-use regression model to investigate the effect of urban greenbelts on ambient PM10 and PM2.5 mitigation during seasons of light and heavy pollution

Heavy metal (HM) contamination in urban road dust (RD) represents a significant environmental and public health concern, particularly in densely populated and industrialized regions. This study investigated the spatial distribution and associated health risks of cadmium (Cd), chromium (Cr), nickel (Ni), lead (Pb), and zinc (Zn) in RD across various land-use types in Arak, Iran. During a nine-month sampling campaign, 160 RD samples were collected from twenty strategic locations representing industrial, residential, commercial, and high-traffic zones. Land-use regression (LUR) modeling was employed to map HM concentrations and identify pollution hotspots. The mean concentrations of Cd, Cr, Ni, Pb, and Zn were 0.48, 64.6, 44.4, 133.9, and 277.6 mg/kg, respectively, substantially exceeding global soil background values. Spatial analysis identified the southern, central, and southeastern sectors as critical pollution hotspots, primarily influenced by vehicular emissions and industrial activities. Health risk assessment revealed ingestion as the dominant exposure pathway, with lead posing the most significant non-carcinogenic risk to children (HI = 0.522). The cumulative hazard index for all metals reached 0.9036 in children, approaching the safety threshold of 1. Furthermore, the total carcinogenic risk for children (2.27 × 10−4) slightly exceeded acceptable levels, with nickel being the predominant contributor. This study provides critical evidence supporting the urgent need for targeted public health interventions, stringent emission controls, and science-based urban planning strategies to mitigate heavy metal exposure risks in vulnerable urban populations.

We designed this study to map environmental noise pollution (ENP) around all elementary schools and kindergartens in Tehran using a land use regression (LUR) approach. Out of 135 spatial predictor variables, seven were identified as significant determinants of ENP. The final model demonstrated strong predictive performance, with an R² of 0.70 and an adjusted R² of 0.65. Additionally, the model had a leave-one-out cross-validation (LOOCV) R² of 0.59 and a root mean squared error (RMSE) of 3.15, indicating acceptable predictive accuracy. Among the significant predictors, green space area, and the distances to the nearest terminals, primary roads, and highways had negative effects on ENP, meaning that increases in these variables reduce noise levels around schools and kindergartens. In contrast, the length of secondary roads, the area of commercial parcels, and the distance to military zones had positive effects, suggesting that increases in these variables contributed to higher ENP. Our findings reveal substantial spatial variation in environmental noise levels across Tehran, with the highest ENP values—ranging from 65.1 dB(A) to 85 dB(A)—concentrated primarily in the central, southern, and southeastern districts of the city. Approximately 36%, 30%, and 13% of educational institutions for children in Tehran are exposed to ENP in the range of 70.1–75 dB(A), 65–70 dB(A), and > 75 dB(A), respectively. Only 4% of these institutions are located in areas with ENP < 60 dB(A). Our findings highlight the importance of infrastructure design changes, such as expanding green spaces around schools and kindergartens, or relocating these educational institutions farther from terminals, primary roads, military zones, and commercial areas.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-21249-2.

Unraveling the impact of prenatal air pollution for neonatal brain maturation

Abstract Background Ambient air pollution has been suggested to be associated with mental health, however the isolated effect of black carbon (BC) has been rarely studied, even though BC was pointed as of special interest by world health organization (WHO). Objectives To examine the associations between annual exposure to particulate matter with a diameter &amp;lt; 2.5 µm (PM2.5), BC and nitrogen dioxide (NO2) and psychological distress in the French CONSTANCES cohort, and assess the effect of BC independently of PM2.5. Methods This cross-sectional study included 117,697 adults. Psychological distress was assessed in 2019 using the General Health Questionnaire-12 (GHQ-12, from 0 to 12). Annual concentrations of PM2.5, BC, and NO2 estimated from land-use regression models, were assigned to each participant's residential address. Negative binomial models adjusted for age, sex, health center of inclusion, education, smoking, season and the French deprivation index were used. The residuals method used to assess BC's independent effect. Results are presented using incident rate ratio (IRR) per interquartile range (IQR) increase in exposure of each pollutant. Stratified analyses by age, sex, education and season were also conducted. Results Mean concentrations were 16.9 µg/m3 for PM2.5 (IQR = 2.59), 1.78 10-5/m for BC (IQR = 0.45) and 25.7 µg/m3 and NO2 (IQR = 11.49). Exposure to all three pollutants was significantly associated with higher GHQ-12 scores (IRR (95%CI) = 1.053 (1.017-1.090) for PM2.5, 1.068 (1.047-1.090) for BC and 1.072 (1.048-1.097) for NO2). BC residuals also showed a significant association (1.20 (1.14 - 1.27)). Stronger associations were found for men, elderly, lower-education and respondents during warm season. Conclusions This study is among the few showing that exposure to ambient air pollution is associated with increased psychological distress in a large adult sample. The findings highlight the need to reduce air pollution to promote population mental health. Key messages • Our study showed that long-term exposure to NO2, PM2.5, and BC, was associated to higher psychological distress, highlighting air quality improvement as a strategy for mental health promotion. • Among the three pollutants associated with higher psychological distress, BC is of particular interest, as it has been not explored before and may open new paths to focused public health actions.

Mobile monitoringcampaigns combined with land use regression(LUR)models effectively capture fine-scale spatial variations in urbanair pollution. However, traditional predictor variables often failto capture the nuances of the built environment and undocumented emissionsources. To address this, we developed a framework integrating customizableobject-level and segmentation-level visual features from street-viewimages into stepwise regression and random-forest-based LUR models.Using 5.7 million mobile air pollution measurements (2019–2020)and 0.37 million street-view images (2008–2024), we mappednitrogen dioxide (NO2), black carbon (BC), and ultrafineparticles (UFP) across 46,664 road segments in Amsterdam, The Netherlands.Incorporating street-view images improved model performance, increasing R2 by 0.01–0.05 and reducing mean absoluteerrors by 0.7–10.3%. Sensitivity analyses indicated that keystreet-view-derived visual features remained stable across years andseasons. Using images from nearby years expanded training instances,thereby enhancing alignment with mobile measurements at fine granularity.Our open-vocabulary object detection module identified influentialbut previously unrecognized object predictors, such as chimneys, trafficlights, and shops. Combined with segmentation-derived features (e.g.,walls, roads, grass), street-view images contributed 8–18%feature importance to model predictions. These findings highlightthe potential of visual data in enhancing hyperlocal air pollutionmapping and exposure assessment.

Land use regression (LUR) models assess air pollution exposure but often struggle with transferability (predicting concentrations in areas without measurements) and generalizability (capturing spatial patterns across neighborhoods). This study evaluated transferability and generalizability of Toronto City LUR models for black carbon (BC) and ultrafine particles (UFP) using mobile monitoring data. Models were developed using multiple linear regression (MLR) and XGBoost under three spatial configurations: Toronto City (TC), Toronto City minus a neighborhood (TCM-NB), and neighborhood-specific (NB). Transferability of TCM-NB models and generalizability of TC models were tested using neighborhood-specific data and compared to NB models. XGBoost outperformed MLR, achieving R2 of 0.77 for UFP and 0.54 for BC in TC models compared to 0.32 and 0.27 for MLR. TC models exhibited poor generalizability, with R2 dropping to 0.1 in certain neighborhoods. Similarly, TCM-NB models exhibited limited transferability, with MLR slightly outperforming XGBoost (R2 of 0.3 vs 0.2). Hyperparameter tuning with spatial cross-validation improved XGBoost transferability and generalizability, with R2 increases of up to 0.2 for both UFP and BC depending on the neighborhood. These findings highlight the importance of monitoring campaigns covering diverse urban environments and adopting tailored modeling approaches to capture neighborhood-specific pollution sources to advance air pollution exposure assessment.

Integrating satellite information, land use regression, and machine learning to estimate the spatiotemporal variation of ionic composition in PM2.5 across Taiwan.

Air pollution and bone health outcomes: Periods of susceptibility from pregnancy to childhood.

This study explores potential associations among ambient particulate matter (PM) exposure, PM load in alveolar macrophage (AM), and biomarkers collected from 53 healthy, adult, nonsmoking residents of the Iztapalapa and Iztacalco municipalities in Mexico City. Ambient PM2.5 concentrations were estimated using an improved Land Use Regression (LUR) model to approximate PM exposure levels. The PM/carbon loading was quantified by the fraction of AM containing PM (%, %AMPM) and the PM area within the AM (µm2) from BAC cytospin microphotography using CellProfiler cell image analysis software. Concentrations of biomarkers were analyzed in bronchoalveolar lavage fluid (BALF), plasma, and urine. Most AM samples contained PM (median = 62.4%, interquartile range [IQR] = 50.0–73.0%). The median PM area in AM was 1.082 µm2 (IQR = 0.607–1.855 µm2). Participant with low %AMPM (< 33 percentile) showed 8% increase in %AMPM per 10 µg/m3 increments of six-month averaged, LUR-estimated PM2.5 concentrations. The %AMPM had a statistically significant, positive association with plasma von Willebrand Factor (vWF) (p = 0.016) and serum lactase dehydrogenase (LDH) (p = 0.026). These findings suggest that ambient urban PM exposure in Mexico City contributes to PM accumulation in AMs and may trigger systemic inflammation and oxidative stress in healthy young residents.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-15878-w.

Chronic kidney disease is a major global health concern, with air pollution increasingly recognized as a key contributor to kidney function decline. This study hypothesizes that exposure to air pollution accelerates kidney function decline, measured by≥30% or≥40% reductions in estimated glomerular filtration rate (eGFR). A nested case-control design was employed using data from the Adult Preventive Healthcare Services database and National Health Insurance claims (2016-2021). The study cohort comprised 871 295 health checkup participants. Cases were defined as participants with an eGFR decline of≥30% or≥40% from baseline, matched 1:1 with controls by time density sampling on sex, age, baseline eGFR, and follow-up duration. Air pollution exposure to six pollutants (PM2.5, PM10, NO2, SO2, CO, and O3) was assessed for the 1-year, 2-year, 3-year, and 5-year period prior to the outcome occurrence using land-use regression combined with machine learning algorithms. Multivariate conditional logistic regression models were used to estimate odds ratios (ORs) for eGFR decline per interquartile range (IQR) increase in pollutant concentrations. The study included 61 239 cases with eGFR declines≥30% and 23 330 cases with declines≥40%. Higher concentrations of all pollutants were associated with significant increases in odds for kidney function decline. CO and PM2.5 exhibited the strongest associations with eGFR decline. For eGFR decline of≥30%, an IQR increase in CO was associated with an aOR of 2.78 (95% CI: 2.69-2.88), while PM2.5 showed an aOR of 2.60 (95% CI: 2.51-2.69). Similarly, for eGFR decline of≥40%, CO had an aOR of 2.46 (95% CI: 2.33-2.60) and PM2.5 an aOR of 2.36 (95% CI: 2.23-2.48). Compared to 3- and 5-year exposure periods, the associations were strongest in the 1- and 2-year periods. Air pollution exposure accelerates kidney function decline, necessitating public health action.

Joint effects of environmental and neighborhood socioeconomic factors on cognitive function in the Heinz Nixdorf Recall Study.

Long-term exposure to air pollution and risk of dementia among older individuals of a Danish nationwide administrative cohort.

Comparison of air pollution mortality effect estimates using different long-term exposure assessment modelling methods.

In the UK, an estimated 15% of asthma patients have concurrent chronic obstructive pulmonary disease (COPD), yet the underlying causes and mechanisms remain largely unexplored. This study aimed to investigate the roles of both ambient air pollution and genetic susceptibility in the progression from asthma to COPD. 46 832 participants with asthma were recruited from the UK Biobank during the baseline period (2006-2010). Particulate matter with a diameter of 2.5 μm (PM2.5) and nitrogen dioxide (NO2) were estimated at baseline address using land-use regression models. Air pollution score reflected joint exposure to air pollution. Polygenic risk score was calculated using novel genetic signals identified for coexistence of asthma+COPD. Cox proportional hazards regression analysis was employed to quantify the risks of both ambient air pollution and genetic scores on incident COPD among asthmatics, adjusting for covariates. Over a median follow-up of 10.84 years, 3759 participants with asthma at baseline developed COPD. For an IQR increase in PM2.5 and NO2, the HR for developing COPD was 1.07 (95% CI: 1.02 to 1.11) and 1.10 (95% CI: 1.04 to 1.15), respectively. Adverse effects could be observed at concentrations as low as 8 µg/m3 for PM2.5 and 12 µg/m3 for NO2. A significant multiplicative interaction was identified between ambient air pollution and genetic susceptibility. Individuals with the highest genetic risk score exhibited the greatest risk, with an HR of 1.13 (95% CI: 1.05 to 1.22) per IQR increase in air pollution score (P interaction <0.05). Ambient air pollution is strongly associated with progression from asthma to comorbidity COPD, particularly among individuals with high genetic risk.