Land Use Regression Research Articles

Modelling spatial & temporal variability of air pollution in an area of unconventional natural gas operations

Despite the growing unconventional natural gas production industry in northeastern British Columbia, Canada, few studies have explored the air quality implications on human health in nearby communities. Researchers who have worked with pregnant women in this area have found higher levels of volatile organic compounds (VOCs) in the indoor air of their homes associated with higher density and closer proximity to gas wells. To inform ongoing exposure assessments, this study develops land use regression (LUR) models to predict ambient air pollution at the homes of pregnant women by using natural gas production activities as predictor variables. Using the existing monitoring network, the models were developed for three temporal scales for 12 air pollutants. The models predicting monthly, bi-annual, and annual mean concentrations explained 23%–94%, 54%–94%, and 73%–91% of the variability in air pollutant concentrations, respectively. These models can be used to investigate associations between prenatal exposure to air pollutants associated with natural gas production and adverse health outcomes in northeastern British Columbia.

Different Impacts of Traffic-Related Air Pollution on Early-Onset and Late-Onset Asthma.

Early-onset asthma (EOA) and late-onset asthma (LOA) are two distinct phenotypes. Air pollution has been associated with an increase in poorer asthma outcomes. The objective of this study was to examine the effects of traffic-related air pollution (TRAP) on asthma outcomes in EOA and LOA patients. A cross-sectional study was conducted on 675 asthma patients (LOA: 415) recruited from a major medical center in Taiwan. The land-use regression (LUR) model was used to estimate the level of exposure to PM10, PM2.5, NO2, and O3 on an individual level. We investigated the association between TRAP and asthma outcomes in EOA and LOA patients, stratified by allergic sensitization status, using a regression approach. An increase in PM10 was associated with younger age of onset, increased asthma duration, and decreased lung function in EOA patients (p<0.05). An increase in PM10 was associated with older age of onset, and decreased asthma duration, eosinophil count, and Asthma Control Test (ACT) score in LOA patients. An increase in PM2.5 was associated with younger age of onset, increased asthma duration, decreased eosinophil count, and lung function in EOA patients (p<0.05). An increase in PM2.5 was associated with decreased lung function and ACT score in LOA patients. An increase in NO2 was associated with increased eosinophil count and decreased lung function in EOA patients (p<0.05). An increase in O3 was associated with decreased lung function in LOA patients (p<0.05). In addition, associations of TRAP with age of onset and eosinophil counts were mainly observed in both EOA and LOA patients with allergic sensitization, and an association with ACT was mainly observed in LOA patients without allergic sensitization. The impact of TRAP on age of onset, eosinophil count, and lung function in EOA patients, and ACT in LOA patients, was affected by the status of allergic sensitization.

Industrial distribution and its effect on land surface temperature (LST) in Sukoharjo Regency in 2001, 2009 and 2021

The expansion of human activities, driven by population growth and various societal needs, has led to significant land-use changes, resulting in microclimate modifications. These changes have a crucial impact on land surface temperature (LST), particularly in urban areas. Urbanization, transportation, industrialization, and urban settlements are the main factors contributing to LST variations. This study focuses on the distribution of industries and their influence on land surface temperature in Sukoharjo Regency, Central Java, during the years 2001, 2009, and 2021. The research utilizes Landsat satellite imagery to analyze the industrial distribution and LST in the study area. The images were acquired on August 27, 2001, June 14, 2009, and July 25, 2021. The methodology includes land-use classification, LST extraction, and regression analysis to assess the relationship between industrial land area and land surface temperature. The results show a significant increase in the number of industries in Sukoharjo Regency over the studied period, with a growth rate of over 100% from 2001 to 2010 and further expansion until 2021. The industrial areas exhibited a clear spatial distribution in all years, indicating the impact of industrialization on land use. The regression analysis revealed a relationship between the industrial land area and land surface temperature, suggesting that expanding industries contributes to higher land surface temperatures. These findings provide valuable insights for urban planning and environmental management in Sukoharjo Regency to mitigate the impacts of climate change and promote sustainable development.

LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.

Associations between community green view index and fine particulate matter from Airboxes

Urban greenery can help to improve air quality, reduce health risks and create healthy livable urban communities. This study aimed to explore the role of urban greenery in reducing air pollution at the community level in Tainan City, Taiwan, using air quality sensors and street-view imagery. We also collected the number of road trees around each air quality sensor site and identified the species that were best at absorbing PM2.5. Three greenness metrics were used to assess community greenery in this study: two Normalized Difference Vegetation Indices (NDVI) from different satellites and the Green View Index (GVI) from Google Street View (GSV) images. Land-use Regression (LUR) was used for statistical analysis. The results showed that a higher GVI within a 500 m buffer was significantly associated with decreased PM2.5. Neither NDVI metrics within a 500 m circular buffer were significantly associated with decreased PM2.5. Evergreen trees were significantly associated with lower ambient PM2.5, compared with deciduous and semi-deciduous trees. Because localized changes in air quality profoundly affect public health and environmental equity, our findings provide evidence for future urban community greenspace planning and its beneficial impacts on reducing air pollution.

Residential exposure to microbial emissions from livestock farms: Implementation and evaluation of land use regression and random forest spatial models

Adverse health effects have been linked with exposure to livestock farms, likely due to airborne microbial agents. Accurate exposure assessment is crucial in epidemiological studies, however limited studies have modelled bioaerosols. This study used measured concentrations in air of livestock commensals (Escherichia coli (E. coli) and Staphylococcus species (spp.)), and antimicrobial resistance genes (tetW and mecA) at 61 residential sites in a livestock-dense region in the Netherlands. For each microbial agent, land use regression (LUR) and random forest (RF) models were developed using Geographic Information System (GIS)-derived livestock-related characteristics as predictors. The mean and standard deviation of annual average concentrations (gene copies/m3) of E. coli, Staphylococcus spp., tetW and mecA were as follows: 38.9 (±1.98), 2574 (±3.29), 20991 (±2.11), and 15.9 (±2.58). Validated through 10-fold cross-validation (CV), the models moderately explained spatial variation of all microbial agents. The best performing model per agent explained respectively 38.4%, 20.9%, 33.3% and 27.4% of the spatial variation of E. coli, Staphylococcus spp., tetW and mecA. RF models had somewhat better performance than LUR models. Livestock predictors related to poultry and pig farms dominated all models. To conclude, the models developed enable enhanced estimates of airborne livestock-related microbial exposure in future epidemiological studies. Consequently, this will provide valuable insights into the public health implications of exposure to specific microbial agents.

Air pollution, lung function and mortality: survival and mediation analyses in UK Biobank.

Air pollution is associated with lower lung function, and both are associated with premature mortality and cardiovascular disease (CVD). Evidence remains scarce on the potential mediating effect of impaired lung function on the association between air pollution and mortality or CVD. We used data from UK Biobank (n∼200 000 individuals) with 8-year follow-up to mortality and incident CVD. Exposures to particulate matter <10 µm (PM10), particulate matter <2.5 µm (PM2.5) and nitrogen dioxide (NO2) were assessed by land-use regression modelling. Lung function (forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and the FEV1/FVC ratio) was measured between 2006 and 2010 and transformed to Global Lung Function Initiative (GLI) z-scores. Adjusted Cox proportional hazards and causal proportional hazards mediation analysis models were fitted, stratified by smoking status. Lower FEV1 and FVC were associated with all-cause and CVD mortality, and incident CVD, with larger estimates in ever- than never-smokers (all-cause mortality hazard ratio per FEV1 GLI z-score decrease 1.29 (95% CI 1.24-1.34) for ever-smokers and 1.16 (95% CI 1.12-1.21) for never-smokers). Long-term exposure to PM2.5 or NO2 was associated with incident CVD, with similar effect sizes for ever- and never-smokers. Mediated proportions of the air pollution-all-cause mortality estimates driven by FEV1 were 18% (95% CI 2-33%) for PM2.5 and 27% (95% CI 3-51%) for NO2. Corresponding mediated proportions for incident CVD were 9% (95% CI 4-13%) for PM2.5 and 16% (95% CI 6-25%) for NO2. Lung function may mediate a modest proportion of associations between air pollution and mortality and CVD outcomes. Results likely reflect the extent of either shared mechanisms or direct effects relating to lower lung function caused by air pollution.

The association of in-utero exposure to air pollution and atherogenic index of plasma in newborns

BackgroundPrenatal exposure to particulate matter (PM) and traffic was associated with the programming of cardiovascular diseases (CVDs) in early life. However, the exact underlying mechanisms are not fully understood. Therefore, we aimed to evaluate the association between in-utero exposure to PMs and traffic indicators with the atherogenic index of plasma (AIP) in newborns, which is a precise index reflecting an enhancement of lipid risk factors for CVDs.MethodsIn this cross-sectional study, a total of 300 mother-newborn pairs were enrolled in Sabzevar, Iran. Spatiotemporal land-use regression models were used to estimate the level of PM1, PM2.5 and PM10 at the mother's residential address. The total length of streets in different buffers (100,300 and 500m) and proximity to major roads were calculated as indicators of traffic. The AIP of cord blood samples was calculated using an AIP calculator. Multiple linear regression models were used to examine the association of PM concentrations as well as traffic indicators with AIP controlled for relevant covariates.ResultsPM2.5 exposure was significantly associated with higher levels of AIP in newborns. Each interquartile range (IQR) increment of PM2.5 concentration at the mothers' residential addresses was associated with a 5.3% (95% confidence interval (CI): 0.0, 10.6%, P = 0.04) increase in the AIP. Associations between PM1, PM10 and traffic indicators with cord blood level of AIP were positive but not statistically significant.ConclusionOur findings showed that in utero exposure to PM2.5 may be associated with CVDs programming through the increase of atherogenic lipids.

Exposure to PM2.5 Metal Constituents and Liver Cancer Risk in REVEAL-HBV.

Ambient particulate matter is classified as a human Class 1 carcinogen, and recent studies found a positive relationship between fine particulate matter (PM2.5) and liver cancer. Nevertheless, little is known about which specific metal constituent contributes to the development of liver cancer. To evaluate the association of long-term exposure to metal constituents in PM2.5 with the risk of liver cancer using a Taiwanese cohort study. A total of 13,511 Taiwanese participants were recruited from the REVEAL-HBV in 1991-1992. Participants' long-term exposure to eight metal constituents (Ba, Cu, Mn, Sb, Zn, Pb, Ni, and Cd) in PM2.5 was based on ambient measurement in 2002-2006 followed by a land-use regression model for spatial interpolation. We ascertained newly developed liver cancer (ie, hepatocellular carcinoma [HCC]) through data linkage with the Taiwan Cancer Registry and national health death certification in 1991-2014. A Cox proportional hazards model was utilized to assess the association between exposure to PM2.5 metal component and HCC. We identified 322 newly developed HCC with a median follow-up of 23.1 years. Long-term exposure to PM2.5 Cu was positively associated with a risk of liver cancer. The adjusted hazard ratio (HR) was 1.13 (95% confidence interval [CI], 1.02-1.25; P = 0.023) with one unit increment on Cu normalized by PM2.5 mass concentration in the logarithmic scale. The PM2.5 Cu-HCC association remained statistically significant with adjustment for co-exposures to other metal constituents in PM2.5. Our findings suggest PM2.5 containing Cu may attribute to the association of PM2.5 exposure with liver cancer.

Potential Effects of Long-Term Exposure to Air Pollution on Dementia: A Longitudinal Analysis in American Indians Aged 55 Years and Older.

(1) Background: American Indians are disproportionately affected by air pollution, an important risk factor for dementia. However, few studies have investigated the effects of air pollution on the risk of dementia among American Indians. (2) Methods: This retrospective cohort study included a total of 26,871 American Indians who were 55+ years old in 2007, with an average follow-up of 3.67 years. County-level average air pollution data were downloaded from land-use regression models. All-cause dementia was identified using ICD-9 diagnostic codes from the Indian Health Service's (IHS) National Data Warehouse and related administrative databases. Cox models were employed to examine the association of air pollution with dementia incidence, adjusting for co-exposures and potential confounders. (3) Results: The average PM2.5 levels in the IHS counties were lower than those in all US counties, while the mean O3 levels in the IHS counties were higher than the US counties. Multivariable Cox regressions revealed a positive association between dementia and county-level O3 with a hazard ratio of 1.24 (95% CI: 1.02-1.50) per 1 ppb standardized O3. PM2.5 and NO2 were not associated with dementia risk after adjusting for all covariates. (4) Conclusions: O3 is associated with a higher risk of dementia among American Indians.

Long-term PM2.5 exposure is associated with asthma prevalence and exhaled nitric oxide levels in children.

Exhaled nitric oxide concentration (FENO) is a marker of airway inflammation. This study aimed to evaluate the association of air pollution exposure with FENO levels and asthma prevalence with respiratory symptoms in school children. We analyzed 4736 school children who reside in six townships near industrial areas in central Taiwan. We evaluated asthmatic symptoms, FENO, and conducted the environmental questionnaire. The personal exposure of PM2.5, NO, and SO2 was estimated using land-use regression models data on children's school and home addresses. Annual exposure to PM2.5 was associated with increased odds of physician-diagnosed asthma (OR = 1.595), exercise-induced wheezing (OR = 1.726), itchy eyes (OR = 1.417), and current nasal problems (OR = 1.334) (P < 0.05). FENO levels in the absence of infection were positively correlated with age, previous wheezing, allergic rhinitis, atopic eczema, near the road, and for children with high exposure to PM2.5 (P < 0.05). An increase of 1 μg/m3 PM2.5 exposure was significantly associated with a 1.0% increase in FENO levels for children after adjusting for potential confounding variables, including exposures to NO and SO2. Long-term exposures to PM2.5 posed a significant risk of asthma prevalence and airway inflammation in a community-based population of children. Annual exposure to PM2.5 was associated with increased odds of physician-diagnosed asthma and nasal problems and itchy eyes. Long-term exposures to PM2.5 were significantly associated with FENO levels after adjusting for potential confounding variables. This is first study to assess the association between FENO levels and long-term air pollution exposures in children near coal-based power plants. An increase of 1 μg/m3 annual PM2.5 exposure was significantly associated with a 1.0% increase in FENO levels. Long-term exposures to PM2.5 posed a significant risk of asthma prevalence and airway inflammation in a community-based population of children.

Development of an integrated model framework for multi-air-pollutant exposure assessments in high-density cities

Abstract. Exposure models for some criteria of air pollutants have been intensively developed in past research; multi-air-pollutant exposure models, especially for particulate chemical species, have been however overlooked in Asia. Lack of an integrated model framework to calculate multi-air-pollutant exposure has hindered the combined exposure assessment and the corresponding health assessment. This work applied the land-use regression (LUR) approach to develop an integrated model framework to estimate 2017 annual-average exposure of multiple air pollutants in a typical high-rise and high-density Asian city (Hong Kong, China) including four criteria of gaseous air pollutants (particulate matter with an aerodynamic diameter equal to or less than 10 µm (PM10) and 2.5 µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3)), as well as four major PM10 chemical species. Our integrated multi-air-pollutant exposure model framework is capable of explaining 91 %–97 % of the variability of measured gaseous air pollutant concentration, with the leave-one-out cross-validation R2 values ranging from 0.73 to 0.93. Using the model framework, the spatial distribution of the concentration of various air pollutants at a spatial resolution of 500 m was generated. The LUR model-derived spatial distribution maps revealed weak-to-moderate spatial correlations between the PM10 chemical species and the criteria of air pollutants, which may help to distinguish their independent chronic health effects. In addition, further improvements in the development of air pollution exposure models are discussed. This study proposed an integrated model framework for estimating multi-air-pollutant exposure in high-density and high-rise urban areas, serving an important tool for multi-air-pollutant exposure assessment in epidemiological studies.

Air pollution prediction and backcasting through a combination of mobile monitoring and historical on-road traffic emission inventories

An important challenge for studies of air pollution and health effects is the derivation of historical exposures. These generally entail some form of backcasting, which refers to a range of approaches that aim to project a current surface into the past. Accurate backcasting is conditional upon the availability of historical data for predictor variables and the ability to capture spatial and temporal trends in these variables. This study proposes a method to backcast traffic-related air pollution surfaces developed using land-use regression models by including temporal variability of traffic and emissions and trends in concentrations measured at reference stations. Nitrogen dioxide (NO2) concentrations collected in the City of Toronto using the Urban Scanner mobile platform were adjusted for historical trends captured at reference stations. The Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST), a powerful tool for time series decomposition, was employed to isolate seasonal variations, annual trends, and abrupt changes in NO2 at reference stations, hence decomposing the signal. Exposure surfaces were generated for a period extending from 2006 to 2020, exhibiting decreases ranging from 10 to 50 % depending on the neighborhood, with an average of 20.46 % across the city. Yearly surfaces were intersected with mobility patterns of Torontonians extracted from travel survey data for 2006 and 2016, illustrating strong spatial gradients in the evolution of NO2 over time, with larger decreases along major roads and highways and in the central core. These findings demonstrate that air pollution improvements throughout the 14 years are inhomogeneous across space.

Prenatal air pollution exposure and childhood obesity: Effect modification by maternal fruits and vegetables intake

Background & aimsPrenatal exposure to air pollution is robustly associated with fetal growth restriction but the extent to which it is associated with postnatal growth and the risk of childhood obesity remains unknown. We examined the association of prenatal exposure to air pollution with offspring obesity related measures and evaluated the possible protective effect of maternal fruits and vegetables intake (FV). MethodsWe included 633 mother-child pairs from the Rhea pregnancy cohort in Crete, Greece. Fine particles (PM2.5 and PM10) exposure levels during pregnancy were estimated using land-use regression models. We measured weight, height and waist circumference at 4 and 6 years of age, and body composition analysis was performed at 6 years using bioimpedance. Maternal diet was evaluated by means of a semi-quantitative food frequency questionnaire in mid-pregnancy. Adjusted associations were obtained via multivariable regression analyses and multiplicative interaction was used to evaluate the potential modifying role of FV intake. ResultsExposure to PMs in utero was not associated with measures of adiposity at 4 or 6 years of age. Associations at 4 years did not differ according to maternal consumption of FV. However, at 6 years, among children whose mothers reported consuming less than 5 servings of FV per day, one SD increase in PM10 during pregnancy was associated with increased BMI (beta 0.41 kg/m2, 95% CI: −0.06, 0.88, p-interaction = 0.037) and increased waist circumference (beta 0.83 cm, 95% CI: −0.38, 2.05, p-interaction = 0.043) and one SD increase in PM2.5 was associated with increased fat mass (beta 0.5 kg, 95% CI: 0.0, 0.99, p-interaction = 0.039) and increased percentage of body fat (beta 1.06%, 95% CI: −0.06, 2.17, p-interaction = 0.035). Similarly, higher prenatal PM2.5 and PM10 exposure was associated with increased risk for obesity and abdominal obesity at 6 years only in the low FV group. ConclusionsExposure to fine particulate matter during pregnancy was not associated with obesity-related measures at 4 and 6 years. However, only among offspring of mothers who consumed inadequate FV, we observed higher obesity-related measures at 6 years. Our results indicate that mothers' diet during pregnancy may play a role in the relationship between air-pollution and childhood obesity.

Prenatal Exposure to Air Pollution and Respiratory Distress in Term Newborns: Results from the MIREC Prospective Pregnancy Cohort.

Respiratory distress is the leading cause of neonatal morbidity and mortality worldwide, and prenatal exposure to air pollution is associated with adverse long-term respiratory outcomes; however, the impact of prenatal air pollution exposure on neonatal respiratory distress has not been well studied. We examined associations between prenatal exposures to fine particular matter () and nitrogen dioxide () with respiratory distress and related neonatal outcomes. We used data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a prospective pregnancy cohort () recruited in the first trimester from 10 Canadian cities. Prenatal exposures to () and () were estimated using land-use regression and satellite-derived models coupled with ground-level monitoring and linked to participants based on residential location at birth. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for associations between air pollution and physician-diagnosed respiratory distress in term neonates in hierarchical logistic regression models adjusting for detailed maternal and infant covariates. Approximately of newborns experienced respiratory distress. Neonates received clinical interventions including oxygen therapy (6%), assisted ventilation (2%), and systemic antibiotics (3%). Two percent received multiple interventions and 4% were admitted to the neonatal intensive care unit (NICU). Median and concentrations during pregnancy were and , respectively. Prenatal exposures to air pollution were not associated with physician-diagnosed respiratory distress, oxygen therapy, or NICU admissions. However, exposures were strongly associated with assisted ventilation (OR per increase in ; 95% CI: 1.02, 1.35), multiple clinical interventions (OR per increase in ; 95% CI: 1.07, 1.26), and systemic antibiotics, (OR per increase in ; 95% CI: 1.04, 1.21). These associations were consistent across exposure periods-that is, during prepregnancy, individual trimesters, and total pregnancy-and robust to model specification. exposure was associated with administration of systemic antibiotics (OR per 1-ppb increase in ; 95% CI: 1.00, 1.06). Prenatal exposures to increased the risk of severe respiratory distress among term newborns. These findings support the development and prioritization of public health and prenatal care strategies to increase awareness and minimize prenatal exposures to air pollution. https://doi.org/10.1289/EHP12880.

Comparison of Air Pollution Exposures and Health Effects Associations Using 11 Different Modeling Approaches in the Women's Health Initiative Memory Study (WHIMS).

Many approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear. Our objective is to compare particulate matter with aerodynamic diameter () concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses. We assigned annual exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region. With a few exceptions, relative agreement of approach-specific exposure estimates was high for concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for . There was no evidence of large differences in health effects associations with among estimation approaches in analyses adjusted for climate region. Different estimation approaches produced similar spatial patterns of concentrations across the contiguous US and in areas with dense monitoring data, and effects associations were similar among estimation approaches. estimates and effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995.

Role of low-density lipoprotein in mediating the effect of air pollution on coronary heart disease: a two-step multivariate Mendelian randomization study.

Air pollution is affecting the health of millions of people all over the world. The causal correlations of PM2.5, PM10, and nitrogen dioxide (NOx), as the main fine particulate matter, and coronary heart disease (CHD) are yet to be explored. Low-density lipoprotein (LDL) has been a principal factor in the pathogenesis of CHD. It is an interesting issue to consider whether LDL mediates the effect of air pollutants in CHD pathogenesis. A genome-wide association study (GWAS) on the European population, followed up from 2010 to 2018, involving over 400,000 participants, was based on a land-use regression model. The annual mean concentrations of major air pollutant particles, PM2.5 (n=423,796), PM10 (n=423,796), and NOx (n=456,380), were recorded. The large GWAS database of CHD covered over ten million SNPs with independent single nucleotide polymorphisms (SNPs). LDL database collected major biochemical blood parameters from over 400,000 patients (n=440,546). Taken together, we conducted independent two-sample Mendelian randomization (MR) analyses for the causality between air pollutants (PM2.5, PM10, and NOx) and CHD. Multivariate MR analysis was conducted using causal relationships to determine the direct effects of exposure on outcome. The fixed-effect inverse variance weighted (IVW2) method was mainly employed to assess this relationship, with a confidence interval of 95% for the odds ratio (OR). Also, MR-Egger, weighted median, maximum likelihood ratio method, and random-effects inverse variance-weighted (IVW1) method were adopted as supplementary methods. Two-sample MR results based on the IVW2 method suggested positive correlations between PM2.5 and CHD [OR 1.875 (1.279-2.748), p=0.001], PM10 and CHD [OR 2.586 (1.479-4.523), p=0.001], and NOx and CHD [OR 2.991 (2.021-4.427), p=4.37E-08]. The direct effect and mediating proportion were calculated using multivariable Mendelian randomization (MVMR). Lastly, the mediating proportions of LDL in the regulatory roles of PM2.5, PM10, and NOx in CHD were 2.82%, 4.73%, and 9.54%, respectively. PM2.5, PM10, and NOx share direct causal associations with CHD, and LDL performs a mediating role in this pathogenic process. Early prevention against air pollution (such as increasing green areas and reducing large-scale industrial dust emissions) and early lipid-lowering treatment can effectively prevent the occurrence of CHD.

Cooking emissions are a major source of racial-ethnic air pollution exposure disparities in the United States

Racial-ethnic minority populations in the US are disproportionately exposed to airborne fine particulate matter (PM2.5), but few national studies have focused individually on the sources that contribute to these disparities. We address this gap by conducting a comprehensive analysis of PM2.5 exposure disparities by race-ethnicity in the US, focusing on three source-categories: mobile-sources, cooking, and all other sources combined. Our approach is based on high-resolution, national land-use regression estimates of source-resolved PM2.5 components, derived from high-resolution aerosol mass spectrometer measurements. We find that each of these sources contributes approximately one-third of the overall PM2.5 exposure disparities by race-ethnicity. While the importance of mobile-source tailpipe emissions is well recognized, our study underscores the significance of cooking emissions in creating PM2.5 exposure disparities. This finding represents a potentially significant opportunity to reduce these disparities, as cooking emissions are currently largely unregulated. It has important implications for policymakers and public health advocates aiming to address the persistent issue of racial-ethnic disparities in air pollution.

Exposure to air pollution concentrations of various intensities in early life and allergic sensitisation later in childhood

BackgroundEvidence on the relationship between air pollution and allergic sensitisation in childhood is inconsistent, and this relationship has not been investigated in the context of smoke events that are predicted to increase with climate change. Thus, we aimed to evaluate associations between exposure in two early life periods to severe levels of particulate matter with an aerodynamic diameter < 2.5 μm (PM2.5) from a mine fire, background PM2.5, and allergic sensitisation later in childhood.MethodsWe measured specific immunoglobulin E (IgE) levels for seven common aeroallergens as well as total IgE levels in a cohort of children who had been exposed to the Hazelwood coal mine fire, either in utero or during their first two years of life, in a regional area of Australia where ambient levels of PM2.5 are generally low. We estimated personal exposure to fire-specific emissions of PM2.5 based on a high-resolution meteorological and pollutant dispersion model and detailed reported movements of pregnant mothers and young children during the fire. We also estimated the usual background exposure to PM2.5 at the residential address at birth using a national satellite-based land-use regression model. Associations between both sources of PM2.5 and sensitisation to dust, cat, fungi, and grass seven years after the fire were estimated with logistic regression, while associations with total IgE levels were estimated with linear regression.ResultsNo association was found between the levels of exposure at either developmental stage to fire-related PM2.5 and allergic sensitisation seven years after the event. However, levels of background exposure were positively associated with sensitisation to dust (OR = 1.90, 95%CI = 1.12,3.21 per 1 μg/m3).ConclusionsChronic but low exposure to PM2.5 in early life could be more strongly associated with allergic sensitisation in childhood than time-limited high exposure levels, such as the ones experienced during landscape fires.

Exploring associations of greenery, air pollution and walkability with cardiometabolic health in people at midlife and beyond.

To examine associations of neighborhood greenery, air pollution and walkability with cardiometabolic disease in adults aged ≥45 years in the Frankston-Mornington Peninsula region, Victoria, Australia. A cross-sectional, ecological study design was used. We assessed mean annual neighborhood greenery using the Normalized Difference Vegetation Index; air pollution (fine particulate matter of diameter ≤2.5 μm [PM2.5] and NO2) using land-use regression models; and walkability using Walk Score (possible values 0-100). Medically diagnosed diabetes (~95% type-2), heart disease and stroke were self-reported in the Australian Census (2021). Multivariable regression was used to model associations between environmental exposures and area-level (neighborhood) cardiometabolic disease prevalence (age group ≥45 years), with socioeconomic status, age and sex as covariates. Air pollution was examined as a mediator of associations between greenery and disease prevalence. Our sample comprised 699 neighborhoods with the following mean (SD) values: Normalized Difference Vegetation Index 0.47 (0.09), PM2.5, 8.5 (0.6) μg/m3 and NO2, 5.2 (1.6) ppb. Disease prevalences were: heart disease, mean 8.9% (4.5%); diabetes, mean 10.3% (4.7%); and stroke, median 1.2% (range 0-10.9%). Greenery was negatively associated with diabetes (β = -5.85, 95% CI -9.53, -2.17) and stroke prevalence (β = -1.26, 95% CI -2.11, -0.42). PM2.5 and NO2 were positively associated with diabetes (β = 1.59, 95% CI 1.00, 2.18; β = 0.42, 95% CI 0.22, 0.62) and stroke prevalence (β = 0.15, 95% CI 0.01, 0.29; β = 0.06, 95% CI 0.01, 0.10). The association between greenery and diabetes was partially mediated by PM2.5 (mediated effect -5.38, 95% CI -7.84, -3.03). Greenery and air pollutants were associated with lower and higher prevalence, respectively, of self-reported diabetes and, to a lesser extent, stroke. These ecological findings require further exploration with stronger, longitudinal study designs to inform public health policy and directions. Geriatr Gerontol Int 2024; 24: 208-214.