The impact of urban configuration types on urban heat islands, air pollution, CO2 emissions, and mortality in Europe: a data science approach.

BACKGROUND: The world is becoming increasingly urbanised. As cities around the world continue to grow, it is important for urban planners and policy makers to understand how different urban configuration patterns affect the environment and human health. However, previous studies have provided mixed findings. We aimed to identify European urban configuration types, on the basis of the local climate zones categories and street design variables from Open Street Map, and evaluate their association with motorised traffic flows, surface urban heat island (SUHI) intensities, tropospheric NO2, CO2 per person emissions, and age-standardised mortality. METHODS: We considered 946 European cities from 31 countries for the analysis defined in the 2018 Urban Audit database, of which 919 European cities were analysed. Data were collected at a 250 m × 250 m grid cell resolution. We divided all cities into five concentric rings based on the Burgess concentric urban planning model and calculated the mean values of all variables for each ring. First, to identify distinct urban configuration types, we applied the Uniform Manifold Approximation and Projection for Dimension Reduction method, followed by the k-means clustering algorithm. Next, statistical differences in exposures (including SUHI) and mortality between the resulting urban configuration types were evaluated using a Kruskal-Wallis test followed by a post-hoc Dunn's test. FINDINGS: We identified four distinct urban configuration types characterising European cities: compact high density (n=246), open low-rise medium density (n=245), open low-rise low density (n=261), and green low density (n=167). Compact high density cities were a small size, had high population densities, and a low availability of natural areas. In contrast, green low density cities were a large size, had low population densities, and a high availability of natural areas and cycleways. The open low-rise medium and low density cities were a small to medium size with medium to low population densities and low to moderate availability of green areas. Motorised traffic flows and NO2 exposure were significantly higher in compact high density and open low-rise medium density cities when compared with green low density and open low-rise low density cities. Additionally, green low density cities had a significantly lower SUHI effect compared with all other urban configuration types. Per person CO2 emissions were significantly lower in compact high density cities compared with green low density cities. Lastly, green low density cities had significantly lower mortality rates when compared with all other urban configuration types. INTERPRETATION: Our findings indicate that, although the compact city model is more sustainable, European compact cities still face challenges related to poor environmental quality and health. Our results have notable implications for urban and transport planning policies in Europe and contribute to the ongoing discussion on which city models can bring the greatest benefits for the environment, climate, and health. FUNDING: Spanish Ministry of Science and Innovation, State Research Agency, Generalitat de Catalunya, Centro de Investigación Biomédica en red Epidemiología y Salud Pública, and Urban Burden of Disease Estimation for Policy Making as a Horizon Europe project.

Associations of air pollution exposures in preconception and pregnancy with birth outcomes and infant neurocognitive development: analysis of the Complex Lipids in Mothers and Babies (CLIMB) prospective cohort in Chongqing, China.

OBJECTIVES: To investigate the associations of traffic-related air pollution exposures in early pregnancy with birth outcomes and infant neurocognitive development. DESIGN: Cohort study. SETTING: Eligible women attended six visits in the maternity clinics of two centres, the First Affiliated Hospital of Chongqing Medical University and Chongqing Health Centre for Women and Children. PARTICIPANTS: Women who were between 20 and 40 years of age and were at 11-14 weeks gestation with a singleton pregnancy were eligible for participation. Women were excluded if they had a history of premature delivery before 32 weeks of gestation, maternal milk allergy or aversion or severe lactose intolerance. 1273 pregnant women enrolled in 2015-2016 and 1174 live births were included in this analysis. EXPOSURES: Air pollution concentrations at their home addresses, including particulate matter with diameter ≤2.5 µm (PM2.5) and nitrogen dioxide (NO2), during pre-conception and each trimester period were estimated using land-use regression models. OUTCOME MEASURES: Birth outcomes (ie, birth weight, birth length, preterm birth, low birth weight, large for gestational age and small for gestational age (SGA) status) and neurodevelopment outcomes measured by the Chinese version of Bayley Scales of Infant Development. RESULTS: An association between SGA and per-IQR increases in NO2 was found in the first trimester (OR: 1.57, 95% CI: 1.06 to 2.32) and during the whole pregnancy (OR: 1.33, 99% CI: 1.01 to 1.75). Both PM2.5 and NO2 exposure in the 90 days prior to conception were associated with lower Psychomotor Development Index scores (ß: -6.15, 95% CI: -8.84 to -3.46; ß: -2.83, 95% CI: -4.27 to -1.39, respectively). Increased NO2 exposure was associated with an increased risk of psychomotor development delay during different trimesters of pregnancy. CONCLUSIONS: Increased exposures to NO2 during pregnancy were associated with increased risks of SGA and psychomotor development delay, while increased exposures to both PM2.5 and NO2 pre-conception were associated with adverse psychomotor development outcomes at 12 months of age. TRIAL REGISTRATION NUMBER: ChiCTR-IOR-16007700.

Assessing long-term effects of gaseous air pollution exposure on mortality in the United States using a variant of difference-in-differences analysis.

Long-term mortality effects of particulate air pollution have been investigated in a causal analytic frame, while causal evidence for associations with gaseous air pollutants remains extensively lacking, especially for carbon monoxide (CO) and sulfur dioxide (SO2). In this study, we estimated the causal relationship of long-term exposure to nitrogen dioxide (NO2), CO, SO2, and ozone (O3) with mortality. Utilizing the data from National Morbidity, Mortality, and Air Pollution Study, we applied a variant of difference-in-differences (DID) method with conditional Poisson regression and generalized weighted quantile sum regression (gWQS) to investigate the independent and joint effects. Independent exposures to NO2, CO, and SO2 were causally associated with increased risks of total, nonaccidental, and cardiovascular mortality, while no evident associations with O3 were identified in the entire population. In gWQS analyses, an interquartile range-equivalent increase in mixture exposure was associated with a relative risk of 1.067 (95% confidence interval: 1.010-1.126) for total mortality, 1.067 (1.009-1.128) for nonaccidental mortality, and 1.125 (1.060-1.193) for cardiovascular mortality, where NO2 was identified as the most significant contributor to the overall effect. This nationwide DID analysis provided causal evidence for independent and combined effects of NO2, CO, SO2, and O3 on increased mortality risks among the US general population.

Prenatal Exposure to Ambient Air Pollution and Cerebral Palsy.

Importance: Air pollution is associated with structural brain changes, disruption of neurogenesis, and neurodevelopmental disorders. The association between prenatal exposure to ambient air pollution and risk of cerebral palsy (CP), which is the most common motor disability in childhood, has not been thoroughly investigated. Objective: To evaluate the associations between prenatal residential exposure to ambient air pollution and risk of CP among children born at term gestation in a population cohort in Ontario, Canada. Design, Setting, and Participants: Population-based cohort study in Ontario, Canada using linked, province-wide health administrative databases. Participants were singleton full term births (≥37 gestational weeks) born in Ontario hospitals between April 1, 2002, and March 31, 2017. Data were analyzed from January to December 2022. Exposures: Weekly average concentrations of ambient fine particulate matter with a diameter 2.5 µm (PM2.5) or smaller, nitrogen dioxide (NO2), and ozone (O3) during pregnancy assigned by maternal residence reported at delivery from satellite-based estimates and ground-level monitoring data. Main outcome and measures: CP cases were ascertained by a single inpatient hospitalization diagnosis or at least 2 outpatient diagnoses for children from birth to age 18 years. Results: The present study included 1 587 935 mother-child pairs who reached term gestation, among whom 3170 (0.2%) children were diagnosed with CP. The study population had a mean (SD) maternal age of 30.1 (5.6) years and 811 745 infants (51.1%) were male. A per IQR increase (2.7 µg/m3) in prenatal ambient PM2.5 concentration was associated with a cumulative hazard ratio (CHR) of 1.12 (95% CI, 1.03-1.21) for CP. The CHR in male infants (1.14; 95% CI, 1.02-1.26) was higher compared with the CHR in female infants (1.08; 95% CI, 0.96-1.22). No specific window of susceptibility was found for prenatal PM2.5 exposure and CP in the study population. No associations or windows of susceptibility were found for prenatal NO2 or O3 exposure and CP risk. Conclusions and relevance: In this large cohort study of singleton full term births in Canada, prenatal ambient PM2.5 exposure was associated with an increased risk of CP in offspring. Further studies are needed to explore this association and its potential biological pathways, which could advance the identification of environmental risk factors of CP in early life.

[Personal nitrogen oxides exposure levels and related influencing factors in adults over 35 years old in Tianjin and Shanghai].

OBJECTIVE: To investigate personal exposures to nitrogen oxides (NOX) and nitrogen di-oxide (NO2) and the influence of baseline personal characteristics, living environment and daily activity patterns of the participants on the exposures among adults over 35 in Tianjin and Shanghai. METHODS: In this panel study, 91 healthy nonsmoking adults aged over 35 from Tianjin and Shanghai participated in our study. The study was conducted in summer and winter. The participants were followed for three times with an interval of at least two weeks. Only participants in Shanghai were followed once in winter because of the COVID-19 pandemic. Twenty-seven participants completed follow-up visits in both seasons. We measured their 24 h personal exposures to NOX and NO2and collected their baseline and time-activity information through questionnaire/diary. The linear mixed model was used to analyze the associations between potential influencing factors and personal NOX and NO2 exposure levels. RESULTS: There were 349 follow-up visits with valid 24 h personal NO2 and NOX exposure measurements in the two cities. The ave-rage 24 h personal exposures to NO2 and NOX (volume fraction) in Tianjin participants were 18.0×10-9 and 26.2×10-9 in summer, and 31.0×10-9 and 54.9×10-9 in winter, respectively; and the average 24 h personal exposures to NO2 and NOX in Shanghai participants were 38.7×10-9 and 100.0×10-9 in summer, and 45.5×10-9 and 139.2×10-9 in winter, respectively. The results of univariate regression analysis showed that their personal NOX exposure levels were significantly associated with city, season, gender, average daily cooking times, and ambient NO2 concentrations measured at fixed-site monitoring stations. In addition to the above factors, the personal NOX exposure levels were also significantly associated with educational level and the personal NO2 exposure levels were also significantly associated with passive smoking, average daily home time, cooking energy type, residential distance from main traffic road, and use of kitchen ventilators. Multivariate regression analysis showed that the personal exposure levels of NO2 and NOX were significantly lower in Tianjin than that in Shanghai, were significantly lower in summer than that in winter, and were significantly and positively associated with ambient NO2 concentrations measured at fixed-site monitoring stations. In addition, personal NOX exposure levels were significantly lower in females than in males, and personal NO2 exposure levels were significantly positively associated with average daily cooking times and significantly inversely associated with average daily home time. For every interquartile range (IQR) increase (12.7×10-9) in ambient NO2, the personal NO2 exposure levels increased by 27.5% (95%CI: 17.0%-38.9%), and personal NOX exposure levels increased by 16.1% (95%CI: 7.1%-25.8%). CONCLUSION: Season, city and ambient NO2 concentrations are significant influencing factors of personal exposure levels of NO2and NOX. At the same time, the personal exposures levels of NO2are also affected by lifestyle factors. Our study provides scientific evidence for making precise air pollution control decisions and reducing the exposure levels of NOX in the population.

Residential air pollution, greenspace, and adverse mental health outcomes in the U.S. Gulf Long-term Follow-up Study.

Air pollution and greenness are environmental determinants of mental health, though existing evidence typically considers each exposure in isolation. We evaluated relationships between co-occurring air pollution and greenspace levels and depression and anxiety. We estimated cross-sectional associations among 9015 Gulf Long-term Follow-up Study participants living in the southeastern U.S. who completed the Patient Health Questionnaire-9 (depression: score ≥ 10) and Generalized Anxiety Disorder Questionnaire-7 (anxiety: score ≥ 10). Participant residential addresses were linked to annual average concentrations of particulate matter (1 km PM2.5) and nitrogen dioxide (1 km NO2), as well as satellite-based greenness (2 km Enhanced Vegetation Index (EVI)). We used adjusted log-binomial regression to estimate prevalence ratios (PR) and 95 % confidence intervals (CI) for associations between exposures (quartiles) and depression and anxiety. In mutually adjusted models (simultaneously modeling PM2.5, NO2, and EVI), the highest quartile of PM2.5 was associated with increased prevalence of depression (PR = 1.17, 95 % CI: 1.06-1.29), whereas the highest quartile of greenness was inversely associated with depression (PR = 0.89, 95 % CI: 0.80-0.99). Joint exposure to greenness mitigated the impact of PM2.5 on depression (PRPM only = 1.20, 95 % CI: 1.06-1.36; PRPM+green = 0.98, 95 % CI: 0.83-1.16) and anxiety (PRPM only = 1.10, 95 % CI: 1.00-1.22; PRPM+green = 0.95, 95 % CI: 0.83-1.09) overall and in subgroup analyses. Observed associations were stronger in urbanized areas and among nonwhite participants, and varied by neighborhood deprivation. NO2 exposure was not independently associated with depression or anxiety in this population. Relationships between PM2.5, greenness, and depression were strongest in the presence of characteristics that are highly correlated with lower socioeconomic status, underscoring the need to consider mental health as an environmental justice issue.

Exposure to Air Pollution, Genetic Susceptibility, and Psoriasis Risk in the UK.

Importance: Psoriasis is a common autoinflammatory disease influenced by complex interactions between environmental and genetic factors. The influence of long-term air pollution exposure on psoriasis remains underexplored. Objective: To examine the association between long-term exposure to air pollution and psoriasis and the interaction between air pollution and genetic susceptibility for incident psoriasis. Design, Setting, and Participants: This prospective cohort study used data from the UK Biobank. The analysis sample included individuals who were psoriasis free at baseline and had available data on air pollution exposure. Genetic analyses were restricted to White participants. Data were analyzed between November 1 and December 10, 2023. Exposures: Exposure to nitrogen dioxide (NO2), nitrogen oxides (NOx), fine particulate matter with a diameter less than 2.5 µm (PM2.5), and particulate matter with a diameter less than 10 µm (PM10) and genetic susceptibility for psoriasis. Main Outcomes and Measures: To ascertain the association of long-term exposure to NO2, NOx, PM2.5, and PM10 with the risk of psoriasis, a Cox proportional hazards model with time-varying air pollution exposure was used. Cox models were also used to explore the potential interplay between air pollutant exposure and genetic susceptibility for the risk of psoriasis incidence. Results: A total of 474 055 individuals were included, with a mean (SD) age of 56.54 (8.09) years and 257 686 (54.36%) female participants. There were 9186 participants (1.94%) identified as Asian or Asian British, 7542 (1.59%) as Black or Black British, and 446 637 (94.22%) as White European. During a median (IQR) follow-up of 11.91 (11.21-12.59) years, 4031 incident psoriasis events were recorded. There was a positive association between the risk of psoriasis and air pollutant exposure. For every IQR increase in PM2.5, PM10, NO2, and NOx, the hazard ratios (HRs) were 1.41 (95% CI, 1.35-1.46), 1.47 (95% CI, 1.41-1.52), 1.28 (95% CI, 1.23-1.33), and 1.19 (95% CI, 1.14-1.24), respectively. When comparing individuals in the lowest exposure quartile (Q1) with those in the highest exposure quartile (Q4), the multivariate-adjusted HRs were 2.01 (95% CI, 1.83-2.20) for PM2.5, 2.21 (95% CI, 2.02-2.43) for PM10, 1.64 (95% CI, 1.49-1.80) for NO2, and 1.34 (95% CI, 1.22-1.47) for NOx. Moreover, significant interactions between air pollution and genetic predisposition for incident psoriasis were observed. In the subset of 446 637 White individuals, the findings indicated a substantial risk of psoriasis development in participants exposed to the highest quartile of air pollution levels concomitant with high genetic risk compared with those in the lowest quartile of air pollution levels with low genetic risk (PM2.5: HR, 4.11; 95% CI, 3.46-4.90; PM10: HR, 4.29; 95% CI, 3.61-5.08; NO2: HR, 2.95; 95% CI, 2.49-3.50; NOx: HR, 2.44; 95% CI, 2.08-2.87). Conclusions and Relevance: In this prospective cohort study of the association between air pollution and psoriasis, long-term exposure to air pollution was associated with increased psoriasis risk. There was an interaction between air pollution and genetic susceptibility on psoriasis risk.

Atmospheric nitrogen dioxide suppresses the activity of phytochrome interacting factor 4 to suppress hypocotyl elongation.

MAIN CONCLUSION: Ambient concentrations of atmospheric nitrogen dioxide (NO2) inhibit the binding of PIF4 to promoter regions of auxin pathway genes to suppress hypocotyl elongation in Arabidopsis. Ambient concentrations (10-50 ppb) of atmospheric nitrogen dioxide (NO2) positively regulate plant growth to the extent that organ size and shoot biomass can nearly double in various species, including Arabidopsis thaliana (Arabidopsis). However, the precise molecular mechanism underlying NO2-mediated processes in plants, and the involvement of specific molecules in these processes, remain unknown. We measured hypocotyl elongation and the transcript levels of PIF4, encoding a bHLH transcription factor, and its target genes in wild-type (WT) and various pif mutants grown in the presence or absence of 50 ppb NO2. Chromatin immunoprecipitation assays were performed to quantify binding of PIF4 to the promoter regions of its target genes. NO2 suppressed hypocotyl elongation in WT plants, but not in the pifq or pif4 mutants. NO2 suppressed the expression of target genes of PIF4, but did not affect the transcript level of the PIF4 gene itself or the level of PIF4 protein. NO2 inhibited the binding of PIF4 to the promoter regions of two of its target genes, SAUR46 and SAUR67. In conclusion, NO2 inhibits the binding of PIF4 to the promoter regions of genes involved in the auxin pathway to suppress hypocotyl elongation in Arabidopsis. Consequently, PIF4 emerges as a pivotal participant in this regulatory process. This study has further clarified the intricate regulatory mechanisms governing plant responses to environmental pollutants, thereby advancing our understanding of how plants adapt to changing atmospheric conditions.

Associations of long-term exposure to air pollution and noise with body composition in children and adults: Results from the LEAD general population study.

BACKGROUND: While long-term air pollution and noise exposure has been linked to increasing cardiometabolic disease risk, potential effects on body composition remains unclear. This study aimed to investigate the associations of long-term air pollution, noise and body composition. METHODS: We used repeated data from the LEAD (Lung, hEart, sociAl, boDy) study conducted in Vienna, Austria. Body mass index (BMI; kg/m2), fat mass index (FMI; z-score), and lean mass index (LMI; z-score) were measured using dual-energy x-ray absorptiometry at the first (t0; 2011-ongoing) and second (t1; 2017-ongoing) examinations. Annual particulate matter (PM10) and nitrogen dioxide (NO2) concentrations were estimated with the GRAMM/GRAL model (2015-2021). Day-evening-night (Lden) and night-time (Lnight) noise levels from transportation were modeled for 2017 following the European Union Directive 2002/49/EC. Exposures were assigned to residential addresses. We performed analyses separately in children/adolescents and adults, using linear mixed-effects models with random participant intercepts and linear regression models for cross-sectional and longitudinal associations, respectively. Models were adjusted for co-exposure, lifestyle and sociodemographics. RESULTS: A total of 19,202 observations (nt0 = 12,717, nt1 = 6,485) from participants aged 6-86 years (mean age at t0 = 41.0 years; 52.9 % female; mean PM10 = 21 µg/m3; mean follow-up time = 4.1 years) were analyzed. Among children and adolescents (age ≤ 18 years at first visit), higher PM10exposure was cross-sectionally associated with higher FMI z-scores (0.09 [95 % Confidence Interval (CI): 0.03, 0.16]) and lower LMI z-scores (-0.05 [95 % CI: -0.10, -0.002]) per 1.8 µg/m3. Adults showed similar trends in cross-sectional associations as children, though not reaching statistical significance. We observed no associations for noise exposures. Longitudinal analyses on body composition changes over time yielded positive associations for PM10, but not for other exposures. CONCLUSION: Air pollution exposure, mainly PM10, was cross-sectionally and longitudinally associated with body composition in children/adolescents and adults. Railway/road-traffic noise exposures showed no associations in both cross-sectional and longitudinal analyses.

Health effects of air pollution on respiratory symptoms: A longitudinal study using digital health sensors.

Previous studies of air pollution and respiratory disease often relied on aggregated or lagged acute respiratory disease outcome measures, such as emergency department (ED) visits or hospitalizations, which may lack temporal and spatial resolution. This study investigated the association between daily air pollution exposure and respiratory symptoms among participants with asthma and chronic obstructive pulmonary disease (COPD), using a unique dataset passively collected by digital sensors monitoring inhaled medication use. The aggregated dataset comprised 456,779 short-acting beta-agonist (SABA) puffs across 3,386 people with asthma or COPD, between 2012 and 2019, across the state of California. Each rescue use was assigned space-time air pollution values of nitrogen dioxide (NO2), fine particulate matter with diameter ≤ 2.5 µm (PM2.5) and ozone (O3), derived from highly spatially resolved air pollution surfaces generated for the state of California. Statistical analyses were conducted using linear mixed models and random forest machine learning. Results indicate that daily air pollution exposure is positively associated with an increase in daily SABA use, for individual pollutants and simultaneous exposure to multiple pollutants. The advanced linear mixed model found that a 10-ppb increase in NO2, a 10 µg m-3 increase in PM2.5, and a 30-ppb increase in O3 were respectively associated with incidence rate ratios of SABA use of 1.025 (95 % CI: 1.013-1.038), 1.054 (95 % CI: 1.041-1.068), and 1.161 (95 % CI: 1.127-1.233), equivalent to a respective 2.5 %, 5.4 % and 16 % increase in SABA puffs over the mean. The random forest machine learning approach showed similar results. This study highlights the potential of digital health sensors to provide valuable insights into the daily health impacts of environmental exposures, offering a novel approach to epidemiological research that goes beyond residential address. Further investigation is warranted to explore potential causal relationships and to inform public health strategies for respiratory disease management.

Assessment of spatial air quality on the East Coast of Peninsular Malaysia utilizing environmetric techniques.

The presence of harmful substances in the atmosphere poses significant risks to the environment and public health. These pollutants can come from natural sources like dust and wildfires, or from human activities such as industrial, transportation, and agricultural practices. The objective of this study was to assess air quality on the East Coast of Peninsular Malaysia by analyzing historical data from the Department of Environment, Malaysia. Daily measurements of PM10, O3, SO2, NO2, and CO were collected from eight monitoring stations over 11 years (2011-2021) and analyzed using environmetric techniques. Hierarchical agglomerative cluster analysis (HACA) classified two stations as belonging to the high pollution cluster (HPC), three stations as part of the moderate pollution cluster (MPC), and three stations as the low pollution cluster (LPC). Discriminant analysis revealed a correct assignment rate of 90.50%, indicating that all five parameters were able to differentiate pollution levels with high significance (p < 0.0001). Principal component analysis (PCA) was conducted to validate the pattern of air quality variables in relation to the identified clusters (HPC, MPC, and LPC). The results showed that two verifactors (VFs) were extracted in HPC and LPC, while three VFs were identified in MPC. The cumulative variance explained by the PCA for HPC, MPC, and LPC was 69.43%, 82.32%, and 62.16%, respectively. Finally, an artificial neural network (ANN) was used to forecast the air pollutant index (API) levels, using the R2 and RMSE performance metrics. The PCA-MLP Model A yielded an R2 value of 0.8470 and an RMSE of 6.6470, while PCA-MLP Model B achieved an R2 value of 0.8591 and an RMSE of 6.3000, both indicating a significant and strong correlation.

Association of Gaseous Ambient Air Pollution and Dementia-Related Neuroimaging Markers in the ARIC Cohort, Comparing Exposure Estimation Methods and Confounding by Study Site.

BACKGROUND: Evidence linking gaseous air pollution to late-life brain health is mixed. OBJECTIVE: We explored associations between exposure to gaseous pollutants and brain magnetic resonance imaging (MRI) markers among Atherosclerosis Risk in Communities (ARIC) Study participants, with attention to the influence of exposure estimation method and confounding by site. METHODS: We considered data from 1,665 eligible ARIC participants recruited from four US sites in the period 1987-1989 with valid brain MRI data from Visit 5 (2011-2013). We estimated 10-y (2001-2010) mean carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxides (NOx), and 8- and 24-h ozone (O3) concentrations at participant addresses, using multiple exposure estimation methods. We estimated site-specific associations between pollutant exposures and brain MRI outcomes (total and regional volumes; presence of microhemorrhages, infarcts, lacunes, and severe white matter hyperintensities), using adjusted linear and logistic regression models. We compared meta-analytically combined site-specific associations to analyses that did not account for site. RESULTS: Within-site exposure distributions varied across exposure estimation methods. Meta-analytic associations were generally not statistically significant regardless of exposure, outcome, or exposure estimation method; point estimates often suggested associations between higher NO2 and NOx and smaller temporal lobe, deep gray, hippocampal, frontal lobe, and Alzheimer disease signature region of interest volumes and between higher CO and smaller temporal and frontal lobe volumes. Analyses that did not account for study site more often yielded significant associations and sometimes different direction of associations. DISCUSSION: Patterns of local variation in estimated air pollution concentrations differ by estimation method. Although we did not find strong evidence supporting impact of gaseous pollutants on brain changes detectable by MRI, point estimates suggested associations between higher exposure to CO, NOx, and NO2 and smaller regional brain volumes. Analyses of air pollution and dementia-related outcomes that do not adjust for location likely underestimate uncertainty and may be susceptible to confounding bias. https://doi.org/10.1289/EHP13906.

Excess deaths and loss of life expectancy attributed to long-term NO2 exposure in the Chinese elderly.

BACKGROUND: Evidence linking nitrogen dioxide (NO2) air pollution to life span of high-vulnerability older adults is extensively scarce in low- and middle-income countries. This study seeks to quantify mortality risk, excess deaths, and loss of life expectancy (LLE) associated with long-term exposure to NO2 among elderly individuals in China. METHODS: A nationwide dynamic cohort of 20352 respondents ≥65 years old were enrolled from the China Longitudinal Health and Longevity Survey during 2005-2018. Residential exposures to NO2 and co-pollutants were assessed by well-validated spatiotemporal prediction models. A Cox regression model with time-dependent covariates was utilized to quantify the association of all-cause mortality with NO2 exposure, controlling for confounders such as demographics, lifestyle, health status, and ambient temperature. NO2-attributable deaths and LLE were evaluated for the years 2010 and 2020 based on the pooled NO2-mortality relation derived from multi-national cohort investigations. Decomposition analyses were conducted to dissociate net shift in NO2-related deaths between 2010 and 2020 into four primary contributing factors. RESULTS: A total of 14313 deaths were recorded during follow-up of approximately 100 hundred person-years (median 3.6 years). We observed an approximately linear relationship (nonlinear P = 0.882) of NO2 exposure with all-cause death across a broad range from 6.6 to 95.7 µg/m3. Every 10-µg/m3 rise in yearly average NO2 concentration was linked to a hazard ratio (HR) of 1.045 (95% confidence interval [CI]: 1.031-1.059). In the updated meta-analysis of this study and 9 existing cohorts, we estimated a pooled HR of 1.043 (95% CI: 1.023-1.063) for each 10-µg/m3 growth in NO2. Reaching a 10-µg/m3 counterfactual target of NO2 concentration in China could avoid 0.33 (95% empirical CI: 0.19-0.49) million premature deaths and an LLE of 0.40 (95% empirical CI: 0.23-0.59) years in 2010, which greatly dropped to 0.24 (95% empirical CI: 0.14-0.36) million deaths and 0.21 (95% empirical CI: 0.12-0.31) years of LLE in 2020. The net fall in NO2-attributable deaths (-26.8%) between 2010 and 2020 was primarily driven by the declines in both NO2 concentration (-41.6%) and mortality rate (-27.1%) under population growth (+41.0%) and age structure transition (+0.9%). CONCLUSIONS: Our findings provide national evidence for increased risk of premature death and loss of life expectancy attributed to later-life NO2 exposure among the elderly in China. In an accelerated aging society, strengthened clean air actions should be formulated to minimize the health burden and regional inequality in NO2-attributable mortality.

From port to planet: Assessing NO2 pollution and climate change effects with Sentinel-5p satellite imagery in maritime zones.

The growing effects of climate change on Malaysia's coastal ecology heighten worries about air pollution, specifically caused by urbanization and industrial activity in the maritime sector. Trucks and vessels are particularly noteworthy for their substantial contribution to gas emissions, including nitrogen dioxide (NO2), which is the primary gas released in port areas. The application of advanced analysis techniques was spurred by the air pollution resulting from the combustion of fossil fuels such as fuel oil, natural gas and gasoline in vessels. The study utilized satellite photos captured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5P satellite to evaluate the levels of NO2 gas pollution in Malaysia's port areas and exclusive economic zone. Before the COVID-19 pandemic, unrestricted gas emissions led to persistently high levels of NO2 in the analyzed areas. The temporary cessation of marine industry operations caused by the pandemic, along with the halting of vessels to prevent the spread of COVID-19, resulted in a noticeable decrease in NO2 gas pollution. In light of these favourable advancements, it is imperative to emphasize the need for continuous investigation and collaborative endeavours to further alleviate air contamination in Malaysian port regions, while simultaneously acknowledging the wider consequences of climate change on the coastal ecology. The study underscores the interdependence of air pollution, maritime activities and climate change. It emphasizes the need for comprehensive strategies that tackle both immediate environmental issues and the long-term sustainability and resilience of coastal ecosystems in the context of global climate challenges.

Source-Specific Air Pollution and Loss of Independence in Older Adults Across the US.

Importance: Air pollution is a recognized risk factor associated with chronic diseases, including respiratory and cardiovascular conditions, which can lead to physical and cognitive impairments in later life. Although these losses of function, individually or in combination, reduce individuals' likelihood of living independently, little is known about the association of air pollution with this critical outcome. Objective: To investigate associations between air pollution and loss of independence in later life. Design, Setting, and Participants: This cohort study was conducted as part of the Environmental Predictors Of Cognitive Health and Aging study and used 1998 to 2016 data from the Health and Retirement Study. Participants included respondents from this nationally representative, population-based cohort who were older than 50 years and had not previously reported a loss of independence. Analyses were performed from August 31 to October 15, 2023. Exposures: Mean 10-year pollutant concentrations (particulate matter less than 2.5 µm in diameter [PM2.5] or ranging from 2.5 µm to 10 µm in diameter [PM10-2.5], nitrogen dioxide [NO2], and ozone [O3]) were estimated at respondent addresses using spatiotemporal models along with PM2.5 levels from 9 emission sources. Main Outcomes and Measures: Loss of independence was defined as newly receiving care for at least 1 activity of daily living or instrumental activity of daily living due to health and memory problems or moving to a nursing home. Associations were estimated with generalized estimating equation regression adjusting for potential confounders. Results: Among 25 314 respondents older than 50 years (mean [SD] baseline age, 61.1 [9.4] years; 11 208 male [44.3%]), 9985 individuals (39.4%) experienced lost independence during a mean (SD) follow-up of 10.2 (5.5) years. Higher exposure levels of mean concentration were associated with increased risks of lost independence for total PM2.5 levels (risk ratio [RR] per 1-IQR of 10-year mean, 1.05; 95% CI, 1.01-1.10), PM2.5 levels from road traffic (RR per 1-IQR of 10-year mean, 1.09; 95% CI, 1.03-1.16) and nonroad traffic (RR per 1-IQR of 10-year mean, 1.13; 95% CI, 1.03-1.24), and NO2 levels (RR per 1-IQR of 10-year mean, 1.05; 95% CI, 1.01-1.08). Compared with other sources, traffic-generated pollutants were most consistently and robustly associated with loss of independence; only road traffic-related PM2.5 levels remained associated with increased risk after adjustment for PM2.5 from other sources (RR per 1-IQR increase in 10-year mean concentration, 1.10; 95% CI, 1.00-1.21). Other pollutant-outcome associations were null, except for O3 levels, which were associated with lower risks of lost independence (RR per 1-IQR increase in 10-year mean concentration, 0.94; 95% CI, 0.92-0.97). Conclusions and Relevance: This study found that long-term exposure to air pollution was associated with the need for help for lost independence in later life, with especially large and consistent increases in risk for pollution generated by traffic-related sources. These findings suggest that controlling air pollution could be associated with diversion or delay of the need for care and prolonged ability to live independently.

Dissecting heritability, environmental risk, and air pollution causal effects using > 50 million individuals in MarketScan.

Large national-level electronic health record (EHR) datasets offer new opportunities for disentangling the role of genes and environment through deep phenotype information and approximate pedigree structures. Here we use the approximate geographical locations of patients as a proxy for spatially correlated community-level environmental risk factors. We develop a spatial mixed linear effect (SMILE) model that incorporates both genetics and environmental contribution. We extract EHR and geographical locations from 257,620 nuclear families and compile 1083 disease outcome measurements from the MarketScan dataset. We augment the EHR with publicly available environmental data, including levels of particulate matter 2.5 (PM2.5), nitrogen dioxide (NO2), climate, and sociodemographic data. We refine the estimates of genetic heritability and quantify community-level environmental contributions. We also use wind speed and direction as instrumental variables to assess the causal effects of air pollution. In total, we find PM2.5 or NO2 have statistically significant causal effects on 135 diseases, including respiratory, musculoskeletal, digestive, metabolic, and sleep disorders, where PM2.5 and NO2 tend to affect biologically distinct disease categories. These analyses showcase several robust strategies for jointly modeling genetic and environmental effects on disease risk using large EHR datasets and will benefit upcoming biobank studies in the era of precision medicine.

Air quality and kidney health: Assessing the effects of PM10, PM2.5, CO, and NO2 on renal function in primary glomerulonephritis.

BACKGROUND: While extensive studies have elucidated the relationships between exposure to air pollution and chronic diseases, such as cardiovascular disorders and diabetes, the intricate effects on specific kidney diseases, notably primary glomerulonephritis (GN)-an immune-mediated kidney ailment-are less well understood. Considering the escalating incidence of GN and conspicuous lack of investigative focus on its association with air quality, investigation is dedicated to examining the long-term effects of air pollutants on renal function in individuals diagnosed with primary GN. METHODS: This retrospective cohort analysis was conducted on 1394 primary GN patients who were diagnosed at Seoul National University Bundang Hospital and Seoul National University Hospital. Utilizing time-varying Cox regression and linear mixed models (LMM), we examined the effect of yearly average air pollution levels on renal function deterioration (RFD) and change in estimated glomerular filtration rate (eGFR). In this context, RFD is defined as sustained eGFR of less than 60 mL/min per 1.73 m2. RESULTS: During a mean observation period of 5.1 years, 350 participants developed RFD. Significantly, elevated interquartile range (IQR) levels of air pollutants-including PM10 (particles ≤10 micrometers, HR 1.389, 95 % CI 1.2-1.606), PM2.5 (particles ≤2.5 micrometers, HR 1.353, 95 % CI 1.162-1.575), CO (carbon monoxide, HR 1.264, 95 % CI 1.102-1.451), and NO2 (nitrogen dioxide, HR 1.179, 95 % CI 1.021-1.361)-were significantly associated with an increased risk of RFD, after factoring in demographic and health variables. Moreover, exposure to PM10 and PM2.5 was associated with decreased eGFR. CONCLUSIONS: This study demonstrates a substantial link between air pollution exposure and renal function impairment in primary GN, accentuating the significance of environmental determinants in the pathology of immune-mediated kidney diseases.

Observation-Based Diagnostics of Reactive Nitrogen Recycling through HONO Heterogenous Production: Divergent Implications for Ozone Production and Emission Control.

Understanding of nitrous acid (HONO) production is crucial to photochemical studies, especially in polluted environments like eastern China. In-situ measurements of gaseous and particulate compositions were conducted at a rural coastal site during the 2018 spring Ozone Photochemistry and Export from China Experiment (OPECE). This data set was applied to investigate the recycling of reactive nitrogen through daytime heterogeneous HONO production. Although HONO levels increase during agricultural burning, analysis of the observation data does not indicate more efficient HONO production by agricultural burning aerosols than other anthropogenic aerosols. Box and 1-D modeling analyses reveal the intrinsic relationships between nitrogen dioxide (NO2), particulate nitrate (pNO3), and nitric acid (HNO3), resulting in comparable agreement between observed and simulated HONO concentrations with any one of the three heterogeneous HONO production mechanisms, photosensitized NO2 conversion on aerosols, photolysis of pNO3, and conversion from HNO3. This finding underscores the uncertainties in the mechanistic understanding and quantitative parametrizations of daytime heterogeneous HONO production pathways. Furthermore, the implications for reactive nitrogen recycling, ozone (O3) production, and O3 control strategies vary greatly depending on the HONO production mechanism. On a regional scale, the conversion of HONO from pNO3 can drastically enhance O3 production, while the conversion from NO2 can reduce O3 sensitivity to NOx changes in polluted eastern China.

Association between residential air pollution exposure and cardiovascular risk factors in adults living in northern France.

Air pollution is associated with elevated cardiovascular mortality and an increase in cardiovascular risk factors. However, the literature data on associations between air pollution and cardiovascular risk factors are contradictory. To explore the relationship between residential exposure to atmospheric pollutants and cardiovascular risk factors (lipid biomarker and blood pressure levels). We studied a sample of 2339 adult participants in the ELISABET study from the Dunkirk and Lille urban areas of France. The mean annual exposure to atmospheric pollutants (PM10, NO2 and SO2) at the home address was estimated via an air dispersion model. The associations were probed in multivariate linear regression models. The mean NO2 level was 26.05 µg/m3 in Lille and 19.96 µg/m3 in Dunkirk. The mean PM10 level was 27.02 µg/m3 in Lille and 26.53 µg/m3 in Dunkirk. We detected a significant association between exposure to air pollutants and the high-density lipoprotein (HDL) (which is a protective factor against cardiovascular diseases) level: for a 2 µg/m3 increment in PM10, the HDL level decreased by 1.72% (p = 0.0037). None of the associations with other lipid variables or with blood pressure were significant. We didn't find evidence significant associations for most of the risk factors but, long-term exposure of adults to moderate levels of ambient air pollution was associated with a decrement in HDL.

IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time.