Association of Maternal Air Pollution Exposure and Infant Lung Function Is Modified by Genetic Propensity to Oxidative Stress.

BACKGROUND AND OBJECTIVE: The association between air pollution and poor respiratory health outcomes is well established. Children are particularly at risk from air pollution, especially during the prenatal period as their organs and systems are still undergoing crucial development. This study investigated maternal exposure to air pollution during pregnancy and oxidative stress (OS), inflammation, and infant lung function at 4 weeks of age. METHODS: Data from the Barwon Infant Study were available for 314 infants. The exposure to NO2 and PM2.5 were estimated. Infant lung function (4 weeks) was measured by multiple-breath washout. Glycoprotein acetyls (GlycA) (36 weeks prenatal), cord blood, and OS biomarkers were measured in maternal urine (28 weeks). A genetic pathway score for OS (gPFSox) was calculated. Linear regression was used and potential modification by the OS genotype was tested. RESULTS: There was no relationship between maternal exposure to air pollution and infant lung function, or with GlycA or OS during pregnancy. We found an association in children with a genetic propensity to OS between NO2 and a lower functional residual capacity (FRC) (ß = -5.3 mls, 95% CI (-9.3, -1.3), p = 0.01) and lung clearance index (LCI) score (ß = 0.46 turnovers, (95% CI 0.10, 0.82), p = 0.01). CONCLUSION: High prenatal exposure to ambient NO2 is associated with a lower FRC and a higher LCI score in infants with a genetic propensity to oxidative stress. There was no relationship between maternal exposure to air pollution with maternal and cord blood inflammation or OS biomarkers.

Long-term exposure to air pollution at residential and workplace addresses and breast cancer risk: A case-control study nested in the French E3N-Générations cohort from 1990 to 2011.

BACKGROUND: An increasing evidence links air pollution to breast cancer (BC) risk. Yet, pollutant exposure estimates at the workplace location in pollution exposure assessment have not been considered. OBJECTIVES: This study investigates the association between particulate matters (PM2·5, PM10) and nitrogen dioxide (NO2) atmospheric concentrations (1990-2011), at the women's residential and workplace locations, and BC risk. METHODS: This case-control study of 2419 BC cases and 2984 controls, was nested in the French prospective E3N cohort. The annual mean PM2·5, PM10 and NO2 concentrations were estimated using a Land Use Regression model (50 m x 50 m resolution) and assigned to the women's geocoded residential and workplace locations, from cohort recruitment to their index date (date of case diagnosis). Odds ratios (OR) and 95 % confidence intervals (CI) were estimated using multivariate logistic regression models. RESULTS: An increased BC risk was observed for a 10 µg/m3 increase of the 1990-2011 average PM2·5 concentration estimates (OR=1·28; CI 1·00, 1·63). An increased risk was suggested for a 10 µg/m3 increase for PM10 (OR=1·09; CI 0·92, 1·30) and NO2 (OR=1·05; CI 0·97, 1·13). No effect modification by menopausal status, nor difference by hormone receptor status were observed. DISCUSSION: This study is the first to estimate BC risk and long-term air pollutant exposure from both, residential and workplace location histories. Results suggest that residential PM2·5, PM10 and NO2 concentrations are strongly correlated with workplace ones, indicating that residential data may serve as proxy for overall exposure. Future studies should consider exposure during commuting.

Multiple air pollutant exposure is associated with higher risk of all-cause mortality in dialysis patients: a French registry-based nationwide study.

Background: Little is known about the effect of combined exposure to different air pollutants on mortality in dialysis patients. This study aimed to investigate the association of multiple exposures to air pollutants with all-cause and cause-specific death in dialysis patients. Materials and methods: This registry-based nationwide cohort study included 90,373 adult kidney failure patients initiating maintenance dialysis between 2012 and 2020 identified from the French REIN registry. Estimated mean annual municipality levels of PM2.5, PM10, and NO2 between 2009 and 2020 were combined in different composite air pollution scores to estimate each participant's exposure at the residential place one to 3 years before dialysis initiation. Adjusted cause-specific Cox proportional hazard models were used to estimate hazard ratios (HRs) per interquartile range (IQR) greater air pollution score. Effect measure modification was assessed for age, sex, dialysis care model, and baseline comorbidities. Results: Higher levels of the main air pollution score were associated with a greater rate of all-cause deaths (HR, 1.082 [95% confidence interval (CI), 1.057-1.104] per IQR increase), regardless of the exposure lag. This association was also confirmed in cause-specific analyses, most markedly for infectious mortality (HR, 1.686 [95% CI, 1.470-1.933]). Sensitivity analyses with alternative composite air pollution scores showed consistent findings. Subgroup analyses revealed a significantly stronger association among women and fewer comorbid patients. Discussion: Long-term multiple air pollutant exposure is associated with all-cause and cause-specific mortality among patients receiving maintenance dialysis, suggesting that air pollution may be a significant contributor to the increasing trend of CKD-attributable mortality worldwide.

High resolution mapping of nitrogen dioxide and particulate matter in Great Britain (2003-2021) with multi-stage data reconstruction and ensemble machine learning methods.

In this contribution, we applied a multi-stage machine learning (ML) framework to map daily values of nitrogen dioxide (NO2) and particulate matter (PM10 and PM2.5) at a 1 km2 resolution over Great Britain for the period 2003-2021. The process combined ground monitoring observations, satellite-derived products, climate reanalyses and chemical transport model datasets, and traffic and land-use data. Each feature was harmonized to 1 km resolution and extracted at monitoring sites. Models used single and ensemble-based algorithms featuring random forests (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), as well as lasso and ridge regression. The various stages focused on augmenting PM2.5 using co-occurring PM10 values, gap-filling aerosol optical depth and columnar NO2 data obtained from satellite instruments, and finally the training of an ensemble model and the prediction of daily values across the whole geographical domain (2003-2021). Results show a good ensemble model performance, calculated through a ten-fold monitor-based cross-validation procedure, with an average R2 of 0.690 (range 0.611-0.792) for NO2, 0.704 (0.609-0.786) for PM10, and 0.802 (0.746-0.888) for PM2.5. Reconstructed pollution levels decreased markedly within the study period, with a stronger reduction in the latter eight years. The pollutants exhibited different spatial patterns, while NO2 rose in close proximity to high-traffic areas, PM demonstrated variation at a larger scale. The resulting 1 km2 spatially resolved daily datasets allow for linkage with health data across Great Britain over nearly two decades, thus contributing to extensive, extended, and detailed research on the long-and short-term health effects of air pollution.

Deployment of low-cost air quality sensors in Rouen: A dataset of one year of hourly concentrations of gas pollutants.

Nitrogen dioxide (NO2) is a major pollutant in urban conditions, as it is mostly produced by traffic. To ensure a good health of cities inhabitants, Europe have set some regulations over maximum NO2 concentrations. This article presents a dataset of NO2 concentrations measured by AirSensEUR sensors and monitoring stations in Rouen, (France). Sensors' data consist of measurements of nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3), and meteorological parameters at ten sites in the city of Rouen, France, over a one-year period. Measurements from three reference stations (two in a traffic area and one in a background area) are also provided, so that comparison between devices and calibration in-situ of the network can be investigated. The dataset consists of CSV files providing the hourly-averaged measurements made by the sensor nodes, and the three reference measurements acquired at the same time. It has been designed to assist organization in investigating the feasibility of a sensor network and its benefits to monitor NO2 concentration in cities. The aim is thus to address the challenge of having a dense network of precise measuring devices at their disposal and make air quality information more accessible for the global population.

Capturing Exposure Disparities with Chemical Transport Models: Evaluating the Suitability of Downscaling Using Land Use Regression.

High resolution exposure surfaces are essential to capture disparities in exposure to traffic-related air pollution in urban areas. In this study, we develop an approach to downscale Chemical Transport Model (CTM) simulations to a hyperlocal level (∼100m) in the Greater Toronto Area (GTA) under three scenarios where emissions from cars, trucks and buses are zeroed out, thus capturing the burden of each transportation mode. This proposed approach statistically fuses CTMs with Land-Use Regression using machine learning techniques. With this proposed downscaling approach, changes in air pollutant concentrations under different scenarios are appropriately captured by downscaling factors that are trained to reflect the spatial distribution of emission reductions. Our validation analysis shows that high-resolution models resulted in better performance than coarse models when compared with observations at reference stations. We used this downscaling approach to assess disparities in exposure to nitrogen dioxide (NO2) for populations composed of renters, low-income households, recent immigrants, and visible minorities. Individuals in all four categories were disproportionately exposed to the burden of cars, trucks, and buses. We conducted this analysis at spatial resolutions of 12, 4, 1 km, and 100 m and observed that disparities were significantly underestimated when using coarse spatial resolutions. This reinforces the need for high-spatial resolution exposure surfaces for environmental justice analyses.

Long-Term Nitrogen Dioxide Exposure as a Possible 5-Year Mortality Risk Factor in Diabetic Patients Treated Using Off-Pump Surgical Revascularization-A Retrospective Analysis.

Background: There is mounting evidence that diabetic-related cardiac metabolism abnormalities with oxidative stress and inflammatory mechanism activation align with the functional impairments that result in atherosclerotic lesion formation. Among the possible non-traditional coronary lesion risk factors, environmental exposure may be significant, especially in diabetic patients. Methods: A total of 140 diabetic patients (115 (82%) males and 25 (18%) females) with a mean age of 65 (60-71) underwent surgical revascularization due to multivessel coronary disease. The possible all-cause mortality risk factors, including demographical and clinical factors followed by chronic air pollution exposure, were identified. Results: All patients were operated on using the off-pump technique and followed for 5.6 (5-6.1) years. The multivariable model for 5-year mortality prediction presented the nitrogen dioxide chronic exposure (HR: 3.99, 95% CI: 1.16-13.71, p = 0.028) and completeness of revascularization (HR: 0.19, 95% CI: 0.04-0.86, p = 0.031) as significant all-cause mortality risk factors. Conclusions: Ambient air pollutants such as an excessive chronic nitrogen dioxide concentration (>15 µg/m3) may increase 5-year all-cause mortality in diabetic patients following surgical revascularization.

Adverse effects of meteorological factors and air pollutants on dry eye disease: a hospital-based retrospective cohort study.

Although previous studies have suggested that meteorological factors and air pollutants can cause dry eye disease (DED), few clinical cohort studies have determined the individual and combined effects of these factors on DED. We investigated the effects of meteorological factors (humidity and temperature) and air pollutants [particles with a diameter ≤ 2.5 µ m (PM2.5), ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO)] on DED. A retrospective cohort study was conducted on 53 DED patients. DED was evaluated by Symptom Assessment in Dry Eye (SANDE), tear secretion, tear film break-up time (TBUT), ocular staining score (OSS), and tear osmolarity. To explore the individual, non-linear, and joint associations between meteorological factors, air pollutants, and DED parameters, we used generalized linear mixed model (GLMM) and Bayesian kernel machine regression (BKMR). After adjusting for all covariates, lower relative humidity or temperature was associated with a higher SANDE (p < 0.05). Higher PM2.5, O3, and NO2 levels were associated with higher SANDE and tear osmolarity (p < 0.05). Higher O3 levels were associated with lower tear secretion and TBUT, whereas higher NO2 levels were associated with higher OSS (p < 0.05). BKMR analyses indicated that a mixture of meteorological factors and air pollutants was significantly associated with increased SANDE, OSS, tear osmolarity, and decreased tear secretion.

Urban Air Pollution and Plant Tolerance: Omics Responses to Ozone, Nitrogen Oxides, and Particulate Matter.

Urban air pollution is a crucial global challenge, mainly originating from urbanization and industrial activities, which are continuously increasing. Vegetation serves as a natural air filter for air pollution, but adverse effects on plant health, photosynthesis, and metabolism can occur. Recent omics technologies have revolutionized the study of molecular plant responses to air pollution, overcoming previous limitations. This review synthesizes the latest advancements in molecular plant responses to major air pollutants, emphasizing ozone (O3), nitrogen oxides (NOX), and particulate matter (PM) research. These pollutants induce stress responses common to other abiotic and biotic stresses, including the activation of reactive oxygen species (ROSs)-scavenging enzymes and hormone signaling pathways. New evidence has shown the central role of antioxidant phenolic compound biosynthesis, via the phenylpropanoid pathway, in air pollution stress responses. Transcription factors like WRKY, AP2/ERF, and MYB, which connect hormone signaling to antioxidant biosynthesis, were also affected. To date, research has predominantly focused on laboratory studies analyzing individual pollutants. This review highlights the need for comprehensive field studies and the identification of molecular tolerance traits, which are crucial for the identification of tolerant plant species, aimed at the development of sustainable nature-based solutions (NBSs) to mitigate urban air pollution.

Choices of morbidity outcomes and concentration-response functions for health risk assessment of long-term exposure to air pollution.

Background: Air pollution health risk assessment (HRA) has been typically conducted for all causes and cause-specific mortality based on concentration-response functions (CRFs) from meta-analyses that synthesize the evidence on air pollution health effects. There is a need for a similar systematic approach for HRA for morbidity outcomes, which have often been omitted from HRA of air pollution, thus underestimating the full air pollution burden. We aimed to compile from the existing systematic reviews and meta-analyses CRFs for the incidence of several diseases that could be applied in HRA. To achieve this goal, we have developed a comprehensive strategy for the appraisal of the systematic reviews and meta-analyses that examine the relationship between long-term exposure to particulate matter with an aerodynamic diameter smaller than 2.5 µm (PM2.5), nitrogen dioxide (NO2), or ozone (O3) and incidence of various diseases. Methods: To establish the basis for our evaluation, we considered the causality determinations provided by the US Environmental Protection Agency Integrated Science Assessment for PM2.5, NO2, and O3. We developed a list of pollutant/outcome pairs based on these assessments and the evidence of a causal relationship between air pollutants and specific health outcomes. We conducted a comprehensive literature search using two databases and identified 75 relevant systematic reviews and meta-analyses for PM2.5 and NO2. We found no relevant reviews for long-term exposure to ozone. We evaluated the reliability of these studies using an adaptation of the AMSTAR 2 tool, which assesses various characteristics of the reviews, such as literature search, data extraction, statistical analysis, and bias evaluation. The tool's adaptation focused on issues relevant to studies on the health effects of air pollution. Based on our assessment, we selected reviews that could be credible sources of CRF for HRA. We also assessed the confidence in the findings of the selected systematic reviews and meta-analyses as the sources of CRF for HRA. We developed specific criteria for the evaluation, considering factors such as the number of included studies, their geographical distribution, heterogeneity of study results, the statistical significance and precision of the pooled risk estimate in the meta-analysis, and consistency with more recent studies. Based on our assessment, we classified the outcomes into three lists: list A (a reliable quantification of health effects is possible in an HRA), list B+ (HRA is possible, but there is greater uncertainty around the reliability of the CRF compared to those included on list A), and list B- (HRA is not recommended because of the substantial uncertainty of the CRF). Results: In our final evaluation, list A includes six CRFs for PM2.5 (asthma in children, chronic obstructive pulmonary disease, ischemic heart disease events, stroke, hypertension, and lung cancer) and three outcomes for NO2 (asthma in children and in adults, and acute lower respiratory infections in children). Three additional outcomes (diabetes, dementia, and autism spectrum disorders) for PM2.5 were included in list B+. Recommended CRFs are related to the incidence (onset) of the diseases. The International Classification of Diseases, 10th revision codes, age ranges, and suggested concentration ranges are also specified to ensure consistency and applicability in an HRA. No specific suggestions were given for ozone because of the lack of relevant systematic reviews. Conclusion: The suggestions formulated in this study, including CRFs selected from the available systematic reviews, can assist in conducting reliable HRAs and contribute to evidence-based decision-making in public health and environmental policy. Future research should continue to update and refine these suggestions as new evidence becomes available and methodologies evolve.

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

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

Residential exposure to ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) and incident breast cancer among young women in Ontario, Canada.

BACKGROUND: Air pollution has been classified as a human carcinogen based largely on findings for respiratory cancers. Emerging, but limited, evidence suggests that it increases the risk of breast cancer, particularly among younger women. We characterized associations between residential exposure to ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) and breast cancer. Analyses were performed using data collected in the Ontario Environmental Health Study (OEHS). METHODS: The OEHS, a population-based case-control study, identified incident cases of breast cancer in Ontario, Canada among women aged 18-45 between 2013 and 2015. A total of 465 pathologically confirmed primary breast cancer cases were identified from the Ontario Cancer Registry, while 242 population-based controls were recruited using random-digit dialing. Self-reported questionnaires were used to collect risk factor data and residential histories. Land-use regression and remote-sensing estimates of NO2 and PM2.5, respectively, were assigned to the residential addresses at interview, five years earlier, and at menarche. Logistic regression was used to estimate odds ratios (OR) and their 95 % confidence intervals (CI) in relation to an interquartile range (IQR) increase in air pollution, adjusting for possible confounders. RESULTS: PM2.5 and NO2 were positively correlated with each other (r = 0.57). An IQR increase of PM2.5 (1.9 µg/m3) and NO2 (6.6 ppb) at interview residence were associated with higher odds of breast cancer and the adjusted ORs and 95 % CIs were 1.37 (95 % CI = 0.98-1.91) and 2.33 (95 % CI = 1.53-3.53), respectively. An increased odds of breast cancer was observed with an IQR increase in NO2 at residence five years earlier (OR = 2.16, 95 % CI: 1.41-3.31), while no association was observed with PM2.5 (OR = 0.96, 95 % CI 0.64-1.42). CONCLUSIONS: Our findings support the hypothesis that exposure to ambient air pollution, especially those from traffic sources (i.e., NO2), increases the risk of breast cancer in young women.

Air pollution exposure during pregnancy and lung function in childhood: The LUIS study.

BACKGROUND: The adverse effects of high air pollution levels on childhood lung function are well-known. Limited evidence exists on the effects of moderate exposure levels during early life on childhood lung function. We investigated the association of exposure to moderate air pollution during pregnancy, infancy, and preschool time with lung function at school age in a Swiss population-based study. METHODS: Fine-scale spatiotemporal model estimates of particulate matter with a diameter <2.5 µm (PM2.5) and nitrogen dioxide (NO2) were linked with residential address histories. We compared air pollution exposures within different time windows (whole pregnancy, first, second, and third trimester of pregnancy, first year of life, preschool age) with forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) measured cross-sectionally using linear regression models adjusted for potential confounders. RESULTS: We included 2182 children, ages 6-17 years. Prenatal air pollution exposure was associated with reduced lung function at school age. In children aged 12 years, per 10 µg·m-3 increase in PM2.5 during pregnancy, FEV1 was 55 mL lower (95% CI -84 to -25 mL) and FVC 62 mL lower (95% CI -96 to -28 mL). Associations were age-dependent since they were stronger in younger and weaker in older children. PM2.5 exposure after birth was not associated with reduced lung function. There was no association between NO2 exposure and lung function. CONCLUSION: In utero lung development is most sensitive to air pollution exposure, since even modest PM2.5 exposure during the prenatal time was associated with reduced lung function, most prominent in younger children.

Association of air pollution with incidence of end-stage kidney disease in two large European cohorts.

End-stage kidney disease (ESKD) poses a high burden on patients and health systems. While numerous studies indicate an association between air pollution and chronic kidney disease, studies on ESKD are rare. We investigated the association of long-term exposure to nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC) and ozone (O3) with ESKD incidence in two large population-based European cohorts. We followed individuals in the Austrian Vorarlberg Health Monitoring and Promotion Program (VHM&PP) and the Italian Rome Longitudinal Study (RoLS) using dialysis and kidney transplant registries. Long-term exposure to pollutants was estimated at the home address using Europe-wide land use regression models at 100x100m scale. Hazard ratios (HR) were determined from Cox-proportional hazard models adjusted for individual and neighbourhood level confounders. We observed 501 events among 136,823 individuals in VHM&PP (mean age 42.1 years; crude incidence rate (IR) 0.14 per 1000 person-years) and 3231 events among 1,939,461 individuals in RoLS (mean age 52.4 years; IR 0.22 per 1000 person-years). In VHM&PP, there was no evidence of an association between PM2.5 or O3 and ESKD. There were elevated HRs but with large confidence intervals for BC (HR 1.17 [95 % confidence interval (CI): 0.98, 1.39] for 0.5*10-5/m), and for NO2 (HR 1.14 [95%CI: 0.96, 1.35] for 10 µg/m3). In RoLS, ESKD was associated with PM2.5 (HR 1.37 [95 % CI: 1.06, 1.76] for an increase of 5 µg/m3), while there was no evidence of association with BC, NO2, or O3 exposure. Our study suggests an association of air pollution with ESKD incidence, which differed between the two cohorts and may possibly be influenced by respective air pollution mixtures.

Hourly effect of atmospheric reactive nitrogen species on the onset of acute ischemic stroke: Insight from the Shanghai Stroke Service System Database.

Acute ischemic stroke (AIS) is one of the most predominant causes of mortality and disability in China. Significant uncertainties in stroke diagnosis and time of onset have resulted in inconsistent evidence on the association between ambient air pollution and the risk of AIS. The present study aimed to evaluate the impact of air pollution on AIS onset based on high time-resolution air pollution data and a stroke-specific registry across the past five years. Hourly concentrations of PM2.5, PM10, O3, SO2, CO, NO2 and nitrous acid (HONO) were monitored from 2017 to 2021, with which a distributed lag non-linear model and conditional logistic regression models coupled with a time-stratified case-crossover design were applied to 106,623 AIS cases recorded in the Shanghai Stroke Service (4S) database during the study period. Results from the conditional logistic regression models indicate that acute exposure to PM2.5, PM10, SO2, NO2 and HONO was found to be associated with AIS onset, respectively. The corresponding cumulative excessive risks of AIS onset were 0.8 %, 1 %, 2.4 %, 2.1 % and 1.8 % for each interquartile range increase in the respective concentration. The longest lag-effect (up to 13 h) was observed for reactive nitrogen species (RNS), such as NO2 and HONO, which remained robust in two-pollutant models. Similar important role of RNS in AIS onset were confirmed by the distributed lag non-linear model. By demonstrating the transient effect of ambient air pollution on AIS, especially the relationships between RNS and AIS for the first time, our study provides stringent evidence for future mitigation strategies for pollution emission and public health.

Air pollution exposure in active versus passive travel modes across five continents: A Bayesian random-effects meta-analysis.

Epidemiological studies on health effects of air pollution usually estimate exposure at the residential address. However, ignoring daily mobility patterns may lead to biased exposure estimates, as documented in previous exposure studies. To improve the reliable integration of exposure related to mobility patterns into epidemiological studies, we conducted a systematic review of studies across all continents that measured air pollution concentrations in various modes of transport using portable sensors. To compare personal exposure across different transport modes, specifically active versus motorized modes, we estimated pairwise exposure ratios using a Bayesian random-effects meta-analysis. Overall, we included measurements of six air pollutants (black carbon (BC), carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM10, PM2.5) and ultrafine particles (UFP)) for seven modes of transport (i.e., walking, cycling, bus, car, motorcycle, overground, underground) from 52 published studies. Compared to active modes, users of motorized modes were consistently the most exposed to gaseous pollutants (CO and NO2). Cycling and walking were the most exposed to UFP compared to other modes. Active vs passive mode contrasts were mostly inconsistent for other particle metrics. Compared to active modes, bus users were consistently more exposed to PM10 and PM2.5, while car users, on average, were less exposed than pedestrians. Rail modes experienced both some lower exposures (compared to cyclists for PM10 and pedestrians for UFP) and higher exposures (compared to cyclist for PM2.5 and BC). Ratios calculated for motorcycles should be considered carefully due to the small number of studies, mostly conducted in Asia. Computing exposure ratios overcomes the heterogeneity in pollutant levels that may exist between continents and countries. However, formulating ratios on a global scale remains challenging owing to the disparities in available data between countries.

Short-term effects of ambient nitrogen dioxide on medical emergency calls for epileptic seizures: A time-series study.

Short-term exposure to air pollution has previously been studied in relation to certain neurological disorders, but there is still a lack of convincing data linking air pollution to epileptic seizures. The study's goal was to investigate how exposure to ambient nitrogen dioxide (NO2) affected the number of patients seeking assistance at the Wuhan Emergency Medical Center due to epileptic seizures. We gathered data on medical emergency calls (MECs), daily ambient air pollution concentrations (SO2, NO2, PM2.5, PM10, CO, and O3), and meteorological variables in Wuhan, China, spanning from January 1, 2017, to November 30, 2019. To investigate the potential influence of ambient nitrogen dioxide on MECs for epileptic seizures, we carried out a time-series investigation using the general additive model (GAM). Additionally, analyses stratified by season, age, and gender were performed. A total of 8989 records of MECs for epileptic seizures were enrolled in our study during the period. Statistical analysis indicates that a rise of 10 µg/m3 in NO2 concentration is linked to a 0.17% increase in daily MECs for epileptic seizures (95% confidence interval [CI]: 0.02%, 0.32%). Furthermore, people aged 14-59 years were more susceptible(2.25%, P < 0.05). The short-term effects of NO2 exposure on daily MECs for epileptic seizures were stronger in warm seasons than in cool seasons (0.55% vs. -0.10%, P < 0.0001). Our findings suggests that short-term exposure to ambient NO2 was positively correlated with daily MECs for epileptic seizures in Wuhan, China. Additionally, we observed that these associations were stronger in patients aged above 14 but under 60 years and the warmer seasons (from April to September).

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.

Potential consequences of nitric oxide release: An improved model informing worker safety.

Both nitric oxide (NO) and nitrogen dioxide (NO2) gasses are toxic to humans but are commonly found in industrial settings such as semiconductor manufacturing sites. Due to the spontaneous oxidation of NO to NO2 under ambient conditions, individuals working with NO may in fact be exposed to both gasses in the case of an accidental release. Unfortunately, most safety materials provided to NO users do not address the potential for associated NO2 toxicity, and, until now, models developed to predict health consequences following a release of NO have not appropriately considered the oxidation kinetics nor the toxicity of both NO and NO2 in their assessments. This paper describes an improved multi-module model that addresses these limitations and explores whether facilities using NO should consider adopting measures that can mitigate the simultaneous health effects of both gasses. The model predicts the morbidity (intoxication/injury), mortality (death), and treatment outcomes that may arise following an industrial NO release by first calculating the doses of both NO and NO2 received by exposed individuals and then applying newly defined toxicity parameters for NO and NO2 to assign dose-dependent probabilities for the onset of intoxication and/or death and the ability of appropriate treatment(s) to save lives. Modeling results indicate low risk to worker health in the likeliest release scenarios while identifying less likely situations that carry substantially higher risk. Moreover, these results indicate that risks to worker health can be mitigated with simple measures like maintaining reliable alarms, adequate ventilation, and on-site supplies of methylene blue, as well as encouraging quick responses by personnel. With appropriate parameterization, the improved modeling framework is generalizable to any chemical release, especially multi-hazard releases resulting from the conversion of one toxic compound into another under likely environmental conditions. By directly addressing the toxicities of multiple compounds, the improved model presents a more realistic picture of the potential health consequences of a chemical release. This generalizable framework for modeling of multi-hazard chemical releases can inform preparedness and risk mitigation strategies for NO release events.

Ambipolar Heterojunction Sensors: Another Way to Detect Polluting Gases.

Gas sensors based on ambipolar materials offer significant advantages in reducing the size of the analytical system and enhancing its efficiency. Here, bilayer heterojunction devices are constructed using different octafluorinated phthalocyanine complexes, with Zn and Co as metal centers, combined with a lutetium bisphthalocyanine complex (LuPc2). Stable p-type behavior is observed for the ZnF8Pc/LuPc2 device under both electron-donating (NH3) and -oxidizing (NO2 and O3) gaseous species, while the CoF8Pc/LuPc2 device exhibits n-type behavior under reducing gases and p-type behavior under oxidizing gases. The nature of majority of the charge carriers of Co-based devices varies depending on the nature of target gases, displaying an ambipolar behavior. Both heterojunction devices demonstrate stable and observable response toward all three toxic gases in the sub-ppm range. Remarkably, the Co-based device is highly sensitive toward ammonia with a limit of detection (LOD) of 200 ppb, whereas the Zn-based device demonstrates exceptional sensitivity toward oxidizing gases, with excellent LOD values of 4.9 and 0.75 ppb toward NO2 and O3, respectively, which makes it one of the most effective organic heterojunction sensors reported so far for oxidizing gases.