Mean NO2 Concentrations Research Articles

Concentration of traffic air pollutants and influencing metrological factors in Hawassa City roadways, Ethiopia

IntroductionThe traffic air pollution caused by transportation is a growing global problem that contributes to millions of deaths each year. Despite its importance, information on pollutant concentration is limited in many developing cities, especially in Ethiopia. This study aimed to determine the concentration levels and spatial and temporal variations of traffic air pollutants in Hawassa and to investigate the influence of metrological parameters on the concentration of traffic air pollutants.MethodsA real-time monitoring system of Aero-Qual Series 300/500 was used to monitor pollutants, and 24 monitoring sites were included on both heavy and low-traffic volume roads. The study monitored morning and afternoon times over 24 days to comprehensively characterize the temporal variations.ResultsThe results showed that the mean PM2.5 concentration on heavy- and low-traffic volume roads was 161.6 ± 26.1 μg/m3 and 95 ± 14.2 μg/m3, respectively, whereas the PM10 concentration was 178.7 ± 20.3 μg/ m3 and 102.3 ± 17.6 μg/m3, respectively. Similarly, the mean NO2 concentrations on roads with heavy and low traffic volumes were 86.4 ± 14.4 μg/m3 and 61.7 ± 14.2 μg/m3, respectively. Significantly higher, concentrations were recorded on traffic light roads, followed by main asphalt roads, for both types of traffic air pollutants. The ratio of PM2.5/PM10 was higher (0.924), in which the pollution sources attributed to anthropogenic sources. Kendall’s tau-b correlation analysis suggested that Meteorological parameters (temperature and relative humidity) were positively correlated with traffic air pollutants. Likewise, stepwise multiple linear regression analysis confirms that the concentrations of traffic air pollutants had a positive relationship with metrological parameters.ImplicationsThe findings of this study therefore showed the need for regular air quality monitoring of the urban areas to copping out the adverse public health impacts. And, it highlighted an urgent need for long-term monitoring of traffic air pollution and the development of emission control programs that can be readily implemented to decrease the emissions from anthropogenic sources. Also, it brings a sense of collaboration among stakeholders to tackle the effects of air pollution by providing an inclusive and sustainable development agenda for Hawassa.

Factors influencing spatiotemporal variability of NO2 concentration in urban area: a GIS and remote sensing-based approach.

The growing global attention on urban air quality underscores the need to understand the spatiotemporal dynamics of nitrogen dioxide (NO2) and its environmental and anthropogenic factors, particularly in cities like Dhaka (Gazipur), Bangladesh, which suffers from some of the world's worst air quality. This study analysed NO2 concentrations in Gazipur from 2019 to 2022 using Sentinel-5P TROPOMI data on the Google Earth Engine (GEE) platform. Correlations and regression analysis were done between NO2 levels and various environmental factors, including land surface temperature (LST), normalized difference vegetation index (NDVI), land use and land cover (LULC), population density, road density, settlement density, and industry density. The results reveal significant seasonal variations. The highest annual meanNO2 concentration (3.1 × 10-4mol/ m2)was recorded for winter 2021, and the lowest (1.1 × 10-4mol/m2) was for monsoon 2022. The study demonstrates a significant positive correlation between NO2 concentrations and LST (0.47), road density (0.55), settlement density (0.44), and industrial density (0.35) and a negative correlation with NDVI (- 0.4). Regression analysis revealed that NO2 concentrations were positively associated with land surface temperature (LST; β = 0.02, R2 = 0.22), road density (β = 0.002, R2 = 0.30), settlement density (β = 0.002, R2 = 0.19), and industrial density (β = 0.007, R2 = 0.12), while a negative association was observed with NDVI (β = - 0.28, R2 = 0.16). This research offers critical insights for policymakers and urban planners, advocating for enhanced green infrastructure, stringent emission controls, and sustainable urban development strategies to mitigate air pollution in Gazipur. Our methodological approach and findings contribute to the broader discourse on urban air quality management in developing countries.

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

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

Effects of traffic-related air and noise pollution exposure on allergic diseases in the elderly: an observational study

Introduction Traffic-related air and noise pollution are important public health issues. The aim of this study was to estimate their effects on allergic/respiratory outcomes in adult and elderly subjects. Materials and methods Six hundred and forty-five subjects living in Pisa (Tuscany, Italy) were investigated through a questionnaire on allergic/respiratory symptoms and diseases. Traffic-related air pollution and noise exposures were assessed at residential address by questionnaire, modelled annual mean NO2 concentrations (1 km and 200 m resolution), and noise level over a 24-h period (Lden). Exposure effects were assessed through logistic regression models stratified by age group (18–64 years, ≥65 years), and adjusted for sex, educational level, occupational exposure, and smoking habits. Results 63.6% of the subjects reported traffic exposure near home. Mean exposure levels were: 28.24 (±3.26 SD) and 27.23 (±3.16 SD) µg/m3 for NO2 at 200 m and 1 km of resolution, respectively; 57.79 dB(A) (±6.12 SD) for Lden. Exposure to vehicular traffic (by questionnaire) and to high noise levels [Lden ≥ 60 dB(A)] were significantly associated with higher odds of allergic rhinitis (OR 2.01, 95%CI 1.09–3.70, and OR 1.99, 95%CI 1.18–3.36, respectively) and borderline with rhino-conjunctivitis (OR 2.20, 95%CI 0.95–5.10, and OR 1.76, 95%CI 0.91–3.42, respectively) only in the elderly. No significant result emerged for NO2. Conclusions Our findings highlighted the need to better assess the effect of traffic-related exposure in the elderly, considering the increasing trend in the future global population’s ageing.

Impact of vegetation cover and land surface temperature on the seasonal tropospheric NO2 level variation from satellite observation

The Southeast Asian Region is one of the world’s regions that has historically had poor air quality conditions due to excess fossil fuel usage, coal combustion, and intensive deforestation activities. As the hub of economic activity, Indonesia’s Jakarta and Palembang cities have drawn significant attention from rural areas, leading to unrestrained urbanization in the past decade. Therefore, this study aimed to integrate the two urban centers of Indonesia, Jakarta and Palembang. It analyzed the variation of nitrogen dioxide (NO2) concentration, vegetation cover, and land surface temperature (LST) across all seasons from 2021 to 2023. We retrieved the tropospheric NO2 concentration data from the ozone monitoring instrument (OMI) aboard Aura Satellite, and obtained LST and the normalized difference vegetation index (NDVI) from Landsat 8 OLI images. We then used a Pearson correlation analysis to determine the relationship between the NO2 concentration, NDVI, and LST. The findings showed that Jakarta had higher levels of air pollution than Palembang, with rising LST and NO2 levels. During the study period, the mean NO2 concentration in Jakarta and Palembang was 3.25 × 1015 mol/cm2 and 0.87 × 1015 mol/cm2, respectively. While, the mean LST values in Jakarta and Palembang were 33.97 °C and 27.17 °C, respectively. The correlation results indicated a negative relationship between NO2 level and NDVI (r = −0.378, p < 0.01), while it obtained a positive association with LST (r = 0.421, p < 0.01). These results can be a chance for the authority to formulate a new strategy for mitigating air pollution, especially for NO2 pollutants in urban areas.

Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model

Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.

Indoor sulfur dioxide prediction through air quality modeling and assessment of sulfur dioxide and nitrogen dioxide levels in industrial and non-industrial areas.

In this study, the levels of sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO2 and NO2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO2 was the highest in summer, while the mean indoor NO2 concentration was the highest in winter in both areas. The ratio of NO2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO2 for the training set (RMSE = 2.108, MAE = 1.780, and R2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant.

RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London

High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33μg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.

ML‐Based Hybrid SAR and Optical Image LULC Mapping and Change Analysis With Variations in the Air Quality of the Imphal Valley, North‐East India

AbstractImphal Valley, situated in Manipur, India, stands as an intermontane valley of great ecological significance, notably hosting Loktak Lake. This research delves into the Land Use Land Cover Change (LULCC) within the Imphal Valley from 2016 to 2021 and assesses their impact on air quality across distinct land cover types. We used Sentinel‐1 &amp; 2 data, ALOS PALSAR Digital Elevation Model, and applied Random Forest (RF), a machine learning algorithm for effective land use and land cover (LULC) mapping. Additionally, Sentinel‐5P data was utilized to monitor air quality parameters (CO, HCHO, NO2, SO2, Aerosol Index) spanning 2019 to 2021. The overall accuracies for the LULC maps employing a k (k = 3) fold approach for accuracy assessment varied between 88% and 92%, with corresponding Kappa coefficient ranging from 0.85 to 0.90. Noteworthy trends emerged from our analysis, revealing an increase in settlements and horticulture farms and a decline in forested areas and phumdis (floating biomass). Our findings highlight a mean concentration of CO ranging between 0.045 mol/m2 and 0.055 mol/m2 in different land cover types during February and March (2019–2021). Furthermore, we observed maximum mean HCHO, NO2, SO2, and aerosol index concentrations in March. Pollution levels surged during forest fires and shifting agriculture seasons, while aerosol levels declined during the lockdown period. This integrated approach emphasized on the comprehensive analysis of the dynamic interplay between LULCC and air quality in the Imphal Valley. This intricate relationship between LULC changes and air quality dynamics in the Imphal Valley, contribute to our understanding of the environmental dynamics in this ecologically vital region.

Investigation of atmospheric VOCs sources and ozone formation sensitivity during epidemic closure and control: A case study of Zhengzhou

This study investigates the pollution characteristics of volatile organic compounds (VOCs) and their impact on ozone formation in the urban area of Zhengzhou. The analysis is based on the air quality data collected during the epidemic control period (P3) from July 20 to August 22, 2021, as well as the same period in 2019 and 2020 (P1 and P2). The results reveal a decreasing trend in the O3 precursors, namely NO2 and VOCs, from 2019 to 2021. During the P3 period, the mean concentrations of NO2 and VOCs were 19 μg/m3 and 56 μg/m3, respectively, a decline of 32% and 36% compared to the levels in P2. However, the mean MDA8 concentration in P3 was 142 μg/m3, 25% higher than that in P2, and ozone-polluted days was 12 d, 6 days more than that in P2. The VOCs composition profiles were similar across three periods, with alkanes accounting for 34%, 36%, and 39% of TVOC, respectively. Six kinds of VOCs sources were quantified by positive matrix factorization (PMF), among which vehicle emissions contributed the most (29%), followed by solvent usage (23%) and industrial processes (22%). The contribution of industrial processes and vehicle emissions declined significantly in P3, while that of stationary combustion and LPG/NG sources increased. The net production rate P(Ox) increased in P3 compared to that in P2, getting closer to the ridge line, indicating that O3 formation was gradually transiting from VOCs control zone to synergistic control zone. Therefore, VOCs and NOx emissions should be reduced synchronously.

How do emission reductions of individual national and local measures impact street-level air quality in a neighbourhood of Madrid, Spain?

AbstractThis paper aims to assess the impact of individual measures for NOx emission reduction on NO2 concentrations at very high spatial resolution in an urban district of Madrid City (Spain). A methodology based on a set of Computational Fluid Dynamics simulations for 16 meteorological scenarios combined with the CHIMERE model for background pollution is used to obtain annual NO2 concentration maps. Two scenarios included in the Spanish National Air Pollution Control Programme are investigated: NOx emission reductions from the installation of more efficient boilers for domestic heating (ECOBOIL) and from the partly substitution of passenger cars with combustion engines by electric cars (EC). This analysis is extended to 9 additional scenarios of more ambitious implementation of electric vehicles in order to determine what the NOx emission reduction required for the annual mean NO2 concentration EU limit value not being exceeded is. The ECOBOIL scenario has a very weak impact on the NO2 concentrations. However, the EC scenario implies a more significant reduction of the NO2 concentrations, but not enough to fully remove NO2 limit value exceedances in the study area. A small additional (compared with the EC scenario) implementation of electric vehicles seems to fulfil that the spatially averaged NO2 concentration be lower than the EU limit value, but the area with exceedances is still very large. However, stronger traffic emission reductions (80%) corresponding to the most ambitious scenarios are needed in order to reach that at least 95% of the domain is free of EU limit value exceedances.

Field intercomparison of continuous ambient FRM and FEM NO2 instruments in the Athabasca Oil Sands Region, Alberta, Canada and the potential impact on ambient regulatory compliance

ABSTRACT The Canadian Federal Government promulgated new and lower NO2 Ambient Air Quality Standards (CAAQS) that went into effect in 2020 with additional decreases scheduled for 2025. The new hourly and annual NO2 CAAQS are 60 and 17 ppb, respectively, and the 2025 hourly and annual CAAQS are 42 and 12 ppb, respectively. The province of Alberta has also promulgated Ambient Air Quality Objectives (AAAQO) for NO2 currently set to 159 and 24 ppb on an hourly and annual basis, respectively. The Wood Buffalo Environmental Association (WBEA) in northeastern Alberta, Canada monitors NO2 at 21 community and industrial sites throughout the Athabasca Oil Sands Region (AOSR), for regulatory compliance using Thermo-Environmental (TEI) Model 42i Federal Reference Method (FRM) designated NO-NO2-NOx analyzers. The 42i measures NO directly via NO-O3 chemiluminescence, and NOx following the reduction of oxidized nitrogen to NO by a heated internal molybdenum converter. The difference between the NOx and NO channels is reported as NO2. This study presents the results of a three-year (2018–2021) WBEA comparison of four continuous NO2 analyzers: TEI 42i FRM; the API Model T500U cavity attenuated phase shift (CAPS) Federal Equivalent Method (FEM); a total reactive odd nitrogen analyzer (TEI Model 42i-Y); and a TEI 42i equipped with an external photolytic converter. The study showed that NO2 data from all analyzers were highly correlated and in general agreement, with r2 values (vs. the CAPS) ranging from 0.990–0.997 and slopes ranging from 0.933–0.992. Mean NO2 concentrations over the study period ranged from 7.2–7.5 ppb. Differences between the TEI 42i, TEI 42i-Y, and PhoNO, relative to the CAPS were all positive and highly significant (p < 0.0001), based upon nonparametric tests. The potential impact from the selection of different FRM/FEM measurement methods on current and future Canadian 2025 regulatory compliance in the region is evaluated. Implications: The study objective was to compare/evaluate different regulatory NO2 measurement techniques from a regional monitoring authority in a routine network operational context. Relatively small NO2 differences resulted in significant differences with respect to regulatory compliance triggers, particularly hourly standards based on daily extreme value statistics (e.g., 99th percentiles). For example, mean hourly NO2 △ differences ranged from 0.02–0.26 ppb over the study period but resulted in 2–3 ppb differences in the 3-year hourly CAAQS metrics. These differences could affect regulatory CAAQS and LARP compliance (management level) at monitoring sites observed during 2019 annual and 2020 hourly LARP trigger exceedances.

Can traffic-related air pollution trigger myocardial infarction within a few hours of exposure? Identifying hourly hazard periods

IntroductionTraffic-related air pollution can trigger myocardial infarction (MI). However, the hourly hazard period of exposure to nitrogen dioxide (NO2), a common traffic tracer, for incident MI has not been fully evaluated. Thus, the current hourly US national air quality standard (100 ppb) is based on limited hourly-level effect estimates, which may not adequately protect cardiovascular health. ObjectivesWe characterized the hourly hazard period of NO2 exposure for MI in New York state (NYS), USA, from 2000 to 2015. MethodsFor nine cities in NYS, we obtained data on MI hospitalizations from the NYS Department of Health Statewide Planning and Research Cooperative System and hourly NO2 concentrations from the US Environmental Protection Agency’s Air Quality System database. We used city-wide exposures and a case-crossover study design with distributed lag non-linear terms to assess the relationship between hourly NO2 concentrations over 24 h and MI, adjusting for hourly temperature and relative humidity. ResultsThe mean NO2 concentration was 23.2 ppb (standard deviation: 12.6 ppb). In the six hours preceding MI, we found linearly increased risk with increasing NO2 concentrations. At lag hour 0, a 10 ppb increase in NO2 was associated with 0.2 % increased risk of MI (Rate Ratio [RR]: 1.002; 95 % Confidence Interval [CI]: 1.000, 1.004). We estimated a cumulative RR of 1.015 (95 % CI: 1.008, 1.021) for all 24 lag hours per 10 ppb increase in NO2. Lag hours 2–3 had consistently elevated risk ratios in sensitivity analyses. ConclusionsWe found robust associations between hourly NO2 exposure and MI risk at concentrations far lower than current hourly NO2 national standards. Risk of MI was most elevated in the six hours after exposure, consistent with prior studies and experimental work evaluating physiologic responses after acute traffic exposure. Our findings suggest that current hourly standards may be insufficient to protect cardiovascular health.

Incident risk and burden of cardiovascular diseases attributable to long-term NO2 exposure in Chinese adults

BackgroundA number of studies suggested a nexus between long-term exposure to nitrogen dioxide (NO2) and the incidence of cardiovascular disease (CVD), while population-based cohort evidence in low- and middle-income countries was extensively sparse. MethodsWe carried out an 8-year longitudinal study (2010–2018) in a nationwide dynamic cohort of 36,948 Chinese adult participants, who were free of CVD at baseline. Annual average estimates of NO2 exposure were predicted using a well-validated spatiotemporal model and assigned to study participants based on their residential counties. Considering death as a competing risk event, Fine-Gray competing risk models with time-varying exposures at an annual scale were used to quantify incident risks of overall CVD, hypertension, and stroke associated with a 10-μg/m3 rise in NO2 exposure. Using the meta-analysis approach, we performed a pooled analysis of hazard ratio (HR) drawn from this and prior multinational cohort studies for the assessment of attributable burden. NO2-attributable overall CVD incidents in China were evaluated by city and province for years 2010 and 2018, referring to a counterfactual exposure level of 10 μg/m3 (2021 World Health Organization [WHO] air quality guidelines). A decomposition method was used to decompose net change in NO2-attributable CVD incidents during 2010 and 2018 into 3 primary contributions of driving factors (i.e., changes in NO2 exposure, population size, and incidence rate). ResultsA total of 4428 overall CVD events (hypertension 2448, stroke 1044) occurred during a median follow-up period of 6.1 years. Annual mean NO2 concentration from 2010 to 2018 was 20.0 μg/m3 (range: 6.9–57.4 μg/m3). An increase of 10-µg/m3 in NO2 was associated with an HR of 1.558 (95% confidence interval [CI]: 1.477, 1.642) for overall CVD, 1.521 (95% CI: 1.419, 1.631) for hypertension, and 1.664 (95% CI: 1.485, 1.865) for stroke. Longitudinal associations of NO2 exposure with incident CVD were nearly linear over the exposure range, suggesting no discernible thresholds. Subgroup analyses indicated significantly higher NO2-associated risks of incident CVD among urban residents and overweight/obese individuals. According to pooled HR of NO2-CVD association (1.108, 95% CI: [1.007, 1.219]) from 10 multinational cohort studies, we estimated totally 1.44 million incident CVD cases attributable to NO2 exposure in 2018, representing a substantial decrease of 0.41 million compared to the estimate in 2010 (1.85 million) in mainland of China. Nationally, from 2010 to 2018, the attributable incident cases greatly dropped by 22.4%, which was dominantly driven by declined NO2 concentration (−47.1%) that had offset far from the rise of CVD incidence rate (+19.6%) and population growth (+5.1%). ConclusionsThis study provided nationwide cohort evidence for elevated risks of CVD incidence associated with long-term ambient NO2 exposure among Chinese adults, particularly in urban areas and among overweight/obese individuals. Our findings highlighted that reducing NO2 exposure below 2021 WHO guideline could help prevent a substantial portion of incident CVD cases in China.

Causal links between long-term exposure to NO2 and the risk of cardiovascular hospitalization

Objective: To determine the causal association between long-term Nitrogen dioxide (NO2) exposure and the risk of cardiovascular hospitalization. Methods: Based on a sub-cohort of a community-based prospective cohort study, a total of 36 271 participants were recruited from 35 communities randomly selected in Guangzhou in 2015. The annual average exposure of NO2, demographic characteristics, lifestyle factors, and information on the causes of hospitalization was collected. We applied marginal structural Cox models to investigate the effect of NO2 on cardiovascular hospitalization. Demographic and behavioral factors also stratified results. Results: The mean age of participants in the present study was (50.9±17.8) years, and the cardiovascular admission rate was 8.7%, with 203 822 person-years of follow-up. The annual mean NO2 concentration was 48.7 μg/m3 during 2015-2020. For each 10 μg/m3 increase in NO2 concentrations, the HRs (95%CIs) of total cardiovascular hospitalization, cardiovascular hospitalization, and cerebrovascular hospitalization were 1.33 (1.16-1.52), 1.36 (1.16-1.60) and 1.25 (1.00-1.55), respectively. Participants who were never married/married, with secondary education, high exercise frequency, or non-smokers/current smokers may be more susceptible than their counterparts. Conclusion: Long-term exposure to NO2 significantly increased hospitalization risk for cardiovascular disease.

Drivers of nocturnal interactions between ground-level ozone and nitrogen dioxide

&lt;p&gt;This study analyses nighttime (7 p.m. to 6 a.m.) light pollution effects on ground-level ozone production in urban and sub-urban sites in Malaysia. In the absence of solar radiation, no photochemical reaction will occur during nighttime, resulting in zero readings of ground-level ozone (O3). O3 and nitrogen dioxide (NO2) data collected in 2020 were analysed to assess time series and diurnal variability at each site and between sites. The highest nighttime mean O3 concentration is Minden (60 ppb); meanwhile, for nighttime mean NO2 concentrations, the highest is Klang (43 ppb). The results show that sub-urban experienced higher O3 variations compared to urban areas. The monthly mean nighttime O3 and NO2 in urban and suburban areas display a gradual increase in O3 and NO2 variations from March to April, followed by a decreasing trend in the mid of the year. These variations in monthly air pollutants are related to the MCO, CMCO, and RMCO in Malaysia during 2020. Putrajaya (sub-urban site) was the darkest site (average lux: 20) for the whole dataset. In contrast, Minden (sub-urban site) was recorded as the brightest site with maximum light pollution (average lux: 70). The relationship between O3 and NO2 shows a negative correlation during nighttime for urban and suburban sites. Light pollution can reach levels that might affect nocturnal O3 and NO2 concentrations; therefore, the long-term variability of light pollution is essential for air pollution studies.&lt;/p&gt;&#x0D;

Association of greenness, nitrogen dioxide with the prevalence of hypertension among the elderly over 65 years old in China

Objective: To investigate the association of mixed exposure to greenness and nitrogen dioxide(NO2) and hypertension among the older adults aged 65 years and over in China. Methods: The study subjects were from the Chinese Longitudinal Healthy Longevity Survey from 2017 to 2018. A total of 15 423 older adults aged 65 years and over meeting the criteria were finally included in the study. A questionnaire survey was used to collect information on demographic characteristics, lifestyle habits and self-reported prevalence of hypertension. Blood pressure values were obtained through physical examination. The level of normalized difference vegetation index(NDVI) was measured by the Medium-resolution Imaging Spectral Radiator(MODIS) of the National Aeronautics and Space Administration(NASA). The concentration of NO2 was from China's surface air pollutant data set. Meteorological data was from NASA MERRA-2. The exposure to NDVI and NO2 for each study subject was calculated based on the area within a 1 km radius around their residence. The association between mixed exposure of NDVI and NO2 as well as their interaction and hypertension in older adults was analyzed by using the multivariate logistic regression model. The restrictive cubic spline(RCS) function was used to explore the exposure-response relationship between greenness and NO2 and the risk of hypertension in study subjects. Results: The mean age of 15 423 older adults were (85.6±11.6). Women accounted for 56.3%(8 685/15 423) and 55.6%(8 578/15 423) lived in urban areas. The mean time of residence was (60.9±28.5) years. 59.8% of participants were with hypertension. The mean NDVI level was 0.41±0.13, and the mean NO2 concentration was (32.18±10.36) μg/cm3. The results of multivariate logistic regression analysis showed that NDVI was inversely and linearly associated with the hypertension in older adults, with the OR(95%CI) value of 0.959(0.928-0.992). Compared with the T1 group of NDVI, the risk of hypertension was lower in the T3 group, with the OR(95%CI) value of 0.852(0.769-0.944), and the trend test was statistically significant(P<0.05). Compared with the T1 group of NO2, the risk of hypertension was higher in the T2 and T3 groups, with OR(95%CI) values of 1.160(1.055-1.275) and 1.244(1.111-1.393), and the trend test was statistically significant (P<0.05). The result of the RCS showed that NDVI was inversely and linearly associated with hypertension in older adults. NO2 was nonlinearly associated with hypertension in older adults. The interaction analysis showed that NDVI and NO2 had a negative multiplicative interaction on the risk of hypertension, with OR(95%CI) value of 0.995(0.992-0.997). Conclusion: Exposure to greenness and NO2 are associated with hypertension in older adults.

Satellite-Based Long-Term Spatiotemporal Trends in Ambient NO2 Concentrations and Attributable Health Burdens in China From 2005 to 2020.

Despite the recent development of using satellite remote sensing to predict surface NO2 levels in China, methods for estimating reliable historical NO2 exposure, especially before the establishment of NO2 monitoring network in 2013, are still rare. A gap-filling model was first adopted to impute the missing NO2 column densities from satellite, then an ensemble machine learning model incorporating three base learners was developed to estimate the spatiotemporal pattern of monthly mean NO2 concentrations at 0.05° spatial resolution from 2005 to 2020 in China. Further, we applied the exposure data set with epidemiologically derived exposure response relations to estimate the annual NO2 associated mortality burdens in China. The coverage of satellite NO2 column densities increased from 46.9% to 100% after gap-filling. The ensemble model predictions had good agreement with observations, and the sample-based, temporal and spatial cross-validation (CV) R 2 were 0.88, 0.82, and 0.73, respectively. In addition, our model can provide accurate historical NO2 concentrations, with both by-year CV R 2 and external separate year validation R 2 achieving 0.80. The estimated national NO2 levels showed a increasing trend during 2005-2011, then decreased gradually until 2020, especially in 2012-2015. The estimated annual mortality burden attributable to long-term NO2 exposure ranged from 305 thousand to 416 thousand, and varied considerably across provinces in China. This satellite-based ensemble model could provide reliable long-term NO2 predictions at a high spatial resolution with complete coverage for environmental and epidemiological studies in China. Our results also highlighted the heavy disease burden by NO2 and call for more targeted policies to reduce the emission of nitrogen oxides in China.

The Association between Vessel Departures and Air Pollution in Helsinki Port Area 2016–2021

European ports are struggling to install enough shore power connections to follow the European Commission initiative, which insists ships that lie alongside to be plugged in and have their auxiliary engines off in EU ports by 2030. The port of Helsinki is one of the busiest passenger ports in the world handling on average more than 10 million international passengers per year. As passenger ships consume more fuel than other vessel types, the shore power regulation poses additional challenges for the port of Helsinki. Passenger ferry and cruise ship terminals are in the middle of the city meaning that their air emissions carry a public health burden in the urban areas. Using port arrivals and departures combined with the EU Monitoring, Reporting and Verifying (MRV), this study estimates that 75–80% of the fuel combusted by ship auxiliary engines falls under the upcoming regulation. However, using statistical methods to find the association and effects between vessel movements and port air quality measurements, ship departures were found to have noticeable increases in the hourly mean NO2 concentration measured at the port terminals. This is most likely caused by starting cold main engines for departure and will not be solved by connecting ships to shore power.

Air pollution and decreased bone mineral density among Women's Health Initiative participants.

Osteoporosis heavily affects postmenopausal women and is influenced by environmental exposures. Determining the impact of criteria air pollutants and their mixtures on bone mineral density (BMD) in postmenopausal women is an urgent priority. We conducted a prospective observational study using data from the ethnically diverse Women's Health Initiative Study (WHI) (enrollment, September 1994-December 1998; data analysis, January 2020 to August 2022). We used log-normal, ordinary kriging to estimate daily mean concentrations of PM10, NO, NO2, and SO2 at participants' geocoded addresses (1-, 3-, and 5-year averages before BMD assessments). We measured whole-body, total hip, femoral neck, and lumbar spine BMD at enrollment and follow-up (Y1, Y3, Y6) via dual-energy X-ray absorptiometry. We estimated associations using multivariable linear and linear mixed-effects models and mixture effects using Bayesian kernel machine regression (BKMR) models. In cross-sectional and longitudinal analyses, mean PM10, NO, NO2, and SO2 averaged over 1, 3, and 5 years before the visit were negatively associated with whole-body, total hip, femoral neck, and lumbar spine BMD. For example, lumbar spine BMD decreased 0.026 (95% CI: 0.016, 0.036) g/cm2/year per a 10% increase in 3-year mean NO2 concentration. BKMR suggested that nitrogen oxides exposure was inversely associated with whole-body and lumbar spine BMD. In this cohort study, higher levels of air pollutants were associated with bone damage, particularly on lumbar spine, among postmenopausal women. These findings highlight nitrogen oxides exposure as a leading contributor to bone loss in postmenopausal women, expanding previous findings of air pollution-related bone damage. US National Institutes of Health.