Ambient NO2 Concentrations Research Articles

Acute effects of ambient nitrogen dioxide pollution on outpatient visits for neurological diseases in Xinxiang, China

BackgroundAccumulating evidence suggests that exposure to air pollution acts as a potential trigger for neurological diseases (NDs), yet the current knowledge regarding the impact of ambient nitrogen dioxide (NO2) on the patients with NDs remains limited. In this study, we conducted a time-series study to evaluate the association between short-term exposure to NO2 and hospital visits for NDs in Xinxiang, China.MethodsAn over-dispersed Poisson generalized additive model was used to analyze the association between ambient NO2 concentrations and daily outpatient visits for NDs from January 1, 2015 to December 31, 2017. The model adjusted for meteorological factors, temporal trends, day of the week, and public holidays. The concentrations of air pollutants were collected from four air quality stations in Xinxiang.ResultsA total of 38, 865 outpatient visits for NDs were retrieved during the study period. 86.5% of the patients were below the age of 65 years. It was revealed that a 10 µg/m3 increase in NO2 at lag 0 was associated with a significant rise of 1.50% (95% CI: 0.45–2.56%) in outpatient visits for NDs, which was stronger during the cold season. However, the overall results from stratified analyses did not reach statistical significance.ConclusionsShort-term exposure to NO2 is associated with increased outpatient visits for NDs. These findings underscore the need for implementing mitigating measures to reduce the neurological health effects of air pollutants.

Global estimates of ambient NO2 concentrations and long-term health effects during 2000–2019

High concentrations of ambient NO2 causes serious air pollution and could also pose great threats to human health. However, the long-term trends (20-year) and potential health effects of ambient NO2 exposure globally still shows high uncertainties. In this work, the field measurements, satellite dataset, GEOS-Chem output, and multiple geographical covariates were incorporated into the multi-stage model to investigate the global evolutions of ambient NO2 during 2000–2019. The results indicated that the cross-validation (CV) R2 values of ambient NO2 based on multi-stage model displayed satisfied performance (R2 = 0.78), which was superior to the individual model. Besides, the out-of-bag R2 was 0.75, which suggested the multi-stage model showed the better transferability. At the spatial scale, the NO2 concentrations followed the order of China (16.9 ± 9.0 μg/m3) > India (15.5 ± 5.6 μg/m3) > United States (10.7 ± 5.6 μg/m3) > Europe (7.7 ± 4.5 μg/m3), which was in consistent with the anthropogenic NOx emission. At the temporal scale, the ambient NO2 levels in China experienced persistent increases (0.29 μg/m3/year) during 2000–2013, whereas they showed slight decreases (−0.23 μg/m3/year) during 2013–2019. The ambient NO2 levels in the United States experienced continuous decreases during 2000–2019 (−0.20 μg/m3/year), while both of India and Europe remained relatively stable. Long-term NO2 exposure inevitably increased premature mortalities. The global premature all-cause mortalities associated with the excessive NO2 exposure increased from 288,169 (95% CI: 43,650, 527,971) to 461,301 (95% CI: 69,973, 843,996) in the past 20 years. This study would provide sufficient policy support for future ambient NO2 mitigation.

The impact of shipping on the air quality in European port cities with a detailed analysis for Rotterdam

Air quality in cities with large maritime ports is considerably impacted by emissions from shipping activity which is of a growing relevance due to an increasing relative contribution. To explore the extent of shipping emissions to ambient air quality, simulations with the chemical transport model LOTOS-EUROS (LOng Term Ozone Simulation – EURopean Operational Smog model) were performed for the year 2018 at an approximate 1 × 1 km resolution for six European cities with large ports, i.e., Rotterdam, Antwerp, Hamburg, Amsterdam, Le Havre, and London. It was found that depending on the investigated city, 6.5%–62% of the nitrogen dioxide (NO2) concentration in the city centres is attributable to shipping activities. This corresponds to contributions of 1.8–11.5 μg/m3 to the ambient air NO2 concentrations. The average NO2 contribution of shipping in these six cities was 28% (7.1 μg/m3). The largest relative contribution was found for Le Havre where 62% (10.8 μg/m3) of the annual average NO2 concentration was caused by shipping emissions. The largest absolute contribution is found for the city centre of Hamburg with 11.5 μg/m3 (41%). The lowest absolute and relative contribution (respectively 1.8 μg/m3 and 6.5%) are found for London, also having the smallest port in terms of tonnage throughput, which is one of the influential factors that determine emission totals, investigated in this study. For the other investigated pollutants, i.e., PM2.5, PM10 and SO2, contributions from shipping were less pronounced with average contribution for all cities of 10% (1.2 μg/m3) 7% (1.5 μg/m3) and 4% (0.16 μg/m3) respectively. To assess the effect of model choices on these results, this study also looked into the choice of simulation resolution and relations between meteorological parameters and NO2 concentrations. Following simulations with varying chemical transport model resolutions (1 × 1 km to 24 × 24 km), it is found that a decrease in ambient air pollutant concentrations away from localized emission sources is more pronounced at higher (1 × 1 km) model resolutions and source contributions are influenced more significantly than total concentrations. Considering meteorology, generally low wind speeds (1–2 m/s) lead to high NO2 concentration in city centres. For the cities where the port is much closer to the city centre (e.g., London, Le Havre, Hamburg and Antwerp) the absolute NO2 concentrations as well as the contributions from shipping emissions become highest for windless conditions. The high concentrations (>60 μg/m3 NO2) only occur when wind speeds fall below 6 m/s.

Air pollution, greenspace exposure and risk of Parkinson's disease: a prospective study of 441,462 participants.

The current understandings of the relationship between air pollution (AP), greenspace exposure and Parkinson's Disease (PD) remain inconclusive. We engaged 441,462 participants from the UK Biobank who were not diagnosed with PD. Utilizing Cox proportional hazard regression model, relationships between AP [nitrogen dioxide (NO2), and nitrogen oxides (NOX), particulate matter < 2.5μm in aerodynamic diameter(PM2.5), coarse particulate matter between 2.5μm and 10μm in aerodynamic diameter(PM2.5-10), particulate matter < 10μm in aerodynamic diameter(PM10)], greenspace exposure, and PD risk were determined independently. Our analyses comprised three models, adjusted for covariates, and affirmed through six sensitivity analyses to bolster the robustness of our findings. Moreover, mediation analysis was deployed to discern the mediating effect of AP between greenspaces and PD. During a median follow-up of 12.23years (5,574,293 person-years), there were 3,293 PD events. Each interquartile (IQR) increment in NO2 and PM10 concentrations were associated with 10% and 8% increase in PD onset risk, while the increases in NOX, PM2.5 and PM2.5-10 were not associated with PD risk. Additionally, greenspace may safeguard by reducing NO2 and PM10 levels, with the effect mediated by NO2 and PM10 in greenspace-PD relationship. Our findings indicate that an IQR increase in ambient NO2 and PM10 concentrations was associated with risk of PD development, while other pollutants (NOX, PM2.5 and PM2.5-10) were not associated with PD risk. Firstly, we find that augmented exposure to greenspace was associated with the lower PD risk by reducing NO2 and PM10 levels.

Contribution of emissions from the oil sands activities in Alberta, Canada to atmospheric concentration and deposition of nitrogen and sulfur species at a downwind site

Oil sands activities in the Athabasca Oil Sands Region in Alberta, Canada, are large sources of atmospheric NOx and SO2. This study investigated the impact of oil sands emissions on the atmospheric deposition of nitrogen and sulfur species at a downwind site, about 350 km from the oil sands facilities. Measurement data are from the Canadian Air and Precipitation Monitoring Network (CAPMoN) from 2015 to 2019, including ambient concentrations of HNO3, pNO3-, NO2, pNH4+, NH3, SO2, pSO42− and base cations, as well as concentrations of NO3−, SO42−, NH4+, and base cations in precipitation. Sector analysis of air mass back trajectories was conducted to distinguish measurements with different air mass origins. Median atmospheric concentrations and dry deposition fluxes of HNO3, pNO3-, NO2, pNH4+, pSO42−, and SO2 on days when the air masses came from the oil sands sector were significantly greater than those with the “Clean” sector by 34–67%, whereas the difference in NH3 concentration was not significant. Contributions of the oil sands emissions to dry deposition fluxes of these species ranged from 3.8 to 13.1%. The precipitation-weighted mean concentrations of NO3−, SO42−, and NH4+ in samples with the oil sands sector were 76 %, 65 % and 81 % greater than those with the “Clean” sector, respectively. Contributions of the oil sands emissions to wet deposition of NO3−, SO42−, and NH4+ were 12.5 ± 8.9 %, 8.7 ± 4.4 %, and 6.0 ± 3.3 %, respectively. The annual total deposition of nitrogen and sulfur were 1.9 kg-N ha−1 and 0.74 kg-S ha−1, respectively, of which 8.0 ± 3.5 % and 8.7 ± 3.6 % were from oil sands emissions. The total deposition of sulfur and nitrogen did not exceed the critical loads (CL) of acidity, but nitrogen deposition exceeded the CLs of nutrient nitrogen in the region.

Machine Learning Classification of Air Quality Monitoring Stations to Achieve Ambient NO2 Objectives Using Emission Scenarios and Chemical Transport Model

Exceeding the latest Canadian Ambient Air Quality Standards (CAAQS) for NO2 concentration in Canadian cities motivates discussions on new policies and directives. Advanced air quality modeling and machine learning clustering algorithms integrating the Weather Research Forecast (WRF) and Community Multiscale Air Quality (CMAQ) models were used to classify air quality monitoring (AQM) stations based on pollution concentration modeling results. The sensitivity of ambient NO2 to the primary anthropogenic emission sources was investigated in Alberta, Canada. Emissions from two main sources, upstream oil and gas (UOG) and transportation sources, have been identified as the reason Alberta fails to meet the newly adopted NO2 CAAQS objectives. The air quality model was validated with ground-level observation data and the atmospheric model accurately replicates spatiotemporal NO2 variations. Despite contributing 62% of Alberta’s total NOx emissions, UOG influences ambient NO2 concentrations modestly in urban areas (<10%) but significantly affects rural regions. In contrast, transportation emission sources, responsible for 23% of NOx emissions, dominate ambient NO2 levels (up to 63%) in large cities. The discrepancy of emission contribution and ground-level concentrations, obtained from the chemical transport model, was resolved using a k-prototypes clustering algorithm to propose a new approach for categorizing AQM stations which led to improving the conventional classifications. The new approach considered the sensitivity of NO2 to emission reduction scenarios and provided an improved classification to be used for emission reduction interventions. Based on the updated classification, one set of AQM stations clearly showed sensitivity to NO2 emission reduction in the transportation sector despite their lower contributions to overall emissions. These stations were categorized as population exposure stations in large cities.

Outdoor air pollution as a risk factor for testing positive for SARS-CoV-2: A nationwide test-negative case-control study in the Netherlands

Air pollution is a known risk factor for several diseases, but the extent to which it influences COVID-19 compared to other respiratory diseases remains unclear. We performed a test-negative case-control study among people with COVID-19-compatible symptoms who were tested for SARS-CoV-2 infection, to assess whether their long- and short-term exposure to ambient air pollution (AAP) was associated with testing positive (vs. negative) for SARS-CoV-2. We used individual-level data for all adult residents in the Netherlands who were tested for SARS-CoV-2 between June and November 2020, when only symptomatic people were tested, and modeled ambient concentrations of PM10, PM2.5, NO2 and O3 at geocoded residential addresses. In long-term exposure analysis, we selected individuals who did not change residential address in 2017–2019 (1.7 million tests) and considered the average concentrations of PM10, PM2.5 and NO2 in that period, and different sources of PM (industry, livestock, other agricultural activities, road traffic, other Dutch sources, foreign sources). In short-term exposure analysis, individuals not changing residential address in the two weeks before testing day (2.7 million tests) were included in the analyses, thus considering 1- and 2-week average concentrations of PM10, PM2.5, NO2 and O3 before testing day as exposure. Mixed-effects logistic regression analysis with adjustment for several confounders, including municipality and testing week to account for spatiotemporal variation in viral circulation, was used. Overall, there was no statistically significant effect of long-term exposure to the studied pollutants on the odds of testing positive vs. negative for SARS-CoV-2. However, significant positive associations of long-term exposure to PM10 and PM2.5 from specifically foreign and livestock sources, and to PM10 from other agricultural sources, were observed. Short-term exposure to PM10 (adjusting for NO2) and PM2.5 were also positively associated with increased odds of testing positive for SARS-CoV-2. While these exposures seemed to increase COVID-19 risk relative to other respiratory diseases, the underlying biological mechanisms remain unclear. This study reinforces the need to continue to strive for better air quality to support public health.

Exposure to ambient air pollution and cognitive function: an analysis of the English Longitudinal Study of Ageing cohort

BackgroundAn increasing number of studies suggest adverse effects of exposure to ambient air pollution on cognitive function, but the evidence is still limited. We investigated the associations between long-term exposure to air pollutants and cognitive function in the English Longitudinal Study of Ageing (ELSA) cohort of older adults.MethodsOur sample included 8,883 individuals from ELSA, based on a nationally representative study of people aged ≥ 50 years, followed-up from 2002 until 2017. Exposure to air pollutants was modelled by the CMAQ-urban dispersion model and assigned to the participants’ residential postcodes. Cognitive test scores of memory and executive function were collected biennially. The associations between these cognitive measures and exposure to ambient concentrations of NO2, PM10, PM2.5 and ozone were investigated using mixed-effects models adjusted for time-varying age, physical activity and smoking status, as well as baseline gender and level of education.ResultsIncreasing long-term exposure per interquartile range (IQR) of NO2 (IQR: 13.05 μg/m3), PM10 (IQR: 3.35 μg/m3) and PM2.5 (IQR: 2.7 μg/m3) were associated with decreases in test scores of composite memory by -0.10 (95% confidence interval [CI]: -0.14, -0.07), -0.02 [-0.04, -0.01] and -0.08 [-0.11, -0.05], respectively. The same increases in NO2, PM10 and PM2.5 were associated with decreases in executive function score of -0.31 [-0.38, -0.23], -0.05 [-0.08, -0.02] and -0.16 [-0.22, -0.10], respectively. The association with ozone was inverse across both tests. Similar results were reported for the London-dwelling sub-sample of participants.ConclusionsThe present study was based on a long follow-up with several repeated measurements per cohort participant and long-term air pollution exposure assessment at a fine spatial scale. Increasing long-term exposure to NO2, PM10 and PM2.5 was associated with a decrease in cognitive function in older adults in England. This evidence can inform policies related to modifiable environmental exposures linked to cognitive decline.

Environmental Injustice in Peel Region: A Cross-Sectional Analysis of Air Pollution and Social Vulnerability

Background: Nitrogen dioxide (NO2), a traffic-related air pollutant, is co-emitted with greenhouse gasses that contribute to climate change and negatively affect health. This research examines the relationship between air pollution exposure and social vulnerability to determine whether communities at a social disadvantage experience environmental injustice. Methods: The 2016 census measures for Peel Region were assessed. Peel Region has 1,381,739 people across Mississauga, Brampton, and Caledon; the spatial unit of analysis was the dissemination area (DA) with an average size of 837 people in Peel. Average ambient NO2 concentrations for 2016 came from the Canadian Urban Environmental Health Research Consortium and resampled to DAs. Social disadvantage was defined by the Ontario Marginalization Index (ON-Marg). Measures within index components came from the Canadian census. Environmental injustice was identified through correlation analysis between ON-Marg and NO2 exposure and an analysis of index components with exposure. Results: The 2016 annual NO2 concentrations were positively correlated with the overall ON-Marg quintiles (Spearman: 0.34, p &lt; .001, 95% CI 0.29 to 0.37). The components and their correlations with NO2 were residential instability (Spearman: 0.37, p &lt; .001, 95% CI 0.33 to 0.42), material deprivation (Spearman: 0.15, p &lt; .001, 95% CI 0.10 to 0.19), dependency (Spearman: 0.31, p &lt; .001, 95% CI 0.26 to 0.35), and ethnic concentration (Spearman: −0.06, p = .02, 95% CI −0.11 to −0.01). Conclusions: In Peel Region, a correlation exists between social vulnerability and air pollution, indicating inequitable exposure to NO2. Marginalized communities may not be able to choose their living environment. Environmental justice research in the context of NO2 will support policy aiming to reduce inequitable exposure.

Integrating traffic pollution dispersion into spatiotemporal NO2 prediction

Accurately predicting ambient NO2 concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach incorporating traffic contribution to NO2 prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO2 models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R2 from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R2 increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO2 concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO2 exposure – especially near major roads in the U.S.

Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO2 concentrations in Accra, Ghana.

Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg m-3). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg m-3 higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.

Advancing shipping NOx pollution estimation through a satellite-based approach.

Estimating shipping nitrogen oxides (NOx) emissions and their associated ambient NO2 impacts is a complex and time-consuming task. In this study, a satellite-based ship pollution estimation model (SAT-SHIP) is developed to estimate regional shipping NOx emissions and their contribution to ambient NO2 concentrations in China. Unlike the traditional bottom-up approach, SAT-SHIP employs satellite observations with varying wind patterns to improve the top-down emission inversion methods for individual sectors amidst irregular emission plume signals. Through SAT-SHIP, shipping NOx emissions for 17 ports in China are estimated. The results show that SAT-SHIP performed comparably with the bottom-up approach, with an R2 value of 0.8. Additionally, SAT-SHIP reveals that the shipping sector in port areas contributes ∼21 and 11% to NO2 concentrations in the Yangtze River Delta and Pearl River Delta areas of China, respectively, which is consistent with the results from chemical transportation model simulations. This approach has practical implications for policymakers seeking to identify pollution sources and develop effective strategies to mitigate air pollution.

Ambient nitrogen dioxide in 47 187 neighbourhoods across 326 cities in eight Latin American countries: population exposures and associations with urban features

Health research on ambient nitrogen dioxide (NO2) is sparse in Latin America, despite the high prevalence of NO2-associated respiratory diseases in the region. This study describes within-city distributions of ambient NO2 concentrations at high spatial resolution and urban characteristics associated with neighbourhood ambient NO2 in 326 Latin American cities. We aggregated estimates of annual surface NO2 at 1 km2 spatial resolution for 2019, population counts, and urban characteristics compiled by the SALURBAL project to the neighbourhood level (ie, census tracts). We described the percentage of the urban population living with ambient NO2 concentrations exceeding WHO air quality guidelines. We used multilevel models to describe associations of neighbourhood ambient NO2 concentrations with population and urban characteristics at the neighbourhood and city levels. We examined 47 187 neighbourhoods in 326 cities from eight Latin American countries. Of the roughly 236 million urban residents observed, 85% lived in neighbourhoods with ambient annual NO2 above WHO guidelines. In adjusted models, higher neighbourhood-level educational attainment, closer proximity to the city centre, and lower neighbourhood-level greenness were associated with higher ambient NO2. At the city level, higher vehicle congestion, population size, and population density were associated with higher ambient NO2. Almost nine out of every ten residents of Latin American cities live with ambient NO2 concentrations above WHO guidelines. Increasing neighbourhood greenness and reducing reliance on fossil fuel-powered vehicles warrant further attention as potential actionable urban environmental interventions to reduce population exposure to ambient NO2. Wellcome Trust, National Institutes of Health, Cotswold Foundation.

Application of roadside air purifiers in urban street canyons: A pilot-scale study in Hong Kong

The implementation of roadside air purifiers has emerged as an effective active control measure to alleviate air pollution in urban street canyons. However, technical questions raised under real conditions remain challenging. In this study, we conducted a pilot-scale investigation involving seven units of self-designed roadside air purifiers in an urban street canyon in Hong Kong. The air cleaning effects were quantified with an air quality sensor network after rigorous quality control. The removal efficiencies of Nitrogen dioxide (NO2), Fine suspended particulates (PM2.5), Carbon monoxide (CO), and Nitric oxide (NO) were determined by comparing with simultaneously measured ambient concentrations, with hourly average efficiencies of 14.0 %–16.9 %, 3.5–10.0 %, 11.9 %–18.7 %, and 19.2 %–44.9 %, respectively. Generally, the purification effects presented variations depending on the ambient pollutants' levels. Higher ambient concentrations of NO2, PM2.5, CO correlated with increased purification effects, while NO presented the opposite trend. The influence of interval distance combined with spatial distribution indicated the operation of purifiers will induce local NO2 attenuation even at an interval distance of four meters. Statistical analysis delivered evidence the air cleaning ability exhibited optimal performance when relative humidity level is ranged from 70 % to 90 %, aligning with the prevailing conditions in Hong Kong. Additionally, improved purification effects were observed at the downwind direction, and their performance was enhanced when the wind speed exceeded 2.5 m/s. Moreover, we estimated the operational lifetime of the air purifiers to be approximately 130 days, offering crucial information regarding the filter replacement cycle. This work serves as a pioneering case study, showcasing the feasibility and deployment considerations of roadside air purifiers in effectively controlling air pollution in urban environments.

Development and validation of a nitrogen dioxide passive sampler

Nitrogen dioxide (NO2) is one of the main indicators of traffic-related air pollution in urban areas. Active sampling methods (common methods) are expensive and need advanced devices. While Passive sampling is a simple and low-cost method for measuring air pollutants, including NO2. Therefore in this study, we developed a passive sampler to measure ambient NO2 and validation its performance by comparing it with active sampling methods. Ambient NO2 was measured for 24 h by both active and passive sampling methods in the same locations (2 m height above grand level and 15 m distance from air pollution sources). Sampling of NO2 was repeated for 18 days to compare ambient NO2 concentrations measured by active and passive methods and validation our developed passive samplers.•To develop passive samplers we used three stainless steel filters impregnated with a combination of triethanolamine and acetone (25:25 mL) in each tube.•Active NO2 sampling was conducted using the modified Satlzman method (standard method).•There was a strong correlation between NO2 concentration obtained from active and passive sampling methods (r = 0.84).

Short-term air pollution exposure and exacerbation events in mild to moderate COPD: a case-crossover study within the CanCOLD cohort

BackgroundInfections are considered as leading causes of acute exacerbations of chronic obstructive pulmonary disease (COPD). Non-infectious risk factors such as short-term air pollution exposure may play a clinically important role....

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.

Impacts of COVID-19's restriction measures on personal exposure to VOCs and aldehydes in Taipei City

The Coronavirus Disease 2019 (COVID-19) pandemic provided an unprecedented natural experiment, that allowed us to investigate the impacts of different restrictive measures on personal exposure to specific volatile organic compounds (VOCs) and aldehydes and resulting health risks in the city. Ambient concentrations of the criteria air pollutants were also evaluated. Passive sampling for VOCs and aldehydes was conducted for graduate students and ambient air in Taipei, Taiwan, during the Level 3 warning (strict control measures) and Level 2 alert (loosened control measures) of the COVID-19 pandemic in 2021–2022. Information on the daily activities of participants and on-road vehicle counts nearby the stationary sampling site during the sampling campaigns were recorded. Generalized estimating equations (GEE) with adjusted meteorological and seasonal variables were used to estimate the effects of control measures on average personal exposures to the selected air pollutants. Our results showed that ambient CO and NO2 concentrations in relation to on-road transportation emissions were significantly reduced, which led to an increase in ambient O3 concentrations. Exposure to specific VOCs (benzene, methyl tert-butyl ether (MTBE), xylene, ethylbenzene, and 1,3-butadiene) associated with automobile emissions were remarkably decreased by ~40–80 % during the Level 3 warning, resulting in 42 % and 50 % reductions of total incremental lifetime cancer risk (ILCR) and hazard index (HI), respectively, compared with the Level 2 alert. In contrast, the exposure concentration and calculated health risks in the selected population for formaldehyde increased by ~25 % on average during the Level 3 warning. Our study improves knowledge of the influence of a series of anti-COVID-19 measures on personal exposure to specific VOCs and aldehydes and its mitigations.

Chronic exposure to ambient PM2.5/NO2 and respiratory health in school children: A prospective cohort study in Hong Kong

Despite increasing concerns about the detrimental effects of air pollution on respiratory health, limited evidence is available on these effects in the Hong Kong population, especially in children. In this prospective cohort study between 2012 and 2017, we aimed to investigate the associations between exposure to air pollution (concentrations of fine particulate matter [PM2.5] and nitrogen dioxide [NO2]) and respiratory health (lung function parameters and respiratory diseases and symptoms) in schoolchildren. We recruited 5612 schoolchildren aged 6–16 years in Hong Kong. We estimated the annual average concentrations of ambient PM2.5 and NO2 at each participant’s address using spatiotemporal models. We conducted spirometry tests on all participants to measure their lung function parameters and used a self-administered questionnaire to collect information on their respiratory diseases and symptoms and a wide range of covariates. Linear mixed models were used to investigate the associations between exposure to air pollution and lung function. Mixed-effects logistic regression models with random effects were used to investigate the associations of exposure to air pollution with respiratory diseases and symptoms. In all of the participants, every 5-μg/m3 increase in the ambient PM2.5 concentration was associated with changes of − 13.90 ml (95 % confidence interval [CI]: −23.65 ml, −4.10 ml), − 4.20 ml (−15.60 ml, 7.15 ml), 27.20 ml/s (−3.95 ml/s, 58.35 ml/s), and − 19.80 ml/s (−38.35 ml/s, −1.25 ml/s) in forced expiratory volume in 1 s, forced vital capacity, peak expiratory flow, and maximal mid-expiratory flow, respectively. The corresponding lung function estimates for every 5-μg/m3 increase in the ambient NO2 concentration were − 2.70 ml (−6.05 ml, 0.60 ml), − 1.40 ml (−5.40 ml, 2.60 ml), − 6.60 ml/s (−19.75 ml/s, 6.55 ml/s), and − 3.05 ml/s (−11.10 ml/s, 5.00 ml/s), respectively. We did not observe significant associations between PM2.5/NO2 exposure and most respiratory diseases and symptoms. Stratified analyses by sex and age showed that the associations between exposure to air pollution and lung function parameters were stronger in male participants and older participants (11–14 year old group) than in female participants and younger participants (6–10 year old group), respectively. Our results suggest that chronic exposure to air pollution is detrimental to the respiratory health of schoolchildren, especially that of older boys. Our findings reinforce the importance of air pollution mitigation to protect schoolchildren’s respiratory health.

Comparison of nitrogen deposition between two ecosystems in the southern edge of Tarim Basin

Quantifying the similarities and differences in atmospheric nitrogen (N) deposition between different ecosystems is important to develop effective measures to reduce air pollution and maintain biodiversity. Here we show that the constitution of N deposition differed significantly between a grassland and a desert ecosystem in Northwestern China. Flux of bulk (wet plus part of dry deposition) and dry (gaseous NH3 and NO2) deposition were continuously monitored from 2018 to 2020. The grassland and desert sites had similar amount of total N deposition, being 7.29 and 6.33 kg N ha−1 yr−1, respectively. However, N deposition at the grassland was dominated by the bulk deposition (4.44 kg N ha−1 yr−1, 61% of the total N deposition), whereas that at the desert was dominated by dry deposition (4.20 kg N ha−1 yr−1, 66% of total deposition). The desert had greater ambient concentrations of NH3 (3.66 μg N m−3) and NO2 (1.52 μg N m−3) than the grassland (2.73 μg NH3–N m−3 and 0.72 μg NO2–N m−3). The amount of reduced N deposition (NH4+ and NH3) was around 3 times of that of oxidized N deposition (NO3− and NO2) in both ecosystems. The N deposition rates in both ecosystems have exceeded the critical load for the fragile ecosystems (5–10 kg N ha−1 yr−1), highlighting the importance of reducing N emission sources that are related with anthropogenic disturbance.