A CNN-SVR model for NO2 profile prediction based on MAX-DOAS observations: The influence of Chinese New Year overlapping the 2020 COVID-19 lockdown on vertical distributions of tropospheric NO2 in Nanjing, China

In this study, a hybrid model, the convolutional neural network-support vector regression model, was adopted to achieve prediction of the NO2 profile in Nanjing from January 2019 to March 2021. Given the sudden decline in NO2 in February 2020, the contribution of the Coronavirus Disease-19 (COVID-19) lockdown, Chinese New Year (CNY), and meteorological conditions to the reduction of NO2 was evaluated. NO2 vertical column densities (VCDs) from January to March 2020 decreased by 59.05% and 32.81%, relative to the same period in 2019 and 2021, respectively. During the period of 2020 COVID-19, the average NO2 VCDs were 50.50% and 29.96% lower than those during the pre-lockdown and post-lockdown periods, respectively. The NO2 volume mixing ratios (VMRs) during the 2020 COVID-19 lockdown significantly decreased below 400 m. The NO2 VMRs under the different wind fields were significantly lower during the lockdown period than during the pre-lockdown period. This phenomenon could be attributed to the 2020 COVID-19 lockdown. The NO2 VMRs before and after the CNY were significantly lower in 2020 than in 2019 and 2021 in the same period, which further proves that the decrease in NO2 in February 2020 was attributed to the COVID-19 lockdown. Pollution source analysis of an NO2 pollution episode during the lockdown period showed that the polluted air mass in the Beijing-Tianjin-Hebei was transported southwards under the action of the north wind, and the subsequent unfavorable meteorological conditions (local wind speed of < 2.0 m/sec) resulted in the accumulation of pollutants.

Reconsidering gas as clean energy: Switching to electricity for household cooking to reduce NO2-attributed disease burden

Nitrogen dioxide (NO2) is a prevalent air pollutant in urban areas, originating from outdoor sources, household gas consumption, and secondhand smoke. The limited evaluation of the disease burden attributable to NO2, encompassing different health effects and contributions from various sources, impedes our understanding from a public health perspective. Based on modeled NO2 exposure concentrations, their exposure-response relationships with lung cancer, chronic obstructive pulmonary disease, and diabetes mellitus, and baseline disability-adjusted life years (DALYs), we estimated that 1,675 (655-2,624) thousand DALYs were attributable to NO2 in urban China in 2019 [138 (54-216) billion Chinese yuan (CNY) economic losses]. The transition from gas to electricity for household cooking was estimated to reduce the attributable economic losses by 35%. This reduction falls within the range of reductions achieved when outdoor air meets the World Health Organization interim target 3 and air quality guidelines for annual NO2, highlighting the significance of raising awareness of gas as a polluting household energy for cooking. These findings align with global sustainable development initiatives, providing a sustainable solution to promote public health while potentially mitigating climate change.

Predicting particulate matter, nitrogen dioxide, and ozone across Great Britain with high spatiotemporal resolution based on random forest models

In Great Britain, limited studies have employed machine learning methods to predict air pollution especially ozone (O3) with high spatiotemporal resolution. This study aimed to address this gap by developing random forest models for four key pollutants (fine and inhalable particulate matter [PM2.5 and PM10], nitrogen dioxide [NO2] and O3) by integrating multiple-source predictors at a daily level and 1-km resolution. The out-of-bag R2 (root mean squared error, RMSE) between predictions from models and measurements from monitoring stations in 2006-2013 was 0.85 (3.63 μg/m3) for PM2.5, 0.77 (6.00 μg/m3) for PM10, 0.85 (9.71 μg/m3) for NO2, and 0.85 (9.39 μg/m3) for maximum daily 8-h average (MDA8) O3 at daily level, and the predicting accuracy was higher at monthly and annual level. The high-resolution predictions captured characterized spatiotemporal patterns of the four pollutants. Higher concentrations of PM2.5, PM10, and NO2 were distributed in densely populated southern regions of Great Britain while O3 showed an inverse spatial pattern in general, which could not be fully depicted by monitoring stations. Therefore, predictions produced in this study could improve exposure assessment with less exposure misclassification and flexible exposure windows for future epidemiological studies to investigate the impact of air pollution across Great Britain.

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.

Air pollution and bronchiolitis: a case-control study in Antwerp, Belgium

This case-control study aimed to investigate the association between short-term (1 to 5 days) and medium-term (31 days) exposure to air pollutants (PM2.5, PM10, BC, NO2) at home/daycare and the risk of 'severe bronchiolitis' (defined as 'requiring hospitalization for bronchiolitis') in children under 2 years in Antwerp, Belgium. We included 118 cases and 79 controls admitted to three general hospitals from October 2020 to June 2021. Exposure levels were predicted using an interpolation model based on fixed measuring stations. We used unconditional logistic regression analysis to assess associations, with adjustment for potential confounders. There were hardly any significant differences in the day-to-day air pollution values between cases and controls. Medium-term (31 days) exposure to PM2.5, PM10, and NO2 was however significantly higher in cases than controls in univariate analysis. Logistic regression revealed an association between severe bronchiolitis and interquartile range (IQR) increases of PM2.5 and PM10 at home and in daycare, as well as IQR increases of NO2 in daycare. Controls were however overrepresented in low pollution periods. Time-adjustment reduced the odds ratios significantly at home for PM2.5 and PM10 (aOR 1.54, 95%CI 0.51-4.65; and 2.69, 95%CI 0.94-7.69 respectively), and in daycare for. PM2.5 (aOR 2.43, 95%CI 0.58-10.1). However, the association between severe bronchiolitis and medium-term air pollution was retained in daycare for IQR increases of PM10 (aOR 5.13, 95%CI 1.24-21.28) and NO2 (aOR 3.88, 95%CI 1.56-9.61) in the time-adjusted model.  Conclusion: This study suggests a possible link between severe bronchiolitis and medium-term (31 days) air pollution exposure (PM10 and NO2), particularly in daycare. Larger studies are warranted to confirm these findings. What is Known: • Bronchiolitis is a leading cause of hospitalization in infants globally and causes a yearly seasonal wave of admissions in paediatric departments worldwide. • Existing studies, mainly from the USA, show heterogeneous outcomes regarding the association between air pollution and bronchiolitis. What is New: • There is a possible link between severe bronchiolitis and medium-term (31 days) air pollution exposure (PM10 and NO2), particularly in daycare. • Larger studies are needed to validate these trends.

Associations between long-term exposure to air pollution and kidney function utilizing electronic healthcare records: a cross-sectional study

BACKGROUND

Chronic kidney disease (CKD) affects more than 38 million people in the United States, predominantly those over 65 years of age. While CKD etiology is complex, recent research suggests associations with environmental exposures.

METHODS

Our primary objective is to examine creatinine-based estimated glomerular filtration rate (eGFRcr) and diagnosis of CKD and potential associations with fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) using a random sample of North Carolina electronic healthcare records (EHRs) from 2004 to 2016. We estimated eGFRcr using the serum creatinine-based 2021 CKD-EPI equation. PM2.5 and NO2 data come from a hybrid model using 1 km2 grids and O3 data from 12 km2 CMAQ grids. Exposure concentrations were 1-year averages. We used linear mixed models to estimate eGFRcr per IQR increase of pollutants. We used multiple logistic regression to estimate associations between pollutants and first appearance of CKD. We adjusted for patient sex, race, age, comorbidities, temporality, and 2010 census block group variables.

RESULTS

We found 44,872 serum creatinine measurements among 7,722 patients. An IQR increase in PM2.5 was associated with a 1.63 mL/min/1.73m2 (95% CI: -1.96, -1.31) reduction in eGFRcr, with O3 and NO2 showing positive associations. There were 1,015 patients identified with CKD through e-phenotyping and ICD codes. None of the environmental exposures were positively associated with a first-time measure of eGFRcr < 60 mL/min/1.73m2. NO2 was inversely associated with a first-time diagnosis of CKD with aOR of 0.77 (95% CI: 0.66, 0.90).

CONCLUSIONS

One-year average PM2.5 was associated with reduced eGFRcr, while O3 and NO2 were inversely associated. Neither PM2.5 or O3 were associated with a first-time identification of CKD, NO2 was inversely associated. We recommend future research examining the relationship between air pollution and impaired renal function.

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.

Long-term variations of air pollutants and public exposure in China during 2000-2020

Since 2000, China has faced severe air pollution challenges，prompting the initiation of comprehensive emission control measures post-2013. The subsequent implementation of these measures has led to remarkable enhancements in air quality. This study aims to enhance our understanding of the long-term trends in fine particulate matter (PM2.5) and gaseous pollutants of ozone (O3) and nitrogen dioxide (NO2) across China from 2000 to 2020. Utilizing the Community Multiscale Air Quality (CMAQ) model, we conducted a nationwide analysis of air quality, systematically quantifying model predictions against observations for pollutants. The CMAQ model effectively captured the trends of air pollutants, meeting recommended performance benchmarks. The findings reveal variations in pollutant concentrations, with initial increases in PM2.5 followed by a decline after 2013. The proportion of the population living in high PM2.5 concentrations (>75 μg/m3) decreased to <5 % after 2015. However, during the period from 2017 to 2020, around 40 % of the population continued to live in regions that did not meet the criteria for Chinese air quality standards (35 μg/m3). From 2000 to 2019, fewer than 20 % of the population met the WHO standard (100 μg/m3) for MDA8 O3. In 2000, 77 % of the population met the NO2 standard (<20 μg/m3), a figure that declined to 60 % between 2005 and 2014, nearly reaching 70 % in 2020. This study offers a comprehensive analysis of the changes in pollutants and public exposure in 2000-2020. It serves as a foundational resource for future efforts in air pollution control and health research.

Chronic exposure to ambient air pollution and the risk of non-alcoholic fatty liver disease: A cross-sectional study in Taiwan and Hong Kong

BACKGROUND

Information on the relation of air pollution with non-alcoholic fatty liver disease (NAFLD) is scarce. We thus conducted a large cross-sectional study in Asia to investigate the role of air pollution in NAFLD.

METHODS

We recruited 329,048 adults (mean age: 41.0 years) without other liver disease (hepatitis and cirrhosis) or excessive alcohol consumption in Taiwan and Hong Kong from 2001 to 2018. The concentrations of nitrogen dioxide (NO2) and ozone (O3) were estimated using a space-time regression model, and the concentrations of fine particulate matter (PM2.5) was evaluated using a satellite-based spatio-temporal model. NAFLD was determined using either the fatty liver index (FLI) or the hepatic steatosis index (HSI). The NAFLD-related advanced fibrosis was defined according to BARD score or the fibrosis-4 (FIB-4). A logistic regression model was adopted to explore the relationships of ambient air pollution with the odds of NAFLD and NAFLD-related advanced fibrosis.

RESULTS

We found positive relationships between PM2.5 and the odds of NAFLD and advanced fibrosis, with every standard deviation (SD, 7.5 µg/m3) increases in PM2.5 exposure being associated with a 10% (95% confidence interval [CI]: 9%-11%) increment in the prevalence of NAFLD and an 8% (95% CI: 7%-9%) increment in the prevalence of advanced fibrosis. Similarly, the prevalence of NAFLD and advanced fibrosis increased by 8% (95% CI: 7%-9%) and 7% (95% CI: 6%-8%) with per SD (18.9 µg/m3) increasement in NO2 concentration, respectively. Additionally, for every SD (9.9 µg/m3) increasement in O3 concentration, the prevalence of NAFLD and advanced fibrosis decreased by 12% (95% CI: 11%-13%) and 11% (95% CI: 9%-12%), respectively.

CONCLUSION

Higher ambient PM2.5 and NO2 are linked with higher odds of NAFLD and advanced fibrosis. Our findings indicate that reducing PM2.5 and NO2 concentrations may be an effective way for preventing NAFLD. Further studies on O3 are warranted.

Risk analysis of air pollutants and types of anemia: a UK Biobank prospective cohort study

Previous studies have suggested that exposure to air pollutants may be associated with specific blood indicators or anemia in certain populations. However, there is insufficient epidemiological data and prospective evidence to evaluate the relationship between environmental air pollution and specific types of anemia. We conducted a large-scale prospective cohort study based on the UK Biobank. Annual average concentrations of NO2, PM2.5, PM2.5-10, and PM10 were obtained from the ESCAPE study using the Land Use Regression (LUR) model. The association between atmospheric pollutants and different types of anemia was investigated using the Cox proportional hazards model. Furthermore, restricted cubic splines were used to explore exposure-response relationships for positive associations, followed by stratification and effect modification analyses by gender and age. After adjusting for demographic characteristics, 3-4 of the four types of air pollution were significantly associated with an increased risk of iron deficiency, vitamin B12 deficiency and folate deficiency anemia, while there was no significant association with other defined types of anemia. After full adjustment, we estimated that the hazard ratios (HRs) of iron deficiency anemia associated with each 10 μg/m3 increase in NO2, PM2.5, and PM10 were 1.04 (95%CI: 1.02, 1.07), 2.00 (95%CI: 1.71, 2.33), and 1.10 (95%CI: 1.02, 1.20) respectively. The HRs of folate deficiency anemia with each 10 μg/m3 increase in NO2, PM2.5, PM2.5-10, and PM10 were 1.25 (95%CI: 1.12, 1.40), 4.61 (95%CI: 2.03, 10.47), 2.81 (95%CI: 1.11, 7.08), and 1.99 (95%CI: 1.25, 3.15) respectively. For vitamin B12 deficiency anemia, no significant association with atmospheric pollution was found. Additionally, we estimated almost linear exposure-response curves between air pollution and anemia, and interaction analyses suggested that gender and age did not modify the association between air pollution and anemia. Our research provided reliable evidence for the association between long-term exposure to PM10, PM2.5, PM2.5-10, NO2, and several types of anemia. NO2, PM2.5, and PM10 significantly increased the risk of iron deficiency anemia and folate deficiency anemia. Additionally, we found that the smaller the PM diameter, the higher the risk, and folate deficiency anemia was more susceptible to air pollution than iron deficiency anemia. No association was observed between the four types of air pollution and hemolytic anemia, aplastic anemia, and other types of anemia. Although the mechanisms are not well understood, we emphasize the need to limit the levels of PM and NO2 in the environment to reduce the potential impact of air pollution on folate and iron deficiency anemia.

Ambient air pollution and rate of spontaneous abortion

Spontaneous abortion (SAB), defined as a pregnancy loss before 20 weeks of gestation, affects up to 30% of conceptions, yet few modifiable risk factors have been identified. We estimated the effect of ambient air pollution exposure on SAB incidence in Pregnancy Study Online (PRESTO), a preconception cohort study of North American couples who were trying to conceive. Participants completed questionnaires at baseline, every 8 weeks during preconception follow-up, and in early and late pregnancy. We analyzed data on 4643 United States (U.S.) participants and 851 Canadian participants who enrolled during 2013-2019 and conceived during 12 months of follow-up. We used country-specific national spatiotemporal models to estimate concentrations of particulate matter <2.5 μm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) during the preconception and prenatal periods at each participant's residential address. On follow-up and pregnancy questionnaires, participants reported information on pregnancy status, including SAB incidence and timing. We fit Cox proportional hazards regression models with gestational weeks as the time scale to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of time-varying prenatal concentrations of PM2.5, NO2, and O3 with rate of SAB, adjusting for individual- and neighborhood-level factors. Nineteen percent of pregnancies ended in SAB. Greater PM2.5 concentrations were associated with a higher incidence of SAB in Canada, but not in the U.S. (HRs for a 5 μg/m3 increase = 1.29, 95% CI: 0.99, 1.68 and 0.94, 95% CI: 0.83, 1.08, respectively). NO2 and O3 concentrations were not appreciably associated with SAB incidence. Results did not vary substantially by gestational weeks or season at risk. In summary, we found little evidence for an effect of residential ambient PM2.5, NO2, and O3 concentrations on SAB incidence in the U.S., but a moderate positive association of PM2.5 with SAB incidence in Canada.

Physical activity attenuates the association of long-term exposure to nitrogen dioxide with sleep quality and its dimensions in Chinese rural older adults

BACKGROUND

Joint impacts of air pollution and physical activity (PA) on sleep quality remain unaddressed. We aimed to investigate whether PA attenuates the association of long-term exposure to nitrogen dioxide (NO2) with sleep quality and its dimensions in older adults.

METHODS

This study included 3408 Chinese rural older adults. Annual NO2 was estimated using the Space-Time Extra-Trees model. PA was assessed by International Physical Activity Questionnaire. Sleep quality was evaluated using Pittsburgh Sleep Quality Index (PSQI) scale. Linear regression models were used to assess the associations of long-term NO2 exposure and PA with sleep quality and its dimensions, and interaction plots were used to depict the attenuating effect of PA on associations of NO2 with sleep quality and its dimensions.

RESULTS

Three-year (3-y) average NO2 (per 0.64-μg/m3 increment) was positively associated with global PSQI (β = 0.41, 95 % CI: 0.23, 0.59), sleep duration (β = 0.16, 95 % CI: 0.11, 0.21), and habitual sleep efficiency (β = 0.22, 95 % CI: 0.17, 0.27), while PA was negatively associated with global PSQI (β = -0.33, 95 % CI: -0.46, -0.20) and five domains of PSQI other than sleep duration and sleep disturbances. The associations of NO2 with global PSQI, sleep duration, and habitual sleep efficiency were attenuated with increased PA (Pinteraction were 0.037, 0.020, and 0.079, respectively).

CONCLUSIONS

PA attenuates the adverse impacts of long-term NO2 exposure on sleep quality, especially on sleep duration, and habitual sleep efficiency, in Chinese rural elderly people. Participating in PA should be encouraged in this population, and continued efforts are still needed to reduce air pollution.

Long-term exposure to ambient air pollution and incident gout: A prospective cohort study in the UK Biobank

Gout is a chronic disorder characterized by the accumulation of uric acid in the body, leading to recurrent episodes of joint inflammation and pain. There remains a lack of studies investigating the association between long-term exposure to ambient air pollution and the incidence of gout. We conducted this prospective cohort study involving participants aged 38-70 from the UK Biobank who were enrolled in 2006-2010 and followed until 2023. Baseline residential concentrations of fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2) and nitrogen oxides (NOx) were predicted using land-use regression models. Cox proportional hazards models were employed to examine the relationship between air pollution and incident gout events. A total of 443,587 individuals were included in the analyses and a total of 6589 incident gout cases were identified over a follow-up of 6,130,439 person-years. There were significant associations between higher levels of air pollution and an increased incidence risk of gout. Higher risk of incident gout was associated with each interquartile range increase in concentrations of PM2.5 (hazard ratio:1.05, 95% confidence intervals: 1.02-1.09), PM10 (1.04, 1.00-1.07), NO2 (1.08, 1.05-1.12) and NOx (1.04, 1.02-1.07). The magnitude of associations was larger at higher concentrations. The association was more prominent among older adults, smokers, and individuals with lower and moderate physical activity. This prospective cohort study provides novel and compelling evidence of increased risk of incident gout associated with long-term air pollution exposures.

BREATH-Net: a novel deep learning framework for NO2 prediction using bi-directional encoder with transformer

Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO2) is a prevalent atmospheric pollutant that can potentially exacerbate respiratory ailments and cardiovascular disorders and contribute to cancer development. The present study introduces a novel approach for monitoring and predicting Delhi's nitrogen dioxide concentrations by leveraging satellite data and ground data from the Sentinel 5P satellite and monitoring stations. The research gathers satellite and monitoring data over 3 years for evaluation. Exploratory data analysis (EDA) methods are employed to comprehensively understand the data and discern any discernible patterns and trends in nitrogen dioxide levels. The data subsequently undergoes pre-processing and scaling utilizing appropriate techniques, such as MinMaxScaler, to optimize the model's performance. The proposed forecasting model uses a hybrid architecture of the Transformer and BiLSTM models called BREATH-Net. BiLSTM models exhibit a strong aptitude for effectively managing sequential data by adeptly capturing dependencies in both the forward and backward directions. Conversely, transformers excel in capturing extensive relationships over extended distances in temporal data. The results of this study will illustrate the proposed model's efficacy in predicting the levels of NO2 in Delhi. If effectively executed, this model can significantly enhance strategies for controlling urban air quality. The findings of this research show a significant improvement of RMSE = 9.06 compared to other state-of-the-art models. This study's primary objective is to contribute to mitigating respiratory health issues resulting from air pollution through satellite data and deep learning methodologies.

Time-sensitive prediction of NO2 concentration in China using an ensemble machine learning model from multi-source data

Nitrogen dioxide (NO2) poses a critical potential risk to environmental quality and public health. A reliable machine learning (ML) forecasting framework will be useful to provide valuable information to support government decision-making. Based on the data from 1609 air quality monitors across China from 2014-2020, this study designed an ensemble ML model by integrating multiple types of spatial-temporal variables and three sub-models for time-sensitive prediction over a wide range. The ensemble ML model incorporates a residual connection to the gated recurrent unit (GRU) network and adopts the advantage of Transformer, extreme gradient boosting (XGBoost) and GRU with residual connection network, resulting in a 4.1%±1.0% lower root mean square error over XGBoost for the test results. The ensemble model shows great prediction performance, with coefficient of determination of 0.91, 0.86, and 0.77 for 1-hr, 3-hr, and 24-hr averages for the test results, respectively. In particular, this model has achieved excellent performance with low spatial uncertainty in Central, East, and North China, the major site-dense zones. Through the interpretability analysis based on the Shapley value for different temporal resolutions, we found that the contribution of atmospheric chemical processes is more important for hourly predictions compared with the daily scale predictions, while the impact of meteorological conditions would be ever-prominent for the latter. Compared with existing models for different spatiotemporal scales, the present model can be implemented at any air quality monitoring station across China to facilitate achieving rapid and dependable forecast of NO2, which will help developing effective control policies.

Gas cooking indoors and respiratory symptoms in the ECRHS cohort

BACKGROUND

Gas cooking is an important source of indoor air pollutants, and there is some limited evidence that it might adversely be associated with respiratory health. Using repeated cross-sectional data from the multi-centre international European Community Respiratory Health Survey, we assessed whether adults using gas cookers have increased risk of respiratory symptoms compared to those using electric cookers and tested whether there was effect modification by a priori selected factors.

METHODS

Data on respiratory symptoms and gas cooking were collected from participants at 26-55 and 38-67 years (median time between examinations 11.4 years) from interviewer-led questionnaires. Repeated associations between gas cooking (versus electric) and respiratory symptoms were estimated using multivariable mixed-effects logistic regression models adjusted for age, sex, study arm, smoking status, education level, and included random intercepts for participants within study centres. Analyses were repeated using a 3-level variable for type of cooker and gas source. Effect modification by ventilation habits, cooking duration, sex, age atopy, asthma, and study arm were examined.

RESULTS

The sample included 4337 adults (43.7% males) from 19 centres in 9 countries. Gas cooking increased the risk of "shortness of breath whilst at rest" (OR = 1.38; 95%CI: 1.06-1.79) and "wheeze with breathlessness" (1.32; 1.00-1.74). For several other symptoms, effect estimates were larger in those who used both gas hobs and ovens, had a bottled gas source and cooked for over 60 min per day. Stratifying results by sex and age found stronger associations in females and younger adults.

CONCLUSION

This multi-centre international study, using repeat data, suggested using gas cookers in the home was more strongly associated than electric cookers with certain respiratory symptoms in adults. As gas cooking is common, these results may play an important role in population respiratory health.

Distribution and influencing factors of atmospheric nitrogen oxides (NOx) over the east coast of China in spring: Indication of the sea as a sink of the atmospheric NOx

An integrated observation of NOx that included coastal cities and oceanic cruises covering the Qingdao coastal waters sites (QDCW) and the Yellow Sea and East China Sea sites (YECS) was conducted in spring. The average concentrations of the coastal cities, the QDCW, and the YECS were 5.4 ± 4.1, 4.2 ± 3.5, and 2.9 ± 6.8 ppb for NO while 18.5 ± 7.2, 9.4 ± 5.2, and 4.9 ± 6.4 ppb for NO2, depicting lowest levels in the open seas. Atmospheric NO and NO2 showed similar spatial variations over the seas, the stations where the air masses originated from land or nearshore regions showed higher levels, but the decisive influencing factors were not the same in the different study areas. The calculated NOx flux value in the YECS (-8.7 × 10-17 mol N cm-2) indicated that the sea surface was a net sink of atmospheric NOx.

Experimental observation of the ν1+3ν3 combination bands of 16O14N18O and 18O14N18O in the near infrared spectral region

The first observation of the ν1+3ν3 combination band of the nitrogen dioxide isotopologue 16O14N18O is presented. The band was measured using Fourier-Transform Incoherent Broad-Band Cavity Enhanced Absorption Spectroscopy (FT-IBBCEAS) in the region between 5870 cm-1 and 5940 cm-1. To confirm the assignment, the band was simulated using a standard asymmetric top Watson Hamiltonian using extrapolated rotational and centrifugal distortion constants. Furthermore, the first experimental observation of the ν1+3ν3 band of the 18O14N18O isotopologue is also reported. The positions of ro-vibrational lines of the ν1+3ν3 band of the naturally most abundant isotopologue 16O14N16O were used for wavenumber calibration of line positions.

Long-term exposure to ambient air pollution and risk of leukemia and lymphoma in a pooled European cohort

Leukemia and lymphoma are the two most common forms of hematologic malignancy, and their etiology is largely unknown. Pathophysiological mechanisms suggest a possible association with air pollution, but little empirical evidence is available. We aimed to investigate the association between long-term residential exposure to outdoor air pollution and risk of leukemia and lymphoma. We pooled data from four cohorts from three European countries as part of the "Effects of Low-level Air Pollution: a Study in Europe" (ELAPSE) collaboration. We used Europe-wide land use regression models to assess annual mean concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and ozone (O3) at residences. We also estimated concentrations of PM2.5 elemental components: copper (Cu), iron (Fe), zinc (Zn); sulfur (S); nickel (Ni), vanadium (V), silicon (Si) and potassium (K). We applied Cox proportional hazards models to investigate the associations. Among the study population of 247,436 individuals, 760 leukemia and 1122 lymphoma cases were diagnosed during 4,656,140 person-years of follow-up. The results showed a leukemia hazard ratio (HR) of 1.13 (95% confidence intervals [CI]: 1.01-1.26) per 10 μg/m3 NO2, which was robust in two-pollutant models and consistent across the four cohorts and according to smoking status. Sex-specific analyses suggested that this association was confined to the male population. Further, the results showed increased lymphoma HRs for PM2.5 (HR = 1.16; 95% CI: 1.02-1.34) and potassium content of PM2.5, which were consistent in two-pollutant models and according to sex. Our results suggest that air pollution at the residence may be associated with adult leukemia and lymphoma.

Changes in industrial air pollution and the onset of childhood asthma in Quebec, Canada

Ambient air pollution has been associated with asthma onset and exacerbation in children. Whether improvement in air quality due to reduced industrial emissions has resulted in improved health outcomes such as asthma in some localities has usually been assessed indirectly with studies on between-subject comparisons of air pollution from all sources and health outcomes. In this study we directly assessed, within small areas in the province of Quebec (Canada), the influence of changes in local industrial fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) concentrations, on changes in annual asthma onset rates in children (≤12 years old) with a longitudinal ecological design. We identified the yearly number of new cases of childhood asthma in 1282 small areas (census tracts or local community service centers) for the years 2002, 2004, 2005, 2006, and 2015. Annual average concentrations of industrial air pollutants for each of the geographic areas, and three sectors (i.e., pulp and paper mills, petroleum refineries, and metal smelters) were estimated by the Polair3D chemical transport model. Fixed-effects negative binomial models adjusted for household income were used to assess associations; additional adjustments for environmental tobacco smoke, background pollutant concentrations, vegetation coverage, and sociodemographic characteristics were conducted in sensitivity analyses. The incidence rate ratios (IRR) for childhood asthma onset for the interquartile increase in total industrial PM2.5, NO2, and SO2 were 1.016 (95% confidence interval, CI: 1.006-1.026), 1.063 (1.045-1.090), and 1.048 (1.031-1.080), respectively. Positive associations were also found with pollutant concentrations from most individual sectors. Results suggest that changes in industrial pollutant concentrations influence childhood asthma onset rates in small localities.

A novel approach to deriving the fine-scale daily NO2 dataset during 2005-2020 in China: Improving spatial resolution and temporal coverage to advance exposure assessment

Surface NO2 pollution can result in serious health consequences such as cardiovascular disease, asthma, and premature mortality. Due to the extensive spatial variation in surface NO2, the spatial resolution of a NO2 dataset has a significant impact on the exposure and health impact assessment. There is currently no long-term, high-resolution, and publicly available NO2 dataset for China. To fill this gap, this study generated a NO2 dataset named RBE-DS-NO2 for China during 2005-2020 at 1 km and daily resolution. We employed the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and utilized an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005-2012 observations from sites in Taiwan province yielded an R2 of 0.72 and RMSE of 10.7 μg/m3, while cross-validation across China during 2013-2020 showed an R2 of 0.73 and RMSE of 9.6 μg/m3. RBE-DS-NO2 better captured spatiotemporal variation of surface NO2 in China compared to the existing publicly available datasets. Exposure assessment using RBE-DS-NO2 show that the population living in non-attainment areas (NO2 ≥ 30 μg/m3) grew from 376 million in 2005 to 612 million in 2012, then declined to 404 million by 2020. Unlike this national trend, exposure levels in several major cities (e.g., Shanghai and Chengdu) continued to increase during 2012-2020, driven by population growth and urban migration. Furthermore, this study revealed that low-resolution dataset (i.e., the 10 km intermediate dataset before the downscaling) overestimated NO2 levels, due to the limited specificity of the low-resolution model in simulating the relationship between NO2 and the predictor variables. Such limited specificity likely biased previous long-term NO2 exposure and health impact studies employing low-resolution datasets. The RBE-DS-NO2 dataset enables robust long-term assessments of NO2 exposure and health impacts in China.

Urban-rural disparities in the association of nitrogen dioxide exposure with cardiovascular disease risk in China: effect size and economic burden

BACKGROUND

Together with rapid urbanization, ambient nitrogen dioxide (NO2) exposure has become a growing health threat. However, little is known about the urban-rural disparities in the health implications of short-term NO2 exposure. This study aimed to compare the association between short-term NO2 exposure and hospitalization for cardiovascular disease (CVD) among urban and rural residents in Shandong Province, China. Then, this study further explored the urban-rural disparities in the economic burden attributed to NO2 and the explanation for the disparities.

METHODS

Daily hospitalization data were obtained from an electronic medical records dataset covering a population of 5 million. In total, 303,217 hospital admissions for CVD were analyzed. A three-stage time-series analytic approach was used to estimate the county-level association and the attributed economic burden.

RESULTS

For every 10-μg/m3 increase in NO2 concentrations, this study observed a significant percentage increase in hospital admissions on the day of exposure of 1.42% (95% CI 0.92 to 1.92%) for CVD. The effect size was slightly higher in urban areas, while the urban-rural difference was not significant. However, a more pronounced displacement phenomenon was found in rural areas, and the economic burden attributed to NO2 was significantly higher in urban areas. At an annual average NO2 concentration of 10 μg/m3, total hospital days and expenses in urban areas were reduced by 81,801 (44,831 to 118,191) days and 60,121 (33,002 to 86,729) thousand CNY, respectively, almost twice as much as in rural areas. Due to disadvantages in socioeconomic status and medical resources, despite similar air pollution levels in the urban and rural areas of our sample sites, the rural population tended to spend less on hospitalization services.

CONCLUSIONS

Short-term exposure to ambient NO2 could lead to considerable health impacts in either urban or rural areas of Shandong Province, China. Moreover, urban-rural differences in socioeconomic status and medical resources contributed to the urban-rural disparities in the economic burden attributed to NO2 exposure. The health implications of NO2 exposure are a social problem in addition to an environmental problem. Thus, this study suggests a coordinated intervention system that targets environmental and social inequality factors simultaneously.

Long-term exposure to ambient air pollution with sarcopenia among middle-aged and older adults in China

BACKGROUND

Few studies have examined the role of outdoor air pollution exposure in sarcopenia in Asia. We aimed to investigate the association of outdoor air pollutants exposure with sarcopenia among Chinese adults.

METHODS

This nationally population-representative study used data from the China Health and Retirement Longitudinal Study (CHARLS) in 2015, 11,700 participants at least 45 years old from 125 Chinese cities were included. Sarcopenia status was identified according to the Asian Working Group for Sarcopenia 2019 (AWGS 2019) criteria. Ambient annual average air pollutants including fine particulate matter (PM2.5), inhalable particles (PM10), coarse particulate matter (PMcoarse), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) were estimated by satellite models and ground-based measurements. Multinomial logistic regression models were performed to examine the associations of air pollutants exposure with different status of sarcopenia (including possible sarcopenia and sarcopenia). Stratified analyses were utilized to assess the effect modifiers.

RESULTS

Among the 11,700 participants (52.6% women), the average age was 61.0 years. Each 10 μg/m3 increment of annual PMcoarse was associated with a higher risk of possible sarcopenia (odds ratio (OR) = 1.08, 95% confidence interval (CI) 1.04-1.11). Stratified analyses showed a positive risk of possible sarcopenia in women after exposure to PM10, PMcoarse, and NO2. Ambient NO2 exposure was positively associated with sarcopenia (OR = 1.13, 95% CI 1.04-1.22) in those aged 65 years and older. However, we have not observed differences by sex, age, residence, smoking, and drinking. Robustness results were found for PMcoarse in the sensitivity analyses.

CONCLUSION

This nationwide study suggested that long-term exposure to outdoor air pollution, especially for PMcoarse, was associated with the risk of sarcopenia among Chinese adults. Our findings provide epidemiological implications for protecting healthy ageing by improving air quality.

Long-term NO2 exposure and mortality: A comprehensive meta-analysis

In response to the World Health Organization's (WHO) revised annual mean nitrogen dioxide (NO2) standard from 40 μg/m3 to 10 μg/m3, reflecting the growing evidence linking long-term exposure to ambient NO2 and excess mortality, we conducted a comprehensive meta-analysis incorporating 11 new studies published since the WHO analysis. Our investigation involved a systematic search of three major databases (PubMed, Web of Science, and Scopus) for articles published until July 1, 2022. We employed random effects models to calculate summarized risk ratios (RR) along with 95% confidence intervals (CIs) for overall and subgroup analyses. Sensitivity analyses were conducted to assess result robustness, and publication bias was evaluated using funnel plots and Egger's linear regression. Out of 2799 identified articles, 56 were included in our meta-analysis. The findings indicate a heightened risk of all-cause, cardiovascular, and respiratory mortality associated with long-term exposure to ambient NO2, with pooled RR values of 1.03 (95% CI: 1.02, 1.05), 1.07 (95% CI: 1.04, 1.10), and 1.03 (95% CI: 1.02, 1.05) per 10 μg/m3 increase, respectively. Substantial heterogeneity (I2 = 84%-96%) among studies was observed. Subgroup analysis revealed significantly elevated RR values in Asia and Oceania (p-value <0.05). The aggregated values for all-cause and cardiovascular mortality were slightly larger than those reported in previous studies. Our study emphasizes the imperative to develop more patient cohorts and conduct age-refined analyses to explore the impact of existing chronic diseases on these associations. Further, additional cohorts in Asia and Oceania are essential to fortify evidence in these regions. Lastly, we recommend using fused multi-source data with higher spatiotemporal resolution for individual exposure representation to minimize heterogeneity among studies in future research.

The impact of short-term exposures to ambient NO2, O3, and their combined oxidative potential on daily mortality

It is widely recognized that air pollution exerts substantial detrimental effects in human health and the economy. The potential for harm is closely linked to the concentrations of pollutants like nitrogen dioxide (NO2) and ozone (O3), as well as their collective oxidative potential (OX). Yet, due to the challenges of directly monitoring OX as an independent factor and the influences of different substances' varying ability to contain or convey OX, uncertainties persist regarding its actual impact. To provide further evidence to the association between short-term exposures to NO2, O3, and OX and mortality, this study conducted multi-county time-series analyses with over-dispersed generalized additive models and random-effects meta-analyses to estimate the mortality data from 2014 to 2020 in Jiangsu, China. The findings reveal that short-term exposures to these pollutants are linked to increased risks of all-cause, cardiovascular, and respiratory mortality, where NO2 demonstrates 2.11% (95% confidence interval: 1.79%, 2.42%), 2.28% (1.91%, 2.66%), and 2.91% (2.13%, 3.69%) respectively per every 10 ppb increase in concentration, and the effect of O3 is 1.11% (0.98%, 1.24%), 1.39% (1.19%, 1.59%), and 1.82% (1.39%, 2.26%), and OX is 1.77% (1.58%, 1.97%), 2.19% (1.90%, 2.48%), and 2.90% (2.29%, 3.52%). Notably, women and individuals aged over 75 years exhibit higher susceptibility to these pollutants, with NO2 showing a greater impact, especially during the warm seasons. The elevated mortality rates associated with NO2, O3, and OX underscore the significance of addressing air pollution as a pressing public health issue, especially in controlling NO2 and O3 together. Further research is needed to explore the underlying mechanisms and possible influential factors of these effects.

Short-term effects of ambient oxidation, and its interaction with fine particles on first-ever stroke: A national case-crossover study in China

Stroke is a significant global cause of disability and death, and its burden has been on the rise, while ambient air pollution has been conclusively linked to stroke incidence. However, knowledge about effects of atmospheric oxidation on stroke and its interactions with fine particles (PM2.5) are still limited. In this study, we investigated the short-term effects of ambient NO2, O3, and their combined oxidation (Owt) on first-ever stroke, based on data from the China National Stroke Screening Survey (CNSSS) conducted from 2013 to 2015. We found significant association between ambient NO2 exposure at lag0 day with first-ever stroke, with a 13.1 % (95 % CI: 3.5 %, 23.6 %) increase in the first-ever stroke risk per 10 μg/m3 exposure. We also found a significant interaction between NO2 and PM2.5 (p < 0.05): first-ever stroke risk increased 23.8 % (95 % CI: 9.6 %, 39.8 %) per 10 μg/m3 NO2 exposure in population exposed to higher PM2.5 concentrations, while no significant association was found in population exposed to lower PM2.5 concentrations. The results of stratified analyses indicated that physical inactivity enhanced the detrimental effects of O3 and Owt exposure, while smoking and transient ischemic attack (TIA) history enhanced the detrimental effects of NO2 exposure. However, TIA history appeared to mitigate the adverse effects of O3 exposure. This study is helpful to better understand the impact of ambient oxidation on stroke, as well as its interaction with PM2.5, and has implications for policies and standards for atmospheric protection and governance.

Net effect of air pollution controls on health risk in the Beijing-Tianjin-Hebei region during the 2022 winter Olympics and Paralympics

Due to the non-linearity in ozone (O3) formation, reducing the emission of nitrogen oxides (NOx) may increase O3 concentration. Given the counteractive O3 response to NOx reduction, overall impact of air pollution controls can be ambiguous when the assessments focus on the changes in pollutant concentrations. In this study, a risk-based method was used to gauge the net effect of air pollution controls on mortality risk in the Beijing-Tianjin-Hebei (BTH) region during the 2022 Winter Olympics and Paralympics (WOP). This mega-event presents a unique opportunity to investigate the efficacy of deep cuts in pollutant emissions. Results show that O3 concentrations greatly increased as nitrogen dioxide (NO2) concentrations decreased in the BTH. Due to the active photochemical formations, O3 became the dominant pollutant that affected human health during the WOP. Despite the substantial O3 increases, the health benefits of NO2 reductions overwhelmed the adverse health effects of O3 increases in most regions of the BTH (at 81 out of 112 stations). After considering the impacts of particulate matter, the integrated health risk of air pollution mixtures declined almost everywhere in the BTH. Our results underscore the great necessity of changing the assessment paradigm of pollution control from using concentration-based methods to using risk-based methods. Together with the carbon neutrality policy, stringent control of NOx emission from combustion sources is a promising way to achieve synergistic control solutions for air pollution and climate change.

Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia

Rapidly urbanizing cities in Latin America experience high levels of air pollution which are known risk factors for population health. However, the estimates of long-term exposure to air pollution are scarce in the region. We developed intraurban land use regression (LUR) models to map long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the five largest cities in Colombia. We conducted air pollution measurement campaigns using gravimetric PM2.5 and passive NO2 sensors for 2 weeks during both the dry and rainy seasons in 2021 in the cities of Barranquilla, Bucaramanga, Bogotá, Cali, and Medellín, and combined these data with geospatial and meteorological variables. Annual models were developed using multivariable spatial regression models. The city annual PM2.5 mean concentrations measured ranged between 12.32 and 15.99 µg/m3 while NO2 concentrations ranged between 24.92 and 49.15 µg/m3. The PM2.5 annual models explained 82% of the variance (R2) in Medellín, 77% in Bucaramanga, 73% in Barranquilla, 70% in Cali, and 44% in Bogotá. The NO2 models explained 65% of the variance in Bucaramanga, 57% in Medellín, 44% in Cali, 40% in Bogotá, and 30% in Barranquilla. Most of the predictor variables included in the models were a combination of specific land use characteristics and roadway variables. Cross-validation suggests that PM2.5 outperformed NO2 models. The developed models can be used as exposure estimate in epidemiological studies, as input in hybrid models to improve personal exposure assessment, and for policy evaluation.

Early life exposure to air pollution and psychotic-like experiences, emotional symptoms, and conduct problems in middle childhood

BACKGROUND

Air pollution has been linked to a variety of childhood mental health problems, but results are inconsistent across studies and the effect of exposure timing is unclear. We examined the associations between air pollution exposure at two time-points in early development and psychotic-like experiences (PLEs), and emotional and conduct symptoms, assessed in middle childhood (mean age 11.5 years).

METHODS

Participants were 19,932 children selected from the NSW Child Development Study (NSW-CDS) with available linked multi-agency data from birth, and self-reported psychotic-like experiences (PLEs) and psychopathology at age 11-12 years (middle childhood). We used binomial logistic regression to examine associations between exposure to nitrogen dioxide (NO2) and particulate matter less than 2.5 μm (PM2.5) at two time-points (birth and middle childhood) and middle childhood PLEs, and emotional and conduct symptoms, with consideration of socioeconomic status and other potential confounding factors in adjusted models.

RESULTS

In fully adjusted models, NO2 exposure in middle childhood was associated with concurrent PLEs (OR = 1.10, 95% CI = 1.02-1.20). Similar associations with PLEs were found for middle childhood exposure to PM2.5 (OR = 1.05, 95% CI = 1.01-1.09). Neither NO2 nor PM2.5 exposure was associated with emotional symptoms or conduct problems in this study.

CONCLUSIONS

This study highlights the need for a better understanding of potential mechanisms of action of NO2 in the brain during childhood.

Air pollution mortality benefits of sustained COVID-19 mobility restrictions in Australian cities

OBJECTIVES

Emissions from road traffic, power generation and industry were substantially reduced during pandemic lockdown periods globally. Thus, we analysed reductions in traffic-related air pollution in Australian capital cities during March-April 2020 and then modelled the mortality benefits that could be realised if similar reductions were sustained by structural policy interventions.

STUDY DESIGN

Satellite, air pollution monitor and land use observations were used to estimate ground-level nitrogen dioxide (NO2) concentrations in all Australian capital cities during: (a) a typical year with no prolonged air pollution events; (b) a hypothetical sustained reduction in NO2 equivalent to the COVID-19 lockdowns.

METHODS

We use the WHO recommended NO2 exposure-response coefficient for mortality (1.023, 95 % CI: 1.008-1.037, per 10 μg/m3 annual average) to assess gains in life expectancy and population-wide years of life from reduced exposure to traffic-related air pollution.

RESULTS

We attribute 1.1 % of deaths to anthropogenic NO2 exposures in Australian cities, corresponding to a total of 13,340 years of life lost annually. Although COVID-19-related reductions in NO2 varied widely between Australian cities during April 2020, equivalent and sustained reductions in NO2 emissions could reduce NO2-attributable deaths by 27 %, resulting in 3348 years of life gained annually.

CONCLUSIONS

COVID-19 mobility restrictions reduced NO2 emissions and population-wide exposures in Australian cities. When sustained to the same extent by policy interventions that reduce fossil fuel consumption by favouring the uptake of electric vehicles, active travel and public transport, the health, mortality and economic benefits will be measurable in Australian cities.

Effects of meteorological conditions on the mixing height of Nitrogen dioxide in China using new-generation geostationary satellite measurements and machine learning

Nitrogen dioxide (NO2) plays a critical role in terms of air quality, human health, ecosystems, and its impact on climate change. While the crucial roles of the vertical structure of NO2 have been acknowledged for some time, there is currently limited knowledge about this aspect in China. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first geostationary satellite instrument capable of measuring the hourly columnar amount of NO2. The study presented here introduces the use of mixing height for NO2 in the atmosphere. A thorough examination of spatiotemporal variations in the mixing height of NO2 was conducted using data from both the GEMS and ground-based air quality monitoring networks. A random forest model based on machine learning techniques was utilized to examine how meteorological parameters affect the mixing height of NO2. The results of our study reveal a notable seasonal fluctuation in the mixing height of NO2, with the highest values observed during the summer and the lowest values during the winter. Additionally, there was an increasing diurnal trend from early morning to mid-afternoon. Moreover, the study discovered elevated NO2 mixing heights in the dry regions of northern China. The results also indicated a positive correlation between the mixing height of NO2 and temperature and wind speed, while negative associations were found with relative humidity and air pressure. The machine learning model's predicted NO2 mixing heights were in good agreement with the measurement-based outcomes, as evidenced by a coefficient of determination (R2) value of 0.96 (0.84 for the 10-fold cross-validation). These findings emphasize the noteworthy influence of meteorological variables on the vertical distribution of NO2 in the atmosphere and enhance our comprehension of the three-dimensional variations in NO2.

Multiple myeloma risk in relation to long-term air pollution exposure - A pooled analysis of four European cohorts

BACKGROUND

Air pollution is a growing concern worldwide, with significant impacts on human health. Multiple myeloma is a type of blood cancer with increasing incidence. Studies have linked air pollution exposure to various types of cancer, including leukemia and lymphoma, however, the relationship with multiple myeloma incidence has not been extensively investigated.

METHODS

We pooled four European cohorts (N = 234,803) and assessed the association between residential exposure to nitrogen dioxide (NO2), fine particles (PM2.5), black carbon (BC), and ozone (O3) and multiple myeloma. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level.

RESULTS

During 4,415,817 person-years of follow-up (average 18.8 years), we observed 404 cases of multiple myeloma. The results of the fully adjusted linear analyses showed hazard ratios (95% confidence interval) of 0.99 (0.84, 1.16) per 10 μg/m³ NO2, 1.04 (0.82, 1.33) per 5 μg/m³ PM2.5, 0.99 (0.84, 1.18) per 0.5 10-5 m-1 BCE, and 1.11 (0.87, 1.41) per 10 μg/m³ O3.

CONCLUSIONS

We did not observe an association between long-term ambient air pollution exposure and incidence of multiple myeloma.

Role of antenatal anxiety in the relationship between maternal exposure to nitrogen dioxide and small for gestational age: A birth cohort study

BACKGROUND

Both Nitrogen dioxide (NO2) exposure and antenatal anxiety have individually been associated with small for gestational age (SGA). Little is known, however, about whether there is effect modification of antenatal anxiety on NO2-related SGA.

METHODS

The prospective birth cohort study included 1823 mother-newborn pairs in Guangzhou, China, from January 2017 to April 2020. Exposure to NO2 during the pre-conceptional and prenatal periods was estimated using an inverse distance weighted method. Antenatal anxiety was assessed by Trait Anxiety Inventory. SGA was determined by the Chinese gestational age- and sex-specific birthweight standards. Cox proportional hazards regression models was used to estimate hazard ratios (HRs) and 95 % confidence intervals (CIs) for SGA as per 10 μg/m3 increase in NO2. Modifying effects of trait anxiety on NO2-related SGA were identified by stratified analyses, and three-dimensional response surface plots and two-dimensional heat maps.

RESULTS

Each 10 μg/m3 increase in NO2 exposure during the third trimester was significantly associated with SGA risk among overall participants (HR = 1.221, 95 % CI: 1.014-1.471) and primipara (HR = 1.271, 95 % CI: 1.023-1.579). We found significant effect modification of anxiety level for NO2-related SGA in the third trimester (Pinteraction < 0.05). Pregnant women with higher levels of trait anxiety were more likely to deliver SGA newborns, particularly for those with high trait anxiety (HR = 1.781, 95 % CI: 1.007-2.945). Primiparous women were more susceptible.

CONCLUSIONS

This study provides evidence that antenatal trait anxiety may modify the effects of maternal NO2 exposure on SGA risk. The third trimester could be a critical window of susceptibility.

Photochemically-induced protein tyrosine nitration in vitro and in cellula by 5-methyl-1,4-dinitro-1H-imidazole (DNI): synthesis and biochemical characterization

The photochemical nitrating agent 5-methyl-1,4-dinitro-1H-imidazole (DNI) has been recently described as an effective tool for nitrating tyrosine residues in proteins under 390 nm irradiation (Long T. et al., 2021). Herein, we describe the one-step synthesis of DNI from the precursor 4-methyl-5-nitro-1H-imidazole with good yield (66%) and high purity (>99%). Spectral analysis of DNI reveals two maximum peaks (228 and 290 nm) with maximum nitration yields and kinetics occurring at 290 nm. Electron paramagnetic resonance (EPR)- and mass spectrometry (MS)- spin trapping analysis evidenced the formation of nitrogen dioxide (•NO2) upon irradiation of DNI, implying the homolysis of the N-N bond in the DNI molecule. Irradiation of DNI at 290, 390 nm, or UVA light (315-400 nm), produced tyrosine nitration, with yields approaching ca. 30% with respect to DNI at 290 nm exposure. Indeed, using alpha-synuclein as a model protein, the main protein post-translational modification triggered by DNI was the generation of 3-nitrotyrosine as shown by MS analysis. Additionally, the formation of di-tyrosine was also observed. Finally, intracellular •NO2 production upon DNI photolysis in bovine aortic endothelial cells was evidenced by the nitration of the tyrosine analog probe p-hydroxyphenylacetic acid (PHPA) and cellular protein tyrosine nitration.

Mendelian-randomization study reveals causal relationships between nitrogen dioxide and gut microbiota

Exposure to nitrogen dioxide might potentially change the makeup and operation of gut microbes. Nitrogen dioxide data was procured from the IEU Open GWAS (N = 456 380). Subsequently, a two-sample Mendelian randomization study was executed, utilizing summary statistics of gut microbiota sourced from the most expansive available genome-wide association study meta-analysis, conducted by the MiBioGen consortium (N = 13 266). The causal relationship between nitrogen dioxide and gut microbiota was determined using inverse variance weighted, maximum likelihood, MR-Egger, Weighted Median, Weighted Model, Mendelian randomization pleiotropy residual sum and outlier, and constrained maximum likelihood and model averaging and Bayesian information criterion. The level of heterogeneity of instrumental variables was quantified by utilizing Cochran's Q statistic. The colocalization analysis was used to examine whether nitrogen dioxide and the identified gut microbiota shared casual variants. Inverse variance weighted estimate suggested that nitrogen dioxide was causally associated with Akkermansia (β = -1.088, 95% CI: -1.909 to -0.267, P = 0.009). In addition, nitrogen dioxide presented a potential association with Bacteroides (β = -0.938, 95% CI: -1.592 to -0.284, P = 0.005), Barnesiella (β = -0.797, 95% CI: -1.538 to -0.055, P = 0.035), Coprococcus 3 (β = 1.108, 95% CI: 0.048-2.167, P = 0.040), Eubacterium hallii group (E. hallii) (β = 0.776, 95% CI: 0.090-1.463, P = 0.027), Holdemania (β = -1.354, 95% CI: -2.336 to -0.372, P = 0.007), Howardella (β = 1.698, 95% CI: 0.257-3.139, P = 0.021), Olsenella (β = 1.599, 95% CI: 0.151-3.048, P = 0.030) and Sellimonas (β = -1.647, 95% CI: -3.209 to -0.086, P = 0.039). No significant heterogeneity of instrumental variables or horizontal pleiotropy was found. The associations of nitrogen dioxide with Akkermansia (PH4 = 0.836) and E. hallii (PH4 = 0.816) were supported by colocalization analysis. This two-sample Mendelian randomization study found that increased exposure to nitrogen dioxide had the potential to impact the human gut microbiota.

Assessing the impact of urban form and urbanization process on tropospheric nitrogen dioxide pollution in the Yangtze River Delta, China

Optimizing urban form through urban planning and management policies can improve air quality and transition to demand-side control. Nitrogen dioxide (NO2) in the urban atmosphere, mainly emitted by anthropogenic sources such as industry and vehicles, is a key precursor of fine particles and ozone pollution. Both NO2 and its secondary pollutants pose health risks for humans. Here we assess the interactions between urban forms and airborne NO2 pollution in different cities with various stages of urbanization in the Yangtze River Delta (YRD) in China, by using the machine learning and geographical regression model. The results reveal a strong correlation between urban fragmentation and tropospheric NO2 vertical column density (TVCD) in YRD cities in 2020, particularly those with lower or higher levels of urbanization. The correlation coefficients (R2) between NO2 TVCD and the largest patch index (a metric of urban fragmentation) in different cities are greater than 0.8. For cities at other urbanization stages, population and road density are strongly correlated with NO2 TVCD, with an R2 larger than 0.61. This study highlights the interdependence among urbanization, urban forms, and air pollution, emphasizing the importance of customized urban landscape management strategies for mitigating urban air pollution.

Middle-term nitrogen dioxide exposure and electrocardiogram abnormalities: A nationwide longitudinal study

BACKGROUND

Recently, professionals, such as those from the World Health Organization, have recommended a rigorous standard for nitrogen dioxide (NO2), a typical urban air pollutant affected by regular traffic emissions, based on its short-term and long-term cardiorespiratory effects. However, the association between middle-term NO2 exposure and cardiovascular disorders remains unknown.

OBJECTIVES

This study was conducted to examine the relationship between NO2 exposure and its middle-term cardiovascular risks indicated by electrocardiogram (ECG) abnormalities.

METHOD

We included 61,094 subjects (132,249 visits) with repeated ECG observations based on longitudinal data from the China National Stroke Screening Survey (CNSSS). The NO2 exposure concentration was derived from a predictive model, measured as the monthly average concentration in the 6 months of preceding the ECG measurement. We used the generalized estimation equation to assess the association between NO2 exposure and ECG abnormalities.

RESULT

For each 10 µg/m3 increase in monthly average NO2 concentration, the odds ratio of ECG abnormalities was 1.10 (95% confidence interval [CI] 1.09-1.12) after multiple adjustments. Stratified regression analyses of urban and rural residents showed associations between middle-term NO2 exposure and ECG abnormalities in urban (OR 1.09 [95% CI 1.08-1.11]) and rural residents (OR 1.14 [95% CI 1.10-1.19]). The association was robust within different subpopulations. Associations generally remained statistically significant (OR 1.03 [95% CI 1.02-1.05]) after extra adjustment for PM2.5. Exposure-response relationship analysis revealed a nearly linear relationship between NO2 exposure and the risk for ECG abnormalities.

CONCLUSION

Using the variation in ECG signals as a potentially reversible indicator for subclinical risk in cardiovascular systems, our study provides additional evidence on the increased risk posed by middle-term NO2 exposure. Our study showed that policies controlling for NO2 concentrations are beneficial to prevent cardiovascular diseases among Chinese adults.

Long-term exposure to low-level air pollution and greenness and mortality in Northern Europe. The Life-GAP project

BACKGROUND

Air pollution has been linked to mortality, but there are few studies examining the association with different exposure time windows spanning across several decades. The evidence for the effects of green space and mortality is contradictory.

OBJECTIVE

We investigated all-cause mortality in relation to exposure to particulate matter (PM2.5 and PM10), black carbon (BC), nitrogen dioxide (NO2), ozone (O3) and greenness (normalized difference vegetation index - NDVI) across different exposure time windows.

METHODS

The exposure assessment was based on a combination of the Danish Eulerian Hemispheric Model and the Urban Background Model for the years 1990, 2000 and 2010. The analysis included a complete case dataset with 9,135 participants from the third Respiratory Health in Northern Europe study (RHINE III), aged 40-65 years in 2010, with mortality follow-up to 2021. We performed Cox proportional hazard models, adjusting for potential confounders.

RESULTS

Altogether, 327 (3.6 %) persons died in the period 2010-2021. Increased exposures in 1990 of PM2.5, PM10, BC and NO2 were associated with increased all-cause mortality hazard ratios of 1.40 (95 % CI1.04-1.87 per 5 μg/m3), 1.33 (95 % CI: 1.02-1.74 per 10 μg/m3), 1.16 (95 % CI: 0.98-1.38 per 0.4 μg/m3) and 1.17 (95 % CI: 0.92-1.50 per 10 μg/m3), respectively. No statistically significant associations were observed between air pollution and mortality in other time windows. O3 showed an inverse association with mortality, while no association was observed between greenness and mortality. Adjusting for NDVI increased the hazard ratios for PM2.5, PM10, BC and NO2 exposures in 1990. We did not find significant interactions between greenness and air pollution metrics.

CONCLUSION

Long term exposure to even low levels of air pollution is associated with mortality. Opening up for a long latency period, our findings indicate that air pollution exposures over time may be even more harmful than anticipated.

Air pollution exposure, SARS-CoV-2 infection, and immune response in a cohort of healthcare workers of a large university hospital in Milan, Italy

Several studies have examined the possible relationship between air pollutants and the risk of COVID-19 but most returned controversial findings. We tried to assess the association between (short- and long-term) exposure to particulate and gaseous pollutants, SARS-CoV-2 infections, and immune response in a population of healthcare workers (HCWs) with well-characterized individual data. We collected occupational and clinical characteristics of all HCWs who performed a nasopharyngeal swab (NPS) for detecting SARS-CoV-2 at the Policlinico Hospital in Milan (Lombardy, Italy) between February 24, 2020 (day after first documented case of COVID-19 in our hospital) and December 26, 2020 (day before start of the vaccination campaign). Each subject was assigned daily average levels of particulate matter ≤10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) retrieved from the air quality monitoring station closest to his/her residential address. Air pollution data were treated as time-dependent variables, generating person-days at risk. Multivariate Poisson regression models were fit to evaluate the rate of positive NPS and to assess the association between air pollution and antibody titer among NPS-positive HCWs. Among 3712 included HCWs, 635 (17.1%) had at least one positive NPS. A 10 μg/m3 increase in NO2 average concentration in the four days preceding NPS was associated with a higher risk of testing positive [Incidence Rate Ratio (IRR) = 1.08, 95% confidence interval (CI): 1.01; 1.16)]. When considering a 1 μg/m3 increase in 2019 annual NO2 average, we observed a higher risk of infection (IRR: 1.02, 95%CI: 1.00; 1.03) and an increased antibody titer (+2.4%, 95%CI: 1.1; 3.6%). Findings on PM10 and O3 were less consistent and, differently from NO2, were not confirmed in multipollutant models. Our study increases the body of evidence suggesting an active role of air pollution exposure on SARS-CoV-2 infection and confirms the importance of implementing pollution reduction policies to improve public health.

Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model

Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.

Long-term effects of air pollutants on respiratory and cardiovascular mortality in a port city along the Adriatic sea

BACKGROUND

Shipping and port-related air pollution has a significant health impact on a global scale. The present study aimed to assess the mortality burden attributable to long-term exposure to ambient particulate matter (PM2.5, PM10) and nitrogen dioxide (NO2) in the city of Ancona (Italy), with one of the leading national commercial harbours.

METHODS

Exposure to air pollutants was derived by dispersion models. The relationship between the long-term exposure of air pollution exposure and cause-specific mortality was evaluated by Poisson regression models, after adjustment for gender, age and socioeconomic status. Results are expressed as percent change of risk (and relative 95% confidence intervals) per 5 unit increases in the exposures. The health impact on the annual number of premature cause-specific deaths was also assessed.

RESULTS

PM2.5 and NO2 annual concentrations were higher in the area close to the harbour than in the rest of the city. Positive associations between each pollutant and most of the mortality outcomes were observed, with estimates of up to 7.6% (95%CI 0.1, 15.6%) for 10 µg/m3 increase in NO2 and cardiovascular mortality and 15.3% (95%CI-1.1, 37.2%) for 10 µg/m3 increase PM2.5 and lung cancer. In the subpopulation living close to the harbour, there were excess risks of up to 13.5%, 24.1% and 37.9% for natural, cardiovascular and respiratory mortality. The number of annual premature deaths due to the excess of PM2.5 and NO2 exposure (having as a reference the 2021 World Health Organization Air Quality Guidelines) was 82 and 25, respectively.

CONCLUSIONS

Our study confirms the long-term health effects of PM and NO2 on mortality and reveals a higher mortality burden in areas close to shipping and port-related emissions. Estimating the source-specific health burdens is key to achieve a deeper understanding of the role of different emission sources, as well as to support effective and targeted mitigation strategies.

Impacts of industrial production and air quality by remote sensing on nitrogen dioxide concentration and related effects: An econometric approach

The high concentration of nitrogen dioxide (NO2) is to blame for West Java's poor Air Quality Index (AQI). So, this study aims to determine the influence of industrial activity as reflected by the value of its imports and exports, wind speed, and ozone (O3) on the high concentration of tropospheric NO2. The method used is the econometric Vector Error Correction Model (VECM) approach to capture the existence of a short-term and long-term relationship between tropospheric NO2 and its predictor variables. The data used in this study is in the form of monthly time series data for the 2018-2022 period sourced from satellite images (Sentinel-5P and ECMWF Climate Reanalysis) and publications of the Central Bureau of Statistics (BPS-Statistics Indonesia). The results explained that, in the short-term, tropospheric NO2 and O3 influence each other as they would in a photochemical reaction. In the long-term, exports from the industrial sector and wind speed have a significant effect on the concentration of tropospheric NO2. The short-term effect occurs directly in the first month after the shock, while the long-term effect occurs in the second month after the shock. Wind gusts originating from industrial areas cause air conditions to be even more alarming because tropospheric NO2 pollutants spread throughout the region in West Java. Based on the coefficient correlation result, the high number of pneumonia cases is one of the impacts caused by air pollution.

Distinct urban-rural gradients of air NO2 and SO2 concentrations in response to emission reductions during 2015-2022 in Beijing, China

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are two major air pollutants in urban environment. Emission reduction policies have thus been implemented to improve urban air quality, especially in the metropolises. However, it remains unclear whether the air concentrations of NO2 and SO2 in and around large cities follow a same spatial pattern and how their characteristics change over time in response to the emission reductions. Using ground-based monitoring datasets of air NO2 and SO2 concentrations in Beijing, China, we tested the hypothesis of urban air pollutant islands and evaluated their seasonal and inter-annual variations during 2015-2022. The results showed that air NO2 concentrations increased significantly towards the urban core, being in line with the hypothesis of urban air pollutant island, while air SO2 concentrations showed no such spatial patterns. The urban air NO2 island varied seasonally, with larger radius and higher air NO2 concentrations in spring and winter. In response to the emission reduction, the annual mean radius of the urban air NO2 island showed a rapid decrease from 45.8 km to zero km during the study period. The annual mean air NO2 concentration at the urban core showed a linear decrease at a rate of 4.5 μg m-3 yr-1. In contrast, air SO2 concentration decreased nonlinearly over time and showed a legacy in comparison to the emission reduction. Our findings suggest different urban-rural gradients of air NO2 and SO2 concentrations and highlight their distinct responses to the regional reductions of anthropogenic emissions.

Spatial variance and estimation of nitrogen dioxide levels as a contributing factor to asthma epidemiology in Rawalpindi, Pakistan

Asthma prevalence and morbidity are increasing rapidly worldwide, especially in developing countries. Previous studies have shown nitrogen dioxide as an important contributor to asthma prevalence along with extreme temperatures, relative humidity, and land use change. The present study aimed to assess the asthma epidemiology and association of nitrogen dioxide, temperature, and land use as a contributing factor for increasing asthma prevalence in Rawalpindi, Pakistan. Secondary data related to the frequency of asthmatics hospital visits were analyzed to figure out the hotspots of asthma by using Getis ord Gi* statistics in ArcGIS 10.2. Moreover, intraurban variation of nitrogen dioxide concentration was analyzed by passive sampling method and its association with the rate of asthmatics hospital visits in Rawalpindi, Pakistan was also researched. Results revealed the random distribution of disease with significant hotspots along with spatial variability of nitrogen dioxide in urban and rural locations. Indoor and outdoor levels of nitrogen dioxide exceed the national and world health organization standards on asthma high risk areas especially in winter season. Congested housing with poor ventilation, unplanned urbanization, cold temperature, and unclean fuel use are revealed as strong determinants of asthma prevalence in Rawalpindi, Pakistan. Extensive monitoring and interventions are needed for the reduction of both indoor and outdoor nitrogen dioxide levels to overcome the increasing rate of asthma prevalence.

Effects of ambient air pollutants on ARDS incidence and outcome: a narrative review

BACKGROUND

Exposure to air pollutants promotes inflammation, cancer, and mortality in chronic diseases. Acute respiratory distress syndrome (ARDS) is a common condition among intensive care unit patients and is associated with a high mortality rate. ARDS is characterized by significant lung inflammation, which can be replicated in animal models by acute exposure to high doses of various air pollutants. Recently, several clinical studies have been conducted in different countries to investigate the role of chronic or acute air pollutant exposure in enhancing both ARDS incidence and severity.

RESULTS

Chronic exposure studies have mainly been conducted in the US and France. The results of these studies suggest that some air pollutants, notably ozone, nitrogen dioxide, and particulate matter, increase susceptibility to ARDS and associated mortality. Furthermore, their impact may differ according to the cause of ARDS. A cohort study conducted in an urbanized zone in China showed that exposure to very high levels of air pollutants in the few days preceding intensive care unit admission was associated with an increased incidence of ARDS. The effects of acute exposure are more debatable regarding ARDS incidence and severity.

CONCLUSION

There is a likely relationship between air pollutant exposure and ARDS incidence and severity. However, further studies are required to determine which pollutants are the most involved and which patients are the most affected. Due to the prevalence of ARDS, air pollutant exposure may have a significant impact and could be a key public health issue.

Assessment of forest fire emissions in Uttarakhand State, India, using Open Geospatial data and Google Earth Engine

In the recent past, forest fires have increased due to the changing climate pattern. It is necessary to analyse and quantify various gaseous emissions so as to mitigate their harmful effects on air pollution. Satellite remote sensing data provides an opportunity to study the greenhouse gases in the atmosphere. The multispectral sensor of the Tropospheric Monitoring Instrument (Sentinel-5) is capable of recording the reflectance of wavelengths vital for measuring the atmospheric concentrations of methane, formaldehyde, aerosol, carbon monoxide, etc., at a spatial resolution of 0.01°. The present study utilized the Google Earth Engine (GEE) platform to study the emissions caused by forest fires in four districts of Uttarakhand State of India, which witnessed unprecedented fires in April-May 2021. All the datasets were ingested in GEE, which has the capability to analyse large datasets without the need to download them. The pre-fire period chosen was September 2020; the fire period was February-May 2021, and the post-fire period was June 2021. The variables chosen were aerosol absorbing index (AAI), carbon monoxide (CO) and nitrogen dioxide (NO2). The climate parameter temperature (Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) and precipitation (from Climate Hazards Group InfraRed Precipitation (CHIRPS) Pentad) were also studied for the period mentioned. The results indicate a different trend for emissions in each district. For AAI, maximum emissions were noted in district Nainital followed by Almora, Tehri Garhwal and Garhwal. For CO emissions, the most affected district was Almora followed by Nainital, Garhwal and Tehri Garhwal. For NO2 emissions, the most affected district was Garhwal, followed by Nainital, Tehri Garhwal and Almora. Delta Normalized Burn Ratio was computed from Sentinel data (difference of pre-fire and post-fire images) to assess the burnt area severity. The Delta Normalized Burn Ratio values observed that the district with the most burnt area is Garhwal, followed by Nainital, Almora and Tehri Garhwal. The elevated temperatures and scanty rainfall patterns regulated the intensity and duration of forest fire. Monitoring the gaseous emissions as a consequence of forest fire in the GEE platform is much easier and more convenient at a regional level. Such data is much needed for mitigation measures to be implemented in time.

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

INTRODUCTION

Traffic-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.

OBJECTIVES

We characterized the hourly hazard period of NO2 exposure for MI in New York state (NYS), USA, from 2000 to 2015.

METHODS

For 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.

RESULTS

The 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.

CONCLUSIONS

We 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.

Association between air pollution exposure and outpatient visits for dermatomyositis in a humid subtropical region of China: a time-series study

In recent years, a growing number of studies have found that air pollution plays critical roles in the onset and development of autoimmune diseases, but few studies have shown an association between air pollutants and dermatomyositis (DM). We sought to investigate the relationship between short-term exposure to air pollution and outpatient visits for DM and to quantify the burden of DM due to exposure to air pollutants in Hefei, China. Daily records of hospital outpatient visits for DM, air pollutants and meteorological factors data in Hefei from January 1, 2018 to December 31, 2021 were obtained. We used a distributed lag non-linear model (DLNM) in conjunction with a generalized linear model (GLM) to explore the association between air pollution and outpatient visits for DM, and conducted stratified analyses by gender, age and season. Moreover, we used attributable fraction (AF) and attributable number (AN) to reflect the burden of disease. A total of 4028 DM clinic visits were recorded during this period. High concentration nitrogen dioxide (NO2) exposure was associated with increased risk of DM outpatient visits (relative risk (RR) 1.063, 95% confidence interval (CI) 1.015-1.114, lag 0-5). Intriguingly, exposure to high concentration ozone (O3) was associated with reduced risk of outpatient visits for DM (RR 0.974, 95% CI 0. 0.954-0.993, lag 0-6). The results of stratified analyses showed that the cold season (vs. warm season) were more susceptible to outpatient visits for DM associated with NO2 and O3 exposure. In addition, we observed that an increased risk of DM outpatient visits was attributable to high concentration NO2 exposure, while high concentration O3 exposure was associated with a decreased risk of DM outpatient visits. This study provided a scientific basis for the etiology research and health protection of DM.

Air quality low-cost sensors and monitoring stations NO2 raw dataset in Rouen (France)

This article presents a dataset comprising measurements made by co-located devices, with the aim of calibrating sensors for an upcoming in-situ use. The dataset includes hourly averaged data from 9 low-cost sensors and 2 traffic monitoring stations (thereafter named QDP and SUD3) in Rouen spanning from October 20, 2021 to March 25, 2022. In addition, the dataset is enriched by covariates measured by the sensors: temperature, relative humidity, atmospheric pressure, plus Ox and CO measures. The experiment was conducted as part of TIGA's call for project, and designed to have a better understanding of sensors' drawbacks, particularly when they are moved or shut down. Knowledge about the effect of air pollution on health has gained significant attention from both the scientific community and citizens, making air quality a growing issue for urban area. As a result, the city of Rouen in Normandy, France, has prioritized air quality monitoring as a key initiative. Concurrently, several means to measure air pollutants have been made more accessible, such as the use of low-cost sensors. Those sensors offer affordability, but are known to be less accurate than monitoring stations. Thus, they need to be cautiously studied so as to be used properly.

Air pollution, alcohol consumption, and the risk of elevated liver enzyme levels: a cross-sectional study in the UK Biobank

Evidences on the association between exposure to air pollution and liver enzymes was scarce in low pollution area. We aimed to investigate the association between air pollution and liver enzyme levels and further explore whether alcohol intake influence this association. This cross-sectional study included 425,773 participants aged 37 to 73 years from the UK Biobank. Land Use Regression was applied to assess levels of PM2.5, PM10, NO2, and NOx. Levels of liver enzymes including AST, ALT, GGT, and ALP were determined by enzymatic rate method. Long-term low-level exposure to PM2.5 (per 5-μg/m3 increase) was significantly associated with AST (0.596% increase, 95% CI, 0.414 to 0.778%), ALT (0.311% increase, 0.031 to 0.593%), and GGT (1.552% increase, 1.172 to 1.933%); The results were similar for PM10; NOX and NO2 were only significantly correlated with AST and GGT Significant modification effects by alcohol consumption were found (P-interaction < 0.05). The effects of pollutants on AST, ALT, and GGT levels gradually increased along with the weekly alcohol drinking frequency. In conclusion, long-term low-level air pollutants exposure was associated with elevated liver enzyme levels. And alcohol intake may exacerbate the effect of air pollution on liver enzymes.