Synergistic or Antagonistic Health Effects of Long- and Short-Term Exposure to Ambient NO2 and PM2.5: A Review

A multi-resolution ensemble model of three decision-tree-based algorithms to predict daily NO2 concentration in France 2005-2022

Understanding and managing the health effects of Nitrogen Dioxide (NO2) requires high resolution spatiotemporal exposure maps. Here, we developed a multi-stage multi-resolution ensemble model that predicts daily NO2 concentration across continental France from 2005 to 2022. Innovations of this work include the computation of daily predictions at a 200 m resolution in large urban areas and the use of a spatio-temporal blocking procedure to avoid data leakage and ensure fair performance estimation. Predictions were obtained after three cascading stages of modeling: (1) predicting NO2 total column density from Ozone Monitoring Instrument satellite; (2) predicting daily NO2 concentrations at a 1 km spatial resolution using a large set of potential predictors such as predictions obtained from stage 1, land-cover and road traffic data; and (3) predicting residuals from stage 2 models at a 200 m resolution in large urban areas. The latter two stages used a generalized additive model to ensemble predictions of three decision-tree algorithms (random forest, extreme gradient boosting and categorical boosting). Cross-validated performances of our ensemble models were overall very good, with a ten-fold cross-validated R2 for the 1 km model of 0.83, and of 0.69 for the 200 m model. All three basis learners participated in the ensemble predictions to various degrees depending on time and space. In sum, our multi-stage approach was able to predict daily NO2 concentrations with a relatively low error. Ensembling the predictions maximizes the chance of obtaining accurate values if one basis learner fails in a specific area or at a particular time, by relying on the other learners. To the best of our knowledge, this is the first study aiming to predict NO2 concentrations in France with such a high spatiotemporal resolution, large spatial extent, and long temporal coverage. Exposure estimates are available to investigate NO2 health effects in epidemiological studies.

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

BACKGROUND

While extensive studies have elucidated the relationships between exposure to air pollution and chronic diseases, such as cardiovascular disorders and diabetes, the intricate effects on specific kidney diseases, notably primary glomerulonephritis (GN)-an immune-mediated kidney ailment-are less well understood. Considering the escalating incidence of GN and conspicuous lack of investigative focus on its association with air quality, investigation is dedicated to examining the long-term effects of air pollutants on renal function in individuals diagnosed with primary GN.

METHODS

This retrospective cohort analysis was conducted on 1394 primary GN patients who were diagnosed at Seoul National University Bundang Hospital and Seoul National University Hospital. Utilizing time-varying Cox regression and linear mixed models (LMM), we examined the effect of yearly average air pollution levels on renal function deterioration (RFD) and change in estimated glomerular filtration rate (eGFR). In this context, RFD is defined as sustained eGFR of less than 60 mL/min per 1.73 m2.

RESULTS

During a mean observation period of 5.1 years, 350 participants developed RFD. Significantly, elevated interquartile range (IQR) levels of air pollutants-including PM10 (particles ≤10 micrometers, HR 1.389, 95 % CI 1.2-1.606), PM2.5 (particles ≤2.5 micrometers, HR 1.353, 95 % CI 1.162-1.575), CO (carbon monoxide, HR 1.264, 95 % CI 1.102-1.451), and NO2 (nitrogen dioxide, HR 1.179, 95 % CI 1.021-1.361)-were significantly associated with an increased risk of RFD, after factoring in demographic and health variables. Moreover, exposure to PM10 and PM2.5 was associated with decreased eGFR.

CONCLUSIONS

This study demonstrates a substantial link between air pollution exposure and renal function impairment in primary GN, accentuating the significance of environmental determinants in the pathology of immune-mediated kidney diseases.

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

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

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.

Spatiotemporal effects of meteorological conditions on global influenza peaks

BACKGROUND

Numerous studies have suggested that meteorological conditions such as temperature and absolute humidity are highly indicative of influenza outbreaks. However, the explanatory power of meteorological factors on the seasonal influenza peaks varied widely between countries at different latitudes.

OBJECTIVES

We aimed to explore the modification effects of meteorological factors on the seasonal influenza peaks in multi-countries.

METHODS

Data on influenza positive rate (IPR) were collected across 57 countries and data on meteorological factors were collected from ECMWF Reanalysis v5 (ERA5). We used linear regression and generalized additive models to investigate the spatiotemporal associations between meteorological conditions and influenza peaks in cold and warm seasons.

RESULTS

Influenza peaks were significantly correlated with months with both lower and higher temperatures. In temperate countries, the average intensity of cold season peaks was stronger than that of warm season peaks. However, the average intensity of warm season peaks was stronfger than of cold season peaks in tropical countries. Temperature and specific humidity had synergistic effects on influenza peaks at different latitudes, stronger in temperate countries (cold season: R2=0.90; warm season: R2=0.84) and weaker in tropical countries (cold season: R2=0.64; warm season: R2=0.03). Furthermore, the effects could be divided into cold-dry and warm-humid modes. The temperature transition threshold between the two modes was 16.5-19.5 °C. During the transition from cold-dry mode to warm-humid mode, the average 2 m specific humidity increased by 2.15 times, illustrating that transporting a large amount of water vapor may compensate for the negative effect of rising temperatures on the spread of the influenza virus.

CONCLUSION

Differences in the global influenza peaks were related to the synergistic influence of temperature and specific humidity. The global influenza peaks could be divided into cold-dry and warm-humid modes, and specific thresholds of meteorological conditions were needed for the transition of the two modes.

Pre- and postnatal particulate matter exposure and blood pressure in children and adolescents: A systematic review and meta-analysis

BACKGROUND

Early life is a susceptible period of air pollution-related adverse health effects. Hypertension in children might be life-threatening without prevention or treatment. Nevertheless, the causative association between environmental factors and childhood hypertension was limited. In the light of particulate matter (PM) as an environmental risk factor for cardiovascular diseases, this study investigated the association of pre- and postnatal PM exposure with blood pressure (BP) and hypertension among children and adolescents.

METHOD

Four electronic databases were searched for related epidemiological studies published up to September 13, 2022. Stata 14.0 was applied to examine the heterogeneity among the studies and evaluate the combined effect sizes per 10 μg/m³ increase of PM by selecting the corresponding models. Besides, subgroup analysis, sensitivity analysis, and publication bias test were also conducted.

RESULTS

Prenatal PM_2.5 exposure was correlated with increased diastolic blood pressure (DBP) in offspring [1.14 mmHg (95% CI: 0.12, 2.17)]. For short-term postnatal exposure effects, PM_2.5 (7-day average) was significantly associated with systolic blood pressure (SBP) [0.20 mmHg (95% CI: 0.16, 0.23)] and DBP [0.49 mmHg (95% CI: 0.45, 0.53)]; and also, PM₁₀ (7-day average) was significantly associated with SBP [0.14 mmHg (95% CI: 0.12, 0.16)]. For long-term postnatal exposure effects, positive associations were manifested in SBP with PM_2.5 [β = 0.44, 95% CI: 0.40, 0.48] and PM₁₀ [β = 0.35, 95% CI: 0.19, 0.51]; DBP with PM₁ [β = 0.45, 95% CI: 0.42, 0.49], PM_2.5 [β = 0.31, 95% CI: 0.27, 0.35] and PM₁₀ [β = 0.32, 95% CI: 0.19, 0.45]; and hypertension with PM₁ [OR = 1.43, 95% CI: 1.40, 1.46], PM_2.5 [OR = 1.65, 95% CI: 1.29, 2.11] and PM₁₀ [OR = 1.26, 95% CI: 1.09, 1.45].

CONCLUSION

Both prenatal and postnatal exposure to PM can increase BP, contributing to a higher prevalence of hypertension in children and adolescents.