Long-Term Trends Worldwide in Ambient NO2 Concentrations Inferred from Satellite Observations

Satellite data to support air quality assessment and management

Satellite data have long been recognized as valuable for air quality applications. These applications are in a stage of rapid growth: new geostationary satellites provide hourly or sub-hourly data; improvements in algorithms convert measured wavelengths into retrievals of atmospheric constituents; advances in machine learning support improved estimates of near-surface pollution; and growing interest among air quality managers has led to a range of new satellite data applications. Considering mainly activities in the United States under the Clean Air Act, we discuss proven applications relevant to air quality management, including: informing epidemiological studies and health risk assessments for setting regulatory standards; evaluating regulatory models; constraining emissions inventories; supporting Exceptional Event Demonstrations through tracking wildfire plumes and other sources; characterizing emission patterns and ozone-forming chemistry for State Implementation Plans; improving air quality forecasting; and tracking long-term trends to evaluate regulatory impact. Air quality professionals are increasingly using satellite data for these and related analyses, but barriers remain. This review provides a summary of satellite products used in applications for air quality and related health assessments; progress in using satellite observations for deriving surface-level air quality information across scales; and their use in air quality management.Implications: The review covers advancements in satellite data for air quality applications over the last 15 years. Success with satellite applications, especially for PM2.5 and NO2, include use in health risk assessment, constraining emissions inventories, and supporting tracking short- and long-term trends with regulatory relevance. Solutions co-developed between researchers and practitioners show promise for continued improvements in the use and value of satellite data for air quality applications.

How growing up without siblings affects the adult brain and behaviour in the CHIMGEN cohort

With the worldwide increase in only-child families, it is crucial to understand the effects of growing up without siblings (GWS) on the adult brain, behaviour and the underlying pathways. Using the CHIMGEN cohort, we investigated the associations of GWS with adult brain structure, function, connectivity, cognition, personality and mental health, as well as the pathway from GWS to GWS-related growth environments to brain and to behaviour development, in 2,397 pairs of individuals with and without siblings well matched in covariates. We found associations linking GWS to higher language fibre integrity, lower motor fibre integrity, larger cerebellar volume, smaller cerebral volume and lower frontotemporal spontaneous brain activity. Contrary to the stereotypical impression of associations between GWS and problem behaviours, we found positive correlations of GWS with neurocognition and mental health. Despite direct effects, GWS affects most brain and behavioural outcomes through modifiable environments, such as socioeconomic status, maternal care and family support, suggesting targets for interventions to enhance children's healthy growth.

Leveraging High-Resolution Satellite-Derived NO2 Estimates to Evaluate NO2 Exposure Representativeness and Socioeconomic Disparities

Research has typically estimated NO2 concentrations over several kilometers; thus, NO2 data at finer spatial resolution remain limited. This study used tropospheric NO2 data from the TROPOspheric Monitoring Instrument (TROPOMI) and traffic-related land use parameters to estimate long-term average NO2 concentrations at a spatial resolution of 500 m in South Korea from 2018 to 2022. Our satellite-land use hybrid regression model showed reasonably high predictability with a cross-validation R2 of 0.81, mean absolute error of 2.28 ppb and root mean squared error of 2.85 ppb. Leveraging these high-resolution data, we assessed the representativeness of ground monitors for population exposure by comparing population-weighted NO2 concentrations from estimated and measured data. Across 17 metropolitan cities and provinces, the ratios of population-weighted estimated to measured NO2 ranged from 0.62 to 1.12, with the ratio of 1 exhibiting the most representative monitoring networks. We further investigated disproportionate NO2 exposures based on socioeconomic status, revealing that NO2 exposures were consistently higher in local districts with higher socioeconomic status because of the unique historical backgrounds of rapid economic development and urban infrastructure design in South Korea. Using high-resolution NO2 data can lead to more comprehensive and precise exposure assessments, enhancing public health and regulatory applications.

The Association Between Air Pollution and Lung Function in Sarcoidosis and Implications for Health Disparities

BACKGROUND

Sarcoidosis is a granulomatous disease with varying courses of disease progression. Environmental exposures are thought to be contributors to disease onset. Exposure to air pollutants such as fine particulate matter of 2.5 μm diameter or smaller (PM2.5) and nitrogen dioxide (NO2) have been identified as contributors to health disparities in lung diseases; little is known about these environmental exposures' associations with disease outcomes in sarcoidosis.

RESEARCH QUESTION

Is higher exposure to PM2.5 and NO2 associated with worse lung function in sarcoidosis?

STUDY DESIGN AND METHODS

We conducted a retrospective, cross-sectional study of individuals with pulmonary sarcoidosis seen from 2005 to 2015. Home addresses at the year of enrollment were geocoded, and exposure to PM2.5 and NO2 was modeled using high-resolution 1 km × 1 km annual surface exposure data during the year of enrollment. Racial and sex differences in exposure were determined. Multivariable linear regression models were used to examine the associations between PM2.5 and NO2 and the pulmonary function test measures FVC, FEV1, and diffusing capacity of the lungs for carbon monoxide (Dlco).

RESULTS

Among the 415 individuals in the analysis, Black individuals had significantly higher exposure to PM2.5 and NO2 compared with non-Hispanic White individuals, 12.2 μg/m3 (SD 2.4) vs 11 μg/m3 (SD 2.2) and 6.3 parts per billion (ppb) (SD 1.9) vs 5.0 ppb (SD 2.0), respectively. Every 1 μg/m3 higher exposure to PM2.5 was associated with 1.12% lower Dlco% predicted (95% CI, -1.83 to -0.41; P < .05). Every 1 ppb higher exposure to NO2 was associated with 1.04% lower Dlco% predicted (95% CI, -1.91 to -0.18; P < .05) in fully adjusted models. There were no significant associations between these pollutants and either FVC or FEV1% predicted.

INTERPRETATION

Higher exposure to PM2.5 and NO2 was associated with worse Dlco% predicted. Black individuals with sarcoidosis were exposed to higher PM2.5 and NO2 than non-Hispanic White individuals. Air pollution exposure may be a contributor to reported health disparities in sarcoidosis.

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

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

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

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

Dissecting heritability, environmental risk, and air pollution causal effects using > 50 million individuals in MarketScan

Large national-level electronic health record (EHR) datasets offer new opportunities for disentangling the role of genes and environment through deep phenotype information and approximate pedigree structures. Here we use the approximate geographical locations of patients as a proxy for spatially correlated community-level environmental risk factors. We develop a spatial mixed linear effect (SMILE) model that incorporates both genetics and environmental contribution. We extract EHR and geographical locations from 257,620 nuclear families and compile 1083 disease outcome measurements from the MarketScan dataset. We augment the EHR with publicly available environmental data, including levels of particulate matter 2.5 (PM2.5), nitrogen dioxide (NO2), climate, and sociodemographic data. We refine the estimates of genetic heritability and quantify community-level environmental contributions. We also use wind speed and direction as instrumental variables to assess the causal effects of air pollution. In total, we find PM2.5 or NO2 have statistically significant causal effects on 135 diseases, including respiratory, musculoskeletal, digestive, metabolic, and sleep disorders, where PM2.5 and NO2 tend to affect biologically distinct disease categories. These analyses showcase several robust strategies for jointly modeling genetic and environmental effects on disease risk using large EHR datasets and will benefit upcoming biobank studies in the era of precision medicine.

The relative contributions of traffic and non-traffic sources in ultrafine particle formations in Tehran mega city

Emissions of ultrafine particles (UFPs; diameter < 100 nm) are strongly associated with traffic-related emissions and are a growing global concern in urban environments. The aim of this study was to investigate the variations of particle number concentration (PNC) with a diameter > 10 nm at nine stations and understand the major sources of UFPs (primary vs. secondary) in Tehran megacity. The study was carried out in Tehran in 2020. NOx and PNC were reported from a total of nine urban site locations in Tehran and BC concentrations were examined at two monitoring stations. Data from all stations showed diurnal changes with peak morning and evening rush hours. The hourly PNC was correlated with NOx. PNCs in Tehran were higher compared to those of many cities reported in the literature. The highest concentrations were at District 19 station (traffic) and the lowest was at Punak station (residential) such that the average PNC varied from 8.4 × 103 to 5.7 × 104 cm-3. In Ray and Sharif stations, the average contributions of primary and secondary sources of PNC were 67 and 33%, respectively. Overall, we conclude that a decrease in primary emission leads to a decrease in the total concentration of aerosols, despite an increase in the formation of new particles by photo nucleation.

Early life ambient air pollution, household fuel use, and under-5 mortality in Ghana

INTRODUCTION

Environmental exposures, such as ambient air pollution and household fuel use affect health and under-5 mortality (U5M) but there is a paucity of data in the Global South. This study examined early-life exposure to ambient particulate matter with a diameter of 2.5 µm or less (PM2.5), alongside household characteristics (including self-reported household fuel use), and their relationship with U5M in the Navrongo Health and Demographic Surveillance Site (HDSS) in northern Ghana.

METHODS

We employed Satellite-based spatiotemporal models to estimate the annual average PM2.5 concentrations with the Navrongo HDSS area (1998 to 2016). Early-life exposure levels were determined by pollution estimates at birth year. Socio-demographic and household data, including cooking fuel, were gathered during routine surveillance. Cox proportional hazards models were applied to assess the link between early-life PM2.5 exposure and U5M, accounting for child, maternal, and household factors.

FINDINGS

We retrospectively studied 48,352 children born between 2007 and 2017, with 1872 recorded deaths, primarily due to malaria, sepsis, and acute respiratory infection. Mean early-life PM2.5 was 39.3 µg/m3, and no significant association with U5M was observed. However, Children from households using "unclean" cooking fuels (wood, charcoal, dung, and agricultural waste) faced a 73 % higher risk of death compared to those using clean fuels (adjusted HR = 1.73; 95 % CI: 1.29, 2.33). Being born female or to mothers aged 20-34 years were linked to increased survival probabilities.

INTERPRETATION

The use of "unclean" cooking fuel in the Navrongo HDSS was associated with under-5 mortality, highlighting the need to improve indoor air quality by introducing cleaner fuels.

Associations between heat waves and chronic kidney disease in China: The modifying role of land cover

The increasing frequency of heat waves under the global urbanization and climate change background poses elevating risks of chronic kidney disease (CKD). Nevertheless, there has been no evidence on associations between long-term exposures to heat waves and CKD as well as the modifying effects of land cover patterns. Based on a national representative population-based survey on CKD covering 47,086 adults and high spatial resolution datasets on temperature and land cover data, we found that annual days of exposure to heat waves were associated with increased odds of CKD prevalence. For one day/year increases in HW_975_4d (above 97.5 % of annual maximum temperature and lasting for at least 4 consecutive days), the odds ratio (OR) of CKD was 1.14 (95 %CI: 1.12, 1.15). Meanwhile, stronger associations were observed in regions with lower urbanicity [rural: 1.14 (95 %CI: 1.12, 1.16) vs urban: 1.07 (95 %CI: 1.03, 1.11), Pinteraction < 0.001], lower water body coverage [lower: 1.14 (95 %CI: 1.12, 1.16) vs higher: 1.02 (95 %CI: 0.98, 1.05), Pinteraction < 0.001], and lower impervious area coverage [lower: 1.16 (95 %CI: 1.14, 1.18) vs higher: 1.06 (95 %CI: 1.03, 1.10), Pinteraction = 0.008]. In addition, this study found disparities in modifying effects of water bodies and impervious areas in rural and urban settings. In rural regions, the associations between heat waves and CKD prevalence showed a consistent decreasing trend with increases in both proportions of water bodies and impervious areas (Pinteraction < 0.05). Nevertheless, in urban regions, we observed significant effect modification by water bodies, but not by impervious areas. Our study indicates the need for targeted land planning as part of adapting to the kidney impacts of heat waves, with a focus on urbanization in rural regions, as well as water body construction and utilization in both rural and urban regions.

Population exposure to multiple air pollutants and its compound episodes in Europe

Air pollution remains as a substantial health problem, particularly regarding the combined health risks arising from simultaneous exposure to multiple air pollutants. However, understanding these combined exposure events over long periods has been hindered by sparse and temporally inconsistent monitoring data. Here we analyze daily ambient PM2.5, PM10, NO2 and O3 concentrations at a 0.1-degree resolution during 2003-2019 across 1426 contiguous regions in 35 European countries, representing 543 million people. We find that PM10 levels decline by 2.72% annually, followed by NO2 (2.45%) and PM2.5 (1.72%). In contrast, O3 increase by 0.58% in southern Europe, leading to a surge in unclean air days. Despite air quality advances, 86.3% of Europeans experience at least one compound event day per year, especially for PM2.5-NO2 and PM2.5-O3. We highlight the improvements in air quality control but emphasize the need for targeted measures addressing specific pollutants and their compound events, particularly amidst rising temperatures.

Ambient PM2.5 components and prevalence of chronic kidney disease: a nationwide cross-sectional survey in China

OBJECTIVES

Chronic kidney disease (CKD) is a global public health concern, and accumulating evidence has indicated that air pollution increases the odds of CKD. However, a limited number of studies have examined the long-term effects of ambient fine particulate matter (PM2.5) components on the risk of CKD among general population; thus, major knowledge gaps remain.

METHODS

Using data from a nationwide representative cross-sectional survey in China and a validated PM2.5 composition dataset, we established generalized linear models to quantify the association between five major components of PM2.5 and CKD prevalence.

RESULTS

There were significant associations between long-term exposure to three PM2.5 components [including black carbon (BC), sulfate (SO42-), organic matter (OM)] and increased odds of CKD prevalence. Along with an interquartile range (IQR) increment in BC (3.3 μg/m3), SO42- (9.7 μg/m3), and OM (16.2 μg/m3) at a 4-year moving average, the odds ratios (ORs) for CKD prevalence were 1.28 (95% CI 1.07, 1.54), 1.23 (95% CI 1.03, 1.45), and 1.23 (95% CI 1.02, 1.47), respectively. We did not detect any significant association of the other two PM2.5 components [nitrate (NO3-) or ammonium (NH4+)] with CKD prevalence. Stratified analyses revealed no differences (P ≥ 0.05) in the effect estimates of subgroups based on administrative region, sex, age, and other demographic characteristics. For instance, along with an IQR increment in BC at a 4-year moving average, the ORs of CKD prevalence among males and females were 1.30 (95% CI 0.98, 1.73) and 1.29 (95% CI 1.01, 1.65), respectively. The odds of CKD were generally higher with increasing PM2.5 composition concentration.

CONCLUSIONS

Our study demonstrated that long-term exposure to specific PM2.5 components including BC, SO42-, and OM increased CKD risk in the general population. This study could provide new insights into source-directed PM2.5 control and CKD prevention.

Long-term observations of NO2 using GEMS in China: Validations and regional transport

In 2019, South Korea launched the Geostationary Environment Monitoring Spectrometer (GEMS) to observe trace gases with an hourly temporal resolution. Compared to previous payloads on polar-orbiting satellites, the GEMS payload has significant advantages in detecting the diurnal variation characteristics of NO2. However, there is still a lack of ground-based validations regarding the overall accuracy of GEMS in the Chinese region. In this study, we conducted a systematic ground validation of GEMS NO2 data in China for the first time. We validated the accuracy of GEMS NO2 data in four typical pollution regions in China, namely the Beijing-Tianjin-Hebei region (JJJ), the Yangtze River Delta region (YRD), the Pearl River Delta region (PRD), and the Sichuan Basin region (SCB), based on MAX-DOAS and CNEMC data. The averaged correlations using the two datasets for validation were 0.81 and 0.57, respectively, indicating a high level of accuracy for the data in China. Using the GEMS seasonal averaged NO2 data, we studied the distribution of NO2 levels in the four regions. We found that the highest NO2 in all four regions occurred during winter with concentrations of 1.84 × 1016 molecules cm-2, 1.59 × 1016 molecules cm-2, 1.58 × 1016 molecules cm-2 and 9.47 × 1015 molecules cm-2, respectively. The distribution of NO2 was closely related to the terrain. Additionally, we observed a significant underestimation issue with TROPOMI, exceeding 30 % in many regions. Based on MAX-DOAS, we investigated the vertical distribution of NO2 in the four regions and found that NO2 was mainly concentrated below 0.5 km. with the HNU station having the lowest concentration, averaging only 2.12 ppb, which was approximately 41 % of the highest concentration recorded at the CQ station. Furthermore, we conducted a study on regional and cross-regional transport using a combination of MAX-DOAS and GEMS data. We found that the transport flux of NO2 could increase by over 500 % within 1 h, making a significant contribution to local NO2 concentrations. The joint observations of GEMS and MAX-DOAS will provide reliable data support for NO2 research and control in China, making a substantial contribution to environmental protection and sustainable development.

Respiratory Health Impacts of Outdoor Air Pollution and the Efficacy of Local Risk Communication in Quito, Ecuador

Relatively few studies on the adverse health impacts of outdoor air pollution have been conducted in Latin American cities, whose pollutant mixtures and baseline health risks are distinct from North America, Europe, and Asia. This study evaluates respiratory morbidity risk associated with ambient air pollution in Quito, Ecuador, and specifically evaluates if the local air quality index accurately reflects population-level health risks. Poisson generalized linear models using air pollution, meteorological, and hospital admission data from 2014 to 2015 were run to quantify the associations of air pollutants and index values with respiratory outcomes in single- and multi-pollutant models. Significant associations were observed for increased respiratory hospital admissions and ambient concentrations of fine particulate matter (PM_2.5), ozone (O₃), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂), although some of these associations were attenuated in two-pollutant models. Significant associations were also observed for index values, but these values were driven almost entirely by daily O₃ concentrations. Modifications to index formulation to more fully incorporate the health risks of multiple pollutants, particularly for NO₂, have the potential to greatly improve risk communication in Quito. This work also increases the equity of the existing global epidemiological literature by adding new air pollution health risk values from a highly understudied region of the world.

Ambient Air Pollution and All-Cause and Cause-Specific Mortality in an Analysis of Asian Cohorts

INTRODUCTION

Much of what is currently known about the adverse effects of ambient air pollution comes from studies conducted in high-income regions, with relatively low air pollution levels. The aim of the current project is to examine the relationship between exposure to ambient air pollution (as predicted from satellite-based models) and all-cause and cause-specific mortality in several Asian cohorts.

METHODS

Cohorts were recruited from the Asia Cohort Consortium (ACC). The geocoded residences of participants were assigned levels of ambient particulate material with aerodynamic diameter of 2.5 μm or less (PM2.5) and nitrogen dioxide (NO2) utilizing global satellite-derived models and assigned for the year of enrollment (or closest available year). The association between ambient exposure and mortality was established with Cox proportional hazard models, after adjustment for common confounders. Both single- and two-pollutant models were generated. Model robustness was evaluated, and hazard ratios were calculated for each cohort separately and combined via random effect meta-analysis for pooled risk estimates.

RESULTS

Six cohort studies from the ACC participated: the Community-based Cancer Screening Program (CBCSCP, Taiwan), the Golestan Cohort Study (Iran), the Health Effects for Arsenic Longitudinal Study (HEALS, Bangladesh), the Japan Public Health Center-based Prospective Study (JPHC), the Korean Multi-center Cancer Cohort Study (KMCC), and the Mumbai Cohort Study (MCS, India). The cohorts represented over 340,000 participants.

Mean exposures to PM2.5 ranged from 8 to 58 μg/m3. Mean exposures to NO2 ranged from 7 to 23 ppb. For PM2.5, a positive, borderline nonsignificant relationship was observed between PM2.5 and cardiovascular mortality. Other relationships with PM2.5 tended toward the null in meta-analysis. For NO2, an overall positive relationship was observed between exposure to NO2 and all cancers and lung cancer. A borderline association between NO2 and nonmalignant lung disease was also observed. The findings within individual cohorts remained consistent across a variety of subgroups and alternative analyses, including two-pollutant models.

CONCLUSIONS

In a pooled examination of cohort studies across Asia, ambient PM2.5 exposure appears to be associated with an increased risk of cardiovascular mortality and ambient NO2 exposure is associated with an increased cancer (and lung cancer) mortality. This project has shown that satellite-derived models of pollution can be used in examinations of mortality risk in areas with either incomplete or missing air pollution monitoring.

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

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

Association between exposure to ambient PM2.5 and the health status in the mobile population from 338 cities in China

There are limited studies investigating the relationship between exposure to PM_2.5 and the health status among the mobile population. A cross-sectional analysis was performed in a nationally representative sample (2017 China Migrants Dynamic Survey data) consisting of 169,469 mobile population. The ordered logistic regression model was used to examine the association between PM_2.5 and the health status in mobile population. Stratified analyses were performed to identify whether the association varied across gender, age group, and regions in China. Overall, every 10 μg/m³ increment in annual average PM_2.5 was associated with increased risk of poor self-reported health (OR = 1.021, 95% CI: 1.012-1.030). Mobile population aged 31-49 years and living in the central region suffers the highest PM_2.5-associated health risk (OR = 1.030, 95% CI: 1.019-1.042; OR = 1.095, 95% CI: 1.075-1.116). Our study suggests that PM_2.5 exposure was associated with an increased risk of poor self-reported health in mobile population, particularly among the population aged 31-49 years and people living in the central region of China. Policymakers should pay more attention to the vulnerable mobile population to tackle the health burden of ambient air pollution.

Regional urbanicity levels modify the association between ambient air pollution and prevalence of obesity: A nationwide cross-sectional survey

Ambient air pollution exposure may increase the risk of obesity, but the population susceptibility associated with urbanicity has been insufficiently investigated. Based on a nationwide representative cross-sectional survey on 44,544 adults, high-resolution night light satellite remote sensing products, and multi-source ambient air pollution inversion data, the present study evaluated the associations of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations with the prevalence of obesity and abdominal obesity. We further calculated the associations in regions with different urbanicity levels characterized by both administrative classification of urban/rural regions and night light index (NLI). We found that 10 μg/m³ increments in PM_2.5 at 1-year moving average and in NO₂ at 5-year moving average were associated with increased prevalence of obesity [odds ratios (OR) = 1.16 (1.14, 1.19); 1.12 (1.09, 1.15), respectively] and abdominal obesity [OR = 1.08 (1.07, 1.10); 1.07 (1.05, 1.09), respectively]. People in rural regions experienced stronger adverse effects than those in urban regions. For instance, a 10 μg/m³ increment in PM_2.5 was associated with stronger odds of obesity in rural regions than in urban regions [OR = 1.27 (1.23, 1.31) vs 1.10 (1.05, 1.14), P for interaction <0.001]. In addition, lower NLI values were associated with constantly amplified associations of PM_2.5 and NO₂ with obesity and abdominal obesity (all P for interaction <0.001). In summary, people in less urbanized regions are more susceptible to the adverse effects of ambient air pollution on obesity, suggesting the significance of collaborative planning of urbanization development and air pollution control, especially in less urbanized regions.

Trade-driven relocation of ground-level SO2 concentrations across Chinese provinces based on satellite observations

The influence of trade on ground-level SO2 concentrations in China was evaluated based on multiregional input-output (MRIO) analysis, using the ozone monitoring instrument (OMI) SO2 columns and SO2 profiles from an atmospheric chemical and transport model, MOZART-4. The provincial sum of ground-level SO2 concentrations has a good consistency with the provincial SO2 emissions (R = 0.65, p < 0.01). The provincial SO2 concentrations presented strong spatial variations, with a range of 5.1-50.6 μg/m3 and an average of 19.7 μg/m3 across China. The international trade increased the SO2 concentrations in all of the provinces and increased the national population-weighted SO2 (PWM-SO2) concentration by 2.9 μg/m3. Interprovincial trade within China decreased the ambient SO2 concentrations in Beijing, Tianjin, and Chongqing and the provinces in southeast and central China, but increased SO2 in the remaining provinces of China. In general, interprovincial trade decreased the national PWM-SO2 concentration by 5.3 μg/m3.

Trends in urban air pollution over the last two decades: A global perspective

Ground-level ozone (O₃), fine particles (PM_2.5), and nitrogen dioxide (NO₂) are the most harmful urban air pollutants regarding human health effects. Here, we aimed at assessing trends in concurrent exposure of global urban population to O₃, PM_2.5, and NO₂ between 2000 and 2019. PM_2.5, NO₂, and O₃ mean concentrations and summertime mean of the daily maximum 8-h values (O₃ MDA8) were analyzed (Mann-Kendall test) using data from a global reanalysis, covering 13,160 urban areas, and a ground-based monitoring network (Tropospheric Ozone Assessment Report), collating surface O₃ observations at nearly 10,000 stations worldwide. At global scale, PM_2.5 exposures declined slightly from 2000 to 2019 (on average, - 0.2 % year^-1), with 65 % of cities showing rising levels. Improvements were observed in the Eastern US, Europe, Southeast China, and Japan, while the Middle East, sub-Saharan Africa, and South Asia experienced increases. The annual NO₂ mean concentrations increased globally at 71 % of cities (on average, +0.4 % year^-1), with improvements in North America and Europe, and increases in exposures in sub-Saharan Africa, Middle East, and South Asia regions, in line with socioeconomic development. Global exposure of urban population to O₃ increased (on average, +0.8 % year^-1 at 89 % of stations), due to lower O₃ titration by NO. The summertime O₃ MDA8 rose at 74 % of cities worldwide (on average, +0.6 % year^-1), while a decline was observed in North America, Northern Europe, and Southeast China, due to the reduction in precursor emissions. The highest O₃ MDA8 increases (>3 % year^-1) occurred in Equatorial Africa, South Korea, and India. To reach air quality standards and mitigate outdoor air pollution effects, actions are urgently needed at all governance levels. More air quality monitors should be installed in cities, particularly in Africa, for improving risk and exposure assessments, concurrently with implementation of effective emission control policies that will consider regional socioeconomic imbalances.

Changes in NO2 and O3 levels due to the pandemic lockdown in the industrial cities of Tehran and Arak, Iran using Sentinel 5P images, Google Earth Engine (GEE) and statistical analysis

Air pollution has very damaging effects on human health. In recent years the Coronavirus disease (COVID-19) pandemic has created a worldwide economic disaster. Although the consequences of the COVID-19 lockdowns have had severe effects on economic and social conditions, these lockdowns also have also left beneficial effects on improving air quality and the environment. This research investigated the impact of the COVID-19 lockdown on NO2 and O3 pollutants changes in the industrial and polluted cities of Arak and Tehran in Iran. Based on this, the changes in NO2 and O3 levels during the 2020 lockdown and the same period in 2019 were investigated in these two cities. For this purpose, the Sentinel-5P data of these two pollutants were used during the lockdown period from November 19 to December 05, 2020, and at the same time before the pandemic from November 19 to December 05, 2019. For better results, the effect of climatic factors such as rain and wind in reducing pollution was removed. The obtained results indicate a decrease in NO2 and O3 levels by 3.5% and 6.8% respectively in Tehran and 20.97% and 5.67% in Arak during the lockdown of 2020 compared to the same time in 2019. This decrease can be caused by the reduction in transportation and socio-economic and industrial activities following the lockdown measures. This issue can be a solid point to take a step toward controlling and reducing pollution in non-epidemic conditions by implementing similar standards and policies in the future.

Green recovery or pollution rebound? Evidence from air pollution of China in the post-COVID-19 era

Under the strict control measures, China has achieved phased victory in combating with the COVID-19, production activities have gradually returned to normal. This paper examined whether air pollution was rebounded or realized green recovery in the post-COVID-19 era with a dataset of weather normalized pollutant concentrations using difference-in-differences models. Results showed that air pollution experienced a significant decline due to the wide range of control measures. With entering the post-epidemic period, air pollution raised due to the orderly production resumption. Specifically, production resumption increased the PM_2.5 concentrations of lockdown cities and non-lockdown cities by 43.2% (22.3 μg/m³) and 35.9% (17.3 μg/m³) compared with that in the period of COVID-19 breakout. Although the economic activities of China have been gradually recovered, PM_2.5 concentrations were 8.8-11.2 μg/m³ lower than the level of pre-epidemic period. In addition, the environmental effects varied across cities. With the process of production resumption, the PM_2.5 concentrations of cities with higher GDP, higher secondary industry output, more private cars and higher export volume rebounded less. Most developed cities realized green recovery by economy growth and air quality improvement, such as Beijing and Shanghai. While cities with heavy industry reflected pollution rebound with slow economy recovery, such as Shenyang and Harbin. Understanding the environmental effects of control measure and production resumption can provide crucial information for developing epidemic recovery policies and dealing with pollution issues for both China and other countries.

A review on the application of the exposome paradigm to unveil the environmental determinants of age-related diseases

Age-related diseases account for almost half of all diseases among adults worldwide, and their incidence is substantially affected by the exposome, which is the sum of all exogenous and endogenous environmental exposures and the human body’s response to these exposures throughout the entire lifespan. Herein, we perform a comprehensive review of the epidemiological literature to determine the key elements of the exposome that affect the development of age-related diseases and the roles of aging hallmarks in this process. We find that most exposure assessments in previous aging studies have used a reductionist approach, whereby the effect of only a single environmental factor or a specific class of environmental factors on the development of age-related diseases has been examined. As such, there is a lack of a holistic and unbiased understanding of the effect of multiple environmental factors on the development of age-related diseases. To address this, we propose several research strategies based on an exposomic framework that could advance our understanding—in particular, from a mechanistic perspective—of how environmental factors affect the development of age-related diseases. We discuss the statistical methods and other methods that have been used in exposome-wide association studies, with a particular focus on multiomics technologies. We also address future challenges and opportunities in the realm of multidisciplinary approaches and genome–exposome epidemiology. Furthermore, we provide perspectives on precise public health services for vulnerable populations, public communications, the integration of risk exposure information, and the bench-to-bedside translation of research on age-related diseases.

[Effect of modification of antihypertensive medications on the association of nitrogen dioxide long-term exposure and chronic kidney disease]

OBJECTIVE

To investigate the potential effect of modification of antihypertensive medications on the association of nitrogen dioxide (NO₂) long-term exposure and chronic kidney disease (CKD).

METHODS

Data of the national representative sample of adult population from the China National Survey of Chronic Kidney Disease (2007-2010) were included in the analyses, and exposure data of NO₂ were collected and matched. Generalized mixed-effects models were used to analyze the associations between NO₂ and CKD, stratified by the presence of hypertension and taking antihypertensive medications. The stratified exposure-response curves of NO₂ and CKD were fitted using the natural spine smoothing function. The modifying effects of antihypertensive medications on the association and the exposure-response curve of NO₂ and CKD were analyzed.

RESULTS

Data of 45 136 participants were included, with an average age of (49.5±15.3) years. The annual average exposure concentration of NO₂ was (7.2±6.4) μg/m³. Altogether 6 517 (14.4%) participants were taking antihypertensive medications, and 4 833 (10.7%) participants were identified as having CKD. After adjustment for potential confounders, in the hypertension population not using antihypertensive medications, long-term exposure to NO₂ was associated with a significant increase risk of CKD (OR: 1.38, 95%CI: 1.24-1.54, P < 0.001); while in the hypertension population using antihypertensive medications, no significant association between long-term exposure to NO₂ and CKD (OR: 0.96, 95%CI: 0.86-1.07, P=0.431) was observed. The exposure-response curve of NO₂ and CKD suggested that there was a non-linear trend in the association between NO₂ and CKD. The antihypertension medications showed significant modifying effects both on the association and the exposure-response curve of NO₂ and CKD (interaction P < 0.001).

CONCLUSION

The association between long-term exposure to NO₂ and CKD was modified by antihypertensive medications. Taking antihypertensive medications may mitigate the effect of long-term exposure to NO₂ on CKD.

Tracing out the effect of transportation infrastructure on NO2 concentration levels with Kernel Density Estimation by investigating successive COVID-19-induced lockdowns

This study aims to investigate the effect of transportation infrastructure on the decrease of NO₂ air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO₂ air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot-coldspot analysis on the relative change in NO₂ air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO₂ air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R² = 0.808). The results showed that the largest decrease in NO₂ air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = -0.904, R² = 0.818). Economic and population predictors also explained with good fit the decrease in NO₂ air pollution during the first lockdown: GDP (R² = 0.511), employees (R² = 0.513), population density (R² = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.

Prenatal exposure to air pollution and the risk of eczema in childhood: a systematic review and meta-analysis

An increasing number of studies investigated the association between air pollution during pregnancy and the risk of eczema in offspring. However, no meta-analysis has confirmed the existence and size of their association to date. We systematically searched PubMed, Web of Science, Cochrane Library, and Embase databases to select the observational controlled studies published from the inception date to October 16, 2021. Quality evaluation was guided by the Newcastle-Ottawa Scale (NOS). Sensitivity analysis was applied to assess the impact of each included study on the combined effects, and publication bias was examined by Begg's tests and Egger's tests. A total of 12 articles involving 69,374 participants met our eligibility criteria. A significant association between the maternal exposure to NO₂ (per 10 μg/m³ increased) and childhood eczema was observed, with a pooled risk estimate of 1.13 (95% CI: 1.06-1.19), but no association was observed between exposure to PM₁₀, PM_2.5, and SO₂ and the risk of eczema in offspring. Besides, the effect of maternal NO₂ exposure on childhood eczema was significant in the first and second trimesters, but not in the third trimester. There was notable variability in geographic location (p = 0.037) and air pollutant concentration (p = 0.031) based on meta-regression. Our findings indicated that prenatal exposure to NO₂ was a risk factor for elevated risk of eczema in childhood, especially in the first and second trimesters. Further studies with larger sample sizes considering different constituents of air pollution and various exposure windows are needed to validate these associations.

Reactive nitrogen compounds and their influence on human health: an overview

Nitrogen (N) is a critical component of food security, economy and planetary health. Human production of reactive nitrogen (Nr) via Haber-Bosch process and cultivation-induced biological N₂ fixation (BNF) has doubled global N cycling over the last century. The most important beneficial effect of Nr is augmenting global food supplies due to increased crop yields. However, increased circulation of Nr in the environment is responsible for serious human health effects such as methemoglobinemia ("blue baby syndrome") and eutrophication of coastal and inland waters. Furthermore, ammonia (NH₃) emission mainly from farming and animal husbandary impacts not only human health causing chronic lung disease, inflammation of human airways and irritation of eyes, sinuses and skin but is also involved in the formation of secondary particulate matter (PM) that plays a critical role in environment and human health. Nr also affects human health via global warming, depletion of stratospheric ozone layer resulting in greater intensity of ultra violet B rays (UVB) on the Earth's surface, and creation of ground-level ozone (through reaction of NO₂ with O₂). The consequential indirect human health effects of Nr include the spread of vector-borne pathogens, increased incidence of skin cancer, development of cataracts, and serious respiratory diseases, besides land degradation. Evidently, the strategies to reduce Nr and mitigate adverse environmental and human health impacts include plugging pathways of nitrogen transport and loss through runoff, leaching and emissions of NH₃, nitrogen oxides (NO _x ), and other N compounds; improving fertilizer N use efficiency; reducing regional disparity in access to N fertilizers; enhancing BNF to decrease dependence on chemical fertilizers; replacing animal-based proteins with plant-based proteins; adopting improved methods of livestock raising and manure management; reducing air pollution and secondary PM formation; and subjecting industrial and vehicular NO _x emission to pollution control laws. Strategic implementation of all these presents a major challenge across the fields of agriculture, ecology and public health. Recent observations on the reduction of air pollution in the COVID-19 lockdown period in several world regions provide an insight into the achievability of long-term air quality improvement. In this review, we focus on complex relationships between Nr and human health, highlighting a wide range of beneficial and detrimental effects.

Analysis of emissions-driven changes in the oxidation capacity of the atmosphere in Europe

Anthropogenic emissions in Europe have been gradually reduced thanks to a combination of factors, including restrictive regulation and policy implementation, fuel switching, technological developments, and improved energy efficiencies. Many measures have been specifically introduced to meet the annual and hourly limit value of NO₂ for the protection of human health, mainly targeting traffic emissions. Due to NO_X reduction policies in Europe, NO₂ levels have generally declined, but O₃ concentrations have been found to increase. This phenomenon would cause changes in the oxidant capacity of the atmosphere, altering the concentration of tropospheric oxidants in urban areas. The Community Multiscale Air Quality (CMAQ) modelling system has been used to study concentration changes of NO₂, O₃ and the main radicals in Europe between 2007 and 2015 for two months representatives of winter and summer conditions (January and July). In addition to describing the general situation in Europe, variations in pollutants along with NO_X emission changes over 67 large European cities have been analysed by means of statistical methods. NO_X emissions and NO₂ concentrations decreased in both seasons during the period in all the selected cities. In most of them O₃ concentrations increased in winter but decreased in summer. The concentration of the OH radical, the main oxidant during the daytime, shows an increase in winter. This is also the case for the main cities in summer although we found a general decrease in continent for this season. The NO₃ radical, the main night-time oxidant, was found to increase in winter and decrease in summer. HNO₃ shows a concentration decline in both seasons. The studied cities are classified in five groups by means of k-mean clustering procedure. We identified five groups with specific patterns, suggesting that the oxidant capacity of the European urban atmospheres has reacted differently to NO_X emission abatement policies.

Emissions of nitrogen dioxide in the northeast U.S. during the 2020 COVID-19 lockdown

We have quantified the emissions of Nitrogen dioxide (NO2) in the Northeast megalopolis of the United States during the COVID-19 lockdown. The measurement of NO2 emission serves as the indicator for the emission of the group of nitrogen oxides (NOx). Approximately 56% of NO2 emissions in the US are from mobile sources, and the remainder is from stationary sources. Since 2002, clean air regulations have resulted in approximately 5% compound annual reduction of NOx emissions in the US (8.2% in the study area). Therefore, when studying the impact of sporadic events like an epidemic on emissions, it is necessary to account for the persistent reduction of emissions due to policy driven emission reduction measures. Using spaceborne sensors, ground monitors, National Emission Inventory data, and the US Motor Vehicle Emission Simulator, we quantified the reduction of total NOx emissions, distinguishing stationary sources from on-road mobile sources (trucks and automobiles). When considering total NOx emissions (stationary and mobile combined), we find that the pandemic restrictions resulted in 3.4% reduction of total NOx emissions in the study area in 2020. This is compared to (and in addition to) the expected 8.2% policy driven reduction of NOx emissions in 2020. This somewhat low reduction of emissions is because most stationary sources (factories, power plants, etc.) were operational during the pandemic. Truck traffic, a significant source of mobile emissions, also did not decline significantly (average 4.8% monthly truck traffic reduction in the study area between March and August 2020), as they were delivering goods during the lockdown. On the other hand, automobile traffic, responsible for 24% of total NOx emissions, dropped significantly, 52% in April, returning to near normal after 5 months. While the reduction of automobile traffic was significant, especially in the early months of the pandemic, its effect on emissions was relatively insignificant.

First satellite-based regional hourly NO2 estimations using a space-time ensemble learning model: A case study for Beijing-Tianjin-Hebei Region, China

Surface Nitrogen dioxide (NO₂) concentrations have been generated with satellite retrievals using multiple statistical algorithms. However, they are often given at coarse frequencies ("snapshot", daily or even longer), limiting their applications in epidemiological studies and assessing the evolution of NO₂ pollution. This study investigated the potential applicability of Himawari-8 derived hourly fine particulate matter concentrations in producing hourly NO₂ concentrations by constructing a space-time ensemble model. The Beijing-Tianjin-Hebei (BTH) region, one of the serious pollution regions in China, is the study region chosen. The proposed model performs well in estimating hourly NO₂ concentration with a high cross-validation (CV) coefficient of determination (R² = 0.81) and low CV root-mean-square (RMSE = 9.71 μg/m³), mean prediction errors (MPE = 6.33 μg/m³), and relative prediction errors (RPE = 22.5%). On daily, monthly, seasonal, and annual time scales, CV R² increases to 0.89, 0.93, 0.97, and 0.99, respectively. The annual mean model estimated NO₂ concentration over BTH region is 28.2 ± 6.5 μg/m³, with relatively higher NO₂ concentrations are seen in southern and southeastern BTH. Winter experiences the most severe NO₂ concentrations, followed by autumn, spring, and summer. Surface NO₂ concentrations are higher (lower) in the morning (afternoon) and tend to decrease gradually with time. The model generally captures the hourly evolution of NO₂ concentrations for the severe pollution episode but shows some underestimations. The annual mean NO₂ concentrations were 2.8% lower on the weekend than on weekdays. In addition, the weekend effects of NO₂ concentrations are larger at rush hour and lower in the noon. The hourly NO₂ products derived from proposed approach are potentially useful for improving our understanding of the source, evolution, and transportation behavior of NO₂ pollution episodes and for exposure- and health-related research. The proposed approach also enriches the potential applications of geostationary satellites (e.g., Himawari-8).

Effects of the physical and social environment on youth cognitive performance

Individual differences in children's cognitive abilities impact life and health outcomes. What factors influence these individual differences during development? Here, we test whether children's environments predict cognitive performance, independent of well-characterized socioeconomic effects. We analyzed data from 9002 9- to 10-year olds from the Adolescent Brain Cognitive Development Study, an ongoing longitudinal study with community samples across the United States. Using youth- and caregiver-report questionnaires and national database registries (e.g., neighborhood crime, walkability), we defined principal components summarizing children's home, school, neighborhood, and cultural environments. In two independent samples (ns = 3475, 5527), environmental components explained unique variance in children's general cognitive ability, executive functioning, and learning/memory abilities. Furthermore, increased neighborhood enrichment was associated with an attenuated relationship between sociodemographics and general cognitive abilities. Thus, the environment accounts for unique variance in cognitive performance in children and should be considered alongside sociodemographic factors to better understand brain functioning and behavior across development.

Assessment of air pollution at Greater Cairo in relation to the spatial variability of surface urban heat island

Greater Cairo, Egypt, which lies in the apex of the Nile Delta, is one of the most populated regions in the world. Air pollution is a profound environmental issue prevailing in the urban/rural landscapes of this crowded megacity. The objectives of the present study were to utilize remotely sensed data in order to address the seasonal variations of the nocturnal surface urban heat island intensity (SUHII) as extracted from the American Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and the related seasonal distribution of selected air pollutants, including nitrogen dioxide (NO2), sulphur dioxide (SO2), and carbon monoxide (CO) as extracted from the European TROPOspheric Monitoring Instrument (TROPOMI) for the period from 2018 to 2021. It is observed that there is clear nocturnal urban heat island over Greater Cairo, particularly at the administrative districts dominated by urban land use with high density of population and at the industrial and power generation locations. The highest SUHII is observed during winter. On the other hand, the selected pollutants also represent an urban pollution island (UPI) capping the regions of high SUHII. At the seasonal level, the highest NO2 correlation with the SUHII occurs during spring (R2 = 0.59), while the CO correlates maximum during winter (R2 = 0.51). Nonetheless, the seasonal SO2 distribution is poorly related to the SUHII as this specific pollutant is significantly associated with the industrial land use. Climatic and topographic factors could intensify the distribution of air pollution in the study area. Results of this study demonstrate the significance of geospatial technology tools in the subtle analysis and addressing regional air pollution. The outputs are also of a paramount implication on the management of urban environment and the adaptation of urban air quality.

Improving Estimates of Sulfur, Nitrogen, and Ozone Total Deposition through Multi-Model and Measurement-Model Fusion Approaches

Earth system and environmental impact studies need high quality and up-to-date estimates of atmospheric deposition. This study demonstrates the methodological benefits of multimodel ensemble and measurement-model fusion mapping approaches for atmospheric deposition focusing on 2010, a year for which several studies were conducted. Global model-only deposition assessment can be further improved by integrating new model-measurement techniques, including expanded capabilities of satellite observations of atmospheric composition. We identify research and implementation priorities for timely estimates of deposition globally as implemented by the World Meteorological Organization.

Nitrogen oxides (NO and NO2) pollution in the Accra metropolis: Spatiotemporal patterns and the role of meteorology

Economic and urban development in sub-Saharan Africa (SSA) may be shifting the dominant air pollution sources in cities from biomass to road traffic. Considered as a marker for traffic-related air pollution in cities, we conducted a city-wide measurement of NO_x levels in the Accra Metropolis and examined their spatiotemporal patterns in relation to land use and meteorological factors. Between April 2019 to June 2020, we collected weekly integrated NO_x (n = 428) and NO₂ (n = 472) samples at 10 fixed (year-long) and 124 rotating (week-long) sites. Data from the same time of year were compared to a previous study (2006) to assess changes in NO₂ concentrations. NO and NO₂ concentrations were highest in commercial/business/industrial (66 and 76 μg/m³, respectively) and high-density residential areas (47 and 59 μg/m³, respectively), compared with peri-urban locations. We observed annual means of 68 and 70 μg/m³ for NO and NO₂, and a clear seasonal variation, with the mean NO₂ of 63 μg/m³ (non-Harmattan) increased by 25-56% to 87 μg/m³ (Harmattan) across different site types. The NO₂/NO_x ratio was also elevated by 19-28%. Both NO and NO₂ levels were associated with indicators of road traffic emissions (e.g. distance to major roads), but not with community biomass use (e.g. wood and charcoal). We found strong correlations between both NO₂ and NO₂/NO_x and mixing layer depth, incident solar radiation and water vapor mixing ratio. These findings represent an increase of 25-180% when compared to a small study conducted in two high-density residential neighborhoods in Accra in 2006. Road traffic may be replacing community biomass use (major source of fine particulate matter) as the prominent source of air pollution in Accra, with policy implication for growing cities in SSA.

Long-term trends in urban NO2 concentrations and associated paediatric asthma incidence: estimates from global datasets

BACKGROUND

Combustion-related nitrogen dioxide (NO₂) air pollution is associated with paediatric asthma incidence. We aimed to estimate global surface NO₂ concentrations consistent with the Global Burden of Disease study for 1990-2019 at a 1 km resolution, and the concentrations and attributable paediatric asthma incidence trends in 13 189 cities from 2000 to 2019.

METHODS

We scaled an existing annual average NO₂ concentration dataset for 2010-12 from a land use regression model (based on 5220 NO₂ monitors in 58 countries and land use variables) to other years using NO₂ column densities from satellite and reanalysis datasets. We applied these concentrations in an epidemiologically derived concentration-response function with population and baseline asthma rates to estimate NO₂-attributable paediatric asthma incidence.

FINDINGS

We estimated that 1·85 million (95% uncertainty interval [UI] 0·93-2·80 million) new paediatric asthma cases were attributable to NO₂ globally in 2019, two thirds of which occurred in urban areas (1·22 million cases; 95% UI 0·60-1·8 million). The proportion of paediatric asthma incidence that is attributable to NO₂ in urban areas declined from 19·8% (1·22 million attributable cases of 6·14 million total cases) in 2000 to 16·0% (1·24 million attributable cases of 7·73 million total cases) in 2019. Urban attributable fractions dropped in high-income countries (-41%), Latin America and the Caribbean (-16%), central Europe, eastern Europe, and central Asia (-13%), and southeast Asia, east Asia, and Oceania (-6%), and rose in south Asia (+23%), sub-Saharan Africa (+11%), and north Africa and the Middle East (+5%). The contribution of NO₂ concentrations, paediatric population size, and asthma incidence rates to the change in NO₂-attributable paediatric asthma incidence differed regionally.

INTERPRETATION

Despite improvements in some regions, combustion-related NO₂ pollution continues to be an important contributor to paediatric asthma incidence globally, particularly in cities. Mitigating air pollution should be a crucial element of public health strategies for children.

FUNDING

Health Effects Institute, NASA.

Global fine-scale changes in ambient NO2 during COVID-19 lockdowns

Nitrogen dioxide (NO2) is an important contributor to air pollution and can adversely affect human health1-9. A decrease in NO2 concentrations has been reported as a result of lockdown measures to reduce the spread of COVID-1910-20. Questions remain, however, regarding the relationship of satellite-derived atmospheric column NO2 data with health-relevant ambient ground-level concentrations, and the representativeness of limited ground-based monitoring data for global assessment. Here we derive spatially resolved, global ground-level NO2 concentrations from NO2 column densities observed by the TROPOMI satellite instrument at sufficiently fine resolution (approximately one kilometre) to allow assessment of individual cities during COVID-19 lockdowns in 2020 compared to 2019. We apply these estimates to quantify NO2 changes in more than 200 cities, including 65 cities without available ground monitoring, largely in lower-income regions. Mean country-level population-weighted NO2 concentrations are 29% ± 3% lower in countries with strict lockdown conditions than in those without. Relative to long-term trends, NO2 decreases during COVID-19 lockdowns exceed recent Ozone Monitoring Instrument (OMI)-derived year-to-year decreases from emission controls, comparable to 15 ± 4 years of reductions globally. Our case studies indicate that the sensitivity of NO2 to lockdowns varies by country and emissions sector, demonstrating the critical need for spatially resolved observational information provided by these satellite-derived surface concentration estimates.

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Implementation of Causal Inference Methods

This report provides a final summary of the principal findings and key conclusions of a study supported by an HEI grant aimed at "Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution." It is the second and final report on this topic. The study was designed to advance four critical areas of inquiry and methods development. First, it focused on predicting short- and long-term exposures to ambient fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) at high spatial resolution (1 km × 1 km) for the continental United States over the period 2000-2016 and linking these predictions to health data. Second, it developed new causal inference methods for estimating exposure-response (ER) curves (ERCs) and adjusting for measured confounders. Third, it applied these methods to claims data from Medicare and Medicaid beneficiaries to estimate health effects associated with short- and long-term exposure to low levels of ambient air pollution. Finally, it developed pipelines for reproducible research, including approaches for data sharing, record linkage, and statistical software. Our HEI-funded work has supported an extensive portfolio of analyses and the development of statistical methods that can be used to robustly understand the health effects of short- and long-term exposure to low levels of ambient air pollution. Our Phase 1 report (Dominici et al. 2019) provided a high-level overview of our statistical methods, data analysis, and key findings, grouped into the following five areas: (1) exposure prediction, (2) epidemiological studies of ambient exposures to air pollution at low levels, (3) sensitivity analysis, (4) methodological contributions in causal inference, and (5) an open access research data platform. The current, final report includes a comprehensive overview of the entire research project.

Considering our (1) massive study population, (2) numerous sensitivity analyses, and (3) transparent assessment of covariate balance indicating the quality of causal inference for simulating randomized experiments, we conclude that conditionally on the required assumptions for causal inference, our results collectively indicate that long-term PM2.5 exposure is likely to be causally related to mortality. This conclusion assumes that the causal inference assumptions hold and, more specifically, that we accounted adequately for confounding bias. We explored various modeling approaches, conducted extensive sensitivity analyses, and found that our results were robust across approaches and models. This work relied on publicly available data, and we have provided code that allows for reproducibility of our analyses.

Our work provides comprehensive evidence of associations between exposures to PM2.5, NO2, and O3 and various health outcomes. In the current report, we report more specific results on the causal link between long-term exposure to PM2.5 and mortality, even at PM2.5 levels below or equal to 12 μg/m3, and mortality among Medicare beneficiaries (ages 65 and older). This work relies on newly developed causal inference methods for continuous exposure.

For the period 2000-2016, we found that all statistical approaches led to consistent results: a 10-μg/m3 decrease in PM2.5 led to a statistically significant decrease in mortality rate ranging between 6% and 7% (= 1 - 1/hazard ratio [HR]) (HR estimates 1.06 [95% CI, 1.05 to 1.08] to 1.08 [95% CI, 1.07 to 1.09]). The estimated HRs were larger when studying the cohort of Medicare beneficiaries that were always exposed to PM2.5 levels lower than 12 μg/m3 (1.23 [95% CI, 1.18 to 1.28] to 1.37 [95% CI, 1.34 to 1.40]).

Comparing the results from multiple and single pollutant models, we found that adjusting for the other two pollutants slightly attenuated the causal effects of PM2.5 and slightly elevated the causal effects of NO2 exposure on all-cause mortality. The results for O3 remained almost unchanged.

We found evidence of a harmful causal relationship between mortality and long-term PM2.5 exposures adjusted for NO2 and O3 across the range of annual averages between 2.77 and 17.16 μg/m3 (included >98% of observations) in the entire cohort of Medicare beneficiaries across the continental United States from 2000 to 2016. Our results are consistent with recent epidemiological studies reporting a strong association between long-term exposure to PM2.5 and adverse health outcomes at low exposure levels. Importantly, the curve was almost linear at exposure levels lower than the current national standards, indicating aggravated harmful effects at exposure levels even below these standards.

There is, in general, a harmful causal impact of long-term NO2 exposures to mortality adjusted for PM2.5 and O3 across the range of annual averages between 3.4 and 80 ppb (included >98% of observations). Yet within low levels (annual mean ≤53 ppb) below the current national standards, the causal impacts of NO2 exposures on all-cause mortality are nonlinear with statistical uncertainty.

The ERCs of long-term O3 exposures on all-cause mortality adjusted for PM2.5 and NO2 are almost flat below 45 ppb, which shows no statistically significant effect. Yet we observed an increased hazard when the O3 exposures were higher than 45 ppb, and the HR was approximately 1.10 when comparing Medicare beneficiaries with annual mean O3 exposures of 50 ppb versus those with 30 ppb.

institutions, including those that support the Health Effects Institute; therefore, it may not reflect the views or policies of these parties, and no endorsement by them should be inferred.

A list of abbreviations and other terms appears at the end of this volume.

Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA

Machine learning (ML) has demonstrated promise in predicting mortality; however, understanding spatial variation in risk factor contributions to mortality rate requires explainability. We applied explainable artificial intelligence (XAI) on a stack-ensemble machine learning model framework to explore and visualize the spatial distribution of the contributions of known risk factors to lung and bronchus cancer (LBC) mortality rates in the conterminous United States. We used five base-learners-generalized linear model (GLM), random forest (RF), Gradient boosting machine (GBM), extreme Gradient boosting machine (XGBoost), and Deep Neural Network (DNN) for developing stack-ensemble models. Then we applied several model-agnostic approaches to interpret and visualize the stack ensemble model's output in global and local scales (at the county level). The stack ensemble generally performs better than all the base learners and three spatial regression models. A permutation-based feature importance technique ranked smoking prevalence as the most important predictor, followed by poverty and elevation. However, the impact of these risk factors on LBC mortality rates varies spatially. This is the first study to use ensemble machine learning with explainable algorithms to explore and visualize the spatial heterogeneity of the relationships between LBC mortality and risk factors in the contiguous USA.

Urbanization, ambient air pollution, and prevalence of chronic kidney disease: A nationwide cross-sectional study

An increasing number of studies have linked ambient air pollution to chronic kidney disease (CKD) prevalence. However, its potential effect modification by urbanization has not been investigated. Based on data of 47,204 adults from the China National Survey of Chronic Kidney Disease (CKSCKD) dataset, night light satellite remote sensing data and high-resolution air pollution inversion products, the present cross-sectional study investigated the association between fine particulate matter <2.5 mm in diameter (PM_2.5), nitrogen dioxide (NO₂), night light index (NLI) and CKD prevalence in China, and the effect modification by urbanization characterized by administrative classification and NLI on the pollutant-health associations. Our results showed that a 10-μg/m³ increase in PM_2.5 at 3-year moving average, a 10-μg/m³ increase in NO₂ at 5-year moving average, and a 10-U increase in NLI at 5-year moving average were significantly associated with increased odds of CKD prevalence [OR = 1.24 (95 %CI:1.14, 1.35); OR = 1.12 (95 %CI:1.09, 1.15); OR = 1.05 (95 %CI:1.02, 1.07)]. Meanwhile, the pollutant-health associations were more apparent in medium-urbanized areas compared to low- and high-urbanized areas. For instance, a 10-μg/m³ increase in PM_2.5 concentration at 2-year moving average was associated with increased odds of CKD in the areas with NLI level in the second [OR = 2.78 (95 %CI:1.77, 4.36)] and third quartiles [OR = 1.49 (95 %CI:1.14, 1.95)], compared to the lowest [OR = 0.96 (95% CI: 0.73, 1.26)] and highest [OR = 0.63 (95% CI: 0.39-1.02)] quartiles. PM_2.5 and NO₂ were associated with increased odds of CKD prevalence, especially in areas with medium NLI levels, suggesting the necessity of strengthening environmental management in medium-urbanized regions.

Calibration of Low-Cost NO2 Sensors through Environmental Factor Correction

Low-cost air quality sensors (LCSs) have become more widespread due to their low cost and increased capabilities; however, to supplement more traditional air quality networks, the performance of these LCSs needs to be validated. This study focused on NO₂ measurements from eight Clarity Node-S sensors and used various environmental factors to calibrate the LCSs. To validate the calibration performance, we calculated the root-mean-square error (RMSE), mean absolute error (MAE), R², and slope compared to reference measurements. Raw results from six of these sensors were comparable to those reported for other NO₂ LCSs; however, two of the evaluated LCSs had RMSE values ~20 ppb higher than the other six LCSs. By applying a sensor-specific calibration that corrects for relative humidity, temperature, and ozone, this discrepancy was mitigated. In addition, this calibration improved the RMSE, MAE, R², and slope of all eight LCS compared to the raw data. It should be noted that relatively stable environmental conditions over the course of the LCS deployment period benefited calibration performance over time. These results demonstrate the importance of developing LCS calibration models for individual sensors that consider pertinent environmental factors.

COVID-19 pandemic reveals persistent disparities in nitrogen dioxide pollution

We leverage the unparalleled changes in human activity during COVID-19 and the unmatched capabilities of the TROPOspheric Monitoring Instrument to understand how lockdowns impact ambient nitrogen dioxide (NO2) pollution disparities in the United States. The least White communities experienced the largest NO2 reductions during lockdowns; however, disparities between the least and most White communities are so large that the least White communities still faced higher NO2 levels during lockdowns than the most White communities experienced prior to lockdowns, despite a ∼50% reduction in passenger vehicle traffic. Similar findings hold for ethnic, income, and educational attainment population subgroups. Future strategies to reduce NO2 disparities will need to target emissions from heavy-duty vehicles.

The unequal spatial distribution of ambient nitrogen dioxide (NO2), an air pollutant related to traffic, leads to higher exposure for minority and low socioeconomic status communities. We exploit the unprecedented drop in urban activity during the COVID-19 pandemic and use high-resolution, remotely sensed NO2 observations to investigate disparities in NO2 levels across different demographic subgroups in the United States. We show that, prior to the pandemic, satellite-observed NO2 levels in the least White census tracts of the United States were nearly triple the NO2 levels in the most White tracts. During the pandemic, the largest lockdown-related NO2 reductions occurred in urban neighborhoods that have 2.0 times more non-White residents and 2.1 times more Hispanic residents than neighborhoods with the smallest reductions. NO2 reductions were likely driven by the greater density of highways and interstates in these racially and ethnically diverse areas. Although the largest reductions occurred in marginalized areas, the effect of lockdowns on racial, ethnic, and socioeconomic NO2 disparities was mixed and, for many cities, nonsignificant. For example, the least White tracts still experienced ∼1.5 times higher NO2 levels during the lockdowns than the most White tracts experienced prior to the pandemic. Future policies aimed at eliminating pollution disparities will need to look beyond reducing emissions from only passenger traffic and also consider other collocated sources of emissions such as heavy-duty vehicles.

COVID-19 pandemic reveals persistent disparities in nitrogen dioxide pollution

The unequal spatial distribution of ambient nitrogen dioxide ([Formula: see text]), an air pollutant related to traffic, leads to higher exposure for minority and low socioeconomic status communities. We exploit the unprecedented drop in urban activity during the COVID-19 pandemic and use high-resolution, remotely sensed [Formula: see text] observations to investigate disparities in [Formula: see text] levels across different demographic subgroups in the United States. We show that, prior to the pandemic, satellite-observed [Formula: see text] levels in the least White census tracts of the United States were nearly triple the [Formula: see text] levels in the most White tracts. During the pandemic, the largest lockdown-related [Formula: see text] reductions occurred in urban neighborhoods that have 2.0 times more non-White residents and 2.1 times more Hispanic residents than neighborhoods with the smallest reductions. [Formula: see text] reductions were likely driven by the greater density of highways and interstates in these racially and ethnically diverse areas. Although the largest reductions occurred in marginalized areas, the effect of lockdowns on racial, ethnic, and socioeconomic [Formula: see text] disparities was mixed and, for many cities, nonsignificant. For example, the least White tracts still experienced ∼1.5 times higher [Formula: see text] levels during the lockdowns than the most White tracts experienced prior to the pandemic. Future policies aimed at eliminating pollution disparities will need to look beyond reducing emissions from only passenger traffic and also consider other collocated sources of emissions such as heavy-duty vehicles.

Early Spread of COVID-19 in the Air-Polluted Regions of Eight Severely Affected Countries

COVID-19 escalated into a pandemic posing several humanitarian as well as scientific challenges. We here investigated the geographical character of the early spread of the infection and correlated it with several annual satellite and ground indexes of air quality in China, the United States, Italy, Iran, France, Spain, Germany, and the United Kingdom. The time of the analysis corresponded with the end of the first wave infection in China, namely June 2020. We found more viral infections in those areas afflicted by high PM 2.5 and nitrogen dioxide values. Higher mortality was also correlated with relatively poor air quality. In Italy, the correspondence between the Po Valley pollution and SARS-CoV-2 infections and induced mortality was the starkest, originating right in the most polluted European area. Spain and Germany did not present a noticeable gradient of pollution levels causing non-significant correlations. Densely populated areas were often hotspots of lower air quality levels but were not always correlated with a higher viral incidence. Air pollution has long been recognised as a high risk factor for several respiratory-related diseases and conditions, and it now appears to be a risk factor for COVID-19 as well. As such, air pollution should always be included as a factor for the study of airborne epidemics and further included in public health policies.

Biomonitoring of Ambient Outdoor Air Pollutant Exposure in Humans Using Targeted Serum Albumin Adductomics

Outdoor air pollution, a spatially and temporally complex mixture, is a human carcinogen. However, ambient measurements may not reflect subject-level exposures, personal monitors do not assess internal dose, and spot assessments of urinary biomarkers may not recapitulate chronic exposures. Nucleophilic sites in serum albumin-particularly the free thiol at Cys³⁴-form adducts with electrophiles. Due to the 4-week lifetime of albumin in circulation, accumulating adducts can serve as intermediate- to long-residence biomarkers of chronic exposure and implicate potential biological effects. Employing nanoflow liquid chromatography-high-resolution mass spectrometry (nLC-HRMS) and parallel reaction monitoring (PRM), we have developed and validated a novel targeted albumin adductomics platform capable of simultaneously monitoring dozens of Cys³⁴ adducts per sample in only 2.5 μL of serum, with on-column limits of detection in the low-femtomolar range. Using this platform, we characterized the magnitude and impact of ambient outdoor air pollution exposures with three repeated measurements over 84 days in n = 26 nonsmoking women (n = 78 total samples) from Qidong, China, an area with a rising burden of lung cancer incidence. In concordance with seasonally rising ambient concentrations of NO₂, SO₂, and PM₁₀ measured at stationary monitors, we observed elevations in concentrations of Cys³⁴ adducts of benzoquinone (p < 0.05), benzene diol epoxide (BDE; p < 0.05), crotonaldehyde (p < 0.01), and oxidation (p < 0.001). Regression analysis revealed significant elevations in oxidation and BDE adduct concentrations of 300% to nearly 700% per doubling of ambient airborne pollutant levels (p < 0.05). Notably, the ratio of irreversibly oxidized to reduced Cys³⁴ rose more than 3-fold during the 84-day period, revealing a dramatic perturbation of serum redox balance and potentially serving as a portent of increased pollution-related mortality risk. Our targeted albumin adductomics assay represents a novel and flexible approach for sensitive and multiplexed internal dosimetry of environmental exposures, providing a new strategy for personalized biomonitoring and prevention.

TROPOMI NO2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO2 Concentrations

Observing the spatial heterogeneities of NO2 air pollution is an important first step in quantifying NOX emissions and exposures. This study investigates the capabilities of the Tropospheric Monitoring Instrument (TROPOMI) in observing the spatial and temporal patterns of NO2 pollution in the continental United States. The unprecedented sensitivity of the sensor can differentiate the fine-scale spatial heterogeneities in urban areas, such as emissions related to airport/shipping operations and high traffic, and the relatively small emission sources in rural areas, such as power plants and mining operations. We then examine NO2 columns by day-of-the-week and find that Saturday and Sunday concentrations are 16% and 24% lower respectively, than during weekdays. We also analyze the correlation of daily maximum 2-m temperatures and NO2 column amounts and find that NO2 is larger on the hottest days (>32°C) as compared to warm days (26°C-32°C), which is in contrast to a general decrease in NO2 with increasing temperature at moderate temperatures. Finally, we demonstrate that a linear regression fit of 2019 annual TROPOMI NO2 data to annual surface-level concentrations yields relatively strong correlation (R 2 = 0.66). These new developments make TROPOMI NO2 satellite data advantageous for policymakers and public health officials, who request information at high spatial resolution and short timescales, in order to assess, devise, and evaluate regulations.

Estimating the Daily NO2 Concentration with High Spatial Resolution in the Beijing–Tianjin–Hebei Region Using an Ensemble Learning Model

Nitrogen dioxide (NO2) is an important pollutant related to human activities, which has short-term and long-term effects on human health. An ensemble learning model was constructed and applied to estimate daily NO2 concentrations in the Beijing–Tianjin–Hebei region between 2010 and 2016. A variety of predictive variables included satellite-based troposphere NO2 vertical column concentration, meteorology, elevation, gross domestic product (GDP), population, land-use variables, and road network. The ensemble learning model achieved two things: a 0.01° × 0.01° grid resolution and the estimation of historical data for the years 2010–2013. The ensemble model showed good performance, whereby the R2 of tenfold cross-validation was 0.72 and the R2 of test validation was 0.71. Meteorological hysteretic effects were incorporated into the model, where the one-day lagged boundary layer height contributed the most. The annual NO2 estimation showed little change from 2010 to 2016. The seasonal NO2 estimation from highest to lowest occurred in winter, autumn, spring, and summer. In the annual maps and seasonal maps, the NO2 estimations in the northwest region were lower than those in the southeast region, and there was a heavily polluted band in the south of the Taihang Mountains. In coastal areas, the annual NO2 estimations were higher than the NO2 monitored values. The drawback of the model is underestimation at high values and overestimation at low values. This study indicates that the ensemble learning model has excellent performance in the simulation of NO2 with high spatial and temporal resolution. Furthermore, the research framework in this study can be a generally applied for drawing implications for other regions, especially for other cities in China.

Reductions in NO2 burden over north equatorial Africa from decline in biomass burning in spite of growing fossil fuel use, 2005 to 2017

Socioeconomic development in low- and middle-income countries has been accompanied by increased emissions of air pollutants, such as nitrogen oxides [NO_x: nitrogen dioxide (NO₂) + nitric oxide (NO)], which affect human health. In sub-Saharan Africa, fossil fuel combustion has nearly doubled since 2000. At the same time, landscape biomass burning-another important NO_x source-has declined in north equatorial Africa, attributed to changes in climate and anthropogenic fire management. Here, we use satellite observations of tropospheric NO₂ vertical column densities (VCDs) and burned area to identify NO₂ trends and drivers over Africa. Across the northern ecosystems where biomass burning occurs-home to hundreds of millions of people-mean annual tropospheric NO₂ VCDs decreased by 4.5% from 2005 through 2017 during the dry season of November through February. Reductions in burned area explained the majority of variation in NO₂ VCDs, though changes in fossil fuel emissions also explained some variation. Over Africa's biomass burning regions, raising mean GDP density (USD⋅km^-2) above its lowest levels is associated with lower NO₂ VCDs during the dry season, suggesting that economic development mitigates net NO₂ emissions during these highly polluted months. In contrast to the traditional notion that socioeconomic development increases air pollutant concentrations in low- and middle-income nations, our results suggest that countries in Africa's northern biomass-burning region are following a different pathway during the fire season, resulting in potential air quality benefits. However, these benefits may be lost with increasing fossil fuel use and are absent during the rainy season.

Systematic review and meta-analysis of cohort studies of long term outdoor nitrogen dioxide exposure and mortality

Objective

To determine whether long term exposure to outdoor nitrogen dioxide (NO₂) is associated with all-cause or cause-specific mortality.

Methods

MEDLINE, Embase, CENTRAL, Global Health and Toxline databases were searched using terms developed by a librarian. Screening, data extraction and risk of bias assessment were completed independently by two reviewers. Conflicts were resolved through consensus and/or involvement of a third reviewer. Pooling of results across studies was conducted using random effects models, heterogeneity among included studies was assessed using Cochran’s Q and I² measures, and sources of heterogeneity were evaluated using meta-regression. Sensitivity of pooled estimates to individual studies was examined and publication bias was evaluated using Funnel plots, Begg’s and Egger’s tests, and trim and fill.

Results

Seventy-nine studies based on 47 cohorts, plus one set of pooled analyses of multiple European cohorts, met inclusion criteria. There was a consistently high degree of heterogeneity. After excluding studies with probably high or high risk of bias in the confounding domain (n = 12), pooled hazard ratios (HR) indicated that long term exposure to NO₂ was significantly associated with mortality from all/ natural causes (pooled HR 1.047, 95% confidence interval (CI), 1.023–1.072 per 10 ppb), cardiovascular disease (pooled HR 1.058, 95%CI 1.026–1.091), lung cancer (pooled HR 1.083, 95%CI 1.041–1.126), respiratory disease (pooled HR 1.062, 95%CI1.035–1.089), and ischemic heart disease (pooled HR 1.111, 95%CI 1.079–1.144). Pooled estimates based on multi-pollutant models were consistently smaller than those from single pollutant models and mostly non-significant.

Conclusions

For all causes of death other than cerebrovascular disease, the overall quality of the evidence is moderate, and the strength of evidence is limited, while for cerebrovascular disease, overall quality is low and strength of evidence is inadequate. Important uncertainties remain, including potential confounding by co-pollutants or other concomitant exposures, and limited supporting mechanistic evidence. (PROSPERO registration number CRD42018084497)

Short-term effects of ambient nitrogen dioxide on years of life lost in 48 major Chinese cities, 2013-2017

BACKGROUND

Evidence on the acute effect of short-term exposure to nitrogen dioxide (NO₂) on years of life lost (YLL) is rare, especially in multicity setting.

METHODS

We conducted a time series study among 48 major Chinese cities covering more than 403 million people from 2013 to 2017. The relative percentage changes of NO₂-YLL were estimated by generalized additive models in each city, then were pooled to generate average effects using random-effect models. In addition, stratified analyses by individual demographic factors and temperature as well as meta-regression analyses incorporating city-specific air pollutant concentrations, meteorological conditions, and socioeconomic indicators were performed to explore potential effect modification.

RESULTS

A 10 μg/m³ increase in two-day moving average (lag01) NO₂ concentration was associated with 0.64% (95% CI: 0.47%, 0.81%), 0.47% (95% CI: 0.27%, 0.68%), and 0.68% (95% CI: 0.34%, 1.02%) relative increments in YLL due to nonaccidental causes, cardiovascular diseases (CVD), and respiratory diseases (RD), respectively. These associations were generally robust to the adjustment of co-pollutants, except for NO₂-CVD that might be confounded by fine particulate matter. The increased YLL induced by NO₂ were more pronounced in elderly people, hotter days, and cities characterized by less severe air pollution or higher temperature.

CONCLUSIONS

Our results demonstrated robust evidence on the associations between NO₂ exposure and YLL due to nonaccidental causes, CVD, and RD, which provided novel evidence to better understand the disease burden related to NO₂ pollution and to facilitate allocation of health resources targeting high-risk subpopulation.

Socioeconomic disparity in the association between long-term exposure to PM2.5 and mortality in 2640 Chinese counties

BACKGROUND

Although the association between long-term exposure to PM_2.5 and mortality has been evaluated intensively, little is known about the socioeconomic disparity in the association.

METHODS

We collected data on annual all-cause mortality, PM_2.5 concentration, socioeconomic and demographic characteristics of 2640 counties from the two most recent Chinese censuses in 2000 and 2010. We applied the difference-in-differences (DID) method to estimate PM_2.5-mortality association for counties at different quartiles of literacy rate, college rate, urbanization rate and GDP per capita, respectively.

RESULTS

Overall, every 10 µg/m³ increase in annual average PM_2.5 was associated with 3.8% (95% confidence interval [CI]: 3.0-5.0) increase of all-cause mortality. The stratified analysis suggested higher health impact of exposure in counties with lower socioeconomic status. For counties of the lowest quartile (Q₁) of literacy rate, college rate, urbanization rate and GDP per capita, the effect estimates were 6.0% (95% CI: 4.2-7.7), 4.4% (95% CI: 2.8-6.0), 3.5% (95% CI: 2.0-5.1) and 4.9% (95% CI: 2.7-7.1), respectively. There was strong evidence for elevated risk in mortality associated with PM_2.5 of all socioeconomic factors in the lowest quartile (Q₁) compared with the highest quartile counties (Q₄) (p-value for difference < 0.05).

CONCLUSIONS

There was socioeconomic disparity in the PM_2.5-mortality association in China. Dwellers living in less developed counties are more vulnerable to long-term exposure to ambient PM_2.5 than those living in developed counties.