Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990-2017

Estimating future climate change impacts on human mortality and crop yields via air pollution

Future climate change may bring local benefits or penalties to surface air pollution, resulting from changing temperature, precipitation, and transport patterns, as well as changes in climate-sensitive natural precursor emissions. Here, we estimate the climate penalties and benefits at the end of this century with regard to surface ozone and fine particulate matter (PM[Formula: see text]; excluding dust and smoke) using a one-way offline coupling between a general circulation model and a global 3-D chemical-transport model. We archive meteorology for the present day (2005 to 2014) and end of this century (2090 to 2099) for seven future scenarios developed for Phase 6 of the Coupled Model Intercomparison Project. The model isolates the impact of forecasted anthropogenic precursor emission changes versus that of climate-only driven changes on surface ozone and PM[Formula: see text] for scenarios ranging from extreme mitigation to extreme warming. We then relate these changes to impacts on human mortality and crop production. We find ozone penalties over nearly all land areas with increasing warming. We find net benefits due to climate-driven changes in PM[Formula: see text] in the Northern Extratropics, but net penalties in the Tropics and Southern Hemisphere, where most population growth is forecast for the coming century.

Exploring the nexus of urban form, transport, environment and health in large-scale urban studies: A state-of-the-art scoping review

BACKGROUND

As the world becomes increasingly urbanised, there is recognition that public and planetary health relies upon a ubiquitous transition to sustainable cities. Disentanglement of the complex pathways of urban design, environmental exposures, and health, and the magnitude of these associations, remains a challenge. A state-of-the-art account of large-scale urban health studies is required to shape future research priorities and equity- and evidence-informed policies.

OBJECTIVES

The purpose of this review was to synthesise evidence from large-scale urban studies focused on the interaction between urban form, transport, environmental exposures, and health. This review sought to determine common methodologies applied, limitations, and future opportunities for improved research practice.

METHODS

Based on a literature search, 2958 articles were reviewed that covered three themes of: urban form; urban environmental health; and urban indicators. Studies were prioritised for inclusion that analysed at least 90 cities to ensure broad geographic representation and generalisability. Of the initially identified studies, following expert consultation and exclusion criteria, 66 were included.

RESULTS

The complexity of the urban ecosystem on health was evidenced from the context dependent effects of urban form variables on environmental exposures and health. Compact city designs were generally advantageous for reducing harmful environmental exposure and promoting health, with some exceptions. Methodological heterogeneity was indicative of key urban research challenges; notable limitations included exposure and health data at varied spatial scales and resolutions, limited availability of local-level sociodemographic data, and the lack of consensus on robust methodologies that encompass best research practice.

CONCLUSION

Future urban environmental health research for evidence-informed urban planning and policies requires a multi-faceted approach. Advances in geospatial and AI-driven techniques and urban indicators offer promising developments; however, there remains a wider call for increased data availability at local-levels, transparent and robust methodologies of large-scale urban studies, and greater exploration of urban health vulnerabilities and inequities.

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 μg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.

Applicability of evaluation metrics/schemes for human health burden attributable to regional ozone pollution: A case study in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), South China

This is a study to identify the applicable/preferable short- and long-term metrics/schemes to evaluate the premature mortality attributable to the ozone pollution in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the most representative populous ozone pollution regions in China, by comprehensively accounting the uncertainty sources. The discrepancy between the observation and the CAQRA reanalysis datasets (2013-2019) was investigated in terms of the concentration variation pattern, which determines the exposure metric change. A set of domestic short-term C-R coefficients for the all-age population were integrated using the meta-analysis respectively corresponding to the metrics of MDA1, MDA8, and Daily average. The dataset-based deviations of the short-term attributable factors (AFs) and their corresponding premature mortalities were respectively about 16.9 ± 13.3 % and <5 % based on MDA8, much smaller than other two metrics; and the MDA8-based evaluation results were the most sensitive to the deteriorative ozone pollution, with the maximum upward trends of 0.095-0.129 %/year. Accordingly, MDA8 was recognized as the most applicable short-term metric. For the long-term exposure, the domestic summer metric SMDA8 could not exactly represent the peak-season ozone maximum level in the GBA, with the deviation from 6MMDA8 as much as 30 %. By considering the ability of metric to represent the peak-season ozone, the relatively smaller dataset-based discrepancies of AFs (6MMDA8-WHO2021: 23.3 ± 16.9 %, AMDA8-T2016: 20.7 ± 15.8 %) and the attributable premature mortalities (6MMDA8-WHO2021: 5 %, AMDA8-T2016: 8 %), and the higher sensitivity of the evaluation results to the deteriorative ozone pollution (6MMDA8-WHO2021: 0.13 %;year, p = 0.01; AMDA8-T2016: 0.15 %/year, p = 0.03), the schemes of 6MMDA8-WHO2021 and AMDA8-T2016 were recognized relatively more preferable for the adult (≥25-year) long-term evaluation. Based on the recognized metric/schemes, the central and the eastern PRE areas of higher NO2 level in the GBA were experiencing the highest health burdens from 2013 to 2019.

Long-term ozone exposure and all-cause mortality: Cohort evidence in China and global heterogeneity by region

BACKGROUND

Cohort evidence linking long-term ozone (O3) exposure to mortality remained largely mixed worldwide and was extensively deficient in densely-populated Asia. This study aimed to assess the long-term effects of O3 exposure on all-cause mortality among Chinese adults, as well as to examine potential regional heterogeneity across the globe.

METHODS

A national dynamic cohort of 42153 adults aged 16+ years were recruited from 25 provinces across Chinese mainland and followed up during 2010-2018. Annual warm-season (April-September) O3 and year-round co-pollutants (i.e., nitrogen dioxide [NO2] and fine particulate matter [PM2.5]) were simulated through validated spatial-temporal prediction models and were assigned to each enrollee in each calendar year. Cox proportional hazards models with time-varying exposures were employed to assess the O3-mortality association. Concentration-response (C-R) curves were fitted by natural cubic spline function to investigate the potential nonlinear association. Both single-pollutant model and co-pollutant models additionally adjusting for PM2.5 and/or NO2 were employed to examine the robustness of the estimated association. The random-effect meta-analysis was adopted to pool effect estimates from the current and prior population-based cohorts (n = 29), and pooled C-R curves were fitted through the meta-smoothing approach by regions.

RESULTS

The study population comprised of 42153 participants who contributed 258921.5 person-years at risk (median 6.4 years), of whom 2382 death events occurred during study period. Participants were exposed to an annual average of 51.4 ppb (range: 22.7-74.4 ppb) of warm-season O3 concentration. In the single-pollutant model, a significantly increased hazard ratio (HR) of 1.098 (95% confidence interval [CI]: 1.023-1.179) was associated with a 10-ppb rise in O3 exposure. Associations remained robust to additional adjustments of co-pollutants, with HRs of 1.099 (95% CI: 1.023-1.180) in bi-pollutant model (+PM2.5) and 1.093 (95% CI: 1.018-1.174) in tri-pollutant model (+PM2.5+NO2), respectively. A J-shaped C-R relationship was identified among Chinese general population, suggesting significant excess mortality risk at high ozone exposure only. The combined C-R curves from Asia (n = 4) and North America (n = 17) demonstrated an overall increased risk of all-cause mortality with O3 exposure, with pooled HRs of 1.124 (95% CI: 0.966-1.307) and 1.023 (95% CI: 1.007-1.039) per 10-ppb rise, respectively. Conversely, an opposite association was observed in Europe (n = 8, HR: 0.914 [95% CI: 0.860-0.972]), suggesting significant heterogeneity across regions (P < 0.01).

CONCLUSIONS

This study provided national evidence that high O3 exposure may curtail long-term survival of Chinese general population. Great between-region heterogeneity of pooled O3-mortality was identified across North America, Europe, and Asia.

Wheat yield response to elevated O3 concentrations differs between the world's major producing regions

Ground-level ozone (O3) concentration is rising in Asia, which accommodates the world's top-two wheat producers (China and India). Because wheat is among the species of high O3 sensitivity, yield loss due to rising O3 in Asia is a major threat to global wheat supply. We estimated the relationships between O3 dose on AOT40 (accumulated daytime O3 concentrations above 40 ppb for 90 days) and relative wheat yield for four wheat producing regions: China, India, Europe and North America using results of O3 elevation experiments conducted therein. When compared on the same AOT40, the estimated yield loss was greatest in China followed by India, Europe, and North America in this order. In China, Europe and North America, the yield loss was primarily due to the reduction of single grain weight, whereas in India reduction of the number of grains contributed more to the yield loss than single grain weight. The greater response of the number of grains to O3 in India can be explained by the earlier start of O3 elevation, but the seasonal change in O3 concentrations cannot explain the lower yield loss in North America than China and India. Referring to the past reports of lower yield sensitivity to O3 in older cultivars, we compared the year of release of cultivars between the regions. In North America, they used cultivars released in 1980s or earlier, whereas in China they used cultivars released in 2000s. In Europe and India, most cultivars were released between those in North America and China. The difference in cultivars could therefore be a cause the differential yield response among the regions. We argue that the O3-induced yield loss should be quantified using the dose-response relationships for each region accounting for the effects of seasonal change in O3 concentrations, cultivars and climate on the yield response.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Solar-induced chlorophyll fluorescence captures the effects of elevated ozone on canopy structure and acceleration of senescence in soybean

Solar-induced chlorophyll fluorescence (SIF) provides an opportunity to rapidly and non-destructively investigate how plants respond to stress. Here, we explored the potential of SIF to detect the effects of elevated O3 on soybean in the field where soybean was subjected to ambient and elevated O3 throughout the growing season in 2021. Exposure to elevated O3 resulted in a significant decrease in canopy SIF at 760 nm (SIF760), with a larger decrease in the late growing season (36%) compared with the middle growing season (13%). Elevated O3 significantly decreased the fraction of absorbed photosynthetically active radiation by 8-15% in the middle growing season and by 35% in the late growing stage. SIF760 escape ratio (fesc) was significantly increased under elevated O3 by 5-12% in the late growth stage due to a decrease of leaf chlorophyll content and leaf area index. Fluorescence yield of the canopy was reduced by 5-11% in the late growing season depending on the fesc estimation method, during which leaf maximum carboxylation rate and maximum electron transport were significantly reduced by 29% and 20% under elevated O3. These results demonstrated that SIF could capture the elevated O3 effect on canopy structure and acceleration of senescence in soybean and provide empirical support for using SIF for soybean stress detection and phenotyping.

Air pollution deaths attributable to fossil fuels: observational and modelling study

OBJECTIVES

To estimate all cause and cause specific deaths that are attributable to fossil fuel related air pollution and to assess potential health benefits from policies that replace fossil fuels with clean, renewable energy sources.

DESIGN

Observational and modelling study.

METHODS

An updated atmospheric composition model, a newly developed relative risk model, and satellite based data were used to determine exposure to ambient air pollution, estimate all cause and disease specific mortality, and attribute them to emission categories.

DATA SOURCES

Data from the global burden of disease 2019 study, observational fine particulate matter and population data from National Aeronautics and Space Administration (NASA) satellites, and atmospheric chemistry, aerosol, and relative risk modelling for 2019.

RESULTS

Globally, all cause excess deaths due to fine particulate and ozone air pollution are estimated at 8.34 million (95% confidence interval 5.63 to 11.19) deaths per year. Most (52%) of the mortality burden is related to cardiometabolic conditions, particularly ischaemic heart disease (30%). Stroke and chronic obstructive pulmonary disease both account for 16% of mortality burden. About 20% of all cause mortality is undefined, with arterial hypertension and neurodegenerative diseases possibly implicated. An estimated 5.13 million (3.63 to 6.32) excess deaths per year globally are attributable to ambient air pollution from fossil fuel use and therefore could potentially be avoided by phasing out fossil fuels. This figure corresponds to 82% of the maximum number of air pollution deaths that could be averted by controlling all anthropogenic emissions. Smaller reductions, rather than a complete phase-out, indicate that the responses are not strongly non-linear. Reductions in emission related to fossil fuels at all levels of air pollution can decrease the number of attributable deaths substantially. Estimates of avoidable excess deaths are markedly higher in this study than most previous studies for these reasons: the new relative risk model has implications for high income (largely fossil fuel intensive) countries and for low and middle income countries where the use of fossil fuels is increasing; this study accounts for all cause mortality in addition to disease specific mortality; and the large reduction in air pollution from a fossil fuel phase-out can greatly reduce exposure.

CONCLUSION

Phasing out fossil fuels is deemed to be an effective intervention to improve health and save lives as part the United Nations' goal of climate neutrality by 2050. Ambient air pollution would no longer be a leading, environmental health risk factor if the use of fossil fuels were superseded by equitable access to clean sources of renewable energy.

Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality

Ever-increasing ambient ozone (O3) pollution in China has been exacerbating cardiopulmonary premature deaths. However, the urban-rural exposure inequity has seldom been explored. Here, we assess population-scale O3 exposure and mortality burdens between 1990 and 2019 based on integrated pollution tracking and epidemiological evidence. We find Chinese population have been suffering from climbing O3 exposure by 4.3 ± 2.8 ppb per decade as a result of rapid urbanization and growing prosperity of socioeconomic activities. Rural residents are broadly exposed to 9.8 ± 4.1 ppb higher ambient O3 than the adjacent urban citizens, and thus urbanization-oriented migration compromises the exposure-associated mortality on total population. Cardiopulmonary excess premature deaths attributable to long-term O3 exposure, 373,500 (95% uncertainty interval [UI]: 240,600-510,900) in 2019, is underestimated in previous studies due to ignorance of cardiovascular causes. Future O3 pollution policy should focus more on rural population who are facing an aggravating threat of mortality risks to ameliorate environmental health injustice.

Ozone-induced neurotoxicity: In vitro and in vivo evidence

Together with cities in higher-income nations, it is anticipated that the real global ozone is rising in densely populated areas of Asia and Africa. This review aims to discuss the possible neurotoxic pollutants and ozone-induced neurotoxicity: in vitro and in vivo, along with possible biomarkers to assess ozone-related oxidative stress. As a methodical and scientific strategy for hazard identification and risk characterization of human chemical exposures, toxicological risk assessment is increasingly being implemented. While traditional methods are followed by in vitro toxicology, cell culture techniques are being investigated in modern toxicology. In both human and rodent models, aging makes the olfactory circuitry vulnerable to spreading immunological responses from the periphery to the brain because it lacks the blood-brain barrier. The ozone toxicity is elusive as it shows ventral and dorsal root injury cases even in the milder dose. Its potential toxicity should be disclosed to understand further the clear mechanism insights of how it acts in cellular aspects. Human epidemiological research has confirmed the conclusions that prenatal and postnatal exposure to high levels of air pollution are linked to behavioral alterations in offspring. O3 also enhances blood circulation. It has antibacterial action, which may have an impact on the gut microbiota. It also activates immunological, anti-inflammatory, proteasome, and growth factor signaling Prolonged O3 exposure causes oxidative damage to plasma proteins and lipids and damages the structural and functional integrity of the mitochondria. Finally, various studies need to be conducted to identify the potential biomarkers associated with ozone and the brain.

The Air Pollution Human Health Burden in Different Future Scenarios That Involve the Mitigation of Near-Term Climate Forcers, Climate and Land-Use

Elevated surface concentrations of ozone and fine particulate matter (PM2.5) can lead to poor air quality and detrimental impacts on human health. These pollutants are also termed Near-Term Climate Forcers (NTCFs) as they can also influence the Earth's radiative balance on timescales shorter than long-lived greenhouse gases. Here we use the Earth system model, UKESM1, to simulate the change in surface ozone and PM2.5 concentrations from different NTCF mitigation scenarios, conducted as part of the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). These are then combined with relative risk estimates and projected changes in population demographics, to estimate the mortality burden attributable to long-term exposure to ambient air pollution. Scenarios that involve the strong mitigation of air pollutant emissions yield large future benefits to human health (25%), particularly across Asia for black carbon (7%), when compared to the future reference pathway. However, if anthropogenic emissions follow the reference pathway, then impacts to human health worsen over South Asia in the short term (11%) and across Africa (20%) in the longer term. Future climate change impacts on air pollutants can offset some of the health benefits achieved by emission mitigation measures over Europe for PM2.5 and East Asia for ozone. In addition, differences in the future chemical environment over regions are important considerations for mitigation measures to achieve the largest benefit to human health. Future policy measures to mitigate climate warming need to also consider the impact on air quality and human health across different regions to achieve the maximum co-benefits.

Excess mortality associated with high ozone exposure: A national cohort study in China

Emerging epidemiological studies suggest that long-term ozone (O₃) exposure may increase the risk of mortality, while pre-existing evidence is mixed and has been generated predominantly in North America and Europe. In this study, we investigated the impact of long-term O₃ exposure on all-cause mortality in a national cohort in China. A dynamic cohort of 20882 participants aged ≥40 years was recruited between 2011 and 2018 from four waves of the China Health and Retirement Longitudinal Study. A Cox proportional hazard regression model with time-varying exposures on an annual scale was used to estimate the mortality risk associated with warm-season (April-September) O₃ exposure. The annual average level of participant exposure to warm-season O₃ concentrations was 100 μg m^-3 (range: 61-142 μg m^-3). An increase of 10 μg m^-3 in O₃ was associated with a hazard ratio (HR) of 1.18 (95% confidence interval [CI]: 1.13-1.23) for all-cause mortality. Compared with the first exposure quartile of O₃, HRs of mortality associated with the second, third, and highest exposure quartiles were 1.09 (95% CI: 0.95-1.25), 1.02 (95% CI: 0.88-1.19), and 1.56 (95% CI: 1.34-1.82), respectively. A J-shaped concentration-response association was observed, revealing a non-significant increase in risk below a concentration of approximately 110 μg m^-3. Low-temperature-exposure residents had a higher risk of mortality associated with long-term O₃ exposure. This study expands current epidemiological evidence from China and reveals that high-concentration O₃ exposure curtails the long-term survival of middle-aged and older adults.

Ambient PM2.5, ozone and mortality in Chinese older adults: A nationwide cohort analysis (2005-2018)

BACKGROUND

Cohort evidence linking long-term survival with exposure to multiple air pollutants (e.g., fine particulate matter [PM_2.5] and ozone) was extensively sparse in low- and middle-income countries, especially among older adults. This study aimed to investigate potential associations of long-term exposures to PM_2.5 and ozone with all-cause mortality in Chinese older adults.

METHODS

A dynamic nationwide prospective cohort comprising 20,352 adults aged ≥65 years were enrolled from the Chinese Longitudinal Healthy Longevity Study and followed up through 2005-2018. Participants' annual exposures to warm-season ozone and year-round PM_2.5 were assigned using satellite-derived spatiotemporal estimates. A directed acyclic graph (DAG) was developed to identify confounding variables. Associations of annual mean exposures to PM_2.5 and ozone with mortality were evaluated using single- and two-pollutant Cox proportional hazards models, adjusting for time-dependent individual risk factors and ambient temperature.

RESULTS

During 100 thousand person-years of follow-up (median: 3.6 years), a total of 14,313 death events occurred. The participants were averagely aged 87.1 years at baseline and exposed to a wide range of annual average concentrations of warm-season maximum 8-hour ozone (mean, 54.4 ppb; range, 23.3-81.6 ppb) and year-round PM_2.5 (mean, 65.5 μg/m³; range, 10.1-162.9 μg/m³). Approximately linear concentration-response relationship was identified for ozone, whereas significant increases in PM_2.5-associated mortality risks were observed only when concentrations were above 60 μg/m³. Rises of 10 ppb in ozone and 10 µg/m³ in PM_2.5 above 60 µg/m³ were associated with increases in all-cause mortality of 13.2% (95% confidence interval [CI]: 10.2-16.2%) and 6.2% (95% CI: 4.6-7.7%) in DAG-based single-pollutant model, and of 9.7% (95% CI: 6.6-13.0%) and 5.3% (95% CI: 3.7-6.9%) in DAG-based two-pollutant model, respectively. We detected significant effect modification by temperature in associations of mortality with ozone (P <0.001 for interaction), suggesting greater ozone-related risks among participants in warmer locations.

CONCLUSIONS

This study provided longitudinal evidence that long-term exposure to ambient PM_2.5 and ozone significantly and independently contributed to elevated risks of all-cause mortality among older adults in China.

Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data

Ozone (O₃) is an important greenhouse gas in the atmosphere. Stratospheric ozone protects human beings, but high near-surface ozone concentrations threaten environment and human health. Owing to the uneven distribution of ground-monitoring stations and the low time resolution of polar orbiting satellites, it is difficult to accurately evaluate the refinement and synergistic pollution of near-surface ozone in China. Besides, atmospheric circulation patterns also affect ozone concentrations greatly. In this study, a new generation of geostationary satellite is used to estimate the hourly near-surface ozone concentration with a spatial resolution of 0.05°. First, the Pearson correlation coefficient and maximum information coefficient were used to study the correlation between the top of atmospheric radiation (TOAR) of Himawari-8 satellite and O₃ concentration; seven TOAR channels were selected. Second, based on an interpretable deep learning model, the hourly ozone concentration in China from September 2015 to August 2021 was obtained using the TOAR-O₃ model. Finally, the self-organizing map method was used to determine six major summer weather circulation patterns in China. The results showed that (1) the near-surface O₃ concentration can be accurately estimated; the R² (RMSE: μg/m³) values of the daily, monthly, and annual tenfold cross validation results were 0.91 (12.74), 0.97 (5.64), and 0.98 (1.75), respectively. The feature importance of the model showed that the temperature, TOAR, and boundary layer height contributed 38 %, 22 %, and 13 %, respectively. (2) The O₃ concentration showed obvious spatiotemporal difference and gradually increased from 10:00 to 15:00 (Beijing time) every day. In most areas of China, O₃ concentration had increased significantly. (3) The O₃ concentration in northern China was the highest under the circulation pattern of the Meiyu front over the Yangtze River Delta, while in southern China, it was the highest under the circulation pattern of the northeast cold vortex controlling most of China.

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.

Ambient NO2 exposure hinders long-term survival of Chinese middle-aged and older adults

INTRODUCTION

Serval longitudinal investigations have reported relationships between long-term nitrogen dioxide (NO₂) exposure and mortality. In developing countries such as China, however, the cohort evidence was extremely rare. In this study, we aimed to establish the concentration-response relationship between long-term exposure to NO₂ and mortality in Chinese adults.

METHODS

We conducted a prospective cohort study followed up from 2011 to 2018, by enrolling 15,440 participants aged ≥45 years from 28 provincial regions of China. NO₂ concentration estimates were derived from high-quality spatiotemporal datasets developed by machine learning methods and were assigned for each participant according to their residential cities. We applied Cox proportional hazard models with time-varying exposures to assess the association of all-cause death with long-term NO₂ exposure. Subgroup analyses were performed to identify effect modifications.

RESULTS

A total of 1646 death events occurred during 105,478.5 person-years' follow-up (median 7.1 years). No evident violation for linear NO₂-mortality relationship (P nonlinear = 0.332) was observed at a range of 7.4-45.0 μg/m³. Per 10-μg/m³ rise in NO₂ was associated with an hazard ratio of 1.220 (95% confidence interval: 1.103-1.350) for all-cause mortality. The association between NO₂ and mortality was generally robust after adjusting for co-pollutants including fine particulate matter or/and ozone. Only participants aged 65 and over (1.351 [1.193-1.531]) suffered from increased risks of death associated with NO₂ exposure, and an evident effect modification by age (P = 0.008) was identified. The elevated risk of death induced by NO₂ was also observed in participants living in rural areas and those with elementary school education or below, though effect modifications were non-significant in these subgroups.

CONCLUSIONS

This study provided novel evidence that long-term NO₂ exposure could be an independent risk for mortality among Chinese middle-aged and older adults. Our findings highlighted the importance of controlling air pollution induced by vehicle emissions.

Progression of a large syphilis outbreak in rural North Carolina through space and time: Application of a Bayesian Maximum Entropy graphical user interface

In 2001, the primary and secondary syphilis incidence rate in rural Columbus County, North Carolina was the highest in the nation. To understand the development of syphilis outbreaks in rural areas, we developed and used the Bayesian Maximum Entropy Graphical User Interface (BMEGUI) to map syphilis incidence rates from 1999-2004 in seven adjacent counties in North Carolina. Using BMEGUI, incidence rate maps were constructed for two aggregation scales (ZIP code and census tract) with two approaches (Poisson and simple kriging). The BME maps revealed the outbreak was initially localized in Robeson County and possibly connected to more urban endemic cases in adjacent Cumberland County. The outbreak spread to rural Columbus County in a leapfrog pattern with the subsequent development of a visible low incidence spatial corridor linking Roberson County with the rural areas of Columbus County. Though the data are from the early 2000s, they remain pertinent, as the combination of spatial data with the extensive sexual network analyses, particularly in rural areas gives thorough insights which have not been replicated in the past two decades. These observations support an important role for the connection of micropolitan areas with neighboring rural areas in the spread of syphilis. Public health interventions focusing on urban and micropolitan areas may effectively limit syphilis indirectly in nearby rural areas.

Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates

Since the publication of the first epidemiological study to establish the connection between long-term exposure to atmospheric pollution and effects on human health, major efforts have been dedicated to estimate the attributable mortality burden, especially in the context of the Global Burden of Disease (GBD). In this work, we review the estimates of excess mortality attributable to outdoor air pollution at the global scale, by comparing studies available in the literature. We find large differences between the estimates, which are related to the exposure response functions as well as the number of health outcomes included in the calculations, aspects where further improvements are necessary. Furthermore, we show that despite the considerable advancements in our understanding of health impacts of air pollution and the consequent improvement in the accuracy of the global estimates, their precision has not increased in the last decades. We offer recommendations for future measurements and research directions, which will help to improve our understanding and quantification of air pollution-health relationships.

Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign

Surface ozone (O₃), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O₃ datasets with coarse spatiotemporal resolution and limited coverage, and the uncertainties of O₃ influential factors seriously restrain related epidemiology and air pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily O₃ concentrations on a fine grid scale (1 km × 1 km) from 2018 to 2020 across China based on machine learning methods using hourly observed ground-level pollutant concentrations data, meteorological data, satellite data, and auxiliary data including digital elevation model (DEM), land use data (LUD), normalized difference vegetation index (NDVI), population (POP), and nighttime light images (NTL), and to identify the difference of influential factors of O₃ on diverse urbanization and topography conditions. Some findings were achieved. The correlation coefficients (R²) between O₃ concentrations and surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 m v-component (MVW), and NDVI were 0.80, 0.40, 0.35, 0.30, and 0.20, respectively. The random forest (RF) demonstrated the highest validation R² (0.86) and lowest validation RMSE (13.74 μg/m³) in estimating O₃ concentrations, followed by support vector machine (SVM) (R² = 0.75, RMSE = 18.39 μg/m³), backpropagation neural network (BP) (R² = 0.74, RMSE = 19.26 μg/m³), and multiple linear regression (MLR) (R² = 0.52, RMSE = 25.99 μg/m³). Our China High-Resolution O₃ Dataset (CHROD) exhibited an acceptable accuracy at different spatial-temporal scales. Additionally, O₃ concentrations showed decreasing trend and represented obviously spatiotemporal heterogeneity across China from 2018 to 2020. Overall, O₃ was mainly affected by human activities in higher urbanization regions, while O₃ was mainly controlled by meteorological factors, vegetation coverage, and elevation in lower urbanization regions. The scheme of this study is useful and valuable in understanding the mechanism of O₃ formation and improving the quality of the O₃ dataset.

Global trends in ozone concentration and attributable mortality for urban, peri-urban, and rural areas between 2000 and 2019: a modelling study

BACKGROUND

Data on long-term trends of ozone exposure and attributable mortality across urban-rural catchment areas worldwide are scarce, especially for low-income and middle-income countries. This study aims to estimate trends in ozone concentrations and attributable mortality for urban-rural catchment areas worldwide.

METHODS

In this modelling study, we used a health impact function to estimate ozone concentrations and ozone-attributable chronic respiratory disease mortality for urban areas worldwide, and their surrounding peri-urban, peri-rural, and rural areas. We estimated ozone-attributable respiratory health outcomes using a modified Global Burden of Diseases, Injuries, and Risk Factors 2019 Study approach. We evaluate long-term trends with linear regressions of annual ozone concentrations and ozone-attributable mortality against time in years, and examined the influence of each health impact function input parameter to temporal changes in ozone-attributable disease burden estimates for 12 946 cities worldwide by region, from 2000 to 2019.

FINDINGS

Ozone-attributable mortality worldwide increased by 46% from 2000 (290 400 deaths [95% CI 151 800-457 600]) to 2019 (423 100 deaths [95% CI 223 200-659 400]). The fraction of global ozone-attributable mortality occurring in peri-urban areas remained unchanged from 2000 to 2019 (56%), whereas urban areas gained in their share of global ozone-attributable burden (from 35% to 37%; 54 000 more deaths). Across all cities studied, average population-weighted mean ozone concentration increased by 11% (46 parts per billion [ppb] to 51 ppb). The number of cities with concentrations above the WHO peak season ozone standard (60 μg/m³) increased from 11 568 (89%) of 12 946 cities in 2000 to 12 433 (96%) cities in 2019. Percent change in ozone-attributable mortality averaged across 11 032 cities within each region from 2000 to 2019 ranged from -62% in eastern Europe to 350% in tropical Latin America. The contribution of ozone concentrations, population size, and baseline chronic respiratory disease rates to the change in ozone-attributable mortality differed regionally.

INTERPRETATION

Ozone exposure is increasing worldwide, contributing to disproportionate ozone mortality in peri-urban areas and increasing ozone exposure and attributable mortality in urban areas worldwide. Reducing ozone precursor emissions in areas affecting urban and peri-urban exposure can yield substantial public health benefits.

FUNDING

NASA Health and Air Quality Applied Sciences Team, the National Institute for Occupational Safety and Health, and the NOAA Co-operative Agreement with the Cooperative Institute for Research in Environmental Sciences.

Ozone modelling and mapping for risk assessment: An overview of different approaches for human and ecosystems health

Tropospheric ozone (O₃) is one of the most concernedair pollutants dueto its widespread impacts on land vegetated ecosystems and human health. Ozone is also the third greenhouse gas for radiative forcing. Consequently, it should be carefully and continuously monitored to estimate its potential adverse impacts especially inthose regions where concentrations are high. Continuous large-scale O₃ concentrations measurement is crucial but may be unfeasible because of economic and practical limitations; therefore, quantifying the real impact of O₃over large areas is currently an open challenge. Thus, one of the final objectives of O₃ modelling is to reproduce maps of continuous concentrations (both spatially and temporally) and risk assessment for human and ecosystem health. We here reviewedthe most relevant approaches used for O₃ modelling and mapping starting from the simplest geo-statistical approaches andincreasing in complexity up to simulations embedded into the global/regional circulation models and pro and cons of each mode are highlighted. The analysis showed that a simpler approach (mostly statistical models) is suitable for mappingO₃concentrationsat the local scale, where enough O₃concentration data are available. The associated error in mapping can be reduced by using more complex methodologies, based on co-variables. The models available at the regional or global level are used depending on the needed resolution and the domain where they are applied to. Increasing the resolution corresponds to an increase in the prediction but only up to a certain limit. However, with any approach, the ensemble models should be preferred.

Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990-2019: A Space-Time Bayesian Neural Network Downscaler

Long-term exposure to ambient ozone (O₃) can lead to a series of chronic diseases and associated premature deaths, and thus population-level environmental health studies hanker after the high-resolution surface O₃ concentration database. In response to this demand, we innovatively construct a space-time Bayesian neural network parametric regressor to fuse TOAR historical observations, CMIP6 multimodel simulation ensemble, population distributions, land cover properties, and emission inventories altogether and downscale to 10 km × 10 km spatial resolution with high methodological reliability (R² = 0.89-0.97, RMSE = 1.97-3.42 ppbV), fair prediction accuracy (R² = 0.69-0.77, RMSE = 5.63-7.97 ppbV), and commendable spatiotemporal extrapolation capabilities (R² = 0.62-0.76, RMSE = 5.38-11.7 ppbV). Based on our predictions in 8-h maximum daily average metric, the rural-site surface O₃ are 15.1±7.4 ppbV higher than urban globally averaged across 30 historical years during 1990-2019, with developing countries being of the most evident differences. The globe-wide urban surface O₃ are climbing by 1.9±2.3 ppbV per decade, except for the decreasing trends in eastern United States. On the other hand, the global rural surface O₃ tend to be relatively stable, except for the rising tendencies in China and India. Using CMIP6 model simulations directly without urban-rural differentiation will lead to underestimations of population O₃ exposure by 2.0±0.8 ppbV averaged over each historical year. Our original Bayesian neural network framework contributes to the deep-learning-driven environmental studies methodologically by providing a brand-new feasible way to realize data fusion and downscaling, which maintains high interpretability by conforming to the principles of spatial statistics without compromising the prediction accuracy. Moreover, the 30-year highly spatial resolved monthly surface O₃ database with multiple metrics fills in the literature gap for long-term surface O₃ exposure tracing.

Approaches to investigate crop responses to ozone pollution: from O3 -FACE to satellite-enabled modeling

Ozone (O₃ ) is a damaging air pollutant to crops. As one of the most reactive oxidants known, O₃ rapidly forms other reactive oxygen species (ROS) once it enters leaves through stomata. Those ROS in turn can cause oxidative stress, reduce photosynthesis, accelerate senescence, and decrease crop yield. To improve and adapt our feed, fuel, and food supply to rising O₃ pollution, a number of Free Air Concentration Enrichment (O₃ -FACE) facilities have been developed around the world and have studied key staple crops. In this review, we provide an overview of the FACE facilities and highlight some of the lessons learned from the last two decades of research. We discuss the differences between C₃ and C₄ crop responses to elevated O₃ , the possible trade-off between productivity and protection, genetic variation in O₃ response within and across species, and how we might leverage this observed variation for crop improvement. We also highlight the need to improve understanding of the interaction between rising O₃ pollution and other aspects of climate change, notably drought. Finally, we propose the use of globally modeled O₃ data that are available at increasing spatial and temporal resolutions to expand upon the research conducted at the limited number of global O₃ -FACE facilities.

Multi-stage ensemble-learning-based model fusion for surface ozone simulations: A focus on CMIP6 models

Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling. However, the simulation outcomes have been reported to vary significantly as a result of the complex mixture of uncertain factors that control the tropospheric ozone budget. Settling the cross-model discrepancies to achieve higher accuracy predictions of surface ozone is thus a task of priority, and methods that overcome structural biases in models going beyond naïve averaging of model simulations are urgently required. Building on the Coupled Model Intercomparison Project Phase 6 (CMIP6), we have transplanted a conventional ensemble learning approach, and also constructed an innovative 2-stage enhanced space-time Bayesian neural network to fuse an ensemble of 57 simulations together with a prescribed ozone dataset, both of which have realised outstanding performances (R² > 0.95, RMSE < 2.12 ppbv). The conventional ensemble learning approach is computationally cheaper and results in higher overall performance, but at the expense of oceanic ozone being overestimated and the learning process being uninterpretable. The Bayesian approach performs better in spatial generalisation and enables perceivable interpretability, but induces heavier computational burdens. Both of these multi-stage machine learning-based approaches provide frameworks for improving the fidelity of composition-climate model outputs for uses in future impact studies.