Informing Urban Flood Risk Adaptation by Integrating Human Mobility Big Data During Heavy Precipitation.

Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between -3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard management.

Water Scarcity Assessment of Hydropower Plants in China under Climate Change, Sectoral Competition, and Energy Expansion.

Hydropower plays a pivotal role in low-carbon electricity generation, yet many projects are situated in regions facing heightened water scarcity risks. This research devised a plant-level Hydropower Water Scarcity Index (HWSI), derived from the ratio of water demand for electricity generation to basin-scale available runoff water. We assessed the water scarcity of 1736 hydropower plants in China for the baseline year 2018 and projected into the future from 2025 to 2060. The results indicate a notable increase in hydropower generation facing moderate to severe water scarcity (HWSI >0.05), rising from 10% in 2018 to 24-34% of the national total (430-630 TWh), with a projected peak in the 2030s-2040s under the most pessimistic scenarios. Hotspots of risk are situated in the southwest and northern regions, primarily driven by decreased river basin runoff and intensified sectoral water use, rather than by hydropower demand expansion. Comparative analysis of four adaptation strategies revealed that sectoral water savings and enhancing power generation efficiency are the most effective, potentially mitigating a high of 16% of hydropower risks in China. This study provides insights for formulating region-specific adaptation strategies and assessing energy-water security in the face of evolving environmental and societal challenges.

Measuring climate change perception in China using mental images: A nationwide open-ended survey.

Current knowledge about public climate change perception mainly covers belief, concern, and attitudes. However, how this discourse is interpreted using individuals' own frame of reference remains largely unknown, particularly in many large emitters from non-Annex I countries such as China. This study, for the first time, performs a nationwide open-ended survey covering 4,037 respondents and collected 12,100 textual answers. Using a semiautomated coding method, we find seven mental images that exclusively represent the Chinese interpretation of the climate change issue, including global warming, distant icons, natural disasters, environmental degradation, cause, solution, and weather. Analysis of influencing factors shows that females, those with lower education levels, lower income, and older individuals tend to connect climate change with natural weather phenomena. Younger and well-educated residents in developed cities are more aware of various consequences and anthropogenic causes of climate change. People with stronger climate change beliefs, policy support, and personal experience of extreme weather are more likely to mention disastrous impacts, carbon emission as causes, and potential solutions. Employing the multilevel regression and post-stratification technique, we map the prevalence of mental images in China at the prefecture-city level. The results reveal significant geographical heterogeneity, with estimated national means ranging from a high of 55% (weather) to a low of 11% (solution). Our findings reveal diverse perspectives and a widespread misconception of climate change in China, suggesting the need for tailored clarification strategies to gain public consent.

Economic Growth Facilitates Household Fuel Use Transition to Reduce PM2.5-Related Deaths in China.

Exposure to ambient and indoor particle matter (PM2.5) leads to millions of premature deaths in China. In recent years, indoor air pollution and premature deaths associated with polluting fuel cooking demonstrate an abrupt decline. However, the driving forces behind the mortality change are still unclear due to the uncertainty in household fuel use prediction. Here, we propose an integrated approach to estimate the fuel use fractions and PM2.5-related deaths from outdoor and indoor sources during 2000-2020 across China. Our model estimated 1.67 and 1.21 million premature deaths attributable to PM2.5 exposure in 2000 and 2020, respectively. We find that the residential energy transition is associated with a substantial reduction in premature deaths from indoor sources, with 100,000 (95% CI: 76,000-122,000) for urban and 265,000 (228,000-300,000) for rural populations during 2000-2020. Economic growth is the dominant driver of fuel use transition and avoids 21% related deaths (357,000, 315,000-402,000) from polluting fuel cooking since 2000, which offsets the adverse impact of ambient emissions contributed by economic growth. Our findings give an insight into the coupled impact of socioeconomic factors in reshaping health burden in exposure pathways.

Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980-2019.

Historical PM2.5 data are essential for assessing the health effects of air pollution exposure across the life course or early life. However, a lack of high-quality data sources, such as satellite-based aerosol optical depth before 2000, has resulted in a gap in spatiotemporally resolved PM2.5 data for historical periods. Taking the United Kingdom as an example, we leveraged the light gradient boosting model to capture the spatiotemporal association between PM2.5 concentrations and multi-source geospatial predictors. Augmented PM2.5 from PM10 measurements expanded the spatiotemporal representativeness of the ground measurements. Observations before and after 2009 were used to train and test the models, respectively. Our model showed fair prediction accuracy from 2010 to 2019 [the ranges of coefficients of determination (R2) for the grid-based cross-validation are 0.71-0.85] and commendable back extrapolation performance from 1998 to 2009 (the ranges of R2 for the independent external testing are 0.32-0.65) at the daily level. The pollution episodes in the 1980s and pollution levels in the 1990s were also reproduced by our model. The 4-decade PM2.5 estimates demonstrated that most regions in England witnessed significant downward trends in PM2.5 pollution. The methods developed in this study are generalizable to other data-rich regions for historical air pollution exposure assessment.

Large Virtual Transboundary Hazardous Waste Flows: The Case of China.

The Basel Convention and prior studies mainly focused on the physical transboundary movements of hazardous waste (transporting waste from one region to another for cheaper disposal). Here, we take China, the world's largest waste producer, as an example and reveal the virtual hazardous waste flows in trade (outsourcing waste by importing waste-intensive products) by developing a multiregional input-output model. Our model characterizes the impact of international trade between China and 140 economies and China's interprovincial trade on hazardous waste generated by 161,599 Chinese enterprises. We find that, in 2015, virtual hazardous waste flows in China's trade reached 26.6 million tons (67% of the national total), of which 31% were generated during the production of goods that were ultimately consumed abroad. Trade-related production is much dirtier than locally consumed production, generating 26% more hazardous waste per unit of GDP. Under the impact of virtual flows, 40% of the waste-intensive production and relevant disposal duty is unequally concentrated in three Chinese provinces (including two least-developed ones, Qinghai and Xinjiang). Our findings imply the importance of expanding the scope of transboundary waste regulations and provide a quantitative basis for introducing consumer responsibilities. This may help relieve waste management burdens in less-developed "waste havens".

Associations between short-term ambient ozone exposure and cause-specific mortality in rural and urban areas of Jiangsu, China.

Most previous studies on the acute health effects of ozone are limited to urban areas, largely due to the paucity of air pollutant measurements in rural areas. We here estimated the county-specific daily maximum 8-h average ozone concentration in Jiangsu Province, China during 2015-2018, using a recently developed spatiotemporal machine learning model at a spatial resolution of 0.1° × 0.1° (∼11 × 11 km). Counties were equally divided into urban and rural groups based on the median of the percentage of urban residents across Jiangsu counties obtained from the National Population Census in 2010. We first conducted time-series analyses to estimate the county-specific effect of ozone using generalized linear models, then pooled the effect estimates by random-effects modeling. A 10 µg/m3 increase in the 4-day moving average (lag 0-3) of ambient ozone exposure was associated with increases of 0.66% (95% confidence interval [CI] 0.36%-0.95%) in daily nonaccidental mortality in rural areas and 0.42% in urban areas (95% CI, 0.27%-0.56%). Short-term ambient ozone exposure was associated with an increased risk of mortality caused by chronic obstructive pulmonary disease, hypertension, ischemic heart disease, and stroke. Our finding suggests that both urban and rural residents suffer adverse health effects from short-term ozone exposure.

Threshold dynamics of a nonlocal and delayed cholera model in a spatially heterogeneous environment.

A nonlocal and delayed cholera model with two transmission mechanisms in a spatially heterogeneous environment is derived. We introduce two basic reproduction numbers, one is for the bacterium in the environment and the other is for the cholera disease in the host population. If the basic reproduction number for the cholera bacterium in the environment is strictly less than one and the basic reproduction number of infection is no more than one, we prove globally asymptotically stability of the infection-free steady state. Otherwise, the infection will persist and there exists at least one endemic steady state. For the special homogeneous case, the endemic steady state is actually unique and globally asymptotically stable. Under some conditions, the basic reproduction number of infection is strictly decreasing with respect to the diffusion coefficients of cholera bacteria and infectious hosts. When these conditions are violated, numerical simulation suggests that spatial diffusion may not only spread the infection from high-risk region to low-risk region, but also increase the infection level in high-risk region.

Decrease in the chronic health effects from PM2.5 during the 13th Five-Year Plan in China: Impacts of air pollution control policies.

The Chinese government implemented a series of policies to improve air quality during the Thirteenth Five-Year Plan (13th FYP). However, the long-term health effects of the 13th FYP air pollution control policies have not been evaluated, and the outbreak of coronavirus disease 2019 (COVID-19) has brought great uncertainty regarding the evaluation of the effects. In this study, we selected 329 cities in mainland China to study the chronic health effects due to the decrease in fine particulate matter (PM2.5) during the 13th FYP. The relative risk (RR) of PM2.5 exposure was obtained from a previous study, and the total premature deaths were calculated. We also applied the grey prediction model to predict the PM2.5 concentration in each city in 2020 to evaluate the impacts of COVID-19. The results showed that the annual PM2.5 concentration was reduced from 49.7 µg/m3 in 2015 to 33.2 µg/m3 in 2020, and premature deaths were reduced from 1,186,201 (95% CI: 910,339-1,451,102) and 446,415 (in key regions, 95% CI: 343,426-544,813) in 2015 to 997,955 (95% CI: 762,167-1,226,652) and 368,786 (in key regions, 95% CI: 282,114-452,567) in 2020, respectively. A total of 188,246 (95% CI: 148,172-224,450) people avoided premature deaths due to the reduction in PM2.5 concentrations from 2015 to 2020. Although the impacts of COVID-19 in 2020 brought a significant reduction of 35.3% in February (14.2 µg/m3, p < 0.0001) and in March by 17.6% (5.8 µg/m3, p = 0.001), we found that COVID-19 showed few obvious influences on China's long-term air pollution control plans. The observed data and predicted data are very close in annual mean values and showed no statistical significance both in all cities (p = 0.98) and in key regions (p = 0.56) in 2020.

Health burdens of ambient PM2.5 pollution across Chinese cities during 2006-2015.

With frequent severe haze and smog episodes in Chinese cities, an increasing number of studies have focused on estimating the impact of fine particulate matter (PM2.5) on public health. However, the current use of national and provincial demographic data might mask regional differences and lead to inaccurate estimations of pollution-related health impacts across cities. We applied the Global Burden of Disease methodology to develop a dataset of premature deaths attributed to ambient PM2.5 in 129 Chinese cities in 2006, 2010 and 2015, based on the information of baseline mortality rates and population densities at the city level. Our results suggested that ambient PM2.5 pollution led to 631,230 (95% confidence interval: 281,460-873,800) premature deaths in those cities in 2015, which was similar to that in 2010, but 42.8% higher than that in 2006. The reduction of premature deaths was not as obvious as the improvement in air quality in recent years, primarily owing to the aging Chinese population. For large and medium/small cities, the effects of PM2.5 abatement on alleviating public health burdens were lower than those for megalopolises and metropolises; however, such large and medium/small cities are at risk of increasing future PM2.5 pollution levels due to rapid development. Significant differences in PM2.5-induced premature deaths indicated the need for specific policies to mitigate the health burden of air pollution in different types of Chinese cities.

Visibility-Based PM2.5 Concentrations in China: 1957-1964 and 1973-2014.

China established ground PM2.5 monitoring network in late 2012 and hence the long-term and large-scale PM2.5 data were lacking before 2013. In this work, we developed a national-scale spatiotemporal linear mixed effects model to estimate the long-term PM2.5 concentrations in China from 1957 to 1964 and from 1973 to 2014 using ground visibility monitoring data as the primary predictor. The overall model-fitting and cross-validation R2 is 0.72 and 0.71, suggesting that the model is not overfitted. Validation beyond the model year (2014) indicated that the model could accurately estimate historical PM2.5 concentrations at the monthly (R2 = 0.71) level. The historical PM2.5 estimates suggest that air pollution is not a new environmental issue that occurs in the recent decades but a problem existing in a longer time before 1980. The PM2.5 concentrations have reached 60-80 µg/m3 in the north part of North China Plain during 1950s-1960s and increased to generally higher than 90 µg/m3 during 1970s. The results also show that the entire China experienced an overall increasing trend (0.19 µg/m3/yr, P < 0.001) in PM2.5 concentrations from 1957 to 2014 with fluctuations among different periods. This paper demonstrated visibility data allow us to understand the spatiotemporal characteristics of PM2.5 pollution in China in a long-term.

Near-UV-enhanced broad-band large third-order optical nonlinearity in aluminum nanorod array film with sub-10 nm gaps.

Plasmonic nanostructures with sub-10 nm gaps possess intense electric field enhancements, leading to their high reputation for exploring various functional applications at nanoscale. Till now, although large amounts of efforts have been devoted into investigation of such structures, few works were emphased on the nonlinear optical properties in near-ultraviolet (UV) region. Here, by combining sputtering technique and an optimized anodic aluminum oxide (AAO) template growing method, we obtain aluminum (Al) nanorod array film (NRAF) with average rod diameter and gap size of 50 and 7 nm, respectively. The Al-NRAF exhibits large third-order optical nonlinear susceptibility (χ(3)) and high figure of merit (χ(3)/α) over a broad wavelength range from 360 to 900 nm, and reaches their maximums at the shortest measured wavelength. In addition, comparisons with Au-NRAF and Ag-NRAF samples further confirm that Al-NRAF has better nonlinear optical properties in the blue and near-UV wavelength regions. These results indicate that Al nanostructures are promising candidates for nonlinear plasmonic applications at blue and near-UV wavelengths.

Plasmon-Enhanced Second-Harmonic Generation Nanorulers with Ultrahigh Sensitivities.

Attainment of spatial resolutions far below diffraction limits by means of optical methods constitutes a challenging task. Here, we design nonlinear nanorulers that are capable of accomplishing approximately 1 nm resolutions by utilizing the mechanism of plasmon-enhanced second-harmonic generation (PESHG). Through introducing Au@SiO2 (core@shell) shell-isolated nanoparticles, we strive to maneuver electric-field-related gap modes such that a reliable relationship between PESHG responses and gap sizes, represented by "PESHG nanoruler equation", can be obtained. Additionally validated by both experiments and simulations, we have transferred "hot spots" to the film-nanoparticle-gap region, ensuring that retrieved PESHG emissions nearly exclusively originate from this region and are significantly amplified. The PESHG nanoruler can be potentially developed as an ultrasensitive optical method for measuring nanoscale distances with higher spectral accuracies and signal-to-noise ratios.

Smog episodes, fine particulate pollution and mortality in China.

BACKGROUND: Starting from early January 2013, northern China was hit by multiple prolonged and severe smog events which were characterized by extremely high-level concentrations of ambient fine particulate matter (PM2.5) with hourly peaks of PM2.5 over 800 µg/m(3). However, the consequences of this severe air pollution are largely unknown. This study investigates the acute effect of the smog episodes and PM2.5 on mortality for both urban and rural areas in northern China. DATA AND METHODS: We collected PM2.5, mortality, and meteorological data for 5 urban city districts and 2 rural counties in Beijing, Tianjin and Hebei Province of China from January 1, 2013 through December 31, 2013. We employed the generalized additive models to estimate the associations between smog episodes or PM2.5 and daily mortality for each district/county. RESULTS: Without any meteorological control, the smog episodes are positively and statistically significantly associated with mortality in 5 out of 7 districts/counties. However, the findings are sensitive to the meteorological factors. After controlling for temperature, humidity, dew point and wind, the statistical significance disappears in all urban districts. In contrast, the smog episodes are consistently and statistically significantly associated with higher total mortality and mortality from cardiovascular/respiratory diseases in the two rural counties. In Ji County, a smog episode is associated with 6.94% (95% Confidence Interval, -0.20 to 14.58) increase in overall mortality, and in Ci County it is associated with a 19.26% (95% CI, 6.66-33.34) increase in overall mortality. The smog episodes kill people primarily through its impact on cardiovascular and respiratory diseases. On average, a smog episode is associated with 11.66% (95% CI, 3.12-20.90) increase in cardiovascular and respiratory mortality in Ji County, and it is associated with a 22.23% (95% CI, 8.11-38.20) increase in cardiovascular and respiratory mortality in Ci County. A 10 µg/m(3) increase in PM2.5 concentration is associated with 0.88% (95% CI, 0.3-1.46) increase in overall mortality and 1.2% (95% CI, 0.55-1.85) in Ji County. A 10 µg/m(3) increase in PM2.5 concentration is associated with 0.55% (95% CI, -0.02 to 1.13) increase in overall mortality in Ci County. The findings suggest that the smog episodes and fine particulate have bigger and more detrimental impacts on rural residents, especially for those living close to big and polluted cities. CONCLUSIONS: The smog episodes and PM2.5 are statistically associated with mortality in rural areas of China. The associations for urban areas are not statistically significant.

Estimating ground-level PM2.5 in China using satellite remote sensing.

Estimating ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geographic region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geographically weighted regression (GWR) model to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor. The results showed that the meteorological and land use information can greatly improve model performance. The overall cross-validation (CV) R(2) is 0.64 and root mean squared prediction error (RMSE) is 32.98 µg/m(3). The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 µg/m(3). Our predicted annual PM2.5 concentrations indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Standard (CNAAQS) Level 2 standard. Our results also confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM2.5 monitoring network in China.

Impact of temperature on childhood pneumonia estimated from satellite remote sensing.

The effect of temperature on childhood pneumonia in subtropical regions is largely unknown so far. This study examined the impact of temperature on childhood pneumonia in Brisbane, Australia. A quasi-Poisson generalized linear model combined with a distributed lag non-linear model was used to quantify the main effect of temperature on emergency department visits (EDVs) for childhood pneumonia in Brisbane from 2001 to 2010. The model residuals were checked to identify added effects due to heat waves or cold spells. Both high and low temperatures were associated with an increase in EDVs for childhood pneumonia. Children aged 2-5 years, and female children were particularly vulnerable to the impacts of heat and cold, and Indigenous children were sensitive to heat. Heat waves and cold spells had significant added effects on childhood pneumonia, and the magnitude of these effects increased with intensity and duration. There were changes over time in both the main and added effects of temperature on childhood pneumonia. Children, especially those female and Indigenous, should be particularly protected from extreme temperatures. Future development of early warning systems should take the change over time in the impact of temperature on children's health into account.

China's progress in synergetic governance of climate change and multiple environmental issues.

Advancing the synergetic control of climate change and environmental crisis is crucial for achieving global sustainable development goals. This study evaluates synergetic governance levels over climate change and four environmental issues at the provincial level in China from 2009 to 2020. Our findings reveal significant progress in China's coordinated efforts to mitigate carbon emissions, reduce air pollutants, and conserve water resources. However, there remains room for improvement in managing solid waste and protecting ecological systems and overall progress in synergetic governance has slowed since 2015. Employing a random forest model, we identify socio-economic factors with great influence on synergetic climate change and environmental governance, such as energy intensity, service sector development, electronic equipment manufacturing, and transportation. Additionally, we reveal nonlinear relationships between some factors and performance of environmental subsystems, including both plateau effects (e.g. output in the smelting of ferrous metals) and U-shaped patterns (e.g. output in the manufacturing of metal products), possibly attributed to constraints in end-of-pipe treatment capacities and complexities in supply chain networks. Furthermore, through hierarchical clustering analysis, we classify provinces into four groups and provide tailored recommendations for policymakers to enhance synergetic governance levels in their respective regions. The framework established in this study also serves as a valuable reference for countries seeking to develop practical and context-specific solutions to mitigate climate and environmental risks.

Advances and future directions of environmental risk research: A bibliometric review.

Environmental risk is one of the world's most significant threats, projected to be the leading risk over the next decade. It has garnered global attention due to increasingly severe environmental issues, such as climate change and ecosystem degradation. Research and technology on environmental risks are gradually developing, and the scope of environmental risk study is also expanding. Here, we developed a tailored bibliometric method, incorporating co-occurrence network analysis, cluster analysis, trend factor analysis, patent primary path analysis, and patent map methods, to explore the status, hotspots, and trends of environment risk research over the past three decades. According to the bibliometric results, the publications and patents related to environmental risk have reached explosive growth since 2018. The primary topics in environmental risk research mainly involve (a) ecotoxicology risk of emerging contaminants (ECs), (b) environmental risk induced by climate change, (c) air pollution and health risk assessment, (d) soil contamination and risk prevention, and (e) environmental risk of heavy metal. Recently, the hotspots of this field have shifted into artificial intelligence (AI) based techniques and environmental risk of climate change and ECs. More research is needed to assess ecological and health risk of ECs, to formulize mitigation and adaptation strategies for climate change risks, and to develop AI-based environmental risk assessment and control technology. This study provides the first comprehensive overview of recent advances in environmental risk research, suggesting future research directions based on current understanding and limitations.

An ensemble machine learning model to uncover potential sites of hazardous waste illegal dumping based on limited supervision experience.

With the soaring generation of hazardous waste (HW) during industrialization and urbanization, HW illegal dumping continues to be an intractable global issue. Particularly in developing regions with lax regulations, it has become a major source of soil and groundwater contamination. One dominant challenge for HW illegal dumping supervision is the invisibility of dumping sites, which makes HW illegal dumping difficult to be found, thereby causing a long-term adverse impact on the environment. How to utilize the limited historic supervision records to screen the potential dumping sites in the whole region is a key challenge to be addressed. In this study, a novel machine learning model based on the positive-unlabeled (PU) learning algorithm was proposed to resolve this problem through the ensemble method which could iteratively mine the features of limited historic cases. Validation of the random forest-based PU model showed that the predicted top 30% of high-risk areas could cover 68.1% of newly reported cases in the studied region, indicating the reliability of the model prediction. This novel framework will also be promising in other environmental management scenarios to deal with numerous unknown samples based on limited prior experience.

Long-term ambient ozone exposure and incident cardiovascular diseases: National cohort evidence in China.

BACKGROUND: Long-term ozone (O3) exposure has been associated with cardiovascular disease (CVD) mortality in mounting cohort evidence, yet its relationship with incident CVD was poorly understood, especially in low- and middle-income countries (LMICs) experiencing high ambient air pollution. METHODS: We carried out a nationwide perspective cohort study from 2010 through 2018 by dynamically enrolling 36948 participants across Chinese mainland. Warm-season (April-September) O3 concentrations were estimated using satellite-based machine-learning models with national coverage. Cox proportional hazards model with time-varying exposures was employed to evaluate the association of long-term O3 exposure with incident CVD (overall CVD, hypertension, stroke, and coronary heart disease [CHD]). Assuming causality, a counterfactual framework was employed to estimate O3-attributable CVD burden based on the exposure-response (E-R) relationship obtained from this study. Decomposition analysis was utilized to quantify the contributions of four key direct driving factors (O3 exposure, population size, age structure, and incidence rate) to the net change of O3-related CVD cases between 2010 and 2018. RESULTS: A total of 4428 CVD, 2600 hypertension, 1174 stroke, and 337 CHD events were reported during 9-year follow-up. Each 10-µg/m³ increase in warm-season O3 was associated with an incident risk of 1.078 (95% confidence interval [CI]: 1.050-1.106) for overall CVD, 1.098 (95% CI: 1.062-1.135) for hypertension, 1.073 (95% CI: 1.019-1.131) for stroke, and 1.150 (95% CI: 1.038-1.274) for CHD, respectively. We observed no departure from linear E-R relationships of O3 exposure with overall CVD (Pnonlinear= 0.22), hypertension (Pnonlinear= 0.19), stroke (Pnonlinear= 0.70), and CHD (Pnonlinear= 0.44) at a broad concentration range of 60-160 µg/m3. Compared with rural dwellers, those residing in urban areas were at significantly greater O3-associated incident risks of overall CVD, hypertension, and stroke. We estimated 1.22 million (10.6% of overall CVD in 2018) incident CVD cases could be attributable to ambient O3 pollution in 2018, representing an overall 40.9% growth (0.36 million) compared to 2010 (0.87 million, 9.7% of overall CVD in 2010). This remarkable rise in O3-attributable CVD cases was primary driven by population aging (+24.0%), followed by increase in O3 concentration (+10.5%) and population size (+6.7%). CONCLUSIONS: Long-term O3 exposure was associated with an elevated risk and burden of incident CVD in Chinese adults, especially among urban dwellers. Our findings underscored policy priorities of implementing joint control measures for fine particulate matter and O3 in the context of accelerated urbanization and population aging in China.