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High spatial resolution PM2.5 data covering a long time period are urgently needed to support population exposure assessment and refined air quality management. In this study, we provided complete-coverage PM2.5 predictions with a 1 km spatial resolution from 2000 to the present under the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/, last access: 3 October 2022) framework. To support high spatial resolution modeling, we collected PM2.5 measurements from both national and local monitoring stations. To correctly reflect the temporal variations in land cover characteristics that affected the local variations in PM2.5, we constructed continuous annual geoinformation datasets, including the road maps and ensemble gridded population maps, in China from 2000 to 2021. We also examined various model structures and predictor combinations to balance the computational cost and model performance. The final model fused 10 km TAP PM2.5 predictions from our previous work, 1 km satellite aerosol optical depth retrievals, and land use parameters with a random forest model. Our annual model had an out-of-bag R2 ranging between 0.80 and 0.84, and our hindcast model had a by-year cross-validation R2 of 0.76. This open-access, 1 km resolution PM2.5 data product, with complete coverage, successfully revealed the local-scale spatial variations in PM2.5 and could benefit environmental studies and policymaking.

In response to the severe air pollution issue, the Chinese government implemented two phases (Phase I: 2013-2017, Phase II: 2018-2020) of clean air actions since 2013, resulting in a significant decline in fine particles (PM2.5) during 2013-2020, while the maximum daily 8 h average ozone (MDA8 O3) increased by 2.6 μg m-3 yr-1 during the same period. Here, we derived the drivers behind the rising O3 concentrations during the two phases of clean air actions by using a bottom-up emission inventory, a regional chemical transport model, and a multiple linear regression model. We found that both meteorological variations (3.6 μg m-3) and anthropogenic emissions (6.7 μg m-3) contributed to the growth of MDA8 O3 from 2013 to 2020, with the changes in anthropogenic emissions playing a more important role. The anthropogenic contributions to the O3 rise during 2017-2020 (1.2 μg m-3) were much lower than that in 2013-2017 (5.2 μg m-3). The lack of volatile organic compound (VOC) control and the decline in nitrogen oxides (NOX) emissions were responsible for the O3 increase in 2013-2017 due to VOC-limited regimes in most urban areas, while the synergistic control of VOC and NOX in Phase II initially worked to mitigate O3 pollution during 2018-2020, although its effectiveness was offset by the penalty of PM2.5 decline. Future mitigation efforts should pay more attention to the simultaneous control of VOC and NOX to improve O3 air quality.

CO2 emission database lays the foundation of climate research and climate governance. Most current global CO2 emission inventories were developed with energy statistics from International Energy Agency (IEA) and were available at country level with limited source categories. Here, as the first step toward a high-resolution and dynamic updated global CO2 emission database, we developed a data-driven approach to construct seamless and highly-resolved energy consumption data cubes for 208 countries/territories, 797 sub-country administrative divisions in 29 countries, 42 fuel types, and 52 sectors, with the fusion of energy consumption from 24 international statistics and 66 regional/local statistics. Global CO2 emissions from fossil fuel combustion and cement production in 1970-2021 were then estimated with highly-resolved source category (1484 of total) and sub-country information (797 of total). Specifically, 73% of global CO2 emissions in 2021 were calculated based on sub-country information, providing considerably improved spatial resolution for global CO2 accounting. With the support of detailed information, the dynamics of global CO2 emissions across sectors and fuels were presented, representing the evolution of global economy and progress of climate governance. Remarkable differences of sectoral contribution were found across sub-country administrative divisions within a given country, revealing the uneven distribution of energy and economic structure among different regions. Our estimates were generally consistent with existing databases at aggregated level for global total or large emitters, while large discrepancies were observed for middle and small emitters. Our database, named Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC) is publicly available through http://meicmodel.org.cn with highly-resolved information and timely update, which provides an independent carbon emission accounting data source for climate research.

Air pollution is one of the most important environmental problems in China. As a major air pollutant, ozone (O3) will endanger human health and terrestrial ecosystems. It is of great practical significance to obtain a continuous full-coverage dataset of ozone with high spatio-temporal resolution to conduct mechanism research from its causes, development, diffusion, impact and other aspects. In this study, a 3-stage machine learning model was developed through multiple data fusion, and the LightGBM method is used to obtain the hourly spatio-temporal distribution dataset of the O3 concentration in China from 2013 to 2020, with a resolution of 0.25 °× 0.25°. We first revise the meteorological reanalysis data using ground observation and propose a data fusion algorithm to achieve the ground level distribution of ozone, which combines ground observation of pollutants, population data, revised reanalysis meteorological conditions, reanalysis of radiation, land and vegetation data, emission inventory and results of chemical transport model simulation. In addition, due to the common phenomenon that the previous prediction models underestimate the extreme value of the pollution periods,therefore, we redefined the heavy pollution event and assimilated it into the 0.25 grid by using the synthetic minimum oversampling technique (SMOTE) method to improve the model performance during the extreme pollution periods.Our model, with the 10-fold CV result of R2 = 71% and RMSE= 25.1μg·m-3, and our hourly O3 concentration results are spatially and spatially continuous with a similar distribution compare to the observation, which proves the reliability of our model. With higher time resolution, various exposure response indicators can be obtained. AOT40 calculated by high-resolution hourly ozone concentration further, which is far more accurate than it when directly predicted by daily indexs modeling.In addition, based on the distribution of AOT40, we assessed the agricultural damage and ecological damage caused by the change of surface ozone pollution during 2013-2020. Our estimation considered the planting area and phenological period of crops that the overestimation of crop RYL in the region can be avoided. The annual avrage production loss of wheat, rice and maize in China from 2013 to 2020 is 55.0, 57.4 and 23.6 Mt, respectively. Besides, The loss of gross primary productivity was also estimated. During 2013-2020, the ozone pollution in China caused an annual average loss of 2.1%, and the loss in the south was much higher than that in the north.

Air pollution has altered the Earth's radiation balance, disturbed the ecosystem, and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant data set with timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, http://tapdata.org.cn/) that combines information from multiple data sources, including ground observations, satellite aerosol optical depth (AOD), operational chemical transport model simulations, and other ancillary data such as meteorological fields, land use data, population, and elevation. Daily full-coverage PM2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method. Our model has an averaged out-of-bag cross-validation R2 of 0.83 for different years, which is comparable to those of other studies, but improves its performance at high pollution levels and fills the gaps in missing AOD on daily scale. The full coverage and near real-time updates of the daily PM2.5 data allow us to track the day-to-day variations in PM2.5 concentrations over China in a timely manner. The long-term records of PM2.5 data since 2000 will also support policy assessments and health impact studies. The TAP PM2.5 data are publicly available through our website for sharing with the research and policy communities.

The contribution of meteorology and emissions to long-term PM 2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used the combination of a machine learning model, statistical method, and chemical transport model to quantify the meteorological impacts on PM 2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM 2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM 2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods to examine the meteorological contribution to PM 2.5 : a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM 2.5 retrievals and the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. We found good agreements between GAM estimations and the CMAQ model estimations of the meteorological contribution to PM 2.5 on a monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM 2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PM 2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and when haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM 2.5 pollution across the North China Plain and central China but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM 2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PM 2.5 and gas precursors in these years. Although emissions dominated the long-term PM 2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM 2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., − 6.3 % to 4.9 % of the annual mean PM 2.5 concentrations in the Beijing-Tianjin-Hebei region, − 5.1 % to 4.3 % in the Fenwei Plain, − 4.8 % to 4.3 % in the Yangtze River Delta, and − 25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM 2.5 and the possible worsening trend of meteorological conditions in the northern part of China where air pollution is severe and population is clustered, stricter clean air actions are needed to avoid haze events in the future.

Long-term exposure to ambient fine particulate matter (PMThe study aimed to explore the relationship between long-term PMA whole-population cohort study was conducted on lung cancer patients diagnosed between 2001 and 2017. An atmospheric chemical transport model was used to estimate exposure under a counterfactual scenario without the policy and then quantified the effect of the policy. Cox regression models were used with the seasonality-adjusted PMA 10 μg/mOur findings suggest that PMLong-term exposure to PM

This study investigates the autumn precipitation in Sichuan Basin (SB), China in combination with reanalysis datasets of aerosol and meteorological fields from 2008 to 2018. To identify the aerosol impacts on precipitation, we carefully selected and divided the data into multiple groups according to vorticity, precipitation amount category, aerosol pollution level and the geopotential height in the upper troposphere. Our data shows that the precipitation in SB are generally reduced and weakened in afternoon to evening, but enhanced and intensified after mid-night under the aerosol polluted condition. By analysing data contrasts between different groups, we find that aerosol pollution in SB reduces shortwave heating near surface, which enhances the tropospheric stability and inhibits the vertical motion and rainfall in the afternoon to early evening. As the stability reduces within the basin, the vertical motions are invigorated to enhance and intensify the rain after mid-night. Therefore, we conclude that the aerosol pollution in SB and the rapid adjustment of aerosol-radiation interaction amplify the diurnal features of precipitation in autumn.

The World Health Organization has issued new air quality guidelines (AQG). Based on 2020 data, achieving the new AQG for PM2.5 could prevent an additional 285,000 chronic deaths and 13,000 acute deaths, across China, compared with the previous AQG. The new AQG can better protect health but cannot be achieved without coordinated air-pollution-control and climate-mitigation efforts.

Since 2013, clean-air actions in China have reduced ambient concentrations of PM2.5. However, recent studies suggest that ground surface O3 concentrations increased over the same period. To understand the shift in air pollutants and to comprehensively evaluate their impacts on health, a spatiotemporal model for O3 is required for exposure assessment. This study presents a data-fusion algorithm for O3 estimation that combines in situ observations, satellite remote sensing measurements, and model results from the community multiscale air quality model. Performance of the algorithm for O3 estimation was evaluated by five-fold cross-validation. The estimates are highly correlated with the in situ observations of the maximum daily 8 h averaged O3 (R2 = 0.70). The mean modeling error (measured using the root-mean-squared error) is 26 μg/m3, which accounts for 29% of the mean level. We also found that satellite O3 played a key role to improve model performance, particularly during warm months. The estimates were further used to illustrate spatiotemporal variation in O3 during 2013-2017 for the whole country. In contrast to the reduced trend of PM2.5, we found that the population-weighted O3 mean increased from 86 μg/m3 in 2013 to 95 μg/m3 in 2017, with a rate of 2.07 (95% CI: 1.65, 2.48) μg/m3 per year at the national level. This increased trend in O3 suggests that it is becoming an important contributor to the burden of diseases attributable to air pollutants in China. The developed method and the results generated from this study can be used to support future health-related studies in China.

Investigations on the chronic health effects of fine particulate matter (PM2.5) exposure in China are limited due to the lack of long-term exposure data. Using satellite-driven models to generate spatiotemporally resolved PM2.5 levels, we aimed to estimate high-resolution, long-term PM2.5 and associated mortality burden in China. The multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) at 1-km resolution was employed as a primary predictor to estimate PM2.5 concentrations. Imputation techniques were adopted to fill in the missing AOD retrievals and provide accurate long-term AOD aggregations. Monthly PM2.5 concentrations in China from 2000 to 2016 were estimated using machine-learning approaches and used to analyze spatiotemporal trends of adult mortality attributable to PM2.5 exposure. Mean coverage of AOD increased from 56 to 100% over the 17-y period, with the accuracy of long-term averages enhanced after gap filling. Machine-learning models performed well with a random cross-validation R2 of 0.93 at the monthly level. For the time period outside the model training window, prediction R2 values were estimated to be 0.67 and 0.80 at the monthly and annual levels. Across the adult population in China, long-term PM2.5 exposures accounted for a total number of 30.8 (95% confidence interval [CI]: 28.6, 33.2) million premature deaths over the 17-y period, with an annual burden ranging from 1.5 (95% CI: 1.3, 1.6) to 2.2 (95% CI: 2.1, 2.4) million. Our satellite-based techniques provide reliable long-term PM2.5 estimates at a high spatial resolution, enhancing the assessment of adverse health effects and disease burden in China.

ATP-binding cassette (ABC) transporters constitute a superfamily of 48 structurally similar membrane transporters that mediate the ATP-dependent cellular export of a plethora of endogenous and xenobiotic substances. Importantly, genetic variants in ABC genes that affect gene function have clinically important effects on drug disposition and can be predictors of the risk of adverse drug reactions and efficacy of chemotherapeutics, calcium channel blockers, and protease inhibitors. Furthermore, loss-of-function of ABC transporters is associated with a variety of congenital disorders. Despite their clinical importance, information about the frequencies and global distribution of functionally relevant ABC variants is limited and little is known about the overall genetic complexity of this important gene family. Here, we systematically mapped the genetic landscape of the entire human ABC superfamily using Next-Generation Sequencing data from 138,632 individuals across seven major populations. Overall, we identified 62,793 exonic variants, 98.5% of which were rare. By integrating five computational prediction algorithms with structural mapping approaches using experimentally determined crystal structures, we found that the functional ABC variability is extensive and highly population-specific. Every individual harbored between 9.3 and 13.9 deleterious ABC variants, 76% of which were found only in a single population. Carrier rates of pathogenic variants in ABC transporter genes associated with autosomal recessive congenital diseases, such as cystic fibrosis or pseudoxanthoma elasticum, closely mirrored the corresponding population-specific disease prevalence, thus providing a novel resource for rare disease epidemiology. Combined, we provide the most comprehensive, systematic, and consolidated overview of ethnogeographic ABC transporter variability with important implications for personalized medicine, clinical genetics, and precision public health.

Multidrug resistance due to facilitated drug efflux mediated by ATP-binding cassette (ABC) transporters is a main cause for failure of cancer therapy. Genetic polymorphisms in ABC genes affect the disposition of chemotherapeutics and constitute important biomarkers for therapeutic response and toxicity. Here we correlated germline variability in ABC transporters with disease-specific survival (DSS) in 960 breast cancer (BRCA), 314 clear cell renal cell carcinoma and 325 hepatocellular carcinoma patients. We find that variant burden in ABCC1 is a strong predictor of DSS in BRCA patients, whereas candidate polymorphisms are not associated with DSS. This association is highly drug-specific for subgroups treated with the MRP1 substrates cyclophosphamide (log-rank p = 0.0011) and doxorubicin (log-rank p = 0.0088) independent of age and tumor stage, whereas no association was found in individuals treated with tamoxifen (log-rank p = 0.13). Structural mapping of significant variants revealed multiple variants at residues involved in protein stability, cofactor stabilization or substrate binding. Our results demonstrate that BRCA patients with high variant burden in ABCC1 are less prone to respond appropriately to pharmacological therapy with MRP1 substrates, thus incentivizing the consideration of genomic germline data for precision cancer medicine.

Background Evidence of the effects of long-term fine particulate matter (PM2.5) exposure on cardiovascular diseases (CVDs) is rare for populations exposed to high levels of PM2.5 in China and in other countries with similarly high levels. Objectives The aim of this study was to assess the CVD risks associated with long-term exposure to PM2.5 in China. Methods A nationwide cohort study, China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China), was used, with 116,972 adults without CVD in 2000 being included. Participants were followed until 2015. Satellite-based PM2.5 concentrations at 1-km spatial resolution during the study period were used for exposure assessment. A Cox proportional hazards model with time-varying exposures was used to estimate the CVD risks associated with PM2.5 exposure, adjusting for individual risk factors. Results Annual mean concentrations of PM2.5 at the China-PAR sites ranged from 25.5 to 114.0 μg/m3. For each 10 μg/m3 increase in PM2.5 exposures, the multivariate-adjusted hazard ratio was 1.251 (95% confidence interval: 1.220 to 1.283) for CVD incidence and 1.164 (95% confidence interval: 1.117 to 1.213) for CVD mortality. The slopes of concentration-response functions of PM2.5 exposure and CVD risks were steeper at high PM2.5 levels. In addition, older residents, rural residents, and never smokers were more prone to adverse effects of PM2.5 exposure. Conclusions This study provides evidence that elevated long-term PM2.5 exposures lead to increased CVD risk in China. The effects are more pronounced at higher PM2.5 levels. These findings expand the current knowledge on adverse health effects of severe air pollution and highlight the potential cardiovascular benefits of air quality improvement in China and other low- and middle-income countries.

Background One of the main hurdles of oncological therapy is the development of drug resistance. The ABC transporter gene family contributes majorly to cancer chemoresistance. However, effects of somatic expression of most ABC transporters on cancer outcomes remain largely unclear. Methods We systematically analyzed expression signatures of all 48 human ABC transporters in samples from 8562 patients across 14 different cancer types. The association between CFTR (ABCC7) expression and outcomes was analyzed experimentally using knock-downs and pharmacological CFTR stimulation. Results Across 720 analyzed clinical associations with patient outcomes, 363 were nominally significant of which 29 remained significant after stringent Bonferroni correction. Among those were various previously known associations, as well as a multitude of novel factors that correlated with poor prognosis or predicted improved outcomes. The association between low CFTR levels and reduced survival in lung adenocarcinoma was confirmed in two independent cohorts of 246 patients with a history of smoking (logrank P = 0.0021, hazard ratio [HR], 0.49) and 143 never-smokers (logrank P = 0.0023, HR 0.31). Further in vitro experiments using naturally CFTR expressing lung adenocarcinoma cells showed that treatment with CFTR potentiators significantly reduced proliferation at therapeutically relevant concentrations. Conclusions These results suggest that CFTR acts as a pharmacologically activatable tumor suppressor and constitutes a promising target for adjuvant therapy in lung adenocarcinoma.

Based on the exposure data sets from the Tracking Air Pollution in China (TAP

Air pollution has altered the Earth's radiation balance, disturbed the ecosystem, and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant data set with timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, http://tapdata.org.cn/) that combines information from multiple data sources, including ground observations, satellite aerosol optical depth (AOD), operational chemical transport model simulations, and other ancillary data such as meteorological fields, land use data, population, and elevation. Daily full-coverage PM

The risk of incident hypertension associated with long-term exposure to fine particulate matter (PM 2.5 ) was still unclear by studies conducted in North America and Europe, and this relationship has rarely been quantified at higher ambient concentrations typically found in developing countries. We aimed to investigate the association between PM 2.5 and incident hypertension using the large-scale prospective cohorts in China. We included 59 456 participants without hypertension aged ≥18 years from the China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China) project. Data on ambient PM 2.5 at participants’ residential address were obtained during 2004 to 2015 using a satellite-based spatial-temporal model. Hazard ratios and 95% CIs were calculated for incident hypertension using stratified Cox proportional hazards models with adjustment of potential confounders. The findings indicated that average PM 2.5 concentration from 2004 to 2015 at study participants’ address was 77.7 μg/m 3 . During the follow-up of 364 947 person-years, we identified 13 981 incident hypertension cases. Compared with the lowest quartile exposure of PM 2.5 , participants in the highest quartile had an increased risk of incident hypertension with a hazard ratio (95% CI) of 1.77 (1.56–2.00). Each 10 μg/m 3 increment of PM 2.5 concentration could increase 11% risk of hypertension (hazard ratio, 1.11; 95% CI, 1.05–1.17). This cohort study provided the first evidence from China that long-term exposure to PM 2.5 was independently associated with incident hypertension at relatively high ambient concentrations. Stringent strategies on PM 2.5 pollution control are warranted to improve the air quality and contribute to the reduction of disease burden of hypertension in China.