Drivers of Increasing Ozone during the Two Phases of Clean Air Actions in China 2013-2020.

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 warm-season (April-September) mean maximum daily 8 h average ozone (MDA8 O3) increased by 2.6 µg m-3 yr-1 in China 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.

The 17-y spatiotemporal trend of PM2.5 and its mortality burden 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.

Tracking PM2.5 and O3 Pollution and the Related Health Burden in China 2013-2020.

Based on the exposure data sets from the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/), we characterized the spatiotemporal variations in PM2.5 and O3 exposures and quantified the long- and short-term exposure related premature deaths during 2013-2020 with respect to the two-stage clean air actions (2013-2017 and 2018-2020). We find a 48% decrease in national PM2.5 exposure during 2013-2020, although the decrease rate has slowed after 2017. At the same time, O3 pollution worsened, with the average April-September O3 exposure increased by 17%. The improved air quality led to 308 thousand and 16 thousand avoided long- and short-term exposure related deaths, respectively, in 2020 compared to the 2013 level, which was majorly attributed to the reduction in ambient PM2.5 concentration. It is also noticed that with smaller PM2.5 reduction, the avoided long-term exposure associated deaths in 2017-2020 (13%) was greater than that in 2013-2017 (9%), because the exposure-response curve is nonlinear. As a result of the efforts in reducing PM2.5-polluted days with the daily average PM2.5 higher than 75 µg/m3 and the considerable increase in O3-polluted days with the daily maximum 8 h average O3 higher than 160 µg/m3, deaths attributable to the short-term O3 exposure were greater than those due to PM2.5 exposure since 2018. Future air quality improvement strategies for the coordinated control of PM2.5 and O3 are urgently needed.

Tracking Daily Concentrations of PM2.5 Chemical Composition in China since 2000.

PM2.5 chemical components play significant roles in the climate, air quality, and public health, and the roles vary due to their different physicochemical properties. Obtaining accurate and timely updated information on China's PM2.5 chemical composition is the basis for research and environmental management. Here, we developed a full-coverage near-real-time PM2.5 chemical composition data set at 10 km spatial resolution since 2000, combining the Weather Research and Forecasting-Community Multiscale Air Quality modeling system, ground observations, a machine learning algorithm, and multisource-fusion PM2.5 data. PM2.5 chemical components in our data set are in good agreement with the available observations (correlation coefficients range from 0.64 to 0.75 at a monthly scale from 2000 to 2020 and from 0.67 to 0.80 at a daily scale from 2013 to 2020; most normalized mean biases within ±20%). Our data set reveals the long-term trends in PM2.5 chemical composition in China, especially the rapid decreases after 2013 for sulfate, nitrate, ammonium, organic matter, and black carbon, at the rate of -9.0, -7.2, -8.1, -8.4, and -9.2% per year, respectively. The day-to-day variability is also well captured, including evolutions in spatial distribution and shares of PM2.5 components. As part of Tracking Air Pollution in China (http://tapdata.org.cn), this daily-updated data set provides large opportunities for health and climate research as well as policy-making in China.

Simultaneous removal of NOX and SO2 from flue gas in an integrated FGD-CABR system by sulfur cycling-mediated Fe(II)EDTA regeneration.

Chemical absorption-biological reduction (CABR) process is an attractive method for NOX removal and Fe(II)EDTA regeneration is important to sustain high NOX removal. In this study a sustainable and eco-friendly sulfur cycling-mediated Fe(II)EDTA regeneration method was incorporated in the integrated biological flue gas desulfurization (FGD)-CABR system. Here, we investigated the NOX and SO2 removal efficiency of the system under three different flue gas flows (100 mL/min, 500 mL/min, and 1000 mL/min) and evaluated the feasibility of chemical Fe(III)EDTA reduction by sulfide in series of batch tests. Our results showed that complete SO2 removal was achieved at all the tested scenarios with sulfide, thiosulfate and S0 accumulation in the solution. Meanwhile, the total removal efficiency of NOX achieved ∼100% in the system, of which 3.2%-23.3% was removed in spray scrubber and 76.7%-96.5% in EGSB reactor along with no N2O emission. The optimal pH and S2-/Fe(III)EDTA for Fe(II)EDTA regeneration and S0 recovery was 8.0 and 1:2. The microbial community analysis results showed that the cooperation of heterotrophic denitrifier (Saprospiraceae_uncultured and Dechloromonas) and iron-reducing bacteria (Klebsiella and Petrimonas) in EGSB reactor and sulfide-oxidizing, nitrate-reducing bacteria (Azoarcus and Pseudarcobacter) in spray scrubber contributed to the efficient removal of NOX in flue gas.

High IGKC-Expressing Intratumoral Plasma Cells Predict Response to Immune Checkpoint Blockade.

Resistance to Immune Checkpoint Blockade (ICB) constitutes the current limiting factor for the optimal implementation of this novel therapy, which otherwise demonstrates durable responses with acceptable toxicity scores. This limitation is exacerbated by a lack of robust biomarkers. In this study, we have dissected the basal TME composition at the gene expression and cellular levels that predict response to Nivolumab and prognosis. BCR, TCR and HLA profiling were employed for further characterization of the molecular variables associated with response. The findings were validated using a single-cell RNA-seq data of metastatic melanoma patients treated with ICB, and by multispectral immunofluorescence. Finally, machine learning was employed to construct a prediction algorithm that was validated across eight metastatic melanoma cohorts treated with ICB. Using this strategy, we have unmasked a major role played by basal intratumoral Plasma cells expressing high levels of IGKC in efficacy. IGKC, differentially expressed in good responders, was also identified within the Top response-related BCR clonotypes, together with IGK variants. These results were validated at gene, cellular and protein levels; CD138+ Plasma-like and Plasma cells were more abundant in good responders and correlated with the same RNA-seq-defined fraction. Finally, we generated a 15-gene prediction model that outperformed the current reference score in eight ICB-treated metastatic melanoma cohorts. The evidenced major contribution of basal intratumoral IGKC and Plasma cells in good response and outcome in ICB in metastatic melanoma is a groundbreaking finding in the field beyond the role of T lymphocytes.

Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion.

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.

Chronic Effects of High Fine Particulate Matter Exposure on Lung Cancer in China.

Rationale: Limited cohort studies have evaluated chronic effects of high fine particulate matter (particulate matter with an aerodynamic diameter ≤2.5 µm [PM2.5]) exposure on lung cancer.Objectives: To investigate the response pattern of lung cancer associated with high PM2.5 exposure.Methods: A Chinese cohort of 118,551 participants was followed up from 1992 to 2015. By incorporating PM2.5 exposure at 1 km spatial resolution generated using the satellite-based model during 2000-2015, we estimated the association between lung cancer and time-weighted average PM2.5 concentration using Cox proportional hazard models.Measurements and Main Results: A total of 844 incident lung cancer cases were identified during 915,053 person-years of follow-up. Among them, 701 lung cancer deaths occurred later. The exposure-response curves for lung cancer associated with PM2.5 exposure were nonlinear, with steeper slopes at the higher concentrations. Adjusted for age, sex, geographical region, urbanization, education level, smoking status, alcohol consumption, work-related physical activity, and body mass index, participants exposed to the second-fifth quintiles of PM2.5 had higher risk for lung cancer incidence than those exposed to the first quintile, with hazard ratios of 1.44 (95% confidence interval [CI], 1.10-1.88), 1.49 (95% CI, 1.12-1.99), 2.08 (95% CI, 1.42-3.04), and 2.45 (95% CI, 1.83-3.29), respectively. The corresponding hazard ratios for lung cancer mortality were 1.83 (95% CI, 1.33-2.50), 1.80 (95% CI, 1.29-2.53), 2.50 (95% CI, 1.62-3.86), and 2.95 (95% CI, 2.09-4.17), respectively.Conclusions: We provide strong evidence that high PM2.5 exposure leads to an elevated risk of lung cancer incidence and mortality, highlighting that remarkable public health benefits could be obtained from the improvement of air quality in highly polluted regions.

Benefits of active commuting on cardiovascular health modified by ambient fine particulate matter in China: A prospective cohort study.

BACKGROUND: Active commuting as a contributor to daily physical activity is beneficial for cardiovascular health, but leads to more chances of exposure to ambient air pollution. This study aimed to investigate associations between active commuting to work with cardiovascular disease (CVD), mortality and life expectancy among general Chinese adults, and to further evaluate the modification effect of fine particulate matter (PM2.5) exposure on these associations. METHODS: We included 76,176 Chinese adults without CVD from three large cohorts of the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project. Information about commuting mode and physical activity were collected by unified questionnaire. Satellite-based PM2.5 concentrations at 1-km spatial resolution was used for estimating PM2.5 exposure of participants. Hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD incidence, mortality and all-cause mortality were estimated using Cox proportional hazards regression models. Multiplicative interaction term of commuting mode and PM2.5 level was tested to investigate potential effect modification. RESULTS: During 448,499 person-years of follow-up, 2230 CVD events and 2777 all-cause deaths were recorded. Compared with the non-active commuters, the multivariable-adjusted HRs (95% CIs) of CVD incidence and all-cause mortality were 0.95(0.85-1.05) and 0.79(0.72-0.87) for walking commuters, respectively. Corresponding HRs (95% CIs) for cycling commuters were 0.71(0.62-0.82) and 0.67(0.59-0.76). Active commuters over 45 years old were estimated to have more CVD-free years and life expectancy than non-active commuters under lower PM2.5 concentration. However, these beneficial effects of active commuting were alleviated or counteracted by long-term exposure to high PM2.5 concentration. Significant multiplicative interaction of commuting mode and PM2.5 level was showed in all-cause mortality, with the lowest risk observed in cycling participants exposed to lower level of PM2.5. CONCLUSIONS: Active commuting was associated with lower risk of CVD, all-cause mortality, and longer life expectancy among Chinese adults under ambient settings with lower PM2.5 level. It will be valuable to encourage active commuting among adults and develop stringent strategies on ambient PM2.5 pollution control for prevention of CVD and prolongation of life expectancy.

Germline variant burden in multidrug resistance transporters is a therapy-specific predictor of survival in breast cancer patients.

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.

Ethnogeographic and inter-individual variability of human ABC transporters.

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.

Estimating Spatiotemporal Variation in Ambient Ozone Exposure during 2013-2017 Using a Data-Fusion Model.

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.

Long-Term Effects of High Exposure to Ambient Fine Particulate Matter on Coronary Heart Disease Incidence: A Population-Based Chinese Cohort Study.

Evidence of long-term effects of high exposure to ambient fine particulate matter (PM2.5) on coronary heart disease (CHD) remains limited. We incorporated the high-resolution satellite-based PM2.5 estimates with a large-scale, population-based Chinese cohort comprising 118 229 individuals, to assess the CHD risk of long-term exposure to high PM2.5. During the follow-up of 908 376 person-years, 1586 incident CHD cases were identified. The long-term average PM2.5 concentration for study population was 64.96 µg/m3, ranging from 31.17 to 96.96 µg/m3. For an increment of 10 µg/m3 in PM2.5, the multivariate-adjusted hazard ratios (HRs) were 1.43 (95% confidence interval [CI]: 1.35-1.51) for total CHD, 1.45 (95% CI: 1.36-1.56) for nonfatal CHD, and 1.38 (95% CI: 1.25-1.53) for fatal CHD, respectively. The effects were different across specific CHD outcomes, with greater effects for unstable angina (HR, 1.71 [95% CI, 1.56-1.88]), and weaker effects for acute myocardial infarction (HR, 1.28 [95% CI, 1.19-1.39]) and other CHD (HR, 1.27 [95% CI, 1.10-1.48]). The exposure-response curve suggested that HRs increased with elevated PM2.5 concentration over the entire exposure range. Elderly and hypertensive individuals were more susceptible to PM2.5-induced CHD. Our findings demonstrate the adverse health effects of severe air pollution and highlight the potential health benefits of air quality improvement.

DNLC: differential network local consistency analysis.

BACKGROUND: The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages. RESULTS: In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible. CONCLUSIONS: The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.

An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data.

The long satellite aerosol data record enables assessments of historical PM2.5 level in regions where routine PM2.5 monitoring began only recently. However, most previous models reported decreased prediction accuracy when predicting PM2.5 levels outside the model-training period. In this study, we proposed an ensemble machine learning approach that provided reliable PM2.5 hindcast capabilities. The missing satellite data were first filled by multiple imputation. Then the modeling domain, China, was divided into seven regions using a spatial clustering method to control for unobserved spatial heterogeneity. A set of machine learning models including random forest, generalized additive model, and extreme gradient boosting were trained in each region separately. Finally, a generalized additive ensemble model was developed to combine predictions from different algorithms. The ensemble prediction characterized the spatiotemporal distribution of daily PM2.5 well with the cross-validation (CV) R2 (RMSE) of 0.79 (21 µg/m3). The cluster-based subregion models outperformed national models and improved the CV R2 by ∼0.05. Compared with previous studies, our model provided more accurate out-of-range predictions at the daily level ( R2 = 0.58, RMSE = 29 µg/m3) and monthly level ( R2 = 0.76, RMSE = 16 µg/m3). Our hindcast modeling system allows for the construction of unbiased historical PM2.5 levels.

Global Land Use Regression Model for Nitrogen Dioxide Air Pollution.

Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the worldwide distribution of NO2 exposure and associated impacts on health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO2) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO2 variation, with a mean absolute error of 3.7 ppb. Regional performance varied from R2 = 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (n = 10,000) demonstrated a robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted R2 within 2%) but not for Africa and Oceania (adjusted R2 within 11%) where NO2 monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO2 concentrations. Variable contributions differed between continental regions, but major roads within 100 m and satellite-derived NO2 were consistently the strongest predictors. The resulting model can be used for global risk assessments and health studies, particularly in countries without existing NO2 monitoring data or models.

Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China.

PM2.5 air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM2.5 exposure spatiotemporally in China, but all these have limitations. This study was to develop a data fusion approach and compare it with kriging and Chemistry Module. Two techniques were applied to create daily spatial cover of PM2.5 in grid cells with a resolution of 10km in North China in 2013, respectively, which was kriging with an external drift (KED) and Weather Research and Forecast Model with Chemistry Module (WRF-Chem). A data fusion technique was developed by fusing PM2.5 concentration predicted by KED and WRF-Chem, accounting for the distance from the central of grid cell to the nearest ground observations and daily spatial correlations between WRF-Chem and observations. Model performances were evaluated by comparing them with ground observations and the spatial prediction errors. KED and data fusion performed better at monitoring sites with a daily model R2 of 0.95 and 0.94, respectively and PM2.5 was overestimated by WRF-Chem (R2=0.51). KED and data fusion performed better around the ground monitors, WRF-Chem performed relative worse with high prediction errors in the central of study domain. In our study, both KED and data fusion technique provided highly accurate PM2.5. Current monitoring network in North China was dense enough to provide a reliable PM2.5 prediction by interpolation technique.

The Regional Impacts of Cooking and Heating Emissions on Ambient Air Quality and Disease Burden in China.

Exposure to air pollution is a major risk factor globally and particularly in Asia. A large portion of air pollutants result from residential combustion of solid biomass and coal fuel for cooking and heating. This study presents a regional modeling sensitivity analysis to estimate the impact of residential emissions from cooking and heating activities on the burden of disease at a provincial level in China. Model surface PM2.5 fields are shown to compare well when evaluated against surface air quality measurements. Scenarios run without residential sector and residential heating emissions are used in conjunction with the Global Burden of Disease 2013 framework to calculate the proportion of deaths and disability adjusted life years attributable to PM2.5 exposure from residential emissions. Overall, we estimate that 341 000 (306 000-370 000; 95% confidence interval) premature deaths in China are attributable to residential combustion emissions, approximately a third of the deaths attributable to all ambient PM2.5 pollution, with 159 000 (142 000-172 000) and 182 000 (163 000-197 000) premature deaths from heating and cooking emissions, respectively. Our findings emphasize the need to mitigate emissions from both residential heating and cooking sources to reduce the health impacts of ambient air pollution in China.

Pediatric emergency department visits and ambient Air pollution in the U.S. State of Georgia: a case-crossover study.

BACKGROUND: Estimating the health effects of ambient air pollutant mixtures is necessary to understand the risk of real-life air pollution exposures. METHODS: Pediatric Emergency Department (ED) visit records for asthma or wheeze (n = 148,256), bronchitis (n = 84,597), pneumonia (n = 90,063), otitis media (n = 422,268) and upper respiratory tract infection (URI) (n = 744,942) were obtained from Georgia hospitals during 2002-2008. Spatially-contiguous daily concentrations of 11 ambient air pollutants were estimated from CMAQ model simulations that were fused with ground-based measurements. Using a case-crossover study design, odds ratios for 3-day moving average air pollutant concentrations were estimated using conditional logistic regression, matching on ZIP code, day-of-week, month, and year. RESULTS: In multipollutant models, the association of highest magnitude observed for the asthma/wheeze outcome was with "oxidant gases" (O3, NO2, and SO2); the joint effect estimate for an IQR increase of this mixture was OR: 1.068 (95% CI: 1.040, 1.097). The group of "secondary pollutants" (O3 and the PM2.5 components SO42-, NO3-, and NH4+) was strongly associated with bronchitis (OR: 1.090, 95% CI: 1.050, 1.132), pneumonia (OR: 1.085, 95% CI: 1.047, 1.125), and otitis media (OR: 1.059, 95% CI: 1.042, 1.077). ED visits for URI were strongly associated with "oxidant gases," "secondary pollutants," and the "criteria pollutants" (O3, NO2, CO, SO2, and PM2.5). CONCLUSIONS: Short-term exposures to air pollution mixtures were associated with ED visits for several different pediatric respiratory diseases.

The co-induced effects of molybdenum and cadmium on the mRNA expression of inflammatory cytokines and trace element contents in duck kidneys.

The aims of this study were determining the co-induced effects of dietary Cadmium (Cd) and high intake of Molybdenum (Mo) on renal toxicity in ducks. 240 healthy 11-day-old ducks were randomly divided into 6 groups, which were treated with Mo or/and Cd at different doses added to the basal diet for 120 days. Ducks of control group were fed with basal diet, LMo and HMo groups were fed with 15mg/kg Mo and 100mg/kg Mo respectively; ducks of Cd group were provided with 4mg/kg Cd which was added into basal diet. Two combination groups were treated with 15mg/kg Mo+4mg/kg Cd and 100mg/kg Mo+4mg/kg Cd respectively. On days 30, 60, 90 and 120, the mRNA expression levels of inflammatory cytokines and contents of trace elements were detected. In addition, transmission electron microscopic examination was used for ultrastructural studies. The results indicated that the mRNA expression levels of tumor necrosis factor-α (TNF-α), nuclear factor-kappa B (NF-κB), and cyclooxygenase-2 (COX-2) showed an upward tendency in treatment groups in comparison with control group, and in the later period of the experiment it showed a significant rise in joint groups compared with the Mo and Cd group (P<0.01); the contents of copper (Cu) and iron (Fe) decreased in joint groups in the later period (P<0.05) while the contents of Mo and Cd significantly increased (P<0.01); zinc (Zn) and selenium (Se) concentration had a slight downtrend in treated groups, but showed no significant difference (P>0.05). The ultrastructural analysis showed that kidney tissues were severely injured in joint groups on day 120. These results suggested that the combination of Mo and Cd could aggravate damages to the kidney. In addition, dietary of Mo or/and Cd caused the decrease of Cu, Fe, Zn, and Se contents, inflammatory response and pathological lesions whose mechanism is somehow linked with Mo and Cd deposition in kidney.