Yixuan Zheng

Recent reduction in NO x emissions over China: synthesis of satellite observations and emission inventories

Tropospheric nitrogen dioxide (NO2) column densities detected from space are widely used to infer trends in terrestrial nitrogen oxide (NO x ) emissions. We study changes in NO2 column densities using the Ozone Monitoring Instrument (OMI) over China from 2005 to 2015 and compare them with the bottom-up inventory to examine NO x emission trends and their driving forces. From OMI measurements we detect the peak of NO2 column densities at a national level in the year 2011, with average NO2 column densities deceasing by 32% from 2011 to 2015 and corresponding to a simultaneous decline of 21% in bottom-up emission estimates. A significant variation in the peak year of NO2 column densities over regions is observed. Because of the reasonable agreement between the peak year of NO2 columns and the start of deployment of denitration devices, we conclude that power plants are the primary contributor to the NO2 decline, which is further supported by the emission reduction of 56% from the power sector in the bottom-up emission inventory associated with the penetration of selective catalytic reduction (SCR) increasing from 18% to 86% during 2011–2015. Meanwhile, regulations for vehicles also make a significant contribution to NO x emission reductions, in particular for a few urbanized regions (e.g., Beijing and Shanghai), where they implemented strict regulations for vehicle emissions years before the national schedule for SCR installations and thus reached their NO2 peak 2–3 years ahead of the deployment of denitration devices for power plants.

Fusing Observational, Satellite Remote Sensing and Air Quality Model Simulated Data to Estimate Spatiotemporal Variations of PM2.5 Exposure in China

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. Through assembling techniques, including linear mixed effect regressions with a spatial-varying coefficient, a maximum likelihood estimator and the spatiotemporal Kriging together, we develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1° over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.0 μg/m3 and R2 = 0.72) and outperforms either AOD-derived PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-derived PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides spatiotemporally complete predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.7 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81 across China during 2014. Fused estimates will be publicly available for future health-related studies.

The Lancet Countdown on PM2·5 pollution-related health impacts of China's projected carbon dioxide mitigation in the electric power generation sector under the Paris Agreement: a modelling study

BACKGROUND Except for comparing the implementation costs of the Paris Agreement with potential health benefits at the national levels, previous studies have not explored the health impacts of the nationally determined contributions (NDCs) by countries and in regional details. In this Lancet Countdown study, we aimed to estimate and monetise the health benefits of Chinas NDCs in the electric power generation sector, and then compare them with the implementation costs, both at the national and regional levels. METHODS In this modelling study, we linked the Multi-regional model for Energy Supply system and their Environmental ImpaCts, the Multi-resolution Emission Inventory for China model, the offline-coupled Weather Research and Forecasting model, the Community Multiscale Air Quality model, and the Integrated Health Impact Assessment model with a time scope from 2010 to 2050. We calculated the PM2·5 concentrations and compared the health impacts and implementation costs between two scenarios that reflect CO2 and air pollutant emissions-the reference (REF) scenario (no climate policy) and the NDC scenario (100% realisation of NDC targets: CO2 emission intensity needs to be about 40% below 2010 emissions by 2030 [roughly 35% below 2030 emissions in REF], and about 90% below 2010 emissions by 2050 [roughly 96% below 2050 emissions in REF]). FINDINGS Under a comparatively optimistic health benefits valuation condition, at the national level, 18-62% of implementation costs could be covered by the health benefits in 2030. In 2050, the overall health benefits would substantially increase to 3-9 times of the implementation costs. However, northwest China would require the highest implementation costs and will also have more premature deaths because of a more carbon-intensive energy structure than business as usual. By 2030, people in northwest China (especially in Gansu, Shaanxi, and Xinjiang provinces) would need to bear worse air quality, and 10 083 (95% CI 3419-16 138) more premature deaths annually. This undesirable situation would diminish by about 2050. A solution that assumes no growth in air pollutant emissions in 2030 at the regional level is technically feasible, but would not be cost-effective. INTERPRETATION Our results suggest that cost-benefit analysis of climate policy that omits regional air pollution could greatly underestimate benefits. A compensation mechanism for inter-regional interests (including financial, technological, and knowledge support) should be established for regions that give up their human health benefits for the sake of the whole nation to realise the climate change targets. FUNDING National Natural Science Foundation of China and Cyrus Tang Foundation.

Differential Susceptibility in Ambient Particle–Related Risk of First-Ever Stroke: Findings From a National Case-Crossover Study

Abstract Different populations may respond differently to exposure to ambient fine particulate matter, defined as particulate matter with an aerodynamic diameter less than or equal to 2.5 &mgr;m (PM2.5); however, less is known about the distribution of susceptible individuals among the entire population. We conducted a time‐stratified case‐crossover study to assess associations between stroke risk and exposure to PM2.5. During 2013‐2015, 1,356 first‐ever stroke events were derived from a large representative sample, the China National Stroke Screening Survey (CNSSS) database. Daily PM2.5 average exposures with a spatial resolution of 0.1° were estimated using a data assimilation approach combining satellite measurements, air model simulations, and monitoring values. The distribution of susceptibility was derived according to individual‐specific associations with PM2.5 modified by different combinations of individual‐level characteristics and their joint frequencies among all of the CNSSS participants (n = 1,292,010). We found that first‐ever stroke was statistically significantly associated with PM2.5 (per 10‐&mgr;g/m3 increment of exposure, odds ratio = 1.049, 95% confidence interval (CI): 1.038, 1.061). This association was modified by demographic (e.g., sex), lifestyle (e.g., overweight/obesity), and medical history (e.g., diabetes) variables. The combined association with PM2.5 varied from 0.966 (95% CI: 0.920, 1.013) to 1.145 (95% CI: 1.080, 1.215) per 10‐&mgr;g/m3 increment in different subpopulations. We found that most of the CNSSS participants were at increased risk of PM2.5‐related stroke, while only a small proportion were highly susceptible.