Abstract. In an attempt to improve the forecasting of atmospheric aerosols, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions (ICs) and emission input. The forecast model, which was expanded by combining the Weather Research and Forecasting with Chemistry (WRF-Chem) model and a forecast model of emission scaling factors, generated both chemical concentration fields and emission scaling factors. The forecast model of emission scaling factors was developed by using the ensemble concentration ratios of the WRF-Chem forecast chemical concentrations and also the time smoothing operator. Hourly surface fine particulate matter (PM 2.5 ) observations were assimilated in this system over China from 5 to 16 October 2014. A series of 48 h forecasts was then carried out with the optimized initial conditions and emissions on each day at 00:00 UTC and a control experiment was performed without data assimilation. In addition, we also performed an experiment of pure assimilation chemical ICs and the corresponding 48 h forecasts experiment for comparison. The results showed that the forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment. In the Yangtze River delta (YRD) and the Pearl River delta (PRD) regions, large reduction of the root-mean-square errors (RMSEs) was obtained for almost the entire 48 h forecast range attributed to assimilation. In particular, the relative reduction in RMSE due to assimilation was about 37.5 % at nighttime when WRF-Chem performed comparatively worse. In the BeijingTianjin-Hebei (JJJ) region, relatively smaller improvements were achieved in the first 24 h forecast but then no improvements were achieved afterwards. Comparing to the forecasts with only the optimized ICs, the forecasts with the joint adjustment were always much better during the night in the PRD and YRD regions. However, they were very similar during daytime in both regions. Also, they performed similarly for almost the entire 48 h forecast range in the JJJ region.

Abstract. Extreme haze events have occurred frequently over China in recent years. Although many studies have investigated the formation mechanisms associated with PM2.5 for heavily polluted regions in China based on observational data, adequately predicting peak PM2.5 concentrations is still challenging for regional air quality models. In this study, we evaluate the performance of one configuration of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) and use the model to investigate the sensitivity of heterogeneous reactions on simulated peak sulfate, nitrate, and ammonium concentrations in the vicinity of Beijing during four extreme haze episodes in October 2014 over the North China Plain. The highest observed PM2.5 concentration of 469 µg m−3 occurred in Beijing. Comparisons with observations show that the model reproduced the temporal variability in PM2.5 with the highest PM2.5 values on polluted days (defined as days in which observed PM2.5 is greater than 75 µg m−3), but predictions of sulfate, nitrate, and ammonium were too low on days with the highest observed concentrations. Observational data indicate that the sulfur/nitric oxidation rates are strongly correlated with relative humidity during periods of peak PM2.5; however, the model failed to reproduce the highest PM2.5 concentrations due to missing heterogeneous/aqueous reactions. As the parameterizations of those heterogeneous reactions are not well established yet, estimates of SO2-to-H2SO4 and NO2/NO3-to-HNO3 reaction rates that depend on relative humidity were applied, which improved the simulation of sulfate, nitrate, and ammonium enhancement on polluted days in terms of both concentrations and partitioning among those species. Sensitivity simulations showed that the extremely high heterogeneous reaction rates and also higher emission rates than those reported in the emission inventory were likely important factors contributing to those peak PM2.5 concentrations.

No abstract

Abstract. An ensemble Kalman filter data assimilation (DA) system has been developed to improve air quality forecasts using surface measurements of PM10, PM2.5, SO2, NO2, O3, and CO together with an online regional chemical transport model, WRF-Chem (Weather Research and Forecasting with Chemistry). This DA system was applied to simultaneously adjust the chemical initial conditions (ICs) and emission inputs of the species affecting PM10, PM2.5, SO2, NO2, O3, and CO concentrations during an extreme haze episode that occurred in early October 2014 over East Asia. Numerical experimental results indicate that ICs played key roles in PM2.5, PM10 and CO forecasts during the severe haze episode over the North China Plain. The 72 h verification forecasts with the optimized ICs and emissions performed very similarly to the verification forecasts with only optimized ICs and the prescribed emissions. For the first-day forecast, near-perfect verification forecasts results were achieved. However, with longer-range forecasts, the DA impacts decayed quickly. For the SO2 verification forecasts, it was efficient to improve the SO2 forecast via the joint adjustment of SO2 ICs and emissions. Large improvements were achieved for SO2 forecasts with both the optimized ICs and emissions for the whole 72 h forecast range. Similar improvements were achieved for SO2 forecasts with optimized ICs only for the first 3 h, and then the impact of the ICs decayed quickly. For the NO2 verification forecasts, both forecasts performed much worse than the control run without DA. Plus, the 72 h O3 verification forecasts performed worse than the control run during the daytime, due to the worse performance of the NO2 forecasts, even though they performed better at night. However, relatively favorable NO2 and O3 forecast results were achieved for the Yangtze River delta and Pearl River delta regions.

No abstract

An aging population could substantially enhance the burden of heat-related health risks in a warming climate because of their higher susceptibility to extreme heat health effects. Here, we project heat-related mortality for adults 65 years and older in Beijing China across 31 downscaled climate models and 2 representative concentration pathways (RCPs) in the 2020s, 2050s, and 2080s. Under a scenario of medium population and RCP8.5, by the 2080s, Beijing is projected to experience 14,401 heat-related deaths per year for elderly individuals, which is a 264.9% increase compared with the 1980s. These impacts could be moderated through adaptation. In the 2080s, even with the 30% and 50% adaptation rate assumed in our study, the increase in heat-related death is approximately 7.4 times and 1.3 times larger than in the 1980s respectively under a scenario of high population and RCP8.5. These findings could assist countries in establishing public health intervention policies for the dual problems of climate change and aging population. Examples could include ensuring facilities with large elderly populations are protected from extreme heat (for example through back-up power supplies and/or passive cooling) and using databases and community networks to ensure the home-bound elderly are safe during extreme heat events.

Abstract. To better characterize anthropogenic emission-relevant aerosol species, the Gridpoint Statistical Interpolation (GSI) and Weather Research and Forecasting with Chemistry (WRF/Chem) data assimilation system was updated from the GOCART aerosol scheme to the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) 4-bin (MOSAIC-4BIN) aerosol scheme. Three years (2015–2017) of wintertime (January) surface PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 µm) observations from more than 1600 sites were assimilated hourly using the updated three-dimensional variational (3DVAR) system. In the control experiment (without assimilation) using Multi-resolution Emission Inventory for China 2010 (MEIC_2010) emissions, the modeled January averaged PM2.5 concentrations were severely overestimated in the Sichuan Basin, central China, the Yangtze River Delta and the Pearl River Delta by 98–134, 46–101, 32–59 and 19–60 µg m−3, respectively, indicating that the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations of 11–12, 53–96 and 22–40 µg m−3 were observed in northeastern China, Xinjiang and the Energy Golden Triangle, respectively. The assimilation experiment significantly reduced both high and low biases to within ±5 µg m−3. The observations and the reanalysis data from the assimilation experiment were used to investigate the year-to-year changes and the driving factors. The role of emissions was obtained by subtracting the meteorological impacts (by control experiments) from the total combined differences (by assimilation experiments). The results show a reduction in PM2.5 of approximately 15 µg m−3 for the month of January from 2015 to 2016 in the North China Plain (NCP), but meteorology played the dominant role (contributing a reduction of approximately 12 µg m−3). The change (for January) from 2016 to 2017 in NCP was different; meteorology caused an increase in PM2.5 of approximately 23 µg m−3, while emission control measures caused a decrease of 8 µg m−3, and the combined effects still showed a PM2.5 increase for that region. The analysis confirmed that emission control strategies were indeed implemented and emissions were reduced in both years. Using a data assimilation approach, this study helps identify the reasons why emission control strategies may or may not have an immediately visible impact. There are still large uncertainties in this approach, especially the inaccurate emission inputs, and neglecting aerosol–meteorology feedbacks in the model can generate large uncertainties in the analysis as well.

Although existing studies have linked high temperature to mortality in a small number of regions, less evidence is available on the variation in the associations between high temperature exposure and cause-specific mortality of multiple regions in China. Our study focused on the use of time series analysis to quantify the association between high temperature and different cause-specific mortalities for susceptible populations for 43 counties in China. Two-stage analyses adopting a distributed lag non-linear model (DLNM) and a meta-analysis allowed us to obtain county-specific estimates and national-scale pooled estimates of the nonlinear temperature-mortality relationship. We also considered different populations stratified by age and sex, causes of death, absolute and relative temperature patterns, and potential confounding from air pollutants. All of the observed cause-specific mortalities are significantly associated with higher temperature. The estimated effects of high temperature on mortality varied by spatial distribution and temperature patterns. Compared with the 90th percentile temperature, the overall relative risk (RR) at the 99th percentile temperature for non-accidental mortality is 1.105 (95%CI: 1.089, 1.122), for circulatory disease is 1.107 (95%CI: 1.081, 1.133), for respiratory disease is 1.095 (95%CI: 1.050, 1.142), for coronary heart disease is 1.073 (95%CI: 1.047, 1.099), for acute myocardial infarction is 1.072 (95%CI: 1.042, 1.104), and for stroke is 1.095 (95%CI: 1.052, 1.138). Based on our findings, we believe that heat-related health effect in China is a significant issue that requires more attention and allocation of existing resources.