PM2.5 during severe winter haze in Beijing, China, has reached levels as high as 880 μg m‐3, with sulfur compounds contributing significantly to PM2.5 composition. This sulfur has been traditionally assumed to be sulfate, although atmospheric chemistry models are unable to account for such large sulfate enhancements under dim winter conditions. Using a 1‐D model, we show that well characterized but previously overlooked chemistry of aqueous‐phase HCHO and S (IV) in cloud droplets to form a S (IV)‐HCHO adduct, hydroxymethane sulfonate (HMS), may explain high particulate sulfur in wintertime Beijing. We also demonstrate in the laboratory that methods of ion chromatography typically used to measure ambient particulates easily misinterpret HMS as sulfate. Our findings suggest that HCHO and not SO2 has been the limiting factor in many haze events in Beijing and that to reduce severe winter pollution in this region, policymakers may need to address HCHO sources such as transportation.
In this study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5observations from ∼ 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998–2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40 % of daily PM2.5variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing–Tianjin–Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of ∼ 0.5 µg m−3 in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small climate benefit, but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections.
Severe PM2.5 air pollution in China and the First Grand National Standard (FGNS), implemented in 2016 (annual PM2.5 concentration target of less than 35 µg m−3), necessitate urgent reduction strategies. This study applied the nested-grid version of the Goddard Earth Observing System (GEOS) chemical transport model (GEOS-Chem) to quantify 2000–2050 changes in PM2.5 air quality and related direct radiative forcing (DRF) in China, based on future emission changes under the representative concentration pathway (RCP) scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP8.5. In the near term (2000–2030), a projected maximum increase in PM2.5 concentrations of 10–15 µg m−3 is found over east China under RCP6.0 and RCP8.5 and less than 5 µg m−3 under RCP2.6 and RCP4.5. In the long term (2000–2050), PM2.5 pollution clearly improves, and the largest decrease in PM2.5 concentrations of 15–30 µg m−3 is over east China under all RCPs except RCP6.0. Focusing particularly on highly polluted regions, we find that Beijing-Tianjin-Hebei (BTH) wintertime PM2.5 concentrations meeting the FGNS occur after 2040 under RCP2.6, RCP4.5, and RCP8.5, and summertime PM2.5 concentrations reach this goal by 2030 under RCP2.6 and RCP4.5. In Sichuan Basin (SCB), wintertime PM2.5 concentrations below the FGNS occur only in 2050 under RCP2.6 and RCP4.5, although future summertime PM2.5 will be well controlled. The difficulty in controlling future PM2.5 concentrations relates to unmitigated high levels of nitrate, although NOxand SO2 emissions show substantial reductions during 2020–2040. The changes in aerosol concentrations lead to positive aerosol DRF over east China (20°–45°N, 100°–125°E) by 1.22, 1.88, and 0.66 W m−2 in 2050 relative to 2000 under RCP2.6, RCP4.5, and RCP8.5, respectively. When considering both health and climate effects of PM2.5 over China, for example, PM2.5concentrations averaged over east China under RCP4.5 (RCP2.6) decrease by 54% (43%) in 2050 relative to 2000, but at the cost of warming with DRF of 1.88 (1.22) W m−2. Our results indicate that it will be possible to mitigate future PM2.5 pollution in China, but it will likely take two decades for polluted regions such as BTH and SCB to meet the FGNS, based on all RCP scenarios. At the same time, the consequent warming effects from reduced aerosols are also significant and inevitable.
Recent field studies have documented a surprisingly strong and consistent methane sink in arctic mineral soils, thought to be due to high-affinity methanotrophy. However, the distinctive physiology of these methanotrophs is poorly represented in mechanistic methane models. We developed a new model, constrained by microcosm experiments, to simulate the activity of high-affinity methanotrophs. The model was tested against soil core-thawing experiments and field-based measurements of methane fluxes and was compared to conventional mechanistic methane models. Our simulations show that high-affinity methanotrophy can be an important component of the net methane flux from arctic mineral soils. Simulations without this process overestimate methane emissions. Furthermore, simulations of methane flux seasonality are improved by dynamic simulation of active microbial biomass. Because a large fraction of the Arctic is characterized by mineral soils, high-affinity methanotrophy will likely have a strong effect on its net methane flux.