Cloud water acidity affects the atmospheric chemistry of sulfate and organic aerosol formation, halogen radical cycling, and trace metal speciation. Precipitation acidity including post-depositional inputs adversely affects soil and freshwater ecosystems. Here, we use the GEOS-Chem model of atmospheric chemistry to simulate the global distributions of cloud water and precipitation acidity as well as the total acid inputs to ecosystems from wet deposition. The model accounts for strong acids (H2SO4, HNO3, and HCl), weak acids (HCOOH, CH3COOH, CO2, and SO2), and weak bases (NH3 as well as dust and sea salt aerosol alkalinity). We compile a global data set of cloud water pH measurements for comparison with the model. The global mean observed cloud water pH is 5.2±0.9, compared to 5.0±0.8 in the model, with a range from 3 to 8 depending on the region. The lowest values are over East Asia, and the highest values are over deserts. Cloud water pH over East Asia is low because of large acid inputs (H2SO4 and HNO3), despite NH3 and dust neutralizing 70 % of these inputs. Cloud water pH is typically 4–5 over the US and Europe. Carboxylic acids account for less than 25 % of cloud water H+ in the Northern Hemisphere on an annual basis but 25 %–50 % in the Southern Hemisphere and over 50 % in the southern tropical continents, where they push the cloud water pH below 4.5. Anthropogenic emissions of SO2 and NOx (precursors of H2SO4 and HNO3) are decreasing at northern midlatitudes, but the effect on cloud water pH is strongly buffered by NH4+ and carboxylic acids. The global mean precipitation pH is 5.5 in GEOS-Chem, which is higher than the cloud water pH because of dilution and below-cloud scavenging of NH3 and dust. GEOS-Chem successfully reproduces the annual mean precipitation pH observations in North America, Europe, and eastern Asia. Carboxylic acids, which are undetected in routine observations due to biodegradation, lower the annual mean precipitation pH in these areas by 0.2 units. The acid wet deposition flux to terrestrial ecosystems taking into account the acidifying potential of NO3- and NH4+ in N-saturated ecosystems exceeds 50 meq m-2 a-1 in East Asia and the Americas, which would affect sensitive ecosystems. NH4+ is the dominant acidifying species in wet deposition, contributing 41 % of the global acid flux to continents under N-saturated conditions.
Sulfur compounds are an important constituent of particulate matter, with impacts on climate and public health. While most sulfur observed in particulate matter has been assumed to be sulfate, laboratory experiments reveal that hydroxymethanesulfonate (HMS), an adduct formed by aqueous phase chemical reaction of dissolved HCHO and SO2, may be easily misinterpreted in measurements as sulfate. Here we present observational and modeling evidence for a ubiquitous global presence of HMS. We find that filter samples collected in Shijiazhuang, China, and examined with ion chromatography within 9 days show as much as 7.6 μg m‐3 of HMS, while samples from Singapore examined 9‐18 months after collection reveal ~0.6 μg m‐3 of HMS. The Shijiazhuang samples show only minor traces of HMS four months later, suggesting that HMS had decomposed over time during sample storage. In contrast, the Singapore samples do not clearly show a decline in HMS concentration over two months of monitoring. Measurements from over 150 sites, primarily derived from the IMPROVE network across the United States, suggest the ubiquitous presence of HMS in at least trace amounts as much as 60 days after collection. The degree of possible HMS decomposition in the IMPROVE observations is unknown. Using the GEOS‐Chem chemical transport model, we estimate that HMS may account for 10% of global particulate sulfur in continental surface air and over 25% in many polluted regions. Our results suggest that reducing emissions of HCHO and other volatile organic compounds may have a co‐benefit of decreasing particulate sulfur.
Wet processes, including aqueous-phase chemistry, wet scavenging, and wet surface uptake during dry deposition, are important for global modeling of aerosols and aerosol precursors. In this study, we improve the treatments of these wet processes in the Goddard Earth Observing System with chemistry (GEOS-Chem) v12.6.0, including pH calculations for cloud, rain, and wet surfaces, the fraction of cloud available for aqueous-phase chemistry, rainout efficiencies for various types of clouds, empirical washout by rain and snow, and wet surface uptake during dry deposition. We compare simulated surface mass concentrations of aerosols and aerosol precursors with surface monitoring networks over the United States, European, Asian, and Arctic regions, and show that model results with updated wet processes agree better with measurements for most species. With the implementation of these updates, normalized mean biases (NMBs) of surface nitric acid, nitrate, and ammonium are reduced from 78 %, 126 %, and 45 % to 0.9 %, 15 %, and 4.1 % over the US sites, from 107 %, 127 %, and 90 % to −0.7 %, 4.2 %, and 16 % over European sites, and from 121 %, 269 %, and 167 % to −21 %, 37 %, and 86 % over Asian remote region sites. Comparison with surface measured SO2, sulfate, and black carbon at four Arctic sites indicated that those species simulated with the updated wet processes match well with observations except for a large underestimate of black carbon at one of the sites. We also compare our model simulation with aircraft measurement of nitric acid and aerosols during the Atmospheric Tomography Mission (ATom)-1 and ATom-2 periods and found a significant improvement of modeling skill of nitric acid, sulfate, and ammonium in the Northern Hemisphere during wintertime. The NMBs of these species are reduced from 163 %, 78 %, and 217 % to −13 %, −1 %, and 10 %, respectively. The investigation of impacts of updated wet process treatments on surface mass concentrations indicated that the updated wet processes have strong impacts on the global means of nitric acid, sulfate, nitrate, and ammonium and relative small impacts on the global means of sulfur dioxide, dust, sea salt, black carbon, and organic carbon.
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.