Improvement of the anthropogenic emission rate estimate in Ulaanbaatar, Mongolia, for 2020-21 winter

Ulaanbaatar (UB), the fast-growing capital of Mongolia, is known for its world's worst level of particulate matter (PM) concentrations in winter. However, current anthropogenic emission inventories over the UB are based on data from more than fifteen years ago, and satellite observations are scarce because UB is in high latitudes. During the winter of 2020-21, the first period of the Fine Particle Research Initiative in East Asia considering the National Differences (FRIEND), several times higher concentrations of PM in UB compared to other urban sites in East Asia were observed but not reproduced with a chemical transport model mainly due to the underestimated anthropogenic emissions. Therefore, we devised a method for sequentially adjusting emissions based on the reactivity of PM precursors using ground observations. We scaled emission rates for the inert species (CO, elemental carbon (EC), and organic carbon (OC)) to reproduce their observed ambient concentrations, followed by SO2 to reproduce the concentration of SO42-, which was examined to have the least uncertainty based on the abundance of observed NH3, and finally NO and NH3 for NO3-, and NH4+. This improved estimation is compared to regional inventories for Asia and suggests more than an order of magnitude increase in anthropogenic emissions in UB. Using the improved emission inventory, we were able to successfully reproduce independent observation data on PM2.5 concentrations in UB in December 2021 from the U.S. Embassy. During the campaign period, we found more than 50% of the SO42-, NO3-, and NH4+ increased in UB due to the improvement could travel to Beijing, China (BJ), and about 20% of the SO42- could travel to Noto, Japan (NT), more than 3000 km away. Also, the anthropogenic emissions in UB can effectively increase OC, NO3-, and NH4+ concentrations in BJ when Gobi dust storms occur.

When and why PM2.5 is high in Seoul, South Korea: Interpreting long-term (2015-2021) ground observations using machine learning and a chemical transport model

Seoul has high PM2.5 concentrations and has not attained the national annual average standard so far. To understand the reasons, we analyzed long-term (2015-2021) hourly observations of aerosols (PM2.5, NO3-, NH4+, SO42-, OC, and EC) and gases (CO, NO2, and SO2) from Seoul and Baekryeong Island, a background site in the upwind region of Seoul. We applied the weather normalization method for meteorological conditions and a 3-dimensional chemical transport model, GEOS-Chem, to identify the effect of policy implementation and aerosol formation mechanisms. The monthly mean PM2.5 ranges between about 20 μg m-3 (warm season) and about 40 μg m-3 (cold season) at both sites, but the annual decreasing rates were larger at Seoul than at Baengnyeong (-0.7 μg m-3 a-1 vs. -1.8 μg m-3 a-1) demonstrating the effectiveness of the local air quality policies including the Special Act on Air Quality in the Seoul Metropolitan Area (SAAQ-SMA) and the seasonal control measures. The weather-normalized monthly mean data shows the highest PM2.5 concentration in March and the lowest concentration in August throughout the 7 years with NO3- accounting for about 40 % of the difference between the two months at both sites. Taking together with the GEOS-Chem model results, which reproduced the elevated NO3- in March, we concluded the elevated atmospheric oxidant level increases in HNO3 (which is not available from the observation) and the still low temperatures in March promote rapid production of NO3-. We used Ox (≡ O3 + NO2) from the observation and OH from the GEOS-Chem as a proxy for the atmospheric oxidant level which can be a source of uncertainty. Thus, direct observations of OH and HNO3 are needed to provide convincing evidence. This study shows that reducing HNO3 levels through atmospheric oxidant level control in the cold season can be effective in PM2.5 mitigation in Seoul.

Trends and drivers of aerosol vertical distribution over China from 2013 to 2020: Insights from integrated observations and modeling

Understanding aerosol vertical distribution is of great importance to climate change and atmospheric chemistry, but there is a dearth of systematical analysis for aerosol vertical distribution amid rapid emission decline after 2013 in China. Here, the GEOS-Chem model and multiple-sourced observations were applied to quantify the changes of aerosol vertical distributions in response to clean air actions. In 2013-2020, the MODIS aerosol optical depth (AOD) presented extensive decreasing trends by -7.9 %/yr to -4.2 %/yr in summer and -6.1 %/yr to -5.8 %/yr in winter in polluted regions. Vertically, the aerosol extinction coefficient (AEC) from CALIPSO decreased by -8.0 %/yr to -5.5 %/yr below ~1 km, but the trends weakened significantly with increasing altitude. Compared with available measurements, the model can reasonably reproduce 2013-2020 trends and seasonality in AOD and vertical AEC. Model simulations confirm that emission reduction was the dominant driver of the 2013-2020 decline in AOD, while the effect of meteorology varied seasonally, with contributions ranging from -2 % to 13 % in summer and -67 % to -2 % in winter. Vertical distributions of emission-driven AEC trends strongly depended on emission reductions, local planetary boundary layer height, and relative humidity. For aerosol components, sulfate accounted for ~50 % of the AOD decline during summer, followed by ammonium and organic aerosol, while in winter the contribution of organic aerosol doubled (24 %-35 %), and nitrate exhibited a weak increasing trend. Chemical production and meteorological conditions (e.g., relative humidity) primarily drove the nitrate contribution, but emission reduction and hygroscopicity were decisive for other components. This work provides an integrated observational and modeling effort to better understand rapid changes in aerosol vertical distribution over China.

Contrasting changes in ozone during 2019-2021 between eastern and the other regions of China attributed to anthropogenic emissions and meteorological conditions

Ozone pollution is one of the most severe air quality issues in China that poses a serious threat to human health and ecosystems. During 2019-2021, the maximum daily 8-h average ozone concentrations in eastern China (110-122.5°E, 26-42°N) and the rest of China (ROC) show different decreasing patterns, with ozone concentrations in eastern China decreasing by 14.9 μg/m3, which is much larger than 4.8 μg/m3 in ROC. Here, based on two independent methods, the atmospheric chemical transport model (GEOS-Chem) simulations and the machine learning (ML) model (LightGBM) predictions, the reasons for the differences in ozone changes between eastern China and ROC during the warm season (April to September) are investigated. According to the GEOS-Chem (LightGBM) results, changes in the meteorological conditions contributed to an ozone decrease by 7.3 (6.8) μg/m3 in eastern China due to decreased chemical production and an ozone decrease by 6.8 (7.0) μg/m3 in ROC attributed to the weakened horizontal and vertical advection. With the influence of meteorological factors excluded, the observations show that changes in anthropogenic emissions resulted in an ozone decrease by 7.6 (8.1) μg/m3 in eastern China and an ozone increase by 2.0 (2.2) μg/m3 in ROC, which is primarily induced by the changes in NOx emissions. The surface measurements and satellite retrievals also indicate that the reduction in NOx emissions in ROC is less efficient than that in the more developed eastern China, leading to contrasting changes in ozone concentrations between eastern China and ROC during 2019-2021. Our results highlight the critical need to reduce ozone precursor emissions in the rest regions of China apart from eastern China.

Observations of the chemistry and concentrations of reactive Hg at locations with different ambient air chemistry

The Hg research community needs methods to more accurately measure atmospheric Hg concentrations and chemistry. The Reactive Mercury Active System (RMAS) uses cation exchange, nylon, and PTFE membranes to determine reactive mercury (RM), gaseous oxidized mercury, and particulate-bound mercury (PBM) concentrations and chemistry. New data for Atlanta, Georgia (NRGT) demonstrated that particulate-bound Hg was dominant and the chemistry was primarily N and S HgII compounds. At Great Salt Lake, Utah (GSL), RM was predominately PBM, with NS > organics > halogen > O HgII compounds. At Guadalupe Mountains National Park, Texas (GUMO), halogenated compound concentrations were lowest when air interacting with the site was primarily derived from the Midwest, and highest when the air was sourced from Mexico. At Amsterdam Island, Southern Indian Ocean, compounds were primarily halogenated with some N, S, and organic HgII compounds potentially associated with biological activity. The GEOS-Chem model was applied to see if it predicted measurements at five field sites. Model values were higher than observations at GSL, slightly lower at NRGT, and observations were an order of magnitude higher than modeled values for GUMO and Reno, Nevada. In general, data collected from 13 locations indicated that N, S, and organic RM compounds were associated with city and forest locations, halogenated compounds were sourced from the marine boundary layer, and O compounds were associated with long-range transport. Data being developed currently, and in the past, suggest there are multiple forms of RM that modelers must consider, and PBM is an important component of RM.

Source Contributions to Fine Particulate Matter and Attributable Mortality in India and the Surrounding Region

Fine particulate matter (PM_2.5) exposure is a leading mortality risk factor in India and the surrounding region of South Asia. This study evaluates the contribution of emission sectors and fuels to PM_2.5 mass for 29 states in India and 6 surrounding countries (Pakistan, Bangladesh, Nepal, Bhutan, Sri Lanka, and Myanmar) by combining source-specific emission estimates, stretched grid simulations from a chemical transport model, high resolution hybrid PM_2.5, and disease-specific mortality estimates. We find that 1.02 (95% Confidence Interval (CI): 0.78-1.26) million deaths in South Asia attributable to ambient PM_2.5 in 2019 were primarily from three leading sectors: residential combustion (28%), industry (15%), and power generation (12%). Solid biofuel is the leading combustible fuel contributing to the PM_2.5-attributable mortality (31%), followed by coal (17%), and oil and gas (14%). State-level analyses reveal higher residential combustion contributions (35%-39%) in states (Delhi, Uttar-Pradesh, Haryana) with high ambient PM_2.5 (>95 μg/m³). The combined mortality burden associated with residential combustion (ambient) and household air pollution (HAP) in India is 0.72 million (95% CI:0.54-0.89) (68% attributable to HAP, 32% attributable to residential combustion). Our results illustrate the potential to reduce PM_2.5 mass and improve population health by reducing emissions from traditional energy sources across multiple sectors in South Asia.

Distinct seasonality in vertical variations of tropospheric ozone over coastal regions of southern China

The surface ozone pollution is strongly coupled with ozone variations above the ground. Using sufficient airborne ozone profiles during 2012-2018, this study reveals the tropospheric ozone distributions over four cities located in coastal regions of southern China. The 7-year mean tropospheric ozone profiles in the four cities consistently show a double-maxima profile, with a local maximum at 1 km altitude and the other in the middle-to-upper troposphere. Seasonally, springtime ozone is larger than the annual mean throughout the troposphere, while ozone in summer is high in the middle-to-upper troposphere, leading to largest vertical variations among seasons. Ozone in the middle-to-upper troposphere is lower in autumn than in spring and summer. The winter ozone is characterized with a minimum in the lower troposphere, and low values in the middle-to-upper troposphere, leading to least vertical variations among seasons. We untangle the causes for these complicated vertical ozone variations using the GEOS-Chem model. The tropospheric ozone over southern China is partitioned into locally produced ozone, regionally transported native ozone, imported ozone from outside of China (foreign ozone) and natural stratospheric ozone. The results suggest that the springtime ozone abundance is due to the enhanced import of foreign and stratospheric ozone and the intensified regional transport processes of native ozone. In summer, local ozone production is enhanced and regional transport of ozone in the middle-to-upper troposphere is strengthened due to upward air motions, while such transport becomes weaker in autumn leaving low ozone in the middle-to-upper troposphere. In winter, the intensive westerly jets promote foreign and stratospheric ozone again in the middle-to-upper troposphere, but the local ozone production and regional transport are sharply reduced, resulting in low ozone near the surface. This study provides new insights into regional ozone profiles and reveals the significance of vertical ozone variations on surface ozone prevention strategy.

Vapors Are Lost to Walls, Not to Particles on the Wall: Artifact-Corrected Parameters from Chamber Experiments and Implications for Global Secondary Organic Aerosol

Atmospheric models of secondary organic aerosol (OA) (SOA) typically rely on parameters derived from environmental chambers. Chambers are subject to experimental artifacts, including losses of (1) particles to the walls (PWL), (2) vapors to the particles on the wall (V2PWL), and (3) vapors to the wall directly (VWL). We present a method for deriving artifact-corrected SOA parameters and translating these to volatility basis set (VBS) parameters for use in chemical transport models (CTMs). Our process involves combining a box model that accounts for chamber artifacts (Statistical Oxidation Model with a TwO-Moment Aerosol Sectional model (SOM-TOMAS)) with a pseudo-atmospheric simulation to develop VBS parameters that are fit across a range of OA mass concentrations. We found that VWL led to the highest percentage change in chamber SOA mass yields (high NO_x: 36-680%; low NO_x: 55-250%), followed by PWL (high NO_x: 8-39%; low NO_x: 10-37%), while the effects of V2PWL are negligible. In contrast to earlier work that assumed that V2PWL was a meaningful loss pathway, we show that V2PWL is an unimportant SOA loss pathway and can be ignored when analyzing chamber data. Using our updated VBS parameters, we found that not accounting for VWL may lead surface-level OA to be underestimated by 24% (0.25 μg m^-3) as a global average or up to 130% (9.0 μg m^-3) in regions of high biogenic or anthropogenic activity. Finally, we found that accurately accounting for PWL and VWL improves model-measurement agreement for fine mode aerosol mass concentrations (PM_2.5) in the GEOS-Chem model.

Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde

The solar absorption spectrometry in the infrared spectral region, using high-resolution Fourier transform infrared (FTIR) spectrometer, has been established as a powerful tool in atmospheric science. These observations cannot be performed continuously, for example, clouds prevent observations. On the other hand, chemical transport models give continuously data. Their results depend on the knowledge of emission inventories, the chemistry involved, and the meteorological fields, yielding to potential biases between measurements and simulations. In our study we concentrated on Formaldehyde (HCHO) and used machine learning approach to fill the gap between the observations, performed on an irregular time scale and having their measurement lacks, and model data, giving continuous data, but having potential variable biases. The proposed machine learning approach is based on the Light Gradient Boosting Machine (LightGBM) algorithm and created by using GEOS-Chem simulations, meteorological fields, emission inventory, and is referred to as the GEOS-Chem-LightGBM model. The results of established GEOS-Chem-LightGBM model have generated consistent HCHO predictions with the ground-based FTIR and satellite (OMI and TROPOMI) observations. In order to understand the GEOS-Chem model to measurement discrepancy, we have investigated the contribution of each input variable to GEOS-Chem-LightGBM model HCHO predictions through the SHapely Additive exPlanations (SHAP) approach. We found that the GEOS-Chem model underestimates the sensitivities of HCHO total column to most photochemical variables, contributing to lower amplitudes of diurnal cycle and seasonal cycle by the GEOS-Chem model. By correcting the model-to-measurement discrepancy, the sensitivities of HCHO total column to all variables by the GEOS-Chem-LightGBM became to be in good agreement with the FTIR observations. As a result, GEOS-Chem-LightGBM model has significantly improved the performance of HCHO predictions compared to the GEOS-Chem alone. The proposed GEOS-Chem-LightGBM model can be extendible to other atmospheric constituents obtained by various measurement techniques and platforms, and is expected to have wide applications.

Diagnosing the Model Bias in Simulating Daily Surface Ozone Variability Using a Machine Learning Method: The Effects of Dry Deposition and Cloud Optical Depth

Machine learning methods are increasingly used in air quality studies to predict air pollution levels, while few applied them to diagnose and improve the underlying mechanisms controlling air pollution represented in chemical transport models (CTMs). Here, we use the random forest (RF) method to diagnose high biases of surface daily maximum 8 h average (MDA8) ozone concentrations in the GEOS-Chem CTM evaluated against measurements from the nationwide monitoring network in summer 2018 over China. The feature importance results show that cloud optical depth (COD), relative humidity, and precipitation are the top three factors affecting CTM high biases. Such results indicate that the high ozone biases in summer over China mainly occur on wet/cloudy days (∼40% biased high), while biases on dry/clear days are small (within 5%). We link the important features with model parameterizations and variables, identifying model underestimates in the dry deposition velocity and COD on wet/cloudy days. By accounting for the enhanced dry deposition on wet plant cuticles and using satellite observation constrained COD, we find that CTM high ozone biases can be halved with an improved agreement in the temporal variability, highlighting the effects of dry deposition and COD on ozone, as suggested by the RF outcomes.

Factors determining the seasonal variation of ozone air quality in South Korea: Regional background versus domestic emission contributions

South Korea has experienced a rapid increase in ozone concentrations in surface air together with China for decades. Here we use a 3-D global chemical transport model, GEOS-Chem nested over East Asia (110 E - 140 E, 20 N-50 N) at 0.25° × 0.3125° resolution, to examine locally controllable (domestic anthropogenic) versus uncontrollable (background) contributions to ozone air quality at the national scale for 2016. We conducted model simulations for representative months of each season: January, April, July, and October for winter, spring, summer, and fall and performed extensive model evaluation by comparing simulated ozone with observations from satellite and surface networks. The model appears to reproduce observed spatial and temporal ozone variations, showing correlation coefficients (0.40-0.87) against each observation dataset. Seasonal mean ozone concentrations in the model are the highest in spring (39.3 ± 10.3 ppb), followed by summer (38.3 ± 14.4 ppb), fall (31.2 ± 9.8 ppb), and winter (24.5 ± 7.9 ppb), which is consistent with that of surface observations. Background ozone concentrations obtained from a sensitivity model simulation with no domestic anthropogenic emissions show a different seasonal variation in South Korea, showing the highest value in spring (46.9 ± 3.4 ppb) followed by fall (38.2 ± 3.7 ppb), winter (33.0 ± 1.9 ppb), and summer (32.1 ± 6.7 ppb). Except for summer, when the photochemical formation is dominant, the background ozone concentrations are higher than the seasonal ozone concentrations in the model, indicating that the domestic anthropogenic emissions play a role as ozone loss via NO_x titration throughout the year. Ozone air quality in South Korea is determined mainly by year-round regional background contributions (peak in spring) with summertime domestic ozone formation by increased biogenic VOCs emissions with persistent NO_x emissions throughout the year. The domestic NO_x emissions reduce MDA8 ozone around large cities (Seoul and Busan) and hardly increase MDA8 in other regions in spring, but it increases MDA8 across the country in summer. Therefore, NO_x reduction can be effective in control of MDA8 ozone in summer, but it can have rather countereffect in spring.

Modeling mercury isotopic fractionation in the atmosphere

Mercury (Hg) stable isotope analysis has become a powerful tool to identify Hg sources and to understand its biogeochemical processes. However, it is challenging to link the observed Hg isotope fractionation to its global cycling. Here, we integrate source Hg isotope signatures and process-based Hg isotope fractionation into a three-dimensional isotope model based on the GEOS-Chem model platform. Our simulated isotope compositions of total gaseous Hg (TGM) are broadly comparable with available observations across global regions. The isotope compositions of global TGM, potentially distinguishable over different regions, are caused by the atmospheric mixture of anthropogenic, natural, and re-emitted Hg sources, superimposed with competing processes, notably gaseous Hg(0) dry deposition and Hg redox transformations. We find that Hg(0) dry deposition has a great impact on the isotope compositions of global TGM and drives the seasonal variation of δ²⁰²Hg in forest-covered regions. The atmospheric photo-reduction of Hg(Ⅱ) dominates over Hg(0) oxidation in driving the global Δ¹⁹⁹Hg (and Δ²⁰¹Hg) distribution patterns in TGM. We suggest that the magnitude of isotope fractionation associated with atmospheric aqueous-phase Hg(Ⅱ) reduction is likely close to aquatic Hg(Ⅱ) reduction. Our model provides a vital tool for coupling the global atmospheric Hg cycle and its isotope fractionation at various scales and advances our understanding of atmospheric Hg transfer and transformation mechanisms.

Source Sector Mitigation of Solar Energy Generation Losses Attributable to Particulate Matter Pollution

Particulate matter (PM) in the atmosphere and deposited on solar photovoltaic (PV) panels reduce PV energy generation. Reducing anthropogenic PM sources will therefore increase carbon-free energy generation and as a cobenefit will improve surface air quality. However, we lack a global understanding of the sectors that would be the most effective at achieving the necessary reductions in PM sources. Here we combine well-evaluated models of solar PV performance and atmospheric composition to show that deep cuts in air pollutant emissions from the residential, on-road, and energy sectors are the most effective approaches to mitigate PM-induced PV energy losses over East and South Asia, and the Tibetan Plateau, Central Asia, and the Arabian Peninsula, and Western Siberia, respectively. Using 2019 PV capacities as a baseline, we find that a 50% reduction in residential emissions would lead to an additional 10.3 TWh yr^-1 (US$878 million yr^-1) and 2.5 TWh yr^-1 (US$196 million yr^-1) produced in China and India, respectively.

Impact of the initial hydrophilic ratio on black carbon aerosols in the Arctic

Black carbon (BC) contributes to patterns of Arctic warming, yet the initial hydrophilic ratio (IHR) of BC emitted from various sources and its impact on Arctic BC remain uncertain. With the use of a tagged tracer method of BC implemented in the global chemistry transport model GEOS-Chem, IHRs were partitioned into 7 BC combustion source categories according to the PKU-BC-v2 emission inventory. The results show that as the IHR increased, the concentration of BC decreased globally. The impact on Arctic BC was mainly reflected in the vertical profile and the burden rather than at the surface. Specifically, the greatest impact of IHR on Arctic BC appeared in summer, with the largest perturbation appearing at an altitude of approximately 600 hPa, reaching 8%. This change in BC vertical profile was mainly caused by the IHR change of wildfire combustion in Russia (44%) and Canada (51%), and the emissions from these two regions were also the two most important contributors to the BC concentration and burden in the middle and lower Arctic atmosphere in summer. In the other three seasons, anthropogenic combustion sources (oil, coal, and biomass) in East Asia, Russia, and Europe accounted for 19-40%, 14-28%, and 7-23%, respectively, of the monthly BC burden. Emissions from Russia were the most important contributor (27-43%) to the monthly BC surface concentration. Due to the large adjustment in IHR from 20% to 70%, biomass burning in Europe was shown to be the dominant contributor causing both burden (39%) and surface concentration (88%) changes in all seasons except summer.

Projected Aerosol Changes Driven by Emissions and Climate Change Using a Machine Learning Method

Projection of future aerosols and understanding the driver of the aerosol changes are of great importance in improving the atmospheric environment and climate change mitigation. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) provides various climate projections but limited aerosol output. In this study, future near-surface aerosol concentrations from 2015 to 2100 are predicted based on a machine learning method. The machine learning model is trained with global atmospheric chemistry model results and projects aerosols with CMIP6 multi-model simulations, creatively estimating future aerosols with all important species considered. PM_2.5 (particulate matter less than 2.5 μm in diameter) concentrations in 2095 (2091-2100 mean) are projected to decrease by 40% in East Asia, 20-35% in South Asia, and 15-25% in Europe and North America, compared to those in 2020 (2015-2024 mean), under low-emission scenarios (SSP1-2.6 and SSP2-4.5), which are mainly due to the presumed emission reductions. Driven by the climate change alone, PM_2.5 concentrations would increase by 10-25% in northern China and western U.S. and decrease by 0-25% in southern China, South Asia, and Europe under the high forcing scenario (SSP5-8.5). A warmer climate exerts a stronger modulation on global aerosols. Climate-driven global future aerosol changes are found to be comparable to those contributed by changes in anthropogenic emissions over many regions of the world in high forcing scenarios, highlighting the importance of climate change in regulating future air quality.

Variability in Aromatic Aerosol Yields under Very Low NOx Conditions at Different HO2/RO2 Regimes

Current chemical transport models generally use a constant secondary organic aerosol (SOA) yield to represent SOA formation from aromatic compounds under low NO_x conditions. However, a wide range of SOA yields (10 to 42%) from m-xylene under low NO_x conditions is observed in this study. The chamber HO₂/RO₂ ratio is identified as a key factor explaining SOA yield variability: higher SOA yields are observed for runs with a higher HO₂/RO₂ ratio. The RO₂ + RO₂ pathway, which can be increasingly significant under low NO_x and HO₂/RO₂ conditions, shows a lower SOA-forming potential compared to the RO₂ + HO₂ pathway. While the traditional low-NO_x chamber experiments are commonly used to represent the RO₂ + HO₂ pathway, this study finds that the impacts of the RO₂ + RO₂ pathway cannot be ignored under certain conditions. We provide guidance on how to best control for these two pathways in conducting chamber experiments to best obtain SOA yield curves and quantify the contributions from each pathway. On the global scale, the chemical transport model GEOS-Chem is used to identify regions characterized by lower surface HO₂/RO₂ ratios, suggesting that the RO₂ + RO₂ pathway is more likely to prove significant to overall SOA yields in those regions. Current models generally do not consider the RO₂ + RO₂ impacts on aromatic SOA formation, but preliminary sensitivity tests with updated SOA yield parameters based on such a pathway suggest that without this consideration, some types of SOA may be overestimated in regions with lower HO₂/RO₂ ratios.

Exploring dust heterogeneous chemistry over China: Insights from field observation and GEOS-Chem simulation

Dust heterogeneous chemistry plays an important role in tropospheric chemistry, but its parameterization in numerical models is often quite simplified, which hampers accurate prediction of particulate matter and its chemical component. In this study, we investigate the evolution of dust heterogeneous chemical process and its potential impacts on gaseous and aerosol components during a dust pollution episode from March 27 to April 2, 2015 over North China. Based on field measurements, the significant role of relative humidity (RH) in dust heterogeneous chemistry is found and a RH-dependent parameterization for uptake coefficients of HNO₃ and SO₂ is incorporated in GEOS-Chem to reproduce the dust heterogeneous chemical process. During the study period, observed dust sulfate (DSO4) and dust nitrate (DNIT) exhibit maximum concentrations of 9.1 and 22.8 μg m^-3 respectively, accompanied by high RH and gaseous precursor concentrations. DSO4 concentrations are positively related to RH. The observed dust sulfate oxidation ratio (DSOR) is elevated evidently with increased RH, especially when RH is higher than ~40%, implying that enhanced RH could promote heterogeneous oxidation of SO₂ to DSO4. Model simulation shows that when incorporating the RH-dependent parameterization, DNIT and DSO4 are generally well captured and the model performance of total sulfate oxidation ratio (TSOR) and total nitrate oxidation ratio (TNOR) are improved. High contribution of DNIT and DSO4 are found to be located over the regions close to source areas (>60%) and downwind regions (>40%), respectively. Sensitivity results show that SO₂ and HNO₃ reduce by 2-24 μg m^-3 and 1-18 μg m^-3 when considering dust heterogeneous impacts, thus leading to reduction in non-dust sulfate and non-dust nitrate concentrations. As a result, simulated NH₃ increases and ammonium reduces by more than 20%. Our study indicates that the contribution of heterogeneous reactions to sulfate formation is 20-30% over North China.

Predicting Spatial Variations in Multiple Measures of Oxidative Burden for Outdoor Fine Particulate Air Pollution across Canada

Fine particulate air pollution (PM_2.5) is a leading contributor to the overall global burden of disease. Traditionally, outdoor PM_2.5 has been characterized using mass concentrations which treat all particles as equally harmful. Oxidative potential (OP) (per μg) and oxidative burden (OB) (per m³) are complementary metrics that estimate the ability of PM_2.5 to cause oxidative stress, which is an important mechanism in air pollution health effects. Here, we provide the first national estimates of spatial variations in multiple measures (glutathione, ascorbate, and dithiothreitol depletion) of annual median outdoor PM_2.5 OB across Canada. To do this, we combined a large database of ground-level OB measurements collected monthly prospectively across Canada for 2 years (2016-2018) with PM_2.5 components estimated using a chemical transport model (GEOS-Chem) and satellite aerosol observations. Our predicted ground-level OB values of all three methods were consistent with ground-level observations (cross-validation R² = 0.63-0.74). We found that forested regions and urban areas had the highest OB, predicted primarily by black carbon and organic carbon from wildfires and transportation sources. Importantly, the dominant components associated with OB were different than those contributing to PM_2.5 mass concentrations (secondary inorganic aerosol); thus, OB metrics may better indicate harmful components and sources on health than the bulk PM_2.5 mass, reinforcing that OB estimates can complement the existing PM_2.5 data in future national-level epidemiological studies.

Interannual Variability of Air-Sea Exchange of Mercury in the Global Ocean: The "Seesaw Effect" in the Equatorial Pacific and Contributions to the Atmosphere

Air-sea exchange of gaseous elemental mercury (Hg(0)) is influenced by different meteorological factors and the availability of Hg in seawater. Here, we use the MITgcm ocean model to explore the interannual variability of this flux and the influence of oceanographic and atmospheric dynamics. We apply the GEOS-Chem model to further simulate the potential impact of the evasion variability on the atmospheric Hg levels. We find a latitudinal pattern in Hg(0) evasion with a relatively small variability in mid-latitudes (3.1-6.7%) and a large one in the high latitudes and Equator (>10%). Different factors dominate the patterns in the equatorial (wind speed), mid- (oceanic flow and temperature), and high-latitudinal (sea-ice, temperature, and dynamic processes) oceans. A seesaw pattern of Hg(0) evasion anomaly (±5-20%) in the equatorial Pacific is found from November to next January between El Niño and La Niña years, owing to the anomalies in wind speed, temperature, and vertical mixing. Higher atmospheric Hg level (2%-5%) are simulated for Hg(0) evasion fluxes with three-month lag, associated with the suppression of upwelling in the beginning of the El Niño event. Despite of the uncertainties, this study elucidates the spatial patterns of the interannual variability of the ocean Hg(0) evasion flux and its potential impact on atmospheric Hg levels.

Impacts of COVID-19 lockdown, Spring Festival and meteorology on the NO2 variations in early 2020 over China based on in-situ observations, satellite retrievals and model simulations

The lockdown measures due to COVID-19 affected the industry, transportation and other human activities within China in early 2020, and subsequently the emissions of air pollutants. The decrease of atmospheric NO2 due to the COVID-19 lockdown and other factors were quantitively analyzed based on the surface concentrations by in-situ observations, the tropospheric vertical column densities (VCDs) by different satellite retrievals including OMI and TROPOMI, and the model simulations by GEOS-Chem. The results indicated that due to the COVID-19 lockdown, the surface NO2 concentrations decreased by 42% ± 8% and 26% ± 9% over China in February and March 2020, respectively. The tropospheric NO2 VCDs based on both OMI and high quality (quality assurance value (QA) ≥ 0.75) TROPOMI showed similar results as the surface NO2 concentrations. The daily variations of atmospheric NO2 during the first quarter (Q1) of 2020 were not only affected by the COVID-19 lockdown, but also by the Spring Festival (SF) holiday (January 24-30, 2020) as well as the meteorology changes due to seasonal transition. The SF holiday effect resulted in a NO2 reduction from 8 days before SF to 21 days after it (i.e. January 17 - February 15), with a maximum of 37%. From the 6 days after SF (January 31) to the end of March, the COVID-19 lockdown played an important role in the NO2 reduction, with a maximum of 51%. The meteorology changes due to seasonal transition resulted in a nearly linear decreasing trend of 25% and 40% reduction over the 90 days for the NO2 concentrations and VCDs, respectively. Comparisons between different datasets indicated that medium quality (QA ≥ 0.5) TROPOMI retrievals might suffer large biases in some periods, and thus attention must be paid when they are used for analyses, data assimilations and emission inversions.

Ship emission of nitrous acid (HONO) and its impacts on the marine atmospheric oxidation chemistry

Nitrous acid (HONO) is an important reservoir of the hydroxyl radical (OH) and thus plays a central role in tropospheric chemistry. Exhaust from engines has long been known as a major primary source of HONO, yet most previous studies focused on vehicle emissions on land. In comparison, ship emissions of HONO have been rarely characterized, and their impacts on the tropospheric oxidation chemistry have not been quantified. In this study, we conducted cruise measurements of HONO and related species over the East China Sea. Contrasting air masses from pristine marine background air to highly polluted ship plumes were encountered. The emission ratio of ΔHONO/ΔNO_x (0.51 ± 0.18%) was derived from a large number of fresh ship plumes. Using the in-situ measured emission ratio, a global ship emission inventory of HONO was developed based on the international shipping emissions of NO_x in the Community Emission Data System inventory. The global shipping voyage emits approximately 63.9 ± 22.2 Gg yr^-1 of HONO to the atmosphere. GEOS-Chem modelling with the addition of ship-emitted HONO showed that HONO concentrations could increase up to 40-100% over the navigation areas, leading to about 5-15% increases of primary OH production in the early-morning time. This study elucidates the potentially considerable effects of ship HONO emissions on the marine atmospheric chemistry, and calls for further studies to better characterize the ship emissions of HONO and other reactive species, which should be taken into account by global and regional models.

Global impact of atmospheric arsenic on health risk: 2005 to 2015

Arsenic is a toxic pollutant commonly found in the environment. Most of the previous studies on arsenic pollution have primarily focused on arsenic contamination in groundwater. In this study, we examine the impact on human health from atmospheric arsenic on the global scale. We first develop an improved global atmospheric arsenic emission inventory and connect it to a global model (Goddard Earth Observing System [GEOS]-Chem). Model evaluation using observational data from a variety of sources shows the model successfully reproduces the spatial distribution of atmospheric arsenic around the world. We found that for 2005, the highest airborne arsenic concentrations were found over Chile and eastern China, with mean values of 8.34 and 5.63 ng/m3, respectively. By 2015, the average atmospheric arsenic concentration in India (4.57 ng/m3) surpassed that in eastern China (4.38 ng/m3) due to the fast increase in coal burning in India. Our calculation shows that China has the largest population affected by cancer risk due to atmospheric arsenic inhalation in 2005, which is again surpassed by India in 2015. Based on potential exceedance of health-based limits, we find that the combined effect by including both atmospheric and groundwater arsenic may significantly enhance the risks, due to carcinogenic and noncarcinogenic effects. Therefore, this study clearly implies the necessity in accounting for both atmospheric and groundwater arsenic in future management.

An inter-comparative evaluation of PKU-FUEL global SO2 emission inventory

PKU-FUEL is a recently developed gridded global emission inventory for multiple air pollutants that uses a bottom-up approach. The inventory includes data collected monthly for the period of 1960 to 2014 and at a 0.1° × 0.1° latitude/longitude resolution. In an effort to evaluate and improve this emission inventory, the PKU-FUEL Sulfur Dioxide (SO₂) emission inventory was compared to other currently available and widely used global SO₂ emission inventories constructed based on bottom-up and top-down approaches, including CEDS and OMI-HTAP. While PKU-FUEL is capable of capturing SO₂ emissions across the globe and particularly in Asia, it misses 41 industrial point sources globally, accounting for 9.3% of Ozone Monitoring Instrument (OMI) remote sensing-measured industrial point sources. Most of these missing point sources are identified in Latin America, the Middle East (~60%), and some remote places. To improve the PKU-FUEL SO₂ inventory, we applied OMI-measured emissions to sources missing from PKU-FUEL. GEOS-Chem model simulations were performed to evaluate original and improved PKU-FUEL SO₂ inventories against measured SO₂ concentrations across the world. Results were further compared to GEOS-Chem modeled SO₂ concentrations using the CEDS inventory. We show that the modeled SO₂ concentrations determined using both CEDS and improved PKU-FUEL inventories to a large extent corroborate sampled data and that the improved PKU-FUEL performs better for those regions lacking monitoring data.

Enhanced regional terrestrial carbon uptake over Korea revealed by atmospheric CO2 measurements from 1999 to 2017

Understanding changes in terrestrial carbon balance is important to improve our knowledge of the regional carbon cycle and climate change. However, evaluating regional changes in the terrestrial carbon balance is challenging due to the lack of surface flux measurements. This study reveals that the terrestrial carbon uptake over the Republic of Korea has been enhanced from 1999 to 2017 by analyzing long-term atmospheric CO₂ concentration measurements at the Anmyeondo Station (36.53°N, 126.32°E) located in the western coast. The influence of terrestrial carbon flux on atmospheric CO₂ concentrations (ΔCO₂ ) is estimated from the difference of CO₂ concentrations that were influenced by the land sector (through easterly winds) and the Yellow Sea sector (through westerly winds). We find a significant trend in ΔCO₂ of -4.75 ppm per decade (p < .05) during the vegetation growing season (May through October), suggesting that the regional terrestrial carbon uptake has increased relative to the surrounding ocean areas. Combined analysis with satellite measured normalized difference vegetation index and gross primary production shows that the enhanced carbon uptake is associated with significant nationwide increases in vegetation and its production. Process-based terrestrial model and inverse model simulations estimate that regional terrestrial carbon uptake increases by up to 18.9 and 8.0 Tg C for the study period, accounting for 13.4% and 5.7% of the average annual domestic carbon emissions, respectively. Atmospheric chemical transport model simulations indicate that the enhanced terrestrial carbon sink is the primary reason for the observed ΔCO₂ trend rather than anthropogenic emissions and atmospheric circulation changes. Our results highlight the fact that atmospheric CO₂ measurements could open up the possibility of detecting regional changes in the terrestrial carbon cycle even where anthropogenic emissions are not negligible.

Simulation of airborne trace metals in fine particulate matter over North America

Trace metal distributions are of relevance to understand sources of fine particulate matter (PM2.5), PM2.5-related health effects, and atmospheric chemistry. However, knowledge of trace metal distributions is lacking due to limited ground-based measurements and model simulations. This study develops a simulation of 12 trace metal concentrations (Si, Ca, Al, Fe, Ti, Mn, K, Mg, As, Cd, Ni and Pb) over continental North America for 2013 using the GEOS-Chem chemical transport model. Evaluation of modeled trace metal concentrations with observations indicates a spatial consistency within a factor of 2, an improvement over previous studies that were within a factor of 3-6. The spatial distribution of trace metal concentrations reflects their primary emission sources. Crustal element (Si, Ca, Al, Fe, Ti, Mn, K) concentrations are enhanced over the central US from anthropogenic fugitive dust and over the southwestern U.S. due to natural mineral dust. Heavy metal (As, Cd, Ni and Pb) concentrations are high over the eastern U.S. from industry. K is abundance in the southeast from biomass burning and high concentrations of Mg is observed along the coast from sea spray. The spatial pattern of PM2.5 mass is most strongly correlated with Pb, Ni, As and K due to their signature emission sources. Challenges remain in accurately simulating observed trace metal concentrations. Halving anthropogenic fugitive dust emissions in the 2011 National Air Toxic Assessment (NATA) inventory and doubling natural dust emissions in the default GEOS-Chem simulation was necessary to reduce biases in crustal element concentrations. A fivefold increase of anthropogenic emissions of As and Pb was necessary in the NATA inventory to reduce the national-scale bias versus observations by more than 80 %, potentially reflecting missing sources.

Performance evaluation of a photochemical model using different boundary conditions over the urban and industrialized metropolitan area of Vitória, Brazil

Metropolitan areas may suffer with increase of air pollution due to the growth of urbanization, transportation, and industrial sectors. The Metropolitan Area of Vitória (MAV) in Brazil is facing air pollution problems, especially because of the urbanization of past years and of having many industries inside the metropolitan area. Developing air quality system is crucial to understand the air pollution mechanism over these areas. However, having a good input dataset for applying on photochemical models is hard and requires quite of research. One input file for air quality modeling which can play a key role on results is the lateral boundary conditions (LBC). This study aimed to investigate the influence of LBC over CMAQ simulation for particulate matter and ozone over MAV by applying four different methods as LBC during August 2010. The first scenario (M1) is based on a fixed, time-independent boundary conditions with zero concentrations for all pollutants; the second scenario (M2) used a fixed, time-independent concentration values, with average values from local monitoring stations; the third CMAQ nesting scenario (M3) used the nested boundary conditions varying with time from a previous simulation with CMAQ over a larger modeling domain, centered on MAV; and finally, the fourth GEOS-Chem scenario (M4) used the boundary conditions varying with time from simulations of global model GEOS-Chem. All scenarios runs are based on the same meteorology conditions and pollutant emissions. The air quality simulations were made over a domain 61 × 79 km centered on coordinates - 20.25° S, - 40.28° W with a resolution of 1 km. The results were evaluated with the measured data from the local monitoring stations. Overall, significant differences on concentrations and number of chemical species between the LBC scenarios are shown across all LBC scenarios. The M3 and M4 dynamic LBC scenarios showed the best performances over ozone estimates while M1 and M2 had poor performance. Although no LBC scenarios do not seem to have a great influence on total PM₁₀ and PM_2.5 concentrations, individual PM_2.5 species like Na, NO₃^-, and NH₄⁺concentrations are influenced by the dynamic LBC approach, since those hourly individual PM_2.5 species from CMAQ nesting approach (M3) and GEOS-Chem model (M4) were used as an input to LBC.

Alpine ice evidence of a three-fold increase in atmospheric iodine deposition since 1950 in Europe due to increasing oceanic emissions

Our measurements show a tripling of iodine in Alpine ice between 1950 and 1990. A 20th century increase in global iodine emissions has been previously found from model simulations, based on laboratory studies, but, up to now, long-term iodine records exist only in polar regions. These polar records are influenced by sea ice processes, which may obscure global iodine trends. Our results suggest that the increased iodine deposition over the Alps is consistent with increased oceanic iodine emissions coupled with a change in the iodine speciation, both driven by increasing anthropogenic NO_x emissions. In turn, the recent increase of iodine emissions implies that iodine-related ozone loss in the troposphere is more active now than in the preindustrial period.

Iodine is an important nutrient and a significant sink of tropospheric ozone, a climate-forcing gas and air pollutant. Ozone interacts with seawater iodide, leading to volatile inorganic iodine release that likely represents the largest source of atmospheric iodine. Increasing ozone concentrations since the preindustrial period imply that iodine chemistry and its associated ozone destruction is now substantially more active. However, the lack of historical observations of ozone and iodine means that such estimates rely primarily on model calculations. Here we use seasonally resolved records from an Alpine ice core to investigate 20th century changes in atmospheric iodine. After carefully considering possible postdepositional changes in the ice core record, we conclude that iodine deposition over the Alps increased by at least a factor of 3 from 1950 to the 1990s in the summer months, with smaller increases during the winter months. We reproduce these general trends using a chemical transport model and show that they are due to increased oceanic iodine emissions, coupled to a change in iodine speciation over Europe from enhanced nitrogen oxide emissions. The model underestimates the increase in iodine deposition by a factor of 2, however, which may be due to an underestimate in the 20th century ozone increase. Our results suggest that iodine’s impact on the Northern Hemisphere atmosphere accelerated over the 20th century and show a coupling between anthropogenic pollution and the availability of iodine as an essential nutrient to the terrestrial biosphere.

Characterization of urban CO2 column abundance with a portable low resolution spectrometer (PLRS): Comparisons with GOSAT and GEOS-Chem model data

We presented the characterization of urban CO₂ column abundance (XCO₂) in Hefei, China using a portable low resolution spectrometer (PLRS). An optimized correction spectrum was introduced in the spectral fitting to improve CO₂ retrieval. A pronounced seasonal cycle and diurnal variation were observed with a precision of ~0.12%. The CO₂ concentrations in winter are about 5-10ppm higher than those in summer. Most diurnal variations exhibited downward trends. The measurement in the early morning is about 2-5ppm higher than the late afternoon observation. The causes of the seasonal and diurnal trends were systematic analyzed. The coincident CO₂ time series were compared with the Greenhouse Gases Observing SATellite (GOSAT) data and the GEOS-Chem global 3-D tropospheric chemistry model data. We found the ground based (g-b) PLRS data are systematically higher than the GOSAT and the GEOS-Chem data. Compared to the GOSAT data, the g-b PLRS data are 0.26ppm (0.07%) higher with a standard deviation of 1.70ppm (0.43%). Compared to the smoothed GEOS-Chem model data, the g-b PLRS data shows a 1.31ppm (0.33%) higher with a standard deviation of 5.30ppm (0.87%). The g-b PLRS generally reproduced the seasonal cycle observed by GOSAT and GEOS-Chem model with correlation coefficients (r) of 0.82 and 0.64, respectively.

Historical and future trends in global source-receptor relationships of mercury

Growing concern about the risk associated with increasing environmental mercury (Hg) concentrations has resulted in a focus on the relationships between intercontinental emitted and accumulated Hg. We use a global biogeochemical Hg model with 8 continental regions and a global ocean to evaluate the legacy impacts of historical anthropogenic releases (2000BCE to 2008AD) on global source-receptor relationships of Hg. Legacy impacts of historical anthropogenic releases are confirmed to be significant on the source-receptor relationships according to our results. Historical anthropogenic releases from Asia account for 8% of total soil Hg in North America, which is smaller than the proportion (~17%) from previous studies. The largest contributors to the global oceanic Hg are historical anthropogenic releases from North America (26%), Asia (16%), Europe (14%) and South America (14%). Although anthropogenic releases from Asia have exceeded North America since the 1970s, source contributions to global Hg receptors from Asia have not exceeded North America so far. Future projections indicate that if Hg emissions are not effectively controlled, Asia will exceed North America as the largest contributor to the global ocean in 2019 and this has a long-term adverse impact on the future environment. For the Arctic Ocean, historical anthropogenic release from North America contributes most to the oceanic Hg reservoir and future projections reveal that the legacy impacts of historical releases from mid-latitudes would lead to the potential of rising Hg in the Arctic Ocean in the future decades, which calls for more effective Hg controls on mid-latitude releases.

High-resolution satellite-based analysis of ground-level PM2.5 for the city of Montreal

Satellite remote sensing offers the opportunity to determine the spatial distribution of aerosol properties and could fill the gap of ground-level observations. Various algorithms have recently been developed in order to retrieve the aerosol optical depth (AOD) at continental scales. However, they are, to some extent, subject to coarse spatial resolutions which are not appropriate for intraurban scales as usually needed in health studies. This paper presents an improved AOD retrieval algorithm for satellite instrument MODIS at 1-km resolution for intraurban scales. The MODIS-retrieved AODs are used to derive the ground-level PM2.5 concentrations using the aerosol vertical profiles and local scale factors obtained from the GEOS-Chem model simulation. The developed method has been applied to retrieve the AODs and to evaluate the ground-level PM2.5 over the city of Montreal, Canada for 2009 on daily, monthly and annual scales. The daily and monthly results are compared with the monitoring values with correlations R(2) ranging from 0.86 to 0.93. Especially, the annual mean PM2.5 concentrations are in good agreement with the measurement values at all monitoring stations (r=0.96, slope=1.0132 ± 0.0025, intercept=0.5739 ± 0.0013). This illustrates that the developed AOD retrieval algorithm can be used to retrieve AODs at a higher spatial resolution than previous studies to further derive the regional full coverage PM2.5 results at finer spatial and temporal scales. The study results are useful in health risk assessment across this region.