Three-dimensional Chemical Transport Model Research Articles

Impact of soil–atmosphere HONO exchange on concentrations of HONO and O3 in the North China Plain

Nitrous acid (HONO) is an important precursor of the hydroxyl radical (OH) and plays a vital role in atmospheric photochemistry and nitrogen cycling. Soil emissions have been considered as a potential source of HONO. Lately, the HONO emission via soil-atmosphere exchange (ESA-exchange) from soil nitrite has been validated and quantified through chamber experiments, but has not been assessed in the real atmosphere. We coupled ESA-exchange and the other seven potential sources of HONO (i.e., traffic, indoor and soil bacterial emissions, heterogeneous reactions on ground and aerosol surfaces, nitrate photolysis, and acid displacement) into the Weather Research and Forecasting model with Chemistry (WRF-Chem), and found that diurnal variations of the soil emission flux at the Wangdu site were well simulated. During the non-fertilization period, ESA-exchange contributed ∼28 % and ∼35 % of nighttime and daytime HONO, respectively, and enhanced the net ozone (O3) production rate by ∼8 % across the North China Plain (NCP). During the preintensive/intensive fertilization period, the maximum ESA-Exchange contributions attained ∼70 %/83 % of simulated HONO in the afternoon across the NCP, definitely asserting its dominance in HONO production. ESA-Exchange enhanced the OH production rate via HONO photolysis by ∼3.5/7.0 times, and exhibited an increase rate of ∼13 %/20 % in the net O3 production rate across the NCP. The total enhanced O3 due to the eight potential HONO sources ranged from ∼2 to 20 ppb, and ESA-exchange produced O3 enhancements of ∼1 to 6 ppb over the three periods. Remarkably, the average contribution of ESA-exchange to the total O3 enhancements remained ∼30 %. This study suggests that ESA-exchange should be included in three-dimensional chemical transport models and more field measurements of soil HONO emission fluxes and soil nitrite levels are urgently required.

Investigation of the renewed methane growth post-2007 with high-resolution 3-D variational inverse modeling and isotopic constraints

Abstract. We investigate the causes of the renewed growth of atmospheric methane (CH4) amount fractions after 2007 by using variational inverse modeling with a three-dimensional chemistry-transport model. Together with CH4 amount fraction data, we use the additional information provided by observations of CH4 isotopic compositions (13C : 12C and D : H) to better differentiate between the emission categories compared to the differentiation achieved by assimilating CH4 amount fractions alone. Our system allows us to optimize either the CH4 emissions only or both the emissions and the source isotopic signatures (δsource(13C,CH4) and δsource(D,CH4)) of five emission categories. Consequently, we also assess, for the first time, the influence of applying random errors to both emissions and source signatures in an inversion framework. As the computational cost of a single inversion is high at present, the methodology applied to prescribe source signature uncertainties is simple, so it can serve as a basis for future work. Here, we investigate the post-2007 increase in atmospheric CH4 using the differences between 2002–2007 and 2007–2014. When random uncertainties in source isotopic signatures are accounted for, our results suggest that the post-2007 increase (here defined using the two periods 2002–2007 and 2007–2014) in atmospheric CH4 was caused by increases in emissions from (1) fossil sources (51 % of the net increase in emissions) and (2) agriculture and waste sources (49 %), which were slightly compensated for by a small decrease in biofuel- and biomass-burning emissions. The conclusions are very similar when assimilating CH4 amount fractions alone, suggesting either that random uncertainties in source signatures are too large at present to impose any additional constraint on the inversion problem or that we overestimate these uncertainties in our setups. On the other hand, if the source isotopic signatures are considered to be perfectly known (i.e., ignoring their uncertainties), the relative contributions of the different emission categories are significantly changed. Compared to the inversion where random uncertainties are accounted for, fossil emissions and biofuel- and biomass-burning emissions are increased by 24 % and 41 %, respectively, on average over 2002–2014. Wetland emissions and agricultural and waste emissions are decreased by 14 % and 7 %, respectively. Also, in this case, our results suggest that the increase in CH4 amount fractions after 2007 (despite a large decrease in biofuel- and biomass-burning emissions) was caused by increases in emissions from (1) fossil fuels (46 %), (2) agriculture and waste (37 %), and (3) wetlands (17 %). Additionally, some other sensitivity tests have been performed. While the prescribed interannual variability in OH can have a large impact on the results, assimilating δ(D,CH4) observations in addition to the other constraints has only a minor influence. Using all the information derived from these tests, the net increase in emissions is still primarily attributed to fossil sources (50 ± 3 %) and agriculture and waste sources (47 ± 5 %). Although our methods have room for improvement, these results illustrate the full capacity of our inversion framework, which can be used to consistently account for random uncertainties in both emissions and source signatures.

Assessing the impact of shipping emissions on ozone concentrations in East Asia: Insights from KORUS-AQ and SIJAQ 2021 campaign periods

This study analyzed the impact of shipping emissions on ozone (O3) concentrations in East Asia during the Korea-United States Air Quality (KORUS-AQ) and the Satellite Integrated Joint monitoring for Air Quality (SIJAQ) 2021 campaign period. Numerical simulations were performed during the KORUS-AQ (May 2016, the KORUS case) and the SIJAQ 2021 (October–November 2021, the SIJAQ case) using Community Multi-scale Air Quality (CMAQ), the three-dimensional chemical transport model. We additionally simulated an experiment without shipping emissions to compare the impact with and without shipping emissions. Using the validated simulation results, the average O3 concentrations in inland regions were found to be 35.63 ppb and 20.62 ppb in the KORUS and SIJAQ cases, respectively. The average O3 concentrations were higher in the KORUS case owing to the difference in weather conditions between the two cases in various seasons. The results of examining the impact of shipping emissions on O3 concentrations in the ocean area indicate that both the KORUS and SIJAQ cases resulted in a decrease in O3 concentrations in the Yellow Sea region, while the O3 concentrations increased in the East Sea and North Pacific regions. In particular, in the Yellow Sea and East China Sea, where the O3 concentrations decreased in the KORUS case, O3 sensitivity results showed volatile organic compounds (VOC)-sensitive regime when the shipping emissions were removed but changed to nitrogen oxide (NOx)-sensitive regime when the shipping emissions were added. Meanwhile, in the inland regions, the SIJAQ case decreased O3 concentrations in most areas, whereas the KORUS case showed an increase in O3 concentrations in the Korean Peninsula and Japan. Analysis of O3 sensitivity revealed that the regions of the Korean Peninsula and Japan, where O3 concentrations increased, exhibited a NOx-sensitive regime. Particularly, in the case of the Korean Peninsula, the initial transport of shipping emissions in the western part led to a decrease in O3 concentrations. However, as shipping emissions were further transported from west to east, they became more diluted, and additional NOx generated by O3 titration reactions was also transported, gradually increasing O3 concentrations. Furthermore, we found that during seasons with abundant BVOC emissions, the transport of shipping emissions could contribute to additional O3 production. These results confirmed that shipping emissions in East Asia primarily reduce O3 concentrations, however, differences in seasonal and regional O3 sensitivity and transportation patterns can contribute to O3 production.

A dynamic parameterization of sulfuric acid–dimethylamine nucleation and its application in three-dimensional modeling

Abstract. Sulfuric acid (SA) is a governing gaseous precursor for atmospheric new particle formation (NPF), a major source of global ultrafine particles, in environments studied around the world. In polluted urban atmospheres with high condensation sinks (CSs), the formation of stable SA–amine clusters, such as SA–dimethylamine (DMA) clusters, usually initializes intense NPF events. Coagulation scavenging and cluster evaporation are dominant sink processes of SA–amine clusters in urban atmospheres, yet these loss processes are not quantitatively included in the present parameterizations of SA–amine nucleation. We herein report a parameterization of SA–DMA nucleation, based on cluster dynamic simulations and quantum chemistry calculations, with certain simplifications to greatly reduce the computational costs. Compared with previous SA–DMA nucleation parameterizations, this new parameterization was able to reproduce the dependences of particle formation rates on temperature and CSs. We then incorporated it in a three-dimensional (3-D) chemical transport model to simulate the evolution of the particle number size distributions. Simulation results showed good consistency with the observations in the occurrence of NPF events and particle number size distributions in wintertime Beijing and represented a significant improvement compared to that using a parameterization without coagulation scavenging. Quantitative analysis shows that SA–DMA nucleation contributes significantly to nucleation rates and aerosol population during the 3-D simulations in Beijing (>99 % and >60 %, respectively). These results broaden the understanding of NPF in urban atmospheres and stress the necessity of including the effects of coagulation scavenging and cluster stability in simulating SA–DMA nucleation in 3-D simulations. Representing these processes is thus likely to improve model performance in particle source apportionment and quantification of aerosol effects on air quality, human health, and climate.

Significant Stratospheric Moistening Following Extreme El Niño Events

The moistening impact of El Niño on the tropical lower stratosphere has been extensively studied, yet a long-standing challenge is its potential nonlinearities regarding the strength of El Niño. Extreme El Niño’s hydration in 2015/2016 was unprecedented in the satellite era, providing a great opportunity to distinguish the differential response of water vapor to extreme and moderate El Niño. Using ERA5 and MERRA-2 reanalysis data from 1979–2019, we compare the composite tropical lower stratospheric water vapor anomalies throughout all extreme and moderate El Niño episodes since the satellite era. We validate the variations in the lower stratospheric water vapor during the two distinct El Niño episodes using a three-dimensional chemistry transport model simulating the same period. The model reproduces the observed pattern in lower stratospheric water vapor. Both demonstrate that robust moistening during extreme El Niño events occurs throughout the tropical lower stratosphere. However, moderate El Niño events seem to have a weak effect on lower stratospheric water vapor. In comparison to moderate El Niño, the strong convective activities induced by extreme El Niño release large amounts of latent heat, causing extensive and intense warming in the tropical upper troposphere and lower stratosphere, thus greatly increasing the water vapor content in the tropical lower stratosphere. Additionally, moderate El Niño events have strong seasonality in their hydration effect in the tropics, whereas the intense moistening effect of extreme El Niño events prevails in all seasons during their episodes.

Investigating the role of organic compounds in intercontinental ozone transport: Reactivity scales and Global Warming Potentials (GWPs)

A global Lagrangian three-dimensional chemistry-transport model has been used to study intercontinental ozone transport from North American sources of organic compounds to receptors in Europe (Mace Head, Ireland) and in Eurasia (Mount Waligaun). Pulses of seventeen volatile organic compounds (VOCs) were emitted across North America, producing short-term ozone responses at the receptors over the first month which decayed rapidly, followed by a longer-term response which decayed more slowly. The short-term ozone responses were highly VOC-dependent and were characterised as VOC-driven. The long-term ozone responses were caused by the VOC chemistry depleting the hydroxyl (OH) radical steady state, slowing down methane oxidation, building up the methane burden and increasing methane-driven ozone production. Indices were designed and employed to quantify the short-term ozone responses, the intercontinental ozone creation potential (IOCP), and the long-term responses, the OH depletion index (OHDI). The radiative forcing consequences of the ozone and methane responses to the seventeen VOC emission pulses were estimated over a one-hundred-year time horizon and the global warming potentials (GWPs) were calculated for each VOC species. The main features of VOC chemistry were delineated and used to explain the relative magnitudes of the IOCPs, OHDIs and GWPs.

Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest

Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. SOA formation involves chemistry in gas phase, aqueous aerosols, and clouds. Simulating these chemical processes involve solving a stiff set of differential equations, which are computationally expensive steps for three-dimensional chemical transport models. Deep neural networks (DNNs) are universal function approximators that could be used to represent the complex nonlinear changes in aerosol physical and chemical processes; however, key challenges such as generalizability to extended time periods, preservation of mass balance, simulating sparse model outputs, and maintaining physical constraints have limited their use in atmospheric chemistry. Here, we develop an approach of using a physics-informed DNN that overcomes previous such challenges and demonstrates its applicability for the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest. The DNN is trained with data generated by simulating IEPOX-SOA over the entire atmospheric column, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The trained DNN is then embedded within WRF-Chem to replace the computationally expensive default solver of IEPOX-SOA formation. The trained DNN predictions generalizes well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution (20 size bins) over several days of simulations in both dry and wet seasons. The embedded DNN reduces the computational expense of WRF-Chem by a factor of 2. Our approach shows promise in terms of application to other computationally expensive chemistry solvers in climate models.

Development of an integrated machine-learning and data assimilation framework for NOx emission inversion

As major air pollutants, nitrogen oxides (NOx, mainly comprising NO and NO2) not only have adverse effects on human health but also contribute to the formation of secondary pollutants, such as ozone and particulate nitrate. To acquire reasonable NOx simulation results for further analysis, a reasonable emission inventory is needed for three-dimensional chemical transport models (3D-CTMs). In this study, a comprehensive emission adjustment framework for NOx emission, which integrates the simulation results of the 3D-CTM, surface NO2 measurements, the three-dimensional variational data assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NOx emissions over China for the summers of 2015 and 2020. Compared with the simulation using prior NOx emissions, the root-mean-square error, normalized mean error, and normalized mean bias decreased by approximately 40 %, 40 %, and 60 % in NO2 simulation using posterior NOx emissions corrected by the framework proposed in this work. Compared with the emissions for 2015, the NOx emission generally decreased by an average of 5 % in the simulation domain for 2020, especially in Henan and Anhui provinces, where the percentage reductions reached 24 % and 19 %, respectively. The proposed framework is sufficiently flexible to correct emissions in other periods and regions. The framework can provide reliable and up-to-date emission information and can thus contribute to both scientific research and policy development relating to NOx pollution.

Development of a CNN+LSTM Hybrid Neural Network for Daily PM2.5 Prediction

A CNN+LSTM (Convolutional Neural Network + Long Short-Term Memory) based deep hybrid neural network was established for the citywide daily PM2.5 prediction in South Korea. The structural hyperparameters of the CNN+LSTM model were determined through comprehensive sensitivity tests. The input features were obtained from the ground observations and GFS forecast. The performance of CNN+LSTM was evaluated by comparison with PM2.5 observations and with the 3-D CTM (three-dimensional chemistry transport model)-predicted PM2.5. The newly developed hybrid model estimated more accurate ambient levels of PM2.5 compared to the 3-D CTM. For example, the error and bias of the CNN+LSTM prediction were 1.51 and 6.46 times smaller than those by 3D-CTM simulation. In addition, based on IOA (Index of Agreement), the accuracy of CNN+LSTM prediction was 1.10–1.18 times higher than the 3-D CTM-based prediction. The importance of input features was indirectly investigated by sequential perturbing input variables. The most important meteorological and atmospheric environmental features were geopotential height and previous day PM2.5. The obstacles of the current CNN+LSTM-based PM2.5 prediction were also discussed. The promising result of this study indicates that DNN-based models can be utilized as an effective tool for air quality prediction.

Changes of PM2.5 population exposure and its sources in the US from 1990 to 2010

KEYWORDS PM25, air pollution sources, exposure BACKGROUND AND AIM The quantification of the contributions of sources of air pollutants, especially PM2.5, can be used for the improvement of our understanding of PM2.5 health effects. Three-dimensional chemical transport models are well suited to address this problem, since they simulate all the major processes that impact PM2.5 concentrations and transport. In this work, we quantified the changes in the concentration, exposure, composition, and sources of PM2.5 in the US. Significant reductions of emissions of SO2, NOx, VOCs and primary PM have taken place. METHODS We evaluate our understanding of the links between these emissions concentration and exposure changes combining a chemical transport model (PMCAMx) with the Particle Source Apportionment Algorithm. RESULTS Results for 1990, 2001 and 2010 are presented. The 63% reduction in PM2.5 sulfate concentrations from electrical generation units during these 20 years has led to a 60% reduction in PM2.5 sulfate exposure. Also, the reductions in elemental carbon (EC) concentrations from road transport by 72% have led to a reduction of PM2.5 EC exposure of 70%. CONCLUSIONS In 1990 90% of the US population was exposed to PM2.5 concentrations to equal and higher than the suggested annual mean by the WHO, but this reduced to 70% in 2010. These results are the basis for an epidemiological study linking PM2.5 sources in the US and their health effect (Pond et al., 2021). REFERENCES Pond, Z. A., Hernandez, C. S., Adams, P. J., Pandis, S. N., Garcia, G. R., Robinson, L. A., Marshall, J. D., Burnett, R., Skyllakou, K., Rivera, P. G., Karnezi, E., Coleman, C. J., and Pope, A. C.: Cardiopulmonary Mortality and Fine Particulate Air Pollution by Species and Source in a National U.S. Cohort, Env. Science & Tech. Article ASAP, 2021.

Socioeconomic disparities in aerosol pollutant exposure may be amplified by ultrafine particles despite declining PM2.5

BACKGROUND AND AIM: No study has investigated the spatiotemporal race-ethnicity and income disparities in ultrafine particles (UFP; ≤0.1µm) exposure, which is increasingly identified as a key subcomponent of PM₂.₅-related health effects. This study aims to establish the importance of distinguishing UFP from PM₂.₅ and quantify their race-ethnicity and income disparities in New York State (NYS). METHODS: Exposure disparities among seven race-ethnicity groups (Hispanics of any race, non-Hispanic Asian, Black, Native, Pacific& White alone, and Other/Mixed) and by household income across spatial scales (state, county, county subdivision, NCHS urban levels) during the period 2013–2020 for UFP and PM₂.₅, were quantified using Census data. A global three-dimensional chemical transport model with state-of-the-science aerosol microphysical processes were used to estimate UFP/PM₂.₅ that have been validated extensively with observations. RESULTS: The average New Yorker was exposed to 5171#·cm⁻³ UFP and 8.2µg·m⁻³ PM₂.₅ in 2013–2020. Minority race-ethnicity groups were invariably exposed to greater daily aerosol pollution in NYS (+51–104% UFP & +1.9–32% PM₂.₅). Race-ethnicity exposure disparities for PM₂.₅ exposure have declined over time; by −6% from 2013–2017 and plateauing thereafter despite increasing PM₂.₅. Crucially and in contrast, these disparities have persisted (+10%) for UFP exposure and even widened in periods of declining UFP. Economic status (median household income) by itself does not reveal significant exposure disparities. However, in tandem with race-ethnicity it uncovers these disparities as disproportionately magnified for socioeconomically disadvantaged subgroups. CONCLUSIONS: PM₂.₅ decline was associated with decline in race-ethnicity exposure disparities. which continued despite a post-2018 reversal in PM₂.₅ trends. However, race-ethnicity UFP exposure disparities were much larger, more disproportionate, and increased or remained constant across various income strata and levels of urbanicity. The need to distinguish ultrafine (UFP) from fine (PM₂.₅) aerosols is established through microphysical, geostatistical, and exposure assessment perspectives. KEYWORDS: air pollution, ultrafine particles, particulate matter, environmental justice, socioeconomic status

An Inversion Framework for Optimizing Non‐Methane VOC Emissions Using Remote Sensing and Airborne Observations in Northeast Asia During the KORUS‐AQ Field Campaign

We aim to reduce uncertainties in CH2O and other volatile organic carbon (VOC) emissions through assimilation of remote sensing data. We first update a three‐dimensional (3D) chemical transport model, GEOS‐Chem with the KORUSv5 anthropogenic emission inventory and inclusion of chemistry for aromatics and C2H4, leading to modest improvements in simulation of CH2O (normalized mean bias (NMB): −0.57 to −0.51) and O3 (NMB: −0.25 to −0.19) compared against DC‐8 aircraft measurements during KORUS‐AQ; the mixing ratio of most VOC species are still underestimated. We next constrain VOC emissions using CH2O observations from two satellites (OMI and OMPS) and the DC‐8 aircraft during KORUS‐AQ. To utilize data from multiple platforms in a consistent manner, we develop a two‐step Hybrid Iterative Finite Difference Mass Balance and four‐dimensional variational inversion system (Hybrid IFDMB‐4DVar). The total VOC emissions throughout the domain increase by 47%. The a posteriori simulation reduces the low biases of simulated CH2O (NMB: −0.51 to −0.15), O3 (NMB: −0.19 to −0.06), and VOCs. Alterations to the VOC speciation from the 4D‐Var inversion include increases of biogenic isoprene emissions in Korea and anthropogenic emissions in Eastern China. We find that the IFDMB method alone is adequate for reducing the low biases of VOCs in general; however, 4D‐Var provides additional refinement of high‐resolution emissions and their speciation. Defining reasonable emission errors and choosing optimal regularization parameters are crucial parts of the inversion system. Our new hybrid inversion framework can be applied for future air quality campaigns, maximizing the value of integrating measurements from current and upcoming geostationary satellite instruments.

Impact of biomass burning and stratospheric intrusions in the remote South Pacific Ocean troposphere

Abstract. The ozone mixing ratio spatiotemporal variability in the pristine South Pacific Ocean is studied, for the first time, using 21-year-long ozone (O3) records from the entire southern tropical and subtropical Pacific between 1994 and 2014. The analysis considered regional O3 vertical observations from ozonesondes, surface carbon monoxide (CO) observations from flasks, and three-dimensional chemistry-transport model simulations of the global troposphere. Two 21-year-long numerical simulations, with and without biomass burning emissions, were performed to disentangle the importance of biomass burning relative to stratospheric intrusions for ambient ozone levels in the region. Tagged tracers of O3 from the stratosphere and CO from various biomass burning regions have been used to track the impact of these different regions on the southern tropical Pacific O3 and CO levels. Patterns have been analyzed based on atmospheric dynamics variability. Considering the interannual variability in the observations, the model can capture the observed ozone gradients in the troposphere with a positive bias of 7.5 % in the upper troposphere/lower stratosphere (UTLS) as well as near the surface. Remarkably, even the most pristine region of the global ocean is affected by distant biomass burning emissions by convective outflow through the mid and high troposphere and subsequent subsidence over the pristine oceanic region. Therefore, the biomass burning contribution to tropospheric CO levels maximizes in the UTLS. The Southeast Asian open fires have been identified as the major contributing source to CO from biomass burning in the tropical South Pacific, contributing on average for the study period about 8.5 and 13 ppbv of CO at Rapa Nui and Samoa, respectively, at an altitude of around 12 km during the burning season in the spring of the Southern Hemisphere. South America is the second-most important biomass burning source region that influences the study area. Its impact maximizes in the lower troposphere (6.5 ppbv for Rapa Nui and 3.8 ppbv for Samoa). All biomass burning sources contribute about 15–23 ppbv of CO at Rapa Nui and Samoa and account for about 25 % of the total CO in the entire troposphere of the tropical and subtropical South Pacific. This impact is also seen on tropospheric O3, to which biomass burning O3 precursor emissions contribute only a few ppbv during the burning period, while the stratosphere–troposphere exchange is the most important source of O3 for the mid troposphere of the South Pacific Ocean, contributing about 15–20 ppbv in the subtropics.

Simulation of the effects of low-volatility organic compounds on aerosol number concentrations in Europe

Abstract. PMCAMx-UF, a three-dimensional chemical transport model focusing on the simulation of the ultrafine particle size distribution and composition has been extended with the addition of reactions of chemical aging of semivolatile anthropogenic organic vapors, the emissions and chemical aging of intermediate-volatility organic compounds (IVOCs), and the production of extremely low-volatility organic compounds (ELVOCs) by monoterpenes. The model is applied in Europe to quantify the effect of these processes on particle number concentrations. The model predictions are evaluated against both ground measurements collected during the PEGASOS 2012 summer campaign across many stations in Europe and airborne observations by a zeppelin measuring above Po Valley, Italy. PMCAMx-UF reproduces the ground level daily average concentrations of particles with a diameter larger than 100 nm (N100) with normalized mean error (NME) of 45 % and normalized mean bias (NMB) close to 10 %. For the same simulation, PMCAMx-UF tends to overestimate the concentration of particles with a diameter larger than 10 nm (N10) with a daily NMB of 23 % and a daily NME of 63 %. The model was able to reproduce more than 75 % of the N10 and N100 airborne observations (zeppelin) within a factor of 2. According to the PMCAMx-UF predictions, the ELVOC production by monoterpenes leads to surprisingly small changes of the average number concentrations over Europe. The total number concentration decreased due to the ELVOC formation by 0.2 %, N10 decreased by 1.1 %, N50 (particles with a diameter larger than 50 nm) increased by 3 %, and N100 increased by 4 % due to this new secondary organic aerosol (SOA) source. This small change is due to the nonlinearity of the system, with increases predicted in some areas and decreases in others, but also the cancelation of the effects of the various processes like accelerated growth and accelerated coagulation. Locally, the effects can be significant. For example, an increase in N100 by 20 %–50 % is predicted over Scandinavia and significant increases (10 %–20 %) are predicted over some parts of central Europe. The ELVOCs contributed on average around 0.5 µg m−3 and accounted for 10 %–15 % of the PM2.5 OA. The addition of IVOC emissions and their aging reactions led to a surprising reduction of the total number of particles (Ntot) and N10 by 10 %–15 % and 5 %–10 %, respectively, and to an increase in the concentration of N100 by 5 %–10 %. These were due to the accelerated coagulation and reduced nucleation rates.

The impacts of transport from South and Southeast Asia on O<sub>3</sub> concentrations in China from 2015 to 2050

<p indent="0mm">Although the significant decrease in PM_2.5 concentrations across China in response to the Air Pollution Prevention and Control Action Plan during 2013–2017, the ozone (O₃) concentrations have increased rapidly. Previous studies have revealed that long-range transboundary transport from South Asia (SA) and Southeast Asia (SEA) greatly influences the O₃ concentrations in China; however, these studies majorly focused on biomass burning events. The impact of anthropogenic emissions from neighboring countries on China’s O₃ pollution has not been comprehensively and quantitatively investigated. In addition, the analysis of the impact of future transport from SA and SEA on O₃ concentrations in China is required. Based on (Shared Socioeconomic Pathways) SSPs, a new set of future emission inventories, this study estimated the MDA8 O₃ concentrations in China, SA, and SEA under different SSPs scenarios from 2015 to 2050 with varying anthropogenic and open burning emissions using a three-dimensional chemical transport model (GEOS-Chem). Several sensitivity experiments were simulated to quantitatively assess the impact of anthropogenic and open burning emission changes in SA and SEA on the future atmospheric O₃ concentrations in China. Transport from SA and SEA can influence MDA8 O₃ concentrations in China widely, covering the Pearl River Delta (PRD), Guangxi, Yunnan, Guizhou, Sichuan Basin (SCB), Qinghai, Tibet and parts of Xinjiang. It contributed to an increase in annual mean MDA O₃ concentrations over above regions by <sc>3.0–19.0 μg m⁻³</sc> in 2015, and led to an increase in MDA8 O₃ concentration in China by <sc>4.0 μg m⁻³.</sc> Monthly variation characteristics of the impacts of SA and SEA transport on concentrations of MDA O₃ in SCB, PRD, and YRD regions were further explored in this study. In 2015, the regional transport from SA and SEA had the highest impact on the MDA8 O₃ concentrations in SCB, increased its annual mean concentration by <sc>6.2 μg m⁻³,</sc> followed by PRD <sc>(+4.7 μg m⁻³),</sc> and YRD <sc>(+0.6 μg m⁻³).</sc> Accompanied by the strong westerly and southwesterly winds on the surface and 850 hPa near 30°N in spring, the influence of transport from SA and SEA on MDA8 O₃ concentrations was highest in SCB in March 2015 <sc>(+10.1 μg m⁻³,</sc> +7.2%), followed by April <sc>(+9.4 μg m⁻³,</sc> +6.4%). Due to the prevailing southwesterly winds at 10°–30°N on the surface and 850 hPa in summer, the contribution attained its peak in July <sc>(+14.0 μg m⁻³,</sc> +10.4%), followed by August <sc>(+9.1 μg m⁻³,</sc> +6.5%) in PRD, while it peaked in July <sc>(+2.5 μg m⁻³,</sc> +1.7%), followed by June <sc>(+1.4 μg m⁻³,</sc> +0.9%) in YRD. Based on medium and long-term perspectives (2015–2030), the impact of transport from SA and SEA on MDA8 O₃ concentrations in SCB, PRD, and YRD from 2015 to 2030 were simulated. Substantive reductions under SSP1, especially under SSP1-1.9 pathway were observed (the largest decline in SCB was <sc>−3.5 μg m⁻³</sc> (−36.5%) in April, and <sc>−3.0 μg m⁻³</sc> (−21.1%) and <sc>−1.1 μg m⁻³</sc> (−43.1%) in PRD and YRD, respectively, in July). The MDA8 O₃ concentrations in SCB, PRD, and YRD during 2015–2030 were projected to increase under SSP3-7.0 scenario (the highest increase in the SCB will be <sc>+1.5 μg m⁻³</sc> (+20.1%) in May, and <sc>+1.2 μg m⁻³</sc> (+8.5%) and <sc>0.2 μg m⁻³</sc> (+7.5%) in July, respectively, in PRD and YRD). Therefore, controlling O₃ pollution due to the transport from SA and SEA in China during 2015–2030 will be easier year by year under the SSP1-1.9 scenario, while greater efforts would be required under the SSP3-7.0 pathway. The long-term (2015–2050) trend of MDA8 O₃ concentrations in the above three regions affected by the transport from SA and SEA is similar to that of the mid-to-long term.

Mortality risk attributable to wildfire-related PM2·5 pollution: a global time series study in 749 locations

Many regions of the world are now facing more frequent and unprecedentedly large wildfires. However, the association between wildfire-related PM2·5 and mortality has not been well characterised. We aimed to comprehensively assess the association between short-term exposure to wildfire-related PM2·5 and mortality across various regions of the world. For this time series study, data on daily counts of deaths for all causes, cardiovascular causes, and respiratory causes were collected from 749 cities in 43 countries and regions during 2000-16. Daily concentrations of wildfire-related PM2·5 were estimated using the three-dimensional chemical transport model GEOS-Chem at a 0·25° × 0·25° resolution. The association between wildfire-related PM2·5 exposure and mortality was examined using a quasi-Poisson time series model in each city considering both the current-day and lag effects, and the effect estimates were then pooled using a random-effects meta-analysis. Based on these pooled effect estimates, the population attributable fraction and relative risk (RR) of annual mortality due to acute wildfire-related PM2·5 exposure was calculated. 65·6 million all-cause deaths, 15·1 million cardiovascular deaths, and 6·8 million respiratory deaths were included in our analyses. The pooled RRs of mortality associated with each 10 μg/m3 increase in the 3-day moving average (lag 0-2 days) of wildfire-related PM2·5 exposure were 1·019 (95% CI 1·016-1·022) for all-cause mortality, 1·017 (1·012-1·021) for cardiovascular mortality, and 1·019 (1·013-1·025) for respiratory mortality. Overall, 0·62% (95% CI 0·48-0·75) of all-cause deaths, 0·55% (0·43-0·67) of cardiovascular deaths, and 0·64% (0·50-0·78) of respiratory deaths were annually attributable to the acute impacts of wildfire-related PM2·5 exposure during the study period. Short-term exposure to wildfire-related PM2·5 was associated with increased risk of mortality. Urgent action is needed to reduce health risks from the increasing wildfires. Australian Research Council, Australian National Health & Medical Research Council.

Simulation of the evolution of biomass burning organic aerosol with different volatility basis set schemes in PMCAMx-SRv1.0

Abstract. A source-resolved three-dimensional chemical transport model, PMCAMx-SR (Particulate Matter Comprehensive Air-quality Model with extensions – Source Resolved), was applied in the continental US to investigate the contribution of the various components (primary and secondary) of biomass burning organic aerosol (bbOA) to organic aerosol levels. Two different schemes based on the volatility basis set were used for the simulation of the bbOA during different seasons. The first is the default scheme of PMCAMx-SR, and the second is a recently developed scheme based on laboratory experiments of the bbOA evolution. The simulations with the alternative bbOA scheme predict much higher total bbOA concentrations when compared with the base case ones. This is mainly due to the high emissions of intermediate-volatility organic compounds (IVOCs) assumed in the alternative scheme. The oxidation of these compounds is predicted to be a significant source of secondary organic aerosol. The impact of the other parameters that differ in the two schemes is low to negligible. The monthly average maximum predicted concentrations of the alternative bbOA scheme were approximately an order of magnitude higher than those of the default scheme during all seasons. The performance of the two schemes was evaluated against observed total organic aerosol concentrations from several measurement sites across the US. The results were different for the different seasons examined. The default scheme performed better during July and September, while the alternative scheme performed a little better during April. These results illustrate the uncertainty of the corresponding predictions and the need to quantify the emissions and reactions of IVOCs from specific biomass sources and to better constrain the total (primary and secondary) bbOA levels.

Investigation of the Production of Trifluoroacetic Acid from Two Halocarbons, HFC-134a and HFO-1234yf and Its Fates Using a Global Three-Dimensional Chemical Transport Model

Trifluoroacetic acid (TFA), a highly soluble and stable organic acid, is photochemically produced by certain anthropogenically emitted halocarbons such as HFC-134a and HFO-1234yf. Both these haloca...

Description and evaluation of a detailed gas-phase chemistry scheme in the TM5-MP global chemistry transport model (r112)

Abstract. This work documents and evaluates the tropospheric gas-phase chemical mechanism MOGUNTIA in the three-dimensional chemistry transport model TM5-MP. Compared to the modified CB05 (mCB05) chemical mechanism previously used in the model, MOGUNTIA includes a detailed representation of the light hydrocarbons (C1–C4) and isoprene, along with a simplified chemistry representation of terpenes and aromatics. Another feature implemented in TM5-MP for this work is the use of the Rosenbrock solver in the chemistry code, which can replace the classical Euler backward integration method of the model. Global budgets of ozone (O3), carbon monoxide (CO), hydroxyl radicals (OH), nitrogen oxides (NOx), and volatile organic compounds (VOCs) are analyzed, and their mixing ratios are compared with a series of surface, aircraft, and satellite observations for the year 2006. Both mechanisms appear to be able to satisfactorily represent observed mixing ratios of important trace gases, with the MOGUNTIA chemistry configuration yielding lower biases than mCB05 compared to measurements in most of the cases. However, the two chemical mechanisms fail to reproduce the observed mixing ratios of light VOCs, indicating insufficient primary emission source strengths, oxidation that is too fast, and/or a low bias in the secondary contribution to C2–C3 organics via VOC atmospheric oxidation. Relative computational memory and time requirements of the different model configurations are also compared and discussed. Overall, the MOGUNTIA scheme simulates a large suite of oxygenated VOCs that are observed in the atmosphere at significant levels. This significantly expands the possible applications of TM5-MP.

Modeling Ozone Source Apportionment and Performing Sensitivity Analysis in Summer on the North China Plain

In recent years, air quality issues due to fine particulate matter have been sufficiently treated. However, ozone (O3) has now become the primary pollutant in summer on the North China Plain (NCP). In this study, a three-dimensional chemical transport model (the Nested Air Quality Prediction Model System, NAQPMS) coupled with an online source apportionment module was applied to investigate the sources of O3 pollution over the NCP. Generally, the NAQPMS adequately captured the observed spatiotemporal features of O3 during the period of July 1st to August 31st in 2017 on the NCP. The results of the source apportionment indicated that the contributions of local emissions and transport from the NCP accounted for the largest proportion of O3, with magnitudes of 25% and 39%, respectively. Compared with those in the average monthly results, the local contribution and regional transport during O3 episodes on the NCP increased by 7% and 10%, respectively. Based on sensitivity tests, two thresholds of the sensitivity indicator P(H2O2)/P(HNO3) were detected, at 0.08 and 0.2. Ozone formation in the urban sites of Beijing, Tianjin, and the southern part of Hebei Province was controlled by VOCs, while the other sites were mainly controlled by NOX. Biogenic emissions contributed approximately 18% to O3 formation in July in the southwestern part of Hebei Province.