Regional Chemical Transport Model Research Articles

Impacts of On‐Road Vehicular Emissions on U.S. Air Quality: A Comparison of Two Mobile Emission Models (MOVES and FIVE)

AbstractOn‐road vehicles are significant contributors to air pollution globally, particularly to nitrogen oxides (NOx) and ozone (O3). Quantifying their contribution to air quality is crucial to understanding the trends of vehicle emissions as low‐ and “zero” emission vehicles join the fleet. Modeling on‐road emissions is complex due to various factors like fleet activities, traffic patterns, and meteorological conditions. We compare on‐road emissions from two mobile models: the National Oceanic and Atmospheric Administration Fuel‐based Inventory of Vehicle Emission (FIVE) and the US Environmental Protection Agency Motor Vehicle Emission Simulator (MOVES), finding they contribute 4%–33% to volatile organic compounds (VOCs), NOx, and fine particulate matter (PM2.5) in the contiguous United States (CONUS). Using a regional chemical transport model, we assess air quality effects under different emission scenarios. Both emission data sets yield satisfactory performance, with MOVES showing lower biases in ozone (O3) and PM2.5 over CONUS, while FIVE performs better at city scales due to higher urban NOx emissions. In January, on‐road emissions increased surface O3 over western and southern US by up to 9.1%–13.1% but decreased by 2.5% over the northeastern US, while PM2.5 predictions vary across the US (−85% to 24%). In July, on‐road emissions elevate O3 and PM2.5 concentrations by 15%–20% across CONUS, except in some west coast cities. They also greatly contribute to nitrogen dioxide (NO2) by more than 80% near roads and in urban areas. This study highlights the significant impact of on‐road emissions on urban air quality and provides insights for improving air quality forecasting and management.

Modelling of atmospheric concentrations of fungal spores: a 2-year simulation over France using CHIMERE

Abstract. Fungal spore organic aerosol emissions have been recognised as a significant source of particulate matter as PM10; however, they are not widely considered in current air quality models. In this work, we have implemented the parameterisation of fungal spore organic aerosol (OA) emissions introduced by Heald and Spracklen (2009) (H&S) and further modified by Hoose et al. (2010) in the CHIMERE regional chemistry-transport model. This simple parameterisation is based on two variables, leaf area index (LAI) and specific humidity. We have validated the geographical and temporal representativeness of this parameterisation on a large scale by using yearly polyol observations and primary biogenic organic aerosol factors from positive matrix factorisation (PMF) analysis at 11 French measurement sites. For a group of sites in northern and eastern France, the seasonal variation of fungal spore emissions, displaying large summer and small winter values, is correctly depicted. However, the H&S parameterisation fails to capture fungal spore concentrations for a smaller group of Mediterranean sites with less data availability in terms of both absolute values and seasonal variability, leading to strong negative biases, especially during the autumn and winter seasons. Two years of CHIMERE simulations with the H&S parameterisation have shown a significant contribution of fungal spore OA to PM10 mass, which is lower than 10 % during winter and reaches up to 20 % during summer in high-emission zones, especially over large forested areas. In terms of contributions to organic matter (OM) concentrations, the simulated fungal spore contribution in autumn is as high as 40 % and reaches at most 30 % of the OM for the other seasons. As a conclusion, the fungal spore OA contribution to the total OM concentrations is shown to be substantial enough to be considered a major PM10 fraction and should then be included in state-of-the-art chemistry-transport models.

Observational evidence reveals the significance of nocturnal chemistry in seasonal secondary organic aerosol formation

Oxidized Organic Aerosol (OOA), a major component of fine atmospheric particles, impacts climate and human health. Previous experiments and atmospheric models emphasize the importance of nocturnal OOA formation from NO3· oxidation of biogenic VOCs. This seasonal study extends the understanding by showing that nocturnal oxidation of biomass-burning emissions can account for up to half of total OOA production in fall and winter. It is the first to distinguish nocturnal OOA characteristics from daytime OOA across all seasons using bulk aerosol measurements. Summer observations of nocturnal OOA align well with regional chemistry transport model predictions, but discrepancies in other seasons reveal a common model deficiency in representing biomass-burning emissions and their nocturnal oxidation. This study underscores the significance of near-ground nocturnal OOA production, proposes a method to differentiate it using bulk aerosol measurements, and suggests model optimization strategies. These findings enhance the understanding and prediction of nighttime OOA formation.

Impact of the 2022 New Mexico, US wildfires on air quality and health

The 2022 wildfires in New Mexico, United States, were unparalleled compared to past wildfires in the state in both their scale and intensity, resulting in poor air quality and a catastrophic loss of habitat and livelihood. Among all wildfires in New Mexico in 2022, six wildfires were selected for our study based on the size of the burn area and their proximity to populated areas. These fires accounted for approximately 90 % of the total burn area in New Mexico in 2022. We used a regional chemical transport model and data-fusion technique to quantify the contribution of these six wildfires (April 6 to August 22) on particulate matter (PM2.5: diameter ≤ 2.5 μm) and ozone (O3) concentrations, as well as the associated health impacts from short-term exposure. We estimated that these six wildfires emitted 152 thousand tons of PM2.5 and 287 thousand tons of volatile organic compounds to the atmosphere. We estimated that the average daily wildfire smoke PM2.5 across New Mexico was 0.3 μg/m3, though 1 h maximum exceeded 120 μg/m3 near Santa Fe. Average wildfire smoke maximum daily average 8-h O3 (MDA8-O3) contribution was 0.2 ppb during the study period over New Mexico. However, over the state 1 h maximum smoke O3 exceeded 60 ppb in some locations near Santa Fe. Estimated all-cause excess mortality attributable to short term exposure to wildfire PM2.5 and MDA8-O3 from these six wildfires were 18 (95 % Confidence Interval (CI), 15–21) and 4 (95 % CI: 3–6) deaths. Additionally, we estimate that wildfire PM2.5 was responsible for 171 (95 %: 124–217) excess cases of asthma emergency department visits. Our findings underscore the impact of wildfires on air quality and human health risks, which are anticipated to intensify with global warming, even as local anthropogenic emissions decline.

Modeling the drivers of fine PM pollution over Central Europe: impacts and contributions of emissions from different sources

Abstract. Fine particulate matter (PM2.5) is among the air pollutants representing the most critical threat to human health in Europe. For designing strategies to mitigate this kind of air pollution, it is essential to identify and quantify the sources of its components. Here, we utilized the regional chemistry transport model CAMx (Comprehensive Air Quality Model with Extensions) to investigate the relationships between emissions from different categories and the concentrations of PM2.5 and its secondary components over Central Europe during the period 2018–2019, both in terms of the contributions of emission categories calculated by the particle source apportionment technology (PSAT) and the impacts of the complete removal of emissions from individual categories (i.e., the zero-out method). During the winter seasons, emissions from other stationary combustion (including residential combustion) were the main contributor to the domain-wide average PM2.5 concentration (3.2 µg m−3), and their removal also had the most considerable impact on it (3.4 µg m−3). During the summer seasons, the domain-wide average PM2.5 concentration was contributed the most by biogenic emissions (0.57 µg m−3), while removing emissions from agriculture–livestock had the most substantial impact on it (0.46 µg m−3). The most notable differences between the contributions and impacts for PM2.5 were associated with emissions from agriculture–livestock, mainly due to the differences in nitrate concentrations, which reached up to 4.5 and 1.25 µg m−3 in the winter and summer seasons, respectively. We also performed a sensitivity test of the mentioned impacts on PM2.5 on two different modules for secondary organic aerosol formation (SOAP and VBS), which showed the most considerable differences for emissions from other stationary combustion (in winter) and road transport (in summer).

Intercomparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1–CMAQ v5.3.1, WRF–Chem v4.1.1, and WRF v3.7.1–CHIMERE v2020r1) in eastern China

Abstract. Two-way coupled meteorology and air quality models, which account for aerosol–radiation–cloud interactions, have been employed to simulate meteorology and air quality more realistically. Although numerous related studies have been conducted, none have compared the performances of multiple two-way coupled models in simulating meteorology and air quality over eastern China. Thus, we systematically evaluated annual and seasonal meteorological and air quality variables simulated by three open-source, widely utilized two-way coupled models (Weather Research and Forecasting (WRF)–Community Multiscale Air Quality (WRF–CMAQ), WRF coupled with chemistry (WRF–Chem), and WRF coupled with a regional chemistry-transport model named CHIMERE (WRF–CHIMERE)) by validating their results with surface and satellite observations for eastern China in 2017. Although we have made every effort to evaluate these three coupled models by using configurations that are as consistent as possible, there are still unavoidable differences between them in their treatments of physical and chemical processes. Our thorough evaluations revealed that all three two-way coupled models captured the annual and seasonal spatiotemporal characteristics of meteorology and air quality reasonably well. Notably, the role of the aerosol–cloud interaction (ACI) in improving the models' performances was limited compared to that of the aerosol–radiation interaction (ARI). The sources of uncertainties and bias in the different ACI schemes in the two-way coupled models were identified. With sufficient computational resources, these models can provide more accurate air quality forecasting to support atmospheric environment management and deliver timely warnings of heavy air pollution events. Finally, we propose potential improvements to two-way coupled models for future research.

Nocturnal ozone enhancement in Shandong Province, China, in 2020–2022: Spatiotemporal distribution and formation mechanisms

Nighttime ozone enhancement (NOE) can increase the oxidation capacity of the atmosphere by stimulating nitrate radical formation and subsequently facilitating the formation of secondary pollutants, thereby affecting air quality in the following days. Previous studies have demonstrated that when nocturnal ozone (O3) concentrations exceed 80 μg/m3, it leads to water loss and reduction of plant yields. In this study, the characteristics and mechanisms of NOE over Shandong Province as well as its 16 cities were analyzed based on observed hourly O3 concentrations from 2020 to 2022. The analysis results show that NOE predominantly occurred in the periods of 0:00–3:00 (41 %). The annual mean frequency of NOE events was ~64 days/year, approximately 4–7 days per month. The average concentration of nocturnal O3 peak (NOP) was ~72.6 μg/m3. Notably, high NOP was observed in the period from April to September with the maximum in June. Coastal cities experienced more NOE events. Typical NOE events characterized by high NOP concentrations in the coastal cities of QingDao, WeiHai and YanTai in June 2021 were selected for detailed analysis with a regional chemical transport model. The results showed that high levels of O3 in eastern coastal cities during NOE events primarily originate from horizontal transport over the sea, followed by vertical transport. During the daytime, O3 and its precursors are transported to the Yellow Sea by westerly winds, leading to the accumulation of O3 near the sea and coastline. Consequently, under the influence of prevailing winds, the movement of O3 pollution belts from the sea to land causes rapid increases in near-surface O3 levels. Meanwhile, vertical transport can also contribute to NOE in coastal areas. The high-level O3 in the upper atmosphere generally originates from long-distance transport and turbulent transport of O3 produced near the ground during the daytime. At night, the absence of chemicals that consume O3 in the upper air and descending air flow carries O3 to the near-surface. The impacts of other O3-depletion processes (such as dry deposition) on NOE are less pronounced than those of transport processes.

Evaluation of WRF-Chem-simulated meteorology and aerosols over northern India during the severe pollution episode of 2016

Abstract. We use a state-of-the-art regional chemistry transport model (WRF-Chem v4.2.1) to simulate particulate air pollution over northern India during September–November 2016. This period includes a severe air pollution episode marked by exceedingly high levels of hourly PM2.5 (particulate matter having an aerodynamic diameter ≤ 2.5 µm) during 30 October to 7 November, particularly over the wider Indo-Gangetic Plain (IGP). We provide a comprehensive evaluation of simulated seasonal meteorology (nudged by ERA5 reanalysis products) and aerosol chemistry (PM2.5 and its black carbon (BC) component) using a range of ground-based, satellite and reanalysis products, with a focus on the November 2016 haze episode. We find the daily and diurnal features in simulated surface temperature show the best agreement followed by relative humidity, with the largest discrepancies being an overestimate of night-time wind speeds (up to 1.5 m s−1) confirmed by both ground and radiosonde observations. Upper-air meteorology comparisons with radiosonde observations show excellent model skill in reproducing the vertical temperature gradient (r>0.9). We evaluate modelled PM2.5 at 20 observation sites across the IGP including eight in Delhi and compare simulated aerosol optical depth (AOD) with data from four AERONET sites. We also compare our model aerosol results with MERRA-2 reanalysis aerosol fields and MODIS satellite AOD. We find that the model captures many features of the observed aerosol distributions but tends to overestimate PM2.5 during September (by a factor of 2) due to too much dust, and underestimate peak PM2.5 during the severe episode. Delhi experiences some of the highest daily mean PM2.5 concentrations within the study region, with dominant components nitrate (∼25 %), dust (∼25 %), secondary organic aerosols (∼20 %) and ammonium (∼10 %). Modelled PM2.5 and BC spatially correlate well with MERRA-2 products across the whole domain. High AOD at 550nm across the IGP is also well predicted by the model relative to MODIS satellite (r≥0.8) and ground-based AERONET observations (r≥0.7), except during September. Overall, the model realistically captures the seasonal and spatial variations of meteorology and ambient pollution over northern India. However, the observed underestimations in pollutant concentrations likely come from a combination of underestimated emissions, too much night-time dispersion, and some missing or poorly represented aerosol chemistry processes. Nevertheless, we find the model is sufficiently accurate to be a useful tool for exploring the sources and processes that control PM2.5 levels during severe pollution episodes.

On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest

Abstract. We present an evaluation of the regional chemical transport model (CTM) WRF-CHIMERE (v2020r2) for the formation of biogenic secondary organic aerosol (BSOA) with a focus over the Finnish boreal forest. Formation processes of biogenic aerosols are still affected by different sources of uncertainties, and model predictions vary greatly depending on the levels of details of the adopted chemical and emissions schemes. In this study, air quality simulations were conducted for the summer of 2019 using different organic aerosol (OA) schemes (as currently available in the literature) to treat the formation of BSOA. First, we performed a set of simulations in the framework of the volatility basis set (VBS) scheme carrying different assumptions for the treatment of the aging processes of BSOA. The results of the model were compared against high-resolution (i.e., 1 h) organic aerosol mass and size distribution measurements performed at the Station for Measuring Ecosystem–Atmosphere Relations (SMEAR-II) site located in Hyytiälä, in addition to other gas-phase species such as ozone (O3), nitrogen oxides (NOx), and biogenic volatile organic compound (BVOC) measurements of isoprene (C5H10) and monoterpenes. We show that WRF-CHIMERE could reproduce well the diurnal variation of the measured OA concentrations for all the investigated scenarios (along with the standard meteorological parameters) as well as the increase in concentrations during specific heat wave episodes. However, the modeled OA concentrations varied greatly between the schemes used to describe the aging processes of BSOA, as also confirmed by an additional evaluation using organic carbon (OC) measurement data retrieved from the EBAS European databases. Comparisons with isoprene and monoterpene air concentrations revealed that the model captured the observed monoterpene concentrations, but isoprene was largely overestimated, a feature that was mainly attributed to the overstated biogenic emissions of isoprene. We investigated the potential consequences of such an overestimation by inhibiting isoprene emissions from the modeling system. Results indicated that the modeled BSOA concentrations increased in the northern regions of the domain (e.g., Finland) compared to southern European countries, possibly due to a shift in the reactions of monoterpene compounds against available radicals, as further suggested by the reduction in α-pinene modeled air concentrations. Finally, we briefly analyze the differences in the modeled cloud liquid water content (clwc) among the simulations carrying different chemical schemes for the treatment of the aging processes of BSOA. The results of the model indicated an increase in clwc values at the SMEAR-II site, for simulations with higher biogenic organic aerosol loads, most likely as a result of the increased number of biogenic aerosol particles capable of activating cloud droplets.

Numerical analysis of CH4 concentration distributions over East Asia with a regional chemical transport model

Atmospheric CH4 concentrations have increased rapidly and exhibited large spatiotemporal variations. But their values were generally fixed in regional air quality models, without considering the processes of emissions, transport, and diffusion. In this study, a regional air quality modeling system RAMS-CMAQ was developed by incorporating the emissions, transport, and diffusion processes of CH4 to produce CH4 concentration distributions. The developed model was applied to simulate CH4 concentrations over East Asia in 2020. As a result, the comparisons between the simulated CH4 vertical column densities (VCDS) and the observations derived from GOSAT showed that the developed model can reasonably reflect the crucial features in CH4 concentration variations with the correlation coefficients larger than 0.94. The temporal-spatial patterns of surface CH4 concentrations showed that concentrations were high (≥2.3 ppm) over Sichuan Basin, central-eastern China and South China as well as India, where the highest was in summer because of the largest agricultural emissions, and the lowest was in spring due to less emissions and stronger OH sinks compared to winter. The low surface CH4 concentrations (≤1.9 ppm) appeared over Northwest China with small seasonal variations (high in summer and low in winter and autumn) and less local emissions. The temporal-spatial patterns of CH4 VCDS showed that VCDS were high (3.6–4.5 × 1019 molec/cm2) over eastern China with significant seasonal variations (high in summer and autumn), and low (≤3.0 × 1019 molec/cm2) over Tibet Plateau and western Mongolia with slight variations.

Dynamic evaluation of modeled ozone concentrations in Germany with four chemistry transport models

Simulating the ozone variability at regional scales using chemistry transport models (CTMs) remains a challenge. We designed a multi-model intercomparison to evaluate, for the first time, four regional CTMs on a national scale for Germany. Simulations were conducted with LOTOS-EUROS, REM-CALGRID, COSMO-MUSCAT and WRF-Chem for January 1st to December 31st, 2019, using prescribed emission information. In general, all models show good performance in the operational evaluation with average temporal correlations of MDA8 O3 in the range of 0.77–0.87 and RMSE values between 16.3 μg m−3 and 20.6 μg m−3. On average, better models' skill has been observed for rural background stations than for the urban background stations as well as for springtime compared to summertime. Our study confirms that the ensemble mean provides a better model-measurement agreement than individual models. All models capture the larger local photochemical production in summer compared to springtime and observed differences between the urban and the rural background. We introduce a new indicator to evaluate the dynamic response of ozone to temperature. During summertime a large ensemble spread in the ozone sensitivities to temperature is found with (on average) an underestimation of the ozone sensitivity to temperature, which can be linked to a systematic underestimation of mid-level ozone concentrations. During springtime we observed an ozone episode that is not covered by the models which is likely due to deficiencies in the representation of background ozone in the models. We recommend to focus on a diagnostic evaluation aimed at the model descriptions for biogenic emissions and dry deposition as a follow up and to repeat the operational and dynamic analysis for longer timeframes.

Assessment of tropospheric ozone simulations in a regional chemical transport model using GEOS-Chem outputs as chemical boundary conditions

Regional chemical transport models (e.g., Community Multiscale Air Quality (CMAQ) Modeling System) are widely used to simulate the physical and chemical process of regional ozone (O3) pollution and its variation trend in recent years. However, chemical boundary condition (CBC) is an important input of these models and contributes to the model bias against observations. In this study, we develop a tool named GC2CMAQ that provides the CMAQ model with the CBCs from the GEOS-Chem simulation. Two experiments using different CBCs were conducted to evaluate their effect on seasonal O3 simulation in China. The Default experiment utilized the model-default static condition (the relatively clean atmosphere in the eastern United States), and the GC experiment employed the GEOS-Chem simulation results. Compared with the observation, the GC experiment has a much better performance in reproducing elevated O3 levels in the higher troposphere and lower stratosphere during different seasons. Near the earth's surface, the simulated concentrations of pollutants O3 (and PM2.5) in the GC experiment were also closer to the observation in April and July. The accuracy of simulation results in provinces close to the boundary was improved by approximately 20 %–30 % relative to the Default experiment. The CBCs provided by GEOS-Chem enabled a better simulation of stratosphere-troposphere O3 exchange in late spring and early summer, which then affected the pollutant concentration near surfaces through vertical transport. This finding was confirmed by a case study in southwestern Tibet on April 28, 2017, in which we quantified the contributions of different physical and chemical processes to O3 variations at different altitudes using the process analysis method. This study highlights the importance of using a reliable CBC for the regional chemical transport model to derive a better performance of O3 simulation.

A multimodel evaluation of the potential impact of shipping on particle species in the Mediterranean Sea

Abstract. Shipping contributes significantly to air pollutant emissions and atmospheric particulate matter (PM) concentrations. At the same time, worldwide maritime transport volumes are expected to continue to rise in the future. The Mediterranean Sea is a major short-sea shipping route within Europe and is the main shipping route between Europe and East Asia. As a result, it is a heavily trafficked shipping area, and air quality monitoring stations in numerous cities along the Mediterranean coast have detected high levels of air pollutants originating from shipping emissions. The current study is a part of the EU Horizon 2020 project SCIPPER (Shipping Contributions to Inland Pollution – Push for the Enforcement of Regulations), which intends to investigate how existing restrictions on shipping-related emissions to the atmosphere ensure compliance with legislation. To demonstrate the impact of ships on relatively large scales, the potential shipping impacts on various air pollutants can be simulated with chemical transport models. To determine the formation, transport, chemical transformation, and fate of particulate matter < 2.5 µm (PM2.5) in the Mediterranean Sea in 2015, five different regional chemical transport models (CAMx – Comprehensive Air Quality Model with Extensions, CHIMERE, CMAQ – Community Multiscale Air Quality model, EMEP – European Monitoring and Evaluation Programme model, and LOTOS-EUROS) were applied. Furthermore, PM2.5 precursors (ammonia (NH3), sulfur dioxide (SO2), nitric acid (HNO3)) and inorganic particle species (sulfate (SO42-), ammonia (NH4+), nitrate (NO3-)) were studied, as they are important for explaining differences among the models. STEAM (see “List of abbreviations” in Appendix A) version 3.3.0 was used to compute shipping emissions, and the CAMS-REG version 2.2.1 dataset was used to calculate land-based emissions for an area encompassing the Mediterranean Sea at a resolution of 12 × 12 km2 (or 0.1∘ × 0.1∘). For additional input, like meteorological fields and boundary conditions, all models utilized their regular configuration. The zero-out approach was used to quantify the potential impact of ship emissions on PM2.5 concentrations. The model results were compared with observed background data from monitoring sites. Four of the five models underestimated the actual measured PM2.5 concentrations. These underestimations are linked to model-specific mechanisms or underpredictions of particle precursors. The potential impact of ships on the PM2.5 concentration is between 15 % and 25 % at the main shipping routes. Regarding particle species, SO42- is the main contributor to the absolute ship-related PM2.5 and to total PM2.5 concentrations. In the ship-related PM2.5, a higher share of inorganic particle species can be found when compared with the total PM2.5. The seasonal variabilities in particle species show that NO3- is higher in winter and spring, while the NH4+ concentrations displayed no clear seasonal pattern in any models. In most cases with high concentrations of both NH4+ and NO3-, lower SO42- concentrations are simulated. Differences among the simulated particle species distributions might be traced back to the aerosol size distribution and how models distribute emissions between the coarse and fine modes (PM2.5 and PM10). The seasonality of wet deposition follows the seasonality of the precipitation, showing that precipitation predominates wet deposition.

The carbon sink in China as seen from GOSAT with a regional inversion system based on the Community Multi-scale Air Quality (CMAQ) and ensemble Kalman smoother (EnKS)

Abstract. Top-down inversions of China's terrestrial carbon sink are known to be uncertain because of errors related to the relatively coarse resolution of global transport models and the sparseness of in situ observations. Taking advantage of regional chemistry transport models for mesoscale simulation and spaceborne sensors for spatial coverage, the Greenhouse Gases Observing Satellite (GOSAT) retrievals of column-mean dry mole fraction of carbon dioxide (XCO2) were introduced in the Models-3 (a flexible software framework) Community Multi-scale Air Quality (CMAQ) and ensemble Kalman smoother (EnKS)-based regional inversion system to constrain China's biosphere sink at a spatiotemporal resolution of 64 km and 1 h. In general, the annual, monthly, and daily variation in biosphere flux was reliably delivered, attributable to the novel flux forecast model, reasonable CMAQ background simulation, well-designed observational operator, and Joint Data Assimilation Scheme (JDAS) of CO2 concentrations and natural fluxes. The size of the assimilated biosphere sink in China was −0.47 Pg C yr−1, which was comparable with most global estimates (i.e., −0.27 to −0.68 Pg C yr−1). Furthermore, the seasonal patterns were recalibrated well, with a growing season that shifted earlier in the year over central and south China. Moreover, the provincial-scale biosphere flux was re-estimated, and the difference between the a posteriori and a priori flux ranged from −7.03 Tg C yr−1 in Heilongjiang to 2.95 Tg C yr−1 in Shandong. Additionally, better performance of the a posteriori flux in contrast to the a priori flux was statistically detectable when the simulation was fitted to independent observations, indicating sufficient to robustly constrained state variables and improved fluxes estimation. This study serves as a basis for future fine-scale top-down carbon assimilation.

Drivers of Increasing Ozone during the Two Phases of Clean Air Actions in China 2013-2020.

In response to the severe air pollution issue, the Chinese government implemented two phases (Phase I, 2013-2017; Phase II, 2018-2020) of clean air actions since 2013, resulting in a significant decline in fine particles (PM2.5) during 2013-2020, while the warm-season (April-September) mean maximum daily 8 h average ozone (MDA8 O3) increased by 2.6 μg m-3 yr-1 in China during the same period. Here, we derived the drivers behind the rising O3 concentrations during the two phases of clean air actions by using a bottom-up emission inventory, a regional chemical transport model, and a multiple linear regression model. We found that both meteorological variations (3.6 μg m-3) and anthropogenic emissions (6.7 μg m-3) contributed to the growth of MDA8 O3 from 2013 to 2020, with the changes in anthropogenic emissions playing a more important role. The anthropogenic contributions to the O3 rise during 2017-2020 (1.2 μg m-3) were much lower than that in 2013-2017 (5.2 μg m-3). The lack of volatile organic compound (VOC) control and the decline in nitrogen oxides (NOx) emissions were responsible for the O3 increase in 2013-2017 due to VOC-limited regimes in most urban areas, while the synergistic control of VOC and NOx in Phase II initially worked to mitigate O3 pollution during 2018-2020, although its effectiveness was offset by the penalty of PM2.5 decline. Future mitigation efforts should pay more attention to the simultaneous control of VOC and NOx to improve O3 air quality.

Exposure Assessment of Ambient PM2.5 Levels during a Sequence of Dust Episodes: A Case Study Coupling the WRF-Chem Model with GIS-Based Postprocessing.

A sequence of dust intrusions occurred from the Sahara Desert to the central Mediterranean in the second half of June 2021. This event was simulated by means of the Weather Research and Forecasting coupled with chemistry (WRF-Chem) regional chemical transport model (CTM). The population exposure to the dust surface PM2.5 was evaluated with the open-source quantum geographical information system (QGIS) by combining the output of the CTM with the resident population map of Italy. WRF-Chem analyses were compared with spaceborne aerosol observations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and, for the PM2.5 surface dust concentration, with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Considering the full-period (17-24 June) and area-averaged statistics, the WRF-Chem simulations showed a general underestimation for both the aerosol optical depth (AOD) and the PM2.5 surface dust concentration. The comparison of exposure classes calculated for Italy and its macro-regions showed that the dust sequence exposure varies with the location and entity of the resident population amount. The lowest exposure class (up to 5 µg m-3) had the highest percentage (38%) of the population of Italy and most of the population of north Italy, whereas more than a half of the population of central, south and insular Italy had been exposed to dust PM2.5 in the range of 15-25 µg m-3. The coupling of the WRF-Chem model with QGIS is a promising tool for the management of risks posed by extreme pollution and/or severe meteorological events. Specifically, the present methodology can also be applied for operational dust forecasting purposes, to deliver safety alarm messages to areas with the most exposed population.

Development of a high-resolution emissions inventory of carbonaceous particulate matters and their growth during 2011–2018 over India

The carbonaceous fine particulate matters (BC and OC) are of significant concern for air quality and climate change. A high-resolution (10 × 10 km2) gridded SAFAR (System of Air Quality and Weather Forecasting And Research) emission inventory of BC-OC has been developed for 2011 and 2018 over India using bottom-up Geographical Information Systems (GIS) based statistical model with the latest activity data and updated emission factors. The emission inventory accounted for major sectors like transport, residential, industries, Thermal Power Plant (TPP), and rest (Mobile towers, Irrigation pumps, Municipal solid waste burning, Waste to energy plants, Construction, Crematorium, Incense stick, Brick kiln). The total national BC and OC emissions for the latest year, 2018 are estimated to be 1480 Gg yr−1 and 3116 Gg yr−1, respectively. The most dominant sector is found to be the transportation sector for BC with 46% share, whereas the residential sector for OC with 39% share. An overall growth of 32% and 36% is estimated in 2018 compared to 2011 for BC and OC, respectively. The highest growth during the past 8 years is found in the transport sector whereas the residential sector has recorded the lowest growth rate. The latter is mainly due to a decline in kerosene oil, and domestic wood after the government introduced the ambitious Ujjwala scheme to replace bio-fuel with LPG. This high-resolution inventory of BC-OC will be helpful to policymakers in identifying the hotspot regions and prioritizing mitigation options, thus benefitting Human health and climate. The current gridded emissions will provide much-needed input for the regional chemistry transport models.

Simulation of organic aerosol, its precursors, and related oxidants in the Landes pine forest in southwestern France: accounting for domain-specific land use and physical conditions

Abstract. Organic aerosol (OA) still remains one of the most difficult components of the atmospheric aerosols to simulate, given the multitude of its precursors, the uncertainty in its formation pathways, and the lack of measurements of its detailed composition. The LANDEX (LANDes Experiment) project, during its intensive field campaign in summer 2017, gives us the opportunity to compare biogenic secondary OA (BSOA) and its precursors and oxidants obtained within and above the Landes forest canopy to simulations performed with CHIMERE, a state-of-the-art regional chemistry transport model. The Landes forest is situated in the southwestern part of France and is one of the largest anthropized forests in Europe (1×106 ha). The majority of the forest is comprised of maritime pine trees, which are strong terpenoid emitters, providing a large potential for BSOA formation. In order to simulate OA buildup in this area, a specific model configuration setup adapted to the local peculiarities was necessary. As the forest is nonhomogeneous, with interstitial agricultural fields, high-resolution 1 km simulations over the forest area were performed. Biogenic volatile organic compound (BVOC) emissions were predicted by MEGAN, but specific land cover information needed to be used and was thus chosen from the comparison of several high-resolution land cover databases. Moreover, the tree species distribution needed to be updated for the specific conditions of the Landes forest. In order to understand the canopy effect in the forest, canopy effects on vertical diffusivity, winds, and radiation were implemented in the model in a simplified way. The refined simulations show a redistribution of BVOCs with a decrease in isoprene and an increase in terpenoid emissions with respect to the standard case, both of which are in line with observations. Corresponding changes to simulated BSOA sources are tracked. Very low nighttime ozone, sometimes near zero, remains overestimated in all simulations. This has implications for the nighttime oxidant budget, including NO3. Despite careful treatment of physical conditions, simulated BSOA is overestimated in the most refined simulation. Simulations are also compared to air quality sites surrounding the Landes forest, reporting a more realistic simulation in these stations in the most refined test case. Finally, the importance of the sea breeze system, which also impacts species concentrations inside the forest, is made evident.

The Z-2018 emissions inventory of COS in Europe: A semiquantitative multi-data-streams evaluation

The anthropogenic emissions of carbonyl sulfide (COS) and the uptake of this gas by terrestrial vegetation are major drivers of COS concentration variations in the atmosphere since the beginning of the industrial era. The fine spatial resolution (i.e., 0.1° × 0.1°) of the gridded anthropogenic emissions inventory of COS developed by Zumkehr et al. (2018), Z-2018 hereafter, is designed for a use by regional chemistry-transport models. In order to anticipate future applications at the regional scale, we carried out a first semiquantitative assessment of direct and indirect (i.e., from CS2 conversion) COS anthropogenic emissions at the sub-country scale in France using historical governmental data. Better agreement between the two inventories was found for direct emissions of COS by coal power plants, than for indirect sources. The use in this latter case of a sub-country spatial scaling, based on industrial N2O emissions, (1) strongly underestimates fluxes from food casings and cellulosic sponge industrial activities responsible for atmospheric CS2 emissions in France, and (2) considerably overestimates emissions from Paris and surrounding areas, which are free of a rayon industry. A list of food casings and cellulosic sponge industrial sites is provided to produce a more realistic inventory of CS2 sources in Europe. Nevertheless, a cluster analysis of wintertime air masses trajectories, with the atmospheric COS monitoring site of Gif-sur-Yvette (GIF) as end point, suggests that Z-2018 correctly identifies northern Germany and Belgium as sources of anthropogenic COS. In winter, when the wind blows from the NE sector, advection of anthropogenic COS from coal power plants is illustrated by synchronous variations in GIF and at the Trainou tall tower (distant of about 80 km) of a series of atmospheric pollution tracers, especially between COS and sulfates. These observations support the use of SO2 emissions as temporal and sub-country spatial scaling factors of COS emissions by the coal industry.

Isoprene and monoterpene simulations using the chemistry–climate model EMAC (v2.55) with interactive vegetation from LPJ-GUESS (v4.0)

Abstract. Earth system models (ESMs) integrate previously separate models of the ocean, atmosphere and vegetation into one comprehensive modelling system enabling the investigation of interactions between different components of the Earth system. Global isoprene and monoterpene emissions from terrestrial vegetation, which represent the most important source of volatile organic compounds (VOCs) in the Earth system, need to be included in global and regional chemical transport models given their major chemical impacts on the atmosphere. Due to the feedback of vegetation activity involving interactions with weather and climate, a coupled modelling system between vegetation and atmospheric chemistry is recommended to address the fate of biogenic volatile organic compounds (BVOCs). In this work, further development in linking LPJ-GUESS, a global dynamic vegetation model, to the atmospheric-chemistry-enabled atmosphere–ocean general circulation model EMAC is presented. New parameterisations are included to calculate the foliar density and leaf area density (LAD) distribution from LPJ-GUESS information. The new vegetation parameters are combined with existing LPJ-GUESS output (i.e. leaf area index and cover fractions) and used in empirically based BVOC modules in EMAC. Estimates of terrestrial BVOC emissions from EMAC's submodels ONEMIS and MEGAN are evaluated using (1) prescribed climatological vegetation boundary conditions at the land–atmosphere interface and (2) dynamic vegetation states calculated in LPJ-GUESS (replacing the “offline” vegetation inputs). LPJ-GUESS-driven global emission estimates for isoprene and monoterpenes from the submodel ONEMIS were 546 and 102 Tg yr−1, respectively. MEGAN determines 657 and 55 Tg of isoprene and monoterpene emissions annually. The new vegetation-sensitive BVOC fluxes in EMAC are in good agreement with emissions from the semi-process-based module in LPJ-GUESS. The new coupled system is used to evaluate the temperature and vegetation sensitivity of BVOC fluxes in doubling CO2 scenarios. This work provides evidence that the new coupled model yields suitable estimates for global BVOC emissions that are responsive to vegetation dynamics. It is concluded that the proposed model set-up is useful for studying land–biosphere–atmosphere interactions in the Earth system.